esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
### Output Analysis  
#### 5.1.1 Parameter Necessity Analysis  
Now that the inputs and outputs have been empirically validated, the model can be used for scenario planning. The purpose of a scenario is to select a combination of operational steps, which increases technology diffusion by the highest possible degree. Operational steps are the product of decision parameters' states. The decision parameters are some of the input parameters that can be altered to monitor the targeted behavior of the system in a specific time domain. The decision parameters of this model are presented in Table 14. The model's outputs are validated against six months ranging from the beginning of summer 2017 to the end of autumn 2017 (see Appendix B), the validation results show that the model's predictive accuracy is very high for each output. Before scenario planning, two preliminary steps should be taken. The first step is to pinpoint on which parameter the diffusion of 4G is necessarily dependent. The second step is to identify the increase in which parameter has a higher effect on the increase of 4G diffusion.  
All decision parameters have a continuous numerical scale (ratio) ranging from 0 to 1, as presented in Table 14. This scale is converted to a Boolean structure where the value of 1 stands for true (T) and 0 for false (F). The purpose of this conversion is to show without which parameter, the diffusion of 4G never happens (step 1). As shown in Figure 14, both compatibility and advertisement are tested for a value of 0. It shows that in case of compatibility = 0, the 4G has not changed in all 100 experiments, while the 4G has changed frequently in all 100 experiments (i.e., runs) in case of advertisement = 0. This result indicates that the compatibility parameter is necessary for the diffusion of 4G because the diffusion of 4G is impossible to happen without the existence of the compatibility parameter. Counter-intuitively, the 4G adoption is necessarily dependent on compatibility degree than the volume  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Decision Parameter** & **Initial Value** & **Direction** & **Target** & **Time Steps** \\ \hline
4G-compatibility & & & & 30 (each step for \\ (4 GComP) & 0.8 & Increase & 4G diffusion & three days) \\ Advertisement (Ad) & 0.6 & Increase & 4G diffusion & 30 (each step for \\  & & & & three days) \\ \hline \end{tabular}
\end{table}
Table 14: Decision parameters.  
Figure 13: Performance of social structures in the simulation of 4G.  
of TV advertisement, which can be rooted in the technology push nature of the MTTs [102; 103; 104] and WSN topology of Iranian social network, where the world-of-mouth can drastically affect the information transfer regarding any artifact [100; 101].
# Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations
## 5 Immersive Audio-Visual Experiments  
As mentioned earlier, in this work, we performed three different perceptual experiments. All three experiments used the immersive experimental methodology described by [PERSON] et al. [16]. With the goal of capturing the real media-consumption experience, this methodology proposes an experimental environment that is supposed to be as natural as possible. The main goal of this methodology is to encourage the subject's engagement with the test content. All experiments used three main aspects of the immersive methodology:  
* _Length of stimuli:_ Longer stimuli (30-60 s) is used along with the three perceptual experiments. According to what was reported in [16], this time length is considered sufficient to capture the participants' attention, transmit an entire idea, and still maintain a tolerable test duration.
* _Content diversity:_ Present each source content only once at each session to prevent fatigue and content memorization. Therefore, the accuracy of the results is associated with the number of different content sources used in the test. In the immersive methodology, for each HRC, each participant should rate five to ten stimuli, which leads to a good estimate of the participant's opinion about each HRC [16];
* _Input media:_ Use audio-visual material and ask participants to rate the overall audio-visual quality and the content. This release participants from the task of separating audio and video quality when presented with audio-visual content.  
In addition, the immersive methodology poses a specific setup to generate the test stimuli for the experiment. One recommendation is to set the number of stimuli sources (\(w\)) in the experiment as an integer multiple of the number of HRCs (\(y\)). The combination of each source stimuli and HRC produce a total of (\(w\cdot y\)) test stimuli. Then, each participant in the experiment rates (\(w/y\)) test stimuli for each HRC under study. It is expected that, when all participant's responses are pooled, (\(n/y\)) participants have rated each individual test stimuli, where \(n\) is the number of human participants.  
