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The increasing load on NASA's deep Space Network, the new capabilities for deep space missions inherent in a next-generation radio telescope, and the potential of new telescope technology for reducing construction and operation costs suggest a natural marriage between radio astronomy and deep space telecommunications in developing advanced radio telescope concepts.
The use of deep learning methods in scientific computing represents a potential paradigm shift in engineering problem solving. One of the most prominent developments is Physics-Informed Neural Networks (PINNs), in which neural networks are trained to satisfy partial differential equations (PDEs). While this method shows promise, the standard version has been shown to struggle in accurately predicting the dynamic behavior of time-dependent problems. To address this challenge, methods have been proposed that decompose the time domain into multiple segments, employing a distinct neural network in each segment and directly incorporating continuity between them in the loss function of the minimization problem. In this work we introduce a method to exactly enforce continuity between successive time segments via a solution ansatz. This hard constrained sequential PINN (HCS-PINN) method is simple to implement and eliminates the need for any loss terms associated with temporal continuity. The method is tested for a number of benchmark problems involving both linear and non-linear PDEs. Examples include various first order time dependent problems in which traditional PINNs struggle, namely advection, Allen–Cahn, and Korteweg–de Vries equations. Furthermore, second and third order time-dependent problems are demonstrated via wave and Jerky dynamics examples, respectively. Notably, the Jerky dynamics problem is chaotic, making the problem especially sensitive to temporal accuracy. Finally, the numerical experiments conducted with the proposed method demonstrated superior convergence and accuracy over both traditional PINNs and the soft-constrained counterparts.
Quantum computing has the potential to significantly improve the early detection of Alzheimer's Disease and Related Dementias (ADRD). Quantum-enhanced machine learning can be used to perform an early screening of Alzheimer's disease using brain imaging data based on dataset of MRI scans from both healthy individuals and those diagnosed with Alzheimer's. This study aims to demonstrate the potential of quantum transfer learning to enhance the performance of the classical deep learning model for dementia detection. Using the MRI sagittal images available in the OASIS-2 (64 demented and 72 non-demented subjects between 60 and 96 years), we show how quantum techniques can transform a suboptimal classical model into a more effective solution for dementia detection, highlighting their potential impact on advancing healthcare technology. We begin with a simple classical deep learning model with a significantly smaller number of parameters, which gives suboptimal performance on the problem. Then, we apply different configurations of quantum transfer learning based on the pre-trained weak classifier (Figure 1). We fix the weak classifier's initial convolutional layers at their fixed pre-trained parameters and replace the last set of dense layers with a dressed quantum circuit (DQN), which we train to enhance performance. We performed 4-fold cross-validation for both the classical and the hybrid quantum models and trained them using Pennylane's `default.qubit' simulator and IonQ's Aria-1 simulator (noisy simulation). We showed that with significantly fewer parameters, the quantum transfer learning-based hybrid models showed significant performance enhancement over the base weak classical deep learning model for dementia detection. To classify between a demented and non-demented subject, the accuracy of quantum-based AI methods improved by 6 to 14% compared to classical methods. The sensitivity of the models improved by 4 to 17%. This shows that there are fewer chances of misclassifying demented patients. Figure 2 compares the performance of the hybrid quantum models and their base classical model, and Table 1 summarizes the results. We illustrated that with assistance from quantum machine learning, it is possible to enhance detection for dementia based on brain images. This shows the potential for practical utility of quantum computing in ADRD research.
