dataesr/scientific-paragraphs-categorization huggingface dataset:
The rationale for allowing machine-actionability is that the amount and complexity of energy data have surpassed the threshold of manageability by humans. Machine support is needed for finding, organizing, and analyzing data. Indeed, a rapidly increasing number of datasets in the energy domain qualify as 'big data' in terms of volume, velocity, variety, and veracity. The trend will intensify with the continued increase of digitization of the energy sector and beyond. An illustrative example is the amount of data generated from household-level electrical load measurements, which easily exceeds one billion data readings when monitoring the inhabitants of a medium-sized European city over a period of two years  [74]. An illustration of the new way of integrating (meta)data is the following. Energy grids can be connected to devices, such as IoT sensors, that collect significant amounts of metadata and place them in real-time storage  [75].
When done correctly, machine-actionability enhances the communication of metadata and supports the efficient processing and integration of data from multiple sources. The FAIR principles have already been designed with machine-actionability in mind. Indeed, the FAIR principles are developed with idea of serving machines first, and then humans with (meta)data. The systematic assigning of persistent identifiers to metadata and the linking of metadata is thereby central. For this purpose, well-established semantic web technologies exist (e. g. , RDF-Resource Description Framework, OWL-Web Ontology Language, and LOD-Linked Open Data). However, the use of these technologies is demanding for researchers outside of the IT community, exceeding the researcher's capacities (in terms of time and skills).
Consequently, activities for machine-actionability need funding, training, guidance by specialists, and supporting tools. In the end, implementing machine-actionability has to be enabled by involving the different (human) user groups of the domain-specific data. The process of implementing machine-actionability is still an ongoing discussion with limited experience gathered so far in the energy domain. However, templates, software tools, and organizational support exist, ready to be customized for and with the energy research community. For example, linked data and RDF are well known and established, but simple tools are missing, so that anyone can leverage on these technologies. A panel discussion as part of the International FAIR Convergence Symposium 2020 was organized to start the community's conversation.
Led by the initiative GO-Fair  [79] , so-called Metadata-for-Machines (M4M) workshops for the energy community were discussed, whereby input was provided from experiences gained in other organizations. M4M workshops intend to kick-start FAIRfication efforts in a community, generating minimally viable metadata components that are modular, reusable, and extended later on. The process starts with the community experts that develop a set of informal terminologies. Then domain experts, with the help of data stewards, go through a formalization process by using metadata templates to describe both the structure and the semantics of metadata. Moreover, care is taken that the results are compliant with FAIR principles. Online tools such as the CEDAR workbench  [80]  are available to make the metadata machine-actionable (i.
e. , the tools convert the templates into semantic components), while sheet2rdf  [81]  and excel2rdf  [82]  are available for simple creation of machine-actionable controlled vocabularies. In one of the first M4M workshops related to the energy domain  [83] , the wind energy community created and employed machine-actionable metadata templates for datasets  [84]. A light weight yet comprehensive metadata specification, such as the one proposed by the Frictionlessdata Framework, has also been adopted by energy-related projects  [85]. The process of documenting the implementation of FAIR principles in a community is known as the 'FAIR Implementation Profile (FIP)'  [86]. In summary, the FIP is a collection of FAIR implementation choices made by a specific community for the FAIR principles. Once realized, FIPs provide guidance and orientation on FAIR infrastructures for different data communities.
Overall, the above process of implementing machine-actionable metadata is echoed by the EERAdata project (data stakeholder involvement-linking to IT experts -developing and agreeing on common standards-FAIRification through support tools). The panel discussion also revealed essential insights for the planning of energy-specific M4M workshops. Most importantly, the community's broad involvement is crucial to ensure that the design of metadata templates reflects energy science's interdisciplinary character. The metadata repositories resulting from FIPs are known as the network of FAIR Data Points. They provide software that allows data owners to expose their data's metadata to the public for seamless access by humans and machines alike. When created by trusted communities, other communities' reuse and uptake can accelerate the overall implementation and convergence of the FAIRification of (meta)data.
