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NASA NTRS2003

Toroidal-Core Microinductors Biased by Permanent Magnets

The designs of microscopic toroidal-core inductors in integrated circuits of DC-to-DC voltage converters would be modified, according to a proposal, by filling the gaps in the cores with permanent magnets that would apply bias fluxes (see figure). The magnitudes and polarities of the bias fluxes would be tailored to counteract the DC fluxes generated by the DC components of the currents in the inductor windings, such that it would be possible to either reduce the sizes of the cores or increase the AC components of the currents in the cores without incurring adverse effects. Reducing the sizes of the cores could save significant amounts of space on integrated circuits because relative to other integrated-circuit components, microinductors occupy large areas - of the order of a square millimeter each. An important consideration in the design of such an inductor is preventing magnetic saturation of the core at current levels up to the maximum anticipated operating current. The requirement to prevent saturation, as well as other requirements and constraints upon the design of the core are expressed by several equations based on the traditional magnetic-circuit approximation. The equations involve the core and gap dimensions and the magnetic-property parameters of the core and magnet materials. The equations show that, other things remaining equal, as the maximum current is increased, one must increase the size of the core to prevent the flux density from rising to the saturation level. By using a permanent bias flux to oppose the flux generated by the DC component of the current, one would reduce the net DC component of flux in the core, making it possible to reduce the core size needed to prevent the total flux density (sum of DC and AC components) from rising to the saturation level. Alternatively, one could take advantage of the reduction of the net DC component of flux by increasing the allowable AC component of flux and the corresponding AC component of current. In either case, permanent-magnet material and the slant (if any) and thickness of the gap must be chosen according to the equations to obtain the required bias flux. In modifying the design of the inductor, one must ensure that the inductance is not altered. The simplest way to preserve the original value of inductance would be to leave the gap dimensions unchanged and fill the gap with a permanent- magnet material that, fortuitously, would produce just the required bias flux. A more generally applicable alternative would be to partly fill either the original gap or a slightly enlarged gap with a suitable permanent-magnet material (thereby leaving a small residual gap) so that the reluctance of the resulting magnetic circuit would yield the desired inductance.

Lieneweg, Udo↗

NASA NTRS2004

On the Positive Bias of Peak Horizontal Velocity from an Idealized Doppler Profiler

In the presence of 3-D turbulence, peak horizontal velocity estimates from an idealized Doppler profiler are found to be positively biased due to an incomplete specification of the vertical velocity field. The magnitude of the bias was estimated by assuming that the vertical and horizontal velocities can be separated into average and perturbation values and that the vertical and horizontal velocity perturbations are normally distributed. Under these assumptions, properties of the Type-I Extreme Value Distribution for maxima, known as the Gumbel distribution, can be used to obtain an analytical solution of the bias. The bias depends on geometric properties of the profiler configuration, the variance in the horizontal velocity, and the unresolved variance in the vertical velocity. When these variances are normalized by the average horizontal velocity, the bias can be mapped as a simple function of the normalized variances.

Short, David A.↗

NASA NTRS2015

Resolving Ice Cloud Optical Thickness Biases Between CALIOP and MODIS Using Infrared Retrievals

Despite its importance as one of the key radiative properties that determines the impact of upper tropospheric clouds on the radiation balance, ice cloud optical thickness (IOT) has proven to be one of the more challenging properties to retrieve from space-based remote sensing measurements. In particular, optically thin upper tropospheric ice clouds (cirrus) have been especially challenging due to their tenuous nature, extensive spatial scales, and complex particle shapes and light scattering characteristics. The lack of independent validation motivates the investigation presented in this paper, wherein systematic biases between MODIS Collection 5 (C5) and CALIOP Version 3 (V3) unconstrained retrievals of tenuous IOT (< 3) are examined using a month of collocated A-Train observations. An initial comparison revealed a factor of two bias between the MODIS and CALIOP IOT retrievals. This bias is investigated using an infrared (IR) radiative closure approach that compares both products with MODIS IR cirrus retrievals developed for this assessment. The analysis finds that both the MODIS C5 and the unconstrained CALIOP V3 retrievals are biased (high and low, respectively) relative to the IR IOT retrievals. Based on this finding, the MODIS and CALIOP algorithms are investigated with the goal of explaining and minimizing the biases relative to the IR. For MODIS we find that the assumed ice single scattering properties used for the C5 retrievals are not consistent with the mean IR COT distribution. The C5 ice scattering database results in the asymmetry parameter (g) varying as a function of effective radius with mean values that are too large. The MODIS retrievals have been brought into agreement with the IR by adopting a new ice scattering model for Collection 6 (C6) consisting of a modified gamma distribution comprised of a single habit (severely roughened aggregated columns); the C6 ice cloud optical property models have a constant g approx. = 0.75 in the mid-visible spectrum, 5-15% smaller than C5. For CALIOP, the assumed lidar ratio for unconstrained retrievals is fixed at 25 sr for the V3 data products.This value is found to be inconsistent with the constrained (predominantly nighttime) CALIOP retrievals. An experimental data set was produced using a modified lidar ratio of 32 sr for the unconstrained retrievals (an increase of 28%), selected to provide consistency with the constrained V3 results. These modifications greatly improve the agreement with the IR and provide consistency between the MODIS and CALIOP products. Based on these results the recently released MODIS C6 optical products use the single habit distribution given above, while the upcoming CALIOP V4 unconstrained algorithm will use higher lidar ratios for unconstrained retrievals.

