dataesr/scientific-paragraphs-categorization huggingface dataset:
For that purpose, we refined the catalogue of phase picks on the temporary stations using EQcorrscan, a matched filter and subspace detection package  (Chamberlain et al. , 2017). After an examination of the signals, seven and five templates were selected at PROK and JL, respectively. The templates span the variety of signals acquired and could mostly correspond to events at various depths. This approach allowed detecting 1800 almost repeating earthquakes within 8 days at PROK, and 1217 within 16 days at JL. These earthquakes include all events picked at NSC at the permanent stations. The additional events represent an increase of 67 % of the database for the considered days. The hypocentral locations of the events detected by template matching were determined with Hypo71 using the NSC velocity model  (Pandey, 1985) , integrating the phase arrival time deduced from template matching and the phases initially picked by the analysts.
ACKNOWLEDGMENTS We thank  DMG (Nepal) as well as DASE (France)  for facilitating the early exchanges during the crisis, as well as the data acquisition and treatment. DMG director general  Sarbjit Prasad Mahato ,  Rajendra Khanal ,  Soma Nath Sapkota  and  Ram Prasad Ghimire  are thanked for support over the years. Nepal Ministry of Home Affairs Nepal  and DASE provided helicopter for installation and service of the temporary seismological station. We acknowledge early discussions with Pascal Bernard (IPGP) and  Héléne Lyon-Caen  ( ENS Paris ) and feedback from  Yves Gaudemer. Kamal Kaphle ,  Arjun Bandhari  and  Destin Nziengui  were involved in the processing. The Ambassador François-Xavier Léger and the  French Embassy in Kathmandu  are thanked for assistance. Two reviewers provided constructive comments that helped improve the original manuscript.
Multi-Mode Transfer Matrix Method (MMTMM) The MMTMM is an efficient simulation-assisted method able to compute not only the phase shift but also the attenuation factor of arbitrary periodic structures  [24] [25] [26] [27] [28]. As any periodic structure is a concatenated system of unit cells, the full structure can be fully characterized by using the ABCD (or transfer) matrix, [T], of the corresponding unit cell. This unit cell is simulated by using any commercial or in-house full-wave software in order to obtain the multimodal scattering matrix, which is later transformed to a multimodal transfer matrix. In this work, the frequency domain solver of the commercial software CST has been used for this purpose. The use of powerful full-wave simulators makes the treatment of very complex unit cells possible (arbitrary geometry, as well as a great variety of materials).
ACK N OWLED G EM ENTS We are grateful to the handling Editor and two anonymous reviewers who helped us improving a previous version of this manuscript with their precious suggestions. D. R. and D. K. were partially supported by the  H2020  Project  SHOWCASE  (Grant agreement No  862480 ). D. R. was also partially supported by the  H2020   COST Action   CA17134  ' Optical synergies for spatiotemporal sensing of scalable ecophysiological traits (SENSECO) '. The research carried out at the  Jet Propulsion Laboratory, California Institute of Technology , was under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ). Government sponsorship is acknowledged. This work was supported by  Friuli Venezia Giulia Region Operative Program ,  European Social Fund - 2014/2020 Program, Specific Action  n.
Database Previous studies have analyzed smart products in the context of large corporations, but not for SMEs  (Raff et al. , 2020). This study fills this gap by uncovering contemporary trends of product capability adoption within Spanish industrial SMEs. Spain is a relevant context for examining SMEs since Spanish firm demographics show a very small presence of large corporations relative to other business environments, and therefore, technological adoption happens mostly in SMEs  (Jin, García, & Salomon, 2019). Regarding firm size and location, we constrained our population to Spanish firms with less than 250 employees. As discussed above, the study applies to industrial firms that possess production and analytic capabilities. To detect a group of relevant industrial firms operating in production and analytic industries, we used the ORBIS database, a service of Bureau Van Dijk (BvD) (https://orbis.
bvdinfo. com). This database provides information of secondary sectors, so it is possible to identify firms that a priori combine manufacturing and technological activities. This empirical strategy has been exploited by previous studies (e. g. Opazo-Basáez, Vendrell-Herrero, & Bustinza, 2018). To construct the population of interest, we followed a two-stage approach. First, we selected the industries of interest. As manufacturing industries, we included NAICS-32 (nonmineral manufacturing including wood, petroleum, plastics and chemical processes, as well as the pharmaceutical industry) and NAICS-33 (mineral manufacturing, including the construction of hardware, vehicles, machines, turbines, and engines). As technological sectors, we considered firms operating in NAICS-54 (Scientific, and Technical Services).
