Revisiting the role of steam methane reforming with CO2 capture and storage for long-term hydrogen production

Road transport is associated with high greenhouse gas emissions due to its current dependence on fossil fuels. In this regard, the implementation of alternative fuels such as hydrogen is expected to play a key role in decarbonising the transport system. Nevertheless, attention should be paid to the suitability of hydrogen production pathways as low-carbon solutions. In this work, an energy systems optimisation model for the prospective assessment of a national hydrogen production mix was upgraded in order to unveil the potential role of grey hydrogen from steam methane reforming (SMR) and blue hydrogen from SMR with CO2 capture and storage (CCS) in satisfying the hydrogen demanded by fuel cell electric vehicles in Spain from 2020 to 2050. This was done by including CCS retrofit of SMR plants in the energy systems model, as a potential strategy within the scope of the European Hydrogen Strategy. Considering three hypothetical years for banning hydrogen from fossil-based plants without CCS (2030, 2035, and 2040), it was found that SMR could satisfy the whole demand for hydrogen for road transport in the short term (2020–2030), while being substituted by water electrolysis in the medium-to-long term (2030–2050). Furthermore, this trend was found to be associated with an appropriate prospective behaviour in terms of carbon footprint.This research has been partly supported by the Spanish Ministry of Economy, Industry and Competitiveness (ENE2015-74607-JIN AEI/FEDER/UE)

Architecture and performance of the KM3NeT front-end firmware

The authors acknowledge the financial support of the funding agencies: Agence Nationale de la Recherche (contract ANR-15-CE31-0020), Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund and Marie Curie Program), Institut Universitaire de France (IUF), LabEx UnivEarthS (ANR-10-LABX-0023 and ANR-18-IDEX-0001), Paris Ile-de-France Region, France; Shota Rustaveli National Science Foundation of Georgia (SRNSFG, FR-18-1268), Georgia; Deutsche Forschungsgemeinschaft (DFG), Germany; The General Secretariat of Research and Technology (GSRT), Greece; Istituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Istruzione, dell'Universita e della Ricerca (MIUR), PRIN 2017 program (Grant NAT-NET 2017W4HA7S) Italy; Ministry of Higher Education Scientific Research and Professional Training, ICTP through Grant AF-13, Morocco; Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands; The National Science Centre, Poland (2015/18/E/ST2/00758); National Authority for Scientific Research (ANCS), Romania; Ministerio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento (refs. PGC2018-096663-B-C41, -A-C42, -B-C43, -B-C44) (MCIU/FEDER), Severo Ochoa Centre of Excellence and MultiDark Consolider (MCIU), Junta de Andalucia (ref. SOMM17/6104/UGR), Generalitat Valenciana: Grisolia (ref. GRISOLIA/2018/119) and GenT (ref. CIDEGENT/2018/034) programs, La Caixa Foundation (ref. LCF/BQ/IN17/11620019), EU: MSC program (ref. 713673), Spain.The KM3NeT infrastructure consists of two deep-sea neutrino telescopes being deployed in the Mediterranean Sea. The telescopes will detect extraterrestrial and atmospheric neutrinos by means of the incident photons induced by the passage of relativistic charged particles through the seawater as a consequence of a neutrino interaction. The telescopes are configured in a three-dimensional grid of digital optical modules, each hosting 31 photomultipliers. The photomultiplier signals produced by the incident Cherenkov photons are converted into digital information consisting of the integrated pulse duration and the time at which it surpasses a chosen threshold. The digitization is done by means of time to digital converters (TDCs) embedded in the field programmable gate array of the central logic board. Subsequently, a state machine formats the acquired data for its transmission to shore. We present the architecture and performance of the front-end firmware consisting of the TDCs and the state machine.French National Research Agency (ANR) ANR-15-CE31-0020Centre National de la Recherche Scientifique (CNRS)Commission Europeenne (FEDER fund), FranceCommission Europeenne (Marie Curie Program), FranceInstitut Universitaire de France (IUF), FranceLabEx UnivEarthS, France ANR-10-LABX-0023 ANR-18-IDEX-0001Paris Ile-de-France Region, FranceShota Rustaveli National Science Foundation of Georgia (SRNSFG), Georgia FR-18-1268German Research Foundation (DFG)Greek Ministry of Development-GSRTIstituto Nazionale di Fisica Nucleare (INFN) NAT-NET 2017W4HA7SMinistry of Education, Universities and Research (MIUR) NAT-NET 2017W4HA7SPRIN 2017 program Italy NAT-NET 2017W4HA7SMinistry of Higher Education Scientific Research and Professional Training, ICTP, Morocco AF-13Netherlands Organization for Scientific Research (NWO) Netherlands GovernmentNational Science Centre, Poland 2015/18/E/ST2/00758National Authority for Scientific Research (ANCS), RomaniaMinisterio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento (MCIU/FEDER), Spain PGC2018-096663-B-C41 PGC2018-096663-A-C42 PGC2018-096663-B-C43 PGC2018-096663-B-C44Severo Ochoa Centre of Excellence and MultiDark Consolider (MCIU), SpainJunta de Andalucia European Commission SOMM17/6104/UGRGeneralitat Valenciana: Grisolia program, Spain GRISOLIA/2018/119Generalitat Valenciana: GenT program, Spain CIDEGENT/2018/034La Caixa Foundation LCF/BQ/IN17/11620019EU: MSC program, Spain 71367

