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This presentation provides an overview of the mechanisms and methods used to implement the XTCE (XML (eXtensible Markup Language) Telemetric and Command Exchange format) schema for Telemetry and Command databases for the GOES-R series satellites.
Distributed Spacecraft Systems are a type of multi-spacecraft mission architecture that can not only provide improved resolution, coverage, and availability of existing missions, but also enable missions that would be previously infeasible using traditional approaches. Distributed Spacecraft Autonomy (DSA) is a project developed by the National Aeronautics and Space Administration that enables distributed spacecraft systems. In previous science swarm missions, the spacecraft involved have not been able to communicate with each other without utilizing a ground station. Now that the spacecraft can perform inter-satellite communication, the spacecraft can be treated as a collective. Swarm autonomy is critical for a growing number of satellites which means novel ways of displaying swarm data needs to be implemented. Such systems introduce unique challenges to traditional approaches for command and control of these spacecraft, due to the large number of spacecraft and the complexity of the interactions between them. The ground data system for DSA addresses these challenges through the creation of a custom user interface that allows a single operator to orchestrate a multi-spacecraft swarm in a scalable way. This plenary describes the details of the autonomy demonstration being performed, the requirements of those using the interface to analyze the spacecraft telemetry to assess demonstration success, and the approach taken by the ground systems team to create an interface that satisfies these requirements. This approach involves the creation of several distinct components that correspond to the level of detail presented to the user. These components are based on conventional user roles in human-robot interaction, including supervisor, operator, and mechanic, extended to accommodate the additional overhead of coordinating actions between agents. One main feature of the interface is the listenability matrix component which will represent inter-satellite communications in a heat mapped matrix. The above work described will enable users to command and interact with the spacecraft as a collective.
After several years of research into Detect and Avoid (DAA) and Command and Control (C2) systems for Unmanned Aircraft Systems (UAS), the National Aeronautics and Space Administration’s (NASA) UAS Integration into the National Airspace System project initiated a focused two-year effort along with the Federal Aviation Administration (FAA) and three industry partners to investigate remaining issues in the specification, test, certification and airspace integration to allow UAS operations in non-segregated airspace. The approach taken had the industry partners propose a flight demonstration approximating a commercial operation, while NASA helped, as needed, the partners through design and test phases and NASA observed interactions with the FAA. During this effort NASA collected best practices intended to be of value to similar UAS endeavors. These best practices can be divided into different sets, including practices that describe the relationship between business considerations to UAS design or describe several UAS development challenges. Another set includes best practices focused on navigating the UAS design and type certification processes. A third set includes best practices that relate to the design of the DAA system. Deploying DAA systems in this timeframe posed unique challenges. Commercial off-the-shelf DAA systems do not exist, necessitating custom development and, for two of the partners, the use of low-size, -weight, and -power (SWaP) sensors not completely specified for DAA. A fourth set of best practices relates to lost-link contingency planning. The final set of best practices relates to the design and testing of C2 systems and obtaining spectrum licenses. The UAS demonstrations were piloted remotely, and for all aspects of flight safety, a C2 system was required to communicate DAA and other UAS subsystem data to the remote pilot and to allow the pilot to issue commands to the vehicle. Throughout the course of this effort, the partners integrated prototype DAA and C2 systems into unmanned aircraft, tested those systems, and laid the groundwork for type certification programs that are expected to continue.
A coherent Doppler lidar has been developed to address the need for a high-performance, compact, and cost-effective velocity and altitude sensor onboard landing vehicles. One of the core subsystems within NDL is its signal processing architecture. The primary function of this subsystem is to digitize the analog output of the homodyned signal received from the photo-diode associated with each telescope, process this signal in the frequency domain, and then turn this information into velocity and range measurements. A secondary function of this subsystem is to setup and control the internal components within NDL and to enable the instrument to interface with external systems by receiving commands, sending telemetry, and synchronizing its measurements with external clocks and pulses. This paper will give an overview of the signal processing architecture within NDL. This will include a discussion on its fundamental processing components and the core signal processing algorithms. Command, telemetry and internal component controls will also be discussed as will how the signal processing architecture has also evolved over the years of development. We will discuss provisions for robust operation including Triple-Mode Redundancy (TMR) within NDL’s Field Programmable Gate Array (FPGA) and storage elements, a fault-tolerant boot process, and Error Detection and Correction (EDAC) on the Static Random-Access Memories (SRAMs). Finally, we conclude this paper with how this architecture could continue to evolve.