The workshop discussions led to identifying several metadata practices and platform developments in the energy domain, most of them being in-house developments. Twelve of the more mature metadata practices were chosen for assessment. These are (1) metadata strings of the Open Energy Platform, (2) the Standard International Energy Product Classi-fication, (3) the Energy Identification Code scheme (ENTSO-E), (4) the CityGML Energy Application Domain Extension, (5) the Common Information Model by the International Electrotechnical Commission,  (6) (7) (8) (9) (10) (11)  metadata models of the projects Hotmaps, EMODnet, ShareWind, PV-GIS, NOMAD, SEMANCO, and  (12)  the Materials Experiment and Analysis Database. The reader should note that the assessment of the metadata practices is not meant to scrutinize these pioneering efforts.
However, this example shows at the same time that the metadata information is only available under commercial licensing and represents a major impediment, preventing the possibility of critically scrutinizing and co-editing the standard (accessibility and transparency criteria). On the opposite end of the spectrum lies the Open Energy Platform, encouraging broad community involvement. The initiative is currently the broadest and most comprehensive approach towards metadata in the energy domain. The biggest challenge for all metadata practices in the energy domain is, however, machine-actionability. Many of the current metadata practices are limited to documentation for humans in the form of, e. g. , widelyused spread sheets and PDF files. A proper functional implementation does not require the use of semantic web technologies but would nonetheless benefit from these technologies.
Lessons Learned A common ground for metadata is essential for communication and validation of results in the energy research domain. The importance of standards will increase with the sustained digitization of energy sector, creating the demand for both human-readability and machine-actionability of data. The stakeholders of energy (meta)data form a diverse group who uses data differently and have specific needs with varying expectations on the coverage, granularity, and accessibility of (meta)data. Also, effective communication of data within and between user groups requires domain-specific metadata, emphasizing the need for agreed-upon standards and user group specific implementations. Multiple conceptual approaches and metadata practices exist. Many of them are, however, islanded solutions and connecting them is a big challenge.
Moreover, the domain-specific translation of the FAIR data guiding principles is a new research area and few mature best practices exist. Science has reached a threshold where data curation is becoming so resource-intensive that an individual researcher becomes overburdened with this task, rendering the organization of a community-wide process to FAIRify domain (meta)data nothing but wishful thinking. The workshop discussions and literature review revealed several specific challenges, which we highlight below. The last section expands the conclusions of this community white paper with the following recommendations.
• Reaching agreements on common standards requires a synthesis of established terminologies, vocabularies, and data formats to create rich and linked, but still comprehensible, metadata that caters to the needs of all stakeholder groups. Metadata concepts to track the domain-and user-specific workflows are still under development. Practices on licensing and access control are uncommon. While the functional implementation should adhere to emerging solutions found in the ICT community (e. g. , the widespread use of persistent identifiers, semantic web technology), they need to tie with established practices in the energy domain and match existing IT competencies. Data curation literacy across user groups. The lack of FAIR data curation skills is an issue for all stakeholders of (low carbon) energy data.
Recommendations Advancing FAIR (meta)data practices for low carbon energy research data supports the energy transition and enables the sector to tap the potential of the knowledge economy. However, the explorations in this community report reveal a number of challenges. We address these with the following recommendations: 1. Augment and synthesize existing metadata practices in the energy domain. The starting point to reach agreements on common standards is the identification, extension, and integration of existing approaches. This includes the search for best practices in other domains. In this way, domain-specific attempts can be matched with emerging solutions. At the same time, researchers are picked up where they are, and their contributions utilized. With this white paper, we invite interested researchers and existing initiatives in the field to organize a joint conference with the Open Energy Modelling Community, the Wind Energy Community, and the EERAdata project, among others.
