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All sources NASA NTRS DOE OSTI

DOE OSTI2023

Understanding structure-processing relationships in metal additive manufacturing via featurization of microstructural images

Understanding and predicting accurate property-structure-processing relationships for additively manufactured components is important for both forward and inverse design of robust, reliable parts and assemblies. While direct mapping of process parameters to properties is sometimes plausible, it is often rendered difficult due to poor microstructural control. Exploring the direct relationship between processing conditions and microstructural features can thus provide significant physical insights and aid the overall design process. Here, in this study, we develop an automated high-throughput framework to simulate an uncertainty-aware additive manufacturing (AM) process, characterize microstructural images, and extract meaningful features/descriptors. A kinetic Monte Carlo (KMC) based model of the AM process is used to simulate microstructural evolution for a diverse set of experimentally relevant processing conditions. We perform a parametric study to explore the relationship between microstructural features and processing conditions. Our results indicate that a many-to-one mapping can exist between processing conditions and typical descriptors; therefore, multiple descriptors are thus necessary to unambiguously represent microstructural images. Our work provides crucial quantitative and qualitative in-formation that would aid in the selection of features for microstructural images. Featurized microstructures could then be utilized to build data-driven models for predictive control of microstructures and thereby properties of additively manufactured components.

36 MATERIALS SCIENCE↗

NASA NTRS2011

Pattern Recognition for a Flight Dynamics Monte Carlo Simulation

The design, analysis, and verification and validation of a spacecraft relies heavily on Monte Carlo simulations. Modern computational techniques are able to generate large amounts of Monte Carlo data but flight dynamics engineers lack the time and resources to analyze it all. The growing amounts of data combined with the diminished available time of engineers motivates the need to automate the analysis process. Pattern recognition algorithms are an innovative way of analyzing flight dynamics data efficiently. They can search large data sets for specific patterns and highlight critical variables so analysts can focus their analysis efforts. This work combines a few tractable pattern recognition algorithms with basic flight dynamics concepts to build a practical analysis tool for Monte Carlo simulations. Current results show that this tool can quickly and automatically identify individual design parameters, and most importantly, specific combinations of parameters that should be avoided in order to prevent specific system failures. The current version uses a kernel density estimation algorithm and a sequential feature selection algorithm combined with a k-nearest neighbor classifier to find and rank important design parameters. This provides an increased level of confidence in the analysis and saves a significant amount of time.

Restrepo, Carolina↗

DOE OSTI2025

Secure Route: Roadway Risk Mapping for Transportation Planners

The secure transport of sensitive materials across U.S. road networks pose unique challenges for local, state, and federal agencies. Threats range from random events (e.g., accidents, medical emergencies, mechanical failures) to opportunistic or organized tactical assaults. Although the probability of such attacks is very low, the consequences of material loss to foreign states or terrorists can be catastrophic, qualifying these scenarios as “grey swan” events—low-probability, high-impact occurrences that are predictable but difficult to quantify. Traditional risk assessments struggle in these contexts, necessitating a shift toward subjective risk perception to inform planning. Risk perception in transport planning is shaped by various factors, including knowledge of adversarial capabilities, vehicle defenses, manifest details, and geographic features along the route. Geographic features such as bridges, tunnels, roadside elevation, and gaps in cellular coverage introduce vulnerabilities, while mitigative features include safe havens, police stations, and medical services. Temporal variables such as congestion, accidents, and weather further complicate route planning. Despite their importance, existing routing tools like Google Maps and commercial software do not explicitly account for geographic risk features, requiring planners to rely on personal familiarity with routes—a time-intensive, non-scalable approach. This work addresses these gaps by: (1) developing datasets that catalog geographic risk features along U.S. roadways, (2) eliciting risk perceptions from experienced transportation security experts, and (3) linking these perceptions to roadway conditions and geographic data. We implement these capabilities within Secure Route a novel mapping tool for classifying route segment risks associated with roadway conditions. This system provides transportation planners with an intuitive interface to assess and contextualize risk along potential routes, improving decision-making for secure transport. We present current progress in this effort and identify next steps.

