esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The Performance is Improved Due to the Addition of CO\({}_{2}\)  
Table 3 demonstrates the fitted formulae and R\({}^{2}\) of SIF-GPP models and SIF-CO\({}_{2}\)-GPP models. R\({}^{2}\) of models surges after the introduction of surface CO\({}_{2}\) concentrations, implying that the CO\({}_{2}\) concentration plays a critical role in modeling GPP. In statistics, the coefficient of determination (R\({}^{2}\)) indicates the ability of the dependent variable to predict the dependent variable. A significant increase in R\({}^{2}\) indicates that surface CO\({}_{2}\) concentration is a non-negligible variable for estimating GPP. In all 12 sites, R\({}^{2}\) was improved to varying degrees at all 12 sites (3%-41%) after the introduction of the surface CO\({}_{2}\) concentrations. Table 3 also shows an interesting a phenomenon that the increase rate has a negative correlation with R\({}^{2}\) of the SIF-GPP model. Table 3 also shows an interesting phenomenon, as 9 of the 12 study sites had negative CO\({}_{2}\) coefficients in the study area. The increase of model performance due to the introduction of CO\({}_{2}\) could be explained by the fact that the effect of CO\({}_{2}\) correction mitigates the non-linear relationship between daily SIF and daily GPP. It is worth noting that it is by no means negative coefficients of CO\({}_{2}\) imply a high CO\({}_{2}\) concentration would cause a decreasing of GPP because the linear regression indicates a correlation but not causality. Details on a possible mechanism on how CO\({}_{2}\) concentrations regulate GPP will be discussed in Sections 4.1 and 4.2. Figure 5 shows linear relationships between modeled GPP and measured GPP in different sites. We used the measured GPP of EC Flux towers represents truth value. Then, we used the method of leave-one-out cross validation to compare the SIF-GPP model with the SIF-CO\({}_{2}\)-GPP model. R of GPP-GPP\({}_{\text{M}}\) is higher than that of GPP-GPP\({}_{\text{S}}\) at all sites. We also calculated root-mean-square error (RMSE), which represents the deviation between the estimates of models and true values (Table 4). The RMSE of SIF-CO\({}_{2}\)-GPP models are lower than those of SIF-GPP models at 8 out of 12 sites, suggesting better performances of SIF-CO\({}_{2}\)-GPP models. In the meantime, slopes of linear relationships between GPP and GPP\({}_{\text{M}}\) are much more close to 1 than those between GPP and GPP\({}_{\text{S}}\). All the above results indicate that the SIF-CO\({}_{2}\)-GPP model is superior to the traditional SIF-GPP model. \begin{table}
\begin{tabular}{c c c} \hline
**Sites** & **SIF-GPP** & **SIF-CO\({}_{2}\)-GPP** \\ \hline US-Srr & 2.37 & 1.80 \\ US-WCr & 1.72 & 1.71 \\ US-Syr & 1.86 & 1.86 \\ US-PFA & 1.51 & 1.20 \\ CA-TP3 & 2.40 & 1.87 \\ CA-TP4 & 2.89 & 1.28 \\ CA-SCC & 2.09 & 1.45 \\ US-EML & 2.11 & 1.26 \\ US-PHM & 1.21 & 1.23 \\ US-Ro4 & 2.43 & 2.45 \\ US-TPD & 3.09 & 2.79 \\ US-Los & 2.74 & 2.80 \\ \hline \end{tabular}
\end{table} 
Table 4: Root-mean-square error (RMSE) of SIF-GPP and SIF-CO\({}_{2}\)-GPP model. The unit is g Cm\({}^{-2}\) day\({}^{-1}\).
# Moving Towards Body-to-Body Sensor Networks for Ubiquitous Applications: A Survey
## 4 BBN Design Challenges and Open Issues
### Wireless Channel Propagation
#### 4.1.1 BBN Users in Stationary Positions  
* Involuntary slight movements of two persons in LOS position, may result in noticeable variations of the body-to-body signal level, in comparison to the log-distance pathloss model prediction.
* In outdoor NLOS situation, when one person turns his body back to the other person, the direct signal path is shadowed and the received signal level decreases consequently.
