esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A  
Table 14 presents a sample of the dataset used for analysis, showing features updated at 80% CWA\({}_{U}\). It includes the hashed IDs of five students, the final grade as the target feature, the weighted average as the performance feature, 24 engagement features, and 10 behavior features.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Userid** & **00554...** & **0068d...** & **01 bb1...** & **01 bcd...** & **03340...** \\ \hline final\_grade & 4.53 & 2.91 & 3.69 & 2.05 & 4.17 \\ weighted\_average & 4.57 & 4.36 & 4.18 & 2.85 & 4.34 \\ days & 101 & 42 & 5 & 105 & 68 \\ mean\_interactions & 49.5 & 22.18 & 95.33 & 39.41 & 53.62 \\ std\_interactions & 39.12 & 17.71 & 54.24 & 34.25 & 63.61 \\ week & 416 & 233 & 137 & 584 & 408 \\ weekend & 178 & 11 & 149 & 86 & 21 \\ course\_navigation & 111 & 62 & 33 & 91 & 12 \\ forum\_viewed & 28 & 0 & 0 & 69 & 0 \\ post\_created & 0 & 0 & 0 & 0 & 0 \\ discussion\_viewed & 119 & 0 & 0 & 141 & 0 \\ discussion\_created & 0 & 0 & 0 & 0 & 0 \\ forum\_searched & 1 & 0 & 0 & 1 & 0 \\ outline\_viewed & 2 & 0 & 0 & 1 & 0 \\ mean\_multimedia\_views & 0.8 & 0.07 & 0.0 & 1.07 & 0.07 \\ std\_multimedia\_views & 0.89 & 0.0 & 0.0 & 0.93 & 0.0 \\ mean\_video\_views & 1.0 & 0.0 & 0.0 & 0.83 & 0.0 \\ std\_video\_views & 2.19 & 0.0 & 0.0 & 0.5 & 0.0 \\ mean\_video\_time & 14.03 & 0.0 & 0.0 & 20.94 & 0.0 \\ std\_video\_time & 8.42 & 0.0 & 0.0 & 1.29 & 0.0 \\ mean\_multimedias\_time & 12.15 & 3.18 & 0.0 & 13.12 & 0.0 \\ std\_multimedias\_time & 7.56 & 0.0 & 0.0 & 0.0 & 0.0 \\ mean\_simulations\_grade & 3.06 & 2.85 & 1.98 & 1.34 & 1.91 \\ std\_simulations\_grade & 0.59 & 0.19 & 0.19 & 2.28 & 0.65 \\ mean\_simulations\_time & 52.57 & 80.54 & 8.4 & 12.96 & 1.72 \\ std\_simulations\_time & 11.96 & 20.62 & 10.93 & 22.92 & 0.42 \\ mean\_simulations\_attempt & 0.67 & 0.67 & 1.33 & 0.83 & 1.83 \\ std\_simulations\_attempt & 0.0 & 0.0 & 0.0 & 0.5 & 0.96 \\ mean\_workshops\_grade & 3.78 & 3.03 & 2.83 & 2.4 & 2.57 \\ std\_workshops\_grade & 0.51 & 0.32 & 0.69 & 1.84 & 0.69 \\ mean\_workshops\_time & 28.03 & 63.34 & 19.61 & 38.86 & 3.81 \\ std\_workshops\_time & 7.24 & 36.18 & 12.83 & 47.24 & 3.64 \\ mean\_workshops\_attempt & 0.83 & 0.67 & 1.5 & 1.0 & 1.83 \\ std\_workshops\_attempt & 0.0 & 0.0 & 0.55 & 0.45 & 0.84 \\ mean\_prior\_knowledge\_grade & 2.75 & 1.5 & 0.0 & 1.75 & 0.75 \\ std\_prior\_knowledge\_grade & 1.77 & 0.0 & 0.0 & 1.06 & 0.35 \\ mean\_prior\_knowledge\_time & 41.52 & 13.92 & 1.56 & 26.77 & 11.07 \\ std\_prior\_knowledge\_time & 26.87 & 0.0 & 0.0 & 24.89 & 7.66 \\ mean\_lessons\_time & 63.82 & 0.0 & 0.0 & 14.24 & 0.0 \\ std\_lessons\_time & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ mean\_lessons\_attempts & 0.5 & 0.0 & 0.0 & 0.5 & 0.0 \\ std\_lessons\_attempts & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample of the dataset used.
