esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment
### Experimental Results and Discussion
#### 5.3.1 Experimental Results of the Six-Layer Models  
In this section, the number of sublayer layers N for both the encoder and decoder was set to six, the number of training rounds (epochs) was 40, and the number of parameters for each model is shown in Table 2:  
The number of parameters for all 5 models was around 50 million, with Transformer_Enc_Dec_Imp and Transformer_Enc_Dec_Imp + Syn having the largest number of parameters, increasing by 1,521,440 compared with the baseline model (1,258,272 + 263,168 = 1,521,440). The number of parameters for Transformer_Enc_Imp and Transformer_Dec_Imp increased by 1,258,272 and 263,168, respectively, compared with the baseline model, accounting for 82.7% and 17.3% of the total increase in parameters, respectively.  
With the cumulative four-gram BLEU [47] score as the primary evaluation metric, adopting the function corpus_bleu() in sacrebleu with smooth_method = \"none\", it is worth pointing out that the BLEU value can vary considerably depending on the choice of smoothing function, and multi-bleu-detok_perl is also used in the BLEU evaluation to ensure the accuracy of the BLEU values by verifying the consistency of the results between the two evaluation tools. The BLEU-4 values for each model were evaluated as shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Number of Parameters** \\ \hline Transformer\_Base & 48,787,909 \\ Transformer\_Enc\_Imp & 50,046,181 \\ Transformer\_Dec\_Imp + Syn & 49,051,077 \\ Transformer\_Enc\_Dec\_Imp & 50,309,349 \\ Transformer\_Enc\_Dec\_Imp + Syn & 50,309,349 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of parameters for each six-layer model.  
It can be seen that the average BLEU value of Transformer_Enc_Dec_Imp was 2.706 higher than that of the baseline model, which indicates that the improved structure was effective. The average BLEU of Transformer_Enc_Dec_Imp + Syn was 3.539 higher than that of Transformer_Enc_Dec_Imp, demonstrating that the addition of dependent syntactic information could improve the quality of translation.  
However, two unexpected results were found:  
1. Transformer_Enc_Dec_Imp + Syn did not have the highest average BLEU score, being lower than Transformer_Enc_Imp 1.161;
2. Transformer_Dec_Imp + Syn performed the worst in the experiment and had lower BLEU values than the baseline model for each random seed, obtaining the two lowest scores in all the experimental results.  
The analysis yielded the two most likely causes of the above phenomenons:  
1. The large disparity in the proportion of model parameters to the total increase in the parameters brought about by the improved encoder and improved decoder resulted in not having enough neural network units on the decoder side to fit the amount of data brought about by the dependent syntax matrix;
2. The information extraction of the Chinese dependency syntax by MMHDPA was not sufficient, and the structure of MMHDPA should continue to be improved to better learn the target language's grammar.
# A Sparse Multiclass Motor Imagery EEG Classification Using 1D-ConvResNet
## 4 Discussion
### Computational Time  
The MI-based classification algorithms require heavy preprocessing of the data, such as artifact removal, filtering (bandpass), feature extraction, feature selection, and classification. Hence, the computational cost and time increase with such procedures. On the other hand, the proposed method extracts the wavelet features directly from the raw EEG without preprocessing, and an overcomplete dictionary was built through uncorrelated sub-matrices corresponding to four different classes. After constructing the overcomplete dictionary, the sparse coefficients were obtained and used for classification. The purpose of sparse representation is to form a redundant dictionary with few coefficients corresponding to each class of data for training the model. As a result, the testing signal requires only the time to calculate a single sparse feature for classification.  
Furthermore, we used 1D-ConvResNet, which efficiently learns and classifies the sparse features and can be used for a multiclass classification instead of combining binary classifiers. Hence, the computational cost was significantly reduced compared to other machine learning algorithms.
## 2 Materials and Methods
### Preprocessing
#### 2.3.2 Outlier Removal and Feature Normalization  
To minimize unnecessary data loss while ensuring the integrity of the dataset, we examined the distribution of each feature. The inspection revealed that nearly all data points fell within the range of -500 to 500, with values beyond this range occurring rarely and likely due to measurement errors or artifacts. Therefore, we applied a consistent threshold by clipping values at \(-500\) and 500 as physiologically plausible limits. Any data point with at least one feature outside this range was considered an outlier and excluded from the training set. As a result, 331 outliers were removed from a total of 4944 samples, yielding 4643 data points for model training.  
Following outlier removal, feature scaling was performed using the MinMaxScaler from scikit-learn to rescale all feature values to the range from 0 to 1. In addition, for selected feature sets where vector magnitude uniformity was important, scikit-learn Normalizer (L2 norm) was applied to ensure that the resulting feature vectors have unit length. These preprocessing steps helped stabilize the training process and improve model convergence.
