Results for “mobility database” — Page 3

NASA NTRS1989

Mobile satellite system fade statistics for shadowing and multipath from roadside trees at UHF and L-band

Field tests related to planned mobile satellite systems were performed, and results that add to the existing database of propagation measurements at L-band (1.5 GHz) are described. They are considered particularly useful in that propagation effects were studied systematically, with repeated and controlled runs pertaining to different path elevation angles, road types, and path geometries defining shadowing and line-of-sight modes. In addition, simultaneous L-band and UHF measurements were performed for the purpose of establishing scaling factors applicable to previous UHF (870 MHz) results. The control of the experimental parameters was made possible by using a helicopter as the source platform and a mobile van to house the receiver.

Goldhirsh, Julius↗

DOE OSTI2024

Baselining the Indirect Effect by Improving Quantification of Sea Spray and Marine Sources at Ascension Island (Final Report)

Oceans cover two-thirds of the Earth and understanding the interactions of aerosols with clouds in these large marine regions requires quantifying the man-made contributions to the budget of cloud-drop forming particles (known as cloud condensation nuclei, or CCN) relative to the non-manmade “baseline” conditions and understanding the meteorology of boundary layer clouds. Modeling studies have shown substantial uncertainties and sensitivities to natural marine CCN sources, meaning that to reduce uncertainties in indirect effects we must be able to better quantify the CCN budget in ocean regions. While models provide important constraints on these uncertainties, actually reducing uncertainties requires substantial observations in open-ocean and coastal regions in order to establish the baseline on which manmade emissions are added. The tropical South Atlantic Ocean is one of the least-sampled regions of the planet, making the comprehensive measurements of the Department of Energy Atmospheric Radiation Measurement Layered Atlantic Smoke Interactions with Clouds (LASIC) campaign provides the longest record of aerosol size distribution measurements from a differential mobility analyzer in a cloud-influenced marine location in the ARM database, with 17 months of ground-based aerosol size distribution measurements. This project addressed the research topic of Aerosol-Cloud Interactions (ACI) by supporting three publications from the LASIC measurements: 1. The ARM measurements were combined with a new value-added technique that was pioneered by the Russell group for quantifying sea salt. This quantification of sea salt uses supermicron scattering measurements for retrieving reasonable sea spray mass concentrations, providing the best-available, observationally-constrained estimate of the sea spray mode properties when supermicron size distribution measurements are not available. 2. The fitted modes of the distribution were used to investigate the signatures of cloud processing for very clean to very smoky aerosol conditions, revealing not only differences in the particles that activate in clouds but also in the mechanisms that control that droplet formation process. In clean air, the size required to form a cloud droplet is influenced by the number of particles, as well as how quickly particles take up water during growth in cloud. 3. Building on this aerosol characterization, cloud and meteorological conditions were used to evaluate aerosol-related changes in cloud albedo and optical depth. We introduced a new method of decomposing the impact of aerosols on clouds known as the Twomey effect by incorporating retrieved supersaturation from two independent sets of observations to constrain the feedback of aerosol particles on cloud properties. The method quantifies the reduction of the Twomey effect at high aerosol concentrations, which has never been explained quantitatively by observations. In addition, the results provide the first direct validation for the conditions observed in the tropical South Atlantic of a parcel-based approach that is embedded in many climate models. Together these findings illustrate how ARM extended field campaigns in stratocumulus-covered regions can be used to constrain ACI processes with direct observations, providing specific radiative effects without models. By making such process-specific constraints available to improve ACI in global climate models, ARM observations play a key role in supporting model development.

