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NextSTEP Q: CIS Capability Studies III – Lunar User Terminals & Network Orchestration and Management System
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By NASA
NASA has selected multiple companies to expand the agency’s Near Space Network’s commercial direct-to-Earth capabilities services, which is a mission-critical communication capability that allows spacecraft to transmit data directly to ground stations on Earth.
The work will be awarded under new Near Space Network services contracts that are firm-fixed-price, indefinite-delivery/indefinite-quantity contracts. Project timelines span from February 2025 to September 2029, with an additional five-year option period that could extend a contract through Sept. 30, 2034. The cumulative maximum value of all Near Space Network Services contracts is $4.82 billion.
Some companies received multiple task orders for subcategories identified in their contracts. Awards are as follows:
Intuitive Machines of Houston will receive two task order awards on its contract for Subcategory 1.2 GEO to Cislunar Direct to Earth (DTE) Services and Subcategory 1.3 xCislunar DTE Services to support NASA’s Lunar Exploration Ground Segment, providing additional capacity to alleviate demand on the Deep Space Network and to meet the mission requirements for unique, highly elliptical orbits. The company also previously received a task order award for Subcategory 2.2 GEO to Cislunar Relay Services. Kongsberg Satellite Services of Tromsø, Norway, will receive two task order awards on its contract for Subcategory 1.1 Earth Proximity DTE and Subcategory 1.2 to support science missions in low Earth orbit and NASA’s Lunar Exploration Ground Segment, providing additional capacity to alleviate demand on the Deep Space Network. SSC Space U.S. Inc. of Horsham, Pennsylvania, will receive two task order awards on its contract for Subcategories 1.1 and 1.3 to support science missions in low Earth orbit and to meet the mission requirements for unique, highly elliptical orbits. Viasat, Inc. of Duluth, Georgia, will be awarded a task order on its contract for Subcategory 1.1 to support science missions in low Earth orbit. The Near Space Network’s direct-to-Earth capability supports many of NASA’s missions ranging from climate studies on Earth to research on celestial objects. It also will play a role in NASA’s Artemis campaign, which calls for long-term exploration of the Moon.
NASA’s goal is to provide users with communication and navigation services that are secure, reliable, and affordable, so that all NASA users receive the services required by their mission within their latency, accuracy, and availability requirements.
These awards demonstrate NASA’s ongoing commitment to fostering strong partnerships with the commercial space sector, which plays an essential role in delivering the communications infrastructure critical to the agency’s science and exploration missions.
As part of the agency’s SCaN (Space Communications and Navigation) Program, teams at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, will carry out the work of the Near Space Network. The Near Space Network provides missions out to 1.2 million miles (2 million kilometers) with communications and navigation services, enabling spacecraft to exchange critical data with mission operators on Earth. Using space relays in geosynchronous orbit and a global system of government and commercial direct-to-Earth antennas on Earth, the network brings down terabytes of data each day.
Learn more about NASA’s Near Space Network:
https://www.nasa.gov/near-space-network
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Joshua Finch
Headquarters, Washington
202-358-1100
joshua.a.finch@nasa.gov
Jeremy Eggers
Goddard Space Flight Center, Greenbelt, Maryland
757-824-2958
jeremy.l.eggers@nasa.gov
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By NASA
4 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
A crane lowers the steel reflector framework for Deep Space Station 23 into position Dec. 18 on a 65-foot-high (20-meter) platform above the antenna’s pedestal that will steer the reflector. Panels will be affixed to the structure create a curved surface to collect radio frequency signals.NASA/JPL-Caltech After the steel framework of the Deep Space Station 23 reflector dish was lowered into place on Dec. 18, a crew installed the quadripod, a four-legged support structure that will direct radio frequency signals from deep space that bounce off the main reflector into the antenna’s receiver.NASA/JPL-Caltech Deep Space Station 23’s 133-ton reflector dish was recently installed, marking a key step in strengthening NASA’s Deep Space Network.
NASA’s Deep Space Network, an array of giant radio antennas, allows agency missions to track, send commands to, and receive scientific data from spacecraft venturing to the Moon and beyond. NASA is adding a new antenna, bringing the total to 15, to support increased demand for the world’s largest and most sensitive radio frequency telecommunication system.
Installation of the latest antenna took place on Dec. 18, when teams at NASA’s Goldstone Deep Space Communications Complex near Barstow, California, installed the metal reflector framework for Deep Space Station 23, a multifrequency beam-waveguide antenna. When operational in 2026, Deep Space Station 23 will receive transmissions from missions such as Perseverance, Psyche, Europa Clipper, Voyager 1, and a growing fleet of future human and robotic spacecraft in deep space.
