esa-sceva/satcom-chunk-collection huggingface dataset:
## Abbreviations  
\begin{tabular}{l l} \hline \hline Abbreviation & Definition \\ \hline PEMWE & Proton exchange membrane water electrolysis \\ BPs & Bipolar plates \\ PANI & Polyaniline \\ PPy & Polypyrrole \\ ICR & Interface contact resistance \\ PEMFC & Proton exchange membrane fuel cell \\ OER & Oxygen evolution reaction \\ HER & Hydrogen evolution reaction \\ CVD & _Chemical vapor deposition_ \\ PVD & Physical vapor deposition \\ EIS & Electrochemical impedance spectroscopy \\ CV & Cyclic voltammetry \\ PAMT & 2-amino-5-mercaptoo-1,3,4-thiadiazole \\ EP & Epoxy resin \\ GO & Graphene oxide \\ TRGO & thermal reduced grapheme oxide \\ BCZT & Ba\({}_{0.85}\)Ca\({}_{0.15}\)Zr\({}_{0.1}\)T\({}_{0.9}\)O\({}_{3}\) \\ FTIR & Fourier transform infrared spectroscopy \\ PI & Polyimide \\ EPD & Electrophoretic deposition \\ PPD & P-phenylenediamine \\ TiN & Titanium nitride \\ RMGO & Reduced magnesium oxide \\ PDADMAC & Polydimethyldiallyammonium chloride \\ Polydopamine & PDA \\ \hline \hline \end{tabular}
# Simultaneous DC Railway Power System Analysis Method Using Model-Based TPS
## 3 Development of Model-Based TPS Construction
### DC Railway Power System Model  
The voltage provided by KEPCO (Korea Electric Power Corporation) is set to 22.9 kV, 60 Hz. For three winding transformers, delta-delta-wye configuration selected to build DC 1500 V for rectifier output voltage. In this case, no load voltage is 1650 V, which is a practical value for the railway substation site. Figure 5 shows the schematic for the DC railway power system model under study. As the paper mainly focuses on the simultaneous mTPS and DC railway power system, circuit breaker (CB) models are not included. Figure 6 shows a circuit of moving train load using varying resistance.  
Train movement by applying varying resistance is calculated as follows [18].  
\[\begin{cases}R_{fwd,l}=R_{fwd}\cdot\left(\left(d_{st}-d_{A}^{*}\right)+s_{i} \right)\\ R_{fwd,r}=R_{fwd}\cdot\left(\left(d_{B}^{*}-d_{st}\right)-s_{i}\right).\end{cases} \tag{13}\]  
\[\begin{cases}R_{ret,l}=R_{ret}\cdot\left(\left(d_{st}-d_{A}^{*}\right)+s_{i} \right)\\ R_{ret,r}=R_{ret}\cdot\left(\left(d_{B}^{*}-d_{st}\right)-s_{i}\right).\end{cases} \tag{14}\]  
where, \(R_{fwd}\) and \(R_{ret}\) is forward rail resistance and return rail resistance in m\(\Omega\)/km, respectively. \(d_{A,B}^{*}\) stands for the location of substation A and B. While \(d_{st}\) is the previous station location. \(s_{i}\) is the distance traveled by train. As the train moves towards the station, \(R_{fwd,l}\) and \(R_{ret,l}\) increases, and \(R_{fwd,r}\) and \(R_{ret,r}\) decreases by the rail resistance.  
Figure 5: Schematics of moving train load using varying resistance.
# Salient Region Detection Using Diffusion Process with Nonlocal Connections
## 2 Proposed Approach  
In this section, our proposed approach is presented in detail, the process of which is shown in Figure 3. First of all, to improve the robustness of multi-scale salient regions, we use the SLIC (simple linear iterative clustering) model [37] to abstract the input image into uniform and compact regions at five scales. Second, a saliency-biased (compactness-biased) Gaussian model is constructed to refine the initial saliency maps generated by the compactness cue, and then, the compactness information is diffused on the 2-layer sparse graph with the nonlocal connections. Similarly, the uniqueness-biased Gaussian model is formed, and the uniqueness information is propagated on the 2-layer sparse graph (without the nonlocal connections). Finally, we put forward a single-layer updating and multi-layer integrating scheme to obtain a more homogeneous salient region.
