Errors and uncertainties in microwave link rainfall estimation explored using drop size measurements and high-resolution radar data

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This paper presents the experimental validation of a linear recursive state estimation (SE) process for hybrid AC/DC microgrids proposed in the authors' previous work. The SE uses a unified and linear measurement model that relies on the use of synchronize ...

We present a statistical framework to benchmark the performance of reconstruction algorithms for linear inverse problems, in particular, neural-network-based methods that require large quantities of training data. We generate synthetic signals as realizati ...

Sweat secreted by the human eccrine sweat glands can provide valuable biomarker information during exercise. Real-time non-invasive biomarker recordings are therefore useful for evaluating the physiological conditions of an athlete such as their hydration ...

In certain cases of astronomical data analysis, the meaningful physical quantity to extract is the ratio R between two data sets. Examples include the lensing ratio, the interloper rate in spectroscopic redshift samples, and the decay rate of gravitational ...

Wind energy potential in complex terrain is still poorly understood and difficult to quantify. With Switzerland’s current efforts to shift to renewable energy resources, it is now becoming even more crucial to investigate the hidden potential of wind energ ...

Water quality prediction in the spatially heterogeneous environment is challenging as the importance of water quality parameters (WQPs) and the performance of prediction models may vary across space. Thus, this study proposed spatially adaptive machine lea ...

Correlated errors of experimental data are a common but often neglected problem in physical sciences. Various tools are provided here for thorough propagation of uncertainties in cases of correlated errors. Discussed are techniques especially applicable to ...

Ice-nucleating particles (INPs) enable ice formation, profoundly affecting the microphysical and radiative properties, lifetimes, and precipitation rates of clouds. Mineral dust emitted from arid regions, particularly potassium-containing feldspar (K-felds ...

Self-exciting point processes, widely used to model arrival phenomena in nature and society, are often difficult to identify. The estimation becomes even more challenging when arrivals are recorded only as bin counts on a finite partition of the observatio ...

Errors and uncertainties in microwave link rainfall estimation explored using drop size measurements and high-resolution radar data

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Experimental Validation of a Unified and Linear State Estimation Method for Hybrid AC/DC Microgrids

A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods

Multisensing wearables for real-time monitoring of sweat electrolyte biomarkers during exercise and analysis on their correlation with core body temperature

An Unbiased Method of Measuring the Ratio of Two Data Sets

Combining Weather Station Data and Short-Term LiDAR Deployment to Estimate Wind Energy Potential with Machine Learning: A Case Study from the Swiss Alps

Spatially adaptive machine learning models for predicting water quality in Hong Kong

Reliable uncertainties: Error correlation, rotated error bars, and linear regressions in three-isotope plots and beyond

Role of K-feldspar and quartz in global ice nucleation by mineral dust in mixed-phase clouds

Estimation of Self-Exciting Point Processes from Time-Censored Data