Magnitude of gravitoinertial force, an independent variable in egocentric visual localization of the horizontal
Magnitude of gravitoinertial force, independent variable in egocentric visual localization of horizontal-space perception
INERTIAL FORCE↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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On changes of independent variable in dynamical systems and applications to regularization.
Independent variable changes in dynamical systems and applications to systems regularization, giving canonical and Lagrangian motion equations with tensor notation
MOTION EQUATION↗
Pragmatic Approach to Device-Independent Color
JPL has been producing images of planetary bodies for over 30 years. The results of an effort to implement device-independent color on three types of devices are described. The goal is to produce near the same eye-brain response when the observer views the image produced by each device under the correct lighting conditions. The procedure used to calibrate and obtain each device profile is described.
device-independent color remote imaging reproducti↗
Programmatic Approach to Device-Independent Color
Describes an effort to produce device-independent color on three types of devices: color monitors, high precesion color film recorder, and color printer using a dye sublimation process.
device-independent color color management system↗
Space invariant independent component analysis and ENose for detection of selective chemicals in an unknown environment
In this paper, we present a space invariant topology to enable the Independent Component Analysis (ICA) to solve chemical detection from two unknown mixing chemical sources.
ENose↗
Space Invariant Independent Component Analysis and ENose for Detection of Selective Chemicals in an Unknown Environment
In this paper, we present a space invariant architecture to enable the Independent Component Analysis (ICA) to solve chemical detection from two unknown mixing chemical sources. The two sets of unknown paired mixture sources are collected via JPL 16-ENose sensor array in the unknown environment with, at most, 12 samples data collected. Our space invariant architecture along with the maximum entropy information technique by Bell and Sejnowski and natural gradient descent by Amari has demonstrated that it is effective to separate the two mixing unknown chemical sources with unknown mixing levels to the array of two original sources under insufficient sampled data. From separated sources, they can be identified by projecting them on the 11 known chemical sources to find the best match for detection. We also present the results of our simulations. These simulations have shown that 100% correct detection could be achieved under the two cases: a) under-completed case where the number of input (mixtures) is larger than number of original chemical sources; and b) regular case where the number of input is as the same as the number of sources while the time invariant architecture approach may face the obstacles: overcomplete case, insufficient data and cumbersome architecture.
ENose↗
An Independent Orbit Determination Simulation for the OSIRIS-REx Asteroid Sample Return Mission
After arriving at the near-Earth asteroid (101955) Bennu in late 2018, the OSIRIS-REx spacecraft will execute a series of observation campaigns and orbit phases to accurately characterize Bennu and ultimately collect a sample of pristine regolith from its surface. While in the vicinity of Bennu, the OSIRIS-REx navigation team will rely on a combination of ground-based radiometric tracking data and optical navigation (OpNav) images to generate and deliver precision orbit determination products. Long before arrival at Bennu, the navigation team is performing multiple orbit determination simulations and thread tests to verify navigation performance and ensure interfaces between multiple software suites function properly. In this paper, we will summarize the results of an independent orbit determination simulation of the Orbit B phase of the mission performed to test the interface between the OpNav image processing and orbit determination software packages.
Orbit↗
Complex Signal Kurtosis and Independent Component Analysis for Wideband Radio Frequency Interference Detection
Radio-frequency interference (RFI) has negatively implicated scientific measurements across a wide variation passive remote sensing satellites. This has been observed in the L-band radiometers SMOS, Aquarius and more recently, SMAP [1, 2]. RFI has also been observed at higher frequencies such as K band [3]. Improvements in technology have allowed wider bandwidth digital back ends for passive microwave radiometry. A complex signal kurtosis radio frequency interference detector was developed to help identify corrupted measurements [4]. This work explores the use of ICA (Independent Component Analysis) as a blind source separation technique to pre-process radiometric signals for use with the previously developed real and complex signal kurtosis detectors.
microwave↗
RFI Detection and Mitigation using Independent Component Analysis as a Pre-Processor
Radio-frequency interference (RFI) has negatively impacted scientific measurements of passive remote sensing satellites. This has been observed in the L-band radiometers Soil Moisture and Ocean Salinity (SMOS), Aquarius and more recently, Soil Moisture Active Passive (SMAP). RFI has also been observed at higher frequencies such as K band. Improvements in technology have allowed wider bandwidth digital back ends for passive microwave radiometry. A complex signal kurtosis radio frequency interference detector was developed to help identify corrupted measurements. This work explores the use of Independent Component Analysis (ICA) as a blind source separation (BSS) technique to pre-process radiometric signals for use with the previously developed real and complex signal kurtosis detectors.
