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Prediction Of Satellite Temperature During An Orbit Of A Cubesat, Michael Reynolds

Honors Theses

This thesis develops a thermal-simulation strategy for predicting CubeSat component temperatures, applied to Jag-Sat-1, a CubeSat developed at the University of South Alabama and deployed from the International Space Station in 2022. The orbit was reconstructed from two-line element (TLE) data using simplified general perturbations (SGP4) propagation, and spacecraft attitude was recovered from onboard gyroscope measurements. Sunlight, penumbra, and umbra intervals were computed geometrically, and the external radiative environment — direct solar, Earth infrared, and albedo heat fluxes — was modeled using orientation-dependent view factors. These time-varying fluxes drove a transient finite-element thermal simulation of the full satellite geometry in …

Fixed-Parameter Extrapolation And Aperiodic Order, Stephen Fenner, Frederic Green, Steven Homer

Mathematics

Fix any λ∈C. We say that a set S⊆C is λ-convex if, whenever a and b are in S, the point (1-λ)a+λb is also in S. We investigate the properties of λ-convex sets and their (topological) closures, and we prove a number of facts about them. Let Qλ⊆C be the least λ-convex superset of {0,1}. Generalizing results of R. G. E. Pinch, we give a sufficient condition on λ for Qλ and some other related λ-convex sets to be discrete by introducing the notion of a strong PV number. These conditions give rise to a number of periodic and aperiodic …

On The Side And Angle Mixtures Expressed By Triangles: Phase Diagrams Based On Intensive State Properties, Daniel Graham

Chemistry: Faculty Publications and Other Works

This is a lecture packet focused on the side and angle mixtures of structures at the center of the planar geometry universe. The intensive states of triangles are charted with the help of phase diagrams—long-standing tools in chemical thermodynamics and materials science. The intended audience consists of students and teachers—college and high school—seeking fresh perspectives, projects, and discussion topics surrounding age-old systems.

Elemental Analysis Of Upper Devonian To Lower Mississippian Organic-Rich Mudstone Cores From Kentucky By Portable X-Ray Fluorescence (Pxrf), Alex M. Washburn

KGS Research Data Set

This dataset contains high-density portable X-ray fluorescence (pXRF) elemental analyses of 1,867 m (6,125 ft) of Upper Devonian to Lower Mississippian organic-rich mudstone cores from Kentucky. Analyses were collected from cores across the Illinois Basin, Cincinnati Arch, and Appalachian Basin to support regional evaluation of elemental variability, chemostratigraphy, and redox-sensitive trace-metal enrichment in Devonian–Mississippian mudstone successions. All measurements were conducted using a Bruker Tracer 5 pXRF analyzer with the factory “Mudrock Air” calibration, using analytical settings optimized for both low-atomic-number elements and transition metals/redox-sensitive trace elements. Reference materials SARM-41 and SGR-1 were analyzed throughout the analytical sequence to monitor instrument …

The Interwoven Dynamics Of Climate, Fire, Resilience And Orangutan Survival In Central Kalimantan’S Peat Swamp Forest, Putu Pujiantari

Graduate Theses and Dissertations

Tropical peat swamp forests are globally significant carbon reservoirs and biodiversity strongholds, yet they face escalating threats from climate change, fire, and anthropogenic disturbances. This dissertation investigates the interwoven dynamics of climate, fire, forest resilience, and orangutan survival in Central Kalimantan, Indonesia, a region which was transformed by the failed Mega Rice Project and recurrent catastrophic wildfires for decades. Using a multi-scale approach that integrates remote sensing time-series analysis (2001–2024),nlandscape-scale disturbance mapping (2005–2025), and fine-scale movement ecology, this research tests three core hypotheses: (1) moisture availability is the dominant climatic driver of wildfire activity, operating through non-linear thresholds; (2) post-fire …

Synoptic Conditions Favoring Tropical Cyclone-Enhanced Southwest Monsoon High Precipitation Events In The Philippines, Alwin Andriel L. Bathan, Lyndon Mark P. Olaguera, Faye Abigail T. Cruz, Jose Ramon T. Villarin, John A. Manalo, Jun Matsumoto