# A Comprehensive Review of Multimodal Analysis in Education
## 2 Background and Theoretical Foundations
### Origins and Development of MMLA  
The conceptualisation of MMLA was initiated with the objective of establishing a unified framework for the integration of multimodal data into the domain of educational research. Early foundational work in multimodal learning can be traced back to studies in science education and semiotics, which explored the interplay between different modes of communication, including speech, gesture, and visual representations. These studies highlighted how multimodal resources allow for richer and more complex meaning-making processes that go beyond purely linguistic descriptions, integrating visual and verbal elements in scientific texts to form cohesive multimodal narratives [19]. Other research has examined how gestures serve as a bridge between physical experiences in science laboratories and the abstract language of scientific discourse, facilitating the transition from concrete actions to symbolic representation [20]. These foundational studies underscored the importance of integrating multiple data streams to capture the full spectrum of human learning, recognising that meaning is constructed through a complex interplay of language, symbols, physical actions, and social interactions.  
The foundational work in this area, including that of [PERSON] et al. [21], established the theoretical underpinnings for the subsequent integration of low-level sensor data into higher-order learning constructs. These conceptual models were found to be of paramount importance in terms of facilitating the harmonisation of terminology and providing a framework for collaborative research initiatives across various academic disciplines.  
Subsequent studies expanded the scope of MMLA by exploring the potential of real-time analysis and adaptive feedback, particularly in digital learning environments. Researchers such as [PERSON] et al. [17] have emphasised the necessity for effective data fusion methods to synthesise diverse data sources, which range from video and electrodermal activity (EDA) to eye-tracking and interaction logs. [PERSON] et al. [22] made a further contribution to the field by proposing a taxonomy of multimodal domains that categorises data into five broad groups. The following terms are to be considered: natural language, video, sensors, human-centred design and environment logs.  
As time has passed, the application of MMLA has become increasingly diverse, encompassing a variety of educational contexts. These include higher education, early childhood education [23], and music instruction [24]. This expansion has also prompted greater scrutiny of the ethical implications of collecting and analysing multimodal data, particularly regarding student privacy [25].
# Unsupervised Machine Learning Techniques for Detecting PLC Process Control Anomalies
## 5 Results and Discussions
### Statistical Hypothesis Test
#### 5.3.1 Analysis of Variance Test (ANOVA)  
ANOVA is a statistical model used to determine if a significant difference between the means of two or more data sets exists [57, 58]. One-way ANOVA is chosen because of the interest in examining one independent variable's influence, which is F1-score. First, OSCVM, OCNN, and IF performances are evaluated on 20 different test samples of the exact sizes as test sets 1, 2, 3, 4, and 5. Next, each detection algorithm's F1-score is computed on 20 different samples of each test set. The assumptions about the data set are  
* data points in each test sample are independent and identically distributed; and
* data points are normally distributed.  
In addition, the hypotheses for the statistical test are  
* null hypothesis (\(H_{0}\)): The mean F1-score of all detection algorithms are equal; and
* alternate hypothesis (\(H_{a}\)): One or more of the mean F1-score are unequal.  
Based on the one-way ANOVA test, the \(F\) value is 14.972, and a \(p\)-value \(<0.001\) is achieved. One-way ANOVA shows significant evidence to reject the null hypothesis. Rejecting the null hypothesis indicates a considerable difference between at least two detection algorithms at a confidence level above 95%. Although one-way ANOVA reveals a difference in the three algorithms' performance, statistically, it is not clear which specific algorithm performs best or worst. Therefore, a post hoc analysis is required to identify the best-performing algorithm.
# Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems
## 5 Experimental Evaluation
### Diabetes Dataset  
Figure 11 shows the best results of supervised algorithms for _Accuracy_, _Precision_, _Recall_, _F1-Score_ and _ROC-AUC-Score_ metrics, applied to diabetes test dataset.  
The Random Forest algorithm obtained the best performance results in all metrics, with _Accuracy_ of 83.8%, _Precision_ of 73.9%, Recall of 72.3%, _F1-Score_ of 73.1% and _ROC-AUC-Score_ of 80.6%. On the other hand, the Decision Tree algorithm presented the worst results for the metrics _Accuracy_ of _73.6%, _F1-Score_ of 59.6% and _ROC-AUC-Score_ of 70.2%. The Naive Bayes algorithm presented the worst result for _Precision_ of 57.1%, while the Logistic Regression and KNN had the worst result for Recall with a value of 53.2%.  
Figure 12 presents the results of the _Silhouette_ Score metric for unsupervised clustering algorithms applied to the diabetes test dataset.  
We can see that the Spectral Clustering algorithm obtained the best _Silhouette_ Score value of 84.8% while Mean Shift had the worst _Silhouette_ Score value of 74.2%. The reason for which Mean Shift had the worst results can be justified by the fact that manually choosing the bandwidth can be non-trivial, and selecting a wrong value can lead to inferior results.  
