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Over the last two years several satellites in Low Earth Orbit (LEO) and geosynchronous orbit (GEO) have experienced serious or catastrophic failures including interruption of desired communications due especially to non linear interference.
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The Anvil Tool GUI allows users to select a Data Type, toggle the map refresh on/off, place labels, and choose the Profiler Type (source of the KSC 50 MHz profiler data), the Date- Time of the data, the Center of Plot, and the Station (location of the RAOB or 50 MHz profiler). If the Data Type is Models, the user selects a Fcst Hour (forecast hour) instead of Station. There are menus for User Profiles, Circle Label Options, and Frame Label Options. Labels can be placed near the center circle of the plot and/or at a specified distance and direction from the center of the circle (Center of Plot). The default selection for the map refresh is "ON". When the user creates a new Anvil Tool map with Refresh Map "ON, the plot is automatically displayed in the AWIPS frame. If another Anvil Tool map is already displayed and the user does not change the existing map number shown at the bottom of the GUI, the new Anvil Tool map will overwrite the old one. If the user turns the Refresh Map "OFF", the new Anvil Tool map is created but not automatically displayed. The user can still display the Anvil Tool map through the Maps dropdown menu* as shown in Figure 4.
Early Warning Inc. of Troy, New York, licensed powerful biosensor technology from Ames Research Center. Incorporating carbon nanotubes tipped with single strands of nucleic acid from waterborne pathogens, the sensor can detect even minute amounts of targeted, disease causing bacteria, viruses, and parasites. Early Warning features the NASA biosensor in its water analyzer, which can provide advance alert of potential biological hazards in water used for agriculture, food and beverages, showers, and at beaches and lakes -- within hours instead of the days required by conventional laboratory methods.
The Space Shuttle program spanned more than the entire length of a solar cycle. Investigations aimed towards understanding the health risks of the astronauts from exposures to space radiation involved mostly physical measurements of the dose and the linear energy transfer (LET) spectrum. Measurement of the dose rate on the Shuttle provided invariable new data for different periods of the solar cycle, whereas measurement of the LET spectrum using the tissue equivalent proportional counter (TEPC) produced the most complete mapping of the radiation environment of the low Earth orbits (LEO). Exposures to the Shuttle astronauts were measured by the personal dosimeter worn by the crewmembers. Analysis of over 300 personal dosimeter readings indicated a dependence on the mission duration, the altitude and inclination of the orbit, and the solar cycle, with the crewmembers on the launch and repair of the Hubble telescope receiving the highest doses due to the altitude of the mission. Secondary neutrons inside the Shuttle were determined by recoil protons or with Bonner spheres, and may contribute significantly to the risks of the crewmembers. In addition, the skin dose and the doses received at different organs were compared using a human phantom onboard a Shuttle mission. A number of radiobiology investigations wer e also performed. The biological doses were determined on six astronauts/cosmonauts on long-duration Shuttle/Mir missions and on two crewmembers on a Hubble repair mission by analyzing the damages in the chromosomes of the crewmembers? white blood cells. Several experiments were also conducted to address the question of possible synergistic effects of spaceflight, microgravity in particular, on the repair of radiation-induced DNA damages. The experimental design included exposure of cells before launch, during flight, or after landing. These physical and biological studies were invaluable in predicting the health risks for astronauts on ISS and future exploration missions. Educational Objectives: A group of high school students flew color negative films on tw o Shuttle missions to detect the radiation environment in orbit. This and other experiments onboard of the Shuttle were aimed at educating the general public of the space program.
Although significant progress has been made in recent years, estimating volcanic ash concentration for the full extent of the airspace affected by volcanic ash remains a challenge. No single satellite, airborne or ground observing system currently exists which can sufficiently inform dispersion models to provide the degree of accuracy required to use them with a high degree of confidence for routing aircraft in and near volcanic ash. Toward this end, the detection and characterization of volcanic ash in the atmosphere may be substantially improved by integrating a wider array of observing systems and advancements in trajectory and dispersion modeling to help solve this problem. The qualitative aspect of this effort has advanced significantly in the past decade due to the increase of highly complementary observational and model data currently available. Satellite observations, especially when coupled with trajectory and dispersion models can provide a very accurate picture of the 3-dimensional location of ash clouds. The accurate estimate of the mass loading at various locations throughout the entire plume, however improving, remains elusive. This paper examines the capabilities of various satellite observation systems and postulates that model-based volcanic ash concentration maps and forecasts might be significantly improved if the various extant satellite capabilities are used together with independent, accurate mass loading data from other observing systems available to calibrate (tune) ash concentration retrievals from the satellite systems.