The decisions made during the Federal Aviation Administration Air Traffic Control System Command Center's planning teleconferences hold significant sway over the National Airspace System. Held every two hours, these teleconferences convene air traffic managers and stakeholders from across the nation to discuss airspace conditions, weather, and constraints, leading to the formulation and adjustment of traffic management initiatives. Given the critical nature of these decisions, the need for accurate and efficient record-keeping is paramount. In recent years, the application of automatic speech recognition has gained popularity across diverse industries, including aviation. While traditional applications focus on transcribing air traffic control communication, this paper explores a unique application of automatic speech recognition by converting the audio from planning teleconferences into text transcriptions. This innovative approach addresses key challenges in the field, presenting potential benefits for quality assurance, real-time participation, and downstream natural language processing tasks. A notable breakthrough in the machine learning community, namely the transformer neural network architecture, forms the backbone of the proposed solution in this paper. The transformer architecture's role in this research represents a paradigm shift in the efficiency of automatic speech recognition models. By reducing the amount of in-domain training data required, this architecture allows for the fine-tuning of such models like Whisper, originally pretrained on vast English speech datasets. The adaptability of the transformer architecture proves invaluable in capturing the nuances of aviation terminology and specific language used in planning teleconferences. Leveraging the Whisper model as a baseline, our research details the fine-tuning and validation using a dataset comprising 20 hours of meticulously transcribed planning teleconferences. Notably, the baseline pretrained Whisper model exhibited a word error rate of 18.77%. Through the fine-tuning process, the model achieved a substantial improvement, demonstrating an impressive performance with a reduced word error rate of 6.82%. This substantial decrease in WER not only highlights the effectiveness of the transformer architecture but also emphasizes the practical advancements achieved through the application of automatic speech recognition in this specific domain. The utilization of automatic speech recognition in planning teleconferences in this work introduces several novelties. Firstly, the creation of text transcriptions offers a valuable tool for quality assurance and facilitates the efficient review of teleconferences. This is an important aspect of the proposed solution, given the time-sensitive and high-stakes nature of decisions made during these meetings. Furthermore, text-searchable transcriptions provide a streamlined approach for locating and validating critical information, potentially saving hours of manual effort in searching through audio recordings. Moreover, our research identifies a key use case for external facilities and stakeholders. In situations where attendance at the planning teleconference is not feasible, having access to text transcriptions in real-time or shortly after the teleconference ends, proves to be a time-saving and informative resource. This feature enhances collaboration and ensures that stakeholders can stay abreast of important discussions and decisions even in their absence. Despite the efficiency gains facilitated by the transformer architecture in automatic speech recognition technology, it is essential to acknowledge the human factors in data creation. Subject matter experts play a crucial role in accurately transcribing planning teleconferences due to the specificity and complexity of the information discussed. The research dataset, consisting of 20 hours of transcribed planning teleconferences, forms the foundation for fine-tuning and validating the Whisper model. The achieved word error rate of 6.82% demonstrates promising advancements, particularly in recognizing essential aviation terminology within the teleconferences. In conclusion, this paper presents a comprehensive exploration of the application of automatic speech recognition in Air Traffic Control System Command Center planning teleconferences, leveraging the transformer architecture for enhanced efficiency. The novel contributions lie in the improved accessibility of decision-making records, real-time participation opportunities for external stakeholders, and the potential for downstream natural language processing advancements. As the aviation industry continues to evolve, the integration of automatic speech recognition technologies holds the promise of revolutionizing decision-making processes and contributing to the overall safety and efficiency of air traffic management.
