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Effective data management and sharing are critical success factors in industry-academia collaboration. This paper explores the motivations and lessons learned from publishing open data sets in such collaborations. Through a survey of participants in a European research project that published 13 data sets, and an analysis of metadata from almost 281 thousand datasets in Zenodo, we collected qualitative and quantitative results on motivations, achievements, research questions, licences and file types. Through inductive reasoning and statistical analysis we found that planning the data collection is essential, and that only few datasets (2.4%) had accompanying scripts for improved reuse. We also found that authors are not well aware of the importance of licences or which licence to choose. Finally, we found that data with a synthetic origin, collected with simulations and potentially mixed with real measurements, can be very meaningful, as predicted by Gartner and illustrated by many datasets collected in our research project., Comment: 15 pages single column, 3 tables, 3 figures

The goal of the project QLEAP (2022-24), funded by Business Finland and participating organizations, was to study using containers as elements of architecture design. Such systems include containerized AI systems, using containers in a hybrid setup (public/hybrid/private clouds), and related security concerns. The consortium consists of four companies that represent different concerns over using containers (Bittium, M-Files, Solita/ADE Insights, Vaadin) and one research organization (University of Jyv\"askyl\"a). In addition, it has received support from two Veturi companies - Nokia and Tietoevry - who have also participated in steering the project. Moreover, the SW4E ecosystem has participated in the project. This document gathers the key lessons learned from the project.

Liver zonation is a fundamental characteristic of hepatocyte spatial heterogeneity, which is challenging to recapitulate in traditional cell cultures. This study presents a novel microfluidic device designed to induce zonation in liver cell cultures by establishing an oxygen gradient using standard laboratory gases. The device consists of two layers; a bottom layer containing a gas channel network that delivers high and low oxygenated gases to create three distinct zones within the cell culture chamber in the layer above. Computational simulations and ratiometric oxygen sensing were employed to validate the oxygen gradient, demonstrating that stable oxygen levels were achieved within two hours. Liver zonation was confirmed using immunofluorescence staining, which showed zonated albumin production in HepG2 cells directly correlating with oxygen levels and mimicking in-vivo zonation behavior. This user-friendly device supports studies on liver zonation and related metabolic disease mechanisms in vitro. It can also be utilized for experiments that necessitate precise gas concentration gradients, such as hypoxia-related research areas focused on angiogenesis and cancer development.

Background: Classification of volatile organic compounds (VOCs) is of interest in many fields. Examples include but are not limited to medicine, detection of explosives, and food quality control. Measurements collected with electronic noses can be used for classification and analysis of VOCs. One type of electronic noses that has seen considerable development in recent years is Differential Mobility Spectrometry (DMS). DMS yields measurements that are visualized as dispersion plots that contain traces, also known as alpha curves. Current methods used for analyzing DMS dispersion plots do not usually utilize the information stored in the continuity of these traces, which suggests that alternative approaches should be investigated. Results: In this work, for the first time, dispersion plots were interpreted as a series of measurements evolving sequentially. Thus, it was hypothesized that time-series classification algorithms can be effective for classification and analysis of dispersion plots. An extensive dataset of 900 dispersion plots for five chemicals measured at five flow rates and two concentrations was collected. The data was used to analyze the classification performance of six algorithms. According to our hypothesis, the highest classification accuracy of 88\% was achieved by a Long-Short Term Memory neural network, which supports our hypothesis. Significance: A new concept for approaching classification tasks of dispersion plots is presented and compared with other well-known classification algorithms. This creates a new angle of view for analysis and classification of the dispersion plots. In addition, a new dataset of dispersion plots is openly shared to public.

This article addresses the challenge of vision-based proximity navigation in asteroid exploration missions and on-orbit servicing. Traditional feature extraction methods struggle with the significant appearance variations of asteroids due to limited scattered light. To overcome this, we propose a lightweight feature extractor specifically tailored for asteroid proximity navigation, designed to be robust to illumination changes and affine transformations. We compare and evaluate state-of-the-art feature extraction networks and three lightweight network architectures in the asteroid context. Our proposed feature extractors and their evaluation leverages both synthetic images and real-world data from missions such as NEAR Shoemaker, Hayabusa, Rosetta, and OSIRIS-REx. Our contributions include a trained feature extractor, incremental improvements over existing methods, and a pipeline for training domain-specific feature extractors. Experimental results demonstrate the effectiveness of our approach in achieving accurate navigation and localization. This work aims to advance the field of asteroid navigation and provides insights for future research in this domain.

