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In this paper, we present a mathematical foundation, including a convergence analysis, for cascading architecture neural network.
This report documents the 2005 Revolutionary System Concept for Aeronautics (RSCA) study entitled "A Robust, Scalable Transportation System Concept". The objective of the study was to generate, at a high-level of abstraction, characteristics of a new concept for the National Airspace System, or the new NAS, under which transportation goals such as increased throughput, delay reduction, and improved robustness could be realized. Since such an objective can be overwhelmingly complex if pursued at the lowest levels of detail, instead a System-of-Systems (SoS) approach was adopted to model alternative air transportation architectures at a high level. The SoS approach allows the consideration of not only the technical aspects of the NAS", but also incorporates policy, socio-economic, and alternative transportation system considerations into one architecture. While the representations of the individual systems are basic, the higher level approach allows for ways to optimize the SoS at the network level, determining the best topology (i.e. configuration of nodes and links). The final product (concept) is a set of rules of behavior and network structure that not only satisfies national transportation goals, but represents the high impact rules that accomplish those goals by getting the agents to "do the right thing" naturally. The novel combination of Agent Based Modeling and Network Theory provides the core analysis methodology in the System-of-Systems approach. Our method of approach is non-deterministic which means, fundamentally, it asks and answers different questions than deterministic models. The nondeterministic method is necessary primarily due to our marriage of human systems with technological ones in a partially unknown set of future worlds. Our goal is to understand and simulate how the SoS, human and technological components combined, evolve.
Considered both a stepping-stone to deep space and a key to unlocking the mysteries of planetary formation, the Moon offers a unique opportunity for scientific study. Robotic precursor missions are being developed to improve technology and enable new approaches to exploration. Robots, lunar landers, and satellites play significant roles in advancing science and technologies, offering close range and in-situ observations. Science and exploration data gathered from these nodes and a lunar science satellite is intended to support future human expeditions and facilitate future utilization of lunar resources. To attain a global view of lunar science, the nodes will be distributed over the lunar surface, including locations on the far side of the Moon. Given that nodes on the lunar far side do not have direct line-of-sight for Earth communications, the planned presence of such nodes creates the need for a lunar communications relay satellite. Since the communications relay capability would only be required for a small portion of the satellite s orbit, it may be possible to include communication relay components on a science spacecraft. Furthermore, an integrated satellite has the potential to reduce lunar surface mission costs. A SCience Hybrid Orbiter and Lunar Relay (SCHOLR) is proposed to accomplish scientific goals while also supporting the communications needs of landers on the far side of the Moon. User needs and design drivers for the system were derived from the anticipated needs of future robotic and lander missions. Based on these drivers and user requirements, accommodations for communications payload aboard a science spacecraft were developed. A team of interns identified and compared possible SCHOLR architectures. The final SCHOLR architecture was analyzed in terms of orbiter lifetime, lunar surface coverage, size, mass, power, and communications data rates. This paper presents the driving requirements, operational concept, and architecture views for SCHOLR within a lunar surface nodal network. Orbital and bidirectional link analysis, between lunar nodes, orbiter, and Earth, as well as a conceptual design for the spacecraft are also presented
Sprites are a category of Transient Luminous Events (TLE's) that occur in the upper atmosphere above the tops of Mesoscale Convective Systems (MCSs). They are commonly associated with lightning strokes that produce large charge moment changes (CMCs). Synergistic use of satellite and radar-retrieved observations together with sounding data, forecasts, and lightning-detection-networks allowed the diagnosis and analysis of the meteorological conditions associated with sprites as well as large-CMC lightning over Oklahoma
Sprites are a category of Transient Luminous Events (TLEs) that occur in the upper atmosphere above the tops of Mesoscale Convective Systems (MCSs). They are commonly associated with lightning strokes that produce large charge moment changes (CMCs). Synergistic use of satellite and radar-retrieved observations together with sounding data, forecasts, and lightning-detection networks allowed the diagnosis and analysis of the meteorological conditions associated with sprites as well as large-CMC lightning over Oklahoma. One goal of the NASA-funded effort reported herein is the investigation of the potential for sprite interference with aerospace activities in the 20- 100km altitude range, including research balloons, space missions and other aviation transports.
Sprites are a category of Transient Luminous Events (TLEs) that occur in the upper atmosphere above the tops of Mesoscale Convective Systems (MCSs). They are commonly associated with lightning that produce large charge moment changes (CMCs). Synergistic use of satellite and radar‐retrieved observations together with sounding data, forecasts, and lightning‐detection networks allowed the diagnosis and analysis of the meteorological conditions associated with sprites as well as large‐CMC lightning over Oklahoma.