Cloud↗

NASA NTRS2019

A Systematic Approach to Isolating the Causes and Impacts of Climate Model Bias Employing Analysis Increments

One of the most promising approaches to isolating the causes and understanding the impacts of climate model formulation errors is through the utilization of information contained in short-term forecast errors. The underlying assumption being that by capturing the errors at the very early stages of growth (before nonlinearities develop), we should be able to associate those errors with specific deficiencies in the model's formulation of the relevant physical processes. Here, we provide an example of utilizing the long-term mean of the 6-hourly analysis increments (first guess forecast minus analysis) produced by MERRA-2 to correct the NASA/GEOS AGCM over well-defined regions, thereby allowing us to quantify how the reduced tendency errors in these regions manifest themselves both locally and remotely through large-scale teleconnections to reduce the model's climatological biases. We extend previous work (focused on boreal summer) to address the full annual cycle of model bias. A key result is the large seasonality of the errors over the Tibet region and their impacts on the Northern Hemisphere jet biases, and related precipitation and temperature biases over North American. Attempts to correct the phase and amplitude errors of the climatological boreal winter ridge over western North America (a likely key requirement for improving winter climate forecasts over North America) reveal a surprising complexity in the sources of the errors in what is otherwise manifest as a rather simple PNA-like bias structure.

Schubert, S.↗

NASA NTRS2021

Improving Surface PM2.5 Forecasts in the United States Using an Ensemble of Chemical Transport Model Outputs: 2. Bias Correction with Satellite Data for Rural Areas

This work serves as the second of a two-part study to improve surface PM2.5 forecasts in the continental U.S. through the integrated use of multi satellite aerosol optical depth (AOD) products (MODIS Terra/Aqua and VIIRS DT/DB), multichemical transport model (CTM) (GEOS-Chem, WRF-Chem, and CMAQ) outputs, and ground observations. In Part I of the study, an ensemble Kalman filter (KF) technique using three CTM outputs and ground observations was developed to correct forecast bias and generate a single best forecast of PM2.5 for next day over non rural areas that have surface PM2.5 measurements in the proximity of 125 km. Here, with AOD data, we extended the bias correction into rural areas where the closest air quality monitoring station is at least 125–300 km away. First, we ensembled all of satellite AOD products to yield the single best AOD. Second, we corrected daily PM2.5 in rural areas from multiple models through the AOD spatial pattern between these areas and non rural areas, referred to as “extended ground truth” or EGT, for the present day. Lastly, we applied the KF technique to reduce the forecast bias for next day using the EGT. Our results find that the ensemble of bias-corrected daily PM2.5 from three CTMs for both today and next day show the best performance. Together, the two-part study develops a multimodel and multi-AOD bias-correction technique that has the potential to improve PM2.5 forecasts in both rural and non rural areas in near real time, and be readily implemented at state levels.

Huanxin Zhang↗

NASA NTRS2006

Real-time Sub-cm Differential Orbit Determination of two Low-Earth Orbiters with GPS Bias Fixing

An effective technique for real-time differential orbit determination with GPS bias fixing is formulated. With this technique, only real-time GPS orbits and clocks are needed (available from the NASA Global Differential GPS System with 10-20 cm accuracy). The onboard, realtime orbital states of user satellites (few meters in accuracy) are used for orbit initialization and integration. An extended Kalman filter is constructed for the estimation of the differential orbit between the two satellites as well as a reference orbit, together with their associating dynamics parameters. Due to close proximity of the two satellites and of similar body shapes, the differential dynamics are highly common and can be tightly constrained which, in turn, strengthens the orbit estimation. Without explicit differencing of GPS data, double-differenced phase biases are formed by a transformation matrix. Integer-valued fixing of these biases are then performed which greatly strengthens the orbit estimation. A 9-day demonstration between GRACE orbits with baselines of approx.200 km indicates that approx.80% of the double-differenced phase biases can successfully be fixed and the differential orbit can be determined to approx.7 mm as compared to the results of onboard K-band ranging.

