MODAQ 2: A Flexible, More Affordable Data System
A fact sheet for MODAQ 2 which is the newest version of the Modular Ocean Acquisition System (MODAQ).
13 HYDRO ENERGY↗
SEARCH · Engineering Papers
Results for “data acquisition systems”
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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Network visualization, intrusion detection, and network healing
The present disclosure is related to a cyber-security system that includes a Supervisory Control and Data Acquisition (SCADA) network monitor configured to receive a data set from a power system network, an event manager, and a mitigation system, where the SCADA network monitor includes an anomaly detector.
Rivera, Joshua Eli↗
TListSpectrum
TListSpectrum is a C++ class developed inside the CERN high-energy physics analysis C++ framework ROOT. This class structure was developed to assist in the processing, visualization, and analysis of list-mode or time-stamped radiation spectroscopy data. The class structure currently contains parsing and functionality to synthesize list-mode data from CAEN and Mirion Lynx radiation spectroscopy digital acquisition systems along with feature functionality to post-process data sets and build coincident data sets from the instrument.
Pierson, Bruce↗
HIDES (Hybrid Intrusion Detection for Energy Systems) [SWR-19-65]
Hybrid Intrusion Detection for Energy Systems (HIDES) is a conglomerate detector that incorporates three major components: (1) Signature-based detection that involves information technology (IT) related anomaly detection rules, (2) Behavior-based detection that utilizes specially crafted rulesets for detecting abnormal behavior regarding the power system's supervisory control and data acquisition (SCADA) communication streams, and (3) Learning-based detection that combines SCADA communication with phasor measurements to identify otherwise non-detectible attack vectors. The software itself interconnects intrusion detection engine (IDE) enabling communications between the generated OT and IT alerts with phasor measurements for the learning-based detection system. The resulting detection system can detect a wide array of IT, OT, and physical attacks in a real-time setting.
Rivera, Joshua↗
Development of a FELIX based readout system for ITk strip hybrid module demonstrator for ATLAS Phase-II Upgrade
The Large Hadron Collider (LHC) will undergo a major upgrade around 2024 with a roughly tenfold increase in luminosity. It results in corresponding increases in particle rates and radiation doses that the current ATLAS Inner Tracking Detector cannot cope with. Therefore the ATLAS experiment will build a new all-silicon tracking system called the ITk (Inner Tracker) that includes an inside pixel detector close to the beam line and a strip detector in outside layer. Meanwhile the ATLAS experiment will adopt the Front-End Link eXchange (FELIX) system as the interface between the data acquisition and front-end electronics for all sub-detectors in the Phase II Upgrade. In this contribution, we present a prototype of FELIX based readout system for the strip hybrid module. Its hardware, firmware and software GUI application will be discussed, as well as integration test results.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Multi-Scale Integrated Monitoring System for Enhancing Methane Emission Detection, Quantification & Prediction
This report details the progress and findings of a comprehensive study on reviewing existing solutions, identifying technology gaps, and formulating an “all-in-one” integrated strategy for developing the next-generation multiscale methane monitoring and modeling platform, conducted under grant number DE-FE0032292. Co-led by Dr. David Ebert, Dr. Binbin Weng, and Dr. Chenghao Wang at the University of Oklahoma, the project’s goal was to develop an integrated approach for building this engineering platform to detect, quantify, and mitigate methane emissions across various temporal scale, spatial scales, and sectors. The planning grant study began with an extensive review of various methane sensing and monitoring technologies and systems, surveying over 100 technology providers globally. This review revealed the prevalence of optical methods over chemical methods in commercially available sensors, with Non-Dispersive Infrared (NDIR), Tunable Diode Laser Absorption Spectroscopy (TDLAS), and Optical Gas Imaging (OGI) cameras being the most prevalent options. A trend towards more advanced optical techniques was observed, driven by increased regulatory focus and technological advancements. The technical evaluation of these sensing technologies provided crucial insights into their capabilities and limitations. The study examined emerging technologies such as Differential Absorption LiDAR (DIAL), which show promise for high-precision and long-range detection. The team then investigated the features and application bandwidth of various sensing platforms, including handheld, fixed/stationary, mobile, aerials, and spaceborne monitors. Pilot field studies were conducted to assess the capabilities of solutions for different emission scenarios. Field work with sensor deployments was conducted at three distinct site types: an oil & gas industry site, a cattle ranching operation, and a waste processing facility. The team also conducted a thorough review of methane flux inverse modeling approaches, focused on physically based methods. These approaches were categorized into simple, intermediate, and advanced methods. A realtime WRF-GHG (Weather Research and Forecasting-Greenhouse Gas) modeling system was developed and applied, incorporating multiple data sources to guide field experiments and inform methane plume detection. The project identified and analyzed numerous categories of methane data sources, including satellite measurements, ground-based sensors, and inventory databases. Key platforms examined include EDGAR, EPA GHGI, NASA TROPOMI, Carbon Mapper, and Climate TRACE, among others. The team proposed an architecture for a comprehensive methane monitoring platform. This system incorporates multi-source data acquisition, advanced data processing and assimilation, interactive visualization tools, and analytical capabilities for emissions forecasting and scenario analysis. The proposed platform aims to provide a user-friendly interface catering to various stakeholders, from researchers to policymakers. The architecture includes sophisticated data ingestion methods, a centralized data warehouse, and advanced analytical tools for data fusion and interpretation. To ensure the relevance and effectiveness of the proposed system, a comprehensive survey was conducted to gather stakeholder input on system requirements. Key findings include a strong need for integrating various data types and formats, a preference for real-time data updates and advanced visualization tools, and a demand for user-friendly interfaces catering to different expertise levels.
03 NATURAL GAS↗
Commissioning of the large-scale lead tungstate scintillating calorimeter
Here, we report on the installation and initial commissioning of a large-scale lead tungstate (PbWO4) scintillating crystal calorimeter developed for high-rate photon detection and precise energy measurement. The calorimeter comprises 1596 high-granularity, high-resolution scintillating crystals optimized for electromagnetic-shower detection over a wide energy range. Scintillation light from each crystal is read out by Hamamatsu R4125 photomultiplier tubes equipped with a custom voltage divider and front-end amplifier to ensure stable gain at high rates. All calorimeter modules were fabricated and characterized using a light-emitting diode–based optical test system prior to installation to verify uniformity and photodetector performance. After installation, the electromagnetic calorimeter was fully integrated into the experiment data acquisition and energy-based trigger systems. The optical response of the modules was equalized using the light-monitoring system, cosmic-ray muons, and photons from Compton-scattering events. Commissioning results demonstrate a reliably calibrated optical response and stable detector performance during the first run. These results validate the calorimeter design and commissioning methodology for large-scale scintillator-based photonic instrumentation.
Analog to digital converters↗
AI-Driven Cross-Domain Knowledge Discoveryand Hypotheses Generation for Enhanced Earth System Predictability
Focal Area(s): 1. Data acquisition, multimodal data fusion and data-to-knowledge and knowledge-to-code transformations enabled by scaling AI and unsupervised deep learning. 2. Insights (cross-domain knowledge) extracted from complex data (both observational and simulated) using AI-driven approaches and large-scale data analytics.
54 ENVIRONMENTAL SCIENCES↗
Oscilloscope Data Push Program
Data acquisition (DAQ) is a complex and costly process. Creating DAQ systems for analyzing a system requires expensive electronics and a dedicated team of engineers for support, posing a challenge for users who readily need data. This project is a proof of concept to create a temporary or one-off DAQ system using equipment commonly available to every team. We aim to automate the data acquisition process from the Rohde \& Schwarz RTO 1044 oscilloscope, convert the acquired binary data into floating point values, and store the results in a CSV file format. By developing a Python program to handle these tasks, we seek to reduce the manual effort involved in data collection, significantly increasing efficiency.
