dataesr/scientific-paragraphs-categorization huggingface dataset:
In brief, griz imaging is used to select targets from SDSS DR9 and PS1. SDSS data were taken between 1998 and 2009, with typically only one epoch per observation. The PS1 3π survey acquired 10-15 epochs of imaging between 2010 and 2013. TDSS uses the SDSS-PS1 comparison as a measure of long-term variability, and the variation among PS1 epochs as a measure of shortterm variability. Adopting the Stripe 82 database as a testbed,  Morganson et al. (2015)  developed an estimator E related to the probability of a specific source being variable based on the short-and long-term variability, and the apparent magnitude. This estimate is applied to a set of isolated point sources with 17 < i < 22 and defined a threshold E above which to select objects as likely variables. Across most of the sky (80%) TDSS randomly selects 10 targets per deg 2 that pass this threshold and are not already eBOSS quasar targets.
Data and metadata from the plate drilling quality assurance process are backed up to the SAS. At APO, the plate-plugging metadata, observing logs, telescope telemetry, and the raw data are transferred each day from the previous night's observing to the SAS  (Weaver et al. 2015)  and backed up on the SAM and HPSS. A similar system is installed at LCO. The eBOSS, MaNGA, and APOGEE-2 pipelines are run automatically on each night's data as they arrive. For eBOSS, this process consists of the full pipeline through the production of 1D calibrated spectra, redshifts, and other parameters, for each completed plate. For MaNGA, this process consists of the Data Reduction Pipeline executed for each completed plate. However, currently the Data Analysis Pipeline is experiencing more development and is not run automatically; it is instead run periodically based on accumulated data and progress in DAP development.
For APOGEE-2, the visit spectrum reductions and radial velocity determinations are performed automatically. However, because the combined spectra require multiple visits and because of its computational expense, the ASPCAP analysis is performed periodically on large sets of plates, again based on accumulated data and progress in ASPCAP development. The primary point of data access for collaboration members is the SAS. Collaboration members can access data on the SAS through ssh connections. SAS also provides http, rsync, and Globus access to the data files. These methods are available also to the astronomical community for publicly released data both for the SAS and SAM. We provide a web interface and an application program interface (API) on SAS to the eBOSS and APOGEE. A similar set of interfaces is being developed for MaNGA called Marvin, which will additionally have a Python module for interaction with the API.
The data directory structure and file format documentation is provided as a "data model. "  6 ublic data releases incorporate both the SAS data interface and the Catalog Archive Server (CAS), hosted at Johns Hopkins University. The CAS contains catalog data from the SDSS imaging and spectroscopic survey; it does not currently include images or spectra (other than JPEG and PNG versions, respectively, for visual browsing). The total database size is approximately 12 Tb, which is dominated by SDSS imaging catalogs. The CAS provides web browser-based access in synchronous mode via the SkyServer web application 7  and in asynchronous mode with the CASJobs batch query service. 8 The SkyServer  (Szalay et al. 2002)  supports multiple levels of data access ranging from simple form-based queries aimed at novice users to raw SQL queries for expert users.
The SkyServer includes interfaces displaying the SDSS and 2MASS imaging and the locations of SDSS spectroscopic and imaging catalog entries, as well as an Explore tool for each object showing the spectra and listing key parameters. CASJobs  (Li & Thakar 2008)  gives each user their own server-side database called MyDB, along with the ability to submit arbitrarily complex SQL queries in batch mode and redirect the output to their MyDB. Users may import their own data to cross-match with the SDSS data. There is a Groups feature to allow users to share their data with collaborators. CASJobs also supports a command-line mode of query submission. For SDSS-IV, SkyServer and CASJobs are integrated into the SciServer collaborative data-driven science framework 9 with seamless single sign-on access to several new services such as Compute, SciDrive, SciScript and SkyQuery.
EDUCATION AND PUBLIC ENGAGEMENT The mission statement of education and public engagement for SDSS-IV is to make the engineering and scientific results of all SDSS surveys accessible to the public through formal education, citizen science, news, and social media. SDSS-IV will continue and expand upon the activities in these areas of its predecessors. SDSS public outreach activities are based on real astronomical data accessed through the same databases as used by professionals. These activities expand the user base of SDSS data and thus its scientific reach, both through training and directly through investigations made possible with these scientific tools. These activities include the public distribution of data, the development of inquiry-led education material suitable for middle school and above, the distribution of SDSS plates to educational venues to support engagement with SDSS data in the classroom, development of new citizen science projects through collaboration with the Zooniverse 10  (building on the success of Galaxy Zoo 11  ), regular blogging 12  and increased social media engagement, including multi-lingual activity.
