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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine Learning-Assisted Distribution System Network Reconfiguration Problem

High penetration from volatile renewable energy resources in the grid and the varying nature of loads raise the need for frequent line switching to ensure the efficient operation of electrical distribution networks. Operators must ensure maximum load delivery, reduced losses, and the operation between voltage limits. However, computations to decide the optimal feeder configuration are often computationally expensive and intractable, making it unfavorable for real-time operations. This is mainly due to the existence of binary variables in the network reconfiguration optimization problem. To tackle this issue, we have devised an approach that leverages machine learning techniques to reshape distribution networks featuring multiple substations. This involves predicting the substation responsible for serving each part of the network. Hence, it leaves simple and more tractable Optimal Power Flow problems to be solved. This method can produce accurate results in a significantly faster time, as demonstrated using the IEEE 37-bus distribution feeder. Compared to the traditional optimization-based approaches, a feasible solution is achieved approximately ten times faster for all the tested scenarios.

deep neural networks↗
NASA NTRS1989

Magellan Prelaunch Mission Operations Report

The Magellan spacecraft will be launched from Kennedy Space Center (KSC) within a 31-day overall launch period extending from April 28 to May 28, 1989. The launch will use the Shuttle Orbiter Atlantis to lift an Inertial Upper Stage (IUS) and the Magellan Spacecraft into low Earth orbit. After the Shuttle achieves its parking orbit, the IUS and attached Magellan spacecraft are deployed from the payload bay. After a short coast time, the two-stage IUS is fired to inject the Magellan spacecraft into an Earth-Venus transfer trajectory. The Magellan spacecraft is powered by single degree of freedom, sun-tracking, solar panels charging a set of nickel-cadmium batteries. The spacecraft is three-axis stabilized by reaction wheels using gyros and a star sensor for attitude reference. The spacecraft carries a solid rocket motor for Venus Orbit Insertion (VOI). A hydrazine propulsion system allows trajectory correction and prevents saturation of the reaction wheels. Communication with Earth through the Deep Space Network (DSN) is provided by S- and X-band telemetry channels, through alternatively a low, medium, or 3.7 m high-gain parabolic antenna rigidly attached to the spacecraft. The high-gain antenna also serves as the radar and radiometer antenna during orbit around Venus.

Source record↗
DOE OSTI2024

Efficient Mixed-Precision Matrix Factorization of the Inverse Overlap Matrix in Electronic Structure Calculations with AI-Hardware and GPUs

In recent years, a new kind of accelerated hardware has gained popularity in the artificial intelligence (AI) community which enables extremely high-performance tensor contractions in reduced precision for deep neural network calculations. In this article, we exploit Nvidia Tensor cores, a prototypical example of such AI-hardware, to develop a mixed precision approach for computing a dense matrix factorization of the inverse overlap matrix in electronic structure theory, S –1 . This factorization of S –1 , written as ZZT = S –1 , is used to transform the general matrix eigenvalue problem into a standard matrix eigenvalue problem. Here we present a mixed precision iterative refinement algorithm where Z is given recursively using matrix–matrix multiplications and can be computed with high performance on Tensor cores. To understand the performance and accuracy of Tensor cores, comparisons are made to GPU-only implementations in single and double precision. Additionally, we propose a nonparametric stopping criteria which is robust in the face of lower precision floating point operations. The algorithm is particularly useful when we have a good initial guess to Z, for example, from previous time steps in quantum-mechanical molecular dynamics simulations or from a previous iteration in a geometry optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Data-driven cyber-attack detection for photovoltaic systems: A transfer learning approach

With increasing exposure to software-based sensing and control, power systems are facing higher risks of cyber/physical attacks. Here, to ensure system stability and minimize the potential economic losses, it is imperative to monitor the operating states and detect those attacks at the early stage. In this paper, a transfer learning method is proposed to detect cyber-attacks in photovoltaic (PV) systems with much less training data. First of all, two PV systems with a different number of PV inverters and power ratings are analyzed and their attack models are studied. Next, an attack detection Convolutional Neural Network (CNN) model was trained with rich amount of data from PV #1. Then, transfer learning was proposed to transfer the well-trained features from PV #1 to PV #2. Lastly, the attack detection model on PV #2 was trained based on the transferred CNN model. The experiment results show that the proposed transfer learning method achieves better accuracy and a faster convergence rate with a much less training dataset than conventional deep learning.

