esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Wheelchair Control via Multi-Modal Integration: Combining Webcam and EEG
## 5 Experimental Results and Discussion
### Evaluation Results
#### 5.2.3 Fusion Command Results  
The study findings, depicted in Figure 11, demonstrated a consistent correlation among the results obtained from the webcam, EEG signals, and their integrated fusion in terms of accurately detecting and recognizing emotions. Additionally, the ear (action unit for the recognition of expression) of emotion was also found to align consistently with the outcomes obtained from the EEG signals and the webcam modalities. This convergence of results across multiple modalities lends further support to the robustness and reliability of our approach in capturing and interpreting emotional states.  
The alignment observed between facial expressions captured by the webcam and neural activity monitored through EEG signals highlights the effectiveness of combining these two modalities in the context of controlling a wheelchair.  
Confusion matrices are a valuable tool for evaluating classification models by summarizing the relationship between predicted and actual labels. They are structured as N \(\times\) N  
Figure 11: Example of experimental result of fusion.  
Figure 10: Experimental result of facial expressions with EEG.  
tables, with one axis representing predicted labels and the other axis representing true labels. In a multiple classification problem with N classes, confusion matrices provide insights into the model's classification accuracy and revealing its strengths and weaknesses. In our multiple classification problem, N = 4.  
Figure 12 shows the confusion matrix of the fusion algorithm for our classification. Where the green color represents correct matrices and the green color represents incorrectly classified matrices. It shows that out of the 400 samples, the actual states of the emotion Wink Left are number 1 in this figure, and the model (fusion) was correctly classified (393) and misclassified (7). Similarly, for these emotions (Wink Right \"2\", Blink \"3\", and Smile \"4\"), 400 samples each (395, 396, 400) were correctly classified, however 5, 4, and 0 were incorrectly classified.
# Video Human Action Recognition Based on Motion-Tempo Learning and Feedback Attention
## 3 The Proposed Critical Action Information Extraction Network
### Feedback Attention-Guided Module (FAGM)
#### 3.2.3 Spatial Attention-Guided Module (SAGM)  
As demonstrated in Figure 7, \(\mathbf{F}_{l}^{T,C_{l}}\) undergoes pooling separately to capture global and local salient information. Then, the results are concatenated to obtain the feature \(\mathbf{F}_{s}\in\mathbb{R}^{N\times 2\times H_{l}\times W_{l}}\). Afterward, \(\mathbf{F}_{s}\) is processed through a convolutional layer for dimension reduction, followed by a sigmoid function. Finally, the result is then element-wise multiplied with \(\mathbf{F}_{l}^{T,C_{l}}\) to obtain \(\mathbf{F}_{l}^{T,C_{l},S}\in\mathbb{R}^{N\times C_{l}\times H_{l}\times W_{ l}}\). The specific formulas are as follows:  
\[\mathbf{F}_{s}=[\text{AvgPool}(\mathbf{F}_{l}^{T,C_{l}});\text{MaxPool}( \mathbf{F}_{l}^{T,C_{l}})] \tag{16}\] \[\mathbf{F}_{l}^{T,C_{l},S}=\sigma[Conv_{\gamma\times 7}(\mathbf{F}_{s })]\odot\mathbf{F}_{l}^{T,C_{l}} \tag{17}\]  
where \(\text{Conv}_{\gamma\times 7}\) represents a convolution operation with a kernel size of \(7\times 7\). By iterating through the above steps multiple times, the model can fully exploit the contextual information within high-level features.  
To enhance the ability of CNNs to activate key sub-actions and motion tempos learning, we propose SMEN. SMEN employs ResNet50 as its backbone and consists of two important components: MgAFM and FAGM. MgAFM is designed to capture motion features of sub-actions with varying tempos and motion dynamics, and adap  
Figure 6: The Channel Attention-Guided Module transformed multi-level features into same dimension along the channel dimension, which are then concatenated accordingly. Then attention weights are generated along the channel dimension via an MLP and applied to the original low-level features. In this way, the contextual information contained in the high-level features is integrated into the low-level features and guides the low-level features to focus on important channel information.  