independent component analysis↗
Chain Formation as a Mechanism for Mass-Independent Fractionation of Sulfur Isotopes in the Archean Atmosphere
The anomalous abundances of sulfur isotopes in ancient sediments provide the strongest evidence for an anoxic atmosphere prior to ∼2.45 Ga, but the mechanism for producing this 'mass-independent' fractionation pattern remains in question. The prevailing hypothesis has been that it is created by differences in the UV photolysis rates of different SO2 isotopologues. We investigate here a recently proposed additional source of fractionation during gas-phase formation of elemental sulfur (S4 and S8). Because two minor S isotopes rarely occur in the same chain, the longer S4 and S8 chains should be strongly, and roughly equally, depleted in all minor isotopes. This gives rise to negative ∆(sup 33) S values and positive ∆(sub 36) S values in elemental sulfur-just the opposite of (and much larger than) what is predicted from SO2 photolysis itself. Back-reactions during chain formation, specifically photolysis of S2 and S3, pass sulfur having the opposite fractionation back to atomic S, and thence to other sulfur species, causing H2S, SO2, sulfate, and short-chain elemental sulfur to have positive ∆(sup 33) S and negative ∆(sup 36) S. Positive ∆(sup 33) S values in elemental sulfur produced in laboratory SO2 photolysis experiments could be caused by the initial fractionation during photolysis, combined with rapid condensation of short-chain sulfur species on the walls of the reaction chamber, along with a scarcity of back-reactions. The simulated fractionations produced by the chain formation mechanism do not directly match fractionations from the rock record. The mismatch might be explained if the isotopic signals leaving the atmosphere were significantly modulated by life, by uncertainties in the rates of reactions of both major and minor isotopic sulfur species, or by the relatively large potential range of atmospheric parameters. Further work is needed to better constrain these uncertainties, but this novel mechanism suggests new avenues to explore in our search for a explanation for the S-MIF record.
Great Oxidation Event↗
Independent Reliability Assessment and Progress Review of the NASA GSFC Laser Transmitter for the LISA Program
NASA Goddard Space Flight Center (GSFC) has been actively developing the laser transmitter for the Laser Interferometer Space Antenna (LISA) program since the late 2017. In 2021 we delivered a prototype laser transmitter to the LISA program for performance evaluation as well as performed an independent technology assessment on the laser design supported by the NASA Engineering and Safety Center (NESC). We continued to further develop the LISA laser with a goal of advancing the technology readiness level (TRL) of the laser to 6 by the end of 2023. In this paper, we report on the progress we made on the laser development for the LISA program as well as the technology assessment findings by the NESC.
Independent Reliability Assessment↗
Earth-Independent Medical Operations (EIMO) Concept of Operations
Compared to the current paradigm for crew health in low-Earth orbit and Lunar missions that rely on constant communication with Mission Control, there is an anticipated shift in medical operations for deep-space exploration missions. This shift stems from mission constraints imposed by the considerable distance from Earth, which include resource limitations due to a lack of resupply, mass, power, volume, and data limitations, challenges imposed by communication latency and the inability to evacuate in case of emergencies. To transition towards a more self-reliant medical approach, a comprehensive strategy is essential to progressively enable crew autonomy and mitigate mission success risks in the challenging environment of space. This transformative shift is collectively referred to as "Earth-Independent Medical Operations" (EIMO), signifying the gradual transfer of medical care and decision-making from terrestrial resources to space-based assets. This transition is aimed at bolstering astronaut health and performance while simultaneously reducing the overall risks associated with space missions. The constraints related to EIMO necessitate an integrated development of medical systems, featuring interoperability with mission planning, vehicle design, spacesuit design, and data architecture. This integration is vital in establishing a robust medical infrastructure that not only supports the well-being of astronauts but also ensures the success of the mission as a whole. The Human Research Program (HRP) Exploration Medical Capability (ExMC) Element has developed a model-based Concept of Operations (ConOps) outlining an initial vision for EIMO. Within this ConOps, a comprehensive view is presented, encompassing stakeholder needs, system objectives, and system goals associated with EIMO. Additionally, it provides illustrative examples of the various activities (scenarios) for which the system will be employed during missions. The selection of these activities has been meticulous, aiming to encompass a wide spectrum of medical conditions, including those falling under different risk categories, such as low-likelihood-low-consequence, low-likelihood-high-consequence, and high-likelihood-low-consequence. The selection of these activities (scenarios) effectively encompasses the wide range of medical events situated within an assumed probability-consequence bell curve. In each scenario, at least one of the five main EIMO components identified is captured. Those EIMO components are: Pre-mission Planning, Acute and Emergent Management Decision Making, Prolonged Medical Management Decision Making, Supplies and Resource Management, and Task Load Management. The ConOps was developed by a multidisciplinary team consisting of systems engineers, scientists, and clinicians across NASA and aims to serve as an initial recommendation to gradually and safely enabling crew autonomy for Mars missions and beyond.
