esa-sceva/satcom-chunk-collection huggingface dataset:
# Eye Tracking and Human Influence Factors' Impact on Quality of Experience of Mobile Gaming
## 2 Background and Methodological Overview
### Quality of Experience and User Delight  
The _Quality of Experience_ (QoE) is defined by ITU-T as _'The degree of delight or annoyance of the user of an application or service'_[45], with reference to the full definition that continues with _'It results from the fulfillment of his or her expectations with respect to the utility and/or enjoyment of the application or service in the light of the user's personality and current state'_[8]. QoE encompasses a range of complex, interrelated dimensions, including psychological factors, networks, applications, etc. Numerous influencing factors (IFs) affect QoE, which are generally classified into three categories: human, system, and contextual factors [8]. Although user satisfaction with an application is integral to the definition of QoE, there is disagreement among researchers regarding the influence of human behavioral tendencies and how to classify emotions triggered by various stimuli. [PERSON] et al. [46] have explored the emotional impact of visual stimuli and the evaluation of this experience.  
QoE Subjective Metrics  
The MOS provides the average ratings of participants in a subjective assessment, and in the case of assessing the delight towards the shown content of two different groups on a binary scale, the MOS can be formulated as follows:  
\[\text{MOS}=\frac{1}{N_{A}+N_{B}}\left(\sum_{i=1}^{N_{A}}R_{A,i}+\sum_{i=1}^{N _{B}}R_{B,i}\right) \tag{1}\]  
where:  
\[N_{A}:\text{Total number of ratings in Group A}.\] \[N_{B}:\text{Total number of ratings in Group B}.\] \[R_{A,i}:\text{Rating given by the $i$-th user in Group A}.\] \[R_{B,i}:\text{Rating given by the $i$-th user in Group B}.\]  
This combined equation allows for a comprehensive assessment of the overall MOS across both user groups. In the case of the MOS for each group where \(x\in\{A,B\}\), we can calculate it as:  
\[\text{MOS}_{x}=\frac{1}{N_{x}}\sum_{i=1}^{N_{x}}R_{x,i} \tag{2}\]where:  
\[N:\text{Total number of ratings}\] \[R_{i}:\text{Rating given by the $i$-th user}\]  
Similarly, GoB or PoW ratios can be easily calculated; the formula for the GoB ratio over an ACR scale can be written as:  
\[\text{GoB}=\frac{N_{4}+N_{5}}{N_{\text{T}}}\]  
where:  
\[N_{4}:\text{Number of ratings with a score of 4 (Good)}\] \[N_{5}:\text{Number of ratings with a score of 5 (Excellent)}\] \[N_{\text{T}}:\text{Total number of ratings}\]  
In this study, we explored how user preference (delight) towards presented content influences their video quality ratings, resulting in the formation of various sub-groups. Beyond the typical statistical analyses using the MOS and confidence intervals in QoE subjective studies, we employed one-way ANOVA [49] to assess the differences and statistical significance when comparing the sub-categories.
# Joint Geoeffectiveness and Arrival Time Prediction of CMEs by a Unified Deep Learning Framework
## 5 Discussion
### Discussion on the Prediction of Geoeffectiveness  
The previous experimental results show that our method is effective for geoeffectiveness prediction. But there are still many misjudgments, we discuss the main source of classification error through the confusion matrix on the testing set. As shown in Figure 7, our method predicts the CME events that are non-geoeffective as geoeffective and 92 CME events are misclassified. This may be caused by the choice of the threshold, therefore the performance of our method under different thresholds is analyzed. As shown in Figure 8, we select 20 thresholds at equal intervals between \([0,1]\) to verify the performance of the method. When the threshold is changed from the default 0.5 to 0.7, we get a higher F1 score and Accuracy, and they are 0.277% and 80.3%, respectively. The main reason is that the threshold is changed from 0.5 to 0.7, the number of CME events incorrectly predicted as geoeffective has greatly decreased while the number of CME events that correctly predicted  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Method** & **MAE (Hours)** & \(\triangle\) \\ \hline \multirow{4}{*}{Convolutional Neural Networks} & ResNet18 & 7.6 & \(-\)1.8 \\ \cline{2-4}  & Vgg16 & 8.6 & \(-\)2.8 \\ \cline{2-4}  & MobilenetV2 & 8.0 & \(-\)2.2 \\ \cline{2-4}  & ResNext & 8.3 & \(-\)2.5 \\ \hline \multirow{2}{*}{Machine learning/deep learning used in CME arrival time prediction} & FCNN & 11.6 & \(-\)5.8 \\ \cline{2-4}  & CAT-PUMA & 5.9 & \(-\)0.1 \\ \cline{2-4}  & Wang’s CNN & 12.4 & \(-\)6.6 \\ \hline \multicolumn{2}{c}{-} & Ours & 5.8 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the arrival time prediction for geoeffective CMEs with other methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Loss** & **MAE (Hours)** \\ \hline L2 Loss & 6.4 \\ cost-sensitive regression loss with \(\beta=2\) & 6.1 \\ cost-sensitive regression loss with \(\beta=3\) & 5.8 \\ cost-sensitive regression loss with \(\beta=4\) & 6.2 \\ cost-sensitive regression loss with \(\beta=4.5\) & 6.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The performance of different loss functions and different values of \(\beta\) based on data expansion and deep residual regression network.  