Stewart, Robert [ORNL] (ORCID:0000000281867559)↗

NASA NTRS1972

Identification and interpretation of tectonic features from ERTS-1 imagery

The author has identified the following significant results. An important fault zone, which is strongly suspected of being seismically active, was identified on RBV images, ERTS E-1013-17305 (101, 201, and 301), in northeastern Utah. This fault zone is not shown on the Geological Map of the United States nor on the Tectonic Map of North America. When the epicenters of historic earthquakes and their magnitudes were plotted on an overlay corresponding to the scene, a major earthquake cluster up to magnitude 4.9 was found through which the fault zone passes. This suspected active fault zone runs in a northwest-southwest direction cutting across the Patmos Mountains and the southwestern side of the East Tavaputs Plateau from near the junction of the Colorado River with the Dolores River to and beyond the town of Dragerton, Utah. The fault zone which will subsequently be referred to as the Dragerton fault zone appears to be an element of a major tectonic lineament which includes the Moab fault, Salt Valley, Spanish and Lisbon Valleys. Because of the limited imagery coverage received so far, the extent of this lineament or its tectonic significance cannot be ascertained. It is suspected, however, that it constitutes a major crustal break in the Colorado Plateau.

Abdel-Gawad, M.↗

NASA NTRSDate not supplied

Abrstract - OnePlace Redesign Spring 2020 Internship

The project I worked on was the OnePlace Redesign. In that, I converted the OnePlace home page to the Service Portal to match NASA OnePlace’s current application design standards, I was able to allow users to easily find applications in OnePlace and I was able to allow users to easily find and request OnePlace services (new app development, new knowledge base, etc.). It was a major accomplishment to be able to remodel a website that soon the entire Langley center that uses OnePlace will use. The website was made through ServiceNow’s Service Portal, and I organized the applications like never before, allowed a clear styling to applications a user does not have access to and/or are new applications, browsing of applications with clear button divisions, and allowing for different options depending if app is accessible or not, with proper error checking in place and very-well documented code. It was amazing to incorporate all this logic into one widget so that whoever wants to utilize this feature has no trouble at all to do so. The 3 most important things I learned were being able to do this work within ServiceNow, as it is such an important enterprise platform for CRM and boosting the connected workforce of NASA, incorporate built-in APIs from ServiceNow Glide Records to connect Server Scripts to Client Scripts and HTML, and be able to use agile to get another experience of managing work capacity with stories, and having proper communication with the team if every feature is done correctly and steps for continuing forward. The part that I enjoyed the most was the training I did for ServiceNow and I freedom I had to experiment to see how to structure the Service Portal. I was not tied down, and I believe this allowed for a better outcome of the product. As part of the internship, I created a very in-depth design document with the help of my team to document the project goals before it was started and technical documentation (including the design document) summarizing the final result of the product in terms of the new features added and the explanation of the logic thoroughly so that anyone can understand the project easily. The technical documentation will be published as a Knowledge Article within OnePlace Knowledge Base documentation.

Ariel Wald↗

NASA NTRS1990

Process and representation in graphical displays

How people comprehend graphics is examined. Graphical comprehension involves the cognitive representation of information from a graphic display and the processing strategies that people apply to answer questions about graphics. Research on representation has examined both the features present in a graphic display and the cognitive representation of the graphic. The key features include the physical components of a graph, the relation between the figure and its axes, and the information in the graph. Tests of people's memory for graphs indicate that both the physical and informational aspect of a graph are important in the cognitive representation of a graph. However, the physical (or perceptual) features overshadow the information to a large degree. Processing strategies also involve a perception-information distinction. In order to answer simple questions (e.g., determining the value of a variable, comparing several variables, and determining the mean of a set of variables), people switch between two information processing strategies: (1) an arithmetic, look-up strategy in which they use a graph much like a table, looking up values and performing arithmetic calculations; and (2) a perceptual strategy in which they use the spatial characteristics of the graph to make comparisons and estimations. The user's choice of strategies depends on the task and the characteristics of the graph. A theory of graphic comprehension is presented.

Gillan, Douglas J.↗

NASA NTRSDate not supplied

Enhancing Air Traffic Control Planning with Automatic Speech Recognition

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.

