Secure & Energy Efficient Sub-Terahertz Wireless System for 6G
Poster for the LDRD 22A1059-031FP titled An Innovative Secure & Energy Efficient Sub-Terahertz Wireless System for 6G.
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Poster for the LDRD 22A1059-031FP titled An Innovative Secure & Energy Efficient Sub-Terahertz Wireless System for 6G.
Machine learning approach can combine mass spectral imaging (MSI) techniques, one with low spatial resolution but intact molecular spectra and the other with nanometer spatial resolution but fragmented molecular signatures, to predict molecular MSI spectra with submicron spatial resolution. The machine learning approach can perform transformations on the spectral image data of the two MSI techniques to reduce dimensionality, and using a correlation technique, find relationships between the transformed spectral image data. The determined relationships can be used to generate MSI spectra of desired resolution.
This paper summarizes my work during the Summer 2025 SULI internship, which focused on two main projects and broader scientific development. The first project involved improving the particle identification (PID) of protons, deuterons, and tritons using PIDA distributions and template fitting, with the goal of modeling nuclear final-state interactions (FSI) and testing the robustness of the method against systematic uncertainties. These techniques pave the way for future application to LArTPC data from the ICARUS detector. The second project centered on the optimization and data-taking of the TinyTPC detector, a compact LArTPC used for high-resolution low-energy measurements. I adjusted gain and threshold parameters, performed hardware validation tests, and developed analysis strategies to extract meaningful physics from collected data. Throughout the summer, I also enhanced my scientific communication and mentorship skills through presentations, collaborative analysis, and peer guidance.
Abstract We design a new strategy to load‐balance high‐intensity sub‐grid atmospheric physics calculations restricted to a small fraction of a global climate simulation's domain. We show why the current parallel load balancing infrastructure of Community Earth System Model (CESM) and Energy Exascale Earth Model (E3SM) cannot efficiently handle this scenario at large core counts. As an example, we study an unusual configuration of the E3SM Multiscale Modeling Framework (MMF) that embeds a binary mixture of two separate cloud‐resolving model grid structures that is attractive for low cloud feedback studies. Less than a third of the planet uses high‐resolution (MMF‐HR; sub‐km horizontal grid spacing) relative to standard low‐resolution (MMF‐LR) cloud superparameterization elsewhere. To enable MMF runs with Multi‐Domain cloud resolving models (CRMs), our load balancing theory predicts the most efficient computational scale as a function of the high‐intensity work's relative overhead and its fractional coverage. The scheme successfully maximizes model throughput and minimizes model cost relative to precursor infrastructure, effectively by devoting the vast majority of the processor pool to operate on the few high‐intensity (and rate‐limiting) high‐resolution (HR) grid columns. Two examples prove the concept, showing that minor artifacts can be introduced near the HR/low‐resolution CRM grid transition boundary on idealized aquaplanets, but are minimal in operationally relevant real‐geography settings. As intended, within the high (low) resolution area, our Multi‐Domain CRM simulations exhibit cloud fraction and shortwave reflection convergent to standard baseline tests that use globally homogenous MMF‐LR and MMF‐HR. We suggest this approach can open up a range of creative multi‐resolution climate experiments without requiring unduly large allocations of computational resources.
Here, in this paper we present the first high-resolution laser spectroscopy results obtained at the GISELE laser laboratory of the GANIL-SPIRAL2 facility, in preparation for the first experiments with the S 3 -Low Energy Branch. Studies of neutron-deficient radioactive isotopes of erbium and tin represent the first physics cases to be studied at S 3 . The measured isotope-shift and hyperfine structure data are presented for stable isotopes of these elements. The erbium isotopes were studied using the 4f 12 6s 2 3 H 6 → 4f 12 ( 3 H)6s6p J = 5 atomic transition (415 nm) and the tin isotopes were studied by the 5s 2 5p 2 ( 3 P 0 ) → 5s 2 5p6s( 3 P 1 ) atomic transition (286.4 nm), and are used as a benchmark of the laser setup. Additionally, the tin isotopes were studied by the 5s 2 5p6s( 3 P 1 ) → 5s 2 5p6p( 3 P 2 ) atomic transition (811.6 nm), for which new isotope-shift data was obtained and the corresponding field-shift F 812 and mass-shift M 812 factors are presented.
This work presents a system upgrade of the High Resolution Thomson Scattering (HRTS) diagnostic on JET that allows it to measure low temperature (1–500 eV) plasma pre- and post-Thermal quench (TQ), which would help us further understand the Shattered Pellet Injection (SPI) physics. The upgrade was done by connecting optic fibers from the original HRTS system to four spectrometers specialized in measuring low temperature plasmas. The upgraded system allows for the measurement of low temperature plasma at up to 12 spatial points, which can be flexibly distributed throughout the JET outer midplane profile during the dedicated SPI experiments. In other JET experiments, four previously unused groups of fibers were used to measure the central plasma to provide disruption data without changing the standard HRTS system. The low temperature Thomson scattering system was installed, commissioned, and cross-calibrated against the standard HRTS diagnostic in a calibration pulse. The system worked reliably during the JET experiments and provided electron density and temperature profiles for pre- and post-TQ low temperature plasmas in the SPI campaign.