2. Establish platforms that gather and document domain-specific metadata practices. The recommendation is to build a knowledge base with supporting tools, guidelines, best practices, FAIR implementation choices, and links to (meta)data repositories. The hub should be anchored in a number of widely frequented energy community websites. The platform should also offer contacts to domain experts and other FAIR data curators (e. g. , linking energy-related working groups of top-down initiatives). This promotes the use of general metadata standards such as Dublin Core and also supports a energy-specific implementation of FAIR (meta)data principles that aligns with the general trends. The FAIR Implementation Profiles  [86]  can serve as an inspiration.
3. Build a FAIR energy data network and foster long-term commitment. A starting point is to utilize existing international and EU-level networks in the field. The idea is to link up with periodic conferences and workshops, which are mainstream meeting places for the energy community already. Established international umbrella organizations that target the wide range of energy data stakeholders from researchers to policy-makers, industries, funders, publishers, and so forth, appear as a good choice. 4 ) promote the building of FAIR data competences and skills.
5. Advocate for broadening funding programs and encourage industry funding. The lack of funding for FAIR data activities is a major barrier to advance community practices, build the necessary skills, and increase awareness for the need for data curation. It is important to actively approach funders to motivate the funding of on-the-job training and the development of new university education on energy data stewardship for instance. The industry should also be targeted, and public-private partnerships seem a way forward in this respect.
Data were recorded in step-scan mode between 4 and 65 • 2θ with 0. 016 • steps and counting 480 s per step. Mid-infrared (MIR) spectroscopy was performed in the transmission mode on pellets, 2 cm in diameter, prepared with 1 mg of sample and 150 mg of KBr salt. The mixture was pressed for 5 min and dried overnight in an oven at 110 • C. MIR spectra were collected with a Thermo Scientific Nicolet 760 FTIR spectrometer equipped with an Ever-Glo source, a KBr beam splitter, and a deuterated triglycine sulfate DTGS-KBr detector. The spectra were recorded over the 400-4000 cm -1 range with a resolution of 4 cm -1 and co-additions of 100 scans. Near-infrared (NIR) spectroscopy was performed on powder samples without any dilution. NIR spectra were obtained using a Thermo Scientific Nicolet 6700 FTIR spectrometer equipped with a white light source, a CaF 2 beam splitter, and a Thermo Scientific Smart NIR integrating sphere equipped with an internal InGaAs detector.
Acknowledgements. The authors thank  Fabien Trotet ,  Pierre Epinoux ,  Philippe Bains ,  Philippe Hoffler ,  Giles Monteil  (SLN),  Julie Michaud  ( SMT/SMCB ),  Yann Dijkstra ,  René Feré  (CFTMC),  Clément Couteau ,  Fédéric Villedieu  (KNS), and  Christian Tessarolo  ( Goro-VALE ) who provided helpful technical reports and assisted with access to mining sites. Pierre Chansigaud (IC2MP spectroscopy platform,  University of Poitiers ) is thanked for technical assistance in the production of the MIR and NIR spectra. We thank the two anonymous reviewers for their detailed and insightful comments. Financial support. This research has been supported by the  Centre National de la Recherche Scientifique  ( CESSUR 2010 : project " Conditions of formation of Mg/Ni silicate ores from New Caledonia "), the  Centre National de Recherche Technologique Nickel et son environnement (CNRT -Nickel  and its environment 2010-2014: project " Ni/Co mineralization factors of laterites derived from ultramafic rocks of New-Caledonia "), and the  European Union (ERDF) "Région Nouvelle Aquitaine ".
In Silico Results In order to better understand the impact of these SAR on AChE binding, a docking study was performed with 3b in comparison with donecopride (Figure  2 ). The docking study was carried out with the aim to predict the AChE inhibition of synthesized compounds compared to donecopride, the initial scaffold of the carried out modulations. The docking study into the hAChE active site (PDB code: 4EY7)  [25]  was performed using Gold 5. 7. 2 software and the ligand 3D models were built from donecopride X-ray structure solved previously. Firstly, the correctness of employed docking procedure was checked by redocking the co-crystallized ligand, donepezil. Gold program regained the crystallographic donepezil position with a RMSD smaller to 1 Å and with a score fit value of 110. 02  [6].