ATM↗

DOE OSTI2020

Improved recovery of cell-cycle gene expression in Saccharomyces cerevisiae from regulatory interactions in multiple omics data

Gene expression is regulated by DNA-binding transcription factors (TFs). Together with their target genes, these factors and their interactions collectively form a gene regulatory network (GRN), which is responsible for producing patterns of transcription, including cyclical processes such as genome replication and cell division. However, identifying how this network regulates the timing of these patterns, including important interactions and regulatory motifs, remains a challenging task. Results We employed four in vivo and in vitro regulatory data sets to investigate the regulatory basis of expression timing and phase-specific patterns cell-cycle expression in Saccharomyces cerevisiae . Specifically, we considered interactions based on direct binding between TF and target gene, indirect effects of TF deletion on gene expression, and computational inference. We found that the source of regulatory information significantly impacts the accuracy and completeness of recovering known cell-cycle expressed genes. The best approach involved combining TF-target and TF-TF interactions features from multiple datasets in a single model. In addition, TFs important to multiple phases of cell-cycle expression also have the greatest impact on individual phases. Important TFs regulating a cell-cycle phase also tend to form modules in the GRN, including two sub-modules composed entirely of unannotated cell-cycle regulators ( STE12-TEC1 and RAP1-HAP1-MSN4 ). Conclusion Our findings illustrate the importance of integrating both multiple omics data and regulatory motifs in order to understand the significance regulatory interactions involved in timing gene expression. This integrated approached allowed us to recover both known cell-cycles interactions and the overall pattern of phase-specific expression across the cell-cycle better than any single data set. Likewise, by looking at regulatory motifs in the form of TF-TF interactions, we identified sets of TFs whose co-regulation of target genes was important for cell-cycle expression, even when regulation by individual TFs was not. Overall, this demonstrates the power of integrating multiple data sets and models of interaction in order to understand the regulatory basis of established biological processes and their associated gene regulatory networks.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Early Alarm: Robust Event Analysis for Power Systems using 1-D Fully Convolutional Network

This work presents a novel deep learning model for early, accurate, and robust detection, recognition, and temporal localization of multi-type events in large-scale power systems. The proposed method develops a unified 1-D fully convolutional network (FCN) model that takes time series of raw frequency signals measured from a power system as input, extracts distinguishing features, and predicts at every temporal point in the time series if an event is happening and what the type of the event is. Compared to existing methods, the proposed model eliminates the necessity for hand-crafted feature extraction or complicated data pre-processing, can flexibly handle input signals of arbitrary length, and precisely infer the event occurrence time. Most importantly, the model is intentionally trained with incomplete patterns, such that it is more robust to partial features of an event which is common in real-world online recognition, resulting in early alarm for power system failures. Extensive experimental results demonstrate that the proposed method achieves superior performance to the state-of-the-art, and also shows strong robustness to noise and system oscillations.

Li, Chengcheng↗

DOE OSTI2024

Large eddy simulation of lean blow-off in swirl-stabilized flame with the subgrid dissipation concept

The lean blow-off mechanism of the premixed swirl flame is numerically investigated by large eddy simulation (LES) with the subgrid dissipation concept (SDC) combustion model. Three simulated cases cover stable, near blow-off, and transient conditions. Compared with the experiment, the LES-SDC approach captures the flow and combustion features for stable and near blow-off conditions. More importantly, the predictions of the blowoff procedure and duration agree satisfactorily with the experiment, indicating that the LES-SDC approach is a promising tool for predicting strong, unsteady turbulent combustion processes. Further, the numerical results are used to investigate the blow-off mechanism. Two stages in the blow-off procedure are specified. The first is the necking and extinction of the downstream flame surface, and the second is the shrinking of the upstream flame surface. The blow-off mechanism is well explained by the theory of stretched flame extinction. At the end of the recirculation zone, the large negative radial velocity pushes the flame to the central line. The combustion process here can be abstracted as the stretched counter-flame of the reactant-reactant configuration. The excessive flame stretch dominates the flame extinction and triggers the blow-off event. The upstream flame resists the intense stretch with the help of hot product recirculation, and the combustion here can be idealized as the counter-flame of the reactant-product configuration. The alignment of the temperature gradient and flow velocity, together with the excessive stretch, clearly indicates the tendency of flame local extinction. A Damköhler number-based blow-off criterion is raised from the mean flow strain rate and laminar flame bulk extinction strain rate.

