Small Uncrewed Aerial Vehicles (sUAVs) are a commonly used tool for agricultural remote sensing due to their capability of carrying a variety of sensors including hyperspectral and LIDAR. Tethered Uncrewed Aerial Vehicles (tUAVs) offer theoretically infinite endurance while maintaining most of the flexibility of a UAV. Proximal sensing carts (PSCs) and Unmanned Ground Vehicles (UGVs) offer large payloads and increased endurance compared to UAVs. Combining UAVs and PSCs can be used to provide a flexible sensor mounting platform that is less dependent on the terrain, is flexible in height, and offers dynamic flexibility in positioning the sensors, all while increasing endurance. Presented here is a bolt-on solution for a marsupial tethered UAV designed to operate either in tandem with or independently from its mother vehicle. It is designed to work either as a part of a Manned-Unmanned Team (MUM-T) with a human powered cart or work autonomously with an UGV. The tradeoffs of the system are analyzed; such as safety as part of a MUM-T, increased weight of the mother vehicle, cost, and complexity.

Optical turbulence in the atmosphere causes defocus, blur, and wander of images captured over long distances, which can significantly degrade their quality. Turbulence is a manifestation of variations in the index of refraction, which are caused by local variations in air temperature, pressure, humidity, gas content, and other factors. Turbulence can be quantified by the refractive index structure function parameter C-n(2). Simulation of images after propagation through an atmosphere of a specific C-n(2), along with measurement of the observed C-n(2) from images, is thus of interest for a variety of agricultural, environmental, and defense applications. We discuss the generation of simulated imagery after propagation through an atmosphere of a defined C-n(2) using various algorithms, then examine methods to determine the observed C-n(2) from the generated images. Finally, we choose and test an algorithm to generate images and another to estimate C-n(2), then compare and contrast the observed C(n)(2)to the defined C-n(2) in each case to observe how the simulation method and measurement method perform.

With an increasing focus on precision agriculture to maximize crop yields and minimize ecological impacts, remote sensing for agriculture has required the deployment of more advanced sensors and processing algorithms. Traditionally, unmanned aerial systems (UAS's) have been the primary choice for phenotyping crops, but these systems are limited in endurance, power, payload, and legality. Medium to large, unmanned ground vehicles (UGV's), however, are not hampered by these limitations. Previous research in the application of phenotyping UGV's for tall crops has been focused on either small systems or very large gantry systems. Described here is a medium sized, low-cost, adjustable UGV that provides a solution by demonstrating the capability to image tall crops into late growth states. It incorporates a sliding mechanism to allow for a greater range in height than previous phenotyping UGV's with the same payload capacity. The UGV's capabilities are analyzed theoretically and practically, including its structural rigidity, handling, and endurance. An overview of parts and assembly is presented to facilitate replication and proliferation of the vehicle. Additionally, the vehicle is primarily fabricated using off-the-shelf components. The few custom components are used based on common materials and simple geometries and can be replicated with standard metalworking equipment. To further reduce costs, a dual RTK-GNSS system is utilized to control the UGV in a semi-autonomous fashion. A Future goal is to use the gathered datasets to produce an algorithm for fully autonomous capabilities.

A measurement of the diffuse astrophysical neutrino spectrum is presented using IceCube data collected from 2011-2022 (10.3 years). We developed novel detection techniques to search for events with a contained vertex and exiting track induced by muon neutrinos undergoing a charged-current interaction. Searching for these starting track events allows us to not only more effectively reject atmospheric muons but also atmospheric neutrino backgrounds in the southern sky, opening a new window to the sub-100 TeV astrophysical neutrino sky. The event selection is constructed using a dynamic starting track veto and machine learning algorithms. We use this data to measure the astrophysical diffuse flux as a single power law flux (SPL) with a best-fit spectral index of $\gamma = 2.58 ^{+0.10}_{-0.09}$ and per-flavor normalization of $\phi^{\mathrm{Astro}}_{\mathrm{per-flavor}} = 1.68 ^{+0.19}_{-0.22} \times 10^{-18} \times \mathrm{GeV}^{-1} \mathrm{cm}^{-2} \mathrm{s}^{-1} \mathrm{sr}^{-1}$ (at 100 TeV). The sensitive energy range for this dataset is 3 - 550 TeV under the SPL assumption. This data was also used to measure the flux under a broken power law, however we did not find any evidence of a low energy cutoff.

This research presents an in-depth investigation into the application of Convolutional Neural Networks (CNN) for acoustic remote sensing on multi-rotor UAVs, with a specific focus on detecting large vehicles on the ground. We used a multi-rotor UAV equipped with a custom audio recorder, calibrated microphones, and uniquely designed microphone mounts for data collection. We explored optimal features for training our CNN, experimented with different normalization techniques, and examined their synergy between various activation functions. The study further explores the fine-tuning of model parameters to enhance detection performance and reliability. The outcome was a CNN model, trained with a combination of both real-world and synthetic data, demonstrating a proficient capability in target detection.