The FAST program has been independently funded by the Sloan Foundation for an initial three-year period. It actively recruits and trains underrepresented minority (URM) talent to participate in SDSS science. To qualify for FAST, at least one team member is expected to be a URM and/or to have a track record serving URM scholars. FAST scholar teams are matched with established SDSS partners to work on a research project of mutual interest and receive specialized training, mentoring, and financial support in order to introduce teams to SDSS science and to cement their participation within the collaboration. FAST team faculty become full members of the SDSS collaboration, with all data rights, access to centralized computing, and ability to lead projects that this implies. We selected our first FAST cohort of three teams in 2015 and recruited five FAST teams in 2016.
In addition, SDSS-IV is in the process of developing a formal Code of Conduct. 10. SUMMARY We have described SDSS-IV, which began operations in 2014 July, with plans to continue until mid-2020. The collaboration has over 1,000 participating astronomers from over 50 institutions worldwide. Three major programs (APOGEE-2, MaNGA, and eBOSS) and two subprograms (TDSS and SPIDERS) will address a number of key scientific topics using dual-hemisphere wide-field spectroscopic facilities. The major elements of this science program are as follows. • Milky Way formation history and evolution, using chemical and dynamical mapping of all of its stellar components with APOGEE-2. • Stellar astrophysics, using APOGEE-2 infrared spectra alone and in combination with asteroseismology, using TDSS's optical observations of variable stars, and using MaNGA's bright-time optical stellar library.
• Formation history and evolution of the diverse array of galaxy types, using chemical and dynamical mapping of stars and gas with MaNGA integral field spectroscopy, using the distant galaxy populations in the eBOSS LRG and ELG programs, and the cluster galaxies in SPIDERS. • Quasar properties and evolution using the massive sample of quasars in eBOSS, reaching nearly down to Seyfert galaxy luminosities out to z ∼ 2, complemented with quasars selected via variability (TDSS) and X-ray emission (SPIDERS). • The most powerful cosmological constraints to date from large-scale structure, precisely investigating the Hubble diagram and the growth of structure in the redshift range 1 < z < 2 for the first time, using the largest volume cosmological large-scale structure survey to date from eBOSS. The science program is coupled to a robust education and public engagement program.
emep. int/; European Air Quality Database AirBase; http://acm. eionet. europa. eu/databases/airbase/) and North American (USEPA Air Quality System AQS: http://www. epa. gov/ttn/airs/airsaqs/; Analysis Facility operated by Environment Canada: http://www. ec. gc. ca/natchem/) monitoring networks. Full details are given in  Solazzo et al. (2017)  and references therein. Following the approach used in previous AQMEII investigations, modelled hourly concentrations in the lowest model layer (∼ 20 m for both models) and corresponding observational data are paired in time and space to provide a verification data sample mod t r , obs t r ; t = 1,. , 8760; r = 1,. , n recs of n recs (number of monitoring stations) record of matched modelled and observational data, where the rth pair mod t0 and obs t0 is evaluated at receptor r at a given time t 0.
The "theory of evaluation" being put forward by the hydrology modelling community  (Nearing et al. , 2016 , and references therein) may provide a template for the air quality community to further advance their model evaluation approaches. Data availability. The modeling and observational data generated for the AQMEII exercise are accessible through the ENSEMBLE data platform (http://ensemble. jrc. ec. europa. eu/) upon contact with the managing organizations. References to the repositories of the observational data used have been provided in Sect. 2. 1. The Supplement related to this article is available online at https://doi. org/10. 5194/acp-17-10435-2017supplement. Competing interests. The authors declare that they have no conflict of interest. Disclaimer. Although this work has been reviewed and approved for publication by the US Environmental Protection Agency, it does not necessarily reflect the views and policies of the agency.
Acknowledgements. We gratefully acknowledge the contribution of various groups to the third  Air Quality Model Evaluation International Initiative  (AQMEII) activity. The following agencies have prepared the data sets used in this study:  US EPA  (North American emissions processing and gridded meteorology);  US EPA, Environment Canada, Mexican Secretariat of the Environment and Natural Resources  ( Secretaría de Medio Ambiente y Recursos Naturales-SEMARNAT ), and  National Institute of Ecology  ( Instituto Nacional de Ecología-INE ) (North American national emissions inventories); TNO (European emissions processing);  ECMWF/MACC  (chemical boundary conditions). Ambient North American concentration measurements were extracted from  Environment Canada 's National Atmospheric Chemistry Database (NAtChem) PM database and provided by several  US and Canadian agencies  ( AQS, CAPMoN, CASTNet, IMPROVE, NAPS, SEARCH , and STN networks); North American precipitation chemistry measurements were extracted from NAtChem's precipitation chemistry database and were provided by several  US and Canadian agencies (CAPMoN, NADP, NBPMN, NSPSN , and REPQ networks); the  WMO World Ozone and Ultraviolet Data Centre (WOUDC)  and its data-contributing agencies provided North American and European ozonesonde profiles;  NASA's AErosol RObotic NETwork (AeroNet)  and its data-contributing agencies provided North American and European AOD measurements; the  MOZAIC Data Centre  and its contributing airlines provided North American and European aircraft takeoff and landing vertical profiles.