The 2018 Department of Defense Nuclear Posture Review emphasized the importance of modernizing the existing nuclear command, control, and communications (NC3) architecture. Heightened awareness of US reliance upon technology developed in the 1970s and emerging challenges posed by frontier technologies spurred Secretary of Defense Mattis to task USSTRATCOM with identifying the technologies, processes, and institutions needed to build a next-generation NC3 system. General Hyten, USAF Commander, has noted that this will require an “overhaul of our existing processes from requirements definition to systems engineering and integration, acquisition, and budgeting. A framework is required for modern processes to enable delivery of a flexible, continuously evolving, threat-driven set of capabilities.” This brief, prepared in response to Gen. Hyten’s call for memoranda to address the “Next Generation NC3 Enterprise,” outlines three distinct approaches to NC3 modernization, each with a system lifecycle of approximately 50 years (2030-2080): upgrading the existing system, a hybrid approach to integrating new technologies, and creating an entirely new NC3 architecture. It is organized as follows: First, it outlines the integral role of NC3 in strategic operations. Second, it examines the current status of NC3 tools and the contemporary challenges that future systems must address. Third, it provides a series of paths forward for USSTRATCOM’s consideration. Fourth and finally, it recommends a “hybrid” approach to maintain the strengths of the current NC3 architecture, increase the resilience of the system, and leverage the advantages of new sensing, ISR, and communication technology.
The APBS software was developed to solve the equations of continuum electrostatics for large biomolecular assemblages. APBS has enjoyed widespread adoption throughout the biomedical community and is used in numerous applications involving biomolecular structures. However, visualization of complex biomolecular electrostatic properties can be challenging for two-dimensional graphics systems. Therefore, advanced visualization and manipulation capabilities have been incorporated. In particular, APBS tools have been integrated with the UnityMolX virtual reality platform to provide an immersive experience for preparing biomolecular systems for calculations as well as visualizing and comparing calculated results.
Purpose: To examine the feasibility of a novel technology platform that enables real-time touchless interaction with radiology images in both a simulated and an actual clinical setting. Materials and Methods: This platform offers three different modes for image interaction. The gesture recognition mode uses a depth camera to detect the user’s hand gestures which are translated to image manipulation commands. The light projection mode uses the same camera to detect finger point-and-tap movements above the icons which are projected on a surface to activate the commands. The capacitive sensing mode is enabled by a handheld, portable device, over which finger movements are detected by capacitive sensors to control the image review. Following initial feedback, light projection and capacitive sensing modes were selected for further testing by comparing with the conventional mode of image interaction in time trials for performing a series of standardized image manipulation tasks. Finally, the usability of the technology platform was examined in actual clinical procedures. Results: The light projection and the capacitive sensing modes were evaluated in the time trials and exhibited 60% and 71% reduction in time, respectively, relative to the control mode (p < 0.001). Clinical feasibility for this platform was demonstrated in three actual interventional radiology cases. Conclusion: Accessing, navigating, and extracting relevant information from patient images intraprocedurally are cumbersome and time-consuming tasks that affect safety, efficiency, and decision-making during image-guided procedures. This study demonstrated that the novel technology addressed this issue by allowing touchless interaction with these images in the sterile field.
We are developing a superconducting camera based on microwave kinetic inductance detectors (MKIDs) to observe 100-GHz continuum with the Nobeyama 45-m telescope. A data acquisition (DAQ) system for the camera has been designed to operate the MKIDs with the telescope. This system is required to connect the telescope control system (COSMOS) to the readout system of the MKIDs (MKID DAQ) which employs the frequency-sweeping probe scheme. The DAQ system is also required to record the reference signal of the beam switching for the demodulation by the analysis pipeline in order to suppress the sky fluctuation. The system has to be able to merge and save all data acquired both by the camera and by the telescope, including the cryostat temperature and pressure and the telescope pointing. A collection of software which implements these functions and works as a TCP/IP server on a workstation was developed. The server accepts commands and observation scripts from COSMOS and then issues commands to MKID DAQ to configure and start data acquisition. We made a commissioning of the MKID camera on the Nobeyama 45-m telescope and obtained successful scan signals of the atmosphere and of the Moon.
