esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Training Method for VR Sickness Reduction
## 3 Results
### Ssq
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Subject**} & \multicolumn{6}{c}{**SSQ**} \\ \cline{2-10}  & \multicolumn{2}{c}{**Nausea (N)**} & \multicolumn{2}{c}{**Oculomotor Discomfort (O)**} & \multicolumn{2}{c}{**Disorientation (D)**} & \multicolumn{2}{c}{**Total Score (T)**} \\ \cline{2-10}  & \multicolumn{2}{c}{**Pre-**} & \multicolumn{2}{c}{**Post-**} & \multicolumn{2}{c}{**Pre-**} & \multicolumn{2}{c}{**Post-**} & \multicolumn{2}{c}{**Pre-**} & \multicolumn{2}{c}{**Post-**} \\  & \multicolumn{2}{c}{**Experiment**} & \multicolumn{2}{c}{**Experiment**} & \multicolumn{2}{c}{**Experiment**} & \multicolumn{2}{c}{**Experiment**} & \multicolumn{2}{c}{**Experiment**} & \multicolumn{2}{c}{**Experiment**} & \multicolumn{2}{c}{**Experiment**} & \multicolumn{2}{c}{**Experiment**} & \multicolumn{2}{c}{**Experiment**} \\ \cline{2-10}  & \multicolumn{2}{c}{**Score**} & \multicolumn{2}{c}{**Score**} & \multicolumn{2}{c}{**Score**} & \multicolumn{2}{c}{**Score**} & \multicolumn{2}{c}{**Score**} & \multicolumn{2}{c}{**Score**} & \multicolumn{2}{c}{**Score**} & \multicolumn{2}{c}{**Score**} \\ \hline S1 & 124.02 & 85.86 & 83.38 & 75.80 & 125.28 & 167.04 & 1244.22 & 1229.33 \\ S2 & 9.54 & 47.70 & 22.74 & 22.74 & 27.84 & 27.84 & 224.84 & 367.56 \\ S3 & 9.54 & 0.00 & 22.74 & 22.74 & 0.00 & 41.76 & 120.72 & 241.23 \\ S4 & 38.16 & 19.08 & 60.64 & 53.06 & 41.76 & 0.00 & 525.69 & 269.80 \\ S5 & 19.08 & 0.00 & 22.74 & 37.90 & 27.84 & 41.76 & 260.52 & 297.92 \\ S6 & 0.00 & 0.00 & 22.74 & 22.74 & 13.92 & 13.92 & 137.10 & 137.10 \\ S7 & 38.16 & 19.08 & 22.74 & 30.32 & 41.76 & 27.84 & 383.94 & 288.87 \\ S8 & 0.00 & 0.00 & 15.16 & 0.00 & 0.00 & 27.84 & 56.69 & 104.12 \\ S9 & 9.54 & 0.00 & 15.16 & 0.00 & 0.00 & 0.00 & 92.37 & 0.00 \\ S10 & 0.00 & 9.54 & 15.16 & 0.00 & 0.00 & 0.00 & 56.69 & 35.67 \\ S11 & 9.54 & 9.54 & 53.06 & 15.16 & 69.60 & 27.84 & 494.42 & 196.49 \\ S12 & 38.16 & 19.08 & 68.22 & 37.90 & 55.68 & 83.52 & 606.10 & 525.47 \\ S13 & 143.10 & 19.08 & 106.12 & 30.32 & 222.72 & 41.76 & 1765.05 & 340.93 \\ \hline Average & 33.76 & 17.61 & 40.82 & 26.82 & 48.18 & 38.55 & 459.10 & 310.35 \\ \hline Rate & \(-\)47.83\% & & \(-\)34.29\% & & \(-\)19.98\% & & \(-\)32.40\% \\ (\(p\)-Value) & (0.04 *) & & (0.02 *) & & (0.47) & & (0.12) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The pre-experiment and post-experiment score results for each category of the SSQ. Figure 7: The pre-experiment and post-experiment results for each category of the SSQ (outliers are excluded from this figure). analysis results confirmed that the subjects who felt more symptoms of VR sickness could experience a greater reduction in the level of VR sickness by the proposed VR sickness reduction training.