# Investigation of Potential of GNSS-R Polarization: Theoretical Simulations
## 2 Model Development
### Random Surface Scattering Models  
In the microwave region, the scattering models commonly used for a natural surface are (1) the geometrical optics (GO) model, (2) the physical optics (PO) model, (3) the small perturbation model (SPM), and (4) the integral equation model (IEM) or advanced integral equation model (AIEM). These models are suitable for natural surfaces with different surface roughness. Broadly, the GO model is best suited for very rough surfaces, the PO model is more suitable for a surface with intermediate scales of roughness, and the SPM is suited for surfaces with a short correlation length, which is the scalar of surface roughness. The ranges of roughness over which these models should be applied are presented in Table 1. The IEM is based on the complex integral model and it is suitable for surfaces with continuous roughness. The AIEM is more complex than the IEM, but it provides comparatively better accuracy [26].
# Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM
## 3 Model Description  
### Model Structure  
The deep RNN models with LSTM units presented in this paper were developed in order to predict PV power values in 1-h resolution over 3 consecutive days. The proposed models were tested in different seasons. A schematic block of the LSTM used to forecast the profile of the power produced by the PV plant is depicted. Figure 4 shows that there are four models for forecasting PV power in different seasons based on next 1 h of meteorological data.  
As for the structure of LSTM in Figure 5, the size of the input layer, the number of hidden layers, and the number of hidden units in each hidden layer are firstly determined. The detailed model is described as follows:  
For the input layer LSTM input: 3D tensor with shape (batch size, timesteps, input dim). The value of timesteps is 4, which can be determined by the set of data during 1 hour, and the input dim is 4 (solar irradiation (_SI_), air temperature (_AT_), relative humidity (_RH_), and wind speed (_WS_)).  
For the hidden layer: The setting of the hidden layer is usually referred to the complexity of power generation process, and the model parameters are different for each season. The operation of LSTM is based on the batch. The'stateful' in Figure 5 means there are state-related between batches. In the  
Figure 4: Flowchart of PV power forecasting design.  
Figure 5: Diagram of PV power forecasting using LSTM.  
LSTM model, sometimes a dropout is set to avoid over-fitting [31]. When a dropout is increased to more than 0.2, the predicted result is usually random and volatile.  
For the LSTM output: final state at \(i\)th hour consists of two operations. In the first operation, the forecasting results in final state are passed to the initial state at next (\(i+1\))th hour. The short-term memory of previous results was achieved during this operation. In other words, the forecasting process in the last hour could be regarded as the training process at (\(i+1\))th hour. In the second operation, the results are passed to the output layer as the PV output (\(P_{i,1}\), \(P_{i,2}\), \(P_{i,3}\), \(P_{i,4}\)) at \(i\)th hour. It requires attention that \(P_{i+1,1}\) is the next sequential value of \(P_{i,4}\). The prediction results for three consecutive days consist of hourly forecasting values.