42 ENGINEERING↗

DOE OSTI2025

Environmental Influences on Deep Convective Upscale Growth Rate in Central Argentina From a Convection‐Permitting Simulation

This study uses a convection‐permitting model simulation to describe the environmental conditions under which convective upscale growth occurs in central Argentina, particularly examining environmental parameters when deep convection initially forms that could differentiate the rate of initial upscale growth. Simulated mesoscale convective systems (MCSs) are separated into slow and rapid growth by the rate of spatial growth from convection initiation until reaching the MCS scale. A low‐level jet (LLJ) is found more frequently near the deep convection that experiences rapid growth to an MCS, but its presence alone is not predictive of rapid growth. Using spatially‐averaged parameters, we find that rapid growth to MCSs also occurs in environments that are significantly more thermodynamically favorable with greater low‐level moisture and instability. Fewer significant differences are found in the kinematic environment with only the 0–2 km vertical wind shear magnitude being significantly larger for rapid growth MCSs compared to slow growth MCSs, potentially related to LLJs often peaking near this height. When focusing only on MCSs with the slowest and fastest growth rates, elevated‐layer shear is significantly smaller for very rapid growth MCSs, suggesting elevated‐layer shear may help discriminate between the upper and lower bounds of growth rate. Finally, when upscale growth occurs near the Sierras de Córdoba (SDC) with a LLJ present, rapid growth is also supported by favorable wind shear orientation. However, this does not hold for upscale growth occurring away from the SDC, highlighting the importance of interpreting shear direction relative to the orientation of features initiating deep convection.

Sasaki, Clayton R. S. [Univ. of Washington, Seattl↗

DOE OSTI2021

Grafting nanometer metal/oxide interface towards enhanced low-temperature acetylene semi-hydrogenation

Metal/oxide interface is of fundamental significance to heterogeneous catalysis because the seemingly “inert” oxide support can modulate the morphology, atomic and electronic structures of the metal catalyst through the interface. The interfacial effects are well studied over a bulk oxide support but remain elusive for nanometer-sized systems like clusters, arising from the challenges associated with chemical synthesis and structural elucidation of such hybrid clusters. We hereby demonstrate the essential catalytic roles of a nanometer metal/oxide interface constructed by a hybrid Pd/Bi 2 O 3 cluster ensemble, which is fabricated by a facile stepwise photochemical method. The Pd/Bi 2 O 3 cluster, of which the hybrid structure is elucidated by combined electron microscopy and microanalysis, features a small Pd-Pd coordination number and more importantly a Pd-Bi spatial correlation ascribed to the heterografting between Pd and Bi terminated Bi 2 O 3 clusters. The intra-cluster electron transfer towards Pd across the as-formed nanometer metal/oxide interface significantly weakens the ethylene adsorption without compromising the hydrogen activation. As a result, a 91% selectivity of ethylene and 90% conversion of acetylene can be achieved in a front-end hydrogenation process with a temperature as low as 44 °C.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Magnetic skyrmionic structures with variable topological charges in engineered Dzyaloshinskii-Moriya interaction systems

Magnetic skyrmionic structures, including magnetic skyrmions and antiskyrmions, are characterized by swirling spin textures with non-trivial topologies. They are featured with specific topological charges, Q , which are of crucial importance in determining their topological properties. Owing to the invariance of the chiral nature, it is generally believed that Q is conserved in a given magnetic skyrmionic structure and is hard to alter. Here, we experimentally realize the control of Q of magnetic skyrmionic structures at room temperature in a Dzyaloshinskii-Moriya interaction (DMI) platform with spatially alternating signs. Depending on how many times it crosses the interfaces between DMI regions with opposite signs, the magnetic skyrmionic structures possess different Q . Modifying the DMI energy landscape through chemisorbed oxygen, a magnetic topological transition is realized. This creation and manipulation of magnetic skyrmionic structures with controllable Q , in particular the DMI-stabilized thin-film antiskyrmions and high- Q skyrmionic structures, enables a new degree of freedom to control their dynamics via a novel DMI confinement effect. Our findings open up an unexplored avenue on various topological magnetic skyrmionic structures and their potential applications.

magnetic properties and materials↗

DOE OSTI2021

Converting tabular data into images for deep learning with convolutional neural networks