# Real-Time Kinematics-Based Self-Collision Avoidance Algorithm for Dual-Arm Robots
## 4 Simulation and Application of the Algorithm
### Experimental Results  
The algorithm was implemented on a dual-arm robot in an office environment, and demonstrated the change of the end-effector velocity. The sampling frequency of the control loop was set at 50 Hz.  
Figures 9 and 10 show the results of the two tasks. In Figure 9, the two arms of the dual-arm robot had a common target, while in Figure 10, each arm had a different target.  
Figure 10(a) shows the snapshots of the experiment. The velocity profile and the trajectory of the end-effect of the arms are shown in Figure 10(b),(c), respectively. As the two arms had the same target, both arms changed trajectory to avoid self-collision. In fact, both arms moved away from the target.  
Figure 10: Process of avoiding self-collision in the dual-arm system using the traditional artificial potential field method. Two arms of the dual-arm had different targets. (**a**) The initial state and final state of the robot; (**b**) Trajectory of the end-effector of each armIn the second experiment, the robotic arms had different targets, as illustrated in Figure 11(a). The velocity profile of the end-effector and the trajectories of the two arms are shown in Figure 11(b),c, respectively.  
Figure 11: Cont.  
Figure 11: Experiment to assess the ability of a dual-arm system to avoid self-collision when the left and right arms have the same target. (**a**) The first experiment; (**b**) Velocity profile of the end-effector of each arm; (**c**) Trajectory of the end-effector of each arm.  
Figure 12: _Cont._  
As the two arms had different targets, each arm changed its motion to find a collision-free trajectory. As illustrated in Figure 12, the repulsive velocity produced the end-effect on the left arm and acted on link 6 of the right arm. In this way, the repulsive velocity was projected onto the end-effect of the left arm. In other words, both arms in the dual-arm system changed their motion to avoid self-collision: the right arm changed its motion to approach the target, whereas the left arm changed its motion by moving away from the target and the right arm.  
Meanwhile, as illustrated in Figure 11b, Figure 12b, respectively, although there were fluctuations in the end-effector velocity, the fluctuation did not affect the results of the experiment.  
These experiments were similar to the simulations, which are described in Section 4.1, and demonstrated the effectiveness of our algorithm in simulation A (robotic arms with the same target) and simulation B (robotic arms with different targets). Meanwhile, these demo of the experiments were upload on the web as illustrate in Supplementary Materials).
## 5 Discussion
### Limitations of MOUNTS and Future Developments
Nevertheless, this architecture limits the ability to process larger amounts of data, which would require the analysis of the entire available Sentinel dataset over hundreds of volcanoes worldwide. To achieve this, cloud computing strategies are preferable, whereby algorithms would run on a platform where the data is hosted, thereby preventing data download, and at the same time offering higher computing power. Commercial platforms offering such services exist: the Copernicus DIAS platforms (Data and Information Access Services, which include Sobloo, Onda, Creodias, Mundi, and Wekeo), the Amazon Web Services (AWS), or the Google Cloud Platform. 5. Analyze the recovered multiparametric volcanic time-trends. Monitoring efforts such as the one presented here, allow the development of consistent multiparametric databases on a variety of volcanic settings (i.e., various volcano types, tectonic settings and magma compositions). Such databases are crucial to decipher eruptive patterns, and potentially better estimate future activity [25; 26; 27; 29]. As a matter of example, decadal heat and gas emission time-trends help decrypt slow mechanisms of magma/gas accumulation and release at active volcanoes [97]. Clustering of these trends in categorically similar patterns, together with time-series analysis and probabilistic approaches (e.g., [98; 99]), should be investigated to help decision making and potentially lead to better eruption forecasting. Moreover, incorporating standardized volcano alert level classifications (whereby color-codes help flag the activity of volcanoes [39; 100]), will help better communicate the level of volcanic unrest and eruption likelihood to local populations and governmental authorities [101; 102], and potentially lead to better early warning systems.
# Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains +
## 2 Related Work
### Human-Computer Interaction
#### 2.2.2 Gestalt Principles  
Taking Fitts' Law a step further is the Gestalt Principles. Gestalt is German for \"shape,\" and the principles look at proximity, similarity, continuity and closure between objects and analyse how the eyes may deal with different examples of these. These principles originated in the 1920s from Gestalt Psychologists, who proposed that humans naturally observe objects in certain patterns and in an organised manner, as the brain naturally perceives objects in this way [33; 34; 35].  