The purpose of the study is to analyze the dynamics of global gene expression changes in the lung tissue of rats exposed to lunar dust particles. Multiple pathways and transcription factors were identified using the Ingenuity Pathway Analysis tool, showing the potential networks of these signaling regulations involved in lunar dust‐induced prolonged proflammatory response and toxicity. The data presented in this study, for the first time, explores the molecular mechanisms of lunar dust induced toxicity. This work contributes not only to the risk assessment for future space exploration, but also to the understanding of the dust‐induced toxicity to humans on earth.
The proposed technology allows use of the latest microcircuit technology with lowest power and fastest speed, with minimal delay and engineering costs, through new Radiation Hardened by Design (RHBD) techniques that do not require extensive process characterization, technique evaluation and re-design at each Moore's Law generation. The separation of critical node groups is explicitly parameterized so it can be increased as microcircuit technologies shrink. The technology will be open access to radiation tolerant circuit vendors. INNOVATION: This technology would enhance computation intensive applications such as autonomy, robotics, advanced sensor and tracking processes, as well as low power applications such as wireless sensor networks. OUTCOME / RESULTS: 1) Simulation analysis indicates feasibility. 2)Compact voting latch 65 nanometer test chip designed and submitted for fabrication -7/2016. INFUSION FOR SPACE / EARTH: This technology may be used in any digital integrated circuit in which a high level of resistance to Single Event Upsets is desired, and has the greatest benefit outside low earth orbit where cosmic rays are numerous.
The Lunar Laser Ranging (LLR) experiment has accumulated 50 years of range data of improving accuracy from ground stations to the laser retroreflector arrays (LRAs) on the lunar surface. The upcoming decade offers several opportunities to break new ground in data precision through the deployment of the next generation of single corner-cube lunar retroreflectors and active laser transponders. This is likely to expand the LLR station network. Lunar dynamical models and analysis tools have the potential to improve and fully exploit the long temporal baseline and precision allowed by millimetric LLR data. Some of the model limitations are outlined for future efforts. Differential observation techniques will help mitigate some of the primary limiting factors and reach unprecedented accuracy. Such observations and techniques may enable the detection of several subtle signatures required to understand the dynamics of the Earth- Moon system and the deep lunar interior. LLR model improvements would impact multi- disciplinary fields that include lunar and planetary science, Earth science, fundamental physics, celestial mechanics and ephemerides.
This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. NASA’s current concept of the future of lunar communication, LunaNet, outlines a complex network of networks. Challenges such as scalability, interoperability and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. The application of machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study three main areas a means to further develop cognitive networking capabilities: networking and flight software development, analysis of wireless data for modeling and simulation, and development of algorithms for a multi-agent system.
Solar and Heliosphere physics are areas of remarkable data-driven discoveries. Recent advances in high cadence, high-resolution multiwavelength observations, growing amounts of data from realistic modeling, and operational needs for uninterrupted science-quality data coverage generate the demand for a solar metadata standardization and overall healthy data infrastructure. This white paper is prepared as an effort of the working group “Uniform Semantics and Syntax of Solar Observations and Events” created within the “Towards Integration of Heliophysics Data, Modeling, and Analysis Tools” EarthCube Research Coordination Network (@HDMIEC RCN), with primary objectives to discuss current advances and identify future needs for the solar research cyberinfrastructure. The white paper summarizes presentations and discussions held during the special working group session at the EarthCube Annual Meeting on June 19th, 2020, as well as community contribution gathered during a series of preceding workshops and subsequent RCN working group sessions. The authors provide examples of the current standing of the solar research cyberinfrastructure, and describe the problems related to current data handling approaches. The list of the top-level recommendations agreed by the authors of the current white paper is presented at the beginning of the paper.
The exponential suppression of macroscopic quantum tunneling (MQT) in the number of elements to be reconfigured is an essential element of broken symmetry phases. This suppression is also a core bottleneck in quantum algorithms, such as traversing an energy landscape in optimization, and adiabatic state preparation more generally. In this work, we demonstrate exponential acceleration of MQT through Floquet engineering with the application of a uniform, high frequency transverse drive field. Using the ferromagnetic phase of the transverse field Ising model in one and two dimensions as a prototypical example, we identify three phenomenological regimes as a function of drive strength. For weak drives, the system exhibits exponentially decaying tunneling rates but robust magnetic order; in the crossover regime at intermediate drive strength, we find polynomial decay of tunnelling alongside vanishing magnetic order; and at very strong drive strengths both the Rabi frequency and time-averaged magnetic order are approximately constant with increasing system size. We support these claims with extensive full wavefunction and tensor network numerical simulations, and theoretical analysis. An experimental test of these results presents a technologically important and novel scientific question accessible on NISQ-era quantum computers.
