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Venusian atmosphere model developed from satellite and earth based radar data for environmental criteria in spacecraft design and mission planning
The local time variations of particle distributions, which can be produced by the sudden enhancement of a cross-tail electric field, are examined by using adiabatic theory. The strong coupling between acceleration, inward motion, and east-west drift is emphasized. The model is used to interpret recent measurements of delay times of newly accelerated electrons at different local times at geostationary orbit. Observations of the asymmetric storm time 'ring current' are interpreted in terms of the model without invoking particle loss.
Primary focus of the study is on the sources, photochemistry and transport of reactive nitrogen species, O3, as well as other O3 precursors such as NMHC and CO. Major objectives are: (1) Identify the major sources of NO(x) and quantify the contribution of each source to the NO(x) distribution in the free troposphere, particularly in the upper troposphere. (2) Understand the odd hydrogen photochemistry and the recycling of NO(x) in the free troposphere. (3) Evaluate the photochemical production and destruction of O3. A combination of modeling and data analysis/interpretation approach is used. We use a three-dimensional model and a box model to simulate, analyze, and interpret the observations. Both models are needed because they are complementary and each has its advantages and limitations. Proven analytical techniques, including correlation of NOx with various tracers and with different air masses, are applied to both observations and model results to deduce useful information toward accomplishing the objectives. In addition, the SONEX observations are compared and analyzed in a systematic way against other airborne experiments conducted over remote atmosphere, especially those from NASA GTE experiments, AASE 1 and 2, and SUCCESS.
Recent explorations of the cosmic microwave background and the large-scale structure of the universe have indicated a preference for sizable neutrino self-interactions, much stronger than what the standard model offers. When interpreted in the context of simple particle-physics models with a light, neutrinophilic scalar mediator, some of the hints are already in tension with the combination of terrestrial, astrophysical, and cosmological constraints. We take a novel approach by considering neutrino self-interactions through a mediator with a smooth, continuous spectral density function. We consider Georgi’s unparticle with a mass gap as a concrete example and point out two useful effects for mitigating two leading constraints. (i) The Unparticle is “broadband’—it occupies a wide range of masses which allows it to pass the early universe constraint on effective number of extra neutrinos ( Δ N eff ) even if the mass gap lies below the MeV scale. (ii) Scattering involving unparticles is less resonant, which lifts the constraint set by IceCube based on a recent measurement of ultra-high-energy cosmogenic neutrinos. Our analysis shows that an unparticle mediator can open up ample parameter space for strong neutrino self-interactions of interest to cosmology and serves a well-motivated target for upcoming experiments. Published by the American Physical Society 2025
The AX-5 is an all metal Extra-vehicular (EVA) space suit currently under consideration for use on Space Station Freedom. A reliability model was developed based on the suit's unique design and on projected joint cycle requirements. Three AX-5 space suit component joints were cycled under simulated load conditions in accordance with NASA's advanced space suit evaluation plan. This paper will describe the reliability model developed, the results of the cycle testing, and an interpretation of the model and test results in terms of projected Mean Time Between Failure for the AX-5. A discussion of the maintenance implications and life cycle for the AX-5 based on this projection is also included.
In the wake of the Columbia accident, the NASA-STD-7009 [1] credibility assessment was developed as a unifying platform to describe model credibility and the uncertainties in its modeling predictions. This standard is now being adapted by NASAs Human Research Program to cover a wide range of numerical models for human research. When used properly, the standard can improve the process of code development by encouraging the use of best practices. It can also give management more insight in making informed decisions through a better understanding of the models capabilities and limitations.To a newcomer, the abstractions presented in NASA-STD-7009 and the sheer volume of information that must be absorbed can be overwhelming. This talk is aimed at describing the credibility assessment, which is the heart of the standard, in plain terms. It will outline how to develop a credibility assessment under the standard. It will also show how to quickly interpret the graphs and tables that result from the assessment and how to drill down from the top-level view to the foundation of the assessment. Finally, it will highlight some of the resources that are available for further study.
