adsabs/AstroCLIMB huggingface dataset:
Paper: Evaluating the Use of Unoccupied Aircraft Systems (UASs) for Planetary Exploration in Mars Analog Terrain
Authors: Brett B. Carr, Matthew Varnam, Nathan Hadland, Jahnavi Shah, Joana R. C. Voigt, Samantha Gwizd, Kathryn M. Stack, Fred Calef, Raymond Francis, Udit Basu, Baldur Björnsson, Colin X. Chen, Elisa Dong, Jeffrey E. Moersch, Michael Phillips, Joshua Springer, Catherine D. Neish, Christopher W. Hamilton
Figure caption: Geologic history of the mission area. The smooth sediment area (1) is the oldest unit in the mission area, followed by the older lava (2; orange outline). The central lava pathway was the first part of the younger lava flow field to be emplaced (3a; yellow outline) followed by the northern flow lobe (3b; red outline). The northeast and northwest flow lobes (3c) were the last parts of the younger lava flow field to be emplaced in this area, as the northern flow lobe inflated and overtopped the older lava. The sand sheet is the youngest surface, as transport of grains is ongoing. The sand sheet has been active since at least the older lava flow was emplaced, as the older lava flow was covered with sediment prior to emplacement of the younger lava flow. The black box in (a) denotes the extent of Figure 3. The white lines in (a) show the trace of the topographic profiles shown in (b) (b–b’) and (c) (c–c’).
Paper: Evaluating the Use of Unoccupied Aircraft Systems (UASs) for Planetary Exploration in Mars Analog Terrain
Authors: Brett B. Carr, Matthew Varnam, Nathan Hadland, Jahnavi Shah, Joana R. C. Voigt, Samantha Gwizd, Kathryn M. Stack, Fred Calef, Raymond Francis, Udit Basu, Baldur Björnsson, Colin X. Chen, Elisa Dong, Jeffrey E. Moersch, Michael Phillips, Joshua Springer, Catherine D. Neish, Christopher W. Hamilton
Figure caption: Point spectrometer data from the UAS mission. The VISIR spectrometer collected data on the lighter sediment (from sol 104), darker sediment (from sol 103), younger lava (from sol 111), and a white discoloration on the younger lava surface (from sol 108). The spectra show a broad absorption for basaltic minerals (olivine, pyroxene) at ∼1000 nm for all surfaces and a strong water absorption at 1950 nm and 1450 nm (overtone band) for the darker sediment and white discoloration. The reflectance in the y-axis for the darker sediment has been offset by 0.02 to improve figure clarity; the reflectance shown for the other three surfaces is the value measured by the instrument.
Paper: Evaluating the Use of Unoccupied Aircraft Systems (UASs) for Planetary Exploration in Mars Analog Terrain
Authors: Brett B. Carr, Matthew Varnam, Nathan Hadland, Jahnavi Shah, Joana R. C. Voigt, Samantha Gwizd, Kathryn M. Stack, Fred Calef, Raymond Francis, Udit Basu, Baldur Björnsson, Colin X. Chen, Elisa Dong, Jeffrey E. Moersch, Michael Phillips, Joshua Springer, Catherine D. Neish, Christopher W. Hamilton
Figure caption: Resource usage during the UAS mission. The UAS activities for each sol were limited by both the power usage (a) and the volume of data that could be transmitted to the science team (b). These limits were often approached by UAS operations, with available power more frequently the limiting resource than data volume.
Paper: HyPyRameter: A Python Toolbox to Calculate Spectral Parameters from Hyperspectral Reflectance Data
Authors: Michael S. Phillips, Christian Tai Udovicic, Jeffrey E. Moersch, Udit Basu, Christopher W. Hamilton
Figure caption: HyPyRameter results on point spectra from samples collected for the Rover–Aerial Vehicle Exploration Network (RAVEN) project. Parameter values for the Freya and Niflheim RAVEN samples are represented as “single pixels” and compared with USGS library spectra for reference. Pyroxene is indicated by high LCPINDEX2, BD905, and BD920_2 values, and H2O bonds are indicated by high BD1900r2 and BD1900_2 values. Small amounts of iron oxides are indicated in both spectra by purple/red color values in the HEM browse product, and hydrated silica is indicated by high D2200, BD2250, and BD2265 values combined with high BD1900_2 values.