The slicing process for additive manufacturing (AM) involves many steps, including slicing the object to be printed and fitting toolpaths to each resulting layer. The toolpaths define the trajectories that the machine must follow during printing to construct the object. Once the slicing is complete and all toolpaths have been found and properly planned, this data must be transferred to the 3D printer in a way the printer can use to construct the object. This is achieved by exporting a file, usually a g-code file, from the slicing software. The g-code file is a text-based file that contains instructions for the machine, controlling the system and defining the motions for each toolpath. The file, which often contains thousands of lines of code, is mostly composed of numbers that command positions for axis motions. This chapter will outline the contents of a g-code file, discuss various commands and their implementation, then provide example codes and files for object construction.
Growing trends in the deployment of inverter-based renewable energy will decrease the inertia and frequency control capability of electric power systems by replacing conventional power plants; thus, the frequency of future power systems might be dynamic. This study proposes a capability-coordinated frequency control (CCFC) scheme of a virtual power plant (VPP) including adjustable-speed pumped storage hydropower (AS-PSH), a wind power plant (WPP), and an energy storage system to support the frequency nadir and reduce the steady-state error of system frequency. The CCFC scheme is based on a hierarchical-control structure in which a CCFC organises the output of local frequency control units. To support the frequency nadir, the CCFC dispatches weighted frequency errors that are proportional to the available headroom of the units; thus, the errors are forwarded separately with a system frequency error to the primary control loop of each unit and thereby arrest the frequency nadir at a higher value than a system without the CCFC. To reduce the steady-state error of the system frequency, the CCFC determines a partial active power command by additionally feeding an integrator of the CCFC with a modified frequency error that depends on the unit with the largest control.
We present full-sky maps of the cosmic microwave background (CMB) and polarized synchrotron and thermal dust emission, derived from the third set of Planck frequency maps. These products have significantly lower contamination from instrumental systematic effects than previous versions. The methodologies used to derive these maps follow closely those described in earlier papers, adopting four methods (Commander, NILC, SEVEM, and SMICA) to extract the CMB component, as well as three methods (Commander, GNILC, and SMICA) to extract astrophysical components. Our revised CMB temperature maps agree with corresponding products in the Planck 2015 delivery, whereas the polarization maps exhibit significantly lower large-scale power, reflecting the improved data processing described in companion papers; however, the noise properties of the resulting data products are complicated, and the best available end-to-end simulations exhibit relative biases with respect to the data at the few percent level. Using these maps, we are for the first time able to fit the spectral index of thermal dust independently over 3° regions. We derive a conservative estimate of the mean spectral index of polarized thermal dust emission of β d = 1.55 ± 0.05, where the uncertainty marginalizes both over all known systematic uncertainties and different estimation techniques. For polarized synchrotron emission, we find a mean spectral index of β s = -3.1 ± 0.1, consistent with previously reported measurements. We note that the current data processing does not allow for construction of unbiased single-bolometer maps, and this limits our ability to extract CO emission and correlated components. The foreground results for intensity derived in this paper therefore do not supersede corresponding Planck 2015 products. For polarization the new results supersede the corresponding 2015 products in all respects.