14 SOLAR ENERGY↗
NASA NTRS1971

The Unified S-band Ground System.

This paper describes how the design of the Unified S-band Ground System evolved from the technology available in the NASA's Deep Space Network and Scientific Satellite Network as well as the Manned Space Flight Network to fulfill the requirements of the Apollo program. The project organization established for this program proved to be an effective management technique for controlling the widely dispersed activities and maintaining the necessary liaison with other organizations involved in the overall program. The Manned Space Flight Network includes systems at three locations employing 26 meter antennas to support the lunar phases of the mission and a number of systems at other locations employing 9 meter antennas for support of the near earth phases of the mission. The tradeoffs of mission requirements and technical capability established the equipment parameters and the network configuration. An extensive checkout program which included the use of aircraft and spacecraft provided a thorough evaluation of the system prior to its commitment to operational support of the missions.

Varson, W. P.↗
NASA NTRS1994

Project ECHO: Electronic Communications from Halo Orbit

The design of a communications relay to provide constant access between the Earth and the far side of the Moon is presented. Placement of the relay in a halo orbit about the L2 Earth-Moon Lagrange point allows the satellite to maintain constant simultaneous communication between Earth and scientific payloads on the far side of the Moon. The requirements of NASA's Discovery-class missions adopted and modified for this design are: total project cost should not exceed $150 million excluding launch costs, launch must be provided by Delta-class vehicle, and the satellite should maintain an operational lifetime of 10 to 15 years. The spacecraft will follow a transfer trajectory to the L2 point, after launch by a Delta II 7925 vehicle in 1999. Low-level thrust is used for injection into a stationkeeping-free halo orbit once the spacecraft reaches the L2 point. The shape of this halo orbit is highly elliptical with the maximum excursion from the L2 point being 35000 km. A spun section and despun section connected through a bearing and power transfer assembly (BAPTA) compose the structure of the spacecraft. Communications equipment is placed on the despun section to provide for a stationary dual parabolic offset-feed array antenna system. The dual system is necessary to provide communications coverage during portions of maximum excursion on the halo orbit. Transmissions to the NASA Deep Space Network 34 m antenna include six channels (color video, two voice, scientific data from lunar payloads, satellite housekeeping and telemetry and uplinked commands) using the S- and X-bands. Four radioisotope thermoelectric generators (RTG's) provide a total of 1360 W to power onboard systems and any two of the four Hughes 13 cm ion thrusters at once. Output of the ion thrusters is approximately 17.8 mN each with xenon as the propellant. Presence of torques generated by solar pressure on the antenna dish require the addition of a 'skirt' extending from the spun section of the satellite for balance. Total mass of the satellite is approximately 900 kg at a cost of $130 million FY99.

Borrelli, Jason↗
DOE OSTI2024

Generalization error guaranteed auto-encoder-based nonlinear model reduction for operator learning

Many physical processes in science and engineering are naturally represented by operators between infinite-dimensional function spaces. The problem of operator learning, in this context, seeks to extract these physical processes from empirical data, which is challenging due to the infinite or high dimensionality of data. An integral component in addressing this challenge is model reduction, which reduces both the data dimensionality and problem size. In this paper, we utilize low-dimensional nonlinear structures in model reduction by investigating Auto-Encoder-based Neural Network (AENet). AENet first learns the latent variables of the input data and then learns the transformation from these latent variables to corresponding output data. Our numerical experiments validate the ability of AENet to accurately learn the solution operator of nonlinear partial differential equations. Furthermore, we establish a mathematical and statistical estimation theory that analyzes the generalization error of AENet. Finally, our theoretical framework shows that the sample complexity of training AENet is intricately tied to the intrinsic dimension of the modeled process, while also demonstrating the robustness of AENet to noise.