# Compact and Broadband Microstrip Band-Stop Filters with Single Rectangular Stubs
## 4 Results and Discussion
### Comparison with Radial Line Stub  
Figure 10 shows \(|\,S_{21}\,|\) and \(|\,S_{11}\,|\) measurement results of a well-known radial stub. Two types of data indicated by line (f) show the case of the center angle \(\alpha=60^{\circ}\) and \(90^{\circ}\) in the pattern (f) in Figure 5. For comparison, the results of the reactance insertion stub in the case where \(Z_{1}/Z_{0}=1/4\) with \(g=7\) mm, which has the same center frequency at 2.5 GHz, are shown by line (d). It is confirmed that the larger the central angle \(\alpha\), the wider the bandwidth is. Table 3 shows the bandwidth change and the transmission zero frequency with respect to the threshold level \(T=-10\) dB. The numerical values in parentheses represent the bandwidth expansion ratio and the increase/decrease ratio of the transmission zero frequency, compared with the case \(Z_{1}/Z_{0}=1\) as a reference. It is found that the stop bandwidth increases by 2.0 times when \(\alpha=60^{\circ}\), and increases by 2.4 times when \(\alpha=90^{\circ}\). Although it is already known that the band-stop characteristics improve when \(\alpha\) is increased, it is considered that \(C\) of the resonator increases. The proposed structure has a similar transmission characteristic to the radial stub, but the bandwidth increases to 3.7 times wider than the reference value. It is also confirmed that the operating principle of the radial stub is equivalent to the circuit model, with reactance inserted in the low impedance stub described in Section 2. The characteristic impedance ratio of the reactance insertion stub used in this study is the case \(Z_{1}/Z_{0}=1/4\), but by using a stub with a smaller impedance ratio, there is a possibility to obtain much broader band-stop characteristics than the radial stub.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Circuit Pattern** & \(Z_{1}/Z_{0}\) & \(-10\) dB BW [GHz] (\({}^{1}\)) & \(f_{0}\) [GHz] (\({}^{1}\)) \\ \hline (d) \(g=19.4\), (a) & \(1/4\) & \(1.75\) (2.9) & \(3.86\) (1.5) \\ (d) \(g=7.0\) & \(1/4\) & \(2.25\) (3.7) & \(2.56\) (1.0) \\ (d) \(g=0.7\) & \(1/4\) & \(1.10\) (1.8) & \(1.66\) (0.6) \\ (e) \(n=4.0\) & \(1/4\) & \(0.55\) (0.9) & \(1.11\) (0.4) \\ (e) \(n=8.0\) & \(1/4\) & \(0.30\) (0.5) & \(0.86\) (0.3) \\ \hline \end{tabular} \({}^{1}\) Indicate the magnification for \(Z_{1}/Z_{0}=1\).  
\end{table}
Table 2: Stop bandwidth in the case of rectangular stubs with the series inductance.  
Figure 9: Measured \(|\,S_{21}\,|\) and \(|\,S_{11}\,|\) results of rectangular stubs of \(Z_{1}/Z_{0}=1/4\) with different series inductances, as shown in Figure 5d.
## 6 Numerical Results
### Impact of Weights on Policy  
Consider a fragment of the policy for different weights in the reward function. Figure 7 illustrates that for a fixed set of weights, and policy \(m\) (size of 1-slice) varies depending on system states. Moreover, the policy is also influenced by the specific set of weights. The provider modifies the weighting according to their prioritization. In scenarios where all parameters are equivalent in value, uniform weights are allocated, exemplified by \((1,1,1)\). Conversely, should a particular criterion--such as one of three fundamental principles--be deemed more pivotal, its associated weight is augmented. For example, should the optimization of resource utilization take precedence, the weight distribution might be adjusted to \((1,1,1000)\).  
Let us examine a specific state \(\mathbf{s}=(6,0,3)\), assuming that \(C=8\). In this state, six units of resources have been reallocated for the 1-slice; there are users in the 1-buffer; and the size of the 2-slice is 2, with one user in the buffer. If all weights in the set are equal, there is no change in the policy (no reallocation occurs). However, with set \(\mathbf{w}=(1,1,1000)\), where the weight corresponding to the principle of maximum resource utilization is given the highest importance, reallocation does occur: the resource of the 1-slice is reduced by 1, and a user from the 2-buffer is processed. This same scenario also applies to set \(\mathbf{w}=(1,1000,1000)\).
