Energy loss functions for electrons and protons in planetary gases.
Energy loss functions for protons and electrons in atomic and molecular gases and various metals, noting application to planetary atmospheres studies
Green, A. E. S.↗
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The relation between ionization yields, cross sections and loss functions.
Three energy dependent quantities examined for gases, describing relation between ionization yields, cross section and loss functions
Green, A. E. S.↗
Effect of coronal elemental abundances on the radiative loss function
The solar photosphere and corona abundances tabulated by Meyer (1985) and the chromospheric abundances given by Murphy (1985) are used here to recalculate radiative loss functions for equilibrium, low-density, optically thin plasmas. Results from a representative standard photospheric abundance set and from coronal and chromospheric abundance sets showing depletions of up to a factor of four in certain elemental abundances are compared. A significant difference is found for both the coronal and chromospheric abundance sets, with the peak of the radiative loss curve shifted closer to 10 to the 6th K than to the standard 2 x 10 to the 5th K found from photospheric abundances. Consequences of these new calculations, in particular for the cool loop model of Antiochos and Noci (1986), are discussed.
Cook, J. W.↗
Using SMAP-Derived Loss Functions to Merge Real-Time Gauge-Based and IMERG Datasets
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Randal Koster↗
Attitude-Independent Magnetometer Calibration for Spin-Stabilized Spacecraft
The paper describes a three-step estimator to calibrate a Three-Axis Magnetometer (TAM) using TAM and slit Sun or star sensor measurements. In the first step, the Calibration Utility forms a loss function from the residuals of the magnitude of the geomagnetic field. This loss function is minimized with respect to biases, scale factors, and nonorthogonality corrections. The second step minimizes residuals of the projection of the geomagnetic field onto the spin axis under the assumption that spacecraft nutation has been suppressed by a nutation damper. Minimization is done with respect to various directions of the body spin axis in the TAM frame. The direction of the spin axis in the inertial coordinate system required for the residual computation is assumed to be unchanged with time. It is either determined independently using other sensors or included in the estimation parameters. In both cases all estimation parameters can be found using simple analytical formulas derived in the paper. The last step is to minimize a third loss function formed by residuals of the dot product between the geomagnetic field and Sun or star vector with respect to the misalignment angle about the body spin axis. The method is illustrated by calibrating TAM for the Fast Auroral Snapshot Explorer (FAST) using in-flight TAM and Sun sensor data. The estimated parameters include magnetic biases, scale factors, and misalignment angles of the spin axis in the TAM frame. Estimation of the misalignment angle about the spin axis was inconclusive since (at least for the selected time interval) the Sun vector was about 15 degrees from the direction of the spin axis; as a result residuals of the dot product between the geomagnetic field and Sun vectors were to a large extent minimized as a by-product of the second step.
Natanson, Gregory↗
Toward Physics-informed Neural Networks for 3D Multi-layer Cloud Mask Reconstruction
Three-dimensional (3D) cloud retrievals are critical for understanding their impact on climate and other applications such as aviation safety, weather prediction, and remote sensing. However, obtaining high-resolution and accurate vertical representation of clouds remains unsolved due to the limitations imposed by satellite instrumentation, viewing conditions, and the complexity of cloud dynamics. Cloud masks are essential for comprehending various cloud vertical properties, but deriving accurate 3D cloud masks from 2D satellite imagery data is a challenging task. To tackle these challenges, we introduce a physics-informed loss function for training deep learning models that can extend 2D cloud images into 3D cloud masks. The proposed loss, called CloudMask Loss, is composed of two domain knowledge-informed loss terms: one for evaluating cloud position and thickness, and the other for measuring the number of layers. By combining these loss terms, we improve the trainability of the deep learning models for more accurate and meaningful results. We apply the proposed loss function to different neural networks and demonstrate significant improvements in multi-layer cloud mask reconstruction. Utilizing the same neural network architecture, our proposed loss outperforms standard binary crossentropy loss in terms of multi-layer cloud classification accuracy, number of layers accuracy, and thickness mean absolute error (MAE). The proposed loss function can be readily integrated into various neural network architectures, resulting in substantial performance gains in 3D cloud mask generation.