The proposed Noctis Landing Site/Exploration Zone (LS/EZ) is shown in Figure 1. Our preliminary study suggests that the proposed site meets all key Science and Resources (incl. Civil Engineering) requirements. The site is of significant interest, as the EZ not only offers a large number and wide range of regions of interest (ROIs) for short-term exploration, it is also located strategically at the crossroads between Tharsis and Valles Marineris, which are key for long-term exploration. The proposed site contains Regions of Interest (ROIs) that meet the following Science requirements: -‐ Access to (1) deposits with a high preservation potential for evidence of past habitability and fossil biosignatures and (2) sites that are promising for present habitability. The site presents a wide variety of ROIs qith likely aqueous features and deposits, including sinous channels and valleys, slope gullies, lobate debris aprons, impact craters with lobate ejecta flows, and "bathtub ring" deposits. Neutron spectrometry also suggests hydrogen is present within the topmost 0.3 m or so of 4 to 10 wt% WEH (Water Equivalent Hydrogen). -‐ Noachian and/or Hesperian rocks in a stratigraphic context that have a high likelihood of containing trapped atmospheric gases. Collapsed canyon rim material with preserved stratigraphy is abundantly present and accessible. -‐ Exposures of at least two crustal units that have regional or global extents, that are suitable for radiometric dating, and that have relative ages that sample a significant range of martian geological time. Canyons floors in Ius Chasma, Tithonium Chasma, and plateau tops on Tharsis and in Sinai Planum offer access to distinct crustal units of regional extent. -‐ Access to outcrops with linked morphological and/or geochemical signatures indicative of aqueous or groundwater/ mineral interactions. Iron and sulfur-bearing deposits on canyon floors in Noctis Labyrinthus, and in Ius Chasma (IC) and Tithonium Chasma (TC) offer many such outcrop options. -‐ Identifiable stratigraphic contacts and cross-cutting relationships from which relative ages can be determined. In place and collapsed canyon walls in NL, TC, and IC offer such opportunities. -‐ Other types of ROIs include access points to surrounding plateau top areas for longer term regional exploration. A key attribute of the proposed Noctic Landing site is its strategic location to allow the shortest possible surface excusions to Tharsis and Valles Marineris (VM). VM is the feature and region on Mars that exposes the longest record of Mars' geology and evolution through time. Tharsis is the region of Mars that has experienced the longest and most extensive volcanic history, and might still be volcanically active. Some of the youngest lava flows on Mars have been identified on the western flanks of the Tharsis Bulge, i.e., within driving range of future longrange (500 - 1000 km) pressurized rover traverses. The proposed site also contains ROIs that offer the following Resources (incl. Civil Engineering) characteristics: -‐ Access to raw material that exhibits the potential to (1) be used as feedstock for water-generating in situ resource utilization (ISRU) processes and (2) yield significant quantities (greater than 100 MT) of water. The raw material is likely in the form of hydrated minerals, and possibly ice/regolith mix. The top of the raw material deposit is at the surface. -‐ Access to a region where infrastructure can be emplaced or constructed. This region is less than 5 km from the LS and contains flat, stable terrain. The region exhibits evidence for an abundant source of loose regolith. Several deep pits in the area combined with the availability of sand suggests that some natural terrain features can be adapted for construction purposes. -‐ Access to raw material that exhibits the potential to be used as metal feedstock for ISRU and construction purposes. Iron and sulfur-rich mineral surface deposits have been identified in CRISM data in many locations in this area. Noctis Landing is the lowest-altitude location on Mars that straddles both the Tharsis region (above average geothermal gradients) and Valles Marineris (minimal crustal thickness from surface (valley floor) to a subsurface liquid water table. Noctis Landing has the potential for being an ideal site for eventual deep drilling on Mars to access deep subsurface liquid water and potentially encountering extant life. Available data remains insufficient to fully qualify the Noctis Landing site. Additional remote sensing data (visible, Near and Mid-IR, and radar) and surface reconnaissance via a high-mobility robotic rover are recommended. In particular, it will be important to assess the trafficability of the site, and its potential for yielding water and metals as a resource. Access to plateau tops from the Noctis Landing site on the canyon floor should be demonstrated. Future exploration of the site would also be enhanced significantly by the availability of robotic (tele-operatable) surveying and sample-collecting drones. Testing of the use of such collaborative science and exploration technologies should be conducted at terrestrial sites such as the Haughton-Mars Project site on Devon Island, High Arctic, among others. Note: Noctis Landing is not an official Mars nomenclature name for this location. Because the area of the proposed LS/EZ had no name, and because it is close to Noctis Labyrinthus to the West while being distinct from it, the provisional name Noctis Landing is proposed. Noctis means night in Latin.