Abstract ITER will be the first tokamak to sustain a fusion-producing, or burning, plasma. If the plasma temperature were to inadvertently rise in this burning regime, the positive correlation between temperature and the fusion reaction rate would establish a destabilizing positive feedback loop. Careful regulation of the plasma’s temperature and density, or burn control, is required to prevent these potentially reactor-damaging thermal excursions, neutralize disturbances and improve performance. In this work, a Lyapunov-based burn controller is designed using a full zero-dimensional nonlinear model. An adaptive estimator manages destabilizing uncertainties in the plasma confinement properties and the particle recycling conditions (caused by plasma–wall interactions). The controller regulates the plasma density with requests for deuterium and tritium particle injections. In ITER-like plasmas, the fusion-born alpha particles will primarily heat the plasma electrons, resulting in different electron and ion temperatures in the core. By considering separate response models for the electron and ion energies, the proposed controller can independently regulate the electron and ion temperatures by requesting that different amounts of auxiliary power be delivered to the electrons and ions. These two commands for a specific control effort (electron and ion heating) are sent to an actuator allocation module that optimally maps them to the heating actuators available to ITER: an electron cyclotron heating system (20 MW), an ion cyclotron heating system (20 MW), and two neutral beam injectors (16.5 MW each). Two different actuator allocators are presented in this work. The first actuator allocator finds the optimal mapping by solving a convex quadratic program that includes actuator saturation and rate limits. It is nonadaptive and assumes that the mapping between the commanded control efforts and the allocated actuators (i.e. the effector model) contains no uncertainties. The second actuator allocation module has an adaptive estimator to handle uncertainties in the effector model. This uncertainty includes actuator efficiencies, the fractions of neutral beam heating that are deposited into the plasma electrons and ions, and the tritium concentration of the fueling pellets. Furthermore, the adaptive allocator considers actuator dynamics (actuation lag) that contain uncertainty. This adaptive allocation algorithm is more computationally efficient than the aforementioned nonadaptive allocator because it is computed using dynamic update laws so that finding the solution to a static optimization problem is not required at every time step. A simulation study assesses the performance of the proposed adaptive burn controller augmented with each of the actuator allocation modules.
Abstract Motivation The detection of APOBEC3F- and APOBEC3G-induced mutations in virus sequences is useful for identifying hypermutated sequences. These sequences are not representative of viral evolution and can therefore alter the results of downstream sequence analyses if included. We previously published the software Hypermut, which detects hypermutation events in sequences relative to a reference. Two versions of this method are available as a webtool. Neither of these methods consider multistate characters or gaps in the sequence alignment. Results Here, we present an updated, user-friendly web and command-line version of Hypermut with functionality to handle multistate characters and gaps in the sequence alignment. This tool allows for straightforward integration of hypermutation detection into sequence analysis pipelines. As with the previous tool, while the main purpose is to identify G to A hypermutation events, any mutational pattern and context can be specified. Availability and implementation Hypermut 3 is written in Python 3. It is available as a command-line tool at https://github.com/MolEvolEpid/hypermut3 and as a webtool at https://www.hiv.lanl.gov/content/sequence/HYPERMUT/hypermutv3.html.
Abstract We introduce RWRtoolkit, a multiplex generation, exploration, and statistical package built for R and command-line users. RWRtoolkit enables the efficient exploration of large and highly complex biological networks generated from custom experimental data and/or from publicly available datasets, and is species agnostic. A range of functions can be used to find topological distances between biological entities, determine relationships within sets of interest, search for topological context around sets of interest, and statistically evaluate the strength of relationships within and between sets. The command-line interface is designed for parallelization on high-performance cluster systems, which enables high-throughput analysis such as permutation testing. Several tools in the package have also been made available for use in reproducible workflows via the KBase web application.
The PathoSystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center funded by the National Institute of Allergy and Infectious Diseases (https://www.patricbrc.org). PATRIC supports bioinformatic analyses of all bacteria with a special emphasis on pathogens, offering a rich comparative analysis environment that provides users with access to over 250 000 uniformly annotated and publicly available genomes with curated metadata. PATRIC offers web-based visualization and comparative analysis tools, a private workspace in which users can analyze their own data in the context of the public collections, services that streamline complex bioinformatic workflows and command-line tools for bulk data analysis. Over the past several years, as genomic and other omics-related experiments have become more cost-effective and widespread, we have observed considerable growth in the usage of and demand for easy-to-use, publicly available bioinformatic tools and services. Here we report the recent updates to the PATRIC resource, including new web-based comparative analysis tools, eight new services and the release of a command-line interface to access, query and analyze data.