This study explores machine learning for decoding Modbus RTU data using K-Nearest Neighbors (KNN) models. An initial KNN model trained on 8,000 packets achieved 95.15% accuracy. Although ML improves generalization, accuracy still falls short of deterministic methods. These findings have implications for Modbus traffic analysis, intrusion detection in industrial networks, and adaptive error correction in real-time monitoring systems. By refining ML-based decoding, future work could enable more efficient anomaly detection and predictive maintenance in industrial automation and cybersecurity applications.
Abstract Plant lignocellulosic biomass, i.e. secondary cell walls of plants, is a vital alternative source for bioenergy. However, the acetylation of xylan in secondary cell walls impedes the conversion of biomass to biofuels. Previous studies have shown that REDUCED WALL ACETYLATION (RWA) proteins are directly involved in the acetylation of xylan but the regulatory mechanism of RWAs is not fully understood. In this study, we demonstrate that overexpression of a Populus trichocarpa PtRWA-C gene increases the level of xylan acetylation and increases the lignin content and S/G ratio, ultimately yielding poplar woody biomass with reduced saccharification efficiency. Furthermore, through gene coexpression network and expression quantitative trait loci (eQTL) analysis, we found that PtRWA-C was regulated not only by the secondary cell wall hierarchical regulatory network but also by an AP2 family transcription factor HARDY (HRD). Specifically, HRD activates PtRWA-C expression by directly binding to the PtRWA-C promoter, which is also the cis-eQTL for PtRWA-C. Taken together, our findings provide insights into the functional roles of PtRWA-C in xylan acetylation and consequently saccharification and shed light on synthetic biology approaches to manipulate this gene and alter cell wall properties. These findings have substantial implications for genetic engineering of woody species, which could be used as a sustainable source of biofuels, valuable biochemicals, and biomaterials.
The results of an extensive system analysis of the megachannel spectrum analyzer currently being developed for use in various applications of the Deep Space Network are presented. The intent of this analysis is to quantify the effects of digital quantization errors on system performance. The results of this analysis provide useful guidelines for choosing various system design parameters to enhance system performance.
Preliminary planning has been performed to support NASA/Marshall Space Flight Center (MSFC) in the coordination of the field experiment to be conducted in Central Tennessee, Northern Alabama, and Mississippi during the Spring/Summer of 1986. The goal of Satellite Precipitation and Cloud Experiments (SPACE) is to investigate mesoscale cloud/precipitation systems and development of associated satellite remote sensing technology. The field program will incorporate remote sensing observations from aircraft, satellite imagery, radar observations, ground based lightning measurements, rawinsonde observations, and various surface meteorological observations. The coordination of existing and special observation networks will provide a data base for analysis of precipitation events and provide ground truth comparisons for remote sensing capabilities. Existing surface-based observational networks include National Weather Service Meso/Alpha Scale Rawinsonde, radar, and surface measurements; the Tennessee Valley Authority automated and manual precpitation recording stations; and NASA/MSFC lightning measurement stations. Special observational features to be implemented include a meso/beta scale rawinsonde network, a special surface observational network within the rawinsonde network, and the installation of a RADAP II/ICRAD data processing unit on the National Weather Service radar at Nashville, TN. Initial coordination of these observational requirements to accomplish the goals of SPACE have been performed.
This presentation discusses an analysis approach to evaluate the interuser interference for Direct-Sequence Spread-Spectrum (DSSS) Systems for Space Network (SN) Users. Part I of this analysis shows that the correlation property of pseudo noise (PN) sequences is the critical factor which determines the interuser interference performance of the DSSS system. For non-standard DSSS systems in which PN sequence s period is much larger than one data symbol duration, it is the partial-period cross-correlation that determines the system performance. This study reveals through an example that a well-designed PN sequence set (e.g. Gold Sequence, in which the cross-correlation for a whole-period is well controlled) may have non-controlled partial-period cross-correlation which could cause severe interuser interference for a DSSS system. Since the analytical derivation of performance metric (bit error rate or signal-to-noise ratio) based on partial-period cross-correlation is prohibitive, the performance degradation due to partial-period cross-correlation will be evaluated using simulation in Part II of this analysis in the future.
Robotic vehicles for lunar and Mars exploration will carry an array of complex instruments requiring real-time data interpretation and fusion. The system described here uses hierarchical multiresolution analysis of visible and multispectral images to extract information on mineral composition, texture and object shape. This information is used to characterize the site geology and choose interesting samples for acquisition. Neural networks are employed for many data analysis steps. A decision tree progressively integrates information from multiple instruments and performs goal-driven decision making. The system is designed to incorporate more instruments and data types as they become available.