Mines Repository (Colorado School of Mines)
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Pyrite on quartz
No full textPhotographed by Ron Wolf.Translucent white prisms of quartz with surface flecks of tiny gold-colored pyrite crystals
Purpurite-heterosite
No full textPhotographed by Ron Wolf.Fine-grained purple purpurite with heterosite, Crystal Mountain district, Larimer County, Colorado
Tincalconite pseudomorph after borax
No full textPhotographed by Ron Wolf.Dull white blocky prisms of tincalconite pseudomorph after borax
Barite on siderite
No full textPhotographed by Ron Wolf.Blocky dull white tabs of barite on pale brown siderite
Gaussian puff atmospheric dispersion model: an analysis at low wind speeds
No full textA prior title of this was "Analyzing the Performance of the Gaussian Puff Atmospheric Dispersion Model at Low Wind Speeds"Monitoring methane emissions from the oil and gas sector is essential for emissions reduction. To do so, we use an inversion algorithm that converts methane concentration observations from in-situ sensors placed around the perimeter of oil and gas sites into estimates of emission source and rate. One step in this inversion involves simulating methane transport from potential emission sources to sensor locations using the Gaussian Puff atmospheric dispersion model. Wind speed and direction are critical inputs to this model, as they primarily control methane movement through the atmosphere. The Gaussian Puff model is known to perform poorly at low wind speeds, but the threshold at which its performance begins to decline remains unknown. This study analyzes limitations of the model at low wind speeds, identifying the exact wind regimes where it begins to underperform and investigating the underlying phenomena leading to this underperformance. For this analysis, we use data from an experiment during which methane releases were conducted from structures at an emissions testing facility at known emission rates. We obtain simulated concentrations from the Gaussian Puff model at each sensor location on the site and compare them to observed concentrations from the sensors. By analyzing discrepancies at different wind speeds, we find that the model begins underperforming at about 3 m/s. This underperformance often occurs because the model fails to capture spikes in observed concentration data. Incorporating these findings allows for omission of times when the model performs unreliably, improving methane emissions understanding and enabling more effective reduction strategies
Beryl var. emerald
No full textPhotographed by Ron Wolf.Pale green glassy opaque emerald (variety of beryl)
At the intersection of land use, flooding, and social vulnerability: a case study of New Orleans, Louisiana
No full textIncludes bibliographical references.2024 Spring.Urban flooding is becoming more frequent and severe due to the effects of climate change, underscoring the urgent need for effective flood risk management. Flood susceptibility maps are tools that assess the likelihood and potential impacts of various flood scenarios. They are generally used for flood risk management in coastal cities like New Orleans, Louisiana, which face recurring flooding events.
This thesis presents a comprehensive investigation into the factors that have influenced flood risk in New Orleans – a city with a history marked by catastrophic flooding events. Storms that overpower drainage systems and natural basins often lead to urban floods (Hirabayashi et al., 2021), which bring significant risks like infrastructure damage, economic setbacks, and loss of life, the latter being the second most common cause of weather-related deaths in the United States (Han & Sharif, 2021).
Furthermore, this research seeks to generate risk maps that show the risk profile of communities (at the census tract level) at the overlap of flood risk, social vulnerability index (SVI), and land use and land cover (LULC) change between 2005 and 2023. Employing satellite imagery and geospatial analysis, the study uses the Modified Normalized Difference Vegetation Index (MNDWI) to indirectly evaluate flood risks and the Normalized Difference Vegetation Index (NDVI) to assist in the assessment of land cover classification through time.