Osei-Tutu, Jason↗
Cybersecurity Anomaly Detection in SCADA-Assisted OT Networks Using Ensemble-Based State Prediction Model
The cybersecurity threats of power system gradually grow due to the increased sophisticated interactions between Information Technology (IT) and Operational Technology (OT) networks. False data injection attack (FDIA) that aims to compromise the Supervisory Control and Data Acquisition (SCADA) measurement and disturb the system operation is one of such cyber threats. Such attacks can potentially lead to significant operational issues at the control centers and substations, and hence, result in severe physical consequences. To avoid catastrophic failure across the power grid resulting from these attacks, it is essential to arm the OT network with real-time vulnerability assessment tools. To this end, this paper outlines various drawbacks of the Purdue architecture model to defend against cyberattacks in the OT network. Furthermore, a novel ensemble-based state prediction model is proposed to detect cybersecurity anomalies in SCADA assisted OT networks. The proposed model uses control center level generation and load forecasts, scheduled, and forced outages, power flow solutions, and the substation level historical data. The hypothesis of the proposed scheme relies on the fact that additional control center and substation data can hardly be accessed and compromised by attackers. One of the vital features of the proposed scheme is an hour-ahead prediction of the operational feasibility of the SCADA measurement range at the control center and substation in real time helps in detecting anomalies in measurements across both substation and the control center.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Intelligent Experiments Through Real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and Future EIC Detectors (Final Report)
The overall vision of this project was to integrate real-time artificial intelligence (AI) directly into the data acquisition and detector-control systems of nuclear physics experiments, including both fast online event selection and an autonomous detector-control feedback loop. The work carried out under the award focused on the fast online event-selection half of that vision: the efficient recording of low-momentum heavy-flavor (HF) hadron decays in proton-proton collisions at the sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC)—an observable that requires fast tracking and topological trigger selection not previously demonstrated at RHIC, and that is essential for QCD studies at future facilities such as the Electron-Ion Collider (EIC). The autonomous detector-control (GPU-based feedback) component named in the project title remained a design concept and was not implemented under this award. The Massachusetts Institute of Technology (MIT) group led the offline simulation and data processing needed to train the machine-learning (ML) models, the translation of trained models to Field-Programmable Gate Array (FPGA) firmware using the hls4ml framework, and the physics validation of heavy-flavor reconstruction. Over the award period, the team developed and hardware-tested the principal components of an AI-based heavy-flavor trigger on simulated and recorded sPHENIX tracker data: a software Bipartite Graph Attention Network (BiGAT) trigger model reaching > 95% signal efficiency at 99% background rejection; an FPGA-native hit clusterizer matching the offline clustering; smaller networks synthesized to FPGA within the required sub-10 µs latency; and an assembled decoder–clusterizer–inference firmware chain exercised on the FELIX readout board. A complete, fully integrated hardware demonstrator was not finished within the award period. This report documents the project goals, the MIT group’s contributions, the technical accomplishments, and the outlook toward applications at the future EIC ePIC detector.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Distributed Resilience in High-Energy Physics Data Acquisition
Historical experience in the High-Performance Computing community teaches us that as computing systems grow, the instance of failures goes from rare to a regular occurrence. A survey of the growth in the size and complexity of Data AcQuisition (DAQ) networks in High-Energy Physics (HEP) experiments reveals that these networks are scaling exponentially, trending to a point where automated fault handling should be considered over the current manual practice, especially given the rarity of data such as in DUNE's mission to observe core-collapse supernovae. We propose a general system, DiDAQt, which is designed to provide fault detection and handling in HEP DAQs specifically, through MPI-like primitives that allow it to be added easily to existing systems. We evaluate the scalability and response time of a prototype on the FABRIC national testbed, with results indicating sufficient scalability for current and near-future DAQs as well as practical response times (under 1 microsecond decision time).