Impulsive hard X-ray and microwave bursts with rise times from 0.1 to 10 seconds are discussed. Source areas calculated by the method of Crannell et al. (1978) were compared with source areas determined from Hinotori and the Hard X-ray Imaging Spectrometer (HXIS) images. The agreement strongly suggests that the method is valid. If the thermal conduction front model for the hard X-ray and microwave source is adopted, then the method enables derivation of area, density, magnetic field, and rise time from hard X-ray and microwave spectral observations. This approach was used to derive these parameters for several rapid impulsive rises in the flares of July 1, 1980, and May 21, 1984. It is shown that the model provides a consistent interpretation of the observations of these impulsive increases. Indeed, the model provides a way to calculate rise times from spectra alone (to within a factor of about three) over more than two orders of magnitude.
Abstract Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve the understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainties with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of the classification of winter precipitation type and regression of surface-layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. To encourage broader adoption of evidential deep learning, we have developed a new Python package, Machine Integration and Learning for Earth Systems (MILES) group Generalized Uncertainty for Earth System Science (GUESS) (MILES-GUESS) ( https://github.com/ai2es/miles-guess ), that enables users to train and evaluate both evidential and ensemble deep learning. Significance Statement This study demonstrates a new technique, evidential deep learning, for robust and computationally efficient uncertainty quantification in modeling the Earth system. The method integrates probabilistic principles into deep neural networks, enabling the estimation of both aleatoric uncertainty from noisy data and epistemic uncertainty from model limitations using a single model. Our analyses reveal how decomposing these uncertainties provides valuable insights into reliability, accuracy, and model shortcomings. We show that the approach can rival standard methods in classification and regression tasks within atmospheric science while offering practical advantages such as computational efficiency. With further advances, evidential networks have the potential to enhance risk assessment and decision-making across meteorology by improving uncertainty quantification, a longstanding challenge. This work establishes a strong foundation and motivation for the broader adoption of evidential learning, where properly quantifying uncertainties is critical yet lacking.
This paper presents two searches for the electroweak production of higgsinos with compressed mass spectra using 140 fb−1 of s=13$$ \sqrt{s}=13 $$ TeV proton-proton collision data collected by the ATLAS experiment at the Large Hadron Collider. Events are required to feature an energetic jet, large missing transverse momentum, and at least one low-momentum charged particle that serves as a candidate higgsino decay product. In the first search, targeting higgsino mass splittings in the range of 0.3–1 GeV, the higgsinos are expected to predominantly decay into pions that are identified as low-momentum charged particles with large transverse impact parameters due to the long higgsino lifetime (cτ ≈ ?(0.1–10 mm)), and neural networks are used to discriminate between signal and background processes. The second search targets larger mass splittings in the range of 1–3 GeV, where the higgsinos are expected to decay promptly into low-momentum leptons, one of which is identified by dedicated low-momentum electron or muon taggers based on neural networks utilising tracking and calorimeter information. No significant excess above the Standard Model prediction is observed in either search and the results are interpreted within simplified models, to set lower limits on the masses of the higgsino-like charginos and neutralinos. Together, these searches exclude chargino masses below 126 GeV at 95% confidence level for mass splittings between the chargino and lightest neutralino in the range of 0.3–2 GeV. This represents the first ATLAS constraints in a portion of this parameter space and surpasses the limits previously set by other experiments.
Work performed on remote sensing of each terrain is outlined. The purpose of the investigation was: (1) development of theoretical models for remote sensing of Earth terrain; (2) data analysis and interpretation using the models developed; (3) scene simulation; and (4) development of new remote sensing approaches and techniques.
In recent years, the FAA has worked with Transport Canada, National Research Council Canada (NRC) and APS Aviation, Inc. to develop allowance times for aircraft operations in ice-pellet precipitation. These allowance times are critical to ensure safety and efficient operation of commercial and cargo flights. Wind-tunnel testing with uncontaminated anti-icing fluids and fluids contaminated with simulated ice pellets had been carried out at the NRC Propulsion and Icing Wind Tunnel (PIWT) to better understand the flowoff characteristics and resulting aerodynamic effects. The percent lift loss on the thin, high-performance wing model tested in the PIWT was determined at 8 angle of attack and used as one of the evaluation criteria in determining the allowance times. Because it was unclear as to how performance degradations measured on this model were relevant to an actual airplane configuration, some means of interpreting the wing model lift loss was deemed necessary. This paper describes how the lift loss was related to the loss in maximum lift of a Boeing 737-200ADV airplane through the Aerodynamic Acceptance Test (AAT) performed for fluids qualification. A loss in maximum lift coefficient of 5.24 percent on the B737-200ADV airplane (which was adopted as the threshold in the AAT) corresponds to a lift loss of 7.3 percent on the PIWT model at 8 angle of attack. There is significant scatter in the data used to develop the correlation related to varying effects of the anti-icing fluids that were tested and other factors. A statistical analysis indicated the upper limit of lift loss on the PIWT model was 9.2 percent. Therefore, for cases resulting in PIWT model lift loss from 7.3 to 9.2 percent, extra scrutiny of the visual observations is required in evaluating fluid performance with contamination.
