Validation in the Software Metric Development Process

In this paper the validation of software metrics will be examined. Two approaches will be combined: representational measurement theory and a validation network scheme. The development process of a software metric will be described, together with validities for the three phases of the metric development process. Representation axioms from measurement theory are used both for the formal and empirical validation. The differentiation of validities according to these phases unifies several validation approaches found in the software metric's literature

A neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine

In order to properly utilize the available fuel and oxidizer of a liquid propellant rocket engine, the mixture ratio is closed loop controlled during main stage (65 percent - 109 percent power) operation. However, because of the lack of flight-capable instrumentation for measuring mixture ratio, the value of mixture ratio in the control loop is estimated using available sensor measurements such as the combustion chamber pressure and the volumetric flow, and the temperature and pressure at the exit duct on the low pressure fuel pump. This estimation scheme has two limitations. First, the estimation formula is based on an empirical curve fitting which is accurate only within a narrow operating range. Second, the mixture ratio estimate relies on a few sensor measurements and loss of any of these measurements will make the estimate invalid. In this paper, we propose a neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine. The estimator is an extension of a previously developed neural network based sensor failure detection and recovery algorithm (sensor validation). This neural network uses an auto associative structure which utilizes the redundant information of dissimilar sensors to detect inconsistent measurements. Two approaches have been identified for synthesizing mixture ratio from measurement data using a neural network. The first approach uses an auto associative neural network for sensor validation which is modified to include the mixture ratio as an additional output. The second uses a new network for the mixture ratio estimation in addition to the sensor validation network. Although mixture ratio is not directly measured in flight, it is generally available in simulation and in test bed firing data from facility measurements of fuel and oxidizer volumetric flows. The pros and cons of these two approaches will be discussed in terms of robustness to sensor failures and accuracy of the estimate during typical transients using simulation data

Hydrometeor Types Associated with GMI Brightness Temperatures

The main goal of this project is to assess and understand how passive microwave brightness temperature values relate to particular hydrometeor types. The hydrometeor types are taken from dual polarization radar hydrometeor identifications in the GPM Validation Network database of matchups between the GPM Microwave Imager (GMI) and dozens of ground radars mostly in the U.S

Global Precipitation Measurement (GPM) Validation Network

The method averages the minimum TRMM PR and Ground Radar (GR) sample volumes needed to match-up spatially/temporally coincident PR and GR data types. PR and GR averages are calculated at the geometric intersection of the PR rays with the individual Ground Radar(GR)sweeps. Along-ray PR data are averaged only in the vertical, GR data are averaged only in the horizontal. Small difference in PR & GR reflectivity high in the atmosphere, relatively larger differences. Version 6 TRMM PR underestimates rainfall in the case of convective rain in the lower part of the atmosphere by 30 to 40 percent

Data Visualization and Analysis Tools for the Global Precipitation Measurement (GPM) Validation Network

The Validation Network (VN) prototype for the Global Precipitation Measurement (GPM) Mission compares data from the Tropical Rainfall Measuring Mission (TRMM) satellite Precipitation Radar (PR) to similar measurements from U.S. and international operational weather radars. This prototype is a major component of the GPM Ground Validation System (GVS). The VN provides a means for the precipitation measurement community to identify and resolve significant discrepancies between the ground radar (GR) observations and similar satellite observations. The VN prototype is based on research results and computer code described by Anagnostou et al. (2001), Bolen and Chandrasekar (2000), and Liao et al. (2001), and has previously been described by Morris, et al. (2007). Morris and Schwaller (2009) describe the PR-GR volume-matching algorithm used to create the VN match-up data set used for the comparisons. This paper describes software tools that have been developed for visualization and statistical analysis of the original and volume matched PR and GR data

Cryptocurrency Constellations across the Three-Dimensional Space: Governance Decentralization, Security, and Scalability

In the post-Bitcoin era, many cryptocurrencies with a variety of goals and purposes have emerged in the digital arena. This article aims to map cryptocurrency protocols across three main defining dimensions, which are governance decentralization, security, and scalability. We theorize about the organizational and technological features that impact these three dimensions. Such features encompass roles permissiveness, validation network size, resource expenditure, and number of transactions per second. We map the different cryptocurrency constellations based on their consensus mechanisms, discussing the organizational and technological features of the various protocols applications and how they experience and play with the tradeoffs among governance decentralization, security, and scalability