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As 5G is entering maturity, the research interest has shifted towards 6G, and specially the new use cases that the future telecommunication infrastructure needs to support. These new use cases encompass much higher requirements, specifically: higher communication data-rates, larger number of users, higher accuracy in localization, possibility to wirelessly charge devices, among others.The radio ac
Predictions of cloud droplet activation in the late summertime (September) central Arctic Ocean are made using κ-Kohler theory with novel observations of the aerosol chemical composition from a high-resolution time-of-flight chemical ionization mass spectrometer with a filter inlet for gases and aerosols (FIGAERO-CIMS) and an aerosol mass spectrometer (AMS), deployed during the Arctic Ocean 2018 e
Dimethyl sulfide (DMS) influences climate via cloud condensation nuclei (CCN) formation resulting from its oxidation products (mainly methanesulfonic acid, MSA, and sulfuric acid, H2SO4). Despite their importance, accurate prediction of MSA and H2SO4 from DMS oxidation remains challenging. With comprehensive experiments carried out in the Cosmics Leaving Outdoor Droplets (CLOUD) chamber at CERN, w
We introduce the Atmospherically Relevant Chemistry and Aerosol box model ARCA box (v.1.2.2). It is a zero-dimensional process model with a focus on atmospheric chemistry and submicron aerosol processes, including cluster formation. A novel feature in the model is its comprehensive graphical user interface, allowing for detailed configuration and documentation of the simulation settings, flexible
Dimethyl sulfide (DMS) is emitted by phytoplankton species in the oceans and constitutes the largest source of naturally emitted sulfur to the atmosphere. The climate impact of secondary particles, formed through the oxidation of DMS by hydroxyl radicals, is still elusive. This study investigates the hygroscopicity and cloud condensation nuclei activity of such particles and discusses the results
A multitude of biogeochemical feedback mechanisms govern the climate sensitivity of Earth in response to radiation balance perturbations. One feedback mechanism, which remained missing from most current Earth System Models applied to predict future climate change in IPCC AR6, is the impact of higher temperatures on the emissions of biogenic volatile organic compounds (BVOCs), and their subsequent
In this study, we modeled the aerosol particle formation along air mass trajectories arriving at the remote Arctic research stations Gruvebadet (67 m a.s.l.) and Zeppelin (474 m a.s.l.), Ny-Ålesund, during May 2018. The aim of this study was to improve our understanding of processes governing secondary aerosol formation in remote Arctic marine environments. We run the Lagrangian chemistry transpor
New-particle formation from vapors through molecular cluster formation is a central process affecting atmospheric aerosol and cloud condensation nuclei numbers, and a significant source of uncertainty in assessments of aerosol radiative forcing. While advances in experimental and computational methods provide improved assessments of particle formation rates from different species, the standard app
Software systems are increasingly depending on data, particularly with the rising use of machine learning, and developers are looking for new sources of data. Open Data Ecosystems (ODE) is an emerging concept for data sharing under public licenses in software ecosystems, similar to Open Source Software (OSS). It has certain similarities to Open Government Data (OGD), where public agencies share da
Bayesian optimization (BO) has become an established framework and popular tool for hyperparameter optimization (HPO) of machine learning (ML) algorithms. While known for its sample-efficiency, vanilla BO can not utilize readily available prior beliefs the practitioner has on the potential location of the optimum. Thus, BO disregards a valuable source of information, reducing its appeal to ML prac
While Weighted Lasso sparse regression has appealing statistical guarantees that would entail a major real-world impact in finance, genomics, and brain imaging applications, it is typically scarcely adopted due to its complex high-dimensional space composed by thousands of hyperparameters. On the other hand, the latest progress with high-dimensional hyperparameter optimization (HD-HPO) methods for
As ultra-massive multiple-input multiple-output (UM-MIMO) has emerged as a key technology for millimeter-wave and terahertz communications, the spherical wave propagation should be considered for channel modeling. Therefore, it is critical to identify the locations and evolving behaviors of scatterers, i.e., the sources of the spherical wavefronts. In this contribution, a novel space-alternating g
Current FPGA programming tools require extensive hardware-specific manual code tuning to achieve performance, which is intractable for most software application teams. We present HPVM2FPGA, a novel end-to-end compiler and autotuning system that can automatically tune hardware-agnostic programs for FPGAs. HPVM2FPGA uses a hardware-agnostic abstraction of parallelism as an intermediate representatio
Big Data analytics and Artificial Intelligence (AI) technologies have become the focus of recent research due to the large amount of data. Dimensionality reduction techniques are recognized as an important step in these analyses. The multidimensional nature of Quality of Experience (QoE) is based on a set of Influence Factors (IFs) whose dimensionality is preferable to be higher due to better QoE
Running regression tests for Modelica models usually takes a long time. This paper presents a safe regression test selection technique for Modelica based on static analysis. The technique tracks dependencies between classes to compute which tests that need to be run given a change. The dependency rules have been verified using mutation testing. The technique has been evaluated on the Modelica Stan
Just like current software systems, models are characterised by increasing complexity and rate of change. Yet, these models only become useful if they can be continuously evaluated and validated. To achieve sufficiently low response times for large models, incremental analysis is required. Reference Attribute Grammars (RAGs) offer mechanisms to perform an incremental analysis efficiently using dyn
Massive multiple-input multiple-output (MIMO) is one of the key candidates for the upcoming 5G wireless generation. It offers a multitude of advantages over traditional techniques, such as reduced latency, reduced interference among user equipments (UEs) and increased spectrum and energy efficiencies. However, to verify the theoretically promised gains in real-life, prototype systems are inevitabl
For a firm in an open source software (OSS) ecosystem, the requirements engineering (RE) process is rather multifaceted. Apart from its typical RE process, there is a competing process, external to the firm and inherent to the firm’s ecosystem. When trying to impose an agenda in competition with other firms, and aiming to align internal product planning with the ecosystem’s RE process, firms need
Context: In the Requirements Engineering (RE) process of an Open Source Software (OSS) community, an involved firm is a stakeholder among many. Conflicting agendas may create miss-alignment with the firm's internal requirements strategy. In communities with meritocratic governance or with aspects thereof, a firm has the opportunity to affect the RE process in line with their own agenda by gaining
In this paper we present fully decentralized methods for calculating an approximate zero- forzing (ZF) equalizer in a large intelligent surface (LIS). A LIS is intended for wireless communication and facilitates unprecedented MU- MIMO performance, far superior to that of Massive MIMO. Antenna modules in the grid connect to their neighbors to exchange messages of information needed for interference
