To support an ongoing observation campaign of transiting extrasolar planets (exoplanets) using the SkyNet Robotic Telescope Network [1], we develop an observation scheduling tool as part of a planned end-to-end observation, reduction, and analysis pipeline. The campaign aims to update transit timing measurements of identified exoplanet targets and contribute to maintaining accurate ephemerides essential for planning of future observations using both ground- and space-based telescopes. Given a desired observation period (e.g., from October to December 2025) and a list of target exoplanets, the tool automatically (1) retrieves the list of transit times from the NASA Exoplanet Archive (NEA), (2) identifies the observatories in the SkyNet Robotic Telescope Network where the full transits will be observable (given a desired maximum airmass), and (3) generates a comprehensive list of observation times for queuing. Additionally, the tool generates airmass plots and sky charts for reference. The tool is developed in Python and utilizes the Python package astroquery [2] for querying external databases, and astroplan [3] and astropy [4] for data visualization. While there are existing online tools from SkyNet and NEA that can generate airmass plots and facilitate the scheduling of observations, there are current limitations in the number of observatory locations and targets that can be simultaneously processed, as well as in the length of the observation window. With our observation scheduling tool, any contributing user can quickly generate a comprehensive observation plan and maximize the use of the SkyNet Robotic Telescope Network, consisting of 32 telescopes in 20 observatories located in 5 countries across 4 continents. We have checked that the generated outputs match those from the above-mentioned established online tools. In addition to using the tool for our own collaboration’s exoplanet campaign, we also plan to release it as an open-source project—making it available to the broader astronomy community for use and further development. Additional improvements could include the expansion of the observatory database beyond SkyNet, and development of a graphical user interface and web application for increased accessibility for students and citizen scientists.

This study introduces a semi-automatic method for extracting farm-to-market roads (FMRs) using very high-resolution (VHR) satellite imagery with only RGB bands. The workflow combines edge detection, thresholding, and morphological operations with vector-based transect analysis to delineate roads and estimate width information. Applied in Nueva Ecija, Philippines, the method produced reliable results despite the absence of multispectral data and processed large images in under a minute. This speed makes the approach practical for monitoring FMR construction, especially in remote areas where site visits are difficult. While VHR imagery provides accuracy and frequent coverage, its use is limited by cost and licensing. To address scalability, the method can be adapted for medium-resolution sources such as PlanetScope or Sentinel-2, with expected trade-offs in precision. Future comparison with UAV-based monitoring may further clarify the balance between cost, coverage, and accuracy, strengthening its application in broader infrastructure planning.

The growing interest in plant space biology is motivated by the need to create sustainable life support systems for long-duration human spaceflight. Plants are essential elements of bio-regenerative systems due to their ability to produce food, generate oxygen, recycle carbon dioxide, and help in water purification. One of the significant issues, however, is the stimulation of plant growth under altered gravitational conditions, such like in microgravity environments (~10⁻³ G). While experiments done in space have yielded valuable results, their practical application is frequently hindered by high costs, limited access, and complicated logistical demands (De Pascale et al., 2021). Ground-based simulators like the random positioning machine (RPM) or 3D clinostat are thus becoming increasingly valuable. By rotating samples continuously around two orthogonal axes, RPM creates a temporal averaging of the gravitational vector, thus simulating microgravity conditions (Herranz et al., 2013). We present an economically viable, table-top random positioning machine (RPM) made from easily sourced parts in the market. The device consists of two independent aluminum frames (20×20 mm extrusion) that rotate and are powered by stepper motors, allowing continuous three-dimensional randomization. Rotational speed, orientation, and time are controlled by the Arduino Uno R3 microcontroller, with real-time optimization allowed through feedback sensors. The sample holder, built from aluminum extrusions, is placed in the center of the device and can hold four standard petri dishes, thus allowing simultaneous biological experiments. Built for modularity and accessibility, this RPM provides a practical platform for microgravity research, particularly in resource-constrained research and academic environments. It is a cost-effective entry to space biology and facilitates local capacity building in preparation for future space science missions.