dataesr/scientific-paragraphs-categorization huggingface dataset:
MeLa has been developed for the Mermaid float, but it could also be used to program other instruments. One of those could be the AudioMoth  [44] [45] [46] , an autonomous acoustic monitoring device that can be programmed with applications, for example, to detect cicadas or bat calls. More generally, the language could be used for most of the sensors used in the Internet of Things  [47, 48]. However, it is presently limited to sensors and cannot be used to program actuators or display devices. Mela is also limited to program instruments that exist, for which a model of operation can be created and measurements, such as execution time or energy, can be done. The development of embedded systems from scratch is not possible with MeLa; it would require many other features, such as adding a large library of configurable software and hardware components to create a system, and also taking account of physical considerations (e.
g. , pressure change, and its effect on actuators power consumption for Mermaid). Industry, such as automotive or spatial industry, have budgets to follow very strict development processes with international standards, quality insurance, static analysis, and the like  [49]. Such processes cannot be followed each time that a scientist wants to add an application to the Mermaid, it would require budgets that many scientists do not have. Thus, instead of following the classical development process that consists of developing a signal processing application in a language such as Matlab (https://fr. mathworks. com/), refine it into embedded software code, integrate it in an embedded software architecture, and test it on the embedded hardware, our approach allows for developing applications in a single step, and by a non-specialist.
FEM numerical method for the studied porous materials For the purpose of the evaluations of these criteria and better understand the mechanical behavior of studied porous medium, finite element method (FEM) are often carried out as reference results. The representative volume element of porous material is here represented by a hollow sphere. Due to its axi-symmetric property, only a quarter of the hollow sphere is considered and it is meshed with 900 quadratic quadrilateral elements of type CAX8 and with 2821 nodes in Abaqus software, as illustrated in Figure  1 (a) for the case f = 0. 15. The solid matrix obeys the von Mises criterion  (1). With the assumption of small strains, the displacement velocity field is prescribed on the exterior boundary of the hollow sphere. A user subroutine MPC (Multi-Points Constraints) is used in this study for the loading condition with a constant macroscopic stress triaxiality T = Σ m /Σ eq , which is realized by calculating the constant stress ratio Σ ρ /Σ z as the one done in  [17, 35].
One can see that the proposed neuron network converges very quickly. At the beginning, there are some fluctuations and the ANN learns 1 The ANN model established in this study is based on the structure of open source code provided in https://github. com/microsoft/ai-edu 2 A training epoch is one total computation of all training data. from the training data. It adjusts itself and the loss drops quickly. At the same time, the accuracy increases rapidly. It is stable after about 10000 epoch. The final values of loss and accuracy are 0. 6 × 10 -5 and 0. 99989 for the training set and 1. 97 × 10 -5 , 0. 99964 for the validation set.
a b s t r a c t This data article relates to and complement the research paper: "Assessment of climate change mitigation policies in 315 cities in the Covenant of Mayors initiative"  [1]. The reported data has been collected and elaborated within the framework of the Covenant of Mayors (CoM) initiative. The dataset is extracted from the overall database of the initiative reported through the platform MyCovenant. The data deals with the Monitoring Emission Inventories submitted by local authorities by 2016. Data has been processed and elaborated to highlights specific features of signatories and the policies they have adopted in the development of their Sustainable Energy Action Plans (SEAP). Available data relates to the mitigation policies in the SEAPs and includes their carbon reduction potential, the status of implementation and the class of governance.
In addition, signatories commit to preparing and submitting an implementation report and a Monitoring Emission Inventory (MEI), which allows appraising the progresses in achieving their target. The data, included in the datasets in the supplementary data, includes 12 307 mitigation policies adopted by 315 local authorities, which submitted their first Monitoring Emission report by 2016. Those 315 local authorities are considered CoM pioneers cities, since they have been able to report their MEI by 2016. Data has been collected from the MEIs, processed and cross related with information regarding mode of Governance as introduced in  [2]  and developed in  [1]  , Gross Domestic Product (GDP) and Heat Degree Days (HDD) and are reported in a spreadsheet. Data is organised in two datasets, which have been built by merging information regarding the signatories and the mitigation policies with the other variables.