Figure 13 shows the results for the metrics _Homogeneity_ Score, _Completeness_ Score, _V Measure_ Score, _Adjusted Mutual Info Score_ and _Adjusted Rand Score_ for the unsupervised clustering algorithms applied to the diabetes test dataset.  
Figure 11: _Accuracy, Precision, Recall, F1-Score_ and _ROC-AUC-Score_ for supervised algorithms on the diabetes test dataset.  
Figure 12: _Silhouette_ Score for unsupervised clustering algorithms of diabetes test dataset.  
All the previous metrics had considerably low values distant from the best value of 1 and \(-1\) for every unsupervised clustering algorithm. This means that the values close to zero are the result of datapoints being randomly assigned in the clusters.  
The best metric results of applying the two preprocessing unsupervised clustering techniques (K-Means and BIRCH) to Logistic Regression, Decision Tree and Naive Bayes supervised algorithms for the diabetes test dataset are shown in Table 1.  
Overall, the result of applying unsupervised clustering techniques for preprocessing supervised algorithms was not improved performance, as can be seen in the metric results. However, for Logistic Regression, compared to Decision Tree and Naive Bayes algorithms, preprocessing results were closer those without any preprocessing technique. Preprocessing Naive Bayes had lower performance results compared to the original Naive Bayes algorithm except for the Recall metric where it had a good result of 86.5% and 68.1%, respectively. Additionally, the preprocessing Decision Tree presented the best precision value of 59.2% compared to the original Decision Tree algorithm with a value of 55.7%.
# Eyelid Dynamics Characterization with 120 GHz mmW Radar
## 7 Conclusions  
In summary, this study confirms that using radar to detect and characterize eye blink dynamics is an alternative to using high-speed cameras or magnetic coils, avoiding the need for good illumination requirements or contact with the patient. The fine beam width and bandwidth of the FMCW radar can isolate the observation area from other parts of the body and surrounding objects. At 120 GHz, the depth of penetration into biological tissues is negligible. For this reason, the measured backscattering changes reflect the alterations in the shape/geometry of the observed body surface within the antenna beam spot. Depending on the direction of observation, the eyelid return may also be affected by echoes from the surrounding face areas, reducing the phase sensitivity of the echo with respect to the eyelid dynamics. A background cancellation technique has been proposed, showing the capability to recover the eyelid echo phase in these situations. Alternatively, observing the eye at a slanted angle with respect to the frontal face direction can also minimize the unwanted face interference. This study also tested the impact of reducing the radar bandwidth to 1 GHz and glasses being worn during measurement, without relevant impacts on the results obtained. Upgrading the radar with a beam-steering capability, for example, using a multiple-input, multiple-output configuration, would allow us to track the eye area automatically, allowing for more robust operation in front-of-body motion resulting from activities such as driving a vehicle.  
Conceptualization, A.B. and D.P.; methodology, A.B., J.R. and D.P.; hardware, A.A.; software, R.W., A.B. and D.P.; validation, R.W. and J.R.; data analysis, D.P., R.W. and J.R.; results optimization, R.W. and A.B.; writing--original draft preparation, D.P.; writing--review and editing, R.W. and A.B.; visualization, R.W.; supervision, A.B. and A.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The work of [PERSON] was supported by a Research Scholarship from the China Scholarship Council (CSC) under grant 202208390068.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Written informed consent was obtained from the subject involved in this study.  
**Data Availability Statement:** The experimental data obtained in this work is available on request from corresponding author due to lack of suitable data repository at this time.  
**Acknowledgments:** The authors are indebted to the Hospital Sant Joan de Deu de Barcelona for their help in the study of healthcare-oriented radar applications.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Discrete Element Simple Shear Test Considering Particle Shape
## 1 Introduction
After the shear begins, the loading rate will affect the formation of new contact between particles and between particles and the wall, and then affect the macro-stress-strain relationship of granular materials. Therefore, the effect of loading rate should be given attention in the PFC numerical test. Spherical particles (or disk particles) were employed to generate the sample of the DEM model in the simple shear test. It can reflect some characteristics of geomaterials, such as anisotropy and noncoaxiality [23], but in the contact position between the ball and the boundary, the ball is easy to slip and roll because of its free rotation and low friction, which will lead to unreliable test data [24]. In terms of the particles themselves, the sphericity of spherical particles seems to be inconsistent with the nonsphericity of geomaterials; nonspherical particles have better slip and roll resistance to avoid this phenomenon and are more consistent with the particle shape of geomaterials. Some numerical simulations employed nonspherical particles and the result shows the difference in comparison to spherical particles; the nonspherical particles have higher peak and residual friction angles [25; 26]. Therefore, the particle morphology of geomaterials should be considered in the simulation of soil mechanics tests. Some researchers have designed particles with different shapes to carry out simple shear tests, which are usually carried out in a two-dimensional plane, and rarely involve particle morphology in three-dimensional space [27]. Under the condition of the two-dimensional plane, the sample is sheared by rotating a rigid wall, and the sample is sheared by simulating laminar sidewalls in the three-dimensional model, and there are differences in the response of the two shear modes inside the sample [28]. Therefore, it is necessary to consider the microstructure of particles in three-dimensional discrete element simulation of geomaterials. In this study, a three-dimensional model of the simple shear test will be established and integrate the microscopic morphology of particles to study the effect of particle shape and confining pressure on the macroscopic stress-strain relationship and noncoaxiality. The sample will be prepared with ellipsoidal particles with different aspect ratios and shear under constant vertical pressure. The effect of particle shape on microscopic mechanical properties and fabric evolution under the principal stress rotation will be investigated to provide a reference for developing constitutive models of granular materials and the deformation behavior of subgrade and other engineering facilities.