Auto-encoder↗
DOE OSTI2020

Optimizing Signal Timing Control for Large Urban Traffic Networks Using an Adaptive Linear Quadratic Regulator Control Strategy

We discuss how traffic signal control is important for intersection safety and efficiency. However, most traffic signal control methods are designed for individual intersections or corridors. Although some adaptive control systems have been developed, the methods used are often proprietary and not published, making it difficult to evaluate their effectiveness. This study proposes an adaptive multi-input and multi-output traffic signal control method that not only can improve network-wide traffic operations in terms of reduced traffic delay and energy consumption, but also is more computationally feasible than existing centralized signal control methods. Considering intersection interactions, a linear dynamic traffic system model was built and adaptively updated to reflect how the signal control input of each intersection affects network-wide vehicle travel delay. Based on the system model, an adaptive linear-quadratic regulator (LQR) was designed to minimize both traffic delay and incremental changes in the control input. The proposed control method was evaluated in a microscopic traffic simulation environment with a 35-intersection network of Bellevue City, Washington. Simulation results show that the proposed method had shorter average traffic delays in the network when compared with the traffic delays controlled by the state-of-the-art max-pressure, self-organizing traffic lights, and independent deep Q network methods.

42 ENGINEERING↗
NASA NTRS2012

Detection Performance of Upgraded "Polished Panel" Optical Receiver Concept on the Deep-Space Network's 34 Meter Research Antenna

The development and demonstration of a "polished panel" optical receiver concept on the 34 meter research antenna of the Deep Space Network (DSN) has been the subject of recent papers. This concept would enable simultaneous reception of optical and microwave signals by retaining the original shape of the main reflector for microwave reception, but with the aluminum panels polished to high reflectivity to enable focusing of optical signal energy as well. A test setup has been installed on the DSN's 34 meter research antenna at Deep Space Station 13 (DSS-13) of NASA's Goldstone Communications Complex in California, and preliminary experimental results have been obtained. This paper describes the results of our latest efforts to improve the point-spread function (PSF) generated by a custom polished panel, in an attempt to reduce the dimensions of the PSF, thus enabling more precise tracking and improved detection performance. The design of the new mechanical support structure and its operation are described, and the results quantified in terms of improvements in collected signal energy and optical communications performance, based on data obtained while tracking the planet Jupiter with the 34 meter research antenna at DSS-13.

photon-bucket receiver↗
DOE OSTI2024

Data-driven models of nonautonomous systems

Nonautonomous dynamical systems are characterized by time-dependent inputs, which complicates the discovery of predictive models describing the spatiotemporal evolution of the state variables of quantities of interest from their temporal snapshots. When dynamic mode decomposition (DMD) is used to infer a linear model, this difficulty manifests itself in the need to approximate the time-dependent Koopman operators. Our approach is to approximate the original nonautonomous system with a modified system derived via a local parameterization of the time-dependent inputs. The modified system comprises a sequence of local parametric systems, which are subsequently approximated by a parametric surrogate model using the DRIPS (dimension reduction and interpolation in parameter space) framework. The offline step of DRIPS relies on DMD to build a linear surrogate model, endowed with reduced-order bases for the observables mapped from training data. The online step interpolates on suitable manifolds to construct a sequence of iterative parametric surrogate models; the target/test parameter points on these manifolds are specified by a local parameterization of the test time-dependent inputs. Here, we use numerical experimentation to demonstrate the robustness of our method and compare its performance with that of deep neural networks.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2016

