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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Sempervirens: A Fast Reconstruction Algorithm for Noisy and Incomplete Binary Matrix Representations of Trees

Applications such as reconstructing cell lineage trees (represented as phylogenetic trees) from single-cell sequencing data require reconstructing a {0,1}-matrix that has many errors and missing entries. We introduce Sempervirens, a very fast matrix reconstruction algorithm for noisy and incomplete matrix representations of phylogenetic trees. Sempervirens uses an iterative maximum-likelihood approach to determine the topology tree represented by the corrupted data. We show that Sempervirens is at least three orders of magnitude faster than other methods on thousand by thousand matrices, with the speed gap widening with larger matrices. We also show that Sempervirens matches state-of-the-art methods in reconstruction accuracy. The speed of Sempervirens enables it to be tractably applied to reconstructing much larger matrices than those that other methods can reconstruct. In addition to experimental results, we justify the algorithm with a mathematical treatment of its subprocedures.

algorithms↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗
DOE OSTI2025

Data and scripts from: “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”

This data package includes data and scripts from the manuscript “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”.The study addressed common challenges faced in environmental sensing and modeling, including uncertain input data, missing sensor observations, and high-dimensional datasets with interrelated but redundant variables. Point-scaled meteorological and soil sensor observations were perturbed with noises and missing values, and denoising autoencoder (DAE) neural networks were developed to reconstruct the perturbed data and further predict evapotranspiration. This study concluded that (1) the reconstruction quality of each variable depends on its cross-correlation and alignment to the underlying data structure, (2) uncertainties from the models were overall stronger than those from the data corruption, and (3) there was a tradeoff between reducing bias and reducing variance when evaluating the uncertainty of the machine learning models.This package includes:(1) Four ipython scripts (.ipynb): “DAE_train.ipynb” trains and evaluates DAE neural networks, “DAE_predict.ipynb” makes predictions from the trained DAE models, “ET_train.ipynb” trains and evaluates ET prediction neural networks, and “ET_predict.ipynb” makes predictions from trained ET models.(2) One python file (.py): “methods.py” includes all user-defined functions and python codes used in the ipython scripts.(3) A “sub_models” folder that includes five trained DAE neural networks (in pytorch format, .pt), which could be used to ingest input data before being fed to the downstream ET models in ‘ET_train.ipynb” or ‘ET_predict.ipynb’.(4) Two data files (.csv). Daily meteorological, vegetation, and soil data is in “df_data.csv”, where “df_meta.csv” contains the location and time information of “df_data.csv”. Each row (index) in “df_meta.csv” corresponds to each row in “df_data.csv”. These data files are formatted to follow the data structure requirements and be directly used in the ipython scripts, and they have been shuffled chronologically to train machine learning models. The meteorological and soil data was collected using point sensors between 2019-2023 at(4.a) Three shrub-dominated field sites in East River, Colorado (named “ph1”, “ph2” and “sg5” in “df_meta.csv”, where “ph1” and “ph2” were located at PumpHouse Hillslopes, and “sg5” was at Snodgrass Mountain meadow) and(4.b) One outdoor, mesoscale, and herbaceous-dominated experiment in Berkeley, California (named “tb” in “df_meta.csv”, short for Smartsoils Testbed at Lawrence Berkeley National Lab).- See "df_data_dd.csv" and "df_meta_dd.csv" for variable descriptions and the Methods section for additional data processing steps. See "flmd.csv" and "README.txt" for brief file descriptions.- All ipython scripts and python files are written in and require PYTHON language software.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1978

Bursty channel errors and the Viterbi decoder

Recent applications have developed for spread spectrum communications, hardware data transfer, high rate digital systems, etc. that use channels for which errors tend to occur in short bursts in addition to those at random, i.e., compound channels. Viterbi decoding algorithms are generally very good for random error channels but are not as efficient for burst errors or for compound channels. This paper presents the results of a computer simulation study of the performance of various Viterbi decoders when receiving data corrupted with burst and random errors on the same channel. Simulations were performed using hard-decision CPSK.