Toward these objectives, thematic mapping and geospatial analysis were used to generate a map overlay of flood risk, SVI, and LULC in New Orleans. Integrating satellite observations with SVI calculations allows for a comprehensive view of flood dynamics and social vulnerability in a major urban setting, examining the relationship between natural and built environments and their effects on flood risks. The final composite products provided insight into zones where past resilience-building and risk-reduction efforts have reduced vulnerability and zones requiring intervention. The findings convey how integrated data-driven analysis can inform urban infrastructure and policy development, advancing discussions on urban resilience and the nuanced understanding of interactions between urban settings and flood risks and potentially aiding in the implementation of adaptive strategies to build resilience in New Orleans
Safeguarding user privacy in the IoT era
No full textIncludes bibliographical references.2024 Spring.The Internet of Things (IoT) has been erupting the world widely over the decade. Smart home and smart building owners are increasingly deploying IoT devices to monitor and control their environments due to the rapid decline in the price of IoT devices. However, serious privacy threat was revealed by recent research and media reports. First, the Internet traffic data generated by IoT devices are collected by Internet Service Providers (ISPs) and IoT device manufacturers, and often shared with third-parties to maintain and enhance user services. Extensive recent research has shown that on-path adversaries can infer and fingerprint user's sensitive privacy information such as occupancy and user in-home activities by analyzing IoT network traffic rates alone. Most recent approaches that aim at defending against these malicious IoT traffic analytics can not sufficiently protect user privacy with reasonable Internet traffic and hardware resource overhead. In particular, many approaches did not consider practical limitations, e.g., network bandwidth, maximum package injection rate or actual user in-home behavior in their design. Second, such privacy threats also shows in some specific types of IoT devices, like smart cameras. Significant recent research has uncovered potential user privacy threats associated with popular commercial camera systems. The manufacture design of these commercial camera systems usually requires smart camera users to relinquish their control of camera recorded data. For instance, these cameras often upload camera recordings to their cloud servers or data centers to enable advanced data analysis for camera app services. To facilitate enhanced camera services, the data may also be shared with on-path vendors, third parties of manufacturers, and cloud providers, potentially allowing them to access video footage or image captures without users' awareness or obtaining meaningful consent.
To address these problems, my research aims at building and implementing computer systems in different scales of implementation, to allow Cyber-Physical System (CPS) and IoT users to regain the comprehensive control over their privacy, and the following contributions were made:
\underline{\textit{SmartAttack}} and \underline{\textit{TrafficSpy}} aim at disaggregating individual IoT devices from both raw and VPN-encrypted IoT network traffic data. I designed and implemented two Machine Learning (ML) and Deep Learning (DL)-based adversarial attack model frameworks to mimic the malicious external adversaries carrying on user activity inference attacks. In addition to proving the severeness of smart home user privacy threat, these two works can also be leveraged by researchers and industrial users from IoT security community, to better evaluate their privacy preserving works.
\underline{\textit{PrivacyGuard}}, as the first prototype, successfully provided an open-sourced, low-cost, user-tunable defense system, that enable users to significantly reduce the private information leaked through IoT device network traffic data, while still permitting sophisticated data analytics or control that is necessary in smart home management. I evaluated PrivacyGuard using IoT network traffic traces of 31 IoT devices from 5 smart homes and deploying a Raspberry Pi 4-based prototype. And the result shows that PrivacyGuard can effectively prevent a wide range of state-of-the-art machine learning-based occupancy and other 9 user-in-home activity detection attacks.
\underline{\textit{PAROS}} - Privacy As a Router Operating system Service, made significant improvement from PrivacyGuard, to lift the requirement for installation of additional hub hardware, and still maintain comparable privacy preserving performance and system overhead. By leveraging Hidden Markov Model (HMM)-based artificial traffic signature injection, and Support Vector Machines (SVM)-based memory replacement scheme, the performance of PAROS was significantly optimized.
\underline{\textit{SecCam}}, designed to solve the second half of the privacy threat, has provided a new open-sourced, adaptive and distributed privacy-preserving camera system. By harnessing the technique of on-device learning, SecCam provided several tiny intelligent camera services that offer the same features found in the commercially available cameras. SecCam enables user to regain the control of their data while still retaining access to regular camera services. The SecCam was evaluated using multiple camera video footage traces and on multiple real camera prototypes.
In the future, I plan to dive deeper on safeguarding the IoT user privacy by improving my current systems, developing new attacking approaches, and doing ethical related work in the field of CPS and IoT
Pyromorphite
No full textPhotographed by Ron Wolf.Small olive-green stubby crystals of pyromorphite on orange matrix