The potential secondary consequences of rapid community growth in deep mining localities and the ability of affected communities to absorb and manage such growth are discussed. Areas discussed include Sweetwater County, Wyoming, and Marion and Monongalia Counties, West Virginia.
A self-calibrating deep space tracking technique is described which can potentially produce two nanoradian angular spacecraft determinations. The technique uses very long base interferometric observations of a spacecraft and several radio sources. The currently employed single source technique is described as a parameter estimation procedure. Then, the number of parameters and observations leads to the proposed local reference frame technique. Station clock, Earth rotation, and tropospheric parameters are estimated along with spacecraft position from the multisource observation sequence. The contributions to spacecraft angular uncertainty from system noise, tropospheric fluctuations, and uncalibrated radio source structure are evaluated. Of these experimental errors, radio source structure dominates the determination of the spacecraft position in the radio reference frame. It is shown, however, that the sensitivity of relative spacecraft position accuracies to time-invariant radio source structure effects may be on the order of 2 nanoradians.
The results of an experiment to determine the feasibility of using asynchronous transfer mode (ATM) technology to support advanced spacecraft missions that require high-rate ground communications and, in particular, full-motion video are reported. Potential nodes in such a ground network include Deep Space Network (DSN) antenna stations, the Jet Propulsion Laboratory, and a set of national and international end users. The experiment simulated a lunar microrover, lunar lander, the DSN ground communications system, and distributed science users. The users were equipped with video-capable workstations. A key feature was an optical fiber link between two high-performance workstations equipped with ATM interfaces. Video was also transmitted through JPL's institutional network to a user 8 km from the experiment. Variations in video depending on the networks and computers were observed, the results are reported.
It is commonplace to begin talks on this topic by noting that oceanographic data are too scarce and sparse to provide complete initial and boundary conditions for large-scale ocean models. Even considering the availability of remotely-sensed data such as radar altimetry from the TOPEX and ERS-1 satellites, a glance at a map of available subsurface data should convince most observers that this is still the case. Data are still too sparse for comprehensive treatment of interannual to interdecadal climate change through the use of models, since the new data sets have not been around for very long. In view of the dearth of data, we must note that the overall picture is changing rapidly. Recently, there have been a number of large scale ocean analysis and prediction efforts, some of which now run on an operational or at least quasi-operational basis, most notably the model based analyses of the tropical oceans. These programs are modeled on numerical weather prediction. Aside from the success of the global tide models, assimilation of data in the tropics, in support of prediction and analysis of seasonal to interannual climate change, is probably the area of large scale ocean modeling and data assimilation in which the most progress has been made. Climate change is a problem which is particularly suited to advanced data assimilation methods. Linear models are useful, and the linear theory can be exploited. For the most part, the data are sufficiently sparse that implementation of advanced methods is worthwhile. As an example of a large scale data assimilation experiment with a recent extensive data set, we present results of a tropical ocean experiment in which the Kalman filter was used to assimilate three years of altimetric data from Geosat into a coarsely resolved linearized long wave shallow water model. Since nonlinear processes dominate the local dynamic signal outside the tropics, subsurface dynamical quantities cannot be reliably inferred from surface height anomalies. Because of its potential for large scale synoptic coverage of the deep ocean, acoustic travel time data should be a natural complement to satellite altimetry. Satellite data give us vertical integrals associated with thermodynamic and dynamic processes.
