Mountain Scholar
Mountain Scholar is an open access repository service that collects, preserves, and provides access to digitized library collections and other scholarly and creative works from Colorado State University and the University Press of Colorado. It also serves as a dark archive for the Open Textbook Library.
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Recent Submissions
How much does media portrayal matter? The impact of a polarized media on politics and political ideology in the United States
(Colorado State University. Libraries, 2025-12-23) Hall, Kaley, author; Stenson, Peter, advisor; Berg, Marni, committee member; Buckley, Cara, committee member
Background: The American political scene continues to grow more polarized, particularly among the two major political parties. Republicans and Democrats dislike, demonize, and look down upon each other more than ever before. This is partially due to the media and its portrayal of the two parties as well as its increasingly apocalyptic messages about one party or the other. The goal of this research is to shed light on how the media portrays each major political party from 2020 to present day and the devastating impact this portrayal has had on American politics and on the American people. Purpose: The purpose of this research is to examine both traditional media forms such as print and cable news as well as contemporary media forms such as social media to determine their impact on politics and individuals' political ideology. The following questions will guide this research: 1) What are the differences in how the media portrays the two major political parties? 2) What are the effects of this difference in the portrayal of the two parties on United States politics? 3) What are the effects of this difference in portrayal of the two parties on the general population and individual political ideology? Findings: This study found that bias in the media manifests differently depending on whether the news outlet is left leaning or right leaning. It also found that this bias has a significant impact on the American public; particularly when it comes to polarization and an overall negative perception of others in the opposite party. This polarization among the people is reflected in the increasing division among lawmakers and political figures.
Denial of service vulnerabilities in commercial vehicles: exploiting diagnostic protocol flaws
(Colorado State University. Libraries, 2025-08-04) Green, Carson, author; Chatterjee, Rik, author; Daily, Jeremy, author; ACM, publisher
Commercial vehicles are a vital component of modern logistics and transportation, forming part of the critical infrastructure and representing safety-critical cyber-physical systems. Contemporary automotive operations are dominated by embedded computing systems that engage through standardized protocols, which constitute the infrastructure of vehicular communication networks. Within the commercial vehicle sector, these systems utilize high-level protocols that operate over the Controller Area Network (CAN) protocol for internal exchanges in medium and heavy-duty vehicles. The Unified Diagnostics Services (UDS) protocol, as described in International Standards Organization (ISO) 14229 (Unified Diagnostic Services - UDS) and ISO 15765 (Diagnostic Communication over CAN), plays a pivotal role by providing vital diagnostic capabilities. This research introduces four specific scenarios that expose deficiencies in the diagnostic protocol standards and how these can be manipulated to initiate attacks on in-vehicle computers within commercial vehicles, circumventing existing security frameworks. In the first three scenarios, we demonstrate three flaws within the ISO 14229 protocol standards. Following this, the fourth and final scenario elucidates a flaw unique to the ISO 15765 protocol standards. For the purpose of demonstration, test setups incorporating actual Electronic Control Units (ECUs) linked to a CAN bus were employed. Further experiments were performed using a fully equipped cab assembly from a 2018 Freightliner Cascadia truck, set up as a testing environment. The experimental outcomes demonstrate how attacks targeting these specific protocols can undermine the integrity of individual ECUs, leading to denial of service. Additionally, within the Freightliner Cascadia configuration, a network architecture typical of contemporary vehicles was observed, featuring a gateway unit that isolates internal ECUs from diagnostic interfaces. Although this gateway is engineered to prevent conventional message injection and spoofing attacks, it permits all diagnostic communications. This selective permeability inadvertently introduces a susceptibility to diagnostic protocol flaws, highlighting an essential area for security improvements within commercial vehicle networks. These insights are vital for engineers and developers tasked with integrating the diagnostic protocols into their network subsystems, underscoring the urgency for improved security provisions.
