esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Biplot Analysis  
The Canonical Biplot, or MANOVA-Biplot, is a multivariate statistical technique that combines _multivariate analysis of variance_ (MANOVA) with Biplot techniques, allowing for a graphical representation of the data. Introduced by [PERSON] (1972, 1995) and further developed by [PERSON] and [PERSON], this technique facilitates the visualization of both the differences between groups and the relationships between variables [56].  
The MANOVA model for \(p\) variables is expressed in matrix form as \(\mathbf{X}=\mathbf{A}\mathbf{B}+\mathbf{U}\), where \(\mathbf{X}\) is the matrix of observations, \(A\) is the design matrix, \(\mathbf{B}\) is the matrix of unknown parameters, and \(\mathbf{U}\) contains the residuals [57]. The general multivariate linear hypothesis is stated as \(H_{0}:\mathbf{C}\mathbf{B}=\mathbf{0}\), where \(\mathbf{C}\) is a contrast matrix. Through _Generalized Singular Value Decomposition_ (GSVD), the Canonical Biplot is constructed by maximizing the separation between groups [58].  
The GSVD is performed as \(\mathbf{R}^{-1/2}\mathbf{\hat{D}}\mathbf{E}^{-1/2}=\mathbf{U}\mathbf{D}_{ \lambda}\mathbf{V}^{\text{T}}\), where \(\mathbf{R}\) is a contrast matrix, \(\mathbf{D}_{\lambda}\) is the diagonal matrix of eigenvalues, and \(\mathbf{\hat{D}}=\mathbf{C}{(\mathbf{A}^{\text{T}}\mathbf{A})}^{-1}\mathbf{A} ^{\text{T}}\mathbf{X}\). The row (observations) and column (variables) markers are calculated as \(\mathbf{P}=\mathbf{R}^{-1/2}\mathbf{U}\mathbf{D}_{\lambda}\) and \(\mathbf{Q}=\mathbf{E}^{-1/2}\mathbf{V}\), respectively, allowing for the Biplot representation of the original data. This graphical representation facilitates the interpretation of relationships between observations and variables in a reduced space. Rows reflect the similarity between observations, while columns indicate the relationships between variables [59]. Additionally, _confidence regions_ around the points representing group means can be added through confidence circles, providing an intuitive visualization of the uncertainty in the observed differences between groups.  
In the Canonical Biplot shown in Figure 3, each point represents a keyword extracted through our text mining process using IRAMUTEQ software with colors indicating their thematic group membership or their study year groups. The larger points denote cluster centroids, which are surrounded by confidence circles that visualize the variability within each lexical group. The vectors in this visualization correspond to the document IDs from our study corpus, where their direction and magnitude reveal the relationship between specific articles and the extracted keywords. Notably, longer vectors indicate documents that contribute more significantly to the differentiation of word clusters, serving as important markers of thematic influence in our analysis. This representation proves particularly valuable for examining the complex relationships between textual patterns (represented by keywords) and their distribution across our research documents, providing a comprehensive view of the semantic structure emerging from our multivariate text analysis.
## 4 Results
### Experimental Setup
#### 4.1.3 Evaluation Metrics  
We assessed our mask quality using established metrics: the \(\mathcal{J}\)-score and \(\mathcal{F}\)-score. These metrics are widely used in video object segmentation and image segmentation tasks and are derived from the Jaccard index and F-measure. The \(\mathcal{J}\)-score measures objects' overlap relative to their combined size. The F-score, also known as the Dice score or boundary score, considers both precision and recall.  
The \(\mathcal{J}\)-index and \(\mathcal{F}\)-index function as a comparison of two masks. The \(\mathcal{J}\)- and \(\mathcal{F}\)-scores for one object are defined using the following equations:  
\[\mathcal{J}(A,B)=\frac{\left|A\cap B\right|}{\left|A\cup B\right|} \tag{1}\]  
\[\mathcal{F}(A,B)=\frac{2\left|A\cap B\right|}{\left|A\right|+\left|B\right|} \tag{2}\]  
\[\mathcal{J}\&\mathcal{F}(A,B)=\frac{\mathcal{J}(A,B)+\mathcal{F}(A,B)}{2} \tag{3}\]  
where \(A\) refers to the binary mask of the ground-truth object, and \(B\) refers to the binary inferred mask.  