as geoeffective is almost unchanged. However, with the threshold continues to increase, almost all CMEs are predicted as non-geoeffective, and due to the imbalance of samples in the test set, samples that are non-geoeffective account for about 90%, so the Accuracy is still continue to improve but F1 score has reduced sharply. This phenomenon also reflects the rationality of our evaluation metrics. For unbalanced data, F1 score can better reflect the performance of the model. When the F1 score of two methods is the same, we can distinguish through Accuracy. In the practical applications, we also can select different thresholds according to the actual needs.
# A Comparative Study of Indoor Accuracies Between SLAM and Static Scanners
## 2 Materials and Methods  
To address the aforementioned aims, an experiment comparing the quality of point clouds acquired with static scanners, old- and new-generation SLAM scanners were devised in an environment suitable for the use of these scanners. An interior building environment with a relatively long, straight corridor with recesses allowing us to analyze the accumulation of systematic and random errors when using SLAM scanners was selected for this study. All analyses of SLAM scanner accuracy were performed against a reference dataset obtained using an accurate static scanner Leica ScanStation P40 (Leica Geosystems AG, Heerbrugg, Switzerland) combined with a highly accurate geodetic network established with the Trimble S9 HP total station (Trimble Inc., Westminster, CO, USA).
## 1 Introduction
According to [PERSON]'s minimum-volume belief [24], all the pixels should be enclosed by the minimum-volume simplex, whose vertices are the endmember vectors (or pure pixels). From the view of unmixing, the minimum-volume belief covers not only spectral signature (i.e., endmember matrix), but also spatial components (i.e., abundance matrix). Thus, incorporating minimum-volume belief into the fusion method may achieve regularization on both spectral and spatial signatures, but it is not enough in a high-noise environment. To address the ill-posedness of fusion method based on spectral unmixing for hyperspectral image super-resolution in a low-SNR environment, this article proposed a novel fusion method based on the spatial-spectral regularization. Our main contributions in this article include the aspects. First of all, we combine spatial-spectral smoothing with minimum volume belief, and propose a CNMF-based fusion method by incorporating the regularization of joint spatial-spectral smoothing in the minimum-volume simplex. Then, the surrogate of the minimum-volume expression, based on the pure pixel algorithm, is redefined as the form of a matrix-vector product. Next, two difference matrices are constructed for spatial and spectral smoothing, in which the former performs spatial smoothing based on vectorial total variation, and the latter handles spectral smoothing of the endmember matrix. Finally, after complexity reduction via Kronecker operators, an efficient solver is carefully designed to test the performance of fusion methods. The experimental results suggest that the proposed method performs better than state-of-the-art methods, and verifies its effectiveness for hyperspectral image enhancement. The remainder of this paper is organized as follows. Section 2 presents signal models and problem formulation by incorporating the regularizers. Section 3 proposes ADMM-based data fusion algorithms. Section 4 evaluates the performance of fusion methods. Section 5 draws a conclusion and suggests the future research. In addition, there are some key notations summarized in Table 1.