SOSE Affiliate: Manila Observatory

The interaction of tropical cyclones (TCs) with the southwest monsoon frequently causes heavy precipitation events (HPEs) and widespread flooding across the Philippines. Despite their substantial socio-economic impacts, the synoptic conditions that favor these events remain insufficiently understood, underscoring the need for systematic investigation to help improve forecasting and risk assessment. Lag composite analysis of daily rainfall records and reanalysis data from July to September for the period 1981–2023 shows that HPEs are more likely to occur when: (1) enhanced monsoon westerlies are present over the tropical Indian Ocean for at least nine days before the HPE, (2) a TC passes …

Book Review: Atlas Of Minerals And Igneous And Metamorphic Rocks In Thin-Section (2025) By Alessandro Da Mommio And Victoria Pease., Kenneth L. Brown

Geology and Environmental Geoscience Faculty Publications

"Optical mineralogy and petrography not only equip us with the skills necessary to address society’s growing need for rock and mineral resources but also awaken a deeper curiosity—inviting us into moments of discovery that change the way we understand these resources. From observing the vibrant and mesmerizing interference colors of a dunite thin section to examining the uniaxial interference figure of calcite, many of us can still recall the first time we peered through the oculars of a polarizing microscope. Atlas of Minerals and Igneous and Metamorphic Rocks in Thin-Section is an exceptional reference textbook, which nurtures that curiosity and …

It's All In The (Exponential) Family: An Equivalence Between Maximum Likelihood Estimation And Control Variates For Sketching Algorithms, Keegan Kang, Kerong Wang, Ding Zhang, Rameshwar Pratap, Bhisham Dev Verma, Benedict Wong

Faculty Conference Papers and Presentations

Maximum likelihood estimators (MLE) and control variate estimators (CVE) have been used in conjunction with known information across sketching algorithms and applications in machine learning. We prove that under certain conditions in an exponential family, an optimal CVE will achieve the same asymptotic variance as the MLE, giving a fixed point algorithm for the MLE. Experiments show the fixed point algorithm is faster and numerically stable compared to other root finding algorithms for the MLE for the bivariate Normal distribution, and we expect this to hold across distributions satisfying these conditions. We show how this algorithm leads to reproducibility for …

Larger Is Not Always Better: Exploring Small Open-Source Language Models In Logging Statement Generation, Renyi Zhong, Yichen Li, Guangba Yu, Wenwei Gu, Jinxi Kuang, Yintong Huo, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Developers use logging statements to create logs that document system behavior and aid in software maintenance. As such, high-quality logging is essential for effective maintenance; however, manual logging often leads to errors and inconsistency. Recent methods emphasize using large language models (LLMs) for automated logging statement generation, but these present privacy and resource issues, hindering their suitability for enterprise use. This paper presents the first large-scale empirical study evaluating small open-source language models (SOLMs) for automated logging statement generation. We evaluate four prominent SOLMs using various prompt strategies and parameter-efficient fine-tuning techniques, such as Low-Rank Adaptation (LoRA) and Retrieval-Augmented Generation …

Activity Transition Graph Generation: How Far Are We?, Jiakun Liu, Peixin Zhang, Han Hu, Yonghui Liu, Wei Minn, Ferdian Thung, Shahar Maoz, Eran Toch, Debin Gao, David Lo

Research Collection School Of Computing and Information Systems

Android applications (i.e., apps) are indispensable nowadays and are getting bigger and bigger with an increasing number offunctionalities. To understand how to access functionalities in an app, prior studies proposed tools to model the transitionsbetween functionalities with the activity transition graph (ATG). ATG is an important data structure and has been used forvarious Android app analyses, including app design, understanding, and testing. However, there is no benchmarking work onATG generation. It is still unclear whether the transitions identified by tools are correct and how many transitions are missed.To fill this gap, we manually identified all transitions in 98 applications to …

Constrained Assortment Optimization Under The Mixed-Logit Model, Hoang Giang Pham, Tien Mai

Research Collection School Of Computing and Information Systems

In this paper, we study the assortment optimization problem under the mixed-logit customer choice model. While assortment optimization has been a central topic in revenue management for decades, the mixed-logit model is widely regarded as one of the most general and flexible frameworks for modeling and predicting customer purchasing behavior. The assortment optimization problem is known to be NP-hard to be approximated to any constant factor, even in the unconstrained case. To address this challenge, we first explore the submodularity properties of a simplified version of the objective function to derive novel semi-constant factor approximation solutions for assortment problems under …

