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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.
Most applications of machine learning in heterogeneous catalysis thus far have used black-box models to predict computable physical properties (descriptors), such as adsorption or formation energies, that can be related to catalytic performance (that is, activity or stability). Here, extracting meaningful physical insights from these black-box models has proved challenging, as the internal logic of these black-box models is not readily interpretable due to their high degree of complexity. Interpretable machine learning methods that merge the predictive capacity of black-box models with the physical interpretability of physics-based models offer an alternative to black-box models. In this Perspective, we discuss the various interpretable machine learning methods available to catalysis researchers, highlight the potential of interpretable machine learning to accelerate hypothesis formation and knowledge generation, and outline critical challenges and opportunities for interpretable machine learning in heterogeneous catalysis.
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 this report a review of the Higgs portal-vector dark matter interpretation of the spin-independent dark matter nu-cleon elastic scattering cross section is presented, where the invisible Higgs decay width measured at the LHC is used. Effective Field Theory and ultraviolet complete models are discussed. LHC interpretations show only the scalar and Majorana dark matter scenarios; we propose including interpretation for vec-tor dark matter in the EFT and UV completion theoretical framework. In addition, our studies suggest an extension of the LHC dark matter interpretations to the sub-GeV regime.
The goal of this work is to address two limitations in autoencoder-based models: latent space interpretability and compatibility with unstructured meshes. This is accomplished here with the development of a novel graph neural network (GNN) autoencoding architecture with demonstrations on complex fluid flow applications. To address the first goal of interpretability, the GNN autoencoder achieves reduction in the number nodes in the encoding stage through an adaptive graph reduction procedure. Further, this reduction procedure essentially amounts to flowfieldconditioned node sampling and sensor identification, and produces interpretable latent graph representations tailored to the flowfield reconstruction task in the form of so-called masked fields. These masked fields allow the user to (a) visualize where in physical space a given latent graph is active, and (b) interpret the time-evolution of the latent graph connectivity in accordance with the time-evolution of unsteady flow features (e.g. recirculation zones, shear layers) in the domain. To address the goal of unstructured mesh compatibility, the autoencoding architecture utilizes a series of multi-scale message passing (MMP) layers, each of which models information exchange among node neighborhoods at various lengthscales. The MMP layer, which augments standard single-scale message passing with learnable coarsening operations, allows the decoder to more efficiently reconstruct the flowfield from the identified regions in the masked fields. Analysis of latent graphs produced by the autoencoder for various model settings are conducted using unstructured snapshot data sourced from large-eddy simulations in a backward-facing step (BFS) flow configuration with an OpenFOAM-based flow solver at high Reynolds numbers.
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.
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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.
X-ray spectroscopy has long been a powerful diagnostic tool for hot, dilute plasmas, providing insights into plasma conditions by measuring line shifts and broadenings of atomic transitions. The technique critically depends on the accuracy of atomic physics models used to interpret spectroscopic measurements for inferring plasma properties such as free-electron density and temperature. Over the past decades, the atomic and plasma physics communities have developed robust atomic physics models to account for various processes in hot, dilute classical plasmas. While these models have been successful in that regime, their applicability becomes uncertain when interpreting x-ray spectroscopy experiments of above-solid-density plasmas. Given that finite-temperature density-functional theory (DFT) offers a more accurate description of dense plasma environments, we present the development of a DFT-based multi-band kinetic model, VERITAS, designed to improve the interpretation of x-ray spectroscopic measurements in high-density plasmas produced by laser-driven spherical implosions. This work details the VERITAS model and its application to both time-integrated and time-resolved x-ray spectra from implosion experiments on OMEGA. The advantages and limitations of the VERITAS model will also be discussed, along with potential directions for advancing x-ray spectroscopy of dense and superdense plasmas.
Increased penetration of renewables for power generation has negatively impacted the dynamics of conventional fossil fuel-based power plants. The power plants operating on the base load are forced to cycle, to adjust to the fluctuating power demands. This results in an inefficient operation of the coal power plants, which leads up to higher operating losses. To overcome such operational challenge associated with cycling and to develop an optimal process control, this work analyzes a set of models for predicting power generation. Moreover, the power generation is intrinsically affected by the state of the power plant components, and therefore our model development also incorporates additional power plant process variables while forecasting the power generation. We present and compare multiple state-of-the-art forecasting data-driven methods for power generation to determine the most adequate and accurate model. We also develop an interpretable attention-based transformer model to explain the importance of process variables during training and forecasting. The trained deep neural network (DNN) LSTM model has good accuracy in predicting gross power generation under various prediction horizons with/without cycling events and outperforms the other models for long-term forecasting. The DNN memory-based models show significant superiority over other state-of-the-art machine learning models for short, medium and long range predictions. The transformer-based model with attention enhances the selection of historical data for multi-horizon forecasting, and also allows to interpret the significance of internal power plant components on the power generation. This newly gained insights can be used by operation engineers to anticipate and monitor the health of power plant equipment during high cycling periods.
The task assignment 3 of the design and validation of digital flight control systems suitable for fly-by-wire applications is studied. Task 3 is associated with formal verification of embedded systems. In particular, results are presented that provide a methodological approach to microprocessor verification. A hierarchical decomposition strategy for specifying microprocessors is also presented. A theory of generic interpreters is presented that can be used to model microprocessor behavior. The generic interpreter theory abstracts away the details of instruction functionality, leaving a general model of what an interpreter does.
We introduce an end-to-end computational framework that allows for hyperparameter optimization using the DeepHyper library, accelerated model training, and interpretable AI inference. The framework is based on state-of-the-art AI models including CGCNN, PhysNet, SchNet, MPNN, MPNN-transformer, and TorchMD-NET. We employ these AI models along with the benchmark QM9, hMOF, and MD17 datasets to showcase how the models can predict user-specified material properties within modern computing environments. We demonstrate transferable applications in the modeling of small molecules, inorganic crystals and nanoporous metal organic frameworks with a unified, standalone framework. We have deployed and tested this framework in the ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and in the Delta supercomputer at the National Center for Supercomputing Applications to provide researchers with modern tools to conduct accelerated AI-driven discovery in leadership-class computing environments. We release these digital assets as open source scientific software in GitLab, and ready-to-use Jupyter notebooks in Google Colab.