The two datasets included in the supplementary material are: 1-"Signatories": the information on local authorities (country, climate characteristics, Gross Domestic Product expressed as Purchasing Power Standard -PPS and the number of policies per mode of governance) 2-"Policies" the mitigation policies included in the MEIs submitted by local authorities, with specification on sectors (such as: buildings, transport, public lighting, etc. ), mode of governance, the corresponding carbon reduction potential (tCO 2 ), the responsible local organisation for the implementation and the status of implementation. In particular, the variables included in the dataset are: The unique code for identifying the action Sector The sector the action refers to key action description The action description Area_of_intervention The area of intervention of the action Policy_Type The related policy type Origin of the action The origin (responsible body) of the action.
Experimental Design, Materials and Methods The experimental design of the study is to relate the local authorities' characteristics with their planned mitigation policies and the governance approach. Detailed studies and investigation on multi-level governance and policies were previously developed and investigated to base the structure of the analysis  [ 5 , 6 ]. The dataset included as supplementary material has been developed from the information submitted by local authorities, which signed the CoM and reported through the MyCovenant official platform  [7]. The data extracted from the platform focused on the sample of local authorities, which submitted a MEI by 2016 (315 local authorities) and on their mitigation policies (12 307). Therefore, the present dataset is the first of its kind to deal with MEIs and urban governance.
Further information was collected and added to the database (GDP, climate characteristics and governance)  [ 1 , 8 , 9 ]. In particular, the datasheet "Policies" includes fields, which specify the policies characteristics (sectors, responsible organisation, description, status of implementation) and assign them to a specific type of governance. This is implemented according to the aggregation procedure developed in  [1]  , in which the fields of area of intervention and policy type are related to different modes of governance as firstly developed by  [2]. The indicator used to define the climatic characteristic of a local authority is the Heating Degree Day (HDD) index, a weather-based technical index designed to describe the need for the heating energy requirements of buildings, it is aggregated and presented on NUTS-2 level, and NUTS-3 level  [10].
The climate characteristics of the local authorities are expressed using a code that goes from "1 to "3 , where "1 refers to cold climate conditions, "2 intermediate and "3 warm  [ 8 , 10 ]. This allows, on one hand to increase the information and build figures related to the local authorities' features and on the other, to associate a mode of governance to the policies planned by local authorities. The data were evaluated by examining their consistency to improve readability and allow cross-relating the mitigation measures to other factors such as sectors, climate  [ 8 , 10 ] , GDP  [ 9 , 11 ] , number of inhabitants, country. The dataset can be used as supporting tool for policy-makers and urban planners dedicated to urban low-carbon development, it provides a database for existing mitigation policies at local level and it may facilitate the identification of best-practice examples and replicable methodologies in similar urban contexts.
Description of data collection Signatories to the Covenant of Mayors report on their strategies and actions included in their Sustainable Energy Action Plans (SEAPs) through the online platform: MyCovenant Data source location Local Authorities who have signed the Covenant of Mayors Initiative and submitted their Monitoring Emission Inventory by 2016 (both in EU and Non EU countries) Data accessibility With the article In the process of being included in the Joint Research centre Data Catalogue with the whole CoM dataset, available at: https://data. jrc. ec. europa. eu/collection/id-00172 Related research article V. Palermo, P. Bertoldi, M. Apostolou, A. Kona, S. Rivas, Assessment of climate change mitigation policies in 315 cities in the Covenant of Mayors initiative, Sustainable Cities and Society.
In this regard, different sequences of Λ P and Λ M i were tested in a test bench that emulate the energy consumption of machine tools and their peripheral devices with the aim to obtain the corresponding outputs S P j and S M i. Afterwards, the energy consumption models for the three machines and the three peripheral devices in the process line were identified by using the n4sid routine of the System Identification Toolbox TM provided by Matlab R. Different values of N were tested to identify the suitable matrices A, B, C, and D, which allow the highest fitting degree between the real and modelled outputs. In Table  2 , the model order and the fitting percentages between the model and real outputs are presented for each one of the machines and peripheral devices. In should be noted that the models of machines correspond to the energy consumption profile along T M i and according to Λ M i.