Longitudinal profiling of water quality via the deployment of sensors from watercraft has advanced the understanding of spatial patterns in large rivers and lakes; however, a similar approach in low- to mid-order streams is lacking. To fill this gap, we developed an unmanned surface vehicle (USV)-water quality measurement platform (the “AquaBOT”). The components of the AquaBOT included a nitrate sensor, multiparameter sonde (temperature, conductivity, turbidity, dissolved oxygen, chlorophyll), quantum sensor, and global positioning system (GPS) mounted to a small pontoon-style USV. The AquaBOT was tested in four streams and rivers in Iowa and Tennessee. All measured water quality parameters varied longitudinally, and greater ranges were generally observed along the low-order, agriculturally influenced streams in Iowa. Nitrate, in particular, was spatially heterogeneous. For example, during one run in early June, concentrations ranged from 10.5 to 12.5 mg N L –1 along a 2.3 km reach and hotspots were observed directly downstream of some tile drains. The spatial resolution of AquaBOT data collected in June was 10× higher than grab sampling data, and measurements were collected in less time and at a comparable cost. Here, the AquaBOT can complement existing measurement approaches and will lead to advancements in understanding the processes driving water quality along the stream-to-river continuum.
Spatial and energy resolutions of state-of-the-art transmission electron microscopes (TEMs) have surpassed 50 pm and 5 meV. However, with respect to the time domain, even the fastest detectors combined with the brightest sources may only be able to reach the microsecond timescale. Thus, conventional methods are incapable of resolving myriad fundamental ultrafast (i.e., attosecond to picosecond) atomic-scale dynamics. The successful demonstration of femtosecond (fs) laser-based (LB) ultrafast transmission electron microscopy (UEM) nearly 20 years ago provided a means to span this nearly 10-order-of-magnitude temporal gap. While nanometer-picosecond UEM studies of dynamics are now well established, ultrafast Å-scale imaging has gone largely unrealized. Further, while instrument development has rightly been an emphasis, and while new modalities and uses of pulsed-beam TEM continue to emerge, the overall chemical and materials application space has been only modestly explored to date. In this Perspectives article, we argue that these apparent shortfalls can be attributed to a simple lack of data and detail. We speculate that present work and continued growth of the field will ultimately lead to the realization that Å-scale fs dynamics can indeed be imaged with minimally modified UEM instrumentation and with repetition rates (f rep ) below - and perhaps even well below - 1 MHz. We further argue that use of low f rep , whether for LB UEM or for chopped/bunched beams, significantly expands the accessible application space. Furthermore, this calls for systematically establishing modality-specific limits so that especially promising technologies can be pursued, thus ultimately facilitating broader adoption as individual instrument capabilities expand.
In a trend toward clean energy alternatives, recent years have seen great strides in the marine energy space. Consequently, there is a pressing need for the design, development, and validation of novel energy harvesting technologies such as hydrokinetic devices, which capture kinetic energy from waves, tides, and currents. However, these devices span numerous concepts and designs that often lack solid benchmark research that can be freely referenced throughout their development. This work focuses on the design process of an open-source composite hydrokinetic turbine blade for a three-bladed marine turbine rotor assembly with a diameter of 2.5 m. The proposed blade consists of two structural composite skins that are bonded with an adhesive and filled with a foam core. This study also explores and contrasts the efficiency and resolution of low-fidelity rapid design methodologies and comprehensive high-fidelity approaches in the context of blade design, modeling, and analysis efforts, a key objective in this research. Blade hydrodynamic loads were modeled and applied to finite-element blade models to study deformations and potential failure. Ongoing and upcoming efforts will result in blade manufacture and structural testing at the National Renewable Energy Laboratory. In future work, multiple blades will be deployed at the Living Bridge site at the University of New Hampshire and will be compared to rigid aluminum blades of the same geometry, developed by Sandia National Laboratories. Ultimately, this research will lay foundational groundwork for researchers and manufacturers, establishing a baseline composite blade design that will serve as a benchmark in the development of future hydrokinetic turbine blades.