Figure 7: The low-level features, enriched with contextual information, are subjected to max pooling and average pooling along the spatial dimension to generate a spatial attention map. The values of the spatial attention map are then constrained within the range of \([0,1]\) using a sigmoid function. Finally, the spatial attention weights are applied to the original features through element-wise multiplication, effectively emphasizing the crucial appearance information.  
motion features with multi-granularity. FAGM leverages contextual information and salient motion frames from high-level features, to guide low-level feature learning, thus focusing on channel and spatial information which facilitates video action classification.
# Multipath Interference Impact Due to Fiber Mode Coupling in C+L+S Multiband Transmission Reach
## 5 Conclusions  
In this work, we have assessed the impact of the coherent MPI, due to mode coupling in G.654E SMFs, in the transmission reach of a C+L+S MB scenario. The applied analytical formulation, based on the GSNR, considers, besides the coherent MPI, the NLI noise and the ASE noise accumulated along several spans. Several transmission scenarios with two modulation formats--QPSK and 16-QAM--and three symbol rate values--32, 64, and 128 Gbaud--were tested and discussed.  
As in G.654E SMFs, the S-band is below the wavelength cut-off, the coherent MPI impact, due to mode coupling, only affects transmitting signals in this band. We concluded that, in the S-band scenario, the transmission reach decreases almost 200 km, which corresponds to an approximately 25% reach decrease when the MPI/span increases to \(-26\) dB/span, considering QPSK signals with a 64 GBaud symbol rate. If L- and C-band signals were not above the wavelength cut-off, the L-band scenario would be more affected by MPI than the C- or S-band scenarios--an approximately 52% reach decrease would be observed for this band. The S-band would be the least impacted band by the MPI due to the reduction in the signal power induced by the ISRS effect in this band. We have also concluded that the symbol rate almost does not influence the impact of the MPI on the transmission reach in a C+L+S MB scenario.  
Conceptualization, L.C. and J.P.; methodology, L.C.; software, L.C.; validation, L.C.; formal analysis, L.C.; investigation, L.C. and J.P.; resources, L.C.; data curation, L.C.; writing--original draft preparation, L.C.; writing--review and editing, L.C. and J.P.; visualization, L.C.; supervision, L.C. and J.P.; project administration, L.C. and J.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by Fundacao para a Ciencia e Tecnologia/ Ministerio da Ciencia, Tecnologia e Ensino Superior (FCT/MCTES) under Project UIDB/50008/2020.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.
## 1 Introduction
### Immersive Technologies in Education  
Virtual reality has emerged as a transformative technology in education, standing out from other ICT tools due to its immersive qualities. VR involves a suite of technologies that generate highly interactive, three-dimensional environments that replicate real or imagined scenarios. These environments allow users to engage through multiple senses and interact with virtual elements in real-time [14]. For maximum educational benefit, VR must be clearly defined and properly integrated into instructional frameworks.  
With technological advancements driving more digitalized and gamified learning experiences, education is increasingly adopting strategies from video games to create immersive and interactive learning environments. In the context of special education, VR's controlled and multisensory environment is particularly appealing for children with ASD. It offers a safe space to practice social interactions without the unpredictable and stressful elements of real-life situations. However, it has not yet been proven whether the use of VR technology combining social stories with visual novel elements can be effective for children with Level 1 ASD.  
This study explores how a VR application could support children with Level 1 ASD in enhancing their social and emotional abilities. Specifically, we investigate whether a VR visual-novel-style social story can effectively improve social interaction and communication skills among children with ASD in a mainstream educational setting. To answer this, a case study was conducted using a VR visual novel designed specifically for primary school children with ASD, situated within a mainstream educational environment. The VR story provided relatable social scenarios and offered users the chance to make decisions, mimicking real-life social interactions. This controlled, virtual setting offered a safe space to practice responses, understand social cues, and build social confidence. Assessments conducted before and after the intervention, along with feedback from teachers and parents, indicated noticeable improvements in participants' social behaviors, including better eye contact, turn-taking, and increased empathy.