Probing invisible neutrino decay with KM3NeT/ORCA

The authors acknowledge the financial support of the funding agencies: Agence Nationale de la Recherche (contract ANR-15-CE31-0020), Centre National de la Recherche Scientifique (CNRS), Commission Européenne (FEDER fund and Marie Curie Program), LabEx UnivEarthS (ANR-10-LABX-0023 and ANR-18-IDEX-0001), Paris Île-de-France Region, France; The General Secretariat of Research and Innovation (GSRI), Greece Istituto Nazionale di Fisica Nucleare (INFN), Ministero dell’Università e della Ricerca (MIUR), PRIN 2017 program (Grant NAT-NET 2017W4HA7S) Italy; Ministry of Higher Education, Scientific Research and Innovation, Morocco, and the Arab Fund for Economic and Social Development, Kuwait; Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands; The National Science Centre, Poland (2021/41/N/ST2/01177); National Authority for Scientific Research (ANCS), Romania; Grants PID2021-124591NB-C41, -C42, -C43 funded by MCIN/AEI/ 10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe”, by the “European Union” or by the “European Union NextGenerationEU/PRTR”, Programa de Planes Complementarios I+D+I (refs. ASFAE/2022/023, ASFAE/2022/014), Programa Prometeo (PROMETEO/2020/019) and GenT (refs. CIDEGENT/2018/034, /2019/043, /2020/049. /2021/23) of the Generalitat Valenciana, Junta de Andalucía (ref. SOMM17/6104/UGR, P18-FR-5057), EU: MSC program (ref. 101025085), Programa María Zambrano (Spanish Ministry of Universities, funded by the European Union, NextGenerationEU), Spain.In the era of precision measurements of the neutrino oscillation parameters, upcoming neutrino experiments will also be sensitive to physics beyond the Standard Model. KM3NeT/ORCA is a neutrino detector optimised for measuring atmospheric neutrinos from a few GeV to around 100 GeV. In this paper, the sensitivity of the KM3NeT/ORCA detector to neutrino decay has been explored. A three-flavour neutrino oscillation scenario, where the third neutrino mass state v3 decays into an invisible state, e.g. a sterile neutrino, is considered. We find that KM3NeT/ORCA would be sensitive to invisible neutrino decays with 1/alpha 3 = T3/m3 < 180 ps/eV at 90% confidence level, assuming true normal ordering. Finally, the impact of neutrino decay on the precision of KM3NeT/ORCA measurements for theta(23), Delta m(31)(2) and mass ordering have been studied. No significant effect of neutrino decay on the sensitivity to these measurements has been found.Commission Européenne FEDER fundCommission Européenn Marie Curie ProgramMCIN/AEI/ 10.13039/501100011033, ID2021-124591NB-C41, -C42, -C43ERDFEuropean UnionEuropean Union NextGenerationEU/PRTRPlanes Complementarios I+D+I, ASFAE/2022/023, ASFAE/2022/014PROMETEO/2020/019Generalitat Valenciana, GenT, CIDEGENT/2018/034, /2019/043, /2020/049, /2021/23Junta de Andalucía SOMM17/6104/UGR, P18-FR-5057EU: MSC program 101025085European Union, NextGenerationEU, Programa María Zambran