Firms were contacted via Computer-Aided Telephone Interviewing (CATI). This method is cost effective and can measure behaviors of interest  (Couper & Hansen, 2002). Surveying yielded a sample of 116 firms. The response rate of 11. 37% (116/1020) is common in survey procedures in which social researchers have neither personal network nor relevant referral  (Chidlow, Ghauri, Yeniyurt, & Cavusgil, 2015). Financial and accounting information for responding and nonresponding firms is available through another BvD service, SABI database (https://sabi. bvdinfo. com). This information is important to ensure the objectivity of the values of interest, including number of employees, profits and productivity. The employment figures also serve to assess the importance of non-response bias in this study by comparing respondents and non-respondents.
Moreover, as a robustness exercise, we account for observed factors that lead some firms to self-select into adopting remote monitoring technologies by implementing a propensity score matching technique based on the estimates of the Logit analysis (see  Deheija & Wahba, 2002). Therefore, we also run the intermediate solution for a matched sample that ensures comparability between firms with and without connected products. We acknowledge that fsQCA has some limitations. For instance, it cannot analyze longitudinal data, nor provide an explanation about the influence of previous cases over later cases  (Goldthorpe, 1997). There are some new methodological developments such-as TQCA (temporal QCA) that considers the temporal succession of factors to produce an outcome. But these are incompatible with the structure of the data used in this study.
Additionally, as explained above, fsQCA is useful when researchers have information about presence/absence of a causal condition  (Marasini, Quatto, & Ripamonti, 2016). Our casual conditions are crisp (i. e. , dichotomous) and can be identified through a membership function, that returns the value 1 "true" if and only if the firm had actually installed a software for developing the different product analytic capabilities (Monitoring, Control, Optimization, and Autonomy). While this is our case, there are other approaches under the fuzzy theory that deal with certain characteristics of causal conditions that do not apply to our data. These other approaches help to operationalize information insufficiency (grey theory), inconsistencies in information (rough set theory), membership degree, non-membership degree, and hesitation degree (intuitionistic fuzzy sets), and functions to returns on a set of membership values for each casual condition (hesitant fuzzy sets).
The first step in fsQCA is to analyze the coverage score. Table  6  shows that all product analytic capabilities (Monitoring, Control, Optimization, and Autonomy) are non-trivial conditions as their Coverage score yield a value clearly distant from 0. Taking a value near to zero would have meant that conditions occur in all cases, being theoretically and empirically trivial  (Schneider & Rohlfing, 2016). Coverage can be interpreted as the coefficient of determination in regression analysis as it captures the amount of explained variation in dependent variables  (Ragin, 2008, p. 63). As  Thiem (2014)  states, coverage value can be influenced by the choice of the crossover threshold and the membership function form -usually a piecewise logistic function is used to calibrate data as it is the default function available on the fsQCA software developed by  Ragin and Davey (2014)  when the "direct method" is chosen.
Sensitivity diagnostics is meant to explore if, depending of the relative location of the crossover threshold, changes in the membership function influence coverage in a positive or negative way  (Thiem, 2014, p. 625). R software -QCAPro package  (Thiem, 2018) -allows to select multiple functions: linear function (calibration by positive end-point concept), logistic (positive and corresponding negative end-point concept), quadratic/S-Shape (negative end-point concept), or inverted S-Shape (negative end-point concept). Because the study uses performance measures as its outcomes, a positive and corresponding negative end-point calibration approach was implemented. As a result, the membership function chosen was logistic. The second step is to analyze the necessary conditions. In doing so, we adopt the Consistence score  (Ragin & Davey, 2014).