Paper: HyPyRameter: A Python Toolbox to Calculate Spectral Parameters from Hyperspectral Reflectance Data
Authors: Michael S. Phillips, Christian Tai Udovicic, Jeffrey E. Moersch, Udit Basu, Christopher W. Hamilton
Figure caption: Flowchart delineating the HyPyRameter protocol. The user inputs hyperspectral data (either an image cube or point spectra), with optional preprocessing. Valid parameters are determined based on the wavelength range of the data and printed for user evaluation. Users can further cull the list of valid parameters or run all parameters in the list. Parameters are calculated and returned as an image cube or series of vectors depending on input data type. If input data is an image cube, browse products are also calculated and returned.
Paper: HyPyRameter: A Python Toolbox to Calculate Spectral Parameters from Hyperspectral Reflectance Data
Authors: Michael S. Phillips, Christian Tai Udovicic, Jeffrey E. Moersch, Udit Basu, Christopher W. Hamilton
Figure caption: Spectral parameters over fumarole vent near Myvatn, Iceland. (A) Custom parameter combination designed to differentiate elemental sulfur, chlorophyll-bearing materials, and iron oxides. (B) Approximate true color browse product, TRU (R637, R550, R463), of the scene shown in (A). (C) Representative spectra from the locations shown in panels (A) and (B).
Paper: HyPyRameter: A Python Toolbox to Calculate Spectral Parameters from Hyperspectral Reflectance Data
Authors: Michael S. Phillips, Christian Tai Udovicic, Jeffrey E. Moersch, Udit Basu, Christopher W. Hamilton
Figure caption: Application of HyPyRameter to a roadcut along TN52, Tennessee. The formation facies (A) containing (hydrated) carbonates (calcite) and clays (illite) are readily distinguishable using spectral parameters (C). Panel (B) shows the outcrop with an FAL browse product (R2529, R1506, R1080). Panel (D) shows representative spectra from the locations indicated in panels (A)–(C).
Paper: HyPyRameter: A Python Toolbox to Calculate Spectral Parameters from Hyperspectral Reflectance Data
Authors: Michael S. Phillips, Christian Tai Udovicic, Jeffrey E. Moersch, Udit Basu, Christopher W. Hamilton
Figure caption: Application of HyPyRameter to select minerals from the USGS SpecLib v7. Parameter values are represented as “single pixels” (black = low values, white = high values). Expected colors for browse product combinations are shown along the right side.
Paper: HyPyRameter: A Python Toolbox to Calculate Spectral Parameters from Hyperspectral Reflectance Data
Authors: Michael S. Phillips, Christian Tai Udovicic, Jeffrey E. Moersch, Udit Basu, Christopher W. Hamilton
Figure caption: Application of HyPyRameter to the CRISM MICA library. Parameter values are represented as “single pixels” (black = low values, white = high values). Expected colors for browse product combinations are shown along the right side.
Paper: HyPyRameter: A Python Toolbox to Calculate Spectral Parameters from Hyperspectral Reflectance Data
Authors: Michael S. Phillips, Christian Tai Udovicic, Jeffrey E. Moersch, Udit Basu, Christopher W. Hamilton
Figure caption: Illustration of four basic parameters in the HyPyRameter library. (A) Band depth parameter, (B) Band height parameter, (C) MIN parameter, and (D) Band Area.
Paper: HyPyRameter: A Python Toolbox to Calculate Spectral Parameters from Hyperspectral Reflectance Data
Authors: Michael S. Phillips, Christian Tai Udovicic, Jeffrey E. Moersch, Udit Basu, Christopher W. Hamilton
Figure caption: Example HyPyRameter output for an EMIT scene over Salar de Pajonales, Chile. (A) TRU browse product. (B) custom parameter combination to highlight significant minerals within and near Salar de Pajonales (kaolinite = red, gypsum = yellow/green, carbonate = blue). (C) The BIO browse product, which highlights potential pitfalls of the NDVI and the D700 parameters for identifying chlorophyll; these two parameters can be high in the presence of oxides (ROI 2) and can miss chlorophyll in water bodies (ROI 3). (D) FM2 browse product highlighting iron oxides (red/pink, ROI 2), pyroxenes (purple, ROI6), and wet regions of the salar (dark blue to cyan).