Given that our experiments were conducted by using a frame-by-frame approach, it was required to select target instances for each frame in order to evaluate our system. To streamline this process, we suggest the following method. It is assumed that there are \(k\) masks \(\{M_{i}\}\) in the ground truth, and our system generates \(n\) masks \(\{S_{j}\}\). All pairs \((M_{i},S_{j})\) are compared to find and match for each ground-truth mask \(M_{i}\). If the value of \(S_{j}\) is greater than the number of ground-truth masks \(k\), we do not display the surplus.
## 4 Discussion
Nevertheless considerable leakage of the dye into the tubules was recorded, even from the AH Plus sealer [16]. In this paper, the hydrophobic AH Plus sealer was used, which in turn is probably the main factor limiting the leaching of both of the used dyes in this experiment, however these assumptions require further investigations with different methodologies. The AH Plus epoxy sealer was used in this study. It is characterized by its effective tightness, superior spatial stability and low density [39], but its hydrophobicity can be a theoretical problem. According to [PERSON] et al., it is more beneficial to leave slightly moisturized dentin, even if a hydrophobic sealer is used [40]. However, the researchers revealed that using alcohol for the final stages of irrigation could improve the penetration of a hydrophobic sealer into the dental tubules in relation to a hypochlorite used as an irrigant. On the other hand, [PERSON] et al. proved that a final rinse of the canal with either 70% isopropyl alcohol or Peridex did not increase sealer penetration, or significantly affected apical microleakage compared to NaOCl. However, in this model of the study, light microscopy was used to investigate the sealer penetration in vitro, so the results cannot be directly compared with those using confocal microscopes [41]. An in vitro study, which investigated the use of 95% ethanol as the final irrigant, in smear layer free dentin, revealed (within the limitations of these studies) an increased sealer penetration into dental tubules and decreased leakage [42]. The main shortcomings of the conducted study relate to the physical and chemical properties of the dyes used in the process of dyeing the sealer. Anatomical conditions of the root canals and histological differences between the samples taken from natural teeth may also be a risk factor for the presented research. The reader has to take these results with criticism and concern, as the sample preparation technique and sample variability can strongly influence the results obtained. The results of this experiment allowed for the null hypothesis to be rejected. The use of an isopropyl alcohol as the final irrigant had a different effect than previously assumed. The greatest sealer depth penetration measurement into the dentinal tubules was observed in the group, where a water-based solution of sodium hypochlorite was used in the final stages of irrigation. The differences were statistically significant mainly in the case of the porphyrin marked sealer. In this study, independently from the experimental group, different sealer depth penetrations were obtained in each section of the root canal. The differences can be influenced by the structure of the dentinal tubules in different sections of the root canal, as well as varied effectiveness of the irrigation and activation of solutions before obturation. Similar research points revealed a comparable relationship between these factors [2; 5; 28; 29; 43].
# Zero Drift Infrared Radiation Thermometer Using Chopper Stabilised Pre-Amplifier
## 3 Results and Discussion
### Statistics Describing the Distributions of Measurements  
Sample tests demonstrated that the measurements acquired from the two IRTs at the zero signal and 700 \({}^{\circ}\)C conditions arose from independent systems. Therefore, any differences between the mean values, variances and excess kurtosis values calculated were interpreted as arising from the different op-amps used to configure the TIAs.  
The mean, variance and excess kurtosis values calculated for the IRT output voltage measurements under the two measurement conditions are presented in Table 1.  
Variances incurred by the measurements using the zero-drift IRT were factors of 0.46 and 0.71 smaller than the variances incurred by the precision IRT, for the zero-and steady-state temperature measurements, respectively. The high excess kurtosis values demonstrated that extremal values within the measurements explained much of the variances calculated whilst the IRTs were blanked and, less so, when sighted at the 700 \({}^{\circ}\)C blackbody. The ratio of excess kurtosis values indicated that the precision IRT was subject to more extreme variations from its mean value than the zero-drift IRT.  