It provides a close-to-reality simulation of the World Ocean dynamics that proves to be specially well suited for simulating surface drifter trajectories  [49]. Trajectories are simulated using the ARIANE Lagrangian trajectory simulation software (www. univ-brest. fr/lpo/ariane). This program uses an accurate quasi-analytical solution of the advection equation  [50]. To produce passive drift trajectories, GAL fed ARIANE with daily surface currents (V c ) provided by G1. In STAMM, we feed ARIANE with turtles' velocities on the ground (V g = V c +V s ) and thus obtain trajectories resulting from the combined effect of current drift and active swimming. Surface current values are used as diving is not explicitly simulated here. Parameterization of the diving activity might be envisioned in subsequent versions of STAMM.
For the sake of simplicity we will refer to this passive-drift simulations and the one performed with STAMM as the passive dispersal and active dispersal simulations respectively. Similarly, simulated individuals will be referred to as passive and active turtles. To perform 18-year-long simulations with 7-year-long forcing data sets (G1 and NPP), we simply loop the forcing fields. This means that the simulations continue after December 2008 using again data starting in January 2002. This process is repeated until the last released turtle reaches the age of 18. All simulations involve 5000 particles (simulated turtles) released off Jamursba-Medi between June and November 2002. Note that out of the 5000 simulated turtles, roughly 3000 (passive and active) individuals finally circulate in the North Pacific while the others drift into the Indonesian seas, the Indian Ocean or the South Pacific.
I. INTRODUCTION IoT is taking over the world, it is estimated that the number of devices connected to the Internet forming the Internet of Things will reach 50 Billion by 2020  [1]. And one critical application is the eHealth Smart Homes. In fact, this technology allows to monitor elderly or individuals with diseases and automatically send the data to a remote server for processing by doctors. This data is recorded in the so called EMR. Secure access to this EMR is problematic considering security and privacy issues, transparency, etc. This is why in order to develop secure and reliable solutions for eHealth smart homes, it requires unprecedented coordination and collaboration between all pieces of the system. All devices must work together and be integrated with all other devices, and all devices must communicate and interact seamlessly with remote systems and infrastructures in a secure way.
IV. MODEL OF THE SYSTEM An interesting approach for solving the problem of the high computational power needed in order to communicate with the Blockchain, is to introduce a centralized-decentralized combined architecture i. e introducing intermediate servers between IoT devices and the Blockchain. A very good candidate for such an intermediate is a cloud server, easily accessible by Fig. 1. Proposed architecture, IoT devices connected to the Publisher node which is connected to the blockchain, clients either connecting to subscriber nodes or directly to the blockchain clients, and can handle high computational power. Another effective approach is the use of a Publisher-Subscriber data sharing mechanism. The authors of reference  [1]  show us that a Publisher-Subscriber mechanism is very efficient in terms of filtering Data, and introducing intermediates to handle electrical power and computational power consuming protocols and technologies such as the Blockchain.
On the other hand, our model is also based on Smart Contracts. Smart contracts allow us to maintain rules, authentication, and communication between different nodes and parties of the system. The final part of the model is the use of an off-chain database, since the transaction of messages containing data of important sizes directly decreases the performance of the Blockchain, and in the nature of this technology which is replication on every single node, the storage of files and data on-chain will cause delays and worse performance. One candidate for off-chain database is IPFS  [10]. We can describe our model by first defining some notations that will be later used in this paper: Function that determines if a certain subscriber is subscribed to a certain publisher Figure  1. describes our proposed architecture in a general matter.
A. Defining the Scenario One of the major project today's are "Smart Cities", "Smart Buildings", and "Smart Homes". How can a person monitor the devices in his house with the complete knowledge that he is the only one that could access that data? Therefore, in order to elaborate our solution, we propose the following scenario: a smart building where sensors on different objects are connected, always online, and users need to monitor the sensors at every moment. The idea is to secure access to the data generated by these sensors. This can be done by protecting the information about the physical location of the data. As described in the previous section, the main components of our system are : the Blockchain, Smart Contracts, Intermediate servers and an Off-chain Database. The main parties of our system are: Publishers and subscriber.
C. The Protocol Description After describing the contracts, this part describes how the publisher node acts upon receiving new data generated by an IoT device connected to it. The steps that should occur are  (1)  storing the data in the off-chain database (IPFS in our case), (2) generate the hash pointing to the location in the database, then (3) sending that hash to every node on the blockchain that is subscribed to this publisher. The transaction are sent through the blockchain client, which is an ethereum client in our case. Publisher node upon IoT device generating new data: Da = Data(d k i ) if (∃a) store(Da) H P = Gen(HashDa) f oreach(S j /S j ∈ sub(P k )) Eth. send(H P ,S j ) The second part describes how the subscriber should act upon receiving a transaction sent by a publisher it is connected to.