Methodological insights and data limitations To measure the performance contribution of smart products in this study, both financial output and labor productivity were used as indicators. Previous studies have found that productivity and operating margins are not necessarily correlated  (Anderson, Fornell, & Rust, 1997; Foster, Haltiwanger, & Syverson, 2008). It is therefore important to analyze the relationships between both these performance indicators. The results of our study, however, are consistent at different performance measures. This in itself is interesting because it means that the firm's analytic capabilities can not only engender greater productivity (which can be interpreted as a process of technology-labor enhancement) but also can increase the firm's economic margins (via greater value-added and competitiveness).
In the context of smart products, it seems that both performance variables go hand in hand. An important methodological contribution of this study comes from the empirical design adopted. Previous studies analyzing the adoption and consequence of economic events have used statistical regression models in two stages, i. e. studies jointly evaluating self-selection into exporting and then learning by exporting  (Chang & Chung, 2017). These models require large databases and are conditioned on the fact that the event under study does not involve additional decisions. This is not so in our case. First, we work with a small database, and secondly, the adoption of connected products involves decisions as to the level of analytic capabilities (Smartness) of these products. It is impossible to know the true economic-financial impact of connected product adoption without also considering this related decision (as well as its business context) -see Fig.
2. As a consequence of these two intricacies, it was decided to introduce an algorithmic analysis (fsQCA) for the second stage of the study's model. The study is therefore amongst the first to analyze the two stages (adoption and consequence) with a combination of parametric and non-parametric analysis, showing the potential synergies between these types of empirical methods. Despite the uniqueness and richness of the data used in this study and of the appropriateness and rigor of the different methodologies used in its analysis, a number of limitations remain. First, despite the fact that the hypotheses developed to capture the factors driving the association between the degree of product smartness and firm performance were largely validated through the use of meticulously applied novel methods, more elaborate quantitative empirical research may be required in order to further investigate these links.
Nevertheless, by contrast with SAFRAN, it is based on extrapolations from meteorological station data only. It was also positively assessed in regions where observational data do not exist, and where precipitation quantities are generally very important: Arctic Alaska, central Norway, Svalbard, Greenland and Antarctica, Wyoming, Colorado, and Idaho  [46] [47] [48] [49] [50] [51] [52] [53]. The objective of this study is twofold: (1) to evaluate and adapt the SAFRAN analysis system to a complex catchment typical of the south Mediterranean region, including an area of water production in the mountain with a marked orography and a downstream plain-the comparison to the MicroMet data-driven approach is also carried out; (2) to disaggregate the future climate scenarios to provide spatially explicit fields of precipitation and temperature.
Meteorological Observations The ground network in the Tensift catchment is sparse. Within this study, several data sets originating from different institutions have been gathered. The location of the stations is provided in Figure  1. Table  1  displays its characteristics. The stations provide either daily data including cumulative rainfall RR, minimum Tmin and maximum Tmax temperatures or 6-hourly observations of rainfall, pressure, relative humidity, wind (strength, direction), temperature and cloud cover. These data cover a hydrological period of 10 years from 1 August 2004 to 31 July 2014. The period was chosen to optimize for data availability. The data was quality-controlled  [57] [58] [59] [60]  to detect outliers. Note that most of the stations in the mountains are installed in a subcatchment of the Tensift named the Rheraya because it has been the support of several hydrological experiments since the beginning of the 2000s  [12, 54].