In this section, we describe the source stimuli, apparatus and physical conditions, experimental procedure, and statistical methods used in the conduction of this set of experiments.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Degradation** & **Conditions** & **Parameters** & **Range** \\ \hline Chop & 4 & Rate & 1, 2, 5 (chops/s) \\  & & Period & 0.02, 0.04 (s) \\  & & Mode & previous, zeros \\ \hline Clip & 4 & Multiplier & 11, 15, 25, 55 \\ \hline Echo & 4 & Alpha & 0.175, 0.3, 0.5 (\%) \\  & & Delay & 25, 100, 140, 180 (ms) \\  & & Feedback & 0, 0.8 (\%) \\ \hline Noise & 4 & Noise type & car, babble, office, road \\  & & SNR & 15, 10, 5 (dB) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Audio Degradations Parameters.
# Comparison of Modern Deep Learning Models for Speaker Verification
## 4 Models Overview
### PyAnnote  
PyAnnote is a framework which provides various modules for speaker diarization, including an embedding module, which is used for speaker embedding extraction [25; 39]. PyAnnote speaker embedding model is based on the canonical x-vectors TDNN-base architecture [40] with filter banks replaced by trainable SincNet features [41]. PyAnnote provides pre-trained PyTorch models which share the same generic PyanNet base architecture.  
PyAnnote's speaker embedding model uses a 512-unit-wide and 3-recurrent-layer deep network, which relies on x-vector-like statistical temporal pooling and has been trained on short 500 ms audio chunks. The speaker embedding model was trained on the VoxCeleb dataset.
## 1 Introduction
A particularly effective alternative is represented by the deterministic method described in [14; 15], where an initial transformation, based on the knowledge of the DoV, from the local topocentric Cartesian system to the local geodetic Cartesian system is followed by a second transformation, from the latter to the global frame. [16] demonstrated that ignoring the first of the two transformations, and therefore neglecting the DoV, may cause an error of one centimeter on the tie vector components, which is totally incompatible with the mm-level precision required for the ITRF computation [17]. The DoV is generally split into two orthogonal components, one in the meridian plane and one in the plane of the prime vertical, denoted \(\xi\) and \(\eta\), respectively [5]. The total deflection \(\theta\) can then be obtained from:  
\[\theta^{2}=\xi^{2}+\eta^{2} \tag{1}\]  
The component of DoV, along a geodetic azimuth direction \(\alpha\), is given by [18]:  
\[\varepsilon=\xi\ cos\alpha+\eta\ sin\alpha \tag{2}\]  
The curvature of the lines of force of gravity causes the value of the DoV to change along the plumb line. There are different definitions of the DoV, depending on where, along the plumb line, it is evaluated [19; 20; 21; 22]. Among these, the definition given by [PERSON], _i.e._, the DoV at the topographical surface (rather than, e.g., at the geoid), yields the value that must be applied for integrating the data acquired by instruments of space geodesy with those acquired by terrestrial instruments. The components of the DoV at the surface can be simply estimated comparing the astronomical latitude and longitude (\(\Phi\), \(\Lambda\)) with the ellipsoidal geodetic coordinates (\(\varphi\), \(\lambda\)) for the same point (astro-geodetic method) [5],  
\[\xi=\Phi-\varphi \tag{3}\] \[\eta=(\Lambda-\lambda)\,cos\varphi\]  
In this study, we compare the DoV values that were obtained with three different methods at the two GNSS-VLBI co-location sites of Medicina (Northern Italy) and Noto (Sicily), managed by the Institute of Radioastronomy (IRA) of the National Institute of Astrophysics (INAF). At both sites, since 2001 the GNSS-VLBI tie vectors have been periodically surveyed using terrestrial techniques and have contributed to the computation of the ITRF [12]. Under this perspective, the knowledge of the DoV might play a crucial role in achieving the utmost level of accuracy in the tie vectors alignment. In the next section, the three different methods that were adopted to estimate the DoV are illustrated: (i) a combination of GNSS and geometric leveling (GNSS-LEV); (ii) the QDaedalus astro-geodetic system developed by the ETH Zurich [23]; and (iii) the estimation of the DoV components using the most accurate Italian gravimetric geoid model available: ITALGEO2005 [24]. Section 3 is devoted to the discussion of the results and the last section contains the conclusions.