58 GEOSCIENCES↗

NASA NTRS2023

Spacesuit Fit and Mobility Assessments by Digital Human Modeling

Spacesuit Fit and Mobility Assessments by Digital Human Modeling K. Han Kim (Leidos, Inc.) Elizabeth A. Benson (KBR, Inc.) Sudhakar L. Rajulu (NASA Johnson Space Center) Spacesuits are required to accommodate safe operations for astronauts across gender and a wide variety of body shapes and sizes. This goal has been of particular importance given the increasing diversity of NASA crewmembers for upcoming Missions. While testing with design prototypes is a critical step for spacesuit development, iterative mockup design, fabrication, and human subject tests can be extremely costly and time consuming. Moreover, testing with a limited subject pool has often raised questions for validity, as test subjects need to represent the entire astronaut population, not only of the past or current, but also the future. This study is aimed at demonstrating how digital human modeling (DHM) tools have been built and directly supported NASA spacesuit developments. With DHM, the computer aided design (CAD) model of a spacesuit was integrated with human body models. Two use scenarios are presented in this paper, namely fit and mobility. For fit assessments, the suit-to-body contact and compression patterns were estimated using 3D human body scan models virtually wearing a spacesuit model. A statistical fit classifier was made from the contact patterns and applied against a large database of body scans (N=2,500). With this technique, the NASA reference design spacesuit Exploration EVA Mobility Unit (xEMU)was verified to accommodate 90% of the astronaut-like population, with the critical dimensions covering 1stto 99thpercentiles of the target body measurements. A similar technique assessed XEMU mobility. The maximum reach envelopes were considered, within which the work objects and critical hardware interfaces should be located for safe and ergonomic operations. While the reach envelope geometry varies significantly with the suit wearer’s body size and strength, the existing test data did not include the subjects critically required to define suit mobility requirements, such as very small females or large males. Using the xEMU virtual model kinematically simulated and permuted for a hypothetical wearer, however, the existing data were statistically transformed and scaled. This method enabled for a parametric estimation of the reach envelopes from the 1stpercentile female or 99thpercentile male. The outcome was successfully incorporated for requirement developments. With the new DHM tools, human integration of the spacesuit was structurally simulated and predictively assessed, which would have been otherwise impossible. Also, the needs for iterative mockup and subject tests were significantly reduced, which resulted in time and cost saving. Additional work is in progress to integrate additional vehicles, tools, and hardware with DHM framework.

K Han Kim↗

DOE OSTI2022

Classification of bacterial plasmid and chromosome derived sequences using machine learning

Plasmids are important genetic elements that facilitate horizonal gene transfer between bacteria and contribute to the spread of virulence and antimicrobial resistance. Most bacterial genome sequences in the public archives exist in draft form with many contigs, making it difficult to determine if a contig is of chromosomal or plasmid origin. Using a training set of contigs comprising 10,584 chromosomes and 10,654 plasmids from the PATRIC database, we evaluated several machine learning models including random forest, logistic regression, XGBoost, and a neural network for their ability to classify chromosomal and plasmid sequences using nucleotide k-mers as features. Based on the methods tested, a neural network model that used nucleotide 6-mers as features that was trained on randomly selected chromosomal and plasmid subsequences 5kb in length achieved the best performance, outperforming existing out-of-the-box methods, with an average accuracy of 89.38% ± 2.16% over a 10-fold cross validation. The model accuracy can be improved to 92.08% by using a voting strategy when classifying holdout sequences. In both plasmids and chromosomes, subsequences encoding functions involved in horizontal gene transfer—including hypothetical proteins, transporters, phage, mobile elements, and CRISPR elements—were most likely to be misclassified by the model. This study provides a straightforward approach for identifying plasmid-encoding sequences in short read assemblies without the need for sequence alignment-based tools.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2013

Engineering of Data Acquiring Mobile Software and Sustainable End-User Applications

The criteria for which data acquiring software and its supporting infrastructure should be designed should take the following two points into account: the reusability and organization of stored online and remote data and content, and an assessment on whether abandoning a platform optimized design in favor for a multi-platform solution significantly reduces the performance of an end-user application. Furthermore, in-house applications that control or process instrument acquired data for end-users should be designed with a communication and control interface such that the application's modules can be reused as plug-in modular components in greater software systems. The application of the above mentioned is applied using two loosely related projects: a mobile application, and a website containing live and simulated data. For the intelligent devices mobile application AIDM, the end-user interface have a platform and data type optimized design, while the database and back-end applications store this information in an organized manner and manage access to that data to only to authorized user end application(s). Finally, the content for the website was derived from a database such that the content can be included and uniform to all applications accessing the content. With these projects being ongoing, I have concluded from my research that the applicable methods presented are feasible for both projects, and that a multi-platform design for the mobile application only marginally drop the performance of the mobile application.