The KM3NeT multi-PMT optical module

The optical module of the KM3NeT neutrino telescope is an innovative multi-faceted large area photodetection module. It contains 31 three-inch photomultiplier tubes in a single 0.44m diameter pressure-resistant glass sphere. The module is a sensory device also comprising calibration instruments and electronics for power, readout and data acquisition. It is capped with a breakout-box with electronics for connection to an electro-optical cable for power and long-distance communication to the onshore control station. The design of the module was qualified for the first time in the deep sea in 2013. Since then, the technology has been further improved to meet requirements of scalability, cost-effectiveness and high reliability. The module features a sub-nanosecond timing accuracy and a dynamic range allowing the measurement of a single photon up to a cascade of thousands of photons, suited for the measurement of the Cherenkov radiation induced in water by secondary particles from interactions of neutrinos with energies in the range of GeV to PeV. A distributed production model has been implemented for the delivery of more than 6000 modules in the coming few years with an average production rate of more than 100 modules per month. In this paper a review is presented of the design of the multi-PMT KM3NeT optical module with a proven effective background suppression and signal recognition and sensitivity to the incoming direction of photons.French National Research Agency (ANR) ANR-15-CE31-0020 Centre National de la Recherche Scientifique (CNRS)Commission Europeenne (FEDER fund) Commission Europeenne (Marie Curie Program)Institut Universitaire de France (IUF)LabEx UnivEarthS ANR-10-LABX-0023 ANR-18-IDEX-0001Paris Ile-de-France Region, FranceShota Rustaveli National Science Foundation of Georgia (SRNSFG), Georgia FR-18-1268German Research Foundation (DFG)Greek Ministry of Development-GSRTIstituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Universita e della Ricerca (MIUR), PRIN 2017 program, Italy NAT-NET 2017W4HA7SMinistry of Higher Education, Scientific Research and Innovation, MoroccoArab Fund for Economic and Social DevelopmentNederlandse organisatie voor Wetenschappelk Onderzoek (NWO), the NetherlandsNational Science Centre, Poland 2015/18/E/ST2/00758National Authority for Scientific Research (ANCS), RomaniaMinisterio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento PGC2018-096663-B-C41 PGC2018-096663-A C42 PGC2018-096663-B-C43 PGC2018-096663-B-C44Center for Forestry Research & Experimentation (CIEF) PROMETEO/2020/019Generalitat Valenciana: Grisolia program GRISOLIA/2018/119 GRISOLIA/2021/192 Generalitat Valenciana: GenT program CIDEGENT/2018/034 CIDEGENT/2019/043 CIDEGENT/2020/049 CIDEGENT/2021/023Junta de Andalucia AFQM-053-UGR18La Caixa Foundation LCF/BQ/IN17/11620019EU: MSC program, Spain 10102508

Determining the neutrino mass ordering and oscillation parameters with KM3NeT/ORCA