Thus, removing the mean value, the small changes associated to load modulation gain relevance and could be easier to capture when the autocorrelation is computed. Autocorrelation is a mathematical tool that allows identifying repetitive patterns in a signal, such as its periodicity. Thus, autocorrelation is defined as the correlation between a signal and with a delayed version of the same signal to measure the similarity between them as a function of the lag. Therefore, correlation coefficients are calculated considering different values of signal delays with respect to itself. In this regard, high values of correlation coefficients correspond to lag values in which the signal is more similar. Afterwards, using the xcorr routine of Matlab, the autocorrelation ux j of the pre-processed signal of u j is computed and the vectors of both correlation coefficients and lags are determined.
This latter fact means that the controller makes decision every second along the prediction horizon, and the required information for the controller is also updated every second before to run the controller again. All simulations were performed using an Intel Core i7-55000U 2. 4 GHz processor with 8G RAM and considering a sampling time τ s = 0. 1s. The simulation results were obtained in Matlab by using the software IBM ILOG CPLEX Optimization Studio  [6]  integrated to YALMIP toolbox  [7]. The obtained optimal activation sequences for both peripheral devices and the percentage of aperture valve for v 1 and v 3 are shown in Figures  4a  and 4b , respectively. According to these results, the device L 1 is activated only when PP 2 is executed, and the periodic behaviour of all devices in the process line is appreciated.
Acknowledgement This work was supported by the  Engineering and Physical Sciences Research Council , grant  EP/N002458/1 , by the  FNRS , the  Innoviris Foundation  and the  Walloon Region , and by the iCODE institute, research project of the  Idex Paris-Saclay. No new data were created in this study. The authors would like to thank  J. Ouaknine  for organising the Bellairs workshop Algorithmic Aspects of  Dynamical Systems, (Barbados, March 2017 ) where this work was initiated.
These characteristics were subsequently assessed using a Google Street View audit and available GIS databases, in 59 administrative residential neighbourhoods in five European urban areas. Car driving (min/week) and individual level characteristics were self-reported by study participants (analytic sample n = 4258). We used linear multilevel regression analyses to assess cross-sectional associations of individual and neighbourhood-level characteristics with weekly minutes of car driving, and assessed explained variance at each level and for the total model. Results: Higher residential density (β:-2. 61, 95%CI: -4. 99; -0. 22) and higher land-use mix (β:-3. 73, 95%CI: -5. 61; -1. 86) were significantly associated with fewer weekly minutes of car driving. At the individual level, higher age (β: 1.
Study design For this study we used data from the Sustainable Prevention of Obesity Through Integrated Strategies (SPOT-LIGHT) study. Details of this study are described elsewhere  [31, 32]. In short, a neighbourhood audit and an individual-level survey were conducted in 60 randomly selected urban neighbourhoods from five European countries (Belgium, France, Hungary, the Netherlands and the United Kingdom). The urban areas were: Ghent and suburbs in Belgium; Paris and suburbs in France; Budapest and suburbs in Hungary; the Randstad (a conurbation including Amsterdam, Rotterdam, the Hague and Utrecht) in the Netherlands, and Greater London in the United Kingdom. In each of these urban areas, 12 neighbourhoods were selected, ensuring variety in residential area density (high and low density: > 2/3 and < 1/3 of area covered by residential buildings, respectively) and socioeconomic status (SES) (high and low: third and first tertile of neighbourhood level income, respectively) at neighbourhood level.
Density Car road density was defined as the percentage of area coverage of fast transit and other roads and associated land per neighbourhood  [11]. Residential density was defined as percentage of the area coverage of residential buildings per neighbourhood  [11]. Data were obtained from the Urban Atlas (European Environment  Agency, 2002) , a Geographic Information System (GIS) database distributed by the European Environmental Agency, which provided high-resolution satellite image data on land use across Europe  [34, 35]. The purpose of the European Environment Agency is to provide high quality data and independent data on the environment (e. g. greenhouse gas emissions, heavy metals in water, landuse). Car road and residential densities were obtained for the five urban areas under study, by intersecting land-use layers with neighbourhood boundaries, in ArcGIS version 10.