Earth Independent Medical Operations↗
Earth-Independent Medical Operations (EIMO) Concept of Operations (ConOps)
Compared to the current paradigm for crew health in low-Earth orbit and Lunar missions that rely on constant communication with Mission Control, there is an anticipated shift in medical operations for deep-space exploration missions. This shift stems from mission constraints imposed by the considerable distance from Earth, which include resource limitations due to a lack of resupply, mass, power, volume, and data limitations, challenges imposed by communication latency and the inability to evacuate in case of emergencies. To transition towards a more self-reliant medical approach, a comprehensive strategy is essential to progressively enable crew autonomy and mitigate mission success risks in the challenging environment of space. This transformative shift is collectively referred to as "Earth-Independent Medical Operations" (EIMO), signifying the gradual transfer of medical care and decision-making from terrestrial resources to space-based assets. This transition is aimed at bolstering astronaut health and performance while simultaneously reducing the overall risks associated with space missions. The constraints related to EIMO necessitate an integrated development of medical systems, featuring interoperability with mission planning, vehicle design, spacesuit design, and data architecture. This integration is vital in establishing a robust medical infrastructure that not only supports the well-being of astronauts but also ensures the success of the mission as a whole. The Human Research Program (HRP) Exploration Medical Capability (ExMC) Element has developed a model-based Concept of Operations (ConOps) outlining an initial vision for EIMO. Within this ConOps, a comprehensive view is presented, encompassing stakeholder needs, system objectives, and system goals associated with EIMO. Additionally, it provides illustrative examples of the various activities (scenarios) for which the system will be employed during missions. The selection of these activities has been meticulous, aiming to encompass a wide spectrum of medical conditions, including those falling under different risk categories, such as low-likelihood-low-consequence, low-likelihood-high-consequence, and high-likelihood-low-consequence. The selection of these activities (scenarios) effectively encompasses the wide range of medical events situated within an assumed probability-consequence bell curve. In each scenario, at least one of the five main EIMO components identified is captured. Those EIMO components are: Pre-mission Planning, Acute and Emergent Management Decision Making, Prolonged Medical Management Decision Making, Supplies and Resource Management, and Task Load Management. The ConOps was developed by a multidisciplinary team consisting of systems engineers, scientists, and clinicians across NASA and aims to serve as an initial recommendation to gradually and safely enabling crew autonomy for Mars missions and beyond.
Earth Independent Medical Operations↗
Minimizing Energy Loss by Designing Multifunctional Solid Additives to Independent Regulation of Donor and Acceptor Layers for Efficient LBL Polymer Solar Cells
Solid additives are crucial in layer-by-layer (LBL) polymer solar cells (PSCs). Despite its importance, the simultaneous application of solid additives into both donor and acceptor layers has been largely overlooked. In this work, two multifunctional solid additives are actively designed, and investigated the synergistic effect on both donor and acceptor layers. Incorporating the multifunctional solid additives into the donor layer could effectively enhance the aggregation and molecular stacking of the donor polymer, leading to reduced energy disorder and minimizing ΔE 2 . When the multifunctional solid additives are introduced into the acceptor layer, they just play a role in optimizing the morphology, thereby reducing the ΔE 3 . Excitedly, the simultaneous addition of the multifunctional solid additives into both donor and acceptor layers produced a synergistic effect for decreasing ΔE 2 and ΔE 3 simultaneously, especially adding SA2, thus enabling an excellent power conversion efficiency (PCE) of 19.95% (certified as 19.68%) with an open-circuit voltage (V oc ) of 0.921 V, a short circuit current density (J sc ) of 27.08 mA cm -2 and a fill factor (FF) of 79.98%. The work highlights the potential of multifunctional solid additives in independently regulating the properties of donor and acceptor layers, which is expected as a promising approach for further developing higher performance PSCs.