Smith, Benton T.↗

DOE OSTI2020

Global root traits (GRooT) database

Motivation: Trait data are fundamental to the quantitative description of plant form and function. Although root traits capture key dimensions related to plant responses to changing environmental conditions and effects on ecosystem processes, they have rarely been included in large-scale comparative studies and global models. For instance, root traits remain absent from nearly all studies that define the global spectrum of plant form and function. Thus, to overcome conceptual and methodological roadblocks preventing a widespread integration of root trait data into large-scale analyses we created the Global Root Trait (GRooT) Database. GRooT provides readyto- use data by combining the expertise of root ecologists with data mobilization and curation. Specifically, we (a) determined a set of core root traits relevant to the description of plant form and function based on an assessment by experts, (b) maximized species coverage through data standardization within and among traits, and (c) implemented data quality checks. Main types of variables contained: GRooT contains 114,222 trait records on 38 continuous root traits. Spatial location and grain: Global coverage with data from arid, continental, polar, temperate and tropical biomes. Data on root traits were derived from experimental studies and field studies. Time period and grain: Data were recorded between 1911 and 2019. Major taxa and level of measurement: GRooT includes root trait data for which taxonomic information is available. Trait records vary in their taxonomic resolution, with subspecies or varieties being the highest and genera the lowest taxonomic resolution available. It contains information for 184 subspecies or varieties, 6,214 species, 1,967 genera and 254 families. Owing to variation in data sources, trait records in the database include both individual observations and mean values. Software format: GRooT includes two csv files. A GitHub repository contains the csv files and a script in R to query the database.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2009

Anthropometric Requirements for Constellation

This slide presentation reviews the requirement from an Anthropometric standpoint for the development of the Constellation's programs hardware, specifically the Orion crew exploration vehicle. The NASA JSC Anthropometry and Biomechanics Facility (ABF) provides anthropometry, strength, mobility, and mass properties requirements; gathers, interprets, manages and maintains the flight crew anthropometry database; and participates and provides input during crew selection. This is used to assist in requirements for vehicle and space suit design and for crew selection.

Raulu, Sudhakar↗

DOE OSTI2023

Ion Mobility Spectrometry-Mass Spectrometry for High-Throughput Analysis

Ion mobility spectrometry is a widely used analytical technique providing gas phase separation of molecules. It has received increasing attention in the recent years with the advancement in technology development and the availability of commercial instruments. In this chapter, we introduced the ion mobility fundamental theory and provided examples of IMS applications, especially for isomer separation, collision cross section database generation, high throughput analysis workflows, software tools for IMS data analysis, and ongoing high resolution SLIM IMS development. While IMS is not yet routinely utilized in drug discovery and pharmaceutical industry, there has been increased interest in high throughput library screening and antibody characterization. With all the ongoing development in IMS technology and informatics, we foresee more and more exciting applications of high throughput IMS analysis in different fields including omics studies, drug discovery and clinical applications in the near future.

Ross, Dylan H.↗

NASA NTRS2018

Changes in Exercise Data Management

The suite of exercise hardware aboard the International Space Station (ISS) generates an immense amount of data. The data collected from the treadmill, cycle ergometer, and resistance strength training hardware are basic exercise parameters (time, heart rate, speed, load, etc.). The raw data are post processed in the laboratory and more detailed parameters are calculated from each exercise data file. Updates have recently been made to how this valuable data are stored, adding an additional level of data security, increasing data accessibility, and resulting in overall increased efficiency of medical report delivery. Questions regarding exercise performance or how exercise may influence other variables of crew health frequently arise within the crew health care community. Inquiries over the health of the exercise hardware often need quick analysis and response to ensure the exercise system is operable on a continuous basis. Consolidating all of the exercise system data in a single repository enables a quick response to both the medical and engineering communities. A SQL server database is currently in use, and provides a secure location for all of the exercise data starting at ISS Expedition 1 - current day. The database has been structured to update derived metrics automatically, making analysis and reporting available within minutes of dropping the inflight data it into the database. Commercial tools were evaluated to help aggregate and visualize data from the SQL database. The Tableau software provides manageable interface, which has improved the laboratory's output time of crew reports by 67%. Expansion of the SQL database to be inclusive of additional medical requirement metrics, addition of 'app-like' tools for mobile visualization, and collaborative use (e.g. operational support teams, research groups, and International Partners) of the data system is currently being explored.