Diversity Land-use mix was defined as heterogeneity in land uses in a given area  [18]. Land use data were derived from the Urban Atlas, as described above, and four land-use categories were included, according to categories predetermined by the Urban Atlas: 1) Industrial, commercial, public, military and private units, 2) Residential areas, 3) Green urban areas, and 4) Sports and leisure facilities. Land-use mix was measured by means of an entropy index (Eq. 1). This entropy index is normalized using the natural logarithm of the number of land uses, and multiplied by 100  [36]. The entropy index was obtained per administrative neighbourhood and ranged from 0 to 100, with higher values indicating higher diversity. Equation 1: a is the total area of the different land-uses (m 2 ), and b refers to a specific land-use category.
Demand management Parking data were obtained in May 2018 from Open-StreetMap (OSM), an open data source where noncommercialised users uploaded data in an online map. The purpose of OSM is to provide a free and editable map at global scale, with local knowledge and expertise. Data collection methods include field audits but also remote sensing, depending on data availability and the choices by the uploader, leading to heterogeneity in data quality. Notwithstanding these limitations, OSM provides data that are not available from traditional GIS sources on a global scale. All available parking facilities identified in OSM were off-street parking facilities. Two variable types were used for parking facilities across the included urban regions: polygons (parking surface in square meter) and point locations (x, y coordinates of parking facilities).
Additionally, generalizability of the results was increased by the assessment of many neighbourhoods, with high-and low density, with high-and low socio-economic status across Europe  [32]. Cross validation of our findings in different datasets and on different populations is recommended. Future studies should also consider investigating the addition of other environmental measures such as walkability, and exploring the relation with other outcomes such as passive/active transport ratio, sedentary behaviour, noncommunicable diseases, air quality, traffic injuries, and traffic congestions. In addition, studies could focus on a broader conceptualization of drivability by including more or other potential characteristics that may influence drivability, such as distance to transit, distance to work, or assess the drivability at both the home, commuting and the work environment  [18] , parking pressure  [43, 44]  and safety.
Acknowledgements The authors would like to thank all participants who took part in the SPOTLIGHT study and the whole WP3 SPOTLIGHT study group. This study group consists of:  Ketevan Glonti ,  Harry Rutter ,  Martin McKee  (The Centre for Health and Social Change,  London School of Hygiene and Tropical Medicine) ,  Helga Bardos ,  Roza Adany  ( Department of Preventive Medicine, University of Debrecen ),  Jean-Michel Oppert ,  Hélène Charreire ,  Célina Roda ,  Thierry Feuillet ,  Maher Ben-Rebah  ( Equipe de Recherche en Epidémiologie Nutritionnelle, Université Paris 13 ),  Jeroen Lakerveld ,  Joreintje Mackenbach  ( Department of Epidemiology and Biostatistics, EMGO Institute for Health and Care Research, VU Medical Center, Amsterdam ), and  Ilse De Bourdeaudhuij ,  Greet Cardon ,  Sofie Compernolle  ( Department of Movement and Sport Sciences, Ghent University ).
Funding This work is part of the  SPOTLIGHT  project funded by the  Seventh Framework Programme  ( CORDISFP7 ) of the  European Commission ,  HEALTH  ( FP7-HEALTH-2011 -two-stage), Grant agreement No. 278186. The content of this article reflects only the authors' views and the  European Commission  is not liable for any use that may be made of the information contained therein. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The similar energy alignment between the two molecular semiconductors and the metal contact allows associating putative differences in the OFET operation to the specific nature of the injecting contact. In this regard, the n-type electrical characteristics of the corresponding OFETs with 70 nm-thick Au electrode were measured and compared. In agreement with the literature,  10, 55  the resulting devices demonstrated a good operation under n-type polarization, with an electron field-effect mobility (µFET) of 0. 5 cm 2 V -1 s -1 and 0. 1 cm 2 V - 1 s -1 for NT4N-and P13-based OFETs, respectively (Figure  S6  and S7). These data were estimated from the saturation regime using the following equation to have a figure-of-merit (FOM) representative of the devices: 𝐼 𝐷𝑆 = 1 2 𝐶 𝐼 𝑊 𝐿 µ 𝐹𝐸𝑇 (𝑉 𝐺𝑆 -𝑉 𝑇 ) 2 In detail, Ci is the capacitance per unit area of the insulating layer, W and L are the width and the length of the channel, respectively, and VT is the threshold voltage extracted from the square root of the drain-source current (IDS 1/2 ) versus VGS characteristics.