## 3 Space--Time Dynamic Analysis Method Based on DCM
### Space-Time Discretization and Numerical Scheme of DC Dynamic Analysis
\begin{array}{ll}\sum\limits_{k=1}^{N}c_{ik}^{(\tau^{2})}y_{k}&=\sum\limits_ {k=B_{3}}\sum\limits_{j=B_{N}}c_{ik}^{(\tau)}c_{kj}^{(\tau)}y_{j}+\sum\limits_ {k=B_{12}}\sum\limits_{j=B_{N}}c_{ik}^{(\tau)}c_{kj}^{(\tau)}y_{j}\\ &=\Delta T\sum\limits_{k=B_{3}}c_{ik}^{(\tau)}\frac{\partial y}{\partial\tau} \Big{|}_{k}+\sum\limits_{k=B_{12}}\sum\limits_{j=B_{22}}c_{ik}^{(\tau)}c_{kj}^ {(\tau)}y_{j}+\sum\limits_{k=B_{12}}\sum\limits_{j=B_{3}}c_{ik}^{(\tau)}c_{kj} ^{(\tau)}y_{j}+\sum\limits_{k=B_{12}}\sum\limits_{j=B_{3}}c_{ik}^{(\tau)}c_{kj }^{(\tau)}y_{j}+\sum\limits_{k=B_{12}}\sum\limits_{j=B_{3}}c_{ik}^{(\tau)}c_{ kj}^{(\tau)}y_{j}\\ \end{array}  \tag{11}\]  
\[ \begin{array}{ll}\sum\limits_{k=1}^{N}c_{ik}^{(\xi^{2}\tau^{2})}y_{k}&= \sum\limits_{k=B_{N}}\sum\limits_{j=B_{3}}c_{ik}^{(\xi^{2})}c_{kj}^{(\tau)}c_{ jl}^{(\tau)}y_{l}+\sum\limits_{k=B_{N}}\sum\limits_{j=B_{12}}\sum\limits_{l= B_{3}}c_{ik}^{(\xi^{2})}c_{kj}^{(\tau)}c_{jl}^{(\tau)}y_{l}\\ &=\Delta T\sum\limits_{k=B_{N}}\sum\limits_{j=B_{3}}c_{ik}^{(\xi^{2})}c_{kj}^ {(\tau)}\frac{\partial y}{\partial\tau}\Big{|}_{j}+\sum\limits_{k=B_{N}}\sum \limits_{j=B_{12}}\sum\limits_{l=B_{12}}c_{ik}^{(\xi^{2})}c_{kj}^{(\tau)}c_{jl }^{(\tau)}y_{l}\\ &+\sum\limits_{k=B_{N}}\sum\limits_{j=B_{12}}\sum\limits_{l=B_{3}}c_{ik}^{( \xi^{2})}c_{kj}^{(\tau)}c_{jl}^{(\tau)}y_{l}+\sum\limits_{k=B_{N}}\sum\limits_{j =B_{12}}\sum\limits_{l=B_{3}}c_{ik}^{(\xi^{2})}c_{kj}^{(\tau)}c_{jl}^{(\tau)}y_{ l}\\ \end{array}  \tag{12}\]  
Substituting the boundary conditions with simple support at both ends, that is, the displacement \(y\) and the first derivative of \(y\) of node group \(B_{3}\) is zero. Substitute the Equations (11)-(13) into the Equation (9), and get  
\[ \begin{array}{ll}\left(\frac{EI}{L}\sum\limits_{k=B_{3}}\sum\limits_{j=B_{N} }\sum\limits_{l=B_{i}}c_{ik}^{(\xi^{2})}c_{kj}^{(\xi^{2})}c_{jl}^{(\xi^{2})}+ \frac{\rho}{\Delta T^{2}}\sum\limits_{k=B_{12}}\sum\limits_{l=B_{i}}c_{ik}^{( \tau)}c_{kl}^{(\tau)}-\frac{\rho\xi}{\Delta T^{2}L^{2}}\sum\limits_{k=B_{N}} \sum\limits_{j=B_{12}}\sum\limits_{l=B_{i}}c_{ik}^{(\xi^{2})}c_{kj}^{(\tau)}c_{ jl}^{(\tau)}\right)y_{l}=F_{i}+\\ \left(\frac{\rho\xi}{\Delta T^{2}L^{2}}\sum\limits_{k=B_{N}}\sum\limits_{j=B_{ 12}}\sum\limits_{l=B_{22}}c_{ik}^{(\xi^{2})}c_{kj}^{(\tau)}c_{jl}^{(\tau)}- \frac{EI}{L}\sum\limits_{k=B_{N}}\sum\limits_{j=B_{N}}\sum\limits_{l=B_{3}}c_{ ik}^{(\xi^{2})}c_{kj}^{(\xi^{2})}c_{jl}^{(\xi^{2})}-\frac{\rho}{\Delta T^{2}}\sum\limits_{k=B_{12}} \sum\limits_{l=B_{3}}c_{ik}^{(\tau)}c_{kl}^{(\tau)}\right)y_{l}\\ +\left(\frac{\rho\xi}{\Delta T^{2}L^{2}}\sum\limits_{k=B_{N}}\sum\limits_{l=B_{3} }c_{ik}^{(\xi^{2})}c_{kl}^{(\tau)}-\frac{\rho}{\Delta T}\sum\limits_{l=B_{3}}c_ {il}^{(\tau)}\right)\frac{\partial y}{\partial\tau}\Big{|}_{l}\\ \end{array}  \tag{13}\]  
where \(i=B_{i}\)  
Equation (13) can be written as a matrix form  
\[\tilde{\tilde{K}}\mathbf{Y}_{B_{i}}=\tilde{\mathbf{F}} \tag{14}\]  
