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Quantification of Surface Layer Turbulence using Sensible Heat Values from Energy Balance versus Aerodynamic Methods
Get PDFSurface layer optical turbulence values in the form of the refractive index structure function C_n^2 are often calculated from surface layer temperature, moisture, and wind characteristics and compared to measurements from sonic anemometers, differential temperature sensors, and imaging systems. A key derived component needed in the surface layer turbulence calculations is the sensible heat value. Typically, the sensible heat is calculated using the bulk aerodynamic method that assumes a certain surface roughness and a friction velocity that approximates the turbulence drag on temperature and moisture mixing from the change in the average surface layer vertical wind velocity. These assumptions/approximations generally only apply in free convection conditions. To obtain the sensible heat, a more robust method, which applies when free convection conditions are not occurring, is via an energy balance method such as the Bowen ratio method. The use of the Bowen ratio––the ratio of sensible heat flux to latent heat flux––allows a more direct assessment of the optical turbulence-driving surface layer sensible heat flux than do more traditional assessments of surface layer sensible heat flux. This study compares surface layer C_n^2 values using sensible heat values from the bulk aerodynamic and energy balance methods to quantifications from sonic anemometers posted at different heights on a sensor tower. The research shows that the sensible heat obtained via the Bowen ratio method provides a simpler, more reliable, and more accurate way to calculate surface layer C_n^2 values than what is required to make such calculations from bulk aerodynamic method-obtained sensible heat
Housing Preferences and Vertical Expansion Potential in Riyadh City
Get PDFThis research investigates the demand for apartment living in Riyadh\u27s northern area and assesses its implications for vertical urban development, aligning with Saudi Arabia\u27s Vision 2030. Employing a quantitative methodology, a structured survey was distributed to a diverse cross-section of Riyadh\u27s population, focusing on housing preferences and perceptions of vertical living. Key findings indicate a significant demand for apartments, primarily from young to middle-aged, smaller households in low to middle-income brackets. Notably, a substantial portion of respondents showed a preference for high-rise living, suggesting a readiness for vertical expansion to manage the city\u27s growing population. The study concludes that Riyadh\u27s emerging housing trends support a shift towards a sustainable, high-density urban model, offering vital insights for urban planning and policy formulation in line with Vision 2030\u27s to increase the city population goal
Signal-to-Image Method for Counterfeit Detection in Layered Security Paradigm
Get PDFNational level attention, resources, and priority regarding critical infrastructure have increased in recent years. This has led to adversaries and defenders exchanging positions between fortification and exploitation. One area that continues to be vulnerable is supply chain attacks like counterfeit insertion. This work investigates the application of converting collected signals into images from devices that may be considered for these critical networks. There are several aspects regarding the conversion of signals into images - specifically Red, Green, Blue (RGB) images. The methodology proposed here is potentially ideal fit for an initial security layer by achieving comparable classification results as more robust detection methods while achieving increased speed and requiring less computational resources
Using Machine Learning to Predict State Compliance with International Legal Obligations for Registration of Space Objects: Comparative Performance of Logistic Regression and Dense Neural Network Models
Get PDFApproximately 12% of satellites and other objects launched into outer space have not been registered with the United Nations (UN) as required by international law. To predict whether States will register a launched space object and understand what factors influence a registration decision, data from a UN online index of space objects was used to train and select the best machine learning model. After preparation, the dataset had 1938 datapoints with 11 features, with categorical features simplified and converted to binary.
Multiple variations of classical logistic regression models were compared to multiple variations of dense neural network models. The best model was a logistic regression model using p-value feature selection for binary classification, which balanced performance and simplicity. It had a precision metric of 0.91 and a recall metric of 0.90 with only 6 input features, besting the trivial model’s precision of 0.73 and 0.85.
The best model predicted registration decisions with 90% accuracy. From its results, inferences can be drawn that States which generate a national designator for their space objects are more likely to register those objects, while space objects that decay or deorbit within five years after launch are less likely to be registered
Detection and Classification of Sporadic E Using Convolutional Neural Networks
Get PDFIn this work, convolutional neural networks (CNN) are developed to detect and characterize sporadic E (Es), demonstrating an improvement over current methods. This includes a binary classification model to determine if Es is present, followed by a regression model to estimate the Es ordinary mode critical frequency (foEs), a proxy for the intensity, along with the height at which the Es layer occurs (hEs). Signal-to-noise ratio (SNR) and excess phase profiles from six Global Navigation Satellite System (GNSS) radio occultation (RO) missions during the years 2008–2022 are used as the inputs of the model. Intensity (foEs) and the height (hEs) values are obtained from the global network of ground-based Digisonde ionosondes and are used as the “ground truth,” or target variables, during training. After corresponding the two data sets, a total of 36,521 samples are available for training and testing the models. The foEs CNN binary classification model achieved an accuracy of 74% and F1-score of 0.70. Mean absolute errors (MAE) of 0.63 MHz and 5.81 km along with root-mean squared errors (RMSE) of 0.95 MHz and 7.89 km were attained for estimating foEs and hEs, respectively, when it was known that Es was present. When combining the classification and regression models together for use in practical applications where it is unknown if Es is present, an foEs MAE and RMSE of 0.97 and 1.65 MHz, respectively, were realized. We implemented three other techniques for sporadic E characterization, and found that the CNN model appears to perform better
Mitigating Code Reuse Attacks on RISC-V Binaries: Minimizing Gadget Availability using the Compressed Extension
No full textEmbedded systems are vital in civilian and military applications, requiring high performance and security. The open RISC-V Instruction Set Architecture (ISA) offers significant advantages, including security through community review and strategic independence in microchip supplies. Brazil’s recent partnership with RISC-V highlights its potential for national technological sovereignty. However, RISC-V is not inherently resistant to code reuse attacks (CRAs), highlighting the need to integrate security measures early in development. The RISC-V Compressed extension, while beneficial for optimizing performance and code flexibility, introduces security trade-offs. As RISC-V adoption grows, particularly in critical systems, addressing these security challenges from the start is crucial for secure deployment
Predictability Limit of the 2021 Pacific Northwest Heatwave From Deep‐Learning Sensitivity Analysis
Get PDFThe traditional method for estimating weather forecast sensitivity to initial conditions uses adjoint models, which are limited to short lead times due to linearization around a control forecast. The advent of deep‐learning frameworks enables a new approach using backpropagation and gradient descent to iteratively optimize initial conditions, minimizing forecast errors. We apply this approach to the June 2021 Pacific Northwest heatwave using the GraphCast model, yielding over 90% reduction in 10‐day forecast errors over the Pacific Northwest. Similar improvements are found for Pangu‐Weather model forecasts initialized with the GraphCast‐derived optimal, suggesting that model error is an unimportant part of the perturbations. Eliminating small scales from the perturbations also yields similar forecast improvements. Extending the length of the optimization window, we find forecast improvement to about 23 days, suggesting atmospheric predictability at the upper end of recent estimates