Buxton, R. E.↗

NASA NTRS2018

Changes in Exercise Data Management

The suite of exercise hardware aboard the International Space Station (ISS) generates an immense amount of data. The data collected, treadmill, cycle ergometer, and resistance strength training hardware, are basic exercise parameters (time, heart rate, speed, load, etc.). The raw data are processed in the laboratory and more detailed parameters are calculated from each exercise data file. Updates recently have been made to how these valuable data are stored, adding an additional level of security, increasing accessibility, and resulting in overall increased efficiency of medical report delivery. Questions regarding exercise performance or how exercise may influence other variables of crew health frequently arise within the crew health care community. Inquiries regarding the health of the exercise hardware often need quick analysis and response to ensure the exercise system is operable on a continuous basis. Consolidating all of the exercise system data in a single repository enables a quick response to both the medical and engineering communities. A SQL server database is currently in use, and provides a secure location for all of the exercise data starting at ISS Expedition 1 to current date. The database has been structured to update derived metrics automatically, making analysis and reporting available within minutes of dropping the in-flight data into the database. Commercial tools were evaluated to help aggregate and visualize data from the SQL database. The Tableau software provides manageable interface, which has improved the laboratory’s output time of crew reports by 67%. Expansion of the SQL database, to be inclusive of additional medical requirement metrics, addition of ‘app-like’ tools for mobile visualization, and collaborative use (e.g., operational support teams, research groups, and International Partners) of the data system, is currently being explored.

Buxton, R. E.↗

NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗

NASA NTRS1993

Opening remarks: Current and future activities

It is our custom to present the Propagation Program's recent accomplishments and future plans at the onset of a NAPEX meeting. A summary is presented. The data analysis phase of the U.S. Olympus Campaign is nearing its end. The final report will be published by Virginia Tech by midsummer 1993. The report will comprise measurement analysis for 12 months. It will include monthly and annual attenuation statistics and statistics on scintillation effects. A number of prediction models will be presented. The ACTS propagation experiment preparations are moving forward as expected. The mobile/personal channel characterization efforts continued during the last year. Data collected by the University of Texas in 1992 are being analyzed and will become available by September 1993. We have recently started a study to characterize LEO mobile/personal channels. Topics such as indoor reception, tree shadowing, blockage, and delay spread will be investigated. These results will become available in one to two years from now. We have also collected Ka-band mobile data using Olympus 20 GHz beacon transmissions. The University of Texas has already collected five years of low-elevation angle, 11GHz propagation data. The work on database for propagation models has progressed very well. The first release is ready, and the participants of this meeting will receive a copy of the software. We had an active year where CCIR is concerned. An input document on land mobile satellite system (LMSS) propagation models was submitted to the U.S. national committee and was approved and sent to Geneva.

Davarian, Faramaz↗

DOE OSTI2023

Metatranscriptomics sheds light on the links between the functional traits of fungal guilds and ecological processes in forest soil ecosystems

Soil fungi belonging to different functional guilds, such as saprotrophs, pathogens, and mycorrhizal symbionts, play key roles in forest ecosystems. To date, no study has compared the actual gene expression of these guilds in different forest soils. We used metatranscriptomics to study the competition for organic resources by these fungal groups in boreal, temperate, and Mediterranean forest soils. Using a dedicated mRNA annotation pipeline combined with the JGI MycoCosm database, we compared the transcripts of these three fungal guilds, targeting enzymes involved in C- and N mobilization from plant and microbial cell walls. Genes encoding enzymes involved in the degradation of plant cell walls were expressed at a higher level in saprotrophic fungi than in ectomycorrhizal and pathogenic fungi. However, ectomycorrhizal and saprotrophic fungi showed similarly high expression levels of genes encoding enzymes involved in fungal cell wall degradation. Transcripts for N-related transporters were more highly expressed in ectomycorrhizal fungi than in other groups. Here, we showed that ectomycorrhizal and saprotrophic fungi compete for N in soil organic matter, suggesting that their interactions could decelerate C cycling. Metatranscriptomics provides a unique tool to test controversial ecological hypotheses and to better understand the underlying ecological processes involved in soil functioning and carbon stabilization.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1992