Figure 3: Allocation of grid node regions. where  
\[
## 5 Conclusions  
This study explores a suite of existing and hypothetical sensors in the viewing of snow-covered terrain as a function of sensor orbital configuration and sensor efficacy. The research explores the viewing of a series of sensors, and constellations of sensors, with the distribution of coincident terrestrial snow. The results help quantify the demands on sensor swath width and orbital configuration for the requirement of 1-day, 3-day, and 30-day integration periods. Viewing extent simulations show that sensors with swath widths of 1100 km, 550 km, and 200 km could meet the demands of 1-day, 3-day, and 30-day repeat intervals, respectively. However, the effective coverage when considering sensor efficacy and cloud attenuation suggests a single sensor cannot observe all snow classes at all locations in the northern hemisphere during all times of the year. Constellations composed of different sensors could better compensate for the shortage of a single sensor in a specific snow class. The combination of the PMW and SAR sensors could perform well for all the snow cover classes except taiga, while the LiDAR sensors could potentially provide some key information of snow with overlaying vegetation. In a future study, we plan to apply the results of this study within a snow OSSE to quantify the expected model improvements as a function of orbital configuration and sensor type. The results of the snow OSSE combined with a cost analysis could help mission planners decide how to get the most snow-related scientific bang for the scientific buck.  
Conceptualization, E.K.; Funding acquisition, B.A.F.; Investigation, L.W.; Methodology, B.A.F. and E.K.; Visualization, L.W.; Writing--original draft, L.W.; Writing--review and editing, B.A.F. and E.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Aeronautics and Space Administration. Grant number 80 NSSC20K0210.  
Not applicable.  
The authors declare no conflict of interest.
# A Source Anonymity-Based Lightweight Secure AODV Protocol for Fog-Based MANET
## 3 Preliminary Knowledge and Analysis
### Aodv
#### 3.1.2 Route Maintenance  
Each node maintains its own broadcast ID counter and sequence number counter. The sequence number counter is a 32-bit loop counter, and the nodes need to increment the sequence number count before an operation. The sequence number represents the new level of a node. AODV always favors newer information, and the nodes update their routing information, called an update function, if they receive a message with a sequence number higher than the last recorded one for that destination.  
After the \(RREQ\) message is broadcasted from the source node, each intermediate node on the route towards the destination node automatically saves the reverse path back to the source node. The reverse path will be maintained for a certain time in the routing table, so that the \(RREP\) message can return to the source node along it.