In Silico Study The X-ray structure of donecopride was used as a 3D model during the docking and four conformers present in the crystal cell were used. The initial model of compounds 7, 3b, and 12b were built from donecopride X-ray structure  [6]. The compound's protonation state at pH 7. 4 was predicted using standard tools of the ChemAxon Package (http://www. chemaxon. com/, accessed on 23 February 2021). The majority microspecie protonated on piperidine nitrogen at this pH was used for docking studies of each compounds. The crystallographic coordinates of human acetylcholinesterase used in this study were obtained from X-ray structure of the donepezil/AChE complex (PDB ID 4EY7, a structure refined to 2. 35 Å with an R factor of 17. 7%)  [25]. The AChE amino acid protonation state was checked before the docking study using the ProPKA software and the proposed protonation for Glu202 was applied.
The docking of the donepezil, donecopride, and compounds 3b, 7, and 12b into the AChE was carried out with the GOLD program (v2020. 2. 0) using the default parameters  [26, 27]. This program applies a genetic algorithm to explore conformational spaces and ligand binding modes. To evaluate the proposed ligand positions, the ChemPLP fitness function was used. The binding site in the AChE model was defined as a 7 Å sphere from the co-crystallized donepezil ligand and a water molecule interacting with protonated piperidine ring of donepezil was conserved during the docking (residue number 931). The second docking with the higher number of docking cycles (20 cycles) was carried out for compound 3b. For the docking studies into 5-HT 4 receptor, the 3D model of the human 5-HT 4 R built previously by homology sequence approach  [28]  was used.
This model was generated using the crystal structure (PBD: 2RH1) of the human β 2 adrenergic receptor-T4 lysozyme fusion protein complexed with the carazolol  [29]  as a template. The docking of the donecopride and 7, 3b, 12b compounds into the generated model was carried out with the GOLD program (v2020. 2. 0) using the default parameters  [26, 27]  and the proposed ligand positions were evaluated by the ChemPLP fitness function. The binding site in the 5-HT 4 R model was defined as a 10 Å sphere centered on the aspartic acid residue Asp 100. As the mutagenesis studies have shown that the interaction between the positively ionizable amine of ligands and Asp100 of 5-HT 4 R is crucial for ligand binding, a hydrogen bond constraint between positively ionizable amine ligand and OD atom of Asp 100 was used during the docking  [30]  Furthermore, special attention was paid during the docking procedure to the following amino acids in the binding site, which were kept flexible: Arg96, Asp100, Thr104, Tyr192, Ser197, and Trp294.
For the docking studies on the 5-HT 6 receptor first a homology model was built. The sequence of the human 5-HT 6 R was retrieved from the UniProt Knowledgebase (UniPro-tKB)  [31]  (ID: P50406_HUMAN). Using screening methods like FUGUE  [32] , SP3  [33] , PSIBLAST,  [34, 35]  HHSEARCH  [36] , and the @tome-2 server  [37] , the β 2 adrenergic receptor was identified as the best 3D experimental template for the homology modeling also for the 5-HT 6 R (Sequence identity = 32%). The high-resolution (2. 4 A) crystal structure of the human β 2 adrenergic receptor (β 2 AR)-T4 lysozyme fusion protein bound to the carazolol (PDB: 2RH1)  [29]  was used as the 3D template. The alignment between the two sequences was manually optimized to avoid insertions and deletions in secondary structure elements.
Disulfide bond: Cys 99 -Cys 180 between the transmembrane helix 3 (TM3) and the extracellular loop (ECL2) was conserved. This alignment (Figure  S2 ) was used as the basis for the homology modeling with the Modeller software  [38]. The resulting model was then evaluated by methods like verify3D  [39]  and Eval23D  [40]. The docking of the donecopride and 7, 3b, 12b compounds into the generated model was carried out with the GOLD program (v2020. 2. 0)  [26, 27]  using the same procedure as for 5-HT 4 R. The binding site in the 5-HT 6 R model was defined as a 10 Å sphere centered on the cysteine residue Cys 110. A hydrogen bond constraint between positively ionizable amine ligand and OD atom of Asp 100 was also applied during the docking and the following amino acids were kept flexible: Trp 102 , Asp 106 , Ser 193 , Thr 196 , and Trp 307.