# Analysis and Optimization of Driveline Bushing for Lateral Ride Vibration under Shock Excitation
###### Abstract  
Ride comfort is increasingly important for automobile companies due to the increasing concern of the demands of customers. Response to shock at low speed, such as single or consecutive pulse excitations from humps, are one of the greatest assessing indices of vehicle ride related to the ride comfort. This paper aims to study the effect of driveline bushing on ride vibration when the vehicle experiences shock excitation and to improve the ride quality by optimizing the bushing parameters. A vehicle level multibody dynamic model with a differential-subframe subsystem is developed and calibrated against field test data. The sensitivities of the bushing stiffness and damping coefficients are analyzed and the most influential bushing parameters on the seat rail vibration are identified. The relationship between the bushing parameters and the ride vibration is developed by a 5-level response surface design. The fitted response functions are validated and adopted as the optimization objectives. The optimized results, including the acceleration, the running r.m.s. acceleration, the jerk, the running r.m.s. jerk, the VDV and the comfort, are compared with those of the baseline vehicle. Results show that the optimized bushings significantly decrease the vibration at the driver and the rear passenger seats by approximately 50% in the lateral direction. A considerable improvement has been achieved in ride comfort that the weighted vibration at seats is reduced to a level below the median perception threshold of human being in the lateral direction.  
ride comfort; vibration optimization; vehicle dynamic modeling; driveline bushing; shock +
Footnote †: journal: Applied Sciences  
1 Tanjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China; [EMAIL_ADDRESS]  
2 State Key Laboratory of Engines, Tianjin University, Tianjin 300580, China; [EMAIL_ADDRESS]
## 2 Materials and Methods
### Cell Viability
#### 2.4.2 Cytotoxic Activity, MTT Assay  
The cytotoxicity assay enabled the evaluation of human gingival fibroblast (HGF-1) cell viability based on mitochondrial enzyme activity. This method effectively quantifies cell proliferation and metabolic activity by reducing thiazolyl blue tetrazolium bromide (MTT), where purple formazan indicates viable cells. The linear relationship between cell number and absorbance allows for the accurate quantification of the cellular response to tested extracts. The HGF-1 cells were seeded in a 96-well flat-bottom microplate and maintained at 37 \({}^{\circ}\)C in 95% humidity and 5% CO\({}_{2}\) overnight. The decoctions were used in the concentration range from 2.5 ug/mL to 2000 ug/mL. Four chemical compounds were also tested at concentrations ranging from 5 ug/mL to 100 ug/mL: epicatechin, procyanidin B2, resveratrol, and piceid. The concentration of DMSO in the samples was equal to or less than 2%. The cells were then incubated for 24 h under the same conditions. The MTT solution (1 mg/mL thiazolyl blue solution; Pol-Aura, Dywity, Poland) was prepared in RPMI 1640 medium without L-glutamine and phenol red (Capricorn Scientific, Ebsdorfergrund, Germany) under a laminar flow hood. After incubation, 50 uL of MTT solution was added to each well. The plates were returned to the incubator for two hours at 37 \({}^{\circ}\)C. After incubation, the MTT solution was removed, and 100 uL of isopropanol was added to each well to dissolve the formazan crystals. The MTT assay was performed in accordance with the ISO 10993-5:2009 (E) standard [19].
## 2 Materials and Methods
### Laboratory Setting--Dataset
#### 2.1.3 Experimental Setup  
Scalp EEG has been recorded using the Waveguard(r) amplifier from ANT Neuro (Hengelo, The Netherlands), with a sample rate of 256 Hz from 64 Ag/AgCl EEG electrodes by following the 10-10 International System [31] referenced to the right mastoid and grounded to the aFz electrode, positioned on the head of the participant by a standard EEG cap in elastic textile. Not all electrodes were used for this study, but a subgroup of electrodes in the Frontal (Fpz, AF3, AF4, AF7, AF8) and Parietal (Pz, P3, P4) areas were selected to mimic the sensors' locations on the wearable headset employed in the second study (Mindtooth EEG system, Brain Products GmbH, Gilching, Germany, www.mindtooth-eeg.com accessed on 18 August 2023). In addition, the theta band on frontal channels and the alpha band on parietal channels' related features have been taken into account, with the aim of catching variations in the mental workload of the user while performing the experimental task [46; 47].  
Surrounding electrodes to those mentioned above were considered to mimic shift occurrences with different seriousness. To induce this shift, two changes were made: the positioning of the electrodes, i.e., the placement of the headset on the head, and the selection of a different electrode as the reference, i.e., the electrode subsequently utilized during the cleaning phase of the EEG trace. In particular:  
* Control data: \(\bigcirc\) Channels: AF3-THETA, AF4-THETA, AF7-THETA, AF8-THETA, FPZ-THETA, P3-ALPHA, P4-ALPHA, PZ-ALPHA \(\bigcirc\) Reference: TP8
* Test data called _Test same Cls diff Ref_: \(\bigcirc\) Channels: AF3-THETA, AF4-THETA, AF7-THETA, AF8-THETA, FPZ-THETA, P3-ALPHA, P4-ALPHA, PZ-ALPHA \(\bigcirc\) Reference: TP8
* Test data called _Test diff Ref diff Cls_: \(\bigcirc\) Channels: F3-THETA, F4-THETA, F7-THETA, F8-THETA, FZ-THETA, PO3-ALPHA, PO4-ALPHA, POZ-ALPHA \(\bigcirc\) Reference: TP8  
The three \"Test\" configurations (i.e., _Test same Cls diff Ref_, _Test same Ref diff Cls_, and _Test diff Ref diff Cls_) were generated to replicate what might happen during a recording session in which EEG headset placement occurs in a position different from an original session (i.e., Control session, used to train the passive BCI classification model).  