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We constrain polarized foreground emission between 30 and 70 GHz with the Planck Low Frequency Instrument (LFI) and WMAP data within the global Bayesian BeyondPlanck framework. We combine for the first time full-resolution Planck LFI time-ordered data with low-resolution WMAP sky maps at 33, 40 and 61 GHz. Spectral parameters are fit with a likelihood defined at the native resolution of each frequency channel. This analysis represents the first implementation of true multi-resolution component separation applied to CMB observations for both amplitude and spectral energy distribution (SED) parameters. For synchrotron emission, we approximate the SED as a power-law in frequency and find that the low signal-to-noise ratio of the current data strongly limits the number of free parameters that may be robustly constrained. We partition the sky into four large disjoint regions (High Latitude; Galactic Spur; Galactic Plane; and Galactic Center), each associated with its own power-law index. We find that the High Latitude region is prior-dominated, while the Galactic Center region is contaminated by residual instrumental systematics. The two remaining regions appear to be signal-dominated, and for these we derive spectral indices of $β$ S Spur = -3.17 ± 0.06 and $β$ S Plane = -3.03 ± 0.07, in good agreement with previous results. For thermal dust emission we assume a modified blackbody model and we fit a single power-law index across the full sky. We find $β$ d = 1.64 ± 0.03, which is slightly steeper than reported from Planck HFI data, but still statistically consistent at the 2σ confidence level.
We discuss a general framework for the evaluation of the gluonic form factors in light hadrons at low momentum transfer, in the QCD instanton vacuum. At medium resolution of the order of the inverse mean instanton size, the glue is mostly localized in single or pairs of pseudoparticles and globally constrained by the fluctuations of their topological charges. These pseudoparticles trap light quarks, giving rise to emerging multiflavor ’t Hooft interactions. We explicitly evaluate the gluonic scalar, pseudoscalar, energy-momentum tensor (EMT), and the leading C -odd and C -even three-gluon hadronic form factors, at next to leading order in the instanton density, including molecular clusters of like and unlike instantons. We use the results for the EMT to address the contribution of the gluons in Ji’s mass and spin sum rules, at low resolution. When evolved, our results for the mass and spin composition of the nucleon are shown to be in good agreement with the recently reported lattice results at higher resolution. Published by the American Physical Society 2024
The pyrochlore magnet Tb 2 Ti 2 O 7 shows a lack of magnetic order to low temperatures and is considered to be a quantum spin liquid candidate. Here we perform time-domain THz spectroscopy on high-quality Tb 2 Ti 2 O 7 crystals and study the low-energy excitations as a function of [111] magnetic field with high energy resolution. The low-energy crystal-field excitations change their energies anomalously under magnetic field. Despite several sharp field-dependent changes, we show that the material's spectrum can be described not by phase transitions but by field-dependent hybridization between the low-energy crystal-field levels. We highlight the strong coupling between spin and lattice degrees of freedom in Tb 2 Ti 2 O 7 as evidenced by the magnetic-field tunable crystal-field environment. Calculations based on single ion physics with field-induced symmetry reduction of the crystal-field environment can reproduce our data.
This dataset contains daily Normalized Difference Snow Index (NDSI) values at 30 m spatial resolution for the East River watershed in Colorado, USA. The temporal range of these data includes water years 2001-2020. These data were created using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). This model fuses low spatial and high temporal resolution data from MODIS (500 m, daily) with high spatial and low temporal resolution data from Landsat (30 m, 16 days) to create a 30m synthetic daily snow product. This product allows for the analysis of historical snow covered area trends in the East River Watershed at fine spatiotemporal resolutions where it was not available previously. This research was performed as a part of the Department of Energy’s Subsurface Biogeochemical Research Program with the primary intent of better understanding the timing and spatial patterns of water delivery to the Critical Zone in mountain watersheds. Each .zip file contains one "water year" of data (October 1 - September 30; i.e., water year 2010 starts October 1, 2010 and ends September 30, 2011). Each zip file contains the following: STARFM daily Normalized Difference Snow Index (NDSI) fusion data files in GeoTiff format with one layer for each day between Landsat data acquisition dates (i.e., for dates of Landsat acquisition, the Landsat image is included for that date). The study area is located in an area of Landsat path overlap, so Landsat dates acquisitions are every 7-9 days. Landsat NDSI files containing the high spatial (30m), low temporal (7-9 days due to Landsat path overlap) resolution data used as input to STARFM in GeoTiff format with one layer for each day. Dates for which no Landsat data were obtained are included as NoData layers. MODIS NDSI files containing the high temporal (daily), low spatial (500m) resolution data used as input to STARFM in GeoTiff format with one layer for each day. Please note the MODIS data were resampled to 30m pixels for input into the STARFM model. The data have a scale factor of 10,000 and a no data value of -32767. The projection of all datasets is WGS 84 (EPSG: 4326), which has a latitude/longitude based degree resolution of 0.0002694946 X 0.0002694946, and approximates to the 30 m spatial resolution mentioned above. The Layer Index files in .csv format. They contain information for each layer in the above GeoTiff files regarding the corresponding date for each layer, the fraction of pixels in the image that contain valid data (missing data is due to either cloud cover or poor data quality; these values are not percent snow cover). Dates of Landsat overpass are indicated in these files. If no Landsat data were able to be obtained due to cloud cover or lack of Landsat Tier 1 data available on Google Earth Engine, this is also noted.