multi-layer clouds↗
A strong loss-of-function mutation in RAN1 results in constitutive activation of the ethylene response pathway as well as a rosette-lethal phenotype
A recessive mutation was identified that constitutively activated the ethylene response pathway in Arabidopsis and resulted in a rosette-lethal phenotype. Positional cloning of the gene corresponding to this mutation revealed that it was allelic to responsive to antagonist1 (ran1), a mutation that causes seedlings to respond in a positive manner to what is normally a competitive inhibitor of ethylene binding. In contrast to the previously identified ran1-1 and ran1-2 alleles that are morphologically indistinguishable from wild-type plants, this ran1-3 allele results in a rosette-lethal phenotype. The predicted protein encoded by the RAN1 gene is similar to the Wilson and Menkes disease proteins and yeast Ccc2 protein, which are integral membrane cation-transporting P-type ATPases involved in copper trafficking. Genetic epistasis analysis indicated that RAN1 acts upstream of mutations in the ethylene receptor gene family. However, the rosette-lethal phenotype of ran1-3 was not suppressed by ethylene-insensitive mutants, suggesting that this mutation also affects a non-ethylene-dependent pathway regulating cell expansion. The phenotype of ran1-3 mutants is similar to loss-of-function ethylene receptor mutants, suggesting that RAN1 may be required to form functional ethylene receptors. Furthermore, these results suggest that copper is required not only for ethylene binding but also for the signaling function of the ethylene receptors.
NASA Discipline Plant Biology↗
Cooling of solar flares plasmas. 1: Theoretical considerations
Theoretical models of the cooling of flare plasma are reexamined. By assuming that the cooling occurs in two separate phase where conduction and radiation, respectively, dominate, a simple analytic formula for the cooling time of a flare plasma is derived. Unlike earlier order-of-magnitude scalings, this result accounts for the effect of the evolution of the loop plasma parameters on the cooling time. When the conductive cooling leads to an 'evaporation' of chromospheric material, the cooling time scales L(exp 5/6)/p(exp 1/6), where the coronal phase (defined as the time maximum temperature). When the conductive cooling is static, the cooling time scales as L(exp 3/4)n(exp 1/4). In deriving these results, use was made of an important scaling law (T proportional to n(exp 2)) during the radiative cooling phase that was forst noted in one-dimensional hydrodynamic numerical simulations (Serio et al. 1991; Jakimiec et al. 1992). Our own simulations show that this result is restricted to approximately the radiative loss function of Rosner, Tucker, & Vaiana (1978). for different radiative loss functions, other scaling result, with T and n scaling almost linearly when the radiative loss falls off as T(exp -2). It is shown that these scaling laws are part of a class of analytic solutions developed by Antiocos (1980).
Cargill, Peter J.↗
Real-Time Attitude Independent Three Axis Magnetometer Calibration
In this paper new real-time approaches for three-axis magnetometer sensor calibration are derived. These approaches rely on a conversion of the magnetometer-body and geomagnetic-reference vectors into an attitude independent observation by using scalar checking. The goal of the full calibration problem involves the determination of the magnetometer bias vector, scale factors and non-orthogonality corrections. Although the actual solution to this full calibration problem involves the minimization of a quartic loss function, the problem can be converted into a quadratic loss function by a centering approximation. This leads to a simple batch linear least squares solution. In this paper we develop alternative real-time algorithms based on both the extended Kalman filter and Unscented filter. With these real-time algorithms, a full magnetometer calibration can now be performed on-orbit during typical spacecraft mission-mode operations. Simulation results indicate that both algorithms provide accurate integer resolution in real time, but the Unscented filter is more robust to large initial condition errors than the extended Kalman filter. The algorithms are also tested using actual data from the Transition Region and Coronal Explorer (TRACE).
Crassidis, John L.↗
Electronic spectra of crystalline NaCl and KCl.
Reflectance spectra, dielectric response function and energy loss function of NaCl and KCl crystals, distinguishing between exciton and interband spectra
Roessler, D. M.↗
Electronic spectrum of crystalline beryllium oxide
Normal incidence reflectance of BeO single crystals, analyzing data by Kramers-Kronig inversion for dielectric function and energy loss function
Loh, E.↗
Mars Terrain Segmentation with Less Labels
Planetary rover systems need to perform terrain segmentation to identify drivable areas as well as identify specific types of soil for sample collection. The latest Martian terrain segmentation methods rely on supervised learning which is very data hungry and difficult to train where only a small number of labeled samples are available. Moreover, the semantic classes are defined differently for different applications (e.g., rover traversal vs. geological) and as a result the network has to be trained from scratch each time, which is an inefficient use of resources. This research proposes a semi-supervised learning framework for Mars terrain segmentation where a deep segmentation network trained in an unsupervised manner on unlabeled images is transferred to the task of terrain segmentation trained on few labeled images. The network incorporates a backbone module which is trained using a contrastive loss function and an output atrous convolution module which is trained using a pixel-wise cross-entropy loss function. Evaluation results using the metric of segmentation accuracy show that the proposed method with contrastive pre-training outperforms plain supervised learning by 2%-10%. Moreover, the proposed model is able to achieve a segmentation accuracy of 91.1% using only 161 training images (1% of the original dataset) compared to 81.9% with plain supervised learning.