In recent years, the FAA has worked with Transport Canada, National Research Council of Canada (NRC) and APS Aviation, Inc. to develop allowance times for aircraft operations in ice-pellet precipitation. These allowance times are critical to ensure safety and efficient operation of commercial and cargo flights. Wind-tunnel testing with uncontaminated anti-icing fluids and fluids contaminated with simulated ice-pellets had been carried out at the NRC Propulsion and Icing Wind Tunnel (PIWT) to better understand the flowoff characteristics and resulting aerodynamic effects. The percent lift loss on the thin, high-performance wing model tested in the PIWT was determined at 8 angle of attack and used as one of the evaluation criteria in determining the allowance times. Because it was unclear as to how performance degradations measured on this model were relevant to an actual airplane configuration, some means of interpreting the wing model lift loss was deemed necessary. In this report, the lift loss was related to the loss in maximum lift of a Boeing 737-200ADV airplane through the Aerodynamic Acceptance Test (AAT) performed for fluids qualification. This report provides a review of the research basis of the AAT in order to understand how this correlation was applied. A loss in maximum lift coefficient of 5.24 percent on the B737-200ADV airplane (which was adopted as the threshold in the AAT) corresponds to a lift loss of 7.3 percent on the PIWT model at 8 degrees angle of attack. There is significant scatter in the data used to develop the correlation related to varying effects of the various antiicing fluids that were tested and other factors. A statistical analysis indicated the upper limit of lift loss on the PIWT model was 9.2 percent. Therefore, for cases resulting in PIWT model lift loss from 7.3 to 9.2 percent, extra scrutiny of the visual observations is required in evaluating fluid performance with contamination. Additional research may result in future changes to this correlation.
In recent years, the FAA has worked with Transport Canada, National Research Council Canada (NRC) and APS Aviation, Inc. to develop allowance times for aircraft operations in ice-pellet precipitation. These allowance times are critical to ensure safety and efficient operation of commercial and cargo flights. Wind-tunnel testing with uncontaminated anti-icing fluids and fluids contaminated with simulated ice pellets had been carried out at the NRC Propulsion and Icing Wind Tunnel (PIWT) to better understand the flow-off characteristics and resulting aerodynamic effects. The percent lift loss on the thin, high-performance wing model tested in the PIWT was determined at 8 deg. angle of attack and used as one of the evaluation criteria in determining the allowance times. Because it was unclear as to how performance degradations measured on this model were relevant to an actual airplane configuration, some means of interpreting the wing model lift loss was deemed necessary. This paper describes how the lift loss was related to the loss in maximum lift of a Boeing 737-200ADV airplane through the Aerodynamic Acceptance Test (AAT) performed for fluids qualification. A loss in maximum lift coefficient of 5.24% on the B737-200ADV airplane (which was adopted as the threshold in the AAT) corresponds to a lift loss of 7.3% on the PIWT model at 8 deg. angle of attack. There is significant scatter in the data used to develop the correlation related to varying effects of the anti-icing fluids that were tested and other factors. A statistical analysis indicated the upper limit of lift loss on the PIWT model was 9.2%. Therefore, for cases resulting in PIWT model lift loss from 7.3% to 9.2%, extra scrutiny of the visual observations is required in evaluating fluid performance with contamination.