ACKNOWLEDGMENT This work received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 644742  ( LEO Project ), grant agreement no. 780839  ( MOLOKO  project), grant agreement no. 646259  ( MOSTOPHOS  project) and grant agreement N. 670173 (PHOSFUN). The authors thank the SPM@ISMN research facility for AFM experimental support and are grateful to  Santiago David Quiroga  and  Francesco Borgatti , for fruitful discussions,  V. Ragona ,  P. Mei ,  T. Bonfiglioli , from  CNR-ISMN , and  F. Corticelli , from  CNR-IMM , for valuable technical contributions. We acknowledge the  CINECA award  under the  ISCRA initiative , for the availability of high performance computing resources and support.
Scanning Electron Microscopy (SEM)-Energy Dispersive X-ray Spectroscopy (EDS) The surface morphology and chemical composition were evaluated using a scanning electron microscope (Quanta 250-FEG (FEI), Hillsboro, OR) operating at an accelerating voltage of 1 kV and equipped with an SDD Brucker detector. Detection limits of EDS analyses depend on the elements and matrix and were below 0. 1 at %. In this work, the detection of the Fluor element, used to track solvent residues in the membrane, was below 0. 1 at % and corresponded to less than 0. 1 wt %, measured by thermogravimetric analysis. Fiber diameters (at least 30 measures) were measured on four areas of the sample with ImageJ software (National Institues of Heath (NIH), 1. 48 VVersion, US).
X-ray Photoelectron Spectroscopy (XPS) Measurements were done using a PHI Quantera SXM instrument (Physical Electronics, Chanhassen, MN) equipped with a 180 • hemispherical electron energy analyzer and a monochromatized Al Kα (1486. 6 eV) source operated at 15 kV and 4 mA. The analysis spot had a diameter of 200 µm and the detection angle relative to the substrate surface was 45 •. Standard deviations were calculated from measurements performed on two different areas. Data were analyzed using the Multipak software (Version 9. 8. 0. 19, Physical Electronics GmbH, Ismaning, Germany). The depth probe of XPS analysis was between 5 and 10 nm.
Fourier-Transform Infrared Spectroscopy (FTIR) FTIR analyses were performed using a Spotlight 400 microspectrometer (Perkin-Elmer, Waltham, MA). FTIR mapping was performed in Attenuated Total Refection (ATR) mode using the ATR-imaging accessory with a germanium crystal. The scanned area was 400 × 400 µm 2 (lateral resolution of 1. 56 µm). The Mercury-Cadmium-Telluride (MCT) detector (Perkin-Elmer, Waltham, MA) covered the range 720-4000 cm -1 with a spectral resolution of 8 cm -1. Principal Component Analysis (PCA) calculation was applied to raw FTIR images using Spectrum IMAGE software (Perkin-Elmer).
Mechanical Characterization The membrane thickness was measured with a laser system (Keyence, France). Samples (n = 3, dog-bone tensile specimens (60 mm × 10 mm)) were tested at 3. 3 mm•min -1 with a tensile system (Multitest1-X, Mecmesin limited, Horsham, UK) updated with an acquisition system (Andilog, Vitrolles, France). Data were analyzed with a Wilcoxon signed-rank test (p < 0. 05). 2. 9. In Vivo Test 2. 9. 1. Animal Protocol Two female rats (Rattus norvegicus, Charles River Laboratories France) weighing 325 ± 60 g (mean ± SD), were involved in the present study. The rats were individually housed in stainless steel suspended cages under laboratory conditions (humidity and temperature recorded daily). Humidity was maintained higher than 30% of relative humidity and temperature was maintained between 20 and 24 • C.