Daily Snow-Covered Area MODIS In order to evaluate the ability of MicroMet and SAFRAN to predict snowfalls, the daily snow-covered fraction (SCF) product MOD10A1 at 500 m resolution version 6 derived from the TERRA satellite data is used. The daily products provided by the National Snow and Ice Data Center (NSIDC) are extracted from August 2004 to July 2014 on the Tensift and on the Rheraya sub-basin. Version 6 of MOD10A1 does not include the snow-covered fraction anymore. In this study, the Normalized Difference Snow Index (NDSI (1);  [64] ) is used following  (Hall et al. [65] ; Baba et al. [66] ) to compute SCF as follows: SCF = 0. 01 + 1. 45 NDSI (1) In a second step, a filter that searches for clear neighbors in space and in time is applied to decrease the number of cloudy images and to minimize the problem of cloud/snow misclassification following Marchane et al.
SAFRAN Assessment The SAFRAN assessment is carried out in three ways: (1) by a station-level validation based on the leave-one-out (LOO) approach  [73]  from August 2004 to July 2014. It consists of removing a station from the database, running SAFRAN without this station and evaluating the SAFRAN reanalysis at it. This validation method is used for a mountain station (Aremd) and the plain station (Chichaoua). Both stations were chosen due to data availability; (2) via a comparison with MicroMet monthly averages over the Rheraya catchment; (3) by comparing the rain/snow partition to the snow surface area derived from the daily MODIS product and to the in situ snow depth measurements.
Data Availability Statement: Not applicable. Acknowledgments: We acknowledge the World Climate Research Programme's Working Group on Regional Climate, and the Working Group on Coupled Modelling, the former coordinating body of CORDEX and the responsible panel for CMIP5 for providing the Euro-CORDEX scenarios available at http://www. euro-cordex. net/060378/index. php. en. The meteorological data can be obtained from the Direction Générale de la Météorologie Maroc and from the Tensift Observatory. This work was carried out within the frame of Joint International Laboratory TREMA (IRD, UCAM, DMN, CNESTEN, ABHT, and ORMVAH) and the FP7 ERANET-MED CHAAMS03-62 project. The SAGESSE PPR/2015/48 "Système d'Aide à la décision pour la GEstion des reSSources en Eau", the H2020 PRIMA IDEWA and ALTOS projects are also acknowledged for additional funding.
Main elements of the setup Internal sizes of the multimode cavity are important parameters to optimize the sample heating. According to previous work  [18] , internal sizes were chosen to be close to nλ/2 (with n being an integer and λ the wavelength corresponding to the frequency of the magnetron) to obtain a homogeneous distribution of the field inside the cavity. In order to get the highest field intensity at the cavity center, n was chosen to be an odd number. Moreover, the cavity sizes must be determined considering also the size and the properties (permittivity and electrical conductivity) of the components constituting the sintering cell (see part 2. 2). Furthermore, sizes of the microwave furnace are also limited by the depth and height of the glovebox (around 800 mm). Taking all these conditions into account, FE simulations were carried out with COMSOL Multiphysics software (Electromagnetic Wave module) to calculate the internal sizes of the cavity (close to nλ/2), with n an odd number and nλ/2 < 800 mm.
Three LEDs (CREE XREWHT-L1-0000-009E7, Conrad, France) are placed on two other chimneys to light the sample at low temperature. For high temperatures (> 700°C), the sample is visible due to its own radiation. Recorded images are processed after sintering with a home-made Labview® software (National Instruments, USA) which automatically detects pellet's edges, to measure its diameter  [15, 16]. The program finally output the evolution of the pellet diameter during sintering.
Control of the thermal cycle A specific control software was developed using Labview® to carry out sintering experiments with controlled heating cycles. This software uses a PID controller based on the temperature measured by pyrometers. The incident power delivered by the generator is continuously adjusted during the sintering cycle to match the measured temperature with the set temperature. Moreover, the impedance tuner is adjusted manually during heating to minimize the reflected power. The power sensors, the camera and the pyrometers are also connected to this Labview® software in order to record the data (power, temperature, images) used to control the thermal treatment and to obtain sintering curves (as conventional sintering in a dilatometer).