## 5 Discussion
### Effects of Complex Activities on Position-Independent HAR  
The effectiveness of systems in recognizing human activities without relying on position information may need to be improved when dealing with intricate and dynamic real-world activities. To assess the capacity of our Att-ResBiGRU model to handle more complex real-world activities, we conducted a multi-label evaluation encompassing locomotive movements and activities of daily living on the Opportunity, PAMAP2, and REALWORLD16 datasets.  
Upon scrutinizing the confusion matrices for the Opportunity and PAMAP2 datasets, we observed robust performance in accurately classifying a significant portion of complex ambulation, gesture, and daily living activities using our Att-ResBiGRU model. The misclassifications tended to concentrate along the main diagonals. Overall accuracy levels exceeded 90% for Opportunity and 96% for PAMAP2 across diverse activity sets, as illustrated in Figure 7 and Figure 8, respectively. Additionally, the accuracy information of the confusion matrix of REALWORLD16 dataset is shown in Figure 9.  
Nevertheless, certain instances posed challenges in accurately distinguishing between subtle sequential pairs of activities, particularly those involving intricate limb or joint movements and swift transitions. To illustrate, about 4% of instances representing the \"Open door 1\" activity in the Opportunity dataset were incorrectly classified as the similar \"Close door 1\" activity, introducing difficulties in discerning the completion of complex interactions.  
Moreover, in the case of PAMAP2, there was a 12.3% rate of confusion between \"Ascending stairs\" and \"Descending stairs.\" Predictably, activities with more consistent signals, such as Cycling, were easier to discern compared to these transient multi-stage sequences. This underscores the potential to incorporate additional positional constraints and environmental triggers to improve the detection of activity commencement and conclusion.
# Attentional Bias Modification Training in Virtual Reality: Evaluation of User Experience
## 3 Results
### Anxiety, Fatigue, and Comfort
Additionally, there was a moderate negative correlation between the anxiety values from series 1 to 4 and their corresponding comfort measures from series 1 to 4, except for the correlation between comfort in PrS and anxiety in PrS (refer to Table 3). Furthermore, a moderate or large negative correlation was identified between each fatigue measure from the pre-session and series 1 to 4 and its corresponding comfort measure from the pre-session and series 1 to 4 (refer to Table 3). This result shows that the participants who experienced lower levels of comfort also felt higher levels of anxiety and fatigue during most of the series. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **N** & **PrS** & **S1** & **S2** & **S3** & **S4** &
\begin{tabular}{c} **ANOVA** \\ **(df = 4272)** \\ \end{tabular} \\ \hline \multirow{2}{*}{**Anxiety**} & \multirow{2}{*}{69} & 14.493 & 19.457 & 20.217 & 25.217 & 22.391 & \(F=7.423\), \(p<0.001\), \\  & & (18.838) & (21.187) & (20.69) & (24.859) & (22.222) & \(\eta^{2}\)p = 0.098 \\ \hline \multirow{2}{*}{**Fatigue**} & \multirow{2}{*}{69} & 34.928 & 49.167 & 53.732 & 58.841 & 61.667 & \(F=57.969\), \(p<0.001\), \\  & & (28.697) & (29.188) & (27.572) & (26.962) & (26.856) & \(\eta^{2}\)p = 0.46 \\ \hline \multirow{2}{*}{**Comfort**} & \multirow{2}{*}{69} & 74.913 & 70.761 & 64.203 & 62.681 & 62.036 & \(F=14.963\), \(p<0.001\), \\  & & (19.75) & (22.785) & (24.814) & (22.91) & (24.257) & \(\eta^{2}\)p = 0.18 \\ \hline \hline \end{tabular} Note: PrS = Pre-session; S1 to S4 = series 1 to series 4; df = degree of freedom.  
\end{table} 
Table 2: Mean values (and standard deviations) obtained by the participants in the three VAS measures, and statistical outcomes of the one-way ANOVAs. No correlation was found between the three subscales employed in the VAS and the time spent on each series.