## 3 Results
### Frost Heaving-Induced Pressure  
#### 3.2.1 Heaving Pressure Behavior  
The variation of the frost heaving-induced pressure (\(p\)) with times for a different \(S\) and \(M\) is shown in Figure 7. Under conditions of confinement, the frost heaving was less  
Figure 6: Summary of frost heaving tests. (**a**) Time to reach ultimate frost heaving (\(t_{\text{ul}}\)), (**b**) heaving ratio (\(\eta\)), and (**c**) total heaving ratio (\(\Delta\eta\)) with respect to mixing ratio.  
than 0.1 mm; therefore, the upward movement can be neglected. When \(M=0\%\) (i.e., pure gravel ballast), no pressure was induced under confinement. As shown in Figure 5a, frost heaving was observed under free confinement. Figure 7a shows that the expansion of water is dissipated within the pore space for the pure gravel ballast. Therefore, the frost heaving pressure can be neglected under a sufficient overburden pressure for pure gravel ballast. However, the pressure increased as \(M\) increased. At \(M=5\%\), \(p\) increases from \(t-300\) min (Figure 7b). As \(M\) increased, the trends in \(p\) were similar each other, but the level of \(p\) increased (Figure 7c,d). A small negative pressure was induced in the initial phase owing to frost shrinkage, but it is negligible. Considered against the irregular increase in \(h\) with time, as shown in Figure 5, the increase in \(p\) showed almost linear. This would indicate that, under confinement, the formation of ice lenses occurred while migrating the pore spaces of the mixtures, resulting in a moderate variation in the frost heaving pressure.  
The minimum value in the \(p\) (\(-2.5\) kPa) was observed at \(t=28.5\) min for p-M25-S70 (Figure 7d). The position of the minimum value corresponds to the position of the largest frost shrinkage for h-M25-S70 (Figure 5f). Frost shrinkage was also affected by the behavior of frost heaving pressure.
# Precise Measurement of Hyperfine Structure of Cesium 7S\({}_{1/2}\) Excited State
## 3 Results  
We measured the hyperfine splitting of \(|7S_{1/2}\rangle\) state employing two different methods, EIS spectroscopy and OODR spectroscopy. The EIS displayed two typical phenomenons, i.e., electromagnetically induced transparency (EIT) and electromagnetically induced absorption (EIA), for coupling laser detuning \(\Delta_{c}\) (see Figure 3). EIT (EIA) is a quantum elimination (constructive) interference among excitation pathways in the three-level atom that led to a cancellation (enhancement) of absorption in the medium. The OODR spectroscopy is an optical resonant spectroscopy that is widely used to investigate the excited state [11; 12]. Below, we present the EIS and OODR spectroscopy to study the hyperfine structure of cesium \(|7S_{1/2}\rangle\) state.  
Figure 2: (color online) Layout of the experimental setup. The 852 and 1470 nm lasers couple the lower and upper transitions and are set to overlap in a counter-propagating geometry inside two separated 75-mm-long Cs room-temperature vapor cells. The purple square indicates the setup of the EIS measurement, where the probe power is detected with a photodiode detector (PD1) after passing through a dichroic mirror (DM1). The red square denotes the setup of the OODR measurement, where the 1470 nm beam is detected with a balanced photodiode detector (BPD) after passing through the other dichroic mirror DM2. In the setup, the probe laser power is kept fixed by using a PID (proportional integral derivative) feedback loop that controls the RF power supplied to an AOM (acousto-optic modulator). The probe frequency is locked to the first side band of an electro-optic modulator (EOM) employing PDH (Pound–Drever–Hall) method with an ultra-stable Fabry–Perot (FP) cavity of a finesse 15,000. The PD2 is used to monitor the 1470 nm laser power.
# Layout Optimisation of Frame Structures with Multiple Constraints and Geometric Complexity Control
## 1 Introduction
### Precedent Work  
#### 1.1.1 Stress  
Two main challenges exist for the stress-constrained layout optimisation of discrete structures, namely, stress singularities and the large number of elemental stress constraints. Stress singularity refers to the phenomenon that as the cross-sectional area of an element approaches zero, the corresponding stress constraint should vanish, while the assigned stress limit in the mathematical formulation remains finite. This phenomenon prevents the total removal of elements and hinders the search for true optima [16; 17; 18]. To address this issue, the stress constraints for elements with small cross-sectional areas should be relaxed. Several strategies have been proposed for their smooth approximation and relaxation, such as the \(\epsilon\)-relaxation method [19; 20], and smooth envelope functions [18; 21]. The second numerical difficulty is associated with the large number of element-wise stress constraints, which requires highly computational-demanding sensitivity analysis, and brings significant challenges to their simultaneous treatment in optimisers. In order to resolve this difficulty, elemental stress constraints can be aggregated into a global measure. Such optimisers then only need to deal with this global measure, as does the sensitivity analysis. Commonly used aggregation functions in stress-constrained optimisation problems include the \(K\)-\(S\) function [22; 23; 24; 25], \(p\)-norm/mean functions [15; 26; 27], etc. Readers are referred to the aforementioned literature for a detailed introduction and implementation of these aggregation functions.