# Identifying Real Estate Opportunities Using Machine Learning
## 5 Evaluation
### Results and Findings  
In this section, we will explore the performance achieved by different machine learning models. The following quality metrics for regression have been computed, which are provided by the scikit-learn application programming interface (API):  
* Explained variance regression score, which measures the extent to which a model accounts for the variation of a dataset. Letting \(\hat{y}\) be the predicted output and \(y\) the actual output, this metric is computed as follows in Equation (1): \[E_{var}\left(y,\hat{y}\right)=1-\frac{Var\left\{y-\hat{y}\right\}}{Var\left\{y \right\}}.\] (1) In this equation, \(Var\) is the variance of a distribution. The best possible score is 1.0, which would occur when \(y=\hat{y}\).
* Mean absolute error, which computes the average of the error for all the instances, computed as follows in Equation (2): \[MAE\left(y,\hat{y}\right)=\frac{1}{n}\sum_{i=0}^{n}\left|y_{i}-\hat{y}_{i} \right|.\] (2) Since this is an error metric, the best possible value is 0.
* Median absolute error, similar to the previous score but computing the median of the distribution of differences between the expected and actual values, as shown in Equation (3): \[MedAE\left(y,\hat{y}\right)=median\left(\left|y_{1}-\hat{y}_{1}\right|,\dots, \left|y_{n}-\hat{y}_{n}\right|\right).\] (3) Again, since this is an error metric, the best possible value is 0.
* Mean squared error, similar to MAE but with all errors squared, and therefore computed as described in Equation (4): \[MSE\left(y,\hat{y}\right)=\frac{1}{n}\sum_{i=0}^{n}\left|y_{i}-\hat{y}_{i} \right|^{2}.\] (4) As it happened with MAE, since this is an error metric, the best possible value is 0.
* Coefficient of determination (\(R^{2}\)), which provides a measure of how well future samples are likely to be predicted. It is computed using Equation (5): \[R^{2}\left(y,\hat{y}\right)=1-\frac{\sum_{i=0}^{n}\left(y_{i}-\hat{y}_{i} \right)^{2}}{\sum_{i=0}^{n}\left(y_{i}-\hat{y}\right)^{2}}.\] (5)In the previous equation, \(\bar{y}\) refers to the average of the real outputs. The maximum value for the coefficient of determination is 1.0, which would be obtained when the predicted output matches the real output for all instances. \(R^{2}\) would be 0 if the model always predicts the average output, but it can also hold negative values, since the model can work arbitrarily worse than just predicting the estimated value.  
In order to discuss the results, we will address relevant findings that can be extracted from the data.
# Deep Learning-Based End-to-End Carrier Signal Detection in Broadband Power Spectrum
## 2 Problem Description
### The End-to-End Detection Process  
Figure 2 shows the end-to-end detection process of the carrier signal detection in the broadband power spectrum. First, for the wideband time-domain signal, we utilize the Welch method [26; 27] to estimate the broadband power spectrum. Then, considering that the broadband power spectrum is an input of the network, the proposed SCN can automatically regress the FC and the corresponding BW of all subcarriers.  
Figure 1: The core task of carrier signal detection problem in the broadband power spectrum.  
The Welch power spectrum estimation is the average of the modified periodograms, namely:  
\[P_{xx}^{W}=\frac{1}{k}\sum_{i=0}^{k-1}\frac{\left|\sum_{n=0}^{M-1}x_{i}(n)\omega( n)e^{-2\pi/n}\right|^{2}}{\sum_{n=0}^{M-1}\omega^{2}(n)}\ \ \ i=0,\ 1,\ \ldots,k-1 \tag{1}\]  
The time-domain signal sequence is separated into \(k\) data segments with an overlap of 50% and a length of 2\(M\). Additionally, \(\omega(n)\) is the window function of a normalization factor for the power estimation results.