Enabling Affordable Communications for the Burgeoning Deep Space Cubesat Fleet

The low costs of development and launch, coupled with new propulsive technologies, have made CubeSats increasingly popular for use in science investigations beyond geosynchronous orbit. As this deep space CubeSat fleet grows in size, the challenge of trying to provide affordable communications for it grows commensurately. The mass, power, and volume constraints inherent to CubeSats limit the antenna size and transmit power that they can use to close the deep space link. As a consequence, CubeSats need to rely more heavily on ground antennas that are characterized by large aperture, low noise temperatures, and relatively high-power transmitters. Such antennas are not in great abundance, nor are they inexpensive to build. For this reason, NASA’s Deep Space Network has been advocating a three-pronged approach to meeting anticipated CubeSat demand: development of simultaneous, shared-beam multi-spacecraft communications capabilities, development of large-antenna cross-support arrangements with other agencies and universities, and development of less uplink-intensive navigation techniques. This paper focuses on the pursuit of simultaneous, shared-beam multi-spacecraft communications capabilities. While the Multiple Spacecraft per Antenna (MSPA) technique has existed for over a decade, it has generally been limited to supporting downlink for just two in-beam spacecraft at a time. This limitation has largely been a function of the number and cost of available receivers. A relatively new technique that potentially overcomes this limitation is Opportunistic MSPA (OMSPA). Instead of relying on additional receivers, OMSPA makes use of a digital recorder at each ground station that is capable of capturing the intermediate frequency (IF) signals from every spacecraft in the antenna beam within the frequency bands of interest. When CubeSat projects see one or more opportunities for their CubeSat(s) to intercept the traditionally scheduled antenna beam of a “host” spacecraft, they can arrange for the CubeSat(s) to transmit open loop during those opportunities. Via a secure Internet site, the CubeSat mission operators can then retrieve the time- and frequency-relevant portions of the digital recording for subsequent demodulation and decoding, or subscribe to a service that does it for them. This “opportunistic” use of a host spacecraft’s ground antenna beam potentially enables CubeSat projects to make use of large ground antennas for downlink without having to compete with bigger, better-funded missions for antenna time in the formal scheduling process. In so doing, it also potentially enables CubeSat projects to avoid the aperture fees associated with formally scheduled downlink time - fees that factor into the “bottom-line” of competitively-bid NASA missions and that actually get charged to non-NASA missions. Taking advantage of these potential OMSPA benefits, however, will require CubeSat projects to pursue mission designs that ensure at least periodic in-beam operations relative to a “host” spacecraft. In the case of a constellation of CubeSats with inter-spacecraft distances that do not extend outside of the beam-width of the desired ground antenna at the given range, one CubeSat can serve as the “host” and have a formally scheduled downlink while the rest of the CubeSats can downlink essentially for “free” via OMSPA. Deep space CubeSats, of course, will need uplink in addition to downlink. Beyond commanding, this need is driven by the use of two-way ranging and Doppler for navigation. While OMSPA may not directly facilitate uplink, it does have the potential to free up antennas for those spacecraft that periodically require formally scheduled links for commanding and two-way radio metrics. NASA is also exploring the physical feasibility of an in-beam, simultaneous multi-spacecraft uplink technique. As with OMSPA, if successful, it will require little new equipment, further enabling affordable deep space CubeSat communications.

Abraham, Douglas S.↗
NASA NTRS2006

Interfacing with USSTRATCOM and UTTR during Stardust Earth Return

The Stardust Sample Return Capsule separated from the main spacecraft four hours prior to atmospheric entry. Between this time and the time at which the SRC touched down at the Utah Test and Training Range, two organizations external to JPL were involved in tracking the Sample Return Capsule. Orbit determination for the Stardust spacecraft during deep space cruise, the encounters of asteroid Annefrank and comet Wild 2, and the final approach to Earth used X-band radio metric Doppler and range data obtained through the Deep Space Network. The SRC lacked the electronics needed for coherently transponded radio metric tracking, so the DSN was not able to track the SRC after it separated from the main spacecraft. Although the expected delivery accuracy at atmospheric entry was well within the capability needed to target the SRC to the desired ground location, it was still desirable to obtain direct knowledge of the SRC trajectory in case of anomalies. For this reason U.S. Strategic Command was engaged to track the SRC between separation and atmospheric entry. Once the SRC entered the atmosphere, ground sensors at UTTR were tasked to acquire the descending SRC and maintain track during the descent in order to determine the landing location, to which the ground recovery team was then directed. This paper discusses organizational interfaces, data products, and delivery schedules, and the actual tracking operations are described.

Utah Test and Training Range (UTTR)↗
DOE OSTI2025

In situ Detection of Plasma Induced Surface Interaction based on Deep Learning based Visual Diagnostics (Technical Report)