* Proximity: This principle states that if objects are within close proximity to each other, the brain naturally groups them compared to those that are further apart;
* Similarity: This principle suggests that the brain groups objects based on their similarity, in relation to colour and shape etc., and distinguishes those that are different as a separate group;
* Continuity: The brain naturally follows and continues lines, even those that intersect with each other, and forms groups based on this continuation;
* Closure: In relation to shapes, if the brain observes lines which form incomplete outlines of certain shapes, we naturally close the gaps to form that shape as the brain prefers completeness and, therefore, initially views the shape as a whole.
# A Database for Tsunamis and Meteotsunamis in the Adriatic Sea
## 4 Discussion
During this process, it was pointed out that the two scales, created for a very similar but nonetheless different phenomenon, are not properly suited for meteotsunami intensity assessment. This is particularly evident for the Papadopoulos-Imamura scale, which has a higher number of degrees (12) with respect to the Ambraseys-Sieberg scale (6 degrees), and the more devastating effects that are described in the higher degrees cannot be produced by meteotsunamis. In fact, even in the case of a very strong meteotsunami, such as the one on 21 June 1978, the observed effects reached a maximum of five on the Ambraseys-Sieberg scale, while they reached a maximum of seven on the Papadopoulos-Imamura scale. The DAMT contains several reliable data, and it is a tool that can contribute to improving the knowledge of tsunami and meteotsunami activity in the Adriatic area and to increase public awareness along these coasts which, especially in the peak season, are some of most densely populated areas in the Mediterranean. The database has already been used several times for educational purposes to increase the awareness of public authorities, citizens and students toward coastal hazards, and it will be one of the main tools for dissemination of the PMO-GATE project results. As mentioned earlier, the availability of long-term observations is essential to characterizing the events in a region. Therefore, particularly with regard to meteotsunamis, in the Adriatic area, two factors could be beneficial in the near future: an increase in tide gauge and barometric instrumentation, especially in \"hot-spots\" that are particularly prone to meteotsunamis on one hand, and on the other hand, in-depth research of bibliographic sources on events that occurred in the past, mainly before 1931. Regarding the latter, a collaboration with Croatian and Albanian researchers is going to start in order to consult local archives and libraries.
# A Comparative Evaluation of Cement and By-Product Petrit T in Soil Stabilization
## 3 Results and Discussion
### Strain at Failure  
Figure 14 shows the axial failure strain versus curing time for untreated and treated soil. For the soil without any treatment, high failure strain is observed (Figure 14a). In contrast, due to the addition of different binder content of cement or Petrit T, failure strain is significantly reduced with more effect from the cement treatment, as shown in Figure 14b-d. Petrit T, compared to cement, exhibits major decreases in failure strain during the first 28 days. This can be attributed to produce more cementing components from the cement than Petrit T. Generally, the average failure strain for the long curing period (90 days) and 7% binder content was less than for shorter curing times (seven days).  
Figure 13: Stress–strain curves for the untreated and treated soil with different binder content after (**a**) 14 days curing time; (**b**) 28 days curing time; (**c**) 60 days curing time; and (**d**) 90 days curing time.  
Figure 15 shows the axial failure strain versus the UCS for both cement and Petrit treatment and all curing times. It is observed that the strength of soil increased, whilst failure strain decreased as the amount of binder increased. A significant reduction in the axial failure strain was noticed regarding different cement and Petrit T treatments, which led to an increase in strength of treated soil to about 150 to 200 kPa. Further increases in soil strength proved less significant on decreasing failure strain. The result is consisted with [15; 32; 54].  
Figure 14: Strain at failure versus curing time for (**a**) untreated soil; (**b**) treated soil with 1 and 2% cement and 2% Petrit T; (**c**) treated soil with 4% of cement and Petrit T and (**d**) treated soil with 7% of cement and Petrit T.
## 3 Approaches Compared for Multi-Parametric Data Analysis
### Semi-Empirical Approach  
The SEA is used to try to predict the time-to-failure of a physical system based on the hypothesis that such a failure represents the final stage of a creeping process, assuming simplified rheological models with their time-by-time calibration based on long-time monitoring time series. Such an approach does not necessarily need of a geologically based model of the slope, since it is not based on cause-to-effect relations. Moreover, it may allow for the temporal prediction of the slope failure, but it could fail for slope processes affected by relevant temporal changes of the controlling and triggering factors.  