## 2 Proposed Methodology
### Evaluation Indicators  
Five indicators were selected for this study to evaluate the accuracy of the model: the coefficient of determination (\(R^{2}\)), the Mean Absolute Error (\(MAE\)), the Root Mean Square Error (\(RMSE\)), the Relative Predictive Deviation (\(RPD\)), and the Ratio of Interquartile Range to RMSE (\(RPIQ\)). The higher the \(R^{2}\), \(RPD\), and \(RPIQ\) and the lower the \(RMSE\) and \(MAE\), the better the model's predictive ability and stability. The calculation formulas are as follows:  
\[\begin{split} MAE&=\frac{1}{n}\sum\limits_{i=1}^{n} \left|y_{r}-y_{p}\right|,\\ RMSE&=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}\left(y_ {r}-y_{p}\right)^{2}},\\ RPD&=\frac{SD}{\sqrt{\frac{\sum\limits_{i=1}^{n} \left(y_{r}-y_{p}\right)^{2}}{n}}},\\ R^{2}&=1-\frac{\sum\limits_{i=1}^{n}\left(y_{r}-y_ {p}\right)^{2}}{\sum\limits_{i=1}^{n}\left(y_{r}-\overline{y}\right)^{2}},\\ RPIQ&=\frac{IQ}{RMSE},\end{split} \tag{29}\]  
Here, \(n\) is the number of samples, \(y_{r}\) and \(y_{p}\) are the true and predicted values of dye concentration and ultrasound power, respectively, \(\overline{y}\) is the mean of the true values, \(SD\) is the standard deviation, and \(IQ\) is the difference between the third and first quartiles of the true values.
## 4 Conclusions  
This paper investigates self-piercing riveted joints between a vibration-damping Al panel and other dissimilar materials (aluminum alloy AL5052-H32, steel alloy GA590 DP, and thermoset CFRP) by examining the joint cross-section quality, the geometrical features, the mechanical performance, and the failure modes. The following conclusions can be made:  
1. When joining the vibration-damping Al panel with AL5052-H23 using the SPR process, the vibration-damping Al panel on top and die type C (nipple) showed the best cross-sectional characteristics and the maximum tensile shear strength.
2. When joining the vibration-damping Al panel with GA590 DP using the SPR process, the vibration-damping Al panel on the bottom showed the best cross-sectional characteristics regardless of the die type. It was found that, due to the high strength of the steel alloy, cavities are formed inside the rivet when the GA590 DP panel is on top, but they do not affect the joining strength between the two materials.
3. When joining the vibration-damping Al panel with CFRP using the SPR process, CFRP must be on top for the successful formation of the joint. The inhomogeneous and brittle characteristics of the CFRP result in an ununiform interface between the rivet and the top panel, and significant cracking can be observed within the rivet head.  
In summary, the study of joining processes of dissimilar materials is becoming more and more critical as the manufacturing industry moves to highly advanced and environmentally friendly processes that integrate novel multi-materials. We hope that this analysis informs other researchers about the strength of the joints formed through an SPR process using various dissimilar materials.  
Conceptualization, D.H.K.; Methodology, T.E.J.; Formal Analysis, D.H.K.; Investigation, T.E.J. and D.H.K.; Resources, D.H.K.; Data Curation, J.K.; Writing--Original Draft Preparation, J.K.; Writing--Review & Editing, J.K.; Visualization, T.E.J.; Supervision, D.H.K. All authors have read and agreed to the published version of the manuscript.  
The authors thank the following agencies for their support: the Korea Institute of Industrial Technology and the Industry Core Technology Development Program funded by the Ministry of Trade, Industry & Energy (Korea) (No. 10063579, 2015R1A6A1A03031833), and the Hongik University new faculty research support fund (2015).  
The authors declare no conflict of interest.
## Materials and Methods
### In-Process Active Cooling System  
In order to regulate the heat dissipation of the upper layers during WAAM, it would be more effective if the cooling device was to be directly attached to the side surface instead of to the substrate.  
Figure 2: Experimental setup: (**a**) Robot system for WAAM; (**b**) Enlarged view of the active cooling system.  