Wolosewicz, A. [IIT, Chicago]↗
The next-generation magnetic recoil spectrometer (MRSnext) on OMEGA and NIF for diagnosing ion temperature, yield, areal density, and alpha heating
The next-generation magnetic recoil spectrometer (MRSnext) is being designed to replace the current MRS at the National Ignition Facility and OMEGA for measurements of the neutron spectrum from an inertial confinement fusion implosion. The MRSnext will provide a far-superior performance and faster data turnaround than the current MRS systems, i.e., a 2× and 6× improvement in energy resolution at the NIF and OMEGA, respectively, and 20× improvement in data turnaround time. The substantially improved performance of the MRSnext is enabled by using electromagnets that provide a short focal plane (12–16 cm) and unprecedented flexibility for a wide range of applications. In addition to being able to measure neutron yield, apparent ion temperature, areal density, and plasma-flow velocity over a wide range of yields, the NIF MRSnext will be able to directly, uniquely assess the alpha heating of the fuel ions through measurements of the alpha knock-on tail in the neutron spectrum. The goal is to implement a radiation-hard electronic detection system capable of providing rapid data acquisition and analysis. The development of the MRSnext will also set the foundation for the more advanced, time-resolving MRSt and serve as a testbed for its implementation on the NIF.
47 OTHER INSTRUMENTATION↗
Ocean Energy: Markets - Currency - Impact. Dimension of & Choices in the Technology Development Space: Preprint
This paper presents considerations of the employment of ocean wave energy to support different energy demand side applications. The key aspect in these considerations is the wave energy supported achievable positive impact and associated tangible contribution in service of common societal good and of the natural commons. The level of impact that can be delivered is dependent on both, the level of contribution of the supported energy use application, and the compatibility and unique suitability of the wave energy resource and its characteristics with the needs of the application. Thus, a variety of ocean wave energy markets, the key value indicators or "currency' in which these markets trade the value delivered and the achievable positive impact, are reflected upon. Ocean wave energy supported acquisition of high quality ocean system data across a wide spectrum of system properties is identified as a highly impactful application enabling and/or improving a comprehensive range of impactful ocean system activities. The technology development process towards these markets and desired impacts requires relevant technology development progress guidance and metrics. Going beyond technology readiness levels and technology performance levels, the notion of further technology development progress scales towards high impact and high contribution are proposed. These scales and the associated technology properties can be regarded as additional technology development dimensions to span-up the technology development space in which desired system capability and functional requirement choices and subsequent ideation, innovation, research and technology development decisions can and are to be made.
data market↗
Remote Radiation Sensing Using Aerial and Ground Platforms
Remote sensing of ionizing radiation has a significant role in waste management, nuclear material management and nonproliferation, and radiation safety. Robotic platforms can surpass the number of tasks that are achieved by humans. With this technique, the operator's radiation exposure can be decreased. Remote sensing allows for the evaluation and monitoring of radiological contamination. Gamma-ray and neutron sensors were integrated onto the robotic platforms. This approach allows for the radiation sensor data to be dynamically tracked and mapped thus enabling further analysis of the radiation flux in temporal and spatial domains. The goal is to complete scheduled tasks while the robot is being irradiated. To achieve this, electronic components must be shielded and radiation hardened. CZT Detector: Cadmium Zinc Telluride (CZT) detector technology has been a promising solution for gamma-ray and x-ray measurements. Detector data is transferred to the Odroid minicomputer that controls and powers the module via the USB. Robot Operating System (ROS) was utilized for data acquisition and data fusion. The Mariscotti method was employed for the spectrum analysis. A function was programmed in ROS for the automatic identification of photopeaks. CLYC Detector: A Cs{sub 2}LiYCl{sub 6}:Ce{sup 3+} (CLYC) detector was used for simultaneous medium-resolution gamma-ray measurements and neutron counting. A 2.54 cm diameter photomultiplier tube (PMT) was equipped with a high voltage supply and a miniature digitizer. Gamma-ray excitation: fast core-to-valence luminescence (CVL) with 1 ns decay constant, and prompt Ce{sup 3+} emission with 50 ns decay constant. Neutron excitation: slow cerium self-trapped excitation (Ce{sup 3+} STE), 1000 ns decay constant. Radiation