Introduction Chemical warfare nerve agents (CWNAs) inhibit acetylcholinesterase (AChE), which leads to status epilepticus (SE), spontaneous recurrent seizures (SRS) and severe neuropathology when treatment is delayed. In addition to binding to AChE, some organophosphorus (OP) CWNAs such as soman (GD) also inhibit carboxylesterase (CaE), which acts as a bioscavenger and can thus reduce the severity of the toxicity of OP agent exposure. Unlike humans, rodents have plasma CaE activity. The CaE knockout (ES1−/−) mouse specifically lacks plasma CaE and might better model human GD exposure compared to wildtype rodents. Delayed treatment of CWNA‐induced SE with midazolam leads to benzodiazepine‐refractory SE. We evaluated combination therapy of midazolam and the NMDA antagonist ketamine in male and female mice for efficacy against soman‐induced lethality, SE and epileptogenesis. Methods Mice implanted with telemetry transmitters for electroencephalography (EEG) seizure identification were exposed to 82 μg/kg GD (~4LD 50 ) and treated with an admix (ip) of atropine sulfate (4 mg/kg) and HI‐6 (50 mg/kg) 1 min after exposure, and with midazolam (3 mg/kg; ip) alone or in combination with ketamine (30 mg/kg, ip) at 40 min after seizure onset. Mice were continuously recorded to evaluate initial seizure duration and SRS and then euthanized 2 weeks after exposure for neuropathology assessment. Results Delayed treatment with midazolam resulted in poor survival, was unable to rapidly terminate behavioral or EEG seizure activity and did not prevent the development of SRS or neuronal loss following GD exposure. Combination therapy of midazolam and ketamine resulted in improved outcome, including an increase in survival from 30% to 78% in female and from 57% to 77% in males compared to midazolam monotherapy. In addition, the incidence of epileptogenesis was lower in mice treated with ketamine and midazolam dual therapy compared to midazolam monotherapy. Conclusions This study demonstrates that delayed treatment of SE with midazolam is not fully protective against the GD‐induced epileptogenesis and neuropathology, exemplifying the need for adjunct treatment to midazolam to prevent or reduce effects of GD‐induced SE. Ketamine combination with midazolam improves outcome. Support or Funding Information Research was supported by the CounterACT Program, NIH OD, and the NINDS (Grant 1R21NS103820‐01 to LA Lumley‐Lange) and by DTRA‐JSTO. Erica Kundrick, Katie Walker, and Sean O'Brien were supported in part by an appointment to the Research Participation Program for the U.S. Army Medical Research and Materiel Command administered by the Oak Ridge Institute for Science and Education through an agreement between the U.S. Department of Energy and U.S. Army Medical Research and Materiel Command. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
As Industry 4.0 advances, the integration of computer numerical control (CNC) machines and advanced manufacturing technologies is transforming production into smart manufacturing systems that blend physical and digital processes as cyber-physical systems. However, this increased cyber-physical connectivity exposes manufacturing systems to cyber threats that can cause severe operational and financial disruptions. This paper presents a comparative study on cyber attacks and anomaly detection techniques in manufacturing, focusing on network traffic from CNC machines. The data extracted from network packets includes machine commands and control signals exchanged between the machine's interface and control system, crucial for maintaining operational integrity. We explore two types of cyber attacks, design modification and command injection, which pose substantial risks to CNC machine productivity and system integrity. Our investigation involves experiments on a real CNC system, highlighting the urgent need for effective detection mechanisms. To address these threats, we evaluate three anomaly detection methods: dynamic time warping (DTW), rolling average, and a deep learning, long short-term memory (LSTM) time-series-based autoencoder. Each is assessed for its effectiveness in identifying anomalous behaviors caused by the attacks. Our findings demonstrate the unique strengths and limitations of each detection technique, providing a deeper understanding of their applicability in realworld manufacturing environments. The comparative analysis indicates that while certain methods are highly effective against specific attack types, others offer broader applicability across different attacks. This study contributes to the accurate detection of anomalies in CNC machining processes, thereby enhancing the reliability and security of smart manufacturing systems against diverse cyber threats.