Cooling methods can be classified according to the medium used to transfer the heat. Commonly used methods include air cooling and liquid cooling. However, strong air flow near the molten pool is likely to disrupt the stability of the arc, whereas liquid cooling requires an additional liquid circulation system. Instead, this study adopted thermoelectric cooling technology [21] with the benefits of no circulating liquid, invulnerability to leak, small size, flexible shape, controllable cooling rate, and long life. A thermoelectric cooler operates by the Peltier effect, and is a solid-state active heat pump that transfers heat from one side of the device to the other with consumption of electrical energy. One side gets cooler while the other gets hotter [22]. As shown in Figure 2b, two thermoelectric coolers were distributed symmetrically on the two sides of the wall. The hot side of each cooler was attached to external fans such that it could remain at ambient temperature. The cold side was attached to the side surface of the wall to regulate the heat dissipation, separated by a highly thermally conductive silicone rubber whose function was to ensure good contact between the cooler and the side surface in the presence of the stair-stepping effect. The thickness of the silicone rubber is 2 mm. A step-motor-driven motion system was used to lift the active cooling system in the \(Z\) direction along with the increase of the wall height. The rated input voltage of the thermoelectric cooler is 12 V, the nominal cooling power is 180 W, and the maximum temperature difference between the two sides is 63 \({}^{\circ}\)C. Generally, the actual cooling power is a function of various factors including ambient temperature, heat sink performance, thermal load, Peltier module geometry, input voltage, etc. The input voltage can be easily adjusted so as to change the cooling power.
# Presenting a Human Pupil Orbit Model (HPOM) for Eye-Gaze Tracking
## 2 Materials and Methods
### PPC Analysis  
The pupil was observed as various elliptic shapes according to the gaze direction. It can be assumed to be a circle with zero eccentricity when pointed directly at the camera placed in front of the face [19]. Based on this, two prerequisites were set to estimate the 3D position of the PCP as follows:  
1. The pupil is a disk with zero eccentricity while located on the surface of the HPOM and being perpendicular to the g-axis.
2. The xy-coordinate system of the camera and HPOM are parallel.  
Based on the established premise, the three-dimensional position of the pupil is estimated by analysing the PPC data that includes various elliptic shapes depending on the gaze direction. When the gaze direction is changed while \(\phi\) is fixed to 0, the HPOM observed by the camera can be represented by the x-z section shown in Figure 2. The shape of the pupil projected onto the camera is shown in Figure 3. When the gaze direction is perpendicular to the camera, \(\theta\) becomes 0. The pupil disk is positioned as shown A in Figure 2. It is projected as a circle with zero eccentricity, as shown A in Figure 3. On the other hand, when the gaze direction is not perpendicular to the plane of the camera, the pupil disk is positioned as shown B in Figure 2. This is due to a three-dimensional rotational movement based on the ORCP as set out in the premise. Due to this, the three-dimensionally rotated and moved pupil disk is projected orthogonally to the camera, resulting in the elliptical shape shown B in Figure 3.  
Figure 1: Relationships between PCP and ORCP in Spherical Coordinates.  
Figure 2: Ellipse change principle of the pupil shown in the cross section of the x-axis and z-axis coordinate system.  
If the gaze direction changes from position B shown in Figure 2 while \(\theta\) is fixed, the PPC appears in the form of B being rotated along the z-axis relative to the ORCP, as shown C in Figure 3. Therefore, \(\theta\) is the Rotation Angle of the Eye (RAE). This causes a change in the pupil's size due to changes in the eccentricity of the PPC and a change in the distance along the z-axis. \(\phi\) is the Rotation Direction Angle of the eye (RDA). This causes rotation of the PPC. In addition, it can be seen that the short axis of the elliptically-changed PPC is formed at the same angle as the RDA.
# Field Test Study on Vertical Bearing Characteristics of Coal Rock
## 3 Results
### Bi-Directional Load Test
#### 3.2.4 Mobilized Side Resistance  
For TP1 and TP2, the interface between the reinforced concrete supporting wall and the coal rock was damaged during the test. Therefore, the outer diameter and length of the reinforced concrete supporting wall was used to calculate the side resistance:  
\[\tau_{i}=\frac{Q_{ui}-W}{\pi D_{\text{w}}h_{\text{w}}} \tag{10}\]  
where \(\tau_{i}\) is the average side resistance per unit area (kPa); \(Q_{ui}\) is the upward loading value (kN); \(W\) is the weight of the pile (kN); \(D_{\text{w}}\) is the outer diameter of the reinforced concrete supporting wall (m); \(h_{\text{w}}\) is the length of the reinforced concrete supporting wall (m).  