3. a). The observed peak heights at each frequency ω -ω q ≈ nχ allow us to determine the cavity photon-number distribution P(n)  [53]. We correct this dataset for the qubit residual thermal population (1 %), the finite fidelity of the π-pulse and readout errors. Interestingly, the peaks are not evenly spaced in frequency due to the higher nonlinear term -K e ĉ † 2 ĉ2 |e e| with K e /2π = 70 kHz. The photon number distribution P(n) are then obtained from the qubit excitation probability at ω q -n(χ + 2K e n) (vertical lines). For comparison, we also measure the photon number distribution P(n) for two other cavity states: a thermal equilibrium state when no pumps are applied (blue in Fig. 3 ) and a thermal state that we create by only applying a sum pump g + /2π = 0. 43 MHz (orange curve). Note that this thermal state is obtained by tracing out the dump mode for the vacuum two mode squeezed state that is stabilized between cavity and dump  [54].
ACKNOWLEDGMENTS We are grateful to  Olivier Arcizet  and  Alexandre Blais  for discussions. This work was initiated during a discussion that happened during Les Houches Summer School in July 2019. We acknowledge IARPA and  Lincoln Labs  for providing a Josephson Traveling-Wave Parametric Amplifier. The device was fabricated in the cleanrooms of  Collège de France, ENS Paris, CEA Saclay , and  Observatoire de Paris. This work is part of a project that has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  820505. AC acknowledges support from the  Air Force Office of Scientific Research MURI program , under Grant No. FA9550-19-1-0399.
The mean time interval of the additionally injected signals is 30 BC with a standard deviation of 10 BC, so that both overlapping and non-overlapping high energy pulses are generated. The AREUS software also allows a simulation of LHC bunch patterns, i. e. regular interruptions in the series of proton-proton collisions. An example sequence for one cell in the barrel section (EMB) of the electromagnetic LAr calorimeter, which is selected for the study presented here, is displayed in the top row of Fig. 1  for a mean number of pileup events, ⟨ ⟩ , of 140. The current readout electronics of the LAr calorimeters applies an optimal filter  [7]  (OF) to determine the energy in each cell. By linear combination of up to five digitized pulse samples, electronic noise and signal pileup are suppressed.
9  Resolution of the transverse energy reconstruction as a function of the gap, i. e. the distance in units of BC, between two consecutive energy deposits for the vanilla RNN sliding-window algorithm Fig. 10  Resolution of the transverse energy reconstruction as a function of the gap, i. e. the distance in units of BC, between two consecutive energy deposits for the CNN algorithm A Python script generates the configuration file from the Keras model files. The script also performs the translation from floating point to fixed point representation with a configurable total and fractional bit width. A bit width of 18 is chosen because it matches the DSP precision of the Stratix-10 FPGA. Of those 18 bits, 10 bits are used for the decimal part of the fixed-point representation. For the sigmoid activation function two implementations are available.
Conversion of RNN to HLS The RNN algorithms are implemented in Intel high-level synthesis (HLS)  [13]. The HLS approach allows for an automated generation of hardware description language from an algorithmic description of the network, similar to C++, with user optimisations of the hardware implementation enforced by inline compiler commands. Thus, the HLS permits a flexible design automatically optimised to a given hardware target. The networks are based on two different functions, the first being the implementation of a single RNN cell, the second one handling the recursive aspect of the network architecture. The LSTM or vanilla RNN cells are coded as template functions. The template is used to pass on the weights and the internal architecture of the cell. The weights and architecture parameters are automatically generated by Python scripts from the Keras model.