# Peptides That Block RAS-p21 Protein-Induced Cell Transformation
## 2 Anti-Onogenic RAS-p21 Peptides from RAS-p21 Itself  
To determine the possible structural effects of oncogenic amino acid substitutions in RAS-p21 on its three-dimensional structure, conformational energy calculations, using two types of sampling methods, i.e., molecular dynamics (using AMBER [16] and DISCOVER [17] potential functions) and the electrostatically driven-Monte Carlo (EDMC) [18] method (using ECEPP potential functions [19]), were performed on the structures of wild-type-RAS-p21 and G12V- and Q61L-RAS-p21 (oncogenic forms) bound to GDP and GTP. These procedures were likewise applied to the two triple mutant proteins [G10V,G12R,A59T]RAS-p21 and [G12R,G15V,A59T]RAS-p21 that do not bind to either GDP or GTP [14]. Both methods determined the low energy structures that lie in the same potential energy well as the energy-minimized X-ray starting structures which have the same chain fold but may differ locally in conformation from this starting structure. Theselow energy Boltzman-weighted structures, including that for the energy-minimized X-ray starting structure, were then used to compute the average structures for the wild-type and oncogenic proteins which were then superimposed on one another. Local domain changes in conformation could then be readily determined from these superpositions. Under the hypothesis that these domains were potential effector peptide segments activated by the oncogenic amino acid substitutions, these peptides were then synthesized and tested in cells to determine if they could act as decoy peptides, i.e., as inhibitors of oncogenic RAS-p21 [15].  
Both sets of approaches identified five highly flexible domains that underwent local structural changes in oncogenic RAS-p21 forms when their average structures were compared with that of the wild-type protein bound to GDP: the 10-16 sequence of the P loop domain containing residue 12, the switch 1 region; residues 35-47, a segment that interacts directly with the ras-binding domain (RBD) of RAF, and with GAP, SOS and PI3K proteins; the switch 2 region involving residues 55-71; and residues 96-110 and 115-126, both found to be involved the binding of RAS-p21 to SOS [15]. The 96-110 segment has also been implicated in the interaction of Val 12-RAS-p21 with JNK [15]. The C\({}^{\alpha}\) traces of each of these segments are shown in Figure 2 for the average structures of wild-type-RAS-p21-GDP (purple), wild-type RAS-p21-GTP (yellow), G12V-RAS-p21-GTP (blue) and Q61L-RAS-p21-GTP (green). Further correlation analysis on the structural changes in the different domains was performed that indicated that changes in conformation in the 10-16 domain around residue 12 are associated with structural changes in the switch 1 region which, in turn, are associated with changes in the switch 2 domain. Structural changes in the switch 2 domain were further found to correlate with changes in the 96-100 and 115-126 domains [20].
## 2 Materials and Methods
### Scanning Process  
A Smarttech 3D Universe scanner was used for scanning; the scanner was connected to a computer to enable the continuous visualization of the process. Detailed data regarding the scanner are presented in Table 1 and in [PERSON] et al. [14]. The scanner was mounted on a tripod, which ensured a constant distance between the scanner and the scanned footprint. Before scanning, markers were placed in the contour of the footprint to clearly separate the scanned area from the background (intact soil). In the first stage of scanning, the so-called triangle mesh was used, from which a three-dimensional (3D) profile of the print was then created in the program. Next, the obtained 3D image was analyzed in a graphic program--a cross-section was made in the vertical plane in the transverse direction (to determine the width and depth of the footprint) and longitudinally (to determine the length of the footprint). In addition, a cross-section was made in the horizontal plane at the height corresponding to the largest transverse deformation--in this case, the aim was to compare the nature of the deformation of a radial and bias-ply tire. The actual surface of the footprint (taking into account vertical deformation) was determined via direct reading in the scanner software (S3 Dmeasure v.20). The Smarttech 3D Universe scanner was calibrated for each 50 h of work. The calibration was realized using a special plate with calibration points.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Description** \\ \hline Scanning technology & White structural light—LED \\ \hline Dimensions (XYZ) of measuring volume (mm) & 400 \(\times\) 300 \(\times\) 230 \\ \hline Distance between points (mm) & 0.156 \\ \hline Accuracy (mm) & 0.08 \\ \hline Power consumption during measurement (W) & 200 \\ \hline Total mass (kg) & 4.40 \\ \hline Working temperature (\({}^{\circ}\)C) & 20 \(\pm\) 0.5 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the Smarttech 3D Universe scanner [14].  
The presented method for the determination of the footprint parameters is innovative, it can simplify the experiment, and it can give high accuracy.