Figure 16 shows the unit side resistance-displacement curve of TP1 and TP2. There is a significant nonlinear relationship between the unit side resistance and the displacement. The hyperbola can be used to represent the load transfer function:  
\[\tau=\frac{s}{a+bs} \tag{11}\]  
where \(s\) is displacement (mm); \(a\) and \(b\) are coefficients.  
In Figure 16, the fitted hyperbola curve matches well with the test points. The initial tangent \(1/a\) of the load transfer functions of TP1 and TP2 are 552.5 and 150.8, respectively, and the limit values of the unit side resistance \(1/b\) of TP1 and TP2 are 166 kPa and 156 kPa, respectively. The difference of the initial tangent is significant, but the limit values of the unit side resistance are close.  
In Figure 6, the lengths of the reinforced concrete supporting wall are 7.5 m for TP1 and 7.7 m for TP2, which are roughly the same. Because the failure occurs at the interface between the reinforced concrete supporting wall and the coal rock, the ultimate side resistance of the two piles is basically the same. That means the \(1/b\) values of TP1 and TP2 are close. However, the reinforced concrete length of the pile body is 6.8 m for TP1 and  
Figure 15: Relationship between deformation modulus and pressure: (**a**) TP1; (**b**) TP2.  
4.0 m for TP2. The stiffness of TP1 is significantly greater than that of TP2, and it makes the initial tangent \(1/a\) of TP1 significantly greater than that of TP2.
# Expansion Lemma--Variations and Applications to Polynomial-Time Preprocessing
## 4 (Basic) Expansion Lemma and Applications  
In this section, we provide the definitions of '\(q\)-expansion' and the expansion lemma that generalize Hall's theorem (Proposition 1). In the following subsections, we give a summary of how expansion lemma has been used for several problems, e.g., \(p\)-component order connectivity, Feedback Vertex Set, Cluster Vertex Deletion, and several other theoretical graph problems.  
**Definition 7** (\(q\)-Expansion).: _Let \(G=(V=A\uplus B,E)\) be a bipartite graph with vertex bipartition \((A,B)\). For a positive integer \(q\), a set of edges \(M\subseteq E(G)\) is called a \(q\)-expansion of \(A\) into \(B\) if_  
* _Every vertex of_ \(A\) _is incident to exactly_ \(q\) _edges of_ \(M\)_, and_
* \(M\) _Saturates exactly_ \(q|A|\) _vertices in_ \(B\)_._  
For \(q=1\), a \(q\)-expansion is matching. We now have expansion lemma, which was introduced by Thomasse [6].  
**Lemma 3** (\(q\)-Expansion Lemma[6]).: _Let \(q\geq 1\) be a positive integer and \(G=(V=A\uplus B,E)\) be a bipartite graph such that_  
* \(|B|\geq q|A|\)_, and_
* _There are no isolated vertices in_ \(B\)_._  
_Then, there exist nonempty vertex sets \(X\subseteq A\) and \(Y\subseteq B\), such that_  
1. _There is a_ \(q\)_-expansion of_ \(X\) _into_ \(Y\)_, and_
2. \(N_{G}(Y)\subseteq X\)_._  
_Furthermore, sets \(X\) and \(Y\) can be found in polynomial time in the size of \(G\) (see Figure 1 for an illustration of \(q=2\))._  
When \(q=1\), note that the sets \(Y\), \(X\), and \(V(G)\setminus(X\cup Y)\) form a crown decomposition with crown, head, and body, respectively. Hence, the expansion lemma can be seen as a generalization of Lemma 2. In particular, it allows us to work with objects that need to be hit that can be of size more than 2. It gives a way to \"encode\" disjoint objects of size \(q+1\) such that any solution must hit these objects. We use this to argue that thereis a solution containing \(X\), noting that every object \(Y\) is part of can be transformed into a solution containing \(X\).  
We illustrate this with a few examples below. To encode objects in this way, we usually construct an auxiliary bipartite graph where we apply the expansion lemma.
## 5 Results  
### Result of Correlation Analysis and Path Analysis  
[PERSON]'s alpha was used to assess the internal consistency of each construct. Table 2 shows a high level of internal consistency of four constructs, suggesting the satisfactory reliability and validity of the measurements.  