This report provides results of a Micrometeoroid and Orbital Debris (MMOD) risk assessment of the Mars Sample Return Earth Entry Vehicle (MSR EEV). The assessment was performed using standard risk assessment methodology illustrated in Figure 1-1. Central to the process is the Bumper risk assessment code (Figure 1-2), which calculates the critical penetration risk based on geometry, shielding configurations and flight parameters. The assessment process begins by building a finite element model (FEM) of the spacecraft, which defines the size and shape of the spacecraft as well as the locations of the various shielding configurations. This model is built using the NX I-deas software package from Siemens PLM Software. The FEM is constructed using triangular and quadrilateral elements that define the outer shell of the spacecraft. Bumper-II uses the model file to determine the geometry of the spacecraft for the analysis. The next step of the process is to identify the ballistic limit characteristics for the various shield types. These ballistic limits define the critical size particle that will penetrate a shield at a given impact angle and impact velocity. When the finite element model is built, each individual element is assigned a property identifier (PID) to act as an index for its shielding properties. Using the ballistic limit equations (BLEs) built into the Bumper-II code, the shield characteristics are defined for each and every PID in the model. The final stage of the analysis is to determine the probability of no penetration (PNP) on the spacecraft. This is done using the micrometeoroid and orbital debris environment definitions that are built into the Bumper-II code. These engineering models take into account orbit inclination, altitude, attitude and analysis date in order to predict an impacting particle flux on the spacecraft. Using the geometry and shielding characteristics previously defined for the spacecraft and combining that information with the environment model calculations, the Bumper-II code calculates a probability of no penetration for the spacecraft.
Through the Evolvable Mars Campaign (EMC) study, the National Aeronautics and Space Administration (NASA) continues to evaluate potential approaches for sending humans beyond low Earth orbit (LEO). A key aspect of these missions is the strategy that is employed to maintain and repair the spacecraft systems, ensuring that they continue to function and support the crew. Long duration missions beyond LEO present unique and severe maintainability challenges due to a variety of factors, including: limited to no opportunities for resupply, the distance from Earth, mass and volume constraints of spacecraft, high sensitivity of transportation element designs to variation in mass, the lack of abort opportunities to Earth, limited hardware heritage information, and the operation of human-rated systems in a radiation environment with little to no experience. The current approach to maintainability, as implemented on ISS, which includes a large number of spares pre-positioned on ISS, a larger supply sitting on Earth waiting to be flown to ISS, and an on demand delivery of logistics from Earth, is not feasible for future deep space human missions. For missions beyond LEO, significant modifications to the maintainability approach will be required.Through the EMC evaluations, several key findings related to the reliability and safety of the Mars spacecraft have been made. The nature of random and induced failures presents significant issues for deep space missions. Because spare parts cannot be flown as needed for Mars missions, all required spares must be flown with the mission or pre-positioned. These spares must cover all anticipated failure modes and provide a level of overall reliability and safety that is satisfactory for human missions. This will require a large amount of mass and volume be dedicated to storage and transport of spares for the mission. Further, there is, and will continue to be, a significant amount of uncertainty regarding failure rates for spacecraft components. This uncertainty makes it much more difficult to anticipate failures and will potentially require an even larger amount of spares to provide an acceptable level of safety. Ultimately, the approach to maintenance and repair applied to ISS, focusing on the supply of spare parts, may not be tenable for deep space missions. Other approaches, such as commonality of components, simplification of systems, and in-situ manufacturing will be required.
We present destructive single-event effects characterization of a variety of silicon Schottky diodes from different manufacturers. We also discuss suitable derating guidelines.
The spectre of cyber attacks on aerospace systems can no longer be ignored given that many of the components and vulnerabilities that have been successfully exploited by the adversary on other infrastructures are the same as those deployed and used within the aerospace environment. An important consideration with respect to the mission/safety critical infrastructure supporting space operations is that an appropriate defensive response to an attack invariably involves the need for high precision and accuracy, because an incorrect response can trigger unacceptable losses involving lives and/or significant financial damage. A highly precise defensive response, considering the typical complexity of aerospace environments, requires a detailed and well-founded understanding of the underlying system where the goal of the defensive response is to preserve critical mission objectives in the presence of adversarial activity. In this paper, a structured approach for modeling aerospace systems is described. The approach includes physical elements, network topology, software applications, system functions, and usage scenarios. We leverage Model-Based Systems Engineering methodology by utilizing the Object Management Group's Systems Modeling Language to represent the system being analyzed and also utilize model transformations to change relevant aspects of the model into specialized analyses. A novel visualization approach is utilized to visualize the entire model as a three-dimensional graph, allowing easier interaction with subject matter experts. The model provides a unifying structure for analyzing the impact of a particular attack or a particular type of attack. Two different example analysis types are demonstrated in this paper: a graph-based propagation analysis based on edge labels, and a graph-based propagation analysis based on node labels.
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