Wilson, Brian D↗
Vibrational and rotational cooling of electrons by water vapor
The cooling of electrons by vibrational and rotational excitation of water molecules plays an important role in the thermal balance of electrons in cometary ionospheres. The energy-loss function for rotational excitation and deexcitation of H2O by electron impact is calculated theoretically. The rotational cooling rate is calculated using this loss function for a wide range of electron and neutral temperatures. The vibrational cooling rate is calculated using measured values of electron-impact vibrational excitation cross sections. Analytical formulas are provided for some of the cooling rates. The interaction of ions with H2O molecules is also discussed, and a formula is suggested for the momentum-transfer collision frequency.
Cravens, T. E.↗
Attitude determination using vector observations: A fast optimal matrix algorithm
The attitude matrix minimizing Wahba's loss function is computed directly by a method that is competitive with the fastest known algorithm for finding this optimal estimate. The method also provides an estimate of the attitude error covariance matrix. Analysis of the special case of two vector observations identifies those cases for which the TRIAD or algebraic method minimizes Wahba's loss function.
Markley, F. Landis↗
Attitude determination using vector observations - A fast optimal matrix algorithm
The attitude matrix minimizing Wahba's loss function is computed directly by a method that is competitive with the fastest known algorithm for finding this optimal estimate. The method also provides an estimate of the attitude error covariance matrix. Analysis of the special case of two vector observations identifies those cases for which the TRIAD or algebraic method minimizes Wahba's loss function.
Markley, F. L.↗
Using model order tests to determine sensory inputs in a motion study
In the study of motion effects on tracking performance, a problem of interest is the determination of what sensory inputs a human uses in controlling his tracking task. In the approach presented here a simple canonical model (FID or a proportional, integral, derivative structure) is used to model the human's input-output time series. A study of significant changes in reduction of the output error loss functional is conducted as different permutations of parameters are considered. Since this canonical model includes parameters which are related to inputs to the human (such as the error signal, its derivatives and integration), the study of model order is equivalent to the study of which sensory inputs are being used by the tracker. The parameters are obtained which have the greatest effect on reducing the loss function significantly. In this manner the identification procedure converts the problem of testing for model order into the problem of determining sensory inputs.
Repperger, D. W.↗
SASS wind ambiguity removal by direct minimization
An objective analysis procedure is presented which combines Seasat-A satellite scatterometer (SASS) data with other available data on wind speeds by minimizing an objective function of gridded wind speed values. The functions are defined as the loss functions for the SASS velocity data, the forecast, the SASS velocity magnitude data, and conventional wind speed data. Only aliases closest to the analysis were included, and a method for improving the first guess while using a minimization technique and slowly changing the parameters of the problem is introduced. The model is employed to predict the wind field for the North Atlantic on Sept. 10, 1978. Dealiased SASS data is compared with available ship readings, showing good agreement between the SASS dealiased winds and the winds measured at the surface. Expansion of the model to take in low-level cloud measurements, pressure data, and convergence and cloud level data correlations is discussed.
Hoffman, R. N.↗
The expanded LaGrangian system for constrained optimization problems
Smooth penalty functions can be combined with numerical continuation/bifurcation techniques to produce a class of robust and fast algorithms for constrainted optimization problems. The key to the development of these algorithms is the Expanded Lagrangian System which is derived and analyzed in this work. This parameterized system of nonlinear equations contains the penalty path as a solution, provides a smooth homotopy into the first-order necessary conditions, and yields a global optimization technique. Furthermore, the inevitable ill-conditioning present in a sequential optimization algorithm is removed for three penalty methods: the quadratic penalty function for equality constraints, and the logarithmic barrier function (an interior method) and the quadratic loss function (an interior method) for inequality constraints. Although these techniques apply to optimization in general and to linear and nonlinear programming, calculus of variations, optimal control and parameter identification in particular, the development is primarily within the context of nonlinear programming.
Poore, A. B.↗