The FLUKA Monte Carlo transport code is a well-known simulation tool in High Energy Physics. FLUKA is a dynamic tool in the sense that it is being continually updated and improved by the authors. We review the progress achieved since the last CHEP Conference on the physics models, some technical improvements to the code and some recent applications. From the point of view of the physics, improvements have been made with the extension of PEANUT to higher energies for p, n, pi, pbar/nbar and for nbars down to the lowest energies, the addition of the online capability to evolve radioactive products and get subsequent dose rates, upgrading of the treatment of EM interactions with the elimination of the need to separately prepare preprocessed files. A new coherent photon scattering model, an updated treatment of the photo-electric effect, an improved pair production model, new photon cross sections from the LLNL Cullen database have been implemented. In the field of nucleus-- nucleus interactions the electromagnetic dissociation of heavy ions has been added along with the extension of the interaction models for some nuclide pairs to energies below 100 MeV/A using the BME approach, as well as the development of an improved QMD model for intermediate energies. Both DPMJET 2.53 and 3 remain available along with rQMD 2.4 for heavy ion interactions above 100 MeV/A. Technical improvements include the ability to use parentheses in setting up the combinatorial geometry, the introduction of pre-processor directives in the input stream. a new random number generator with full 64 bit randomness, new routines for mathematical special functions (adapted from SLATEC). Finally, work is progressing on the deployment of a user-friendly GUI input interface as well as a CAD-like geometry creation and visualization tool. On the application front, FLUKA has been used to extensively evaluate the potential space radiation effects on astronauts for future deep space missions, the activation dose for beam target areas, dose calculations for radiation therapy as well as being adapted for use in the simulation of events in the ALICE detector at the LHC.
NASA employs Design Reference Missions (DRMs) to define potential architectures for future human exploration missions to deep space, the Moon, and Mars. While DRMs to these destinations share some components, each mission has different needs. This paper focuses on the human and automation/robotic integration needs for these future missions, evaluating them with respect to NASA research gaps in the area of space human factors engineering. The outcomes of our assessment is a human and automation/robotic (HAR) task list for each of the four DRMs that we reviewed (i.e., Deep Space Sortie, Lunar Visit/Habitation, Deep Space Habitation, and Planetary), a list of common critical HAR factors that drive HAR design.
State of the art deployable structures are mainly being designed for medium to large size satellites. The lack of reliable deployable structural systems for low cost, small volume, rideshare-class spacecraft severely constrains the potential for using small satellite platforms for affordable deep space science and exploration precursor missions that could be realized with solar sails. There is thus a need for reliable, lightweight, high packaging efficiency deployable booms that can serve as the supporting structure for a wide range of small satellite systems including solar sails for propulsion. The National Air and Space Administration (NASA) is currently investing in the development of a new class of advanced deployable shell-based composite booms to support future deep space small satellite missions using solar sails. The concepts are being designed to: meet the unique requirements of small satellites, maximize ground testability, permit the use of low-cost manufacturing processes that will benefit scalability, be scalable for use as elements of hierarchical structures (e.g. trusses), allow long duration storage, have high deployment reliability, and have controlled deployment behavior and predictable deployed dynamics. This paper will present the various rollable boom concepts that are being developed for 5-20 m class size deployable structures that include solar sails with the so-called High Strain Composites (HSC) materials. The deployable composite booms to be presented are being developed to expand the portfolio of available rollable booms for small satellites and maximize their length for a given packaged volume. Given that solar sails are a great example of volume and mass optimization, the booms were designed to comply with nominal solar sail system requirements for 6U CubeSats, which are a good compromise between those of smaller form factors (1U, 2U and 3U CubeSats) and larger ones (12 U and 27 U future CubeSats, and ESPA-class microsatellites). Solar sail missions for such composite boom systems are already under consideration and development at NASA, as well as mission studies that will benefit from planned scaled-up versions of the composite boom technologies to be introduced. The paper presents ongoing research and development of thin-shell rollable composite booms designed under the particular stringent and challenging system requirements of relatively large solar sails housed on small satellites. These requirements will be derived and listed. Several new boom concepts are proposed and other existing ones are improved upon using thin-ply composite materials to yield unprecedented compact deployable structures. Some of these booms are shown in Fig. 1. For every boom to be introduced the scalable fabrication process developed to keep the overall boom system cost down will be shown. Finally, the initial results of purposely designed boom structural characterization test methods with gravity off-loading will be presented to compare their structural performance under expected and general load cases.