## 3 Results and Discussion
### Evaluation of Volatile Aroma Compounds
#### 3.1.11. Lactones  
In young wines, only \(\gamma\)-nonalactone was detected with even amounts across the treatments (Table 1). Aging introduced _trans_-oak lactone, _cis_-oak lactone, and \(\gamma\)-decalactone, with increased \(\gamma\)-nonalactone levels altering the lactone ratios between the treatments. This also resulted in changes in lactone ratios compared to young wines. TM21-A treatment wine had the highest lactone concentrations after maturation, mostly attributed to increased oak lactone levels. TM10-A and especially TM7-A wine had lower concentrations than the remaining treatments. Particular lactones can originate from grapes [7] or be produced by yeasts during fermentation [38]. The oak lactones, which significantly impact wine sensory qualities, originate exclusively from the wood of the barrel. Formation can occur through cyclization or enzymatic processes, with glutamic acid often serving as a precursor [49]. The extraction of lactones from oak barrels depends on factors such as the oak origin, toasting level, and drying method, as well as wine's chemical composition [71]. It was found that total acidity affects lactone extraction, with higher acidity leading to lower concentrations of _trans_- and _cis_-oak lactones and vice versa [72]. This observation aligns with the findings of this study, where TM21-Y wines, which had the lowest total acidity,exhibited the highest levels of _trans_- and _cis_-oak lactones. The lower total acidity in TM21-Y may have facilitated the extraction and stability of these lactones. Although specific acidity data are not shown in a table, the trend observed supports the significant impact of acidity on lactone concentrations.  
Increased lactone concentrations after aging, especially with prolonged maceration, have been observed [30]. Lactones have been identified as markers of oak aging [12]. In this study, _trans_-oak lactone concentrations were lower than those of _cis_-oak lactone, consistent with previous findings [70].
## 2 Principles and Methods
### Active Controller and Actuator Design
#### 2.4.2 Actuator Design  
The actuator is an important component in the active control system. Its function is to apply control force to the system according to the control signal to change the system response. Voice coil motor actuator is a new type of actuator based on magnetic force, which has the advantages of fast response, good robustness, and large control force. The voice coil motor actuator can be simplified as the model, shown in Figure 6.  
In Figure 6, \(i(t)\) is the control current, \(R\) is the moving coil resistance, \(L\) is the moving coil voltage, \(u(t)\) is the control voltage, and \(K_{F}\) is the gain. The dynamic analysis of the model of the actuator, shown in Figure 6, shows that the force of the electrified coil in the magnetic field is  
\[F=K_{F}\dot{  
Figure 5: Optomechanical coupling active control system of the spatial filter.  
Figure 6: Model diagram of voice coil actuator.  
According to Kirchhoff voltage law, the electric balance equation of the voice coil motor is obtained.  
\[L\frac{di(t)}{dt}+Ri(t)=u(t) \tag{4}\]  
The AVM50-HF-10 voice coil motor of AKribis company is selected as the actuator for vibration control according to the Optomechanical structural modal parameters of the spatial filter. The parameters of the voice coil motor are \(K_{F}=28\) N/A, \(L=0.0053\) H, \(R=8.6\)\(\Omega\), peak thrust \(F_{A\max}=140\) N, and peak voltage \(u_{\max}=43.2\) V.  
In this paper, when the voice coil motor is used in the actual active control system, the mass of the Optomechanical structure is much larger than that of the motor mover, so the mass of the motor mover can be ignored, and the output force of the motor is used to push the Optomechanical structure, so the transfer function of the voice coil motor can be obtained as follows.  
\[H=\frac{F(s)}{V(s)}=\frac{K_{F}}{Ls+R} \tag{5}\]  
The displacement, velocity, and acceleration of the push rod and the Optomechanical structure are equal because the push rod of the actuator is fixedly connected with the Optomechanical structure. This shows that there is a coupling relationship between the actuator and the Optomechanical structure. The active control force of the voice coil motor actuator is  
\[f_{c}=-H\cdot H_{c}=-\frac{K_{F}}{Ls+R}(g_{I}+gps)X \tag{6}\]  
In this paper, the active actuator uses voice coil motor to provide control force, and adopts a PI control algorithm to control displacement feedback gain and speed feedback gain, which can suppress response amplitude and achieve fast convergence.