Source Localization: Maximum Likelihood Estimation (MLE) and gradient-based methods were used to locate the position of a radiation source based on measured radiation intensities. Multi-Particle Transport Code FLUKA: Estimation of radiation damage of the electronic components is important in order to optimize the robot's operational time while it is irradiated. Displacement per atom (DPA) represents the radiation damage in materials exposed to the ionizing radiation. Various shielding layers of different thickness t were analyzed (< 5% statistical error). The model of the controller of the UAS was designed in FLUKA. Conclusion: CZT and CLYC detectors were integrated onto the robotic platforms. Radiation source localization and contour mapping using robotic platforms were studied. Functions for data analysis and fusion were developed in ROS. FLUKA code was utilized to analyze DPA values. Layers of low-density and high-density materials were used to shield the UAS electronics.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography
Parallel-beam tomography systems at synchrotron facilities have limited field of view (FOV) determined by the available beam size and detector system coverage. Scanning the full size of samples bigger than the FOV requires various data acquisition schemes such as grid scan, 360-degree scan with offset center-of-rotation (COR), helical scan, or combinations of these schemes. Though straightforward to implement, these scanning techniques have not often been used due to the lack of software and methods to process such types of data in an easy and automated fashion. The ease of use and automation is critical at synchrotron facilities where using visual inspection in data processing steps such as image stitching, COR determination, or helical data conversion is impractical due to the large size of datasets. Here, we provide methods and their implementations in a Python package, named Algotom, for not only processing such data types but also with the highest quality possible. The efficiency and ease of use of these tools can help to extend applications of parallel-beam tomography systems.
36 MATERIALS SCIENCE↗
Practical and Optimal Sequential Bayesian Experimental Design for Complex Systems Incorporating Human Experimenter Preferences (Final Scientific/Technical Report)
Experiments are indispensable for developing models of complex systems. Carefully designed experiments can provide substantial savings for these expensive data-acquisition opportunities. However, designs based on heuristics are often suboptimal for systems with multiphysics, nonlinear dynamics, and uncertain and noisy environments. Optimal experimental design, while leveraging predictive models, seeks to systematically quantify and maximize the value of experiments. In this project, we focused on the design of multiple experiments, where current approaches are largely suboptimal: batch-design does not adapt to new data acquired during the experiment campaign (no feedback), and greedy/myopic design ignores future dynamics and consequences (no lookahead). We developed the mathematical framework and computational methods for sequential optimal experimental design (sOED) for complex systems. We enabled tractable model-based sOED in a rigorous manner through novel algorithms based on reinforcement learning, and investigated the effects of human experimenters on the design process. Our methods are fully Bayesian, able to quantify and update uncertainty in a principled manner. The traits aimed by our approach—mathematical rigor and optimality, human effects and uncertainty quantification, computational practicality—are crucial for elevating the standards of artificial intelligence (AI) to support decision-making in scientific domains, and contribute toward trust and realistic adoption of AI in experimental design practice.
97 MATHEMATICS AND COMPUTING↗
Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter
The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle data acquisition problems to avoid data quality loss. In this study, we present a semi-supervised spatio-temporal anomaly detection (AD) monitoring system for the physics particle reading channels of the Hadron Calorimeter (HCAL) of the CMS using three-dimensional digi-occupancy map data of the DQM. We propose the GraphSTAD system, which employs convolutional and graph neural networks to learn local spatial characteristics induced by particles traversing the detector and the global behavior owing to shared backend circuit connections and housing boxes of the channels, respectively. Recurrent neural networks capture the temporal evolution of the extracted spatial features. We validate the accuracy of the proposed AD system in capturing diverse channel fault types using the LHC collision data sets. The GraphSTAD system achieves production-level accuracy and is being integrated into the CMS core production system for real-time monitoring of the HCAL. We provide a quantitative performance comparison with alternative benchmark models to demonstrate the promising leverage of the presented system.
43 PARTICLE ACCELERATORS↗