The two central configurations (i.e., _\"Test same Cls diff Ref\"_ and _\"Test same Ref diff Cls\"_) also allowed us to investigate whether a variation in channel positioning had more or less impact than a variation in the reference.  
Figure 2: Image of the Multi-Attribute Task Battery (MATB) interface: In the top left corner (**A**), is the emergency lights sub-task; in the bottom left corner (**B**), is the radio communication task; at the top, in the center (**C**), is the cursor tracking task, and finally, at the center bottom (**D**), is the fuel managing task.
## 6 Conclusions  
We have introduced an interactive event-detection mode that operates through a cycle of \"machine-recommended event triggers\(\rightarrow\)human review and correction\(\rightarrow\)machine incremental learning\" to facilitate event detection. To address the challenges of class-incremental learning, we introduced the ICD method. To mitigate forgetting of old-class events, we designed a replay strategy based on representative and confusable samples. Additionally, we applied semantic boundary smoothing techniques to construct positive and negative samples around gold triggers for contrastive learning, thereby addressing the overfitting problem in new-class event learning. Finally, we incorporated hierarchical distillation techniques to alleviate the model's forgetting issues.  
Our experimental results demonstrate that our approach excels in class-incremental learning tasks related to interactive event detection. However, when tested in more complex environments, such as the ACE dataset, we observe that the recall rate drops below 90% after several rounds of class-incremental learning. This indicates a significant gap between our model's performance and the high precision requirements needed for effective application.  
Figure 7: Average time consumption per epoch of ICD and baseline methods under different settings.  
Future work will focus on addressing these challenges by enhancing the model's ability to handle diverse and complex scenarios, such as the subjunctive mood, multiple negation, and so on. This will involve refining our approach to better manage continuous class-incremental learning, exploring advanced strategies to further reduce forgetting, and optimizing performance in more challenging environments. In addition, we will also focus on another common scenario in incremental learning: sample increment, aiming to investigate how to address issues related to sample distribution shift and missing data. By advancing these, our goal is to enhance the efficiency of the interactive event-detection mode in real-world applications, thereby establishing a positive feedback loop.  
Conceptualization, X.Z. and J.D.; methodology, J.D. and X.Z.; validation, J.D.; investigation, J.D. and X.Z.; resources, X.Z.; data curation, J.D.; writing--original draft preparation, J.D.; writing--review and editing, X.Z.; visualization, J.D.; supervision, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China under the grants of NO. 62102431, the National University of Defense Technology under the grant of NO. ZK21-32, and Science and Technology on Information Systems Engineering Laboratory under the grant of NO. 6142101220209.  
Not applicable.  
The data used in this study have been cited and referenced [36]. They are available at [[https://ai.baidu.com/broad/introduction](https://ai.baidu.com/broad/introduction)]([https://ai.baidu.com/broad/introduction](https://ai.baidu.com/broad/introduction)) (accessed on 18 August 2024). Other data are available on request.  
The authors declare no conflicts of interest.