It is characteristic for many plasma devices to undergo plasma-material interaction leading to surface erosion. These processes, often not easily detectable, lead to changes in device performance and lifespan. State-of-the-art lifetime tests and wear experiments require over 1000s hours. A self-consistent model for accurately predicting the erosion's effects is not available. In situ detection of these processes is not a trivial task since the surface variations at the early stages have a micron scale. Such limitations not only restrict testing and prediction capabilities but also slow the development of new thrusters and limit mission duration. To address these challenges, an in-situ diagnostic for real-time erosion assessment has been developed, aiming to expedite lifetime testing and broaden experimental campaigns. Several works were dedicated to real-time and in situ monitoring of material erosion during plasma exposure using laser holography, microscopy, and with telemicroscopes. However, the applicability of these approaches is limited due to complexity, cost and less flexibility as they often require placing diagnostic equipment inside the vacuum chamber. In collaboration with Princeton Collaborative Research Facility (PCRF), Princeton Plasma Physics Laboratory (PPPL), a new diagnostic approach is developed, where geometry modifications to the ceramic channel walls were introduced that would result in accelerated channel erosion. We employed Long-distance microscope (LDM) imagery, combined with Deep-Learning based Shape from focus or depth from focus (DFF or SFF) approach, that provides an accessible and cost-effective solution. LDM employs focus variation techniques to continuously capture multiple images of the target object at distinct focal planes. DFF, an optical focus variation method, generates a 3D topographical surface depth map from a sequence of variably focused images. Combined with the developed diagnostic, this approach offers a controllable means to study erosion under accelerated conditions. In this work, we develop Neural Network-based DFF algorithm applicable for LDM data to quantitatively evaluate plasma induced surface modification from LDM data. Next, we develop Deep Learning-based super-resolution depth map image reconstruction technique to increase the resolution of depth maps obtained from DFF algorithm to improve the accuracy of erosion measurements. Thirdly, we develop several image processing techniques to remove noise and improve the quality of depth map image. Here we report the results of initial tests for this approach. An experimental setup designed and built in PPPL was employed that consists of a 3-cm gridded ion source that produces a neutralized argon beam with energies up to 600 eV. A hexagonal boron nitride (h-BN) ceramic target, designed based on computational predictions, was used. Tests were conducted to reconstruct the complex geometry of the target under the lighting conditions of the operated ion source.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Robustness of Deep Learning Classification to Adversarial Input on GPUs: Asynchronous Parallel Accumulation Is a Source of Vulnerability

The ability of machine learning (ML) classification models to resist small, targeted input perturbations—known as adversarial attacks—is a key measure of their safety and reliability. We show that floating-point non associativity (FPNA) coupled with asynchronous parallel programming on GPUs is sufficient to result in misclassification, without any perturbation to the input. Additionally, we show that this misclassification is particularly significant for inputs close to the decision boundary and that standard adversarial robustness results may be overestimated up to 4.6 when not considering machine-level details. We first study a linear classifier, before focusing on standard Graph Neural Network (GNN) architectures and datasets used in robustness assessments. We develop a novel black-box attack using Bayesian optimization to discover external workloads that can change the instruction scheduling which bias the output of reductions on GPUs and reliably lead to misclassification. Motivated by these results, we present a new learnable permutation (LP) gradient-based approach to learning floating-point operation orderings that lead to misclassifications. The LP approach provides a worst-case estimate in a computationally efficient manner, avoiding the need to run identical experiments tens of thousands of times over a potentially large set of possible GPU states or architectures. Finally, using instrumentation-based testing, we investigate parallel reduction ordering across different GPU architectures under external background workloads, when utilizing multi-GPU virtualization, and when applying power capping. Our results demonstrate that parallel reduction ordering varies significantly across architectures under the first two conditions, substantially increasing the search space required to fully test the effects of this parallel scheduler-based vulnerability. These results and the methods developed here can help to include machine-level considerations into adversarial robustness assessments, which can make a difference in safety and mission critical applications.

Shanmugavelu, Sanjif [Maxeler Technologies, a Groq↗
DOE OSTI2024

Tokamak edge localized mode onset prediction with deep neural network and pedestal turbulence

Abstract A neural network, BES-ELMnet, predicting a quasi-periodic disruptive eruption of the plasma energy and particles known as edge localized mode (ELM) onset is developed with observed pedestal turbulence from the beam emission spectroscopy system in DIII-D. BES-ELMnet has convolutional and fully-connected layers, taking two-dimensional plasma fluctuations with a temporal window of size 128 µ s and generating a scalar output which can be interpreted as a probability of the upcoming ELM onset. As approximately labeled inter-ELM broadband ( 15 kHz ⩽ f ⩽ 150 kHz ) fluctuations are given to the network, BES-ELMnet learns by itself ELM-related precursors arising before the onsets through supervised learning scheme. BES-ELMnet achieves the gradually increasing ELM onset probabilities between two consecutive ELMs during the inter-ELM phases and can forecast the first ELM onsets which occur after the high confinement mode transition. We further investigate the network generality in terms of the selected frequency band to ensure the use of BES-ELMnet for various operation regimes without changing the trained architecture. Therefore, our novel prediction method will enhance a proactive high confinement mode control of fusion-grade plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Fault Injection for TensorFlow Applications