Ingels, F.↗
NASA NTRS1987

A model-free method for mass spectrometer response correction

A new method for correction of mass spectrometer output signals is described. Response-time distortion is reduced independently of any model of mass spectrometer behavior. The delay of the system is found first from the cross-correlation function of a step change and its response. A two-sided time-domain digital correction filter (deconvolution filter) is generated next from the same step response data using a regression procedure. Other data are corrected using the filter and delay. The mean squared error between a step response and a step is reduced considerably more after the use of a deconvolution filter than after the application of a second-order model correction. O2 consumption and CO2 production values calculated from data corrupted by a simulated dynamic process return to near the uncorrupted values after correction. Although a clean step response or the ensemble average of several responses contaminated with noise is needed for the generation of the filter, random noise of magnitude not above 0.5 percent added to the response to be corrected does not impair the correction severely.

Shykoff, Barbara E.↗
NASA NTRS1993

Measurement methods and algorithms for comparison of local and remote clocks

Several methods for characterizing the performance of clocks with special emphasis on using calibration information that is acquired via an unreliable or noisy channel is discussed. Time-domain variance estimators and frequency-domain techniques such as cross-spectral analysis are discussed. Each of these methods has advantages and limitations that will be illustrated using data obtained via GPS, ACTS, and other methods. No one technique will be optimum for all of these analyses, and some of these problems cannot be completely characterized by any of the techniques discussed. The inverse problem of communicating frequency and time corrections to a real-time steered clock are also discussed. Methods were developed to mitigate the disastrous problems of data corruption and loss of computer control.

Levine, Judah↗
NASA NTRS1995

Estimating the Cramer-Rao bound for restored astronomical observations

This work addresses the problem of assigning confidence intervals to estimated photometry data obtained from astronomical observations. The proposed solution is to estimate the Cramer-Rao bound, which is an analytical expression that describes the minimum obtainable mean square error associated with a given estimate of a parameter. This Letter presents a compact and simple form for the bound associated with a linear estimator such as a Wiener filter estimator. A prescription for estimating the variance associated with each element in a restored object was developed using an analytical model for observed data corrupted by either Poisson or Gaussian noise. Both one- and two-dimensional examples are presented.

Zaccheo, T. S.↗
NASA NTRS1998

A new frame-based registration algorithm

This paper presents a new algorithm for frame registration. Our algorithm requires only that the frame be comprised of straight rods, as opposed to the N structures or an accurate frame model required by existing algorithms. The algorithm utilizes the full 3D information in the frame as well as a least squares weighting scheme to achieve highly accurate registration. We use simulated CT data to assess the accuracy of our algorithm. We compare the performance of the proposed algorithm to two commonly used algorithms. Simulation results show that the proposed algorithm is comparable to the best existing techniques with knowledge of the exact mathematical frame model. For CT data corrupted with an unknown in-plane rotation or translation, the proposed technique is also comparable to the best existing techniques. However, in situations where there is a discrepancy of more than 2 mm (0.7% of the frame dimension) between the frame and the mathematical model, the proposed technique is significantly better (p < or = 0.05) than the existing techniques. The proposed algorithm can be applied to any existing frame without modification. It provides better registration accuracy and is robust against model mis-match. It allows greater flexibility on the frame structure. Lastly, it reduces the frame construction cost as adherence to a concise model is not required.

NASA Center ARC↗
NASA NTRS2005

Designing and Implementing a Distributed System Architecture for the Mars Rover Mission Planning Software (Maestro)

Distributed systems allow scientists from around the world to plan missions concurrently, while being updated on the revisions of their colleagues in real time. However, permitting multiple clients to simultaneously modify a single data repository can quickly lead to data corruption or inconsistent states between users. Since our message broker, the Java Message Service, does not ensure that messages will be received in the order they were published, we must implement our own numbering scheme to guarantee that changes to mission plans are performed in the correct sequence. Furthermore, distributed architectures must ensure that as new users connect to the system, they synchronize with the database without missing any messages or falling into an inconsistent state. Robust systems must also guarantee that all clients will remain synchronized with the database even in the case of multiple client failure, which can occur at any time due to lost network connections or a user's own system instability. The final design for the distributed system behind the Mars rover mission planning software fulfills all of these requirements and upon completion will be deployed to MER at the end of 2005 as well as Phoenix (2007) and MSL (2009).