The similarity of variance and excess kurtosis values evaluated for the IRTs measuring 700 \({}^{\circ}\)C steady-state temperature, indicated that the two configurations would work similarly well for the measurement of higher temperature sources. The zero-drift IRT did still achieve lower variance than the precision IRT but the difference between the IRTs was not as marked with these higher intensity signals. The zero-drift IRT would be expected to afford lower minimum resolvable temperature measurement, owing to the lower noise and offset, whilst achieving a similar maximum temperature measurable to the precision IRT.  
From the zero-signal measurement variances, the noise equivalent power (NEP) within each configuration was calculated to be \(1.63\times 10^{-11}\) W/V/Hz and \(2.43\times 10^{-11}\) W/V/Hz for the zero-drift IRT and precision IRT, respectively. These NEP figures provide an assessment of the minimum detectable power within each IRT, combining both the photodiode and amplifier circuitry.
## 7 Limitations and Future Directions  
Although this study achieves the research objectives and provides several valuable implications, this study has several limitations. First, the study was limited to the school experiment at the senior high school level in collaboration with the Syiah Kuala University in Indonesia. Therefore, the findings should be generalized with caution. Future studies should use larger samples from various countries. Second, this study also used TPB as the base model, while student satisfaction toward STEM education could still be analyzed in many models. Additionally, it was a quantitative study, implying the need for qualitative and longitudinal studies in the future. The results showed how attitude and collaborative learning in STEM education affect student satisfaction. However, future studies could consider constructs such as student academic achievement, intention to use, and actual use of STEM education. The findings could provide additional knowledge on marginal processes and situations that describe STEM education's effect on student satisfaction. Conceptualization, T.T.W. and M.M. ; methodology, T.T.W. ; software, A.H.; validation M.M. ; formal analysis, T.T.W. and A.H.; investigation, M.M. ; funding acquisition, J.Z. ; project administration, J.Z. ; writing--original draft, T.T.W., M.M. and A.H.; writing--review and editing, T.T.W., M.M., A.H. and J.Z. All authors have read and agreed to the published version of the manuscript. This research was supported by the Hainan Provincial Natural Science Foundation of China (622 RC670); Project of the 13 th Five Year Plan of Educational Science in Hainan Province of China (QJY20191071). The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of Syiah Kuala University (protocol code 3806/UN11.1.6/TU/01.01/2022 and 1 May 2022). Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy. The authors declare no conflict of interest.
## 3 Results and Discussion
### UL-94 Flammability  
The results of the UL-94 flammability of all samples are summarized in Table 3. The pure polymer (TPU-1) showed high UL-94 flammability results. It caught fire easily, and the fire propagated considerably along with the melting of the polymer. Upon adding 3% plasticizer (TPU-2), the polymer's ability to catch fire and its propagation decreased to some extent. In TPU-3, the antimony trioxide (primary flame retardant) presented good flammability behavior. In TPU-4, the ability to catch fire, as well as the propagation, was reduced, but dripping still occurred. In TPU-5, the dripping of the polymer was very little,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **LOI** & **UL-Ranking** & **UL-Dripping** & **t\({}_{1}\)/t\({}_{2}\) (s) a ** & **N b ** \\ \hline TPU-1 & 19 & V-2 & yes & \textgreater{}50 & \textgreater{}28 \\ TPU-2 & 19 & V-2 & yes & 42/15 & \textgreater{}18 \\ TPU-3 & 21 & V-1 & yes & 20/10 & \textgreater{}12 \\ TPU-4 & 21 & V-1 & yes & 18/8 & \textgreater{}10 \\ TPU-5 & 26 & V-1 & yes & 9/7 & 0/3 \\ TPU-6 & 29 & V-0 & no & 0/2 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Flame retardant performance of composites.  
and the flame resistance of the polymer was greatly enhanced. The lowest flammability properties out of all the samples were shown by TPU-6. It did not catch fire, and neither did it melt or drip.