Towards surrogate models with hybrid spatial neural networks: a summary of results
(Colorado State University. Libraries, 2025-11-03) Zhang, Shengya, author; Sharma, Arun, author; Farhadloo, Majid, author; Yang, Mingzhou, author; Zeng, Ruolei, author; Ghosh, Subhankar, author; Zhang, Yao, author; Hong, Mu, author; Liu, Licheng, author; Mulla, David, author; Shekhar, Shashi, author; ACM, publisher
The goal is to develop an efficient and accurate surrogate model for Daycent, a widely used but computationally expensive ecosystem model. This problem is important due to its societal applications in sustainable agriculture. Challenges include balancing the trade-off between prediction time and solution quality (e.g., accuracy), as well as the need to capture spatial relationships both within and across sites, while also accounting for varied crop management practices that introduce irregular and non-stationary patterns, reducing predictability. Related work on surrogate models with traditional feed-forward artificial neural networks (SM-ANN) has shown that these models have limited accuracy and often fail to capture spatial dependencies. To address these limitations, we explore novel Surrogate Models with Hybrid Spatial Neural Networks (SM-Hybrid) capable of explicitly modeling spatial autocorrelation and tele-connections. Experimental results show that the proposed SM-Hybrid is more accurate than SM-ANN and is twice as fast as the Daycent model.
Page-overwrite data sanitization in 3D NAND flash: challenges, feasibility, and the PULSE solution
(Colorado State University. Libraries, 2025-10-01) Buddhanoy, Matchima, author; Milenkovic, Aleksandar, author; Pasricha, Sudeep, author; Ray, Biswajit, author; ACM, publisher
Instant data deletion (or sanitization) in NAND flash devices is essential for achieving data privacy, but it remains challenging due to the mismatch between erase and write granularities, which leads to high overhead and accelerated wear. While page-overwrite-based instant data sanitization has proven effective for 2D NAND, its applicability to 3D NAND is limited due to the unique sub-block architecture. In this study, we experimentally evaluate page-overwrite-based sanitization on commercial 3D NAND flash memory chips and uncover significant threshold voltage disturbances in erased cells on adjacent pages within the same layer but across different sub-blocks. Our key findings reveal that page-overwrite sanitization increases the median raw bit error rate (RBER) beyond correction limits (exceeding 0.93%) in Floating-Gate (FG) Single-Level Cell (SLC) technology, whereas Charge-Trap (CT) SLC 3D NAND flash memories exhibit higher robustness. In Triple-Level Cell (TLC) 3D NAND, page-overwrite sanitization proves impractical, with the median RBER of ∼13% for FG and ∼5% for CT devices. To overcome these challenges, we propose PULSE, a low-disturbance sanitization technique that balances sanitization efficiency (ηsan) and data integrity (RBER). Experimental results show that PULSE eliminates RBER increases in SLC devices and reduces the median RBER to below 0.57% for FG and 0.79% for CT in fresh TLC blocks, demonstrating its practical viability for 3D NAND flash sanitization.
GATE: graph attention neural networks with real-time edge construction for robust indoor localization using mobile embedded devices
(Colorado State University. Libraries, 2025-10-01) Gufran, Danish, author; Pasricha, Sudeep, author; ACM, publisher
Accurate indoor localization is crucial for enabling spatial context in smart environments and navigation systems. Wi-Fi Received Signal Strength (RSS) fingerprinting is a widely used indoor localization approach due to its compatibility with mobile embedded devices. Deep Learning (DL) models improve accuracy in localization tasks by learning RSS variations across locations, but they assume fingerprint vectors exist in a Euclidean space, failing to incorporate spatial relationships and the non-uniform distribution of real-world RSS noise. This results in poor generalization across heterogeneous mobile devices, where variations in hardware and signal processing distort RSS readings. Graph Neural Networks (GNNs) can improve upon conventional DL models by encoding indoor locations as nodes and modeling their spatial and signal relationships as edges. However, GNNs struggle with non-Euclidean noise distributions and suffer from the GNN blind spot problem, leading to degraded accuracy in environments with dense access points (APs). To address these challenges, we propose GATE, a novel framework that constructs an adaptive graph representation of fingerprint vectors while preserving an indoor state-space topology, modeling the non-Euclidean structure of RSS noise to mitigate environmental noise and address device heterogeneity. GATE introduces (1) a novel Attention Hyperspace Vector (AHV) for enhanced message passing, (2) a novel Multi-Dimensional Hyperspace Vector (MDHV) to mitigate the GNN blind spot, and (3) a new Real-Time Edge Construction (RTEC) approach for dynamic graph adaptation. Extensive real-world evaluations across multiple indoor spaces with varying path lengths, AP densities, and heterogeneous devices demonstrate that GATE achieves 1.6 × to 4.72 × lower mean localization errors and 1.85 × to 4.57 × lower worst-case errors compared with state-of-the-art indoor localization frameworks.