Introduction We are living a deep change in our society due to digitization and computing power. A transition in the health sector is slowly taking place with a move from conventional medicine to precision medicine, also known as personalized medicine  [1]. Backed by powerful algorithms to interpret large amounts of data, the goal of precision medicine is to adapt the treatment of each individual with respect to its genomics, its environment and its lifestyle in order to maximize effectiveness and decrease the side effects of treatments  [2, 3]. This approach is supported by health authorities such as the Food and Drug Administration (FDA) who provides a list of drugs associated with specific actions to be undertaken according to a list of biomarkers  [4]. Another example is provided by the National Institute of Health which founded in 2013 "ClinVar" an open centralized database to collect and improve the understanding on genomic variations and their implication for the patient  [5].
Distribution of studies and involvement of different actors around the world In total, 72 articles were analyzed. From these, 70 articles were gathered from the databases Pubmed and Science Direct and 2 articles were added from the top-down bibliographic approach. The systematic research ended up in 2019. The objectives and main findings of included articles are presented in Supplemental Data 2. It was observed that FDM studies are unevenly performed around the world. Regarding the country of affiliation of the first author, the United Kingdom (UK) leads the production of articles regarding this subject with 27 articles (37. 5%) published so far. It is followed by the United States of America (USA) (11%), China (7%) and Italy (7%). It is worth noting that the UK has the largest number of universities (6 in total) working on this subject which provides a prolific network between research teams working on the topic.
Control variability on computer aided design, slicers and printers The design is drawn on a Computer Aided Design (CAD) software and is conventionally saved in a stl. file (as stereolithography file), the format most widely used in 3D printing. Then, another software called "slicer" transforms in several seconds the digital design (from the stl. file) into a sequence of instructions to be carried out by the printer, generating a Gcode file. These instructions include for example the working temperatures of the bed and the extruder, the displacements of the printing head, the quantities of filament to be extruded over time, etc. Once this Gcode is loaded into the printer, the printing can start. Depending on the complexity of the shape to be produced, designing on CAD software can take few seconds (for simple geometries such as a cylinder or a sphere) and a few hours for complex models.
There is a broad variety of CAD softwares available in the market. In the studies reviewed fifteen different softwares were used. Despite 10% missing data due to articles' lack of information two of the most used softwares are commercialized by Autodesk®: AutoCAD ® (used by 23% of the studies) and 3DS MAX Design® (used by 19% of the studies). Stereolithography files (stl. ) were used to save the design drawn from the CAD software in all studies mentioning the files format (90%). Based on a triangular geometry, it retains the external surface of the drawing by transforming it into a sum of small triangles in space. From our point of view, the diversity of CADs used is not a source of variability for the final product. The lack in variability comes from the use of common mathematical functions that for simple forms such as conventional medicines do not differ one from the other.
Dataset The Rhodophyta dataset used in this analysis consisted of ten red algal species, including three members of the Florideophyceae, two members of the Bangiophyceae, one member of the Porphyridiophyceae, and four members of the Cyanidiales (Figure  1 ). A set of thirty species was used in the phylogenetic analysis, consisting of the above ten members of the Rhodophyta, plus two members of the Cryptophyta, six members of the Chlorophyta, two streptophyte algae of the KCM grade (comprising the basal lineages Klebsormidiales, Chlorokybales, and Mesostigmatales), three streptophyte algae of the ZCC grade (comprising the sister lineages of extant land plants, namely Zygnematophyceae, Coleochaetophyceae, and Charophyceae), and seven members of the Embryophyta. The names, five-letter codes, and the data sources of the species used are shown in Supplementary Table  S1.
If more than the 25% of the total number of sequences fulfilled the criteria, new sequences were randomly eliminated. MAFFT version 7 was used to construct an alignment for each of the six domains  [31]. The parameter -add was used to add unaligned sequences into the existing alignment. In addition, -auto was used to automatically optimize the algorithm according to the data size and properties. The alignment was then trimmed using BioEdit version 7. 2. 5  [32]  and manually curated so that only the domain of interest was present in the alignment. The alignment was trimmed based on the original seed alignment from PFAM, and the sequences were rechecked for completeness. New profile HMMs were calculated from these alignments using hmmbuild from the HMMER software package version 3. 3. 2 [33].