Paper: HyPyRameter: A Python Toolbox to Calculate Spectral Parameters from Hyperspectral Reflectance Data
Authors: Michael S. Phillips, Christian Tai Udovicic, Jeffrey E. Moersch, Udit Basu, Christopher W. Hamilton
Figure caption: BIO browse product over a fumarole vent near Myvatn, Iceland highlighting different chlorophyll parameters. The NDVI and D700 parameters capture the chlorophyll “red edge” and are better for vegetated areas on land, and the CPLINDEX is better for low signal and water (see Figure 8). The spectral plot shows representative spectra from the seafoam green and burgundy regions of the scene, highlighting chlorophyll with a strong and weak red edge, respectively.
Paper: HyPyRameter: A Python Toolbox to Calculate Spectral Parameters from Hyperspectral Reflectance Data
Authors: Michael S. Phillips, Christian Tai Udovicic, Jeffrey E. Moersch, Udit Basu, Christopher W. Hamilton
Figure caption: Spectra from ROIs 1–6 in Figure 8.
Paper: HyPyRameter: A Python Toolbox to Calculate Spectral Parameters from Hyperspectral Reflectance Data
Authors: Michael S. Phillips, Christian Tai Udovicic, Jeffrey E. Moersch, Udit Basu, Christopher W. Hamilton
Figure caption: Assessment of spectral parameter response to the addition of stochastic noise at 0.1%, 1%, and 5% levels. Parameter values are largely unaffected at the 1% level (differences max at ∼0.04) but are significantly different at the 5% level.
Paper: HyPyRameter: A Python Toolbox to Calculate Spectral Parameters from Hyperspectral Reflectance Data
Authors: Michael S. Phillips, Christian Tai Udovicic, Jeffrey E. Moersch, Udit Basu, Christopher W. Hamilton
Figure caption: Comparison of HyPyRameter and CRISM Analysis Toolkit (CAT) spectral parameter products. Stretch values for both scenes are set to the same values.
Paper: HyPyRameter: A Python Toolbox to Calculate Spectral Parameters from Hyperspectral Reflectance Data
Authors: Michael S. Phillips, Christian Tai Udovicic, Jeffrey E. Moersch, Udit Basu, Christopher W. Hamilton
Figure caption: Difference images between HyPyRameter and CAT (CRISM Analysis Toolkit) calculated parameters.
Paper: Ocean Circulation on Tide-locked Lava Worlds. II. Scalings
Authors: Yanhong Lai, Wanying Kang, Jun Yang
Figure caption: The general equations for horizontal momentum (a) and potential temperature (b) in the interior ocean, where  v  = u  i  + v  j  + w  k ; u, v, and w are the zonal, meridional, and vertical velocities, respectively;  u  = u  i  + v  j  is the horizontal velocity; f is the Coriolis parameter; ρ  c  is the reference density; p is pressure; A  h  and A  z  are the horizontal and vertical viscosity coefficients, respectively; θ is the potential temperature; and k  h  and k  z  are the horizontal and vertical diffusivities, respectively. The characteristic scales of each term are presented below the equations. The dominant terms of the momentum equation in Regimes I, II, and III are shown in red, blue, and green brackets, respectively. The dominant terms of the thermal equation in thermal-dominated and wind-dominated systems are shown in gold and purple brackets, respectively.
Paper: Ocean Circulation on Tide-locked Lava Worlds. I. An Idealized 2D Numerical Model
Authors: Yanhong Lai, Jun Yang, Wanying Kang
Figure caption: Snapshot results of the simulation with thermal forcing only. (a) Temperature (T) in K, (b) density (ρ) in kg m−3, (c) zonal current speed (u) in m s−1 (colors) and velocity stream function (ψ) in m2 s−1 (contours) with an interval of 2.0 m2 s−1 and (d) SSH in m. The black solid lines in panels (a) and (b) indicate the location of the magma ocean boundary, which is determined by the liquidus. Solid lines in panel (c) correspond to positive values (a clockwise overturning circulation), and dashed lines correspond to negative values (an anticlockwise overturning circulation). As the ocean circulation is confined to the upper ∼1 km, panel (c) shows only the zonal velocity within the upper 2 km.