He connects to the IPFS node in our case, and uses the hash to fetch the data In figure  4 , we can observe each part of the subscriber and publisher nodes. For the subscriber node, the Graphical user interface (GUI) interacts with the user, the eth client connects the node to the blockchain, and the local database stores the data received, it works like a cache for fast access in the future. For the publisher node, eth client IoT devices are connected to the node, the publisher code manages the publisher subscriber mechanism in the publisher, the off-chain database contains all the data generated by IoT devices from this publisher and the Database Access control manages the access to the database. It is also important to notice one important step which is mining, since with every transaction taking place, nodes on the blockchain (miners) need to validate the transaction which will make it longer for the message to be delivered from publisher to subscriber.
Nt In the same way this can be generalized on data generated by all publishers to obtain N P n=1 N P d i × r P d i ≥ Nt T b This study aims to calculate the time needed for a transaction to be mined and a bloc to be validated, without taking into consideration the work and time taken by the subscriber and publisher mechanism. To implement our solution, we have chosen to use a framework called Embark  [11]. Embark is based on the idea of Decentralized applications (DApps). It implements ethereum blockchain, IPFS database, and Whisper protocol used to send messages between multiple DApps. We have chosen to work with the go ethereum (geth) client, along with SolidityC  [12]  language for smart contracts programming, HTML, Javascript and JQuery for frontend and GUI programming. We have successfully created our private blockchain, with a couple of nodes, and notification smart contracts upon new data updates.
VII. CONCLUSION AND FUTURE WORKS The objective of this work was to consider the possibility to use blockchain technology in the area of security in IoT. We have presented an architecture solution designed for that purpose that is based on contract model between a provider and a consumer of data. To cope with the size of the data to store, we have proposed to associate the blockchain with complementary off-chain database technology. The block contains the main contract information as well as reference to where the complete data is stored. We have also presented an analytic model to study the performances of the mining process that has a high impact on the overall response time of the system. In our future work, we plan to go further in the implementation of the system and perform large scale tests with physical and virtual IoT devices.
AMBRE ITSs are based on a knowledge-based system coded in Prolog, a programming language for knowledge representation. This knowledge-based system relies on a problem solver and uses two main types of knowledge: knowledge about the method to be taught and knowledge to guide the learner when he/she solves problems, providing assistance and diagnosing his/her answers. Building an AMBRE ITS, like any other ITS, is a labor-intensive process that requires expertise in the domain application and in programming  (Murray 2003; Woolf & Cunningham 1987). Our goal is to reduce the effort of making AMBRE ITSs by building an authoring tool that can generate the domain models. This tool should provide assistance to the authors during knowledge elicitation process. Our intention is to enable authors with no Prolog programming expertise to build their own ITS in the domain they are interested in.
Cell motility is governed by a complex molecular machinery that converts physico-chemical cues into whole-cell movement. Understanding the underlying biophysical mechanisms requires the ability to measure physical quantities inside the cell in a simple, reproducible and preferably non-invasive manner. To this end, we developed BioFlow, a computational mechano-imaging method and associated software able to extract intracellular measurements including pressure, forces and velocity everywhere inside freely moving cells in two and three dimensions with high spatial resolution in a non-invasive manner. This is achieved by extracting the motion of intracellular material observed using fluorescence microscopy, while simultaneously inferring the parameters of a given theoretical model of the cell interior.
We illustrate the power of BioFlow in the context of amoeboid cell migration, by modelling the intracellular actin bulk flow of the parasite Entamoeba histolytica using fluid dynamics, and report unique experimental measures that complement and extend both theoretical estimations and invasive experimental measures. Thanks to its flexibility, BioFlow is easily adaptable to other theoretical models of the cell, and alleviates the need for complex or invasive experimental conditions, thus constituting a powerful tool-kit for mechano-biology studies. BioFlow is open-source and freely available via the Icy software.
Also, as mentioned earlier, these implementations are tailored specifically to the theoretical model being validated, and cannot easily be applied to other theoretical models, dimensions or cell types. Here we describe BioFlow, a user-and experimenter-friendly mechano-imaging method able to estimate biophysical quantities everywhere inside the cell in a non-invasive manner, in two or three dimensions, using live cell imaging. This is achieved by combining two mathematical techniques in an integrated framework: (i) optical flow  27  , an image processing method that extracts motion information from video sequences and overcomes several limitations of PIV, and (ii) variational data assimilation, a highly-scalable mathematical framework designed to infer the parameters of a given theoretical model based on a limited set of observations (also called realisations of the model)  28.