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CMS: Simulated Physical-Biogeochemical Data, SABGOM Model, Gulf of Mexico, 2005-2010

This dataset contains monthly mean ocean surface physical and biogeochemical data for the Gulf of Mexico simulated by the South Atlantic Bight and Gulf of Mexico (SABGOM) model on a 5-km grid from 2005 to 2010. The simulated data include ocean surface salinity, temperature, dissolved inorganic nitrogen (DIN), dissolved inorganic carbon (DIC), partial pressure of CO2 (pCO2), air-sea CO2 flux, surface currents, and primary production. The SABGOM model is a coupled physical-biogeochemical model for studying circulation and biochemical cycling for the entire Gulf of Mexico to achieve an improved understanding of marine ecosystem variations and their relations with three-dimensional ocean circulation in a gulf-wide context.

OCO-3 Level 2 geolocated XCO2 retrievals results, physical model, Forward Processing V11 (OCO3_L2_Standard) at GES DISC

Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11. The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations. The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol.

OCO-2 Level 2 geolocated XCO2 retrievals results, physical model V11.2 (OCO2_L2_Standard) at GES DISC

Version 11.2 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.2. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection is the output from the algorithm retrieving the column-averaged CO2 dry air mole fraction XCO2 and other quantities from the spectra collected by the Orbiting Carbon Observatory-2 (OCO-2).

OCO-3 Level 2 geolocated XCO2 retrievals results, physical model, Forward Processing V10 (OCO3_L2_Standard) at GES DISC

Version 10 is the current version of the data set. Older versions will no longer be available and are superseded by Version 10. The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations. The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol.

opencc-by-4.0Dec 2018View details →

zenodo20/100

Mapping the intertidal microphytobenthos Gross Primary Production. Part II: Merging remote sensing and physical-biological coupled modelling.

Simulation outputs with MARS-3D of the MPB GPP on the Brouage mudflat in 2015.

ClinicalTrials.gov20/100

The Effect of Transtheoretical Model-Based Exercise Interventions on Increasing Physical Activity in Office Workers

ClinicalTrials.gov study NCT07087457. IPD Sharing: Not stated. Countries: 0. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov20/100

Integration of a Trained Language Model to Improve Glycemic Control Through Increased Physical Activity: a Fully Digital My Heart Counts Smartphone App Randomized Trial

ClinicalTrials.gov study NCT06596330. IPD Sharing: NO. Countries: 0. Publications: 0.

closedIPD-NOFeb 2026View details →

Physics of Failure Models for Capacitor Degradation in DC-DC Converters

This paper proposes a combined energy-based model with an empirical physics of failure model for degradation analysis and prognosis of electrolytic capacitors in DC-DC power converters. Electrolytic capacitors and MOSFET’s have higher failure rates than other components in DC-DC converter systems. For example, in avionics systems where the power supply drives a GPS unit, ripple currents can cause glitches in the GPS position and velocity output, and this may cause errors in the Inertial Navigation (INAV) system causing the aircraft to fly off course. We have employed a topological energy based modeling scheme based on the bond graph (BG) modeling language for building parametric models of electrical domain systems. Our current work adopts a physics of failure model (Arrhenius Law) for equivalent series resistance (ESR) increase in electrolytic capacitors subjected to electrical and thermal stresses. Experiments for capacitor degradation were conducted for collecting degradation ESR data. Parameter re-estimation for the failure model is done using the experimental data. The derived degradation model of the capacitor is reintroduced into the DC-DC converter system model to study changes in the system performance using Monte Carlo simulation methods. Stochastic simulation methods applied to the combined model help us predict how system performance deteriorates with time.

Prognostics Health Management and Physics based failure Models for Electrolytic Capacitors

This paper proposes first principles based modeling and prognostics approach for electrolytic capacitors. Electrolytic capacitors and MOSFETs are the two major components, which cause degradations and failures in DC-DC converters. This type of capacitors are known for its low reliability and frequent breakdown on critical systems like power supplies of avionics equipment and electrical drivers of electro-mechanical actuators. Some of the more prevalent fault effects, such as a ripple voltage surge at the power supply output can cause glitches in the GPS position and velocity output, and this, in turn, if not corrected will propagate and distort the navigation solution. Prognostics provides a way to assess remaining useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. In this paper, we study the effects of accelerated aging due to thermal stress on sets of capacitors. Our focus is on deriving rst principles degradation models for thermal stress conditions. The degradation data form the basis for developing the model based remaining life prediction algorithm. Our overall goal is to derive accurate models of capacitor degradation, and use them to predict performance changes in DC-DC converters.

Physics Based Electrolytic Capacitor Degradation Models for Prognostic Studies under Thermal Overstress

Electrolytic capacitors are used in several applications rang- ing from power supplies on safety critical avionics equipment to power drivers for electro-mechanical actuators. This makes them good candidates for prognostics and health management research. Prognostics provides a way to assess remaining use- ful life of components or systems based on their current state of health and their anticipated future use and operational con- ditions. Past experiences show that capacitors tend to degrade and fail faster under high electrical and thermal stress condi- tions that they are often subjected to during operations. In this work, we study the effects of accelerated aging due to thermal stress on different sets of capacitors under different conditions. Our focus is on deriving first principles degra- dation models for thermal stress conditions. Data collected from simultaneous experiments are used to validate the de- sired models. Our overall goal is to derive accurate models of capacitor degradation, and use them to predict performance changes in DC-DC converters.

Using Markov Models of Fault Growth Physics and Environmental Stresses to Optimize Control Actions

A contrived example of a dice throwing game was considered in order to provide some insight into the general problem developing prognostics-based control routines that utilize uncertain models of component fault dynamics and future environmental stresses to assess and mitigate risk. A generalized Markov modeling representation of fault dynamics was developed for the case that available modeling of fault growth physics and available modeling of future environ- mental stresses are represented by two independent Markov process models. A finite horizon dynamic programming algorithm was given for a Markov decision process representation of the prognostics-based control problem and this algorithm was used to identify an optimal control policy for the dice throwing game that is considered in this paper. The outcomes obtained from simulations of the optimizing control policy were observed to differ only slightly from the outcomes that would have been achievable if all modeling uncertainties were removed from the example dice throwing game.

Physics based Degradation Modeling and Prognostics of Electrolytic Capacitors under Electrical Overstress Conditions

This paper proposes a physics based degradation modeling and prognostics approach for electrolytic capacitors. Electrolytic capacitors are critical components in electronics systems in aeronautics and other domains. Degradation's in capacitor and MOSFET components are often the cause of failures in DC-DC converters. For example, prevalent fault effects, such as a ripple voltage surge at the power supply output, can damage interconnected critical subsystems leading to cascading fault propagation. Prognostics in general and in this case electronics components in particular is concerned with the prediction of remaining useful life (RUL) of components and systems. It performs a condition-based health assessment by estimating the current state of health. Furthermore, it leverages the knowledge of the device physics and degradation physics to predict remaining useful life as a function of current state of health and anticipated operational and environmental conditions. Physics-based models capture degradation phenomena in terms of component geometry and energy based principles that define the effect of stressors on the component behavior. This is in contrast to the traditional approach for deriving degradation models from empirical data. Implementing the degradation modeling techniques present a general methodology for estimating lifetimes due to specific failure mechanisms. The failure rate models can be tuned to include parameters that relate to the present health of the device/system and the expected conditions under which it will be operated. The models and algorithms are applied to data from degradation experiments of several COTS capacitors. Results show the efficiency of the approach chosen.