Paper: Ocean Circulation on Tide-locked Lava Worlds. I. An Idealized 2D Numerical Model
Authors: Yanhong Lai, Jun Yang, Wanying Kang
Figure caption: Same as Figure 4, except that both thermal and wind forcings are included and A  z  = 102 m2 s−1 at the liquidus in this simulation. The interval of the velocity stream function is 30.0 m2 s−1 in the contours of panel (c).
Paper: Ocean Circulation on Tide-locked Lava Worlds. I. An Idealized 2D Numerical Model
Authors: Yanhong Lai, Jun Yang, Wanying Kang
Figure caption: The OHT divergence (﹩{F}_{o}=\displaystyle \frac{\partial }{\partial x}{\int }_{-{ \mathcal D }}^{0}{c}_{p}\rho {uTdz}﹩) (panels (a) and (b)) and temperature anomaly induced by ocean circulation (panels (d) and (e)) under thermal forcing only (left panels) and under both thermal and wind forcings (middle panels). Note that positive values of OHT divergence correspond to heat loss and negative values of OHT divergence correspond to heat gain. Results shown in the left and middle panels are based on the simulation results shown in Figures 4 and 6, respectively. The stellar insolation of different lava planets is shown for comparison (panel (c)).
Paper: Ocean Circulation on Tide-locked Lava Worlds. I. An Idealized 2D Numerical Model
Authors: Yanhong Lai, Jun Yang, Wanying Kang
Figure caption: The zonal momentum budget at different depths of the simulations shown in Figure 4 (left panels) and Figure 6 (right panels). From top to bottom panels, they are zonal momentum budget at the surface (panels (a) and (b)), at a depth of 200 m (panels (c) and (d)), and at a depth of 1000 m (panels (e) and (f)), respectively. Due to the substantial difference in zonal momentum between the interior and exterior magma ocean, only the zonal momentum budget within ±70° is shown.
Paper: Ocean Circulation on Tide-locked Lava Worlds. I. An Idealized 2D Numerical Model
Authors: Yanhong Lai, Jun Yang, Wanying Kang
Figure caption: Time series of the simulations shown in Figure 4 (top panels) and Figure 6 (bottom panels). (a, d) Zonal-mean kinetic energy at different depths, (b, e) temperature of the substellar point at varying depths, and (c, f) SSH at different angles, as a function of time. Note that the system is initialized with a rest state.
Paper: Ocean Circulation on Tide-locked Lava Worlds. I. An Idealized 2D Numerical Model
Authors: Yanhong Lai, Jun Yang, Wanying Kang
Figure caption: Comparison of vertical temperature profiles of this study with those in Earth’s oceans. (a, b) Vertical temperature profiles at varying angles away from the substellar point under thermal forcing only and under both thermal and wind forcings, respectively. (c) Vertical potential temperature profiles in the North Pacific of Earth at varying latitudes at 170° west.
Paper: Ocean Circulation on Tide-locked Lava Worlds. II. Scalings
Authors: Yanhong Lai, Wanying Kang, Jun Yang
Figure caption: Schematics of thermal-driven (a) and wind-driven (b) ocean circulations. In the schematics, the external forcings, surface height, ocean circulation, and magma ocean depth are shown. The red solid line represents the boundary of the magma ocean, and the gray lines represent the isotherms, indicating that temperature decreases as depth increases within the ocean. For more details, please see Part I.