The authors acknowledge the financial support of the funding agencies: Agence Nationale de la Recherche (contract ANR-15-CE31-0020), Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund and Marie Curie Program), Institut Universitaire de France (IUF), LabEx UnivEarthS (ANR-10-LABX-0023 and ANR-18-IDEX-0001), Paris Ile-de-France Region, France; Shota Rustaveli National Science Foundation of Georgia (SRNSFG, FR-18-1268), Georgia; Deutsche Forschungsgemeinschaft (DFG), Germany; The General Secretariat of Research and Technology (GSRT), Greece; Istituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Universita e della Ricerca (MIUR), PRIN 2017 program (Grant NAT-NET 2017W4HA7S) Italy; Ministry of Higher Education Scientific Research and Professional Training, ICTP through Grant AF-13, Morocco; Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands; The National Science Centre, Poland (2015/18/E/ST2/00758); National Authority for Scientific Research (ANCS), Romania; Ministerio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento (refs. PGC2018-096663-B-C41, -A-C42, -B-C43, -B-C44) (MCIU/FEDER), Severo Ochoa Centre of Excellence and MultiDark Consolider (MCIU), Junta de Andalucia (ref. SOMM17/6104/UGR), Generalitat Valenciana: Grisolia (ref. GRISOLIA/2018/119) and GenT (ref. CIDEGENT/2018/034 and CIDEGENT/2019/043) programs, La Caixa Foundation (ref. LCF/BQ/IN17/11620019), EU: MSC program (ref. 713673), Spain.The next generation of water Cherenkov neutrino telescopes in the Mediterranean Sea are under construction offshore France (KM3NeT/ORCA) and Sicily (KM3NeT/ARCA). The KM3NeT/ORCA detector features an energy detection threshold which allows to collect atmospheric neutrinos to study flavour oscillation. This paper reports the KM3NeT/ORCA sensitivity to this phenomenon. The event reconstruction, selection and classification are described. The sensitivity to determine the neutrino mass ordering was evaluated and found to be 4.4 sigma if the true ordering is normal and 2.3 sigma if inverted, after 3 years of data taking. The precision to measure Delta m(32)(2) and theta(23) were also estimated and found to be 85.10(-6) eV(2) and ((+1.9)(-3.1))degrees for normal neutrino mass ordering and, 75.10(-6) eV(2) and ((+2.0)(-7.0))degrees for inverted ordering. Finally, a unitarity test of the leptonic mixing matrix by measuring the rate of tau neutrinos is described. Three years of data taking were found to be sufficient to exclude (nu)over-left-right-arrow tau event rate variations larger than 20% at 3 sigma level.French National Research Agency (ANR) ANR-15-CE31-0020 Centre National de la Recherche Scientifique (CNRS) Commission Europeenne (FEDER fund), France Institut Universitaire de France (IUF), France LabEx UnivEarthS, France ANR-10-LABX-0023 ANR-18-IDEX-0001 Paris Ile-de-France Region, FranceShota Rustaveli National Science Foundation of Georgia (SRNSFG), Georgia FR-18-1268German Research Foundation (DFG)The General Secretariat of Research and Technology (GSRT), GreeceIstituto Nazionale di Fisica Nucleare (INFN)Ministry of Education, Universities and Research (MIUR)PRIN 2017 program, Italy NAT-NET 2017W4HA7SMinistry of Higher Education Scientific Research and Professional Training, ICTP, Morocco AF-13Netherlands Organization for Scientific Research (NWO) Netherlands GovernmentThe National Science Centre, Poland 2015/18/E/ST2/00758National Authority for Scientific Research (ANCS), RomaniaMinisterio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento, Spain PGC2018-096663-B-C41 PGC2018-096663-A-C42 PGC2018-096663-B-C43 PGC2018-096663-B-C44 Generalitat Valenciana: Grisolia program, Spain GRISOLIA/2018/119 La Caixa Foundation LCF/BQ/IN17/11620019EU: MSC program, Spain 713673Commission Europeenne (Marie Curie Program), FranceGeneralitat Valenciana: GenT programs, Spain CIDEGENT/2018/034 CIDEGENT/2019/043Ministerio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Severo Ochoa Centre of ExcellenceMinisterio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): MultiDark Consolider (MCIU)Junta de Andalucia European Commission SOMM17/6104/UG

Prospective techno-economic and environmental assessment of a national hydrogen production mix for road transport

Fuel cell electric vehicles arise as an alternative to conventional vehicles in the road transport sector. They could contribute to decarbonising the transport system because they have no direct CO2 emissions during the use phase. In fact, the life-cycle environmental performance of hydrogen as a transportation fuel focuses on its production. In this sense, through the case study of Spain, this article prospectively assesses the techno-economic and environmental performance of a national hydrogen production mix by following a methodological framework based on energy systems modelling enriched with endogenous carbon footprint indicators. Taking into account the need for a hydrogen economy based on clean options, alternative scenarios characterised by carbon footprint restrictions with respect to a fossil-based scenario dominated by steam methane reforming are evaluated. In these scenarios, the steam reforming of natural gas still arises as the key hydrogen production technology in the short term, whereas water electrolysis is the main technology in the medium and long term. Furthermore, in scenarios with very restrictive carbon footprint limits, biomass gasification also appears as a key hydrogen production technology in the long term. In the alternative scenarios assessed, the functional substitution of hydrogen for conventional fossil fuels in the road transport sector could lead to high greenhouse gas emission savings, ranging from 36 to 58 Mt CO2 eq in 2050. Overall, these findings and the model structure and characterisation developed for the assessment of hydrogen energy scenarios are expected to be relevant not only to the specific case study of Spain but also to analysts and decision-makers in a large number of countries facing similar concerns.This research has been partly supported by the Spanish Ministry ofEconomy, Industry and Competitiveness (ENE2015-74607-JIN AEI/FEDER/UE