# Entropy Analysis of a Flexible Markovian Queue with Server Breakdowns
## 2 Model Formulation and Previous Results
### Entropy Solution Using Both First and Second Moments
Third, the efficiency of the best method is directly proportional to the values of \(\mu_{2}\) and the efficiency of the other two methods is inversely proportional._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mu_{1}\) & Exact & Entropy 1 & Entropy 2 & Entropy 1\&2 \\ \hline
0.1 & 0.3312 & 0.4124 & 0.0136 & 0.3065 \\
0.2 & 0.5429 & 0.5306 & 0.0126 & 0.5349 \\
0.3 & 0.6756 & 0.6353 & 0.5493 & 0.6730 \\
0.4 & 0.7526 & 0.7131 & 0.6338 & 0.7517 \\
0.5 & 0.8009 & 0.7685 & 0.7000 & 0.8007 \\
0.6 & 0.8337 & 0.8080 & 0.7520 & 0.8337 \\
0.7 & 0.8573 & 0.8369 & 0.7925 & 0.8574 \\
0.8 & 0.8751 & 0.8587 & 0.8237 & 0.8752 \\
0.9 & 0.8889 & 0.8756 & 0.8478 & 0.8890 \\
1 & 0.9000 & 0.8890 & 0.8665 & 0.9001 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Effect of \(\mu_{1}\) on \(p_{0}\). Figure 4: Effect of \(\mu_{1}\) on the percentage errors of \(p_{0}\). Again observe from Figure 5 that we always have \(PE\ 1\&2<PE\ 1<PE\ 2\), which confirms our conclusions from Table 7 stated above. **Effect of \(c\) on The Initial Probability Vector \(P_{i}\). **  
_The results are summarized in Table 8 and Figure 6. Superiority of Entropy 1&2 is demonstrated for all values of \(c\)._  
_We read from Table 8 and Figure 6 three points: First, obviously the approximation results obtained using Entropy 1 & 2 are clearly better than the ones obtained by the two other methods. Second, if we are using a single moment, then better use Entropy 2 than Entropy 1 for large values of \(c\) and for small values of \(c\) there is no big difference between the two methods. Third, the efficiency of all three methods is inversely proportional to the values of \(c\)._  
_From our previous sensitivity analysis, we conclude that if a single moment is used to estimate the probabilities, then it makes a difference whether we use the first moment or the second moment. Also, the more information we feed the maximum entropy technique, the more accurate the results are. Although we did not do it, we conjecture that inclusion of the third moment would confirm our findings that including more information would result in higher accuracy._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mu_{2}\) & Exact & Entropy 1 & Entropy 2 & Entropy 1\&2 \\ \hline
4 & 0.7516 & 0.7422 & 0.6861 & 0.7586 \\
5 & 0.7518 & 0.7299 & 0.6623 & 0.7558 \\
6 & 0.7520 & 0.7241 & 0.6516 & 0.7544 \\
7 & 0.7521 & 0.7210 & 0.6460 & 0.7536 \\
8 & 0.7522 & 0.7190 & 0.6426 & 0.7531 \\
9 & 0.7523 & 0.7177 & 0.6404 & 0.7528 \\
10 & 0.7523 & 0.7168 & 0.6389 & 0.7525 \\
11 & 0.7523 & 0.7161 & 0.6378 & 0.7524 \\
12 & 0.7524 & 0.7155 & 0.6370 & 0.7523 \\
13 & 0.7524 & 0.7151 & 0.6364 & 0.7522 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Effect of \(\mu_{2}\) on \(p_{0}\). Figure 5: Effect of \(\mu_{2}\) on the percentage errors of \(p_{0}\).
## 2 Materials and Methods
### Statistical Analysis  
Reliability and level of agreement of the force variables between sessions (test-retest) were determined by the intraclass correlation coefficient (ICC), typical error (TE), coefficient of variation (CV%) and minimum detectable change (MDC). The intraclass coefficient correlation (ICC) was calculated using the two-way mixed-effects ICC. ICC values were interpreted as poor (<0.50), moderate (0.50-0.75), good (0.76-0.90) and excellent (>0.90) [31]. The CV was calculated relative to the TE as a percentage (CV% = 100 TE/mean of the test-retest). A combination of ICC > 0.75 and CV < 15% was used as key statistical parameters to categorize measurements with good reliability [32]. In addition, MDC was calculated through TE to represent the error expected from a given measurement [33], using the following formula: MDC = TE\(\cdot\)/2\(\cdot\)1.96 [32]. The ICC, TE and MDC were calculated with their corresponding 95% confidence interval (CI). Bland-Altman plots were used to assess and display the agreement along the entire spectrum of loads. Systematic difference (bias) and its 95% limits of agreement (LoA = bias \(\pm\) 1.96 SD) were calculated [34]. Calculations were performed using a specific spreadsheet for consecutive pairs of trials ([[http://www.sportsci.org/resource/stats/xrely.xls](http://www.sportsci.org/resource/stats/xrely.xls)]([http://www.sportsci.org/resource/stats/xrely.xls](http://www.sportsci.org/resource/stats/xrely.xls)) (accessed on 15 December 2020)) and the GraphPad Prism 6.0 (GraphPad Software Inc., San Diego, CA, USA).  
Figure 1: Image of the custom-built bench, placement of the portable strain gauge and participant’s position for the measurement of hamstring and quadriceps strength using a field-based test.