Multimodal Contrastive Spatiotemporal Self-Organizing Neural Networks For In-Home Activity Learning Of Mild Cognitive Impairment, Seng Khoon Teh, Ah-Hwee Tan, Kar Way Tan, Iris Rawtaer

Research Collection School Of Computing and Information Systems

In-home spatiotemporal data, such as the movement trajectory data and the spatial time series data, contains potential predictive utility for detection of geriatric conditions including Mild Cognitive Impairment (MCI), frailty, and cognitive frailty. However, few have explored spatiotemporal learning models for learning and fusion of such disparate spatiotemporal data, owing to the lack of a generalized machine learning model that can jointly model these different spatiotemporal data types. This work reports a multimodal spatiotemporal machine learning model based on a class of self-organizing neural networks that can integrate different spatiotemporal data types for MCI detection. Specifically, Episodic Memory Adaptive Resonance …

Beyond Hard Constraints: Budget-Conditioned Reachability For Safe Offline Reinforcement Learning, Brahmanage Janaka Chathuranga Thilakarathna, Akshat Kumar

Research Collection School Of Computing and Information Systems

Sequential decision-making using Markov Decision Process underpins many real-world applications. Both model-based and model-free methods have achieved strong results in these settings. However, real-world tasks must balance reward maximization with safety constraints, often conflicting objectives, that can lead to unstable min–max, adversarial optimization. A promising alternative is safety reachability analysis, which precomputes a forward-invariant safe state–action set, ensuring that an agent starting inside this set remains safe indefinitely. Yet, most reachability-based methods address only hard safety constraints, and little work extends reachability to cumulative cost constraints. To address this, first, we define a safety-conditioned reachability set that decouples reward maximization …

Anomaly Management In Unmanned Aerial Vehicles: A Systematic Literature Review, Ivan Tan Wei Han, Christopher M. Poskitt, Lingxiao Jiang, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in safety-critical applications such as logistics, surveillance, disaster response, and urban air mobility. While their autonomy enables powerful capabilities, it also introduces vulnerabilities due to hardware faults, software defects, communication failures, and adversarial interference. This survey presents a comprehensive review of research studies closely related to UAV anomalies published between 2015 and 2025, covering 111 papers from academic and industrial sources. We introduce a unified five-pillar taxonomy—anomaly generation, prevention, detection, recovery, and analysis—that organizes existing work across the full anomaly management lifecycle. In contrast to prior surveys that focus primarily on detection algorithms, …

Co-Matching: Towards Human–Model Collaborative Legal Case Matching, Chen Huang, Xinwei Yang, Yang Deng, Wenqiang Lei, Jiancheng Lv, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Recent efforts have aimed to improve AI models in legal case matching by integrating legal domain knowledge. However, successful legal case matching requires the tacit knowledge of legal practitioners, which is difficult to verbalize and encode into models. This emphasizes the crucial role of involving legal practitioners in high-stakes legal case matching. To address this, we propose a collaborative matching framework called Co-Matching, which encourages both the model and the legal practitioner to participate in the matching process, integrating tacit knowledge. Unlike existing methods that rely solely on the model, Co-Matching allows both the legal practitioner and the model to …

New Algorithms For Fully-Dynamic K-Center With Outliers, Junyu Huang, Zhize Li, Zhen Zhang, Xujia Li, Jianxin Wang, Qilong Feng

Research Collection School Of Computing and Information Systems

In this paper, we study the fully dynamic k-center with outliers problem, where points are inserted and deleted over time and the goal is to maintain an approximate clustering while discarding up to z outliers. Existing algorithms typically rely on radius guessing to maintain cluster representations, leading to update and query times that depend explicitly on the aspect ratio Δ. We propose a layered-sampling framework that avoids radius guessing by maintaining a hierarchy of sampled structures, which can separate most inliers from potential outliers and refine the remaining uncertain points. The resulting algorithm achieves Õ(k²/ε⁴) update and query time independent …

Draft Final 2023 Insufficiently Reclaimed Sites Sampling: Bres No. 93 – Soudan Dump Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Beyond Semantic Matching: Integrating Graph Structures, Boolean Logic, And Interactive Aggregation In Modern Retrieval, Quan Mai

