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Abstract: Deep and frequent Global Positioning System (GPS) signal fading due to strong ionospheric scintillation is a major concern for GPS-guided aviation in equatorial areas during high solar activity. A GPS aviation receiver may lose carrier tracking lock under deep fading, and a lost channel cannot be used for position calculation until lock is reestablished. Hence, frequent loss of lock due to frequent fading can significantly reduce the availability of GPS aviation. However, the geometric diversity of the satellites can mitigate scintillation impact on GPS aviation depending on the correlation level of deep fades between satellites. This paper proposes a metric to measure the correlation level of two fading channels from the perspective of GPS aviation. Using this metric, the satellite-to-satellite correlation is studied based on real scintillation data. The low satellite-to-satellite correlation shown in this paper envisions notable availability benefit from the geometric diversity of satellites under strong scintillation. In addition, this paper proposes a way to generate correlated fading processes with arbitrary correlation coefficients. Using this correlated fading process model, the availability of Localizer Performance with Vertical guidance (LPV)-200 under severe scintillation scenarios is analyzed. The result emphasizes the importance of a fast reacquisition capability of an aviation receiver after a brief outage, which is not currently mandated by the aviation receiver performance standards. [Copyright &y& Elsevier]

Abstract This study applies a novel landscape approach to empirically assess the linkage between terrestrial landscape alteration such as urbanization and aquatic ecosystem degradation from a hydrological sensitive area (HSA) perspective in 141 selected northern New Jersey watersheds. HSAs are hydrological "hotspots" in a watershed that actively contribute to runoff generation and were delineated using a soil topographic index. Land use metrics captured landscape alterations in terms of percentages of varying land uses in these watersheds and their HSAs. Aquatic ecosystem integrity was represented by a High Gradient Macroinvertebrate Index (HGMI) specifically developed for the stream types assessed in this study. Multiple linear regression (MLR) analysis was used to understand the relationships between land use metrics and HGMI score at the watershed- and HSA-scales and a data fitting procedure called Least Absolute Shrinkage and Selection Operator (LASSO) was used to identify the most statistically significant land use attributes to be retained in the MLR models. The modeling results at the HSA-scale showed more parsimonious and robust relationships between landscape alteration and aquatic integrity than at the watershed-scale in terms of both variable selection and statistical inference. While high intensity urbanization is a known stressor that can significantly degrade aquatic ecosystem integrity, the results indicate that landscapes developed more strategically by way of low intensity urbanization (e.g., rural residential) or on less hydrologically sensitive areas may lessen the detrimental effects of urbanization on aquatic ecosystem integrity. These findings support the premise that it is not just the extent of urbanization in a watershed that matters, but also the intensity and location of the disturbance on the landscape that affects aquatic ecosystem integrity. Such findings may encourage more flexible landscape planning and management practices that better protect HSAs from urban development in support of long-term aquatic ecosystem protection and restoration. Graphical abstract Unlabelled Image Highlights • Landscape change in hydrological hotspots has disproportionate impact on stream integrity. • Urban lands with high development intensity negatively affect stream integrity. • Deliberately managed urban landscape has potential of improving stream integrity. • Forest preservation is the most effective policy to improve stream integrity. • LASSO is a powerful tool for effective multiple linear regression modeling. [ABSTRACT FROM AUTHOR]