# Characterization of Invar Syntactic Foams Obtained by Spark Plasma Sintering
## 3 Results and Discussion
The peaks of the chemical compounds from the NiCrSiB superalloy powder were no longer highlighted, possibly due to their small amount (below the detection limit of diffractometer), as well as the fact that the spherical NiCrSiB particles are hollow. The mean sizes of the Invar crystallites, computed with the Scherrer formula, increased from 14 nm (mechanically alloyed powder) to 30 nm (600 \({}^{\circ}\)C spark plasma sintered material). Figure 5: EDX maps of element distributions of Invar/20%NiCrSiB syntactic foam – mixed elements map distribution (**a**), map of the Fe (**b**), map of the Ni (**c**), map of the Cr (**d**), map of the Si (**e**), map of the B (**f**) and map of the C (**g**). Invar/20%NiCrSiB foams were subjected to dilatometric analysis by heating up to 400 \({}^{\circ}\)C. The variation in sample elongation (\(\Delta\)I) with increasing temperature are graphically rendered in Figure 8 and compared with results obtained for (SPS) sintered Invar from a previous study [17]. In the case of syntactic foams, an insignificant change in elongation can be observed up to a temperature of 150 \({}^{\circ}\)C. In the temperature range of 150-400 \({}^{\circ}\)C, elongation increases continuously and proportionally with temperature. This suggests that the increase in the temperature range of 150-400 \({}^{\circ}\)C is due to the increase in the temperature range of 150-400 \({}^{\circ}\)C. This suggests that the increase in the temperature range of 150-400 \({}^{\circ}\)C is due to the increase in the temperature range of 150-400 \({}^{\circ}\)C.  
Figure 6: SEM image and EDX line scan of Invar/20%NiCrSiB sample. Figure 7: XRD diffraction patterns of Invar 16 h milled powders (**a**), NiCrSiB hollow particles (**b**) and Invar/20%NiCrSiB spark plasma sintered composite foam (**c**). the Invar/20%NiCrSiB porous structure is characterized by two different coefficients of thermal expansion (CTE), \(\alpha_{1}\) for the 25-150 \({}^{\circ}\)C and \(\alpha_{2}\) for the 150-400 \({}^{\circ}\)C temperature range. The values of the coefficient of thermal expansion (\(\alpha\)) were computed using the following relationship:  
\[\alpha\ =\ \frac{1}{\mathrm{I_{o}}}\cdot\frac{\Delta\mathrm{I}}{\Delta\mathrm{T}} \tag{5}\]  
where \(\mathrm{I_{o}}\) is the original sample length, \(\Delta\mathrm{I}\) is the sample elongation and \(\Delta\mathrm{T}\) is the temperature range. Table 2 shows the CTE values of the Invar/20% NiCrSiB spark plasma sintered foam. Based on the data presented in Figure 8 and Table 2, it can be concluded that the addition of hollow spherical NiCrSiB particles in the Invar matrix leads to an increase in the coefficient of thermal expansion, from 0.6 \(\times\) 10\({}^{-6}\)\({}^{\circ}\)C\({}^{-1}\) of the reference Invar [17] to 2.52 \(\times\) 10\({}^{-6}\)\({}^{\circ}\)C\({}^{-1}\), up to 150 \({}^{\circ}\)C.
## 4 Test Results and Discussion
### Characteristic Loads  
#### 4.3.1 Cracking Stress  
In the test, the characteristic loads were recorded, and the equivalent cracking stress was calculated using Equation (1). The characteristic loads and cracking stress of the panel specimens are presented in Table 8.  
\[\sigma_{cr}=\frac{M}{W}+\frac{P}{A} \tag{1}\]  
where \(M\) is the bending moment at the center of the curved section, \(M=P\cdot L\)\(P\) is the cracking load of the specimen, \(L\) is the length of the moment arm equal to the distance from the center of the loading cell to the center of the cracked section. \(L\) is measured during the test. \(W\) is the section modulus, \(W=bl^{2}/6\), \(b\) and \(h\) are the length and thickness of the curved section, respectively; \(A\) is the sectional area of the curved section; and \(P\) is the test load.  