Graduate Theses and Dissertations

Modern Natural Language Processing (NLP) and Information Retrieval (IR) systems have achieved remarkable success in semantic understanding; however, they continue to struggle with complex logical reasoning, structural interactions, and dynamic aggregation. This dissertation presents a comprehensive framework to enhance the structural, logical, and interactive capabilities of language models and retrieval systems across four progressive studies. First, we address the challenge of modeling dynamic conversational logic in online debates. We introduce a Sequence Graph Network (SGN) that captures the temporal and interactive exchange of ideas—such as counterarguments and reinforcements—by updating node features sequentially through a novel Sequence Graph Attention (SGA) layer. …

Potassium Fertilization Timing And Critical Stalk Potassium Concentration As Tools To Improve Corn Production, Gabriela Andrade Leite Mengez

Graduate Theses and Dissertations

Potassium (K) is a common yield-limiting nutrient in Mid-Southern U.S. corn (Zea mays L.) production, emphasizing the importance of effective diagnostic tools to support fertilizer decision-making. This study integrated two approaches to improve K management in irrigated corn systems: (i) evaluating K fertilization timing to identify in-season opportunities for correcting K deficiency, and (ii) developing a corn stalk-based diagnostic test to assess end-of-season K and nitrate-nitrogen (NO₃-N) status and fertilizer adequacy. Seven (2024-2025) and eight (2023-2024) field trials were conducted across Arkansas for the K application timing and stalk testing studies, respectively. For the K timing study, fertilizer-K (112 kg …

Localizing And Repairing Backdoor-Sensitive Layers In Pre-Trained Language Models For Secure Fine-Tuning, Sunanda Das

Graduate Theses and Dissertations

Task-agnostic backdoor attacks can contaminate pre-trained language models (PLMs) in a way that survives downstream adaptation, even under full fine-tuning, making it difficult for practitioners to trust third-party checkpoints. Existing defenses often rely on privileged assumptions (e.g., access to poisoned data or trigger/target knowledge), thereby limiting their applicability in realistic settings. We present DiSec (Disentanglement of potentially adversarial weights for Secure fine-tuning), a robust and label-efficient purification framework that uses only clean auxiliary text and does not rely on downstream supervision or attack signatures. DiSec elicits model-internal signals from this clean data to separate suspicious parameter components that are inconsistent …

Impacts Of Climate Hazards On Employment And Earnings By Hazard Type, Industry, Race, And Ethnicity, Dania V. Francis, Keren M. Horn

Sustainable Solutions Lab

We examine the relationship between climate-related disasters and subsequent county-level aggregate employment and earnings. We combine earnings and employment data from 2000 to 2023 with data on disasters to estimate local projection impulse response functions relating extreme disaster events to cumulative percent changes in employment and earnings. On average, disasters appear to reduce employment growth three to five years post disaster. There are small positive impacts on earnings. These results are largely driven by responses to hurricanes and tropical storms. Key rebuilding industries such as construction see short-term gains in employment that turn to losses in the longer-term that are …

Utah County-Level Emergency Manager Survey, Bailey Holdaway, Courtney Flint, Gina Gilson, Richard Rushforth

Environment and Society Student Research

County emergency managers are central to preparing for and responding to hazards. This brief highlights preliminary survey findings and highlights opportunities to strengthen Utah's statewide hazard resilience.

Glacier Elevation Difference Data For The Conterminous U.S., Andrew G. Fountain, Bryce Allen Glenn, Christopher Mcneil, Brian Menounos

Geology Faculty Datasets

Historic and recent elevation data for the glaciers in the conterminous US were differenced to estimate glacier mass and volume change.

Historic glacier elevations were obtained from the USGS National Elevation Dataset (https://doi.org/10.15760/geology-data.04), a digitized product from the USGS 1:24,000 paper topographic maps that included the glacier extent (Gesch, 2007). Recent glacier elevations were derived from aerial lidar and from satellite radar. The lidar data were largely obtained from the USGS 3D Elevation Program (Stoker and Miller, 2022; US Geological Survey, 2024), and filtered by acquisition date using only those data collected between mid-summer and early autumn. The …

Online Ppo-Based Multi-Hop Task Offloading Strategy For Vehicular Edge Computing, Wenzhu Zhang, Yuewei Bian, Fuli Xiong, Siqi Cai