## 3 Numerical Simulation of Early-Age Behaviour of Small Specimens to Actual Rc Deck Slabs
### Experimental Outline and Model Validation via Small-Scale Specimens (Decimetres)  
As mentioned above, the Shinkesen Bridge deck was implemented with BFS concrete. In accordance with small-scale specimens, OPC concretes were simultaneously used as a reference. To reflect on the properties of micropore structures and evaluate free volumetric change, deformation and cracks due to shrinkage, mass loss and shrinkage tests were conducted on 200 \(\times\) 200 \(\times\) 800 mm\({}^{3}\) prismatic OPC specimens. Then, using the mixture designs proposed for the Shinkesen Bridge, compression tests were executed on cylindrical specimens with radius of 100 mm and height of 200 mm according to JIS A 1108 specification to determine the concrete with the most appropriate strength for application on the bridge. Because curing conditions adversely influence the properties of concrete with BFS, especially its early strength to resist thermal and shrinkage-induced cracks, shrinkage tests were then performed on 100 \(\times\) 100 \(\times\) 400 mm\({}^{3}\) prismatic specimens by applying various prolonged seal-curing durations. It is important to mention that, in addition to prolonged curing, expansive additives were used to lessen the cracking tendency. Finally, to study the effects of restraints imposed by reinforcing bars, 400 \(\times\) 400 \(\times\) 200 mm\({}^{3}\) prismatic specimens were used to trace the strain progress of all proposed mixture designs. Each test was designed for different environments, from a controlled chamber to ambient conditions, incorporating details such as temperature, humidity, solar radiation and rainfall. The details of each experiment and its corresponding validation will be explicitly displayed in the following sections.  
The numerical models have been well-verified in environmental controlled condition. Yet, the fluctuation in environmental conditions necessitates further model validation because it significantly affects the microscopically thermodynamic phenomena of the whole concrete body and, consequently, the mechanical properties. Furthermore, since our current DuCOM-COM3 computational platform does not possess a sophisticated expansive agent model, the model validation process will also aim to ensure the capability in predicting the behavior under these complex actual considerations.
# Mycoremediation as a Potentially Promising Technology: Current Status and Prospects--A Review
## 7 Factors Influencing Mycoremediation
### Temperature  
Temperature affects the bioavailability of the pollutants and the development of fungal species, both of which are involved in bioremediation. Fungi are classified as mesophilic (5 \({}^{\circ}\)C to 35 \({}^{\circ}\)C), thermophilic (above 40 \({}^{\circ}\)C), or psychrophilic (below 5 \({}^{\circ}\)C) based on the temperature of their optimal growth. The degradation of organic matter was accelerated by higher temperatures, which also improved the bioavailability of contaminants and HM absorption. For mycoremediation, fungi can tolerate high temperatures [170,226]. At 30 \({}^{\circ}\)C, [PERSON] et al. [222] found that _Rhodotorula mucilaginousa_ and _Beauveria bussiana_ had higher levels of Pb and Zn in their bodies [222]. At 10-20 \({}^{\circ}\)C, _Yarrowia lipolytica_ showed the most increased activity to degrade diesel oil (up to 41%). Similarly, temperatures around 15 \({}^{\circ}\)C resulted in 20% higher oil degradation activity of the inoculum (20%) [227]. In the temperature range from (30-50) \({}^{\circ}\)C, the biosorption of the fungi increases significantly [225]. At 37 \({}^{\circ}\)C, Kluyveromyces marxianus IMB3 successfully removed the most colour from the diazo dye. In addition, binding affinity and enzyme activity in fungal physiology are two other crucial factors that are influenced by temperature. The optimal temperature not only affects the growth of fungi and forms and the availability of pollutant uptake, but also affects the enzyme that converts or degrades the pollutant.
## 3 Proposed Method
### Detection of Incorrectly Segmented Short Lines  
As discussed in Section 3.1, short lines are the result of incorrect segmentation due to the spaces created by characters written above or below a letter. Depending on the handwriting, this spacing will vary. Short lines contain sparsely scattered fragments of characters and therefore the height will be smaller. Hence, the threshold to detect short lines depends on the height of the line, the width of the characters present in the line and the number of non-zero vertical projection values. Because of this dependency, thresholds for detecting short lines are developed and are given in Equations (11)-(14).  
1. Determine the threshold value \(t_{sh_{1}}\) using the equation \[t_{sh_{1}}=1.9\times Avg_{ch_{\text{mid}}}\] (11) where \(Avg_{ch_{\text{mid}}}\) is the average character width in a document image.
2. A second threshold value \(t_{sh_{2}}\) is determined as follows. If (\(M_{L_{ht}}\leq 3\times Avg_{ch_{\text{mid}}}\)), \[t_{sh_{2}} =\frac{t_{sh_{1}}}{3}.\] (12) If (\(M_{L_{ht}}>3\times Avg_{c_{ht}}\) and \(M_{L_{ht}}\leq 4\times Avg_{c_{ht}}\) ), \[t_{sh_{2}} =\frac{t_{sh_{1}}}{4}.\] (13) If (\(M_{L_{ht}}>4\times Avg_{c_{ht}}\) ), \[t_{sh_{2}} =\frac{t_{sh_{1}}}{7}.\] (14)3. Compute the number of non-zero vertical projection values, VPN, in the given text line segmented from the handwritten document. This is determined to check the sparsity of character fragments present in the short line.