# Mechanical Properties of Concrete with Bamboo Chips
## 4 Conclusions  
Several studies have focused on using bamboo splints to replace steel reinforcing bar or fiber. However, the use of bamboo chips to replace aggregates has rarely been investigated. In this study, 1 cm \(\times\) 1 cm \(\times\) 0.5 cm (thickness) bamboo chips were wetted, dried, and coated. The chips were used to replace 10, 20, and 30% of the coarse aggregates in concrete. Only wetted bamboo chips were used to completely replace the natural coarse aggregates. The chips were then cured under water for 7 and 28 days, and the corresponding compressive and splitting tensile strengths were subsequently evaluated. The major results of this study are summarized as follows:  
* Regardless of the bamboo type, both compressive and splitting tensile strengths decreased with increasing BCC. The strength of the specimen with 30% dried bamboo was 87% of the strength associated with ordinary concrete without bamboo. When natural coarse aggregates were replaced entirely with wetted bamboo chips, the compressive and tensile strengths decreased to \(\sim\)10% of the plain-concrete strength. This results from the fact that the bamboo chip had lower strength and a weaker interface than the natural coarse aggregate.
* The specimen with wetted bamboo exhibited the highest strength, whereas the specimen with coated and then dried bamboo exhibited the lowest strength. During the curing process, the dried bamboo absorbs water, causing the volume to expand, resulting in micro cracks in the concrete and leading to a reduction in the strength of the concrete.
* The elastic modulus decreased from \(2.2\times 10^{4}\) MPa to \(0.32\times 10^{4}\) MPa, when the BCC increased from 0 to 30%. Moreover, except for the toughness ratio of the plain concrete, the toughness ratio decreased slightly with increasing BCC.  
S.-S.P.--Supervise, conceptualization, writing and methodology evolution: Y.-L.H.--Experimentation, data interpretation and writing: J.-C.L.--Supervision, logical interpretation and writing: S.-W.J.--Data interpretation, writing and constructive suggestions.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (No. NRF-2018R1A5A1025137).  
The authors declare no conflict of interest.
# A Seamless Image-Stitching Method Based on Human Visual Discrimination and Attention
## 3 Methods
### Visual Perception Quantification
#### 3.1.4 Visual Saliency  
In addition to the visual discrimination restricted by HVS physiological properties analyzed above, there is also a vital psychological feature called visual attention mechanism, which has an impact to the selection of visual information. According to the feature integration theory proposed by [35], independent early features are extracted but not perceived at first time, and then the whole scene is gradually perceived with the selection and transfer of focus of attention (FOA). VS is used to represent the tendency of FOA selection. There is an increasing number of researchers studying VS detection to achieve the consistent effect with the human eyes in recent years. Some researchers are focusing on image quality assessment based on VS for reasonable evaluation results [14; 36].  
Each pixel has a unique saliency value, and those pixels with higher saliency have greater impact on image quality. If the discontinuous stitching line is also with a high visual saliency, it will have a negative effect on the mosaic result. Therefore, it is of great significance to avoid high VS regions when searching the stitching line. In this paper, we choose the VS model SDSP to calculate the VS value of each pixel. The value is defined as Equation (8):  
\[\text{VS}(X)=\text{VS}_{\text{F}}(X)\text{-VS}_{\text{D}}(X)\text{-VS}_{ \text{C}}(X), \tag{8}\]  
where \(X\) refers to different pixels in input image pairs. The final saliency map VS combines three saliency maps, which include saliency map \(\text{VS}_{\text{F}}(X)\) modeled by band-pass filtering, location saliency \(\text{VS}_{\text{D}}(X)\), and color saliency \(\text{VS}_{\text{C}}(X)\) based on whether it is warm or cold respectively.