Journal of System Simulation

Abstract: To address the problems of frequent communication link interruptions caused by dynamic network topologies and the sharp increase in computational complexity triggered by high-dimensional decision spaces in the vehicular edge computing (VEC) environment, a multi-hop task offloading strategy for VEC based on an online PPO algorithm was proposed. A multi-hop task offloading optimization model simultaneously considering link effective time, transmission rate, and computing resource constraints was constructed; a multi-hop A* path search algorithm integrating link stability and end-to-end delay was designed; an online offloading decision framework based on PPO was proposed, which transformed the 0-1 mixed integer nonlinear programming …

Research On Temporal Action Localization Methods For Cross-Modal Understanding, Jinwei Li, Xiaoyang Liu, Rusheng Ju

Journal of System Simulation

Abstract: To address the problems of insufficient localization accuracy and high model complexity in the temporal action localization (TAL) task for video-text cross-modal understanding, an anchor-free action transformer (AFAT) model was proposed. Based on the anchor-free framework, the local self-attention mechanism of Transformer was introduced to enhance the global modeling capability of temporal features. A multi-scale feature pyramid structure was combined to strengthen the representation of actions with different durations, and a lightweight predictor was adopted to reduce computational redundancy. Experimental results show that the average precision of this model on the THUMOS14 dataset is significantly improved compared with the …

Developing Solvent-Based Recycling Methods At Ambient Conditions To Regenerate Bisphenol A Diglycidyl Ether And Recover Metal Oxides From Epoxy Nanocomposites, Heaven Smith

Rose-Hulman Undergraduate Research Publications

No abstract provided.

Influence Of Stormwater Flooding Events On The Mobile County School District Student Population, Kadence Kali Johnston

Honors Theses

Mobile County, Alabama, receives among the highest annual rainfall totals in the United States resulting in frequent stormwater flooding that intersects with existing socioeconomic vulnerabilities. Within the Mobile County Public School System (MCPSS), 77% of students are economically disadvantaged and over 10% experience homelessness, creating heightened risk for educational disruption during flood events. This study investigates how stormwater flooding influences school attendance, closures, and academic performance across MCPSS, providing insight into how climate-driven hazards exacerbate educational inequities. The research integrates flood frequency, school performance metrics, and GIS flood-zone mapping and teacher perceptions to understand how flooding affects student learning opportunities. …

What We Know About Accounting Ratios: Methodological Considerations, Wojciech Kuryłek, Oskar Kowalewski

Studia i Materiały Wydział Zarządzania Uniwersytet Warszawski

Purpose: This paper provides a comprehensive literature review of the methodological aspects of financial ratio analysis, consolidating dispersed knowledge on the computation, statistical properties, and appropriate usage of accounting ratios.

Design/Methodology/Approach: The study adopts a narrative literature review methodology, systematically surveying published research on financial ratio distributions, normality testing, data transformations, outlier handling, the proportionality assumption, dimensionality reduction techniques, compositional data analysis, and recommended ratio sets for corporate financial research.

Findings: Financial ratios predominantly deviate from normal distributions, exhibiting skewness, excess kurtosis, and sensitivity to outliers. Transformation techniques such as logarithmic, square root, and Box‑Cox methods yield mixed results in …

Establishing Ferromagnetism In Van Der Waals Antiferromagnets Mps3, Dinesh Upreti

Graduate Theses and Dissertations

Recent discoveries of magnetism in low dimensions not only have gained fundamental scientific interest but also holds significant potential in numerous technological applications, such as spintronics. This has led to a wide range of efforts in the synthesis of new layered materials or tuning their existing magnetic properties, which has been instrumental in understanding the novel physical concept and development of new technological frontiers. Transition metal thiophosphate, MPS3, is a class of layered antiferromagnet family. The rich magnetic/crystal structure with delicate choice of transition metal (M) together with intriguing optical, electronic, and ferroelectric properties have placed them as a model …

Enhancing Deep Learning Image Reconstruction In Breast Ct With Task-Driven Training And Physics-Informed Models, Megan Ariel Murphy

Dissertations (1934 -)

Dedicated breast X-ray computed tomography (breast CT) is a developing imaging modality that has potential as an alternative to mammography. Unlike traditional mammography, breast CT offers high-resolution, fully 3D anatomical detail, which aids in the detection of microcalfications and subtle lesions that are potential indicators of cancer. To be effective for screening, breast CT must limit radiation exposure by reducing CT view angles. However, traditional image reconstruction methods yield noisy, artifacted images in this sparse-view CT setting. This dissertation develops image restoration and reconstruction methods that address these limitations by integrating physics-informed iterative methods with machine learning tools, and a …