In recent decades, wildfires have become increasingly widespread and hazardous. Dryer, hotter weather combined with more frequent heat waves leave forest areas susceptible to sudden, intense, and fast-growing forest fires. To protect private property and mitigate the damage, Hot Shot fire fighters are deployed into these dangerous situations. Extensive satellite and aerial platforms possess optical techniques for monitoring wildfire risks and boundary tracking. sUAS (small unmanned aerial system) based EO/IR systems provide a solution for real-time, high resolution, targeted response to acquire information critical to the safety and efficacy of wildfire mitigation. Real-time imagery from a sUAS of the position of Hot Shots and the progression of the fire boundary would be easily obtained and offer a method of ensuring safe deployment. An ideal sensor system for situational awareness in this environment would be able to image the ambient terrain and firefighters with good contrast while also detecting fire signatures and imaging through the smoke. The longer wavelength infrared bands have demonstrated imaging through the smoke of forest fires. However, near the wildfire where the Hot Shots work, they also receive strong radiometric signal from the temperature of the smoke. The emitted signal of the smoke can obscure the line of sight similarly to the scattering effect of wildfire smoke in the visible spectrum. The reflective and emissive components of a wildfire scene are studied and compared in the visible (VIS, 0.4 - 0.7 mu m), shortwave infrared (SWIR, 1.0-1.7 mu m), extended SWIR (eSWIR, 2.0-2.5 mu m), and longwave infrared (LWIR, 8-14 mu m). Both a radiometric model and calibrated field measurements find a band that has the highest probability for a continuous line of sight for terrain, firefighters, and fire signatures in a wildfire scene.

Coherent light propagation in the atmosphere, as occurs with lasers, is significantly impacted by fluctuations in the refractive index of air, which are a function of temperature and pressure. The fluctuations cause beam degradations, including spatial incoherence, power fades, and surges. Conventionally, numerical wave propagation methods with phase screens are used for modeling imaging and optical transmission. Phase screens assume turbulence isotropy and thin turbulence regions to simplify complex turbulence behaviors in the atmosphere. However, these assumptions may result in large deviations due to shear and inhomogeneous regions in the atmospheric boundary layer. An alternate optical turbulence model is proposed using a spherical bubble packing scheme. The broad spectrum of turbulent length scales is represented by the bubbles with radius based on power law distribution based on a linear-eddy model approach. The refractive index of each bubble is prescribed based on the Tatarskii spectrum. Imaging is accomplished by a ray tracing algorithm through Snell's law. The effects of length scales, path, and refractive-index structure function coefficient, C-n(2) are investigated by imaging analysis. We take several steps to assess model uncertainty and inadequacy for validation and verification. Images generated from ray tracing are used for verification of the bubble model. A numerical wave propagation approach with phase screens is used to validate imaging techniques used on the bubble model with prescribed C-n(2) values and profiles.

Gamma-ray bursts (GRBs) have long been considered a possible source of high-energy neutrinos. While no correlations have yet been detected between high-energy neutrinos and GRBs, the recent observation of GRB 221009A - the brightest GRB observed by Fermi-GBM to date and the first one to be observed above an energy of 10 TeV - provides a unique opportunity to test for hadronic emission. In this paper, we leverage the wide energy range of the IceCube Neutrino Observatory to search for neutrinos from GRB 221009A. We find no significant deviation from background expectation across event samples ranging from MeV to PeV energies, placing stringent upper limits on the neutrino emission from this source.

Abstract Atmospheric muon neutrinos are produced by meson decays in cosmic-ray-induced air showers. The flux depends on meteorological quantities such as the air temperature, which affects the density of air. Competition between decay and re-interaction of those mesons in the first particle production generations gives rise to a higher neutrino flux when the air density in the stratosphere is lower, corresponding to a higher temperature. A measurement of a temperature dependence of the atmospheric $$\nu _{\mu }$$ ν μ flux provides a novel method for constraining hadronic interaction models of air showers. It is particularly sensitive to the production of kaons. Studying this temperature dependence for the first time requires a large sample of high-energy neutrinos as well as a detailed understanding of atmospheric properties. We report the significant ( $$> 10 \; \sigma $$ > 10 σ ) observation of a correlation between the rate of more than 260,000 neutrinos, detected by IceCube between 2012 and 2018, and atmospheric temperatures of the stratosphere, measured by the Atmospheric Infrared Sounder (AIRS) instrument aboard NASA’s AQUA satellite. For the observed 10 $$\%$$ % seasonal change of effective atmospheric temperature we measure a 3.5(3) $$\%$$ % change in the muon neutrino flux. This observed correlation deviates by about 2-3 standard deviations from the expected correlation of 4.3 $$\%$$ % as obtained from theoretical predictions under the assumption of various hadronic interaction models.

