Retrieval of refractivity fields from GNSS tropospheric delays: theoretical and data-based evaluation of collocation methods and comparisons with GNSS tomography.

This paper focuses on the retrieval of refractivity fields from GNSS measurements by means of least-squares collocation. Collocation adjustment estimates parameters that relate delays and refractivity without relying on a grid. It contains functional and stochastic models that define the characteristics of the retrieved refractivity fields.

The increasingly dominant role of climate change on length of day variations.

The melting of ice sheets and global glaciers results in sea-level rise, a pole-to-equator mass transport increasing Earth's oblateness and resulting in an increase in the length of day (LOD). Here, we use observations and reconstructions of mass variations at the Earth's surface since 1900 to show that the climate-induced LOD trend hovered between 0.3 and 1.

Can large-scale tree planting in China compensate for the loss of climate connectivity due to deforestation?

Extensive deforestation has been a major reason for the loss of forest connectivity, impeding species range shifts under current climate change. Over the past decades, the Chinese government launched a series of afforestation and reforestation projects to increase forest cover, yet whether the new forests can compensate for the loss of connectivity due to deforestation-and where future tree planting would be most effective-remains largely unknown. Here, we evaluate changes in climate connectivity across China's forests between 2015 and 2019.

Raw GNSS data collected using smartphones and low-cost receiver under optimal and sub-optimal conditions.

The miniaturisation and decrease of price are amongst the main current trends in the area of Global Navigation Satellite Systems (GNSS) receivers. Besides standalone receivers also receivers incorporated into Android devices can provide raw GNSS measurements thus enabling much wider options, formerly restricted to devices of much higher price. The article describes two datasets.

Fine-Grained 3D Modeling and Semantic Mapping of Coral Reefs Using Photogrammetric Computer Vision and Machine Learning.

Corals play a crucial role as the primary habitat-building organisms within reef ecosystems, forming expansive structures that extend over vast distances, akin to the way tall buildings define a city's skyline. However, coral reefs are vulnerable to damage and destruction due to their inherent fragility and exposure to various threats, including the impacts of climate change. Similar to successful city management, the utilization of advanced underwater videography, photogrammetric computer vision, and machine learning can facilitate precise 3D modeling and the semantic mapping of coral reefs, aiding in their careful management and conservation to ensure their survival.

Progressive Model-Driven Approach for 3D Modeling of Indoor Spaces.

This paper focuses on the 3D modeling of the interior spaces of buildings. Three-dimensional point clouds from laser scanners can be considered the most widely used data for 3D indoor modeling. Therefore, the walls, ceiling and floor are extracted as the main structural fabric and reconstructed.

Differencing strategies for SLR observations at the Wettzell observatory.

The precise estimation of geodetic parameters using single- and double-differenced SLR observations is investigated. While the differencing of observables is a standard approach for the GNSS processing, double differences of simultaneous SLR observations are practically impossible to obtain due to the SLR basic principle of observing one satellite at a time. Despite this, the availability of co-located SLR telescopes and the use of the alternative concept of allow the forming of SLR differences under certain assumptions, thus enabling the use of these processing strategies.

Weak Calibration of a Visible Light Positioning System Based on a Position-Sensitive Detector: Positioning Error Assessment.

Reduced deployment and calibration requirements are key for scalable and cost-effective indoor positioning systems. In this work, we propose a low-complexity, weak calibration procedure for an indoor positioning system based on infrastructure lighting and a positioning-sensitive detector. The proposed calibration relies on genetic algorithms to obtain the relevant system parameters in the real positioning environment without a priori information, and requires a low number of simple measurements.

Can Machine Learning and PS-InSAR Reliably Stand in for Road Profilometric Surveys?

This paper proposes a methodology for correlating products derived by Synthetic Aperture Radar (SAR) measurements and laser profilometric road roughness surveys. The procedure stems from two previous studies, in which several Machine Learning Algorithms (MLAs) have been calibrated for predicting the average vertical displacement (in terms of mm/year) of road pavements as a result of exogenous phenomena occurrence, such as subsidence. Such algorithms are based on surveys performed with Persistent Scatterer Interferometric SAR (PS-InSAR) over an area of 964 km in the Tuscany Region, Central Italy.