Paper: Interpolation and Synthesis of Sparse Samples in Exoplanet Atmospheric Modeling
Authors: Jacob Haqq-Misra, Eric T. Wolf, Thomas J. Fauchez, Ravi K. Kopparapu
Figure caption: Kriging can be used to interpolate a sparse sample as well as to synthesize models of different complexity. The sparse sample calculated with the full-complexity ExoCAM model (top left) and ordinary kriging contour fit (top right) are identical to Figure 3 but only using points with instellation 1600 W m−2 or less. A grid of solutions is shown using the intermediate-complexity ExoPlaSim model across the full parameter space (middle left), with the contour plot (middle right) indicating an underprediction of warm cases compared to ExoCAM. The results shown in the bottom row are obtained by using universal kriging to map the ExoCAM sparse sample (bottom left, outlined points) across the parameter space by using the ExoPlaSim solutions (bottom left, colored points) as a drift function. Universal kriging can theoretically synthesize results from compatible models but may also introduce spurious results or nonphysical fine structure (bottom right).
Paper: Interpolation and Synthesis of Sparse Samples in Exoplanet Atmospheric Modeling
Authors: Jacob Haqq-Misra, Eric T. Wolf, Thomas J. Fauchez, Ravi K. Kopparapu
Figure caption: The values shown in the left panel are obtained by using ordinary kriging to map the sparse sample from Figure 1 onto a regularly spaced grid, with the original sparse sample outlined in black. The right panel plots the kriged points with contour interpolation, with the original sparse sample shown as black dots.
Paper: Mapping “Brain Terrain” Regions on Mars Using Deep Learning
Authors: Kyle A. Pearson, Eldar Noe, Daniel Zhao, Alphan Altinok, Alexander M. Morgan
Figure caption: Decoding times of various JPEG2000 images in the HiRISE archive using a single thread on a 2.4 GHz 8-Core Intel Core i9 CPU. The decoding time improves if the image is opened at a lower resolution. However, a lower resolution may not always retain enough signal-to-noise ratio for a computer vision algorithm to act reliably. The image size on disk is listed in the plot legend.
Paper: Mapping “Brain Terrain” Regions on Mars Using Deep Learning
Authors: Kyle A. Pearson, Eldar Noe, Daniel Zhao, Alphan Altinok, Alexander M. Morgan
Figure caption: A mosaic of training samples for the brain terrain class. Each tile shown here has a size of 512 × 512 pixels, which corresponds roughly to a ∼128 × 128 m2 area at the native resolution. The tiles shown here are reminiscent of the data used to train the segmentation algorithm. However, images pertaining to the background class are not shown here. The brain terrain exhibits pitted mounds next to small ridges that range in size from 1 to 10 m. Most of the examples shown here are open-cell brain terrain (e.g., ESP_057317_2210 and ESP_061075_2195). Closed-cell terrain can be found in PSP_001410_2210.
Paper: Ocean Circulation on Tide-locked Lava Worlds. II. Scalings
Authors: Yanhong Lai, Wanying Kang, Jun Yang
Figure caption: Scaling laws of the magma ocean depth (D; top), horizontal velocity (U; middle), and ocean heat transport convergence (H; bottom) in Regime I. Left panels: sensitivity of D, U, and H to vertical viscosity (A  z ) and vertical diffusivity (k  z ) when dominated by thermal forcing. Right panels: sensitivity of D, U, and H to wind stress (τ) and substellar temperature (T sub) when dominated by wind forcing. Note that in the right panels, the results are still from the thermal-forcing-dominated scaling when the wind stress is zero. Colors are the results from scaling laws, and filled circles are the results from numerical simulations. For the numerical results, their exact values are indicated.
Paper: Ocean Circulation on Tide-locked Lava Worlds. II. Scalings
Authors: Yanhong Lai, Wanying Kang, Jun Yang
Figure caption: Comparison between results from scaling laws and from numerical simulations in Regime I. From left to right columns: sensitivity of the magma ocean depth D (top), horizontal velocity U (middle), and ocean heat transport convergence H (bottom) to vertical viscosity (A  z ), vertical diffusivity (k  z ), substellar temperature (T sub), and wind stress (τ). The solid lines and scattered asterisks are the results from scaling laws and numerical simulations, respectively. By default, wind forcing is not included, except for the rightmost series of results.