4. A short line is detected If \((VPN<2\times t_{sh_{1}}\) and \(VPN>t_{sh_{2}})\) Or If \((VPN/9\times t_{sh_{1}}\)and \(VPN)t_{sh_{2}}\)and \(L_{ht}<0.5\times M_{L_{ht}})\). Depending on the handwriting, the values of \(Avg_{ct_{ht}}\), \(Avg_{ch_{mid}}\), \(M_{L_{ht}}\) may vary from one document to another.
## 3 Results
### Barc--Hyperion Surface Reflectance Derivative Analysis  
At this USDA site, targets were chosen with varying degrees of seasonal differences to determine if derivative analysis displays a consistent signal throughout the Hyperion time-series. The spectral reflectance and derivatives from four targets (corn field, evergreen trees, deciduous trees, and top of  
Figure 6: Upper chart: NDVI maps from EO-1 Hyperion images over the Howland Forest area in Maine across four seasons from: (**a**) Spring; (**b**) Summer; (**c**) Fall; (**d**) Winter. Three flux tower sites are indicated with black squares. Lower graph: NDVI difference between Hyperion and Moderate Resolution Imaging Spectroradiometer (MODIS) for the three flux tower sites in the Howland forest area of Maine from 2003 to 2014.  
building) are shown in Figures 7 and 8. As can be seen in the figures, the corn field has a very different spectral reflectance between the five dates due to the differences in planting and harvesting schedules in those years. The Evergreen patch and the top of a building, not surprisingly, have a much more uniform reflectance among dates. The derivatives show the consistency between different dates for a target and keep different features for different targets.  
Figure 7: Spectrum (top row) and first, second, and third derivatives (rows 2 to 4) of a Corn field (**column A**) and Evergreen patch (**column B**).
# Underwater Acoustic Target Tracking: A Review
## 6 Discussion, Challenges, Countermeasures, and Lessons Learned
### Challenges  
Due to the special and complex underwater environment characteristics, the acoustic wave becomes the only suitable carrier for realizing underwater target tracking. However, the problem of acoustic communication and the complexity of the marine environment pose great challenges to target tracking:  
_The low target detection probability._ Due to the complexity of the underwater environment, tracking using imaging sensors has the problem of low contrast and visibility which even makes it hard for humans to detect the presence of a target. Tracking using acoustic echoes also has difficulty in detecting due to the attenuation of the acoustic waves.  
_Sound speed variation:_ Many tracking algorithms based on distance measurements obtained by TDOA or TOA assume that the acoustic waves are transmitted at a constant speed in the marine environment, which is impractical and results in large tracking errors, especially in trilateration. Therefore, how to construct a stable and accurate sound speed model is a challenge.  
_PS ambiguity:_ When using a sensor array to realize underwater target tracking, PS ambiguity appears due to the structural symmetry of the array. Because the ambiguity makes tracking algorithms more complicated and results in noticeable performance degradation, the process of removing such artifacts is dispensable.  
_The balance between tracking accuracy and energy consumption:_ If USWNs are exploited for tracking, the energy consumption is a key factor. However, as Section 5 introduces, reducing the energy cost means sacrificing certain tracking accuracy. There are few papers taking both into consideration to design tracking algorithms. It is still a challenge to design a tracking algorithm which can automatically adjust the energy consumption and the tracking accuracy according to the application requirement.