# A Hybrid Model of Conformer and LSTM for Ocean Wave Height Prediction
## 2 Materials and Methods
### Algorithm Process
For a given batch of data, calculate the mean of each feature [26]:  
\[\mu=\frac{\mathbf{1}}{m}{\sum_{i=1}^{m}}{x_{i}} \tag{6}\]  
Calculate the variance of each feature:  
\[\sigma^{2}=\frac{\mathbf{1}}{m}{\sum_{i=1}^{m}}({x_{i}}{-\mu})^{2} \tag{7}\]  
\[\overset{\kappa}{x_{i}}=\frac{x_{i}{-\mu}}{\sqrt{\sigma^{2}{+}\epsilon}} \tag{8}\]  
where \(\epsilon\) is a small number to avoid division by 0. \[\mathbf{y}_{i}=\gamma\overset{\kappa}{x_{i}}{+}\beta \tag{9}\]  
where \(\gamma\) and \(\beta\) are scale and shift parameters, respectively, obtained through training. In Figure 4, batch normalization is applied to the output of the convolutional layer, which helps with the stability and convergence speed during the model training process. Three dense layers are used, with the first dense layer using the ReLU activation function to process the output of the convolutional layer, adding nonlinear processing capabilities. The second dense layer is used to adjust the input dimensions to match the added output of the adjusted feedforward network. The third dense layer calculates the weight of each input feature through learning, which is used to adjust the output of the feedforward network to emphasize more important features. An LSTM layer is used to process the output of the Conformer block. The LSTM layer is configured with 32 units, does not return a sequence, and only returns the output of the last time step. LSTM can handle long-term dependencies in time series, and its gating mechanism helps the model remember and forget information, thereby capturing dynamic changes in time series data. By combining Conformer and LSTM, the model leverages the strengths of both architectures. The Conformer's self-attention mechanism effectively captures spatial relationships and temporal dynamics, while the LSTM ensures that long-term dependencies are adequately managed. This hybrid approach results in a model that is better equipped to handle the complex, non-linear, and dynamic nature of ocean wave data. The final output section includes a flattening layer followed by two dense layers. The flattening layer converts the output of the LSTM into a one-dimensional array to meet the input requirements of the fully connected layer. The first dense layer uses the ReLU  
Figure 4: Conformer-LSTM network structure diagram. activation function, and the second one directly outputs the predicted result without an activation function. The dense layer is responsible for mapping the processed features to the final predicted target.
# Model Based Open-Loop Wind Farm Control Using Active Power for Power Increase and Load Reduction
## 3 Simulation Result
### Total Power
In the frequency domain analysis, it is known that most energy components in turbulent winds are included within 1 rad/s, and therefore the bandwidth of the pitch controller is commonly chosen to be around or higher than 1 rad/s to cover the wind speed variations [36]. In addition, the bandwidth of the pitch actuators is commonly higher than 100 rad/s, and therefore, as shown in Figure 8b,h, the pitch actuator responds rapidly according to the wind speed variations [21]. Figure 8d,i shows the electrical power generated from the wind turbines for both baseline and proposed wind farm controllers. For the most upstream wind turbines, one with the proposed control generates slightly less power than the other with the baseline control. However, for the two downstream wind turbines, those with the proposed controller generate more than those with the baseline controller. In the end, for the total power, the proposed wind farm controller yields more power than the baseline controller, as shown in Figure 8e,j.
## 4 Discussion
### Orowan Bypass-Like Strengthening Mechanism in Architected Metamaterials
#### 4.2.1 Hindering Effect of Meta-Precipitates  
The strengthening mechanism in architected metamaterials is largely dependent on the hindering effect of meta-precipitates on the shear band formation. In the experimental and numerical results, the initial deformation localization deviates from its diagonal direction to a meander path. When such path approaches the interface between the matrix and meta-precipitates, the shear band bows or even stops to bypass the meta-precipitates, which is commonly observed in experiments and FE predictions  
Figure 8: Diagram of: (**a**) slip band in single crystals, (**b**) shear band in architected metamaterials.  
in P-1, P-2 and P-4 structure, as is shown in Figure 9. As the consequence, the overall performance of the architected metamaterials is substantially enhanced due to the control of shear band propagation. This phenomenon illustrates the hindering effect of meta-precipitates.  