Goldgof, Gregory M.↗
NASA NTRS2004

Using software security analysis to verify the secure socket layer (SSL) protocol

nal Aeronautics and Space Administration (NASA) have tens of thousands of networked computer systems and applications. Software Security vulnerabilities present risks such as lost or corrupted data, information the3, and unavailability of critical systems. These risks represent potentially enormous costs to NASA. The NASA Code Q research initiative 'Reducing Software Security Risk (RSSR) Trough an Integrated Approach '' offers, among its capabilities, formal verification of software security properties, through the use of model based verification (MBV) to address software security risks. [1,2,3,4,5,6] MBV is a formal approach to software assurance that combines analysis of software, via abstract models, with technology, such as model checkers, that provide automation of the mechanical portions of the analysis process. This paper will discuss: The need for formal analysis to assure software systems with respect to software and why testing alone cannot provide it. The means by which MBV with a Flexible Modeling Framework (FMF) accomplishes the necessary analysis task. An example of FMF style MBV in the verification of properties over the Secure Socket Layer (SSL) communication protocol as a demonstration.

software↗
NASA NTRS2004

Model based verification of the Secure Socket Layer (SSL) Protocol for NASA systems

The National Aeronautics and Space Administration (NASA) has tens of thousands of networked computer systems and applications. Software Security vulnerabilities present risks such as lost or corrupted data, information theft, and unavailability of critical systems. These risks represent potentially enormous costs to NASA. The NASA Code Q research initiative 'Reducing Software Security Risk (RSSR) Trough an Integrated Approach' offers formal verification of information technology (IT), through the creation of a Software Security Assessment Instrument (SSAI), to address software security risks.

software security↗
NASA NTRS2013

Behavior Prediction Tools Strengthen Nanoelectronics

Several years ago, NASA started making plans to send robots to explore the deep, dark craters on the Moon. As part of these plans, NASA needed modeling tools to help engineer unique electronics to withstand extremely cold temperatures. According to Jonathan Pellish, a flight systems test engineer at Goddard Space Flight Center, "An instrument sitting in a shadowed crater on one of the Moon s poles would hover around 43 K", that is, 43 kelvin, equivalent to -382 F. Such frigid temperatures are one of the main factors that make the extreme space environments encountered on the Moon and elsewhere so extreme. Radiation is another main concern. "Radiation is always present in the space environment," says Pellish. "Small to moderate solar energetic particle events happen regularly and extreme events happen less than a handful of times throughout the 7 active years of the 11-year solar cycle." Radiation can corrupt data, propagate to other systems, require component power cycling, and cause a host of other harmful effects. In order to explore places like the Moon, Jupiter, Saturn, Venus, and Mars, NASA must use electronic communication devices like transmitters and receivers and data collection devices like infrared cameras that can resist the effects of extreme temperature and radiation; otherwise, the electronics would not be reliable for the duration of the mission.

Source record↗
NASA NTRS2012

CASSIUS: The Cassini Uplink Scheduler

The Cassini Uplink Scheduler (CASSIUS) is cross-platform software used to generate a radiation sequence plan for commands being sent to the Cassini spacecraft. Because signals must travel through varying amounts of Earth's atmosphere, several different modes of constant telemetry rates have been devised. These modes guarantee that the spacecraft and the Deep Space Network agree with respect to the data transmission rate. However, the memory readout of a command will be lost if it occurs on a telemetry mode boundary. Given a list of spacecraft message files as well as the available telemetry modes, CASSIUS can find an uplink sequence that ensures safe transmission of each file. In addition, it can predict when the two on-board solid state recorders will swap. CASSIUS prevents data corruption by making sure that commands are not planned for memory readout during telemetry rate changes or a solid state recorder swap.

Cassini mission↗
NASA NTRS1993

Instrumental Biases in Ionospheric Measurement Derived from GPS Data

Line-of-sight ionosphere measurements derived from differencing dual frequency GPS range data are corrupted by instrumental biases in both receiver and GPS satellite transmitters due to hardware delays in the L1 and L2 signal paths. Using a global ionospheric shell model to fit GPS based ionospheric delay data from a world wide network of 30 to 40 receivers, we can estimate, with a single fit, satellite biases for the entire GPS constellation and receiver biases for all the uncalibrated receivers.