Paper: Ocean Circulation on Tide-locked Lava Worlds. II. Scalings
Authors: Yanhong Lai, Wanying Kang, Jun Yang
Figure caption: Regime diagram in (Ω, A  z )-space in thermal-forcing-dominated (a) and wind-forcing-dominated (b) systems. The regions in red, blue, and green colors correspond to the parameter space in Regimes I (viscosity-dominant), II (advection-dominant), and III (rotation-dominant), respectively. The parameters used to obtain this diagram, including vertical diffusivity (k  z ), gravity (g), thermal expansion coefficient (α), temperature difference (ΔT), planet radius (a), horizontal range of the magma ocean (L), and wind stress (τ), are listed on the right side of panel (b). Note that wind stress is not included (τ = 0) in the thermal-forcing-dominated case. Due to the simplicity of these scaling laws, there is some overlap between different regimes, and blank areas are not resolved by these scalings.
Paper: Ocean Circulation on Tide-locked Lava Worlds. II. Scalings
Authors: Yanhong Lai, Wanying Kang, Jun Yang
Figure caption: Scaling laws of the magma ocean depth (D; top), horizontal velocity (U; middle), and ocean heat transport convergence (H; bottom) in Regime II. Left panels: sensitivity of D, U, and H to vertical diffusivity (k  z ) and substellar temperature (T sub) when dominated by thermal forcing. Right panels: sensitivity of D, U, and H to wind stress (τ) and vertical viscosity (A  z ) when dominated by wind forcing.
Paper: Ocean Circulation on Tide-locked Lava Worlds. II. Scalings
Authors: Yanhong Lai, Wanying Kang, Jun Yang
Figure caption: Scaling laws of the magma ocean depth (D; top), horizontal velocity (U; middle), and ocean heat transport convergence (H; bottom) in Regime III. Left panels: sensitivity of D, U, and H to planetary rotation rate (Ω) and substellar temperature (T sub) when dominated by thermal forcing. Right panels: sensitivity of D, U, and H to planetary rotation rate (Ω) and wind stress (τ) when dominated by wind forcing.
Paper: Ocean Circulation on Tide-locked Lava Worlds. I. An Idealized 2D Numerical Model
Authors: Yanhong Lai, Jun Yang, Wanying Kang
Figure caption: (a) EOS used in the model; (c) horizontal (A  h ) and vertical (A  z ) viscosity coefficients as a function of temperature. (b) External forcings imposed at the surface. Surface temperature with a substellar temperature of 3000 K and a temperature of 50 K at ±110°; (d) zonal wind stress with a maximum value of ∼100 N m−2 near 40°, where a positive (negative) value corresponds to eastward (westward) wind stress.
Paper: Ocean Circulation on Tide-locked Lava Worlds. I. An Idealized 2D Numerical Model
Authors: Yanhong Lai, Jun Yang, Wanying Kang
Figure caption: Vertical temperature profile and magma ocean depth when they are adiabat determined (top panels) and ocean circulation determined (bottom panels). (a, c) Vertical temperature profile with a substellar temperature of 3000 K (red line) and the liquidus curve (black line). (b, d) The magma ocean depth as a function of the angle away from the substellar point under a surface temperature shown in Figure 2(b). Note that the liquidus in panel (c) increases with depth as in panel (a), but it is not evident within the shallow depth. The vertical range is different between the top and bottom panels.
Paper: Ocean Circulation on Tide-locked Lava Worlds. I. An Idealized 2D Numerical Model
Authors: Yanhong Lai, Jun Yang, Wanying Kang
Figure caption: Schematics of the evolution of magma ocean under thermal and wind forcings. Here t 0, t 1, t 2 correspond to the initial state, the intermediate state, and the final equilibrium state, respectively. The determination processes of magma ocean depth are shown in the equilibrium state.
Paper: Dust Production Rates in Jupiter-family Comets: A Two Year Study with ATLAS Photometry
Authors: A. Fraser Gillan, Alan Fitzsimmons, Larry Denneau, Robert J. Siverd, Ken W. Smith, John L. Tonry, David R. Young
Figure caption: Maximum A(0°)f ρ vs. days from perihelion for each JFC is 							shown. The green vertical line represents the perihelion for each 							individual comet and the plot is color coded according to orbital 							eccentricity.