36 MATERIALS SCIENCE↗
Reductant‐ or Light‐Driven ATP‐Independent Reduction of CO 2 by Nitrogenase MoFe Protein
Nitrogenase is a versatile metalloenzyme that activates and reduces small molecules like N 2 , CO, and CO 2 into value-added chemicals at ambient conditions. Previously, it is shown that the Mo-nitrogenase could reduce CO 2 to CO, but not to hydrocarbons, in an ATP-dependent reaction. Here, it is reported that the ability of the catalytic component of Mo-nitrogenase (MoFe protein) enables ATP-independent reduction of CO 2 to up to C 4 hydrocarbons in room-temperature reactions driven by a chemical reductant (Eu II –DTPA) or visible light (via CdS@ZnS (CZS) quantum dots). Moreover, an opposite deuterium isotope effect is observed on the Eu II –DTPA driven reactions of CO 2 reduction by MoFe protein and its V-counterpart (VFe protein), in that the former displays higher activities in H 2 O, and the latter displays higher activities in D 2 O. Furthermore, these results provide an important foundation for further mechanistic exploration of the nitrogenase-enabled, atypical Fischer–Tropsch type reaction that uses CO 2 instead of CO as a substrate; moreover, they serves as a potential template for the future development of nitrogenase-based applications that effectively recycle the greenhouse gas CO 2 into valuable fuel products.
C-C coupling↗
Crystal Orientation Independent Strong TERS Response in Anisotropic ReS2 on Gold
Rhenium disulfide (ReS2) crystallizes in a distorted 1T' lattice with triclinic symmetry arising from Re–Re dimerization, which produces in-plane anisotropic properties and a dense set of Raman-active modes with mixed atomic displacements. Unlike far-field Raman spectroscopy, which shows strong polarization and orientation dependence, tip-enhanced Raman spectroscopy (TERS) probes out-of-plane Raman tensor components. Here we use 785 nm (1.58 eV) gap-mode TERS to investigate monolayer to four-layer thick ReS2 crystals.. TERS maps and averaged spectra reveal that all modes remain invariant under 90° rotation of the crystal, and the only thickness-dependent feature is the emergence of the multilayer breathing mode. These results demonstrate that TERS yields a crystal-orientation-independent Raman response in ReS2 and provides direct access to Raman tensor components that are not easily accessible in conventional far-field measurements.
Valencia Acuna, Pavel A.↗
A resolution independent neural operator
The Deep operator network (DeepONet) is a powerful yet simple neural operator architecture that utilizes two deep neural networks to learn mappings between infinite-dimensional function spaces. This architecture is highly flexible, allowing the evaluation of the solution field at any location within the desired domain. However, it imposes a strict constraint on the input space, requiring all input functions to be discretized at the same locations; this limits its practical applications. Here, in this work, we introduce a general framework for operator learning from input–output data with arbitrary number and locations of sensors. This begins by introducing a resolution-independent DeepONet (RI-DeepONet), enabling it to handle input functions that are arbitrarily, but sufficiently finely, discretized. To this end, we propose two dictionary learning algorithms to adaptively learn a set of appropriate continuous basis functions, parameterized as implicit neural representations (INRs), from correlated signals defined on arbitrary point cloud data. These basis functions are then used to project arbitrary input function data as a point cloud onto an embedding space (i.e., a vector space of finite dimensions) with dimensionality equal to the dictionary size, which can be directly used by DeepONet without any architectural changes. In particular, we utilize sinusoidal representation networks (SIRENs) as trainable INR basis functions. The introduced dictionary learning algorithms are then used in a similar way to learn an appropriate dictionary of basis functions for the output function data, which defines a new neural operator architecture referred to as the R esolution I ndependent N eural O perator (RINO). In the RINO, the operator learning task simplifies to learning a mapping from the coefficients of input basis functions to the coefficients of output basis functions. We demonstrate the robustness and applicability of RINO in handling arbitrarily (but sufficiently richly) sampled input and output functions during both training and inference through several numerical examples.
Deep operator network (DeepONet)↗
Recommendations for Comprehensive and Independent Evaluation of Machine Learning‐Based Earth System Models
Abstract Machine learning (ML) is a revolutionary technology with demonstrable applications across multiple disciplines. Within the Earth science community, ML has been most visible for weather forecasting, producing forecasts that rival modern physics‐based models. Given the importance of deepening our understanding and improving predictions of the Earth system on all time scales, efforts are now underway to develop Earth‐system models (ESMs) capable of representing all components of the coupled Earth system (or their aggregated behavior) and their response to external changes over long timescales. Building trust in ESMs is a much more difficult problem than for weather forecast models, not least because the model must represent the alternate (e.g., future or paleoclimatic) coupled states of the system for which there are no direct observations. Given that the physical principles that enable predictions about the response of the Earth system are often not explicitly coded in these ML‐based models, demonstrating the credibility of ML‐based ESMs thus requires us to build evidence of their consistency with the physical system. To this end, this paper puts forward five recommendations to enhance comprehensive, standardized, and independent evaluation of ML‐based ESMs to strengthen their credibility and promote their wider use.
54 ENVIRONMENTAL SCIENCES↗