Quantitative and objective evaluation tools are essential for assessing the performance of machine learning (ML)-based magnetic resonance imaging (MRI) reconstruction methods. However, the commonly used fidelity metrics, such as mean squared error (MSE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR), often fail to capture fundamental and clinically relevant MR image quality aspects. To address this, we propose evaluation of ML-based MRI reconstruction using digital image quality phantoms and automated evaluation methods. Our phantoms are based upon the American College of Radiology (ACR) large physical phantom but created in k-space to simulate their MR images, and they can vary in object size, signal-to-noise ratio, resolution, and image contrast. Our evaluation pipeline incorporates evaluation metrics of geometric accuracy, intensity uniformity, percentage ghosting, sharpness, signal-to-noise ratio, resolution, and low-contrast detectability. We demonstrate the utility of our proposed pipeline by assessing an example ML-based reconstruction model across various training and testing scenarios. The performance results indicate that training data acquired with a lower undersampling factor and coils of larger anatomical coverage yield a better performing model. The comprehensive and standardized pipeline introduced in this study can help to facilitate a better understanding of the performance and guide future development and advancement of ML-based reconstruction algorithms.
This paper describes the software Data Reduction and Analysis for GRACES (DRAGraces), which is a pipeline reducing spectra from Gemini Remote Access to the CFHT ESPaDOnS Spectrograph (GRACES) at the Gemini North Telescope. The code is written in the IDL language. It is designed to find all the GRACES frames in a given directory, automatically determine the list of bias, flat, arc, and science frames, and perform the whole reduction and extraction within a few minutes. We compare the output from DRAGraces with that of the Open source Pipeline for ESPaDOnS Reduction and Analysis (OPERA), a pipeline developed at the Canada–France–Hawaii Telescope (CFHT) that also can extract GRACES spectra. Both pipelines were developed completely independently, yet they give very similar extracted spectra. They both have their advantages and disadvantages. For instance, DRAGraces is more straightforward and easy to use and is less likely to be derailed by a parameter that needs to be tweaked, while OPERA offers a more careful extraction that can be significantly superior when the highest resolution is required and when the signal-to-noise ratio is low. One should compare both before deciding which one to use for their science. Yet, both pipelines deliver a fairly comparable resolution power (R ~ 52.8k and 36.6k for DRAGraces and R ~ 58k and 40k for OPERA in high- and low-resolution spectral modes, respectively), wavelength solution, and signal-to-noise ratio per resolution element.
Nitrogen is a primary constituent of Earth’s atmosphere and an essential component of many biological processes. Recent developments in understanding N incorporation into silicate minerals suggest that the solid earth and lithosphere could provide a substantial reservoir for the planetary N budget. Furthermore, determining the concentration and distribution of low abundances of N in silicates presents a significant challenge in microanalysis. We tested capabilities for high spatial resolution quantification of low levels of N using EPMA with the goal of developing standards to be used in coupled N and noble gas analyses of silicate minerals. We investigated the concentration and distribution of N in a suite of ammonium-bearing silicate minerals that reflect the dominant N-bearing phases of the lithosphere: tobelite, buddingtonite, ammonioleucite, tsaregorodtsevite, and hyalophane. These minerals form during NH4-rich alteration of K-bearing silicate minerals, and the bonding environment of N in each mineral is likely to differ based on crystal structure. Using an optimized analytical routine for N quantification enables sensitivity approaching 0.02 weight% N and high-resolution quantitative mapping of sub-micron variations in N contents.
Nitrogen is a primary constituent of Earth’s atmosphere and an essential component of many biological processes. Recent developments in understanding N incorporation into silicate minerals suggest that the solid earth and lithosphere could provide a substantial reservoir for the planetary N budget. Furthermore, determining the concentration and distribution of low abundances of N in silicates presents a significant challenge in microanalysis. We tested capabilities for high spatial resolution quantification of low levels of N using EPMA with the goal of developing standards to be used in coupled N and noble gas analyses of silicate minerals. We investigated the concentration and distribution of N in a suite of ammonium-bearing silicate minerals that reflect the dominant N-bearing phases of the lithosphere: tobelite, buddingtonite, ammonioleucite, tsaregorodtsevite, and hyalophane. These minerals form during NH 4 -rich alteration of K-bearing silicate minerals, and the bonding environment of N in each mineral is likely to differ based on crystal structure. Using an optimized analytical routine for N quantification enables sensitivity approaching 0.02 weight% N and high-resolution quantitative mapping of sub-micron variations in N contents.