As machine learning (ML) has seen increasing adoption in safety-critical domains (e.g., autonomous vehicles), the reliability of ML systems has also grown in importance. While prior studies have proposed techniques to enable efficient error-resilience (e.g., selective instruction duplication), a fundamental requirement for realizing these techniques is a detailed understanding of the application’s resilience. In this work, we present TensorFI 1 and TensorFI 2, high-level fault injection (FI) frameworks for TensorFlow-based applications. TensorFI 1 and 2 are able to inject both hardware and software faults in any general TensorFlow 1 and 2 program respectively. Both are configurable FI tools that are flexible, easy to use, and portable. They can be integrated into existing TensorFlow programs to assess their resilience for different fault types (e.g., bit-flips in particular operations or layers). We use the TensorFI 1 and TensorFI 2 to evaluate the resilience of 12 and 10 ML programs written in TensorFlow, including DNNs used in the autonomous vehicle domain. The results give us insights into why some of the models are more resilient. We also measure the performance overheads of the two injectors, and present 4 case studies, two for each tool, to demonstrate their utility.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2008

Juno Mission Simulation

The Juno spacecraft is planned to launch in August of 2012 and would arrive at Jupiter four years later. The spacecraft would spend more than one year orbiting the planet and investigating the existence of an ice-rock core; determining the amount of global water and ammonia present in the atmosphere, studying convection and deep- wind profiles in the atmosphere; investigating the origin of the Jovian magnetic field, and exploring the polar magnetosphere. Juno mission management is responsible for mission and navigation design, mission operation planning, and ground-data-system development. In order to ensure successful mission management from initial checkout to final de-orbit, it is critical to share a common vision of the entire mission operation phases with the rest of the project teams. Two major challenges are 1) how to develop a shared vision that can be appreciated by all of the project teams of diverse disciplines and expertise, and 2) how to continuously evolve a shared vision as the project lifecycle progresses from formulation phase to operation phase. The Juno mission simulation team addresses these challenges by developing agile and progressive mission models, operation simulations, and real-time visualization products. This paper presents mission simulation visualization network (MSVN) technology that has enabled a comprehensive mission simulation suite (MSVN-Juno) for the Juno project.

space vehicles↗
NASA NTRS2010

Antenna Controller Replacement Software

The Antenna Controller Replacement (ACR) software accurately points and monitors the Deep Space Network (DSN) 70-m and 34-m high-efficiency (HEF) ground-based antennas that are used to track primarily spacecraft and, periodically, celestial targets. To track a spacecraft, or other targets, the antenna must be accurately pointed at the spacecraft, which can be very far away with very weak signals. ACR s conical scanning capability collects the signal in a circular pattern around the target, calculates the location of the strongest signal, and adjusts the antenna pointing to point directly at the spacecraft. A real-time, closed-loop servo control algorithm performed every 0.02 second allows accurate positioning of the antenna in order to track these distant spacecraft. Additionally, this advanced servo control algorithm provides better antenna pointing performance in windy conditions. The ACR software provides high-level commands that provide a very easy user interface for the DSN operator. The operator only needs to enter two commands to start the antenna and subreflector, and Master Equatorial tracking. The most accurate antenna pointing is accomplished by aligning the antenna to the Master Equatorial, which because of its small size and sheltered location, has the most stable pointing. The antenna has hundreds of digital and analog monitor points. The ACR software provides compact displays to summarize the status of the antenna, subreflector, and the Master Equatorial. The ACR software has two major functions. First, it performs all of the steps required to accurately point the antenna (and subreflector and Master Equatorial) at the spacecraft (or celestial target). This involves controlling the antenna/ subreflector/Master-Equatorial hardware, initiating and monitoring the correct sequence of operations, calculating the position of the spacecraft relative to the antenna, executing the real-time servo control algorithm to maintain the correct position, and monitoring tracking performance.

Chao, Roger Y.↗