Building a Raspberry Pi school magnetometer network in the UK

As computing and geophysical sensor components have become increasingly affordable over the past decade, it is now possible to design and build a cost-effective system for monitoring the Earth’s natural magnetic field variations, in particular for space weather events. Modern fluxgate magnetometers are sensitive down to the sub-nanotesla (nT) level, which far exceeds the level of accuracy required to detect very small variations of the external magnetic field. When the popular Raspberry Pi single-board computer is combined with a suitable digitiser it can be used as a low-cost data logger. We adapted off-the-shelf components to design a magnetometer system for schools and developed bespoke Python software to build a network of low-cost magnetometers across the UK. We describe the system and software and how it was deployed to schools around the UK. In addition, we show the results recorded by the system from one of the largest geomagnetic storms of the current solar cycle

Mars Riometer System

A riometer (relative ionospheric opacity meter) measures the intensity of cosmic radio noise at the surface of a planet. When an electromagnetic wave passes through the ionosphere collisions between charged particles (usually electrons) and neutral gases remove energy from the wave. By measuring the received signal intensity at the planet's surface and comparing it to the expected value (the quietday curve) a riometer can deduce the absorption (attenuation) of the trans-ionospheric signal. Thus the absorption measurements provide an indication of ionisation changes occurring in the ionosphere. To avoid the need for orbiting sounders riometers use the cosmic noise background as a signal source. Earth-based systems are not subject to the challenging power, volume and mass restriction that would apply to a riometer for Mars. Some Earth-based riometers utilise phased-array antennas in order to provide an imaging capability

Minimizing Variability of Cascade Impaction Measurements in Inhalers and Nebulizers

The purpose of this article is to catalogue in a systematic way the available information about factors that may influence the outcome and variability of cascade impactor (CI) measurements of pharmaceutical aerosols for inhalation, such as those obtained from metered dose inhalers (MDIs), dry powder inhalers (DPIs) or products for nebulization; and to suggest ways to minimize the influence of such factors. To accomplish this task, the authors constructed a cause-and-effect Ishikawa diagram for a CI measurement and considered the influence of each root cause based on industry experience and thorough literature review. The results illustrate the intricate network of underlying causes of CI variability, with the potential for several multi-way statistical interactions. It was also found that significantly more quantitative information exists about impactor-related causes than about operator-derived influences, the contribution of drug assay methodology and product-related causes, suggesting a need for further research in those areas. The understanding and awareness of all these factors should aid in the development of optimized CI methods and appropriate quality control measures for aerodynamic particle size distribution (APSD) of pharmaceutical aerosols, in line with the current regulatory initiatives involving quality-by-design (QbD)

A multi-instrument data analysis toolbox

In the study of solar-terrestrial physics there is frequently a requirement to combine and compare data from different instruments, of either the same type or of different types. This paper presents a Multi-Instrument Analysis (MIA) toolbox for Matlab. By using object-oriented programming techniques it is shown that the same tools can be applied to data from different instruments, or even instruments of different types. A coherent structure enables MIA to display image plots, keograms and movies for all imaging instruments, regardless of type. Data files are joined automatically so that file boundaries do not interrupt data processing. Although a graphical user interface is available all operations can be performed by scripts, thereby permitting automated data processing. By simplifying data processing MIA aids the creation of new data products such as energy maps and event databases. MIA currently supports riometers and imaging riometers, magnetometers and all-sky cameras

Digital beam-forming imaging riometer systems

The design and operation of a new generation of digital imaging riometer systems developed by Lancaster University are presented. In the heart of the digital imaging riometer is a field-programmable gate array (FPGA), which is used for the digital signal processing and digital beam forming, completely replacing the analog Butler matrices which have been used in previous designs. The reconfigurable nature of the FPGA has been exploited to produce tools for remote system testing and diagnosis which have proven extremely useful for operation in remote locations such as the Arctic and Antarctic. Different FPGA programs enable different instrument configurations, including a 4 × 4 antenna filled array (producing 4 × 4 beams), an 8 × 8 antenna filled array (producing 7 × 7 beams), and a Mills cross system utilizing 63 antennas producing 556 usable beams. The concept of using a Mills cross antenna array for riometry has been successfully demonstrated for the first time. The digital beam forming has been validated by comparing the received signal power from cosmic radio sources with results predicted from the theoretical beam radiation pattern. The performances of four digital imaging riometer systems are compared against each other and a traditional imaging riometer utilizing analog Butler matrices. The comparison shows that digital imaging riometer systems, with independent receivers for each antenna, can obtain much better measurement precision for filled arrays or much higher spatial resolution for the Mills cross configuration when compared to existing imaging riometer systems