We describe a new data sample of IceCube DeepCore and report on the latest measurement of atmospheric neutrino oscillations obtained with data recorded between 2011-2019. The sample includes significant improvements in data calibration, detector simulation, and data processing, and the analysis benefits from a detailed treatment of systematic uncertainties, with significantly higher level of detail since our last study. By measuring the relative fluxes of neutrino flavors as a function of their reconstructed energies and arrival directions we constrain the atmospheric neutrino mixing parameters to be $\sin^2\theta_{23} = 0.51\pm 0.05$ and $\Delta m^2_{32} = 2.41\pm0.07\times 10^{-3}\mathrm{eV}^2$, assuming a normal mass ordering. The resulting 40\% reduction in the error of both parameters with respect to our previous result makes this the most precise measurement of oscillation parameters using atmospheric neutrinos. Our results are also compatible and complementary to those obtained using neutrino beams from accelerators, which are obtained at lower neutrino energies and are subject to different sources of uncertainties.

Core-collapse supernovae are a promising potential high-energy neutrino source class. We test for correlation between seven years of IceCube neutrino data and a catalog containing more than 1000 core-collapse supernovae of types IIn and IIP and a sample of stripped-envelope supernovae. We search both for neutrino emission from individual supernovae as well as for combined emission from the whole supernova sample, through a stacking analysis. No significant spatial or temporal correlation of neutrinos with the cataloged supernovae was found. All scenarios were tested against the background expectation and together yield an overall p -value of 93%; therefore, they show consistency with the background only. The derived upper limits on the total energy emitted in neutrinos are 1.7 × 10 48 erg for stripped-envelope supernovae, 2.8 × 10 48 erg for type IIP, and 1.3 × 10 49 erg for type IIn SNe, the latter disfavoring models with optimistic assumptions for neutrino production in interacting supernovae. We conclude that stripped-envelope supernovae and supernovae of type IIn do not contribute more than 14.6% and 33.9%, respectively, to the diffuse neutrino flux in the energy range of about [ 10 3 –10 5 ] GeV, assuming that the neutrino energy spectrum follows a power-law with an index of −2.5. Under the same assumption, we can only constrain the contribution of type IIP SNe to no more than 59.9%. Thus, core-collapse supernovae of types IIn and stripped-envelope supernovae can both be ruled out as the dominant source of the diffuse neutrino flux under the given assumptions.

We present a catalog of likely astrophysical neutrino track-like events from the IceCube Neutrino Observatory. IceCube began reporting likely astrophysical neutrinos in 2016, and this system was updated in 2019. The catalog presented here includes events that were reported in real time since 2019, as well as events identified in archival data samples starting from 2011. We report 275 neutrino events from two selection channels as the first entries in the catalog, the IceCube Event Catalog of Alert Tracks, which will see ongoing extensions with additional alerts. The Gold and Bronze alert channels respectively provide neutrino candidates with a 50% and 30% probability of being astrophysical, on average assuming an astrophysical neutrino power-law energy spectral index of 2.19. For each neutrino alert, we provide the reconstructed energy, direction, false-alarm rate, probability of being astrophysical in origin, and likelihood contours describing the spatial uncertainty in the alert's reconstructed location. We also investigate a directional correlation of these neutrino events with gamma-ray and X-ray catalogs, including 4FGL, 3HWC, TeVCat, and Swift-BAT.

The Reed-Xiaoli Detection (RX) algorithm is a classic algorithm commonly used to detect anomalies in hyperspectral image data, i.e. regions which are spectrally distinct from the image background. Such regions may represent interesting objects to human observers. We investigate the possibility of applying the RX algorithm to a VNIR pushbroom hyperspectral image sensor in real time onboard a small uncrewed aerial system (UAS). The generated anomaly information is much more concise and can be transmitted much faster than the raw hyperspectral data. This would enable anomalies to be automatically detected, then communicated to a ground station for immediate attention by a human observer. However, the UAS payload capacities impose strict size, weight, and power constraints. We show in what contexts the algorithm can be successfully applied and how the UAS constraints bound algorithm performance and parameters.

This paper presents the results of a search for neutrinos that are spatially and temporally coincident with 22 unique, nonrepeating fast radio bursts (FRBs) and one repeating FRB (FRB 121102). FRBs are a rapidly growing class of Galactic and extragalactic astrophysical objects that are considered a potential source of high-energy neutrinos. The IceCube Neutrino Observatory's previous FRB analyses have solely used track events. This search utilizes seven years of IceCube cascade events which are statistically independent of track events. This event selection allows probing of a longer range of extended timescales due to the low background rate. No statistically significant clustering of neutrinos was observed. Upper limits are set on the time-integrated neutrino flux emitted by FRBs for a range of extended time windows.