global positioning system (GPS)↗

NASA NTRS2021

Social Bias in AI and its Implications

Previous studies have documented many different types of biases that exist in artificial intelligence (AI) and machine learning (ML) systems. We reviewed the literature on AI and ML bias with a focus on social implications and found that bias in AI and ML can potentially have harmful social impacts on individuals and/or groups of people. By affecting people differently according to characteristics such as race, gender, or sexual orientation, AI and ML systems may lead to harm by exacerbating social inequities. We recount examples of issues that have occurred in systems that use technology that might be used at NASA and elsewhere so that similar issues might be identified and mitigated in future systems. We also provide interested parties with a gateway into existing work on social bias in AI and ML systems.

artificial intelligence (AI)↗

NASA NTRS2022

Regression Bias in using Solar Wind Measurements

Simultaneous solar wind measurements from the solar wind monitors, WIND and ACE, differ due to the spatial and temporal structure of the solar wind. Correlation studies that use these measurements as input may infer an incorrect correlation due to uncertainties arising from this spatial and temporal structure, especially at extreme and rare solar wind values. In particular, regression analysis will lead to a regression function whose slope is biased towards the mean value of the measurement parameter. This article demonstrates this regression bias by comparing simultaneous ACE and WIND solar wind measurements. A non-linear regression analysis between them leads to a perception of underestimation of extreme values of one measurement on average over the other. Using numerical experiments, we show that popular regression analysis techniques such as linear least-squares, orthogonal least-squares, and non-linear regression are not immune to this bias. Hence while using solar wind parameters as an independent variable in a correlation or regression analysis, random uncertainty in the independent variable can create unintended biases in the response of the dependent variable. More generally, the regression to the mean effect can impact both event-based, statistical studies of magnetospheric response to solar wind forcing.

magnetosphere↗

NASA NTRSDate not supplied

Coronal FIP Bias: From Full-Sun X-Ray Spectroscopy to Imaging Spectroscopy

The First Ionization Potential (FIP) bias, whereby the abundances of the low FIP elements in different coronal structures vary from their photospheric values and may also vary with time, has been known for a long time, but still poorly understood. X-ray spectroscopic observations of the Sun are very crucial to study the spatio-temporal variation, and to understand the physical mechanisms giving rise to the FIP bias. Recent X-ray spectroscopic observations of the Sun in disk-integrated mode by Solar X-ray Monitor (XSM) onboard Chandrayaan-2 enhanced our knowledge of the temporal variation of FIP bias during solar flares and in hot AR cores. Here we will summarize the results from these recent studies. Also we will discuss the importance of spatially resolved spectroscopic observation to understand the FIP bias. In this context we will explore the role of upcoming X-ray imaging spectrographs.

solar corona↗

NASA NTRS1974

A solution to the problem of optimizing the fuel bias for a liquid propellant rocket by an application of the central limit theorem

A method of determining the fuel bias for a bipropellant liquid rocket that minimizes outage associated penalties on payload potential is presented. A fuel bias so derived is normally called the optimum fuel bias. The subjects discussed are: (1) probability density function of outage, (2) computer program listing, and (3) choosing the optimum fuel bias.

Viera, W. J.↗

NASA NTRS1975

A note on optimal filtering in the presence of unknown biases

This note considers some aspects of the optimal filtering problem for linear processes in the presence of unknown biases in the input and the observations. It is proved via duality that the optimal filtering problem in the presence of an input bias is equivalent to a certain optimal regulator problem incorporating integral feedback. The question of observability of the augmented system used in the state and bias estimation is answered by deriving necessary and sufficient conditions when bias is present (1) in the input, (2) in the observations and (3) both in the input and the observations.

Joshi, S. M.↗

NASA NTRS1977

Bias-field equalizer for bubble memories

Magnetoresistive Perm-alloy sensor monitors bias field required to maintain bubble memory. Sensor provides error signal that, in turn, corrects magnitude of bias field. Error signal from sensor can be used to control magnitude of bias field in either auxiliary set of bias-field coils around permanent magnet field, or current in small coils used to remagnetize permanent magnet by infrequent, short, high-current pulse or short sequence of pulses.

Keefe, G. E.↗

NASA NTRS1980

Forward- and reverse-bias tunneling effects in n/+/p silicon solar cells

Excess currents due to field-assisted tunneling in both forward and reverse bias directions have been observed in n(+)-p silicon solar cells. These currents arise from the effect of conducting paths produced in the depletion layer by n(+) diffusion and cell processing. Forward-bias data indicate a small potential barrier with height of 0.04 eV at the n(+) end of conducting paths. Under reverse bias, excess tunneling currents involve a potential barrier at the p end of the conducting paths, the longer paths being associated with smaller barrier heights and dominating at the lower temperatures. Low-reverse-bias data give energy levels of 0.11 eV for lower temperatures (253-293 K) and 0.35 eV for higher temperatures (293-380 K). A model is suggested to explain the results.