Abstract Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume a spatial relationship between features, and thus are unsuitable for modeling using CNNs. To meet this challenge, we develop a novel algorithm, image generator for tabular data (IGTD), to transform tabular data into images by assigning features to pixel positions so that similar features are close to each other in the image. The algorithm searches for an optimized assignment by minimizing the difference between the ranking of distances between features and the ranking of distances between their assigned pixels in the image. We apply IGTD to transform gene expression profiles of cancer cell lines (CCLs) and molecular descriptors of drugs into their respective image representations. Compared with existing transformation methods, IGTD generates compact image representations with better preservation of feature neighborhood structure. Evaluated on benchmark drug screening datasets, CNNs trained on IGTD image representations of CCLs and drugs exhibit a better performance of predicting anti-cancer drug response than both CNNs trained on alternative image representations and prediction models trained on the original tabular data.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Near-infrared C-term MCD spectroscopy of octahedral uranium (V) complexes

C-term magnetic circular dichroism (MCD) spectroscopy is a powerful method for probing d–d and f–f transitions in paramagnetic metal complexes. However, this technique remains underdeveloped both experimentally and theoretically for studies of U(V) complexes of Oh symmetry, which have been of longstanding interest for probing electronic structure, bonding, and covalency in 5f systems. In this study, C-term NIR MCD of the Laporte forbidden f–f transitions of [UCl 6 ] - and [UF 6 ] - are reported, demonstrating the significant fine structure resolution possible with this technique including for the low energy Γ 7 → Γ 8 transitions in [UF 6 ] - . The experimental NIR MCD studies were further extended to [U(OC 6 F 5 ) 6 ] - , [U(CH 2 SiMe 3 ) 6 ] - , and [U(NC(tBu)(Ph)) 6 ] - to evaluate the effects of ligand-type on the f–f MCD fine structure features. Theoretical calculations were conducted to determine the Laporte forbidden f–f transitions and their MCD intensity experimentally observed in the NIR spectra of the U(V) hexahalide complexes, via the inclusion of vibronic coupling, to better understand the underlying spectral fine structure features for these complexes. These spectra and simulations provide an important platform for the application of MCD spectroscopy to this widely studied class of U(V) complexes and identify areas for continued theoretical development.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

A quantitative model for a nanoscale switch accurately predicts thermal actuation behavior

Manipulation of temperature can be used to actuate DNA origami nano-hinges containing gold nanoparticles. We develop a physical model of this system that uses partition function analysis of the interaction between the nano-hinge and nanoparticle to predict the probability that the nano-hinge is open at a given temperature. The model agrees well with experimental data and predicts experimental conditions that allow the actuation temperature of the nano-hinge to be tuned over a range of temperatures from 30 °C to 45 °C. Additionally, the model identifies microscopic interactions that are important to the macroscopic behavior of the system, revealing surprising features of the system. This combination of physical insight and predictive potential is likely to inform future designs that integrate nanoparticles into dynamic DNA origami structures or use strand binding interactions to control dynamic DNA origami behavior. Furthermore, our modeling approach could be expanded to consider the incorporation, stability, and actuation of other types of functional elements or actuation mechanisms integrated into nucleic acid devices.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Tau tridents at accelerator neutrino facilities

We present the first detailed study of Standard Model neutrino tridents involving tau leptons at the near detectors of accelerator neutrino facilities. The rates of these processes were previously thought to be negligible, even at future facilities. Our full 2 → 4 calculation, including both coherent and incoherent scatterings, reveals that the DUNE near detector could observe a considerable number of tau tridents—an important background to new physics searches. We identify promising kinematic features that may allow distinction of tau tridents from the usual neutrino charged-current background at DUNE, and thus establish the observation of tau tridents for the first time. We also comment on the detection prospects at other accelerator and collider neutrino experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Topology-empowered membrane devices for terahertz photonics

Control of terahertz waves offers a profound platform for next-generation sensing, imaging, and information communications. However, all conventional terahertz components and systems suffer from bulky design, sensitivity to imperfections, and transmission loss. We propose and experimentally demonstrate on-chip integration and miniaturization of topological devices, which may address many existing drawbacks of the terahertz technology. We design and fabricate topological devices based on valley-Hall photonic structures that can be employed for various integrated components of on-chip terahertz systems. We demonstrate valley-locked asymmetric energy flow and mode conversion with topological waveguide, multiport couplers, wave division, and whispering gallery mode resonators. Our devices are based on topological membrane metasurfaces, which are of great importance for developing on-chip photonics and bring many features into terahertz technology.

47 OTHER INSTRUMENTATION↗