For slope stability applications, the SEA was originally experienced by [PERSON] [77] and based on results from laboratory tests that output relation between the velocity and the acceleration of displacement during the tertiary creep under stress and temperature constant conditions. Further semi-empirical methods known as material failure forecasting methods (FFMs) were developed to forecast the time-to-failure that induce hazardous events in a physical system [78]. Those methods are distinguished in: (i) graphical [77]; (ii) log rate vs. log acceleration [78]; (iii) linearized least square [76]; and (iv) non-linear least square [78]. These techniques take into account datasets from new monitoring technologies, such as SAR interferometry [79] and geotechnical devices [80], on which the inverse velocity model can be applied to forecast the time-to-failure of large-scale rock landslides [57].  
To apply the SEA, a very long displacement dataset must be available to detect the transition to the tertiary creep phase as well as the definition of a rheological model of the physical slope system that is connected to a well-defined long-term mechanical response to external forces (e.g., deformation responses of the rock mass driven by viscous rheology or by thermomechanical processes). To define the most correct rheological models two approaches can be followed: (i) assuming a mechanical behaviour  
Figure 5: Flow chart illustrating the tools of the compiled script for rate analysis of the Acuto dataset, propaedeutic to cross-correlation statistical analysis of the obtained time series of rates.  
based on the typology of the natural system and its composition; (ii) perform a machine learning through the recorded big-to-mega data from the monitoring system itself [81,82]. These models must be able to justify any long-term drifts in the rock mass deformation that can be interpreted as changes in the trend and, therefore, in the temporal evolution of a deformation process. In our case, we present preliminary results from only a 3 years of strain dataset, which, at most, can provide suggestions on the long-term evolution of the monitored process but not yet able to predict the failure time because the strain time series does not show deviations from their trend.
# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 2 Knowledge-Based Approaches
### Categories of Knowledge Representation and Reasoning Approaches
#### 2.1.1 Probabilistic Formalisms  
These formalisms employ probabilistic models including non-standard logic models to represent uncertainty and causal relationships between variables. They enable reasoning under uncertainty by quantifying the likelihood of different events and updating beliefs based on evidence. Uncertainties may be due to incomplete, imprecise, vague knowledge or knowledge that is probabilistic and/or causally inferred. The latter corresponds to Bayesian and/or causal reasoning that is governed by the conditional probabilities and/or causal relationships represented by probabilistic and causal models, respectively. The former can be alternatively addressed by approaches such as non-monotonic logic, fuzzy logic, and temporal logic. These reasoning methods are suitable for application scenarios with varying levels of certainty and requirements for timely adapting to dynamic and uncertain environments [9; 10; 11].
## 2 Methods, Study Area and Data Analysis  
The methods selected to address the objectives were to measure the seasonal variation in LAI in a broadleaved deciduous plot at a woodland site in north-west England and to use a series of DMC images to track the variations in LAI of the plot. Broadleaved deciduous woodland in the U.K. follows a clear phenological pattern with bud-burst in early spring, leaf development through to mid-summer, followed by senescence and eventual leaf fall in late autumn. This sequence thus exhibits a wide range of LAI states with which to test the applicability of the DMC data.
## 3 System Performance
### Data Drop  
Data drop represents the higher layer data traffic dropped by the all the WLAN MACs in the network as a result of consistently failing retransmissions. On the one hand, data dropped by buffer overflow is due to: (a) full higher layer data buffer, or (b) the size of the higher layer package, which is greater than the maximum allowed data size defined in the IEEE 802.11 standard. On the other hand, data dropped by the retry threshold exceeded the number of the higher layer packages that are dropped because the MAC could not receive any ACKs for retransmitting those packages or their fragments, and the packages' short or long retry counts reached the MAC's short retry limit or long retry limit, respectively.  
In Figure 8, we show the data dropped by the global network formed by eight patients. We can see that the average data dropped by buffer overflow is 14,475 bps and 11,145 bps in the case of average data dropped by the exceeded retry threshold. These data decrease. If we focus our attention on one patient, in this case the average data dropped by buffer overflow is around 230 bps and there is no data dropped through exceeding the retry threshold.