# 4 KSecure: A Universal Method for Active Manipulation Detection in Images of Any Resolution
## 3 Method
### Evaluation
#### 3.4.1 Transparency Metrics Obtained
Unlike steganography, which primarily focuses on covert communication, or classical watermarking, which often embeds additional information for ownership verification or copyright protection, our approach--active marking--is designed specifically for image integrity verification and tamper detection, while maintaining an exceptionally high level of transparency. This comparison serves to highlight the minimal perceptual impact of our approach relative to existing high-performance data-hiding techniques. The results demonstrate that 4 KSecure achieves significantly higher transparency (higher SSIM and PSNR values) compared to prior methods, while remaining suitable for active integrity verification across various resolutions. Figure 7: Average LPIPS for different resolutions of test images (our dataset). The obtained results demonstrate the superiority of the 4 KSecure algorithm over other data embedding methods in terms of transparency. Notably, this solution enables the efficient active marking of images at virtually any resolution, with successful testing conducted on materials up to a 4K resolution (\(3840\times 2160\)). Unlike some fully convolutional models such as SteganoCNN, which theoretically support images of any size, 4 KSecure does not require excessive memory resources when increasing resolution. This allows it to achieve very high SSIM and PSNR values by distributing integrity markers over a larger surface area and leveraging the semantic richness of the entire image, providing more potential locations for embedding verification features compared to approaches that operate only on patches. The described algorithm was tested on both a proprietary dataset and the ImageNet dataset, covering over 160,000 images. A key observation is the minimal variance in results between these two datasets. The SSIM values obtained, 0.9996 and 0.9995, differ by only about 0.01%, while the PSNR values (60.9391 vs. 60.7033) show a difference of less than 0.4%. An additional advantage of the 4 KSecure method is that it does not require separate training for different input and output resolutions, allowing it to efficiently process images with various aspect ratios, from 1:1 to 16:9, without modifying the network parameters.
## 2 System Model
### Channel
Without losing generality, we introduce the assumption that the mean of squared envelope value equals one, i.e.,  
\[\overline{r^{2}}=V_{1}^{2}+V_{2}^{2}+2\sigma_{F}^{2}=1. \tag{5}\]From (4) and (5), it follows that  
\[V_{1}=\sqrt{K/((1+\Gamma^{2})(1+K))},\quad V_{2}=V_{1}\cdot\Gamma, \tag{6}\]  
\[\sigma_{F}=\sqrt{1/(2(1+K))}. \tag{7}\]  
Based on the derivation approach from [11], the PDF of the fading envelope can be expressed in the form of  
\[p_{R}(r)=\frac{r}{\sigma_{F}^{2}}\,\mathrm{e}^{-\frac{r^{2}+V_{1}^{2}+V_{2}^{2} }{2r_{F}^{2}}}\left\{\mathrm{I}_{0}\left(\frac{rV_{1}}{\sigma_{F}^{2}}\right) \mathrm{I}_{0}\left(\frac{rV_{2}}{\sigma_{F}^{2}}\right)\mathrm{I}_{0}\left( \frac{V_{1}V_{2}}{\sigma_{F}^{2}}\right)+2\sum_{m=1}^{+\infty}(-1)^{m}\, \mathrm{I}_{m}\left(\frac{rV_{1}}{\sigma_{F}^{2}}\right)\mathrm{I}_{m}\left( \frac{rV_{2}}{\sigma_{F}^{2}}\right)\mathrm{I}_{m}\left(\frac{V_{1}V_{2}}{ \sigma_{F}^{2}}\right)\right\}, \tag{8}\]  
where \(\mathrm{I}_{\
u}(\cdot),\
u=0,1,\cdots\) denotes the modified Bessel function of the first kind and order \(\
u\) ([18], Equation (8.431)).