As shown in Table 8, the mean cracking stress of UHPC panel specimens was 8.12 MPa, slightly greater than that of UHPC tension specimens (7.8 MPa). It indicates that the UHPC can maintain its crack resistance well in this kind of thin panel. In contrast, the mean cracking stress of GRC panel specimens was 4.51 MPa, 33.7% lower than that of GRC tension specimens (6.8 MPa) because the reinforcement effect of glass fibers on the cement matrix was not effective in thin elements, resulting in a lower cracking stress. It can be concluded that UHPC is more suitable for producing ultra-thin and irregular components than GRC. The cracking stress and ultimate load of UHPC panels were higher than those of GRC panels by 64.3% to 123.0% and 29.2% to 115.0% at the same curved section thicknesses, respectively. It indicates that the replacement of GRC with UHPC is capable of improving both the crack resistance and bearing capacity of decorative panels.  
Figure 11: Load-displacement curves of panels.
# High-Efficiency Quantum Dot Lasers as Comb Sources for DWDM Applications
## 3 Performance of CPMLLs
### Comb Efficiency  
In order to analyze the efficiency of the different devices, the WPE of comb lines was analyzed. For a given comb, the comb line with the lowest power will also have the lowest WPE. Therefore, the PoLL values in Section 3.3 are divided by the electrical power consumption of the CPMLL for each state to calculate the WPE of the 20 th channel (WPE20). The WPE20 for the devices from Figure 3 are shown in Figure 4d-f, and the maximum and average WPE20 values are recorded in Table 1. All WPE20 values are stated in dB and the PoLL used to calculate WPE20 includes a 2 dB chip-to-fiber coupling loss. The electrical power consumption was measured in a four-wire configuration. The WPE20 values closely track the PoLL values; however, states with lower drive current tend to have higher WPE20, which reflects the trend of the plots in Figure 3b. The 6% and 10% devices can both achieve similar WPE20, despite the 6% device having a shorter SA and a higher overall WPE for the same SA bias. The 6% device requires higher reverse bias to achieve a more efficient (20-line) comb, which degrades its WPE. In the 18% device, the saturable loss is considerably higher, which reduces the average efficiency of comb generation and moves efficient states to higher drive currents and lower SA biases. This demonstrates that the bias values of efficient comb generation can be tuned by changing the SA length; however, 10% offers the most efficient comb and the largest area of efficient combs. The state with the highest WPE is shown in Figure 5c from the 10% device at a drive current of 140 mA and an SA bias of 1.8 V. This state consumes 214 mW of electrical power and produces 20 comb lines with powers ranging from \(-\)5.62 to \(-\)4.35 dBm. Another comb from the 10% device is shown in Figure 5d with a lower power consumption, and a 0.12 dB reduction in efficiency from the state shown in Figure 5c. The comb is produced with 144 mW of electrical power, at an SA bias of 1.8 V and a drive current of 100 mA, and supplies 20 lines ranging from \(-\)7.46 to \(-\)4.37 dB. This demonstrates the ability of CPMLLs to produce efficient combs at a range of output powers.
# GazeRayHand: Combining Gaze Ray and Hand Interaction for Distant Object Manipulation
## 2 Related Work
### Asynchronous and Synchronous Inputs of Gaze and Hand for Manipulation  
Gaze and hand manipulation can be categorized into two types. In the first type, gaze and hand inputs are asynchronously performed in a sequential manner by switching the input mode between them [19; 33; 34; 35]. Hence, the gaze and hand inputs are not performed and applied simultaneously. For example, [PERSON] et al. [36] introduced the GG method that uses gaze to create a selected area for distant dense objects and then supports translation or rotation using a hand within the area without taking any input from the user's gaze. Additionally, [PERSON] et al. [32] proposed a method for object selection, and the gaze input is deactivated when the hand is used for object selection, and vice versa.  
Recently, researchers have developed interaction techniques that simultaneously take gaze and hand inputs for object selection and manipulation [14; 16; 17]. [PERSON] et al. [15] proposed a method that selects an object which is gazed at when placing a hand on a gaze ray. In a user study, they found their selection method was faster and had fewer errors than hand-only and Gaze&Pinch [19] techniques. Additionally, [PERSON] et al. [14] introduced the GazeHand technique for object translation. This technique first creates a gaze ray to an object which a user is looking at and then places virtual hands nearby the object. The GazeHand takes gaze and hand inputs simultaneously and allows for concurrent gaze and hand manipulation after grabbing the object.