L- and K-band LMSS propagation measurements using MARECS-B, OLYMPUS, and ACTS

L-band measurements of land mobile satellite systems (LMSS) propagation effects were last made at the end of 1988, but some voids were left in the database, making modeling of low elevation roadside tree shadowing and multipath reflections difficult for some path geometries. Transmission of a pilot tone from MARECS-B at 55 deg West during Sep. and Dec. 1991 gave an opportunity to fill the gaps in the experimental results. Two campaigns during which fade data were obtained at elevation angles from 7 deg to 40 deg are described. Below 15 deg, specular terrain reflections in a non-shadowing, hilly environment were observed to introduce significant fading. Although the reflecting surface was at a distance of up to several km, it is shown that the reflected signals are delayed by less than 1 microsec. Mobile measurements were also attempted receiving the 20 GHz Olympus beacon, but antenna pointing problems restricted first results to straight-line driving.

Vogel, W. J.↗

NASA NTRSDate not supplied

Assessing High-Intensity Radiated Fields (HIRF) from High-Power Antennas for Air Vehicle Safety

Advanced Air Mobility (AAM), including Urban Air Mobility (UAM), vehicles may be required to meet stringent High-Intensity Radiated Fields (HIRF) certification requirements. HIRF can cause interference and even damage to vehicle systems. A recently proposed map-based avoidance approach can help reduce HIRF protection costs compared to the standard approach. However, it necessitates knowing the locations, frequencies, and transmit powers of high-power antennas within the operating area. This study aims to provide the necessary transmitter data by utilizing regulatory license databases. Using New York City as a representative urban area, fixed transmitter data are presented for AM, FM, TV, satellite uplink, land-mobile radio, microwave link, weather radars, and others. The maximum effective isotropic radiated powers of the antenna are reported. The associated electric field envelopes at 100 feet (30.48 m) distance are compared against the current rotorcraft HIRF standard. The results show that the current standards for protecting vehicles are inadequate. Maps of regions with high HIRF are illustrated. Data for several other cities are also being considered.

HIRF↗

NASA NTRS2024

Assessing High-Intensity Radiated Fields (HIRF) from High-Power Antennas for Air Vehicle Safety

Advanced Air Mobility (AAM), including Urban Air Mobility (UAM), vehicles may be required to meet stringent High-Intensity Radiated Fields (HIRF) certification requirements. HIRF can cause interference and even damage to vehicle systems. A recently proposed map-based avoidance approach can help reduce HIRF protection costs compared to the standard approach. However, it necessitates knowing the locations, frequencies, and transmit powers of high-power antennas within the operating area. This study aims to provide the necessary transmitter data by utilizing regulatory license databases. Using New York City as a representative urban area, fixed transmitter data are presented for AM, FM, TV, satellite uplink, land-mobile radio, microwave link, weather radars, and others. The maximum effective isotropic radiated powers of the antenna are reported. The associated electric field envelopes at 100 feet (30.48 m) distance are compared against the current rotorcraft HIRF standard. The results show that the current standards for protecting vehicles are inadequate. Maps of regions with high HIRF are illustrated. Data for several other cities are also being considered.

HIRF↗

NASA NTRSDate not supplied

HIRF Avoidance Approach for Advanced Air Mobility Vehicles

Advanced Air Mobility (AAM), including Urban Air Mobility (UAM), vehicles may be required to meet stringent High-Intensity Radiated Fields (HIRF) certification requirements. HIRF can cause interference and even damage to vehicle systems. A recently proposed map-based HIRF avoidance approach can help reduce HIRF protection costs compared to the standard approach. However, it necessitates knowing the locations, frequencies, and transmit powers of high-power antennas within the operating area. This study aims to provide the necessary transmitter data by utilizing regulatory license databases. Using New York City as a representative urban area, fixed transmitter data are presented for AM, FM, TV, satellite uplink, land-mobile radio, microwave link, weather radars, and others. The maximum antenna effective isotropic radiated powers are reported. The associated electric field envelopes at 100 feet (30.48 m) distance are compared against the current rotorcraft HIRF standard. The results show the current standards are far adequate in protecting the vehicles. Maps of regions with high HIRF are illustrated. Data for several other cities are also being considered.

HIRF↗