The reinforcement of the structure is similar to the Orowan bypass strengthening mechanism [45]. In Orowan effect, precipitations serve as the obstacles of dislocation movement in precipitation-hardened alloys [30], and the slip plane bows under the interaction between dislocation and precipitates [46]. In precipitate-type architected metamaterials, the embedded meta-precipitates also hinder the shear band propagation during compression, and the meander fracture route substantially improves the strength. This similarity between precipitation-hardened alloys and precipitate-type architected metamaterials demonstrates the applicability of combining metallurgical hardening principles with structural design.
# Multi-Channel Expression Recognition Network Based on Channel Weighting
###### Abstract  
Accurate expression interpretation occupies a huge proportion of human-to-human communication. The control of expressions can facilitate more convenient communication between people. Expression recognition technology has also been transformed from relatively mature laboratory-controlled research to natural scenes research. In this paper, we design a multi-channel attention network based on channel weighting for expression analysis in natural scenes. The network mainly consists of three parts: Multi-branch expression recognition feature extraction network, which combines residual network ResNet18 and ConvNeXt network ideas to improve feature extraction and uses adaptive feature fusion to build a complete network; Adaptive Channel Weighting, which designs adaptive weights in the auxiliary network for feature extraction, performs channel weighting, and highlights key information areas; and Attention module, which designs and modifies the spatial attention mechanism and increases the proportion of feature information to accelerate the acquisition of important expression feature information areas. The experimental results show that the proposed method achieves better recognition efficiency than existing algorithms on the dataset FER2013 under uncontrolled conditions, reaching 73.81%, and also achieves good recognition accuracy of 89.65% and 85.24% on the Oulu_CASIA and RAF-DB datasets, respectively.  
facial expression recognition; convolution neural network; deep learning +
Footnote †: journal: Applied sciences  
2023
# A Method for Broken Rotor Bars Diagnosis Based on Sum-Of-Squares of Current Signals
## 4 Experimental Validation and Discussion
### Results and Discussion
#### 4.2.5 Comparison of Computation Cost  
It can be seen from Section 3 that the principle of the SS method proposed is uncomplicated, which only needs to calculate the sum-of-squares of three-phase current signals. In theory, the calculation cost of the SS method is lower compared with other popular methods.  
In order to investigate the computation cost of the SS method, different methods are used to process the same group data. The process is repeated 10,000 times, and the calculation time is compared. The results are shown in Table 3. The runtime of the SS method is the smallest among these methods. Figure 9 shows the ratio of runtime of other methods to the SS method. There is an obvious improvement compared with HT, d-q transformation, and Park's vector modulus. This shows that the computation cost of the SS method is smaller than other popular methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Load Level**} & \multirow{2}{*}{
\begin{tabular}{c} **Healthy** \\ **Condition** \\ \end{tabular} } & \multicolumn{5}{c}{**Runtime of Different Algorithms (s)**} \\ \cline{3-7}  & & **SS** & **HT** & **TEO** & **d-q** & **PARK** \\ \hline \multirow{2}{*}{\(100\%\)} & faulty motor & 0.000219 & 0.001289 & 0.000357 & 0.002605 & 0.000627 \\  & healthy motor & 0.000247 & 0.001596 & 0.000277 & 0.002257 & 0.000667 \\ \hline \multirow{2}{*}{\(60\%\)} & faulty motor & 0.000164 & 0.000908 & 0.000202 & 0.00203 & 0.000475 \\  & healthy motor & 0.000187 & 0.000994 & 0.000252 & 0.002269 & 0.000577 \\ \hline \multirow{2}{*}{\(20\%\)} & faulty motor & 0.000117 & 0.001015 & 0.000166 & 0.002034 & 0.000258 \\  & healthy motor & 0.000155 & 0.000914 & 0.000158 & 0.002051 & 0.000407 \\ \hline \multicolumn{7}{c}{mean} & 0.000182 & 0.001119 & 0.000235 & 0.002208 & 0.000502 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The runtime of different algorithms.  
Figure 8: The spectra of data analysis using different algorithms.