The results of measurement items' mean values and standard deviation values are described in Table 2. The results show that the means of EE and PPS items are at a high level above 6.00 (with the highest level at 7 points). Most of the participants expressed their acceptance and intention to use IEG in the future. Most of the items' SD is lower than 1.00 which indicates that participants' opinions are similar. A higher level of SD is reported in PE4 (SD= 1.130) and BI 2 (SD = 1.122), which indicates moderate variables by participants expressed in these items.  
Table 3 shows the correlation results of PE, EE, PPS, BI, and age. Overall, the correlation analysis revealed significant relationships between PE and PPS, PE and BI, EE and age. A significant positive correlation is observed between PE and BI at a high level of statistical significance (\(p<0.01\)). Additionally, a significant negative correlation is found between age and EE at \(p<0.05\). The result indicates that: 1. age is negatively correlated with effort expectancy, meaning that younger people are more likely than others to find the immersive exergames easy to play; 2. PE is negatively correlated with PPS, indicating that individuals who feel safer during physical activities have lower performance expectations. In contrast, individuals who think less physically safe tend to be more focused on the expectation of attaining health benefits; 3. PE is positively correlated with BI, indicating that older adults with higher performance expectations in immersive exergames are more likely to use IEG in the future.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Constructions** & **Measurement Items** \\ \hline \multirow{4}{*}{Perceived Physical Safety (PPS)} & PPS1: I would feel my body safe while playing VR exergames. \\  & PPS2: I would feel in harmony with my body while playing VR exergames. \\  & PPS3: I feel that I’m in a safe physical environment while playing VR exergames. \\  & PPS4: I trust in my body’s health condition while playing VR exergames. \\  & PPS5: Overall, I find VR exergames would be safe. \\ \hline \multirow{4}{*}{Behavioral Intention to Use (BI)} & BI1: Assuming I had access to a VR exergame, I would intend to use it. \\  & BI2: I predict I will play VR exergames on a regular basis in the future. \\  & BI3: I intend to play VR exergames in the future. \\  & BI4: Given that I had access to a VR exergame, I would play with it to keep healthy. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
Table 4 shows the results of the path coefficients' estimates and the results of H 1 and H 2 in developing the structure model. The results of path analysis of the impacts of PE, EE, and PPS on BI are not significant.
## 3 Experimental Results
### Lateral Drifts  
The average total drift for each SCMSW specimen is presented in Table 4 at the maximum observed lateral load and at failure, which is defined in this study as 80% from the maximum observed lateral load. At maximum load, walls with an aspect ratio of 1.0, 2.0, 2.6 and 3.6 had an average drift of 1.51%, 1.32%, 1.56% and 1.65%, respectively. Results showed that drift capacity for SCMSW specimens increased as aspect ratio increased, except SW1, which experienced a higher drift capacity. This can be referred to the contribution of sliding in the lateral displacement and slip in lab splices at the base [19; 20]. The average total drift at failure ranged from 1.56% to 2.27%. The results revealed that the drift capacity was not significantly affected by the amount of the axial load in the constant of the aspect ratio and the flexural reinforcement.
## 1 Introduction
(2016) [29], many studies have investigated the dynamical drivers of precipitation variability in the Caribbean, aiming to improve the skill of weather and climate forecasting. Most of these studies used different GPP without a comprehensive evaluation of their abilities to represent the main features of precipitation in the Caribbean, and/or conclude that their results were limited to the large-scale because the datasets employed do not have a horizontal resolution appropriate for the Caribbean islands. An accurate description of the temporal and spatial variations of precipitation in the Caribbean requires products that are capable of reproducing regional features at the appropriate spatial resolution. Theoretically, higher resolution GPP could best fulfill this requirement, as compared with lower resolution ones. However, since higher resolution does not necessarilymean a better representation of the observed climate features, it is critical to always evaluate the GPP performance. This work aims to: (1) assess the ability of 16 GPP in representing the characteristics of precipitation in the Caribbean as compared to surface stations, and (2) determine which products perform best over the region using common statistical metrics. To do so, we conduct a comprehensive evaluation and intercomparison of several GPP across the Caribbean, including the latest versions of some previously evaluated for the region and others that have not yet been evaluated for the Caribbean. Results from this work might be used as a reference to select the most appropriate GPP (e.g., for model evaluation and climate change studies), based on their advantages and limitations. This paper is organized as follows. Section 2 presents the study domain, the data, and methods used in this work. In Section 3, we describe the results, while Section 4 provides the conclusions of the study.