“This addition to the Deep Space Network represents a crucial communication upgrade for the agency,” said Kevin Coggins, deputy associate administrator of NASA’s SCaN (Space Communications and Navigation) program. “The communications infrastructure has been in continuous operation since its creation in 1963, and with this upgrade we are ensuring NASA is ready to support the growing number of missions exploring the Moon, Mars, and beyond.”
This time-lapse video shows the entire day of construction activities for the Deep Space Station 23 antenna at the NASA Deep Space Network’s Goldstone Space Communications Complex near Barstow, California, on Dec. 18. NASA/JPL-Caltech Construction of the new antenna has been under way for more than four years, and during the installation, teams used a crawler crane to lower the 133-ton metal skeleton of the 112-foot-wide (34-meter-wide) parabolic reflector before it was bolted to a 65-foot-high (20-meter-high) alidade, a platform above the antenna’s pedestal that will steer the reflector during operations.
“One of the biggest challenges facing us during the lift was to ensure that 40 bolt-holes were perfectly aligned between the structure and alidade,” said Germaine Aziz, systems engineer, Deep Space Network Aperture Enhancement Program of NASA’s Jet Propulsion Laboratory in Southern California. “This required a meticulous emphasis on alignment prior to the lift to guarantee everything went smoothly on the day.”
Following the main lift, engineers carried out a lighter lift to place a quadripod, a four-legged support structure weighing 16 1/2 tons, onto the center of the upward-facing reflector. The quadripod features a curved subreflector that will direct radio frequency signals from deep space that bounce off the main reflector into the antenna’s pedestal, where the antenna’s receivers are housed.
In the early morning of Dec. 18, a crane looms over the 112-foot-wide (34-meter-wide) steel framework for Deep Space Station 23 reflector dish, which will soon be lowered into position on the antenna’s base structure.NASA/JPL-Caltech Engineers will now work to fit panels onto the steel skeleton to create a curved surface to reflect radio frequency signals. Once complete, Deep Space Station 23 will be the fifth of six new beam-waveguide antennas to join the network, following Deep Space Station 53, which was added at the Deep Space Network’s Madrid complex in 2022.
“With the Deep Space Network, we are able to explore the Martian landscape with our rovers, see the James Webb Space Telescope’s stunning cosmic observations, and so much more,” said Laurie Leshin, director of JPL. “The network enables over 40 deep space missions, including the farthest human-made objects in the universe, Voyager 1 and 2. With upgrades like these, the network will continue to support humanity’s exploration of our solar system and beyond, enabling groundbreaking science and discovery far into the future.”
NASA’s Deep Space Network is managed by JPL, with the oversight of NASA’s SCaN Program. More than 100 NASA and non-NASA missions rely on the Deep Space Network and Near Space Network, including supporting astronauts aboard the International Space Station and future Artemis missions, monitoring Earth’s weather and the effects of climate change, supporting lunar exploration, and uncovering the solar system and beyond.
For more information about the Deep Space Network, visit:
https://www.nasa.gov/communicating-with-missions/dsn
News Media Contact
Ian J. O’Neill
Jet Propulsion Laboratory, Pasadena, Calif.
818-354-2649
ian.j.oneill@jpl.nasa.gov
2024-179
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Last Updated Dec 20, 2024 Related Terms
Deep Space Network Jet Propulsion Laboratory Space Communications & Navigation Program Space Operations Mission Directorate Explore More
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By NASA
3 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
Regolith Adherence Characterization, or RAC, is one of 10 science and technology instruments flying on NASA’s next Commercial Lunar Payload Services (CLPS) flight as part of the Blue Ghost Misison-1. Developed by Aegis Aerospace of Webster, Texas, RAC is designed to study how lunar dust reacts to more than a dozen different types of material samples, located on the payload’s wheels. Photo courtesy Firefly Aerospace The Moon may look like barren rock, but it’s actually covered in a layer of gravel, pebbles, and dust collectively known as “lunar regolith.” During the Apollo Moon missions, astronauts learned firsthand that the fine, powdery dust – electromagnetically charged due to constant bombardment by solar and cosmic particles – is extremely abrasive and clings to everything: gloves, boots, vehicles, and mechanical equipment. What challenges does that dust pose to future Artemis-era missions to establish long-term outposts on the lunar surface?
That’s the task of an innovative science instrument called RAC-1 (Regolith Adherence Characterization), one of 10 NASA payloads flying aboard the next delivery for the agency’s CLPS (Commercial Lunar Payload Services) initiative and set to be carried to the surface by Firefly Aerospace’s Blue Ghost 1 lunar lander.
Developed by Aegis Aerospace of Webster, Texas, RAC will expose 15 sample materials – fabrics, paint coatings, optical systems, sensors, solar cells, and more – to the lunar environment to determine how tenaciously the lunar dust sticks to each one. The instrument will measure accumulation rates during landing and subsequent routine lander operations, aiding identification of those materials which best repel or shed dust. The data will help NASA and its industry partners more effectively test, upgrade, and protect spacecraft, spacesuits, habitats, and equipment in preparation for continued exploration of the Moon under the Artemis campaign.