KM3NeT broadcast optical data transport system

The optical data transport system of the KM3NeT neutrino telescope at the bottom of the Mediterranean Sea will provide more than 6000 optical modules in the detector arrays with a point-to-point optical connection to the control stations onshore. The ARCA and ORCA detectors of KM3NeT are being installed at a depth of about 3500 m and 2500 m, respectively and their distance to the control stations is about 100 kilometers and 40 kilometers. In particular, the two detectors are optimised for the detection of cosmic neutrinos with energies above about 1 TeV (ARCA) and for the detection of atmospheric neutrinos with energies in the range 1 GeV-1 TeV (ORCA). The expected maximum data rate is 200 Mbps per optical module. The implemented optical data transport system matches the layouts of the networks of electro-optical cables and junction boxes in the deep sea. For efficient use of the fibres in the system the technology of Dense Wavelength Division Multiplexing is applied. The performance of the optical system in terms of measured bit error rates, optical budget are presented. The next steps in the implementation of the system are also discussed

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The authors acknowledge the financial support of the funding agencies: Agence Nationale de la Recherche (contract ANR-15-CE31-0020), Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund and Marie Curie Program), Institut Universitaire de France (IUF), LabEx UnivEarthS (ANR-10-LABX-0023 and ANR-18-IDEX-0001), Paris Ile-de-France Region, France; Shota Rustaveli National Science Foundation of Georgia (SRNSFG, FR-18-1268), Georgia; Deutsche Forschungsgemeinschaft (DFG), Germany; The General Secretariat of Research and Technology (GSRT), Greece; Istituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Universita e della Ricerca (MUR), PRIN 2017 program (Grant NAT-NET 2017W4HA7S) Italy; Ministry of Higher Education, Scientific Research and Professional Training, Morocco; Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands; The National Science Centre, Poland (2015/18/E/ST2/00758); National Authority for Scientific Research (ANCS), Romania; Ministerio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento (refs. PGC2018-096663-B-C41, -A-C42, -B-C43, -B-C44) (MCIU/FEDER), Severo Ochoa Centre of Excellence and MultiDark Consolider (MCIU), Junta de Andalucia (ref. SOMM17/6104/UGR), Generalitat Valenciana: Grisolia (ref. GRISOLIA/2018/119) and GenT (ref. CIDEGENT/2018/034) programs, La Caixa Foundation (ref. LCF/BQ/IN17/11620019), EU: MSC program (ref. 713673), Spain.The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches.French National Research Agency (ANR) ANR-15-CE31-0020Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund)European Union (EU)Institut Universitaire de France (IUF)LabEx UnivEarthS ANR-10-LABX-0023 ANR-18-IDEX-0001Shota Rustaveli National Science Foundation of Georgia FR-18-1268German Research Foundation (DFG)Greek Ministry of Development-GSRTIstituto Nazionale di Fisica Nucleare (INFN)Ministry of Education, Universities and Research (MIUR) Research Projects of National Relevance (PRIN)Ministry of Higher Education, Scientific Research and Professional Training, MoroccoNetherlands Organization for Scientific Research (NWO)National Science Centre, Poland 2015/18/E/ST2/00758National Authority for Scientific Research (ANCS), RomaniaMinisterio de Ciencia, Innovacion, Investigacion y Universidades PGC2018-096663-B-C41 A-C42 B-C43 B-C44Severo Ochoa Centre of ExcellenceJunta de Andalucia SOMM17/6104/UGRGeneralitat Valenciana: Grisolia GRISOLIA/2018/119 CIDEGENT/2018/034La Caixa Foundation LCF/BQ/IN17/11620019EU: MSC program 71367