Phase Stability under Thermal Drifts in Photodiode-Conditioning Transimpedance Amplifiers for Distance Metrology.

Transimpedance amplifiers (TIA) are widely used for front-end signal conditioning in many optical distance measuring applications in which high accuracy is often required. Small effects due to the real characteristics of the components and the parasitic elements in the circuit board may cause the error to rise to unacceptable levels. In this work we study these effects on the TIA delay time error and deduce analytic expressions, taking into account the trade-off between the uncertainties caused by the delay time instability and by the signal-to-noise ratio.

Kalman Filter-Based Fusion of Collocated Acceleration, GNSS and Rotation Data for 6C Motion Tracking.

The ground motion of an earthquake or the ambient motion of a large engineered structure not only has translational motion, but it also includes rotation around all three axes. No current sensor can record all six components, while the fusion of individual instruments that could provide such recordings, such as accelerometers or Global Navigation Satellite System (GNSS) receivers, and rotational sensors, is non-trivial. We propose achieving such a fusion via a six-component (6C) Kalman filter (KF) that is suitable for structural monitoring applications, as well as earthquake monitoring.

Accuracy and Precision Assessment of AoA-Based Indoor Positioning Systems Using Infrastructure Lighting and a Position-Sensitive Detector.

Unlike GNSS-based outdoor positioning, there is no technological alternative for Indoor Positioning Systems (IPSs) that generally stands out from the others. In indoor contexts, the measurement technologies and localization strategies to be used depend strongly on the application requirements and are complementary to each other. In this work, we present an optical IPS based on a Position-Sensitive Detector (PSD) and exploiting illumination infrastructure to determine the target position by Angle of Arrival (AoA) measurements.

Rail Track Detection and Projection-Based 3D Modeling from UAV Point Cloud.

The expansion of the railway industry has increased the demand for the three-dimensional modeling of railway tracks. Due to the increasing development of UAV technology and its application advantages, in this research, the detection and 3D modeling of rail tracks are investigated using dense point clouds obtained from UAV images. Accordingly, a projection-based approach based on the overall direction of the rail track is proposed in order to generate a 3D model of the railway.

Spatial analysis and risk assessment of urban BTEX compounds in Urmia, Iran.

Land Use Regression models (LUR) are the most common tools to estimate intra-urban air pollutant exposure in epidemiological studies. However, number of available and published models in developing and middle up income countries is still scarce. Here, we developed seasonal and overall LUR models for the spatial distribution of benzene, toluene, ethylbenzene and xylene (BTEX) based on 20 monitoring stations and 166 potentially predictive variables (PPVs) in Urmia, Iran.

An Indoor Positioning Approach Based on Fusion of Cameras and Infrared Sensors.

A method for infrared and cameras sensor fusion, applied to indoor positioning in intelligent spaces, is proposed in this work. The fused position is obtained with a maximum likelihood estimator from infrared and camera independent observations. Specific models are proposed for variance propagation from infrared and camera observations (phase shifts and image respectively) to their respective position estimates and to the final fused estimation.

Estimate annual and seasonal PM, PM and PM concentrations using land use regression model.

Exposure to ambient particulate matter (PM) can increase mortality and morbidity in urban area. In this study, annual and seasonal spatial pattern of PM, PM and PM pollutants were assessed using land use regression (LUR) models in Sabzevar, Iran. The studied pollutants were measured at 26 monitoring stations of different microenvironments in the study area.

Stand-Alone GNSS Sensors as Velocity Seismometers: Real-Time Monitoring and Earthquake Detection.