# A Multi-Site Anti-Interference Neural Network for ASD Classification
## 2 Materials and Methods
### Representation Learning
In addition, the purposes of sampling under the pooling layer are to reduce the amount of computation, to prevent overfitting, and to increase the receptive field of subsequent convolution CNN (1D). In order to ensure the corresponding relationship between convolutional kernel weights and brain regions, the range of receptive fields is always the same as the number of brain regions, and pooling of the previous layer is not required to increase receptive fields. At the same time, if the calculation amount is reduced by the pooling layer, the corresponding relationship between convolutional kernel weights and brain regions will be destroyed, resulting in the mixed features of different brain regions, and the importance of specific brain regions cannot be studied. Moreover, it has been proved that downsampling of the pooling layer does not necessarily improve the performance of the CNN network [22]. The convolution operation is shown in Formula (4), where \(x_{j}^{m}\) is the output of the current layer, \(x_{j}^{m-1}\) is the output of the previous layer, \(k_{ij}^{l}\) is the parameter of the current layer, \(\times\) is the dot product operation of the corresponding receptive field, and \(b_{j}^{m}\) is the bias of the current layer. Figure 4: Representation learning. Considering that the range of the functional connection matrix is \(-1\) to \(1\), the representation learning module selects the activation function tanh with the same range. The classification layer uses Softmax function to calculate the probability of each category, as shown in Formula (5). The Dropout layer and L2 regularization are added to prevent overfitting of the model, while the InstanceNorm layer is used to normalize the features in each channel. \[x_{j}^{m}=f\left(\sum\limits_{i\in M_{j}}x_{i}^{m-1}\times k_{ij}^{m}+b_{j}^{m}\right) \tag{4}\]  
\[p(y=j)=\frac{e^{x^{T}}W_{j}}{\sum\limits_{k=1}^{K}e^{x^{T}W_{k}}} \tag{5}\]
# Evaluation of _Aspergillus flavus_ Growth on Weathered HDPE Plastics Contaminated with Diesel Fuel
## 3 Results
### Composition of Weathered Diesel Fuel Extracted from HDPE Fuel Container Surfaces  
The diesel fuel extracted from HDPE plastic fragments exhibited a significantly altered composition due to prolonged weathering. A marked reduction in volatile compounds (C10-C14) and light aromatic hydrocarbons was observed, while the proportion of heavier n-alkanes (C21-C25) and unidentified residuals increased substantially, accounting for over 80% of the sample. These results indicate extensive loss of components through evaporation, oxidation, and both chemical and biological transformation over time. Detailed results are presented in Table 1 and compared to commercial diesel fuel composition.
## 2 Theoretical Considerations about Electrical Contacts  
As described in the Introduction section, an electrical contact consists of a pair of usually metallic contact elements, by the touch of which conduction in an electrical circuit is established. In practice, the touching of the two elements is achieved by pressing one element against the other by means of a force. This contact force can be produced by either springs (by compression or stretching) or bolts that mechanically join the two elements.  
In contacts with a large area, the effective contact area is smaller as compared to the apparent touching area, because surfaces that appear smooth at real scale are actually rough and contaminated at the microscopic scale [68].  
No matter how finely the contact surfaces are polished, the actual touch is achieved only in certain areas called contact points, where the current lines of flow are bent and undergo a constriction as shown in Figure 1. These micro-points of contact, which are grouped into contact areas, are called contact spots or a-spots, where the material is deformed. If the area of contact points \(A_{F}\) on which the contact force \(F\) is exerted is only a tiny fraction (0.01-0.05) of the apparent area \(A_{a}\), there is a surface contact, as illustrated in Figure 2, where contact is achieved through three a-spots, noted as 1, 2, and 3.  
Each contact spot consists of micro-areas where the material is deformed either plastically, elastically or both. The dependence between the contact force \(F\) and the contact area \(A_{F}\) on which this force is exerted under plastic deformation conditions can be expressed using the formula of [PERSON] [69]:  
\[A_{F}=\frac{F}{\xi\cdot H}=n\cdot\pi\cdot a^{2} \tag{1}\]  
where \(a\) is the radius of the equivalent circle for each of the \(n\) contact zones.  
For the calculation of the constriction resistance, two models can be applied [70], namely, the infinite conductivity sphere model and the flattened ellipsoid model.  
Figure 1: constriction of the current lines of flow.  
Figure 2: Surface contact illustration of effective and actual contact areas.