## 3 The Proposed Algorithm
### Partitioning Phase  
In the partitioning stage, a partitioning strategy that considers both density and distance is proposed, which aims to partition the data into high-density regions and ensure that the data points in the same dense region have highly similar densities. The details are as follows. Before calculating local densities, use the box plots to remove outliers from the data to prevent their impact on the partitioning results, and the filtered data consist of points within the upper and lower limits of the box plots, with the calculations for these limits provided in Equations (1) and (2).  
\[UB_{d_{i}}=Q3_{d_{i}}+\left(e*IQR_{d_{i}}\right) \tag{1}\]  
\[LB_{d_{i}}=Q1_{d_{i}}-\left(e*IQR_{d_{i}}\right) \tag{2}\]  
where \(LB_{d_{i}}\) and \(UB_{d_{i}}\) represent the upper and lower bounds of the \(d_{i}\) dimension, respectively; \(Q1_{d_{i}}\) and \(Q3_{d_{i}}\) denote the upper quartile and the lower quartile of the \(d_{i}\) dimension; and \(IQR_{d_{i}}=Q3_{d_{i}}-Q1_{d_{i}}\). \(IQR_{d_{i}}\) is used to define the range of outliers. In addition, based on the statistical properties of the normal distribution, e is set to 1.5 [34]. This is because about 0.7 percent of the samples outside \(LB_{d_{i}}\) and \(UB_{d_{i}}\) are considered outliers under the normal distribution. This threshold balances sensitivity and robustness.  
Secondly, according to the kernel density estimation, calculate the local density of the points after removing the outliers, construct the density curve to detect the density peak in each dimension, and use the coordinates of the peak of the density curve as the benchmark to establish a Kd-Tree to quickly attract the data points around the initial subcluster division. Taking the intersection of the partitioning results from each dimension to obtain several subclusters.
# Comprehensive Analysis of PPP-B2b Service and Its Impact on BDS-3/GPS Real-Time PPP Time Transfer
## 3 Results
### Quality Evaluation of PPP-B2b Corrections
#### 3.2.3 PPP-B2b DCB Corrections  
Since three common DCB products were included in the PPP-B2b and CAS relative to the B3I signal (i.e., B1I-, B1 Cp-, and B2 ap-B3I DCB), only these three DCB products were evaluated. The PPP-B2b and CAS DCBs for each satellite are plotted in different colors in Figure 9. The PPP-B2b DCBs of B1I- and B1 Cp-B3I showed good agreement with the CAS DCBs, while the B2 ap-B3I DCB products indicated a strong systematic bias between PPP-B2b and CAS. We further summarized the biases of the PPP-B2b DCBs with respect to the CAS for each satellite, as shown in Figure 10. The B2 ap-B3I DCB has the largest  
Figure 7: STDs of the PPP-B2b and CNES for BDS-3 satellite clock.  
difference, with an average value of 10.31 ns. However, the differences of B1I- and B1 Cp-B3I, DCB are almost the same, with average values of 0.91 ns and 1.36 ns, respectively. The STDs of biases are 0.68 ns, 0.54 ns, and 0.48 ns for B1I-, B1 Cp-, and B2 ap-B3I, respectively. Obviously, B1I-B3I DCB has the lowest stability. Therefore, the accuracy of the PPP-B2b, DCB should be increased soon.  
Figure 8: STDs of the PPP-B2b and CNES for GPS satellite clock.  
Figure 9: The DCB values of PPP-B2b and CAS for B1I-, B1 Cp-, and B2 ap-B3I.
# Damage Identification Method Using Additional Virtual Mass Based on Damage Sparsity
## 4 Numerical Simulation of Simply Supported Beam and Space Truss
### Space Truss Model
#### 4.2.2 Damage Identification  
In this section, considering the complexity of the space truss, the non-parameter Gaussian kernel regression model is adopted. The first frequency of the 51 virtual structures under real damage situation, which is constructed by respectively adding 1.2 kg virtual mass on each bar, is chosen to optimize the damage factors. The results of OMP and IOMP method are shown in Table 3.  
As known from Table 3, the identification results of IOMP method is more accurate than OMP method. Either the selected damaged substructures or the residual stiffness of OMP method has more error, which might be caused by the complexity of space truss or the non-parameter regression model.