By means of the time derivatives of Global Navigation Satellite System (GNSS) carrier-phase measurements, the instantaneous velocity of a stand-alone, single GNSS receiver can be estimated with a high precision of a few mm/s; it is feasible to even obtain the level of tenths of mm/s. Therefore, only data from the satellite navigation message are needed, thus discarding any data from a reference network. Combining this method with an efficient movement-detection algorithm opens some interesting applications for geohazard monitoring; an example is the detection of strong earthquakes.

SNR Degradation in Undersampled Phase Measurement Systems.

A wide range of measuring applications rely on phase estimation on sinusoidal signals. These systems, where the estimation is mainly implemented in the digital domain, can generally benefit from the use of undersampling to reduce the digitizer and subsequent digital processing requirements. This may be crucial when the application characteristics necessarily imply a simple and inexpensive sensor.

A Comparative Study of the Applied Methods for Estimating Deflection of the Vertical in Terrestrial Geodetic Measurements.

This paper compares three different methods capable of estimating the deflection of the vertical (DoV): one is based on the joint use of high precision spirit leveling and Global Navigation Satellite Systems (GNSS), a second uses astro-geodetic measurements and the third gravimetric geoid models. The working data sets refer to the geodetic International Terrestrial Reference Frame (ITRF) co-location sites of Medicina (Northern, Italy) and Noto (Sicily), these latter being excellent test beds for our investigations. The measurements were planned and realized to estimate the DoV with a level of precision comparable to the angular accuracy achievable in high precision network measured by modern high-end total stations.

Automatic tracking of vessel-like structures from a single starting point.

The identification of vascular networks is an important topic in the medical image analysis community. While most methods focus on single vessel tracking, the few solutions that exist for tracking complete vascular networks are usually computationally intensive and require a lot of user interaction. In this paper we present a method to track full vascular networks iteratively using a single starting point.

Lunisolar tidal force and its relationship to chlorophyll fluorescence in Arabidopsis thaliana.

The yield of chlorophyll fluorescence Ft was measured in leaves of Arabidopsis thaliana over periods of several days under conditions of continuous illumination (LL) without the application of saturating light pulses. After linearization of the time series of the chlorophyll fluorescence yield (ΔFt), oscillations became apparent with periodicities in the circatidal range. Alignments of these linearized time series ΔFt with the lunisolar tidal acceleration revealed high degrees of synchrony and phase congruence.

Intra-urban variation of ultrafine particles as evaluated by process related land use and pollutant driven regression modelling.

The microscale intra-urban variation of ultrafine particle concentrations (UFP, diameter Dp<100 nm) and particle number size distributions was studied by two statistical regression approaches. The models were applied to a 1 km2 study area in Braunschweig, Germany. A land use regression model (LUR) using different urban morphology parameters as input is compared to a multiple regression type model driven by pollutant and meteorological parameters (PDR).

Delay tracking of spread-spectrum signals for indoor optical ranging.

Delay tracking of spread-spectrum signals is widely used for ranging in radio frequency based navigation. Its use in non-coherent optical ranging, however, has not been extensively studied since optical channels are less subject to narrowband interference situations where these techniques become more useful. In this work, an early-late delay-locked loop adapted to indoor optical ranging is presented and analyzed.

The effect of different Global Navigation Satellite System methods on positioning accuracy in elite alpine skiing.

In sport science, Global Navigation Satellite Systems (GNSS) are frequently applied to capture athletes' position, velocity and acceleration. Application of GNSS includes a large range of different GNSS technologies and methods. To date no study has comprehensively compared the different GNSS methods applied.

GPT2: Empirical slant delay model for radio space geodetic techniques.

Up to now, state-of-the-art empirical slant delay modeling for processing observations from radio space geodetic techniques has been provided by a combination of two empirical models. These are GPT (Global Pressure and Temperature) and GMF (Global Mapping Function), both operating on the basis of long-term averages of surface values from numerical weather models. Weaknesses in GPT/GMF, specifically their limited spatial and temporal variability, are largely eradicated by a new, combined model GPT2, which provides pressure, temperature, lapse rate, water vapor pressure, and mapping function coefficients at any site, resting upon a global 5° grid of mean values, annual, and semi-annual variations in all parameters.