## 3 Potential Function Solution and Stress-Displacement Calculation
### Stress-Displacement Calculation  
After determining the potential function, the stresses and displacements can be calculated. The formula for the stress of an arbitrary point is  
\[\sigma_{x}+\sigma_{y}=2\left[\varphi^{\prime}(z)+\overline{\varphi^{\prime}(z )}\right] \tag{29}\]\[\sigma_{x}-\sigma_{y}+2i\sigma_{xy}=2\big{[}\overline{z}\varphi^{\prime \prime}(z)+\psi^{\prime}(z)\big{]} \tag{30}\]  
The formula for the displacement of an arbitrary point is  
\[u+iv=\frac{1}{2G}\Big{[}x\varphi(z)-z\overline{\varphi^{\prime}(z)}-\overline{ \psi(z)}\Big{]} \tag{31}\]  
where \(\sigma_{x}\), \(\sigma_{y}\), and \(\sigma_{yy}\) are the \(x\)-direction normal stress, \(y\)-direction normal stress, and \(xy\)-plane stress at point \(z\), respectively, and \(u\), \(v\) are the \(x\)-direction displacement and \(y\)-direction displacement at point \(z\), respectively.  
It is worth pointing out that, in order to eliminate the rigid body displacement, a point far enough away from the pipe jacking and the ground surface can be chosen, approximated as the \"infinity point\". This means that the displacement component at any point is equal to the corresponding displacement component at that point in the calculation minus the corresponding displacement component at a sufficiently far distance in the calculation. Since the displacement of the rigid body does not change the stress, this treatment does not affect the stress calculation.
# _k_-Labelsets Method for Multi-Label ECG Signal Classification Based on SE-ResNet
## 3 Experimental Results
### Multi-Label Classification Results Based on Superclass
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Exact Match** & **Accuracy** & **F1-Score** & **Precision** & **Recall** \\ \hline ResNet-18 & 60.67 & 87.93 & 92.06 & 90.38 & 93.81 \\ ResNet-34 & 60.51 & 88.02 & 92.03 & 91.05 & 93.02 \\ ResNet-50 & 59.89 & 87.88 & 91.95 & 91.06 & 92.87 \\ ResNet-101 & 59.89 & 87.69 & 91.90 & 90.26 & 93.60 \\ \hline Average & 60.24 & 87.88 & 91.99 & 90.69 & 93.33 \\ \hline SE-ResNet-18 & 61.29 & 88.22 & 92.23 & 90.82 & 93.68 \\ SE-ResNet-34 & 61.42 & 88.48 & 92.40 & 90.99 & 93.84 \\ SE-ResNet-50 & 61.29 & 88.33 & 92.31 & 90.77 & 93.90 \\ SE-ResNet-101 & 60.99 & 88.18 & 92.18 & 91.00 & 93.39 \\ \hline Average & 61.25 & 88.30 & 92.28 & 90.90 & 93.70 \\ \hline
1-labelset & 60.21 & 88.48 & 92.41 & 90.83 & 94.05 \\
2-labelsets & 63.05 & 89.10 & **92.84** & 90.90 & **94.87** \\
3-labelsets & 62.98 & 89.02 & 92.79 & 90.87 & 94.80 \\
4-labelsets & **63.76** & **89.16** & 92.82 & **91.62** & 94.60 \\ \hline Average & 62.50 & 88.94 & 92.72 & 91.06 & 94.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification performance for each model based on superclass (the proposed model with \(k\)-labelsets uses SE-ResNet-34 as a classifier).
## 6 Discussion
### Angle Accuracy  
The angle error for the three interaction methods was not statistically significant, but the average angle error for Head Interaction was less than that for Hand Interaction. For the angle error around the x- and z-axes, Head Interaction and Finger Interaction were less than Hand Interaction. However, in Manipulation 3, the angle error around the \(y\)-axis for Hand Interaction was larger than that for Hand Interaction and the angle error around the \(y\)-axis for Finger Interaction was larger than the \(y\)-axis rotation error of Hand Interaction in Manipulation 4. The reason the angle error around the \(y\)-axis for Head Interaction and Finger Interaction was larger than that for Hand Interaction is similar to the reason the position error along the \(x\)-axis for Head Interaction and Finger Interaction was larger than that for Hand Interaction. As the rotation of the virtual object was performed with the head movement and release was performed with a Tap gesture recognized by AnywhereTouch, there was a little gap in the angle error between Head Interaction and Hand Interaction. However, as both translation of the virtual object and release were performed with finger movement recognized by AnywhereTouch, a gap between the position errors of Head Interaction and Hand Interaction occurred.