We derive the atomic hydrogen content for a total of 1900 spirals in the fields of eighteen nearby clusters. By comparing the HI deficiency distributions of the galaxies inside of and outside of one Abell radius of each cluster, we find that two thirds of the clusters in our sample show a dearth of neutral gas in their interiors. Possible connections between the gaseous deficiency and the characteristics of both the underlying galaxies and their environment are investigated in order to gain insight into the mechanisms responsible for HI depletion. While we do not find a statistically significant variation of the fraction of HI-deficient spirals in a cluster with its global properties, a number of correlations emerge that argue in favor of the interplay between spiral disks and their environment. In the clusters in which neutral gas deficiency is pronounced, we see clear indications that the degree of HI depletion is related to the morphology of the galaxies and not to their optical size: early-type and, probably, dwarf spirals are more easily emptied of gas than the intermediate Sbc--Sc types. The wealth of 21-cm data collected for the Virgo region has made it possible to study the 2D pattern of HI deficiency in that cluster. We also find evidence that gas-poor spirals in HI-deficient clusters move on orbits more radial than those of the gas-rich objects. The implications of all these results on models of how galaxies interact with their environment are reviewed. Hydrodynamic effects appear as the most plausible cause of HI removal. (abridged)Comment: Accepted for publication in the Astrophysical Journa

Fast precise point positioning (Fast-PPP) is a satellite-based navigation technique using an accurate real-time ionospheric modeling to achieve high accuracy quickly. In this paper, an end-to-end performance assessment of Fast-PPP is presented in near-maximum Solar Cycle conditions; from the accuracy of the Central Processing Facility corrections, to the user positioning. A planetary distribution of permanent receivers including challenging conditions at equatorial latitudes, is navigated in pure kinematic mode, located from 100 to 1300 km away from the nearest reference station used to derive the ionospheric model. It is shown that satellite orbits and clocks accurate to few centimeters and few tenths of nanoseconds, used in conjunction with an ionosphere with an accuracy better than 1 Total Electron Content Unit (16 cm in L1) reduce the convergence time of dual-frequency Precise Point Positioning, to decimeter-level (3-D) solutions. Horizontal convergence times are shortened 40% to 90%, whereas the vertical components are reduced by 20% to 60%. A metric to evaluate the quality of any ionospheric model for Global Navigation Satellite System is also proposed. The ionospheric modeling accuracy is directly translated to mass-market single-frequency users. The 95th percentile of horizontal and vertical accuracies is shown to be 40 and 60 cm for single-frequency users and 9 and 16 cm for dual-frequency users. The tradeoff between the formal and actual positioning errors has been carefully studied to set realistic confidence levels to the corrections.

Ionospheric scintillation produces strong disruptive effects on global navigation satellite system (GNSS) signals, ranging from degrading performances to rendering these signals useless for accurate navigation. The current paper presents a novel approach to detect scintillation on the GNSS signals based on its effect on the ionospheric-free combination of carrier phases, i.e. the standard combination of measurements used in precise point positioning (PPP). The method is implemented using actual data, thereby having both its feasibility and its usefulness assessed at the same time. The results identify the main effects of scintillation, which consist of an increased level of noise in the ionospheric-free combination of measurements and the introduction of cycle-slips into the signals. Also discussed is how mis-detected cycle-slips contaminate the rate of change of the total electron content index (ROTI) values, which is especially important for low-latitude receivers. By considering the effect of single jumps in the individual frequencies, the proposed method is able to isolate, over the combined signal, the frequency experiencing the cycle-slip. Moreover, because of the use of the ionospheric-free combination, the method captures the diffractive nature of the scintillation phenomena that, in the end, is the relevant effect on PPP. Finally, a new scintillation index is introduced that is associated with the degradation of the performance in navigation.

Single-frequency users of the global navigation satellite system (GNSS) must correct for the ionospheric delay. These corrections are available from global ionospheric models (GIMs). Therefore, the accuracy of the GIM is important because the unmodeled or incorrectly part of ionospheric delay contributes to the positioning error of GNSS-based positioning. However, the positioning error of receivers located at known coordinates can be used to infer the accuracy of GIMs in a simple manner. This is why assessment of GIMs by means of the position domain is often used as an alternative to assessments in the ionospheric delay domain. The latter method requires accurate reference ionospheric values obtained from a network solution and complex geodetic modeling. However, evaluations using the positioning error method present several difficulties, as evidenced in recent works, that can lead to inconsistent results compared to the tests using the ionospheric delay domain. We analyze the reasons why such inconsistencies occur, applying both methodologies. We have computed the position of 34 permanent stations for the entire year of 2014 within the last Solar Maximum. The positioning tests have been done using code pseudoranges and carrier-phase leveled (CCL) measurements. We identify the error sources that make it difficult to distinguish the part of the positioning error that is attributable to the ionospheric correction: the measurement noise, pseudorange multipath, evaluation metric, and outliers. Once these error sources are considered, we obtain equivalent results to those found in the ionospheric delay domain assessments. Accurate GIMs can provide single-frequency navigation positioning at the decimeter level using CCL measurements and better positions than those obtained using the dual-frequency ionospheric-free combination of pseudoranges. Finally, some recommendations are provided for further studies of ionospheric models using the position domain method.

Inertial measurement units (IMUs) are key components of various applications including navigation, robotics, aerospace, and automotive systems. IMU sensor characteristics have a significant impact on the accuracy and reliability of these applications. In particular, noise characteristics and bias stability are critical for proper filter settings to perform a combined GNSS/IMU solution. This paper presents an analysis based on the Allan deviation of different IMU sensors that correspond to different grades of micro-electromechanical systems (MEMS)-type IMUs in order to evaluate their accuracy and stability over time. The study covers three IMU sensors of different grades (ascending order): Rokubun Argonaut navigator sensor (InvenSense TDK MPU9250), Samsung Galaxy Note10 phone sensor (STMicroelectronics LSM6DSR), and NovAtel PwrPak7 sensor (Epson EG320N). The noise components of the sensors are computed using overlapped Allan deviation analysis on data collected over the course of a week in a static position. The focus of the analysis is to characterize the random walk noise and bias stability, which are the most critical for combined GNSS/IMU navigation and may differ or may not be listed in manufacturers’ specifications. Noise characteristics are calculated for the studied sensors and examples of their use in loosely coupled GNSS/IMU processing are assessed. This work proposes a structured and reproducible approach for working with sensors for their use in navigation tasks in combination with GNSS, and can be used for sensors of different levels to supplement missing or incorrect sensor manufacturers’ data.