Many guidelines for responsible AI have been suggested to help AI practitioners in the development of ethical and responsible AI systems. However, these guidelines are often neither grounded in regulation nor usable by different roles, from developers to decision makers. To bridge this gap, we developed a four-step method to generate a list of responsible AI guidelines; these steps are: (1) manual coding of 17 papers on responsible AI; (2) compiling an initial catalog of responsible AI guidelines; (3) refining the catalog through interviews and expert panels; and (4) finalizing the catalog. To evaluate the resulting 22 guidelines, we incorporated them into an interactive tool and assessed them in a user study with 14 AI researchers, engineers, designers, and managers from a large technology company. Through interviews with these practitioners, we found that the guidelines were grounded in current regulations and usable across roles, encouraging self-reflection on ethical considerations at early stages of development. This significantly contributes to the concept of `Responsible AI by Design' -- a design-first approach that embeds responsible AI values throughout the development lifecycle and across various business roles., Comment: 28 pages, 5 figures, 3 tables

Long-range correlations for pairs of charged particles with two-particle angular correlations are studied in $pp$ at ${\sqrt{{\textit s}}}=13$~TeV with various Monte Carlo generators. The correlation functions are constructed as functions of relative azimuthal angle $\Delta\varphi$ and pseudorapidity separation $\Delta\eta$ for pairs of different particle species with the identified hadrons such as $\pi$, $K$, $p$, and $\Lambda$ in wide $\Delta\eta$ ranges. Fourier coefficients are extracted for the long-range correlations in several -multiplicity classes using a low-multiplicity template fit method. The method allows to subtract the enhanced away-side jet fragments in high-multiplicity with respect to low-multiplicity events. However, we found that due to a kinematic bias on jets and differing model implementation of flow and jet components, subtracting the non-flow contamination in small systems can bias the results. It is found that PYTHIA8 Default model where the presence of the collective flow is not expected but the bias results in very large flow. Also extracting flow signal from the EPOS4 and PYTHIA8 String Shoving models is not possible because of flow signal introduced in the low-multiplicity events. Only AMPT String Melting model among studied model calculations is free from this bias, and shows a mass ordering at low $p_{\mathrm{T}}$ and particle type grouping in the intermediate $p_{\mathrm{T}}$ range. This feature has first found in large systems but the mass ordering in small systems is different from what is observed in the large collision systems., Comment: 16 pages, 16 figures

Radio waves provide a useful diagnostic tool to investigate the properties of the ionosphere because the ionosphere affects the transmission and properties of High Frequency (HF) electromagnetic waves. We have conducted a transionospheric HF-propagation research campaign with a nanosatellite on a low-Earth polar orbit and the EISCAT HF transmitter facility in Troms{\o}, Norway, in December 2020. In the active measurement, the EISCAT HF facility transmitted sinusoidal 7.953 MHz signal which was received with the HEARER radio spectrometer onboard 1 Unit (size: 10 cm x 10 cm x 10 cm) Suomi 100 space weather nanosatellite. Data analysis showed that the EISCAT HF signal was detected with the satellite's radio spectrometer when the satellite was the closest to the heater along its orbit. Part of the observed variations seen in the signal was identified to be related to the heater's antenna pattern and to the transmitted pulse shapes. Other observed variations can be related to the spatial and temporal variations of the ionosphere and its different responses to the used transmission frequencies and to the transmitted O- and X-wave modes. Some trends in the observed signal may also be associated to changes in the properties of ionospheric plasma resulting from the heater's electromagnetic wave energy. This paper is, to authors' best knowledge, the first observation of this kind of "self-absorption" measured from the transionospheric signal path from a powerful radio source on the ground to the satellite-borne receiver., Comment: 27 pages, 10 images

Auroras can be regarded as the most fascinating manifestation of space weather and they are continuously observed by ground-based and, nowadays more and more, also by space-based measurements. Investigations of auroras and geospace comprise the main research goals of the Suomi 100 nanosatellite, the first Finnish space research satellite, which has been measuring the Earth's ionosphere since its launch on Dec. 3, 2018. In this work, we present a case study where the satellite's camera observations of an aurora over Northern Europe are combined with ground-based observations of the same event. The analyzed image is, to the authors' best knowledge, the first auroral image ever taken by a cubesat. Our data analysis shows that a satellite vantage point provides complementary, novel information of such phenomena. The 3D auroral location reconstruction of the analyzed auroral event demonstrates how information from a 2D image can be used to provide location information of auroras under study. The location modelling also suggests that the Earth's limb direction, which was the case in the analyzed image, is an ideal direction to observe faint auroras. Although imaging on a small satellite has some large disadvantages compared with ground-based imaging (the camera cannot be repaired, a fast moving spinning satellite), the data analysis and modelling demonstrate how even a small 1-Unit (size: 10 cm x 10 cm x 10 cm) CubeSat and its camera, build using cheap commercial off-the-shelf components, can open new possibilities for auroral research, especially, when its measurements are combined with ground-based observations., Comment: Accepted manuscript 34 pages, 17 figures