Garlick, G. F. J.↗

NASA NTRS1983

Multisatellite attitude determination/optical aspect bias determination (MSAD/OABIAS) system description and operating guide. Volume 1: Introduction and analysis

The Multisatellite Attitude Determination/Optical Aspect Bias Determination (MSAD/OABIAS) System, designed to determine spin axis orientation and biases in the alignment or performance of optical or infrared horizon sensors and Sun sensors used for spacecraft attitude determination is described. MSAD/OABIAS uses any combination of eight observation models to process data from a single onboard horizon sensor and Sun sensor to determine simultaneously the two components of the attitude of the spacecraft, the initial phase of the Sun sensor, the spin rate, seven sensor biases, and the orbital in-track error associated with the spacecraft ephemeris information supplied to the system. In addition, the MSAD/OABIAS System provides a data simulator for system and performance testing, an independent deterministic attitude system for preprocessing and independent testing of biases determined, and a multipurpose data prediction and comparison system.

Joseph, M.↗

NASA NTRS1983

Multisatellite attitude determination/optical aspect bias determination (MSAD/OABIAS) system description and operating guide. Volume 3: Operating guide

The Multisatellite Attitude Determination/Optical Aspect Bias Determination (MSAD/OABIAS) System, designed to determine spin axis orientation and biases in the alignment or performance of optical or infrared horizon sensors and Sun sensors used for spacecraft attitude determination, is described. MSAD/OABIAS uses any combination of eight observation models to process data from a single onboard horizon sensor and Sun sensor to determine simultaneously the two components of the attitude of the spacecraft, the initial phase of the Sun sensor, the spin rate, seven sensor biases, and the orbital in-track error associated with the spacecraft ephemeris information supplied to the system. In addition, the MSAD/OABIAS system provides a data simulator for system and performance testing, an independent deterministic attitude system for preprocessing and independent testing of biases determined, and a multipurpose data prediction and comparison system.

Joseph, M.↗

NASA NTRS1982

An experimental verification of laser-velocimeter sampling bias and its correction

The existence of 'sampling bias' in individual-realization laser velocimeter measurements is experimentally verified and shown to be independent of sample rate. The experiments were performed in a simple two-stream mixing shear flow with the standard for comparison being laser-velocimeter results obtained under continuous-wave conditions. It is also demonstrated that the errors resulting from sampling bias can be removed by a proper interpretation of the sampling statistics. In addition, data obtained in a shock-induced separated flow and in the near-wake of airfoils are presented, both bias-corrected and uncorrected, to illustrate the effects of sampling bias in the extreme.

Johnson, D. A.↗

NASA NTRS1983

Discharges on a negatively biased solar array in a charged particle environment

The charging behavior of a negatively biased solar cell array when subjected to a charged particle environment is studied in the ion density range from 200 to 12 000 ions/sq cm with the applied bias range of -500 to -1400 V. The profile of the surface potentials across the array is related to the presence of discharges. At the low end of the ion density range the solar cell cover slides charge to from 0 to +5 volts independent of the applied voltage. No discharges are seen at bias voltages as large as -1400 V. At the higher ion densities the cover slide potential begins to fluctuate, and becomes significantly negative. Under these conditions discharges can occur. The threshold bias voltage for discharges decreases with increasing ion density. A condition for discharges emerging from the experimental observations is that the average coverslide potential must be more negative than -4 V. The observations presented suggest that the plasma potential near the array becomes negative before a discharge occurs. This suggests that discharges are driven by an instability in the plasma.

Snyder, D. B.↗

NASA NTRS1985

Discharges on a negatively biased solar cell array in a charged-particle environment

The charging behavior of a negatively biased solar cell array when subjected to a charged particle environment is studied in the ion density range from 200 to 12,000 ions/sq cm with the applied bias range of -500 to -1400 V. The profile of the surface potentials across the array is related to the presence of discharges. At the low end of the ion density range the solar cell cover slides charge to from 0 to +5 volts independent of the applied voltage. No discharges are seen at bias voltages as large as -1400 V. At the higher ion densities the cover slide potential begins to fluctuate, and becomes significantly negative. Under these conditions discharges can occur. The threshold bias voltage for discharges decreases with increasing ion density. A condition for discharges emerging from the experimental observations is that the average coverslide potential must be more negative than -4 V. The observations presented suggest that the plasma potential near the array becomes negative before a discharge occurs. This suggests that discharges are driven by an instability in the plasma.

Snyder, D. B.↗