BioSentinel is one of 13 secondary payloads to be deployed on Exploration Mission 1 (EM-1) in 2019. We will use the budding yeast Saccharomyces cerevisiae as a biosensor to determine how deep-space radiation affects living organisms and to potentially quantify radiation levels through radiation damage analysis. Radiation can damage DNA through double strand breaks (DSBs), which can normally be repaired by homologous recombination. Two yeast strains will be air-dried and stored in microfluidic cards within the payload: a wild-type control strain and a radiation sensitive rad51 mutant that is deficient in DSB repairs. Throughout the mission, the microfluidic cards will be rehydrated with growth medium and an indicator dye. Growth rates of each strain will be measured through LED detection of the reduction of the indicator dye, which correlates with DNA repair and the amount of radiation damage accumulated. Results from BioSentinel will be compared to analog experiments on the ISS and on Earth. It is well known that desiccation can damage yeast cells and decrease viability over time. We performed a screen for desiccation-tolerant rad51 strains. We selected 20 re-isolates of rad51 and ran a weekly screen for desiccation-tolerant mutants for five weeks. Our data shows that viability decreases over time, confirming previous research findings. Isolates L2, L5 and L14 indicate desiccation tolerance and are candidates for whole-genome sequencing. More time is needed to determine whether a specific strain is truly desiccation tolerant. Furthermore, we conducted an intracellular trehalose assay to test how intracellular trehalose concentrations affect or protect the mutant strains against desiccation stress. S. cerevisiae cell and reagent concentrations from a previously established intracellular trehalose protocol did not yield significant absorbance measurements, so we tested varying cell and reagent concentrations and determined proper concentrations for successful protocol use.
BioSentinel is one of 13 secondary payloads to be deployed on Exploration Mission 1 (EM-1) in 2019. We will use the budding yeast Saccharomyces cerevisiae as a biosensor to determine how deep-space radiation affects living organisms and to potentially quantify radiation levels through radiation damage analysis. Radiation can damage DNA through double strand breaks (DSBs), which can normally be repaired by homologous recombination. Two yeast strains will be air-dried and stored in microfluidic cards within the payload: a wild-type control strain and a radiation sensitive rad51 mutant that is deficient in DSB repairs. Throughout the mission, the microfluidic cards will be rehydrated with growth medium and an indicator dye. Growth rates of each strain will be measured through LED detection of the reduction of the indicator dye, which correlates with DNA repair and the amount of radiation damage accumulated. Results from BioSentinel will be compared to analog experiments on the ISS and on Earth. It is well known that desiccation can damage yeast cells and decrease viability over time. We performed a screen for desiccation-tolerant rad51 strains. We selected 20 re-isolates of rad51 and ran a weekly screen for desiccation-tolerant mutants for five weeks. Our data shows that viability decreases over time, confirming previous research findings. Isolates L2, L5 and L14 indicate desiccation tolerance and are candidates for whole-genome sequencing. More time is needed to determine whether a specific strain is truly desiccation tolerant. Furthermore, we conducted an intracellular trehalose assay to test how intracellular trehalose concentrations affect or protect the mutant strains against desiccation stress. S. cerevisiae cell and reagent concentrations from a previously established intracellular trehalose protocol did not yield significant absorbance measurements, so we tested varying cell and reagent concentrations and determined proper concentrations for successful protocol use.