“Lunar regolith is a sticky challenge for long-duration expeditions to the surface,” said Dennis Harris, who manages the RAC payload for NASA’s CLPS initiative at the agency’s Marshall Space Flight Center in Huntsville, Alabama. “Dust gets into gears, sticks to spacesuits, and can block optical properties. RAC will help determine the best materials and fabrics with which to build, delivering more robust, durable hardware, products, and equipment.”
Under the CLPS model, NASA is investing in commercial delivery services to the Moon to enable industry growth and support long-term lunar exploration. As a primary customer for CLPS deliveries, NASA aims to be one of many customers on future flights. NASA’s Marshall Space Flight Center in Huntsville, Alabama, manages the development of seven of the 10 CLPS payloads carried on Firefly’s Blue Ghost lunar lander.
Learn more about. CLPS and Artemis at:
https://www.nasa.gov/clps
Alise Fisher
Headquarters, Washington
202-358-2546
Alise.m.fisher@nasa.gov
Headquarters, Washington
202-358-2546
Alise.m.fisher@nasa.gov
Corinne Beckinger
Marshall Space Flight Center, Huntsville, Ala.
256-544-0034
corinne.m.beckinger@nasa.gov
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Last Updated Dec 20, 2024 EditorBeth RidgewayContactCorinne M. Beckingercorinne.m.beckinger@nasa.govLocationMarshall Space Flight Center Related Terms
Commercial Lunar Payload Services (CLPS) Artemis Marshall Space Flight Center Explore More
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By NASA
Download PDF: Statistical Analysis Using Random Forest Algorithm Provides Key Insights into Parachute Energy Modulator System
Energy modulators (EM), also known as energy absorbers, are safety-critical components that are used to control shocks and impulses in a load path. EMs are textile devices typically manufactured out of nylon, Kevlar® and other materials, and control loads by breaking rows of stitches that bind a strong base webbing together as shown in Figure 1. A familiar EM application is a fall-protection harness used by workers to prevent injury from shock loads when the harness arrests a fall. EMs are also widely used in parachute systems to control shock loads experienced during the various stages of parachute system deployment.
Random forest is an innovative algorithm for data classification used in statistics and machine learning. It is an easy to use and highly flexible ensemble learning method. The random forest algorithm is capable of modeling both categorical and continuous data and can handle large datasets, making it applicable in many situations. It also makes it easy to evaluate the relative importance of variables and maintains accuracy even when a dataset has missing values.
Random forests model the relationship between a response variable and a set of predictor or independent variables by creating a collection of decision trees. Each decision tree is built from a random sample of the data. The individual trees are then combined through methods such as averaging or voting to determine the final prediction (Figure 2). A decision tree is a non-parametric supervised learning algorithm that partitions the data using a series of branching binary decisions. Decision trees inherently identify key features of the data and provide a ranking of the contribution of each feature based on when it becomes relevant. This capability can be used to determine the relative importance of the input variables (Figure 3). Decision trees are useful for exploring relationships but can have poor accuracy unless they are combined into random forests or other tree-based models.
The performance of a random forest can be evaluated using out-of-bag error and cross-validation techniques. Random forests often use random sampling with replacement from the original dataset to create each decision tree. This is also known as bootstrap sampling and forms a bootstrap forest. The data included in the bootstrap sample are referred to as in-the-bag, while the data not selected are out-of-bag. Since the out-of-bag data were not used to generate the decision tree, they can be used as an internal measure of the accuracy of the model. Cross-validation can be used to assess how well the results of a random forest model will generalize to an independent dataset. In this approach, the data are split into a training dataset used to generate the decision trees and build the model and a validation dataset used to evaluate the model’s performance. Evaluating the model on the independent validation dataset provides an estimate of how accurately the model will perform in practice and helps avoid problems such as overfitting or sampling bias. A good model performs well on
both the training data and the validation data.
The complex nature of the EM system made it difficult for the team to identify how various parameters influenced EM behavior. A bootstrap forest analysis was applied to the test dataset and was able to identify five key variables associated with higher probability of damage and/or anomalous behavior. The identified key variables provided a basis for further testing and redesign of the EM system. These results also provided essential insight to the investigation and aided in development of flight rationale for future use cases.
For information, contact Dr. Sara R. Wilson. sara.r.wilson@nasa.gov
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By Space Force
The mission successfully achieved a complex effort across multiple Space Force organizations to pull an existing GPS III satellite from storage, accelerate integration and launch vehicle readiness, and rapidly process for launch.
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