e mind is introduced as a set of simple intelligent units (say, neurons, or interacting agents), which can communicate by exchange of information without explicit global control. Incomplete information is compensated by a sequence of random guesses symmetrically distributed around expectations with prescribed variances. Both the expectations and variances are the invariants characterizing the whole class of agents. These invariants are stored as parameters of the collective mind, while they contribute into dynamical formalism of the agents' evolution, and in particular, into the reflective chains of their nested abstract images of the selves and non-selves. The proposed model consists of the system of stochastic differential equations in the Langevin form representing the motor dynamics, and the corresponding Fokker-Planck equation representing the mental dynamics (Motor dynamics describes the motion in physical space, while mental dynamics simulates the evolution of initial errors in terms of the probability density). The main departure of this model from Newtonian and statistical physics is due to a feedback from the mental to the motor dynamics which makes the Fokker-Planck equation nonlinear. Interpretation of this model from mathematical and physical viewpoints, as well as possible interpretation from biological, psychological, and social viewpoints are discussed. The model is illustrated by the dynamics of a dialog.
In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R 2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms.
Total vertical column amounts of NO have been derived from infrared solar absorption spectra recorded near sunrise and sunset with the 0.01 per cm resolution Fourier transform interferometer at the national Solar Observatory on Kitt peak (elevation 2095 m, latitude 31.9 degrees N) on February 23, 1981. The results show an increase in NO concentration in the morning, late afternoon values about 40 percent higher than in the morning, and a decrease in NO concentration prior to sunset. The measured diurnal changes in the total vertical column amount are compared with values obtained from time-dependent photochemical calculations.
Knowledge of the formation, distribution, physical, chemical and optical characteristics of interstellar, interplanetary, and planetary dust grains provide valuable information about many issues dealing with the origin and formation of the solar system bodies, interplanetary and interstellar environments as well as various industrial processes. Understanding the microphysics of individual grains and their interaction with the surrounding environment is key to properly model various conditions and interpret existing data. The theory and models of individual dust grains are well developed for environments that vary from dense planetary atmospheres to dusty plasmas to diffuse environments such is interplanetary space. However, experimental investigations of individual dust grains in equilibrium are less common, perhaps due to the difficult of these experiments. Laboratory measurements of dust grains have primarily measured ensemble properties or transient properties of single grains. A technique developed in the 1950's for ion spectroscopy, known as a quadrupole trap or 'Paul Trap', has recently been used to investigate single micron-sized dust grains. This scaled ion trap called an electrodynamic balance has been used for atmospheric aerosol research. A description of this technique is provided. Recent results from experiments to investigate the equilibrium potential of dust grains exposed to far ultraviolet light or to -,in electron or ion beam are presented. This laboratory technique ]ends itself to many applications that relate to planetary atmospheres, heliospheric environments, pre-stellar and pre-planetary conditions, and industrial settings. Several planned experimental approaches are presented. Potential experiments to investigate the interaction of multiple dust grains using an electrodynamic balance are proposed.
Knowledge of the formation and distribution of interstellar, interplanetary, and planetary dust grains, and their physical, chemical and optical characteristics provide valuable information about many issues dealing with the origin and formation of the solar system bodies, interplanetary and interstellar environments as well as various industrial processes. Understanding the microphysics of individual grains and their interaction with the surrounding, environment is key to properly model various conditions and interpret existing data. The theory and models of individual dust grains are well developed for environments that vary from dense planetary atmospheres to dusty plasmas to diffuse environments such as interplanetary space. However, experimental investigations of individual dust grains in equilibrium are less common, perhaps due to the difficulty of these experiments. Laboratory measurements of dust grains have primarily measured ensemble properties or transient properties of single grains. A technique developed in the 1950's for ion spectroscopy, generally referred to as a quadrupole trap has recently been employed as an electrodynamic balance to investigate single micron-sized dust grains and for atmospheric aerosol research. A description of the theoretical basis and the experimental setup of the electrodynamic balance being developed in our laboratory are given. This laboratory technique lends itself to many applications that relate to planetary atmospheres, heliospheric environments, pre-stellar and pre-planetary conditions, and industrial settings. We present results from some recent experiments carried out to investigate the equilibrium potential of dust grains exposed to far ultraviolet light or to an electron beam. Some future experiments using an electrodynamic balance to investigate the optical characteristics, and condensation process involving dust grains in various astrophysical environments are discussed.