While the idea that downdraft vertical velocity is influenced by thermodynamic stability may seem obvious, there remains a gap in the scientific literature surrounding the interaction between downdrafts and stable layers. A number of questions remain unanswered. Can a stable layer stop the downward progression of a downdraft or merely slow its velocity? Related to this, can a stable layer stop the injection of low entropy air into the boundary layer? Do downdrafts modify the environment in a way that removes pre-existing stable layers (limiting the influence of the stable layer)? Is it possible for evaporative cooling to generate and maintain a stable layer that is sufficiently strong to stop the downward progression of downdrafts?The present study aims to answer these questions using a combination of high-altitude airborne radar and dropsonde observations, both collected within four tropical cyclones (Gaston, Hermine, Karl, and Matthew) during the 2016 NOAA SHOUT field campaign, as well as idealized simulations of tropical deep convection. This presentation will focus on the potential for a stable layer to halt the downward progression of a downdraft (and the associated low entropy air) with particular attention given to conditions and processes that might enhance or mitigate the influence of the stable layer.
The international scientific community has, through a Committee on Space Research (COSPAR) meeting series, participated in a seven-year review of knowledge gaps in planetary protection for crewed missions to Mars. This meeting series first identified a prioritized list of knowledge gaps that, if addressed, would permit development of an implementable planetary protection policy for the initial crewed Mars mission. Next, a consideration of the available venues (ground, LEO, deep space, planetary surfaces) yielded a roadmap of potential future investigations needed to close the knowledge gaps. Finally, the meeting series evaluated topic-specific measurements that are essential to establishing the figures of merit needed for hardware engineering design requirements. The three main topic threads considered within the meeting series were: microbial and human health monitoring; natural transport of terrestrial biological contamination on Mars, and; technologies and operations for contamination control (of spacecraft systems). Participants at the meeting series included engineers, scientists, managers and various subject matter experts from space agencies, academic and research institutions, commercial companies, and other government departments. This paper provides a detailed summary review of the findings of the meeting series, including descriptions of the basis for each of the figures of merit (where they were identified), and considerations of how planetary protection implementation and policy for crewed missions may progress from the current state of the art.
The LHC produces an intense beam of highly energetic neutrinos of all three flavors in the forward direction, and the Forward Physics Facility (FPF) has been proposed to house a suite of experiments taking advantage of this opportunity. In this study, we investigate the FPF’s potential to probe the neutrino electromagnetic properties, including neutrino millicharge, magnetic moment, and charge radius. We find that, due to the large flux of tau neutrinos at the LHC, the FPF detectors will be able to provide more sensitive constraints on the tau neutrino magnetic moment and millicharge than previous measurements at DONUT, by searching for excess in low recoil energy electron scattering events. We also find that, by precisely measuring the rate of neutral current deep inelastic scattering events, the FPF detectors have the potential to obtain the strongest experimental bounds on the neutrino charge radius for the electron neutrino, and one of the leading bounds for the muon neutrino flavor. The same signature could also be used to measure the weak mixing angle, and we estimate that sin 2 θ W could be measured to about 3% precision at a scale Q ∼ 10 GeV , shedding new light on the longstanding NuTeV anomaly. Published by the American Physical Society 2025
The project has evolved to that of using Green's functions to predict properties of deep defects in narrow gap materials. Deep defects are now defined as originating from short range potentials and are often located near the middle of the energy gap. They are important because they affect the lifetime of charge carriers and hence the switching time of transistors. We are now moving into the arena of predicting formation energies of deep defects. This will also allow us to make predictions about the relative concentrations of the defects that could be expected at a given temperature. The narrow gap materials mercury cadmium telluride (MCT), mercury zinc telluride (MZT), and mercury zinc selenide (MZS) are of interest to NASA because they have commercial value for infrared detecting materials, and because there is a good possibility that they can be grown better in a microgravity environment. The uniform growth of these crystals on earth is difficult because of convection (caused by solute depletion just ahead of the growing interface, and also due to thermal gradients). In general it is very difficult to grow crystals with both radial and axial homogeneity.
Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.