The first global 883 GHz cloud ice survey: IceCube Level 1 data calibration, processing and analysis

Sub-millimeter (200–1000 GHz) wavelengths contribute a unique capability to fill in the sensitivity gap between operational visible–infrared (VIS–IR) and microwave (MW) remote sensing for atmospheric cloud ice and snow. Being able to penetrate clouds to measure cloud ice mass and microphysical properties in the middle to upper troposphere, a critical spectrum range, is necessary for us to understand the connection between cloud ice and precipitation processes. As the first spaceborne 883 GHz radiometer, the IceCube mission was NASA's latest spaceflight demonstration of commercial sub-millimeter radiometer technology. Successfully launched from the International Space Station, IceCube is essentially a free-running radiometer and collected valuable 15-month measurements of atmosphere and cloud ice. This paper describes the detailed procedures for Level 1 (L1) data calibration, processing and validation. The scientific quality and value of IceCube data are then discussed, including radiative transfer model validation and evaluation, as well as the unique spatial distribution and diurnal cycle of cloud ice that are revealed for the first time on a quasi-global scale at this frequency. IceCube Level 1 dataset is publicly available at Gong and Wu (2021) (https://doi.org/10.25966/3d2p-f515).

X-ray Fluorescence Spectroscopy Features of Micro- and Nanoscale Copper and Nickel Particle Compositions

The study is devoted to X-ray fluorescence spectroscopy (XRF) features of micro- and nanosized powder mixtures of copper and nickel. XRF is a high accuracy method that allows for both qualitative and quantitative analysis. However, the XRF measurement error due to the size of the studied particles is not usually taken into account, which limits the use of the method in some cases, such as analysis of Ni-Cu mixtures and coatings. In this paper, a method for obtaining copper and nickel nanoparticles was investigated, and the XRF of powder compositions was considered in detail. The initial micro- and nanoparticles of copper and nickel were studied in detail using SEM, TEM, XRD, and EDX. Based on experimental data, calibration curves for copper-nickel powder compositions of various sizes were developed. According to the results, it was experimentally established that the calibration curves constructed for nanoscale and microscale powders differ significantly. The presented approach can be expanded for other metals and particle sizes.

Development of technique of remote spectral sensing of technogenically loaded territories

Introduction. The article considers the problem of monitoring technologically loaded landscapes. To solve it, the authors proposed an innovative method for studying the chemical composition of objects using direct spectral sensing means. Problem Statement. The objective of this study is to consider the possibility of using spectral sensing to control the composition of soils in technogenically loaded territories. Practical Part. To confirm the hypothesis that observing changes in the parameters of reflection spectra in non-selective areas will make it possible to establish the presence of basic biogenic macroelements for plants in the soil and evaluate its fertility, or determine the degree of contamination of the territory, a laboratory experiment was conducted using modern spectral equipment and multidimensional data calibration was performed. Conclusion. The results of the analysis show the fundamental possibility of using spectral sensing in the monitoring of technogenically loaded territories using methods of multidimensional data analysis.

Raw EPIC Data Calibration

Earth Polychromatic Imaging Camera (EPIC) raw level-0 (L0) data in one channel is a 12-bit 2,048 × 2,048 pixels image array plus auxiliary data such as telemetry, temperature, etc. The EPIC L1a processor applies a series of correction steps on the L0 data to convert them into corrected count rates (level-1a or L1a data): Dark correction, Enhanced pixel detection, Read wave correction, Latency correction, Non-linearity correction, Temperature correction, Conversion to count rates, Flat fielding, and Stray light correction. L1a images should have all instrumental effects removed and only need to be multiplied by one single number for each wavelength to convert counts to radiances, which are the basis for all higher-level EPIC products, such as ozone and sulfur dioxide total column amounts, vegetation index, cloud, aerosol, ocean surface, and vegetation properties, etc. This paper gives an overview of the mathematics and the pre-launch and on-orbit calibration behind each correction step.

ON THE ORGANIZATION AND VALIDATION OF A PILOT TEST OF A MOBILE CROWDSOURCED AIR QUALITY MONITORING SYSTEM

The development of new tools that allow continuous monitoring of air quality is essential for the study of actions, in order to improve the levels of pollutants in the air that are harmful to the health of citizens. Cardiovascular and respiratory diseases have been identified as risk factors for death in patients with COVID-19; at the same time, exposure to air pollution is associated with these diseases. In this article, we present the pilot tests of the Crowdsourced Air Quality Monitoring (C-AQM) system, which allows the generation of reliable air pollution maps, using data provided by low-cost sensor nodes. The results verify that the system is correct after performing a data calibration; an improvement in NO2 pollution has been observed on weekends, as well as a situation of less air pollution by NO2 between the first and second pandemic waves in Spain.

ApneaDetector

Sleep apnea is a sleep disorder in which breathing is briefly and repeatedly interrupted. Polysomnography (PSG) is the standard clinical test for diagnosing sleep apnea. However, it is expensive and time-consuming which requires hospital visits, specialized wearable sensors, professional installations, and long waiting lists. To address this problem, we design a smartwatch-based system called ApneaDetector, which exploits the built-in sensors in smartwatches to detect sleep apnea. Through a clinical study, we identify features of sleep apnea captured by smartwatch, which can be leveraged by machine learning techniques for sleep apnea detection. However, there are many technical challenges such as how to extract various special patterns from the noisy and multi-axis sensing data. To address these challenges, we propose signal denoising and data calibration techniques to process the noisy data while preserving the peaks and troughs which reflect the possible apnea events. We identify the characteristics of sleep apnea such as signal spikes which can be captured by smartwatch, and propose methods to extract proper features to train machine learning models for apnea detection. Through extensive experimental evaluations, we demonstrate that our system can detect apnea events with high precision (0.9674), recall (0.9625), and F1-score (0.9649).

A Simple PSO-Based Method for Power Distribution System Components Reliability Parameters Calibration

In this paper, a proposed algorithm based on Particle Swarm Optimization (PSO) is used to present a simple method for data calibration of reliability indices in electrical power distribution networks. The main feature of the proposed method is its comprehensiveness, since the whole reliability indices can be calibrated using a proper objective function. In order to evaluate the effectiveness of the suggested algorithm, calculations are made on the well-known IEEE-RBTS Bus2 test system. The results confirm the simplicity and validation of the proposed method, and verify that by applying the proposed method, the computation speed for data calibration can be reduced as well.