GPS↗
NASA NTRS2021

Automated Data Accountability for Missions in Mars Rover Data

As the Mars Curiosity Rover transmits data to the JPL Ground Data System (GDS), it frequently observes data loss and corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the downlink process. As new missions are launched, the GDSA team redistributes analysts to these new missions, causing shortages in previous missions. The GDSA team can significantly benefit from the automation and optimization of the downlink process of telemetry data. In fact, there is a need for a better understanding of why the data is corrupted, so that the GDSA team can best determine the root cause of the issues in the GDS. This paper presents machine learning and deep learning based approaches to automate and optimize the detection of data loss. We first created a pipeline to automatically accumulate data from the telemetry databases (MAROS, Telemetry Data Storage, and GDS Elastic Search Database) in the downlink process. With our newly created datasets, we perform feature selection to supplement the GDSA understanding of the downlink process and provide supplemental analysis on the importance of different features. We implement various machine learning and deep learning based models, including support vector machines, ensemble methods, and deep neural networks and evaluate their accuracies in identifying whether a downlink process is complete or incomplete. We utilize fast hyperparameter optimization methods that allow our models to quickly be re-trained, allowing them to quickly be tuned and optimized on daily incoming data in real time. This hyperparameter optimization also allows our methods to be quickly integrated into other JPL missions. Our results show that our best-performing machine learning and deep learning based models outperform the existing GDSA detection software by 6 accuracy points and can aid analysts by providing insights into the data accountability problem. Since these various machine learning and deep learning approaches vary significantly in interpretability, we provide a discussion on the tradeoffs between their performance and trustworthiness in helping detect issues in data transmission.

Divsalar, Dariush↗
NASA NTRS2010

ICER-3D Hyperspectral Image Compression Software

Software has been developed to implement the ICER-3D algorithm. ICER-3D effects progressive, three-dimensional (3D), wavelet-based compression of hyperspectral images. If a compressed data stream is truncated, the progressive nature of the algorithm enables reconstruction of hyperspectral data at fidelity commensurate with the given data volume. The ICER-3D software is capable of providing either lossless or lossy compression, and incorporates an error-containment scheme to limit the effects of data loss during transmission. The compression algorithm, which was derived from the ICER image compression algorithm, includes wavelet-transform, context-modeling, and entropy coding subalgorithms. The 3D wavelet decomposition structure used by ICER-3D exploits correlations in all three dimensions of sets of hyperspectral image data, while facilitating elimination of spectral ringing artifacts, using a technique summarized in "Improving 3D Wavelet-Based Compression of Spectral Images" (NPO-41381), NASA Tech Briefs, Vol. 33, No. 3 (March 2009), page 7a. Correlation is further exploited by a context-modeling subalgorithm, which exploits spectral dependencies in the wavelet-transformed hyperspectral data, using an algorithm that is summarized in "Context Modeler for Wavelet Compression of Hyperspectral Images" (NPO-43239), which follows this article. An important feature of ICER-3D is a scheme for limiting the adverse effects of loss of data during transmission. In this scheme, as in the similar scheme used by ICER, the spatial-frequency domain is partitioned into rectangular error-containment regions. In ICER-3D, the partitions extend through all the wavelength bands. The data in each partition are compressed independently of those in the other partitions, so that loss or corruption of data from any partition does not affect the other partitions. Furthermore, because compression is progressive within each partition, when data are lost, any data from that partition received prior to the loss can be used to reconstruct that partition at lower fidelity. By virtue of the compression improvement it achieves relative to previous means of onboard data compression, this software enables (1) increased return of hyperspectral scientific data in the presence of limits on the rates of transmission of data from spacecraft to Earth via radio communication links and/or (2) reduction in spacecraft radio-communication power and/or cost through reduction in the amounts of data required to be downlinked and stored onboard prior to downlink. The software is also suitable for compressing hyperspectral images for ground storage or archival purposes.

Xie, Hua↗
NASA NTRSDate not supplied

Maneuver Reconstruction, Tracking Scheduling, and Bias Estimation in the Near Rectilinear Halo Orbit (NRHO)

NASA’s Gateway program will build a crew-tended station in an Earth-Moon Near Rectilinear Halo Orbit (NRHO) to support deep space missions to the lunar surface and heliocentric space. The station in the NRHO will be tracked by the Deep Space Network (DSN) with 2-way radiometric tracking data to produce an estimated state that is utilized to target Orbit Maintenance Maneuvers (OMMs). However, the tracking data is corrupted with measurement noise and range bias, which results in state estimation error and OMM execution error. This paper reports on analysis performed to improve the geometry of the tracking data problem with the inclusion of cross-hemispheric partner sites and explores the performance impact of handovers between partner sites and DSN sites. The OMM execution error is estimated directly using a Batch Least Squares (BLS) process, and the range bias estimation is analyzed among both original DSN sites and a combined DSN + partner sites scenario.

Clark P Newman↗