dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants also indicated the need for positive framing (e. g. , 'improving blood pressure' as opposed to 'working on high blood pressure') and easy (non-medical) language, for example a 63 year old female interview participant: "[The app has to be] understandable! Don't go tossing around big words and medical terms and all that. " For this, we have adapted the wording throughout the mobile application. Another important aspect of a user-friendly intervention was trustworthy and easy-to-understand material. Lastly, we have simplified login procedures, to facilitate easy access (Supplementary Table  2 ). Based on wishes from the Chinese target population, we made the Chinese mobile application available as a WeChat subapplication or "mini-program. " WeChat is a widely used Chinese multi-purpose messaging, social media, and mobile payment application with a wide range of such mini-programs.
DISCUSSION In this paper, we describe the design, development, and piloting of an mHealth portal and mobile application for the prevention of dementia in the context of the PRODEMOS trial, building upon the existing evidence and experience from the HATICE eHealth platform. Based on extensive input from all stakeholders involved, we developed a platform for behavior change for older adults, with adaptations for specific needs from the low SES population in the UK and the general population in China. For the thorough development of an mHealth platform, many stakeholders from several backgrounds need to be involved, including researchers, healthcare professionals, software developers, and the target population. We believe clear communication is crucial to understand each other's idioms and ways of thinking during development and evaluation.
ACKNOWLEDGMENTS The authors thank the European Union's  Horizon 2020 Research and Innovation Programme  and  National Key R&D Programme of China  for the funding of the trial. The authors thank  Kevin Hekert ,  Louwrens Knulst ,  Michiel van Dam ,  Martijn Bos ,  Jan Eikelenboom , and  Mart Hoitink  for their substantial contribution in building the platform. The authors thank  Jihui Lyu ,  Wei Zhang ,  Haifeng Hou ,  Yixuan Niu ,  Yueyi Yu ,  Hui Yuan ,  Hongmei Liu ,  Siqi Ge ,  Mingyue He ,  Dan Li ,  Qiang Zeng ,  Dong Li ,  Baoliang Sun ,  Jinxia Zhang ,  Xiaoyu Zhang ,  Mingyang Cao ,  Bin Jiang ,  Danning Li ,  Weijiao Zhang , and  Cancan Li  for their contributions to the trial logistics. The authors thank  Ron Handels  for his comments on the manuscript. The authors also thank all patient participants and other stakeholders who took part in patient and public involvement groups for their contribution to the platform design.
Introduction A dataset in the ATLAS experiment [1] at the LHC is the aggregation of multiple files in one logical and operational unit in a distributed computing environment. Only datasets, not single files, are transferred between sites and replicated. For this reason, in our study the popularity of data refers to the popularity of datasets. As data replication and deletion strategies in ATLAS are typically based on manually defined static policies, sooner or later we face situations when popular datasets do not have enough replicas, or unpopular datasets, on the contrary, take up too much space. These inconsistencies may cause significant delays in tasks execution processes that could have been avoided if the policies were more automated and dynamic. Methods for assessing data popularity in order to automate data management policies have already been investigated in earlier studies [2, 3], but none of them have been integrated in the production system of the ATLAS computing infrastructure.
A possible reason that they are not used is due to insufficient integration of the following sources of information about ATLAS data: DDM (Distributed Data Management) Rucio [4], Rucio Traces [5], EOS [6] Report Logs, WMS (Workload Management System) PanDA [7]. These sources have specific sets of metrics that can be used to assess the popularity of datasets. Our studies have proved that measurements obtained from these four sources are consistent with each other. This means that data on popularity can be integrated without sacrificing the measurement accuracy, but with a gain in the number of consolidated metrics. Additionally, we demonstrate how popularity can be evaluated based on different sources of information and which auxiliary metrics can be calculated for further integration work.
Rucio Traces Every data access made on the grid, either through the WMS or directly through the DDM system is tracked by the Tracer system. The trace dictionary is on a file level and includes information like the filename and scope, the corresponding dataset, the endpoint where it was accessed, the type of access, e. g. Analysis or Production upload, the user account, some file information and timings. From the Rucio server the traces are forwarded to a central ActiveMQ broker from where different consumers read this data. One consumer is the Kronos daemon in Rucio. This daemon takes the traces and updates the last access timestamp for files and datasets in the Rucio catalogue, which is used to sort replicas for the deletion. Furthermore, it also updates the access_cnt field for files and datasets, which simply counts the number of times when a file or any file in a dataset was accessed.
PanDA Database PanDA (Production and Distributed Analysis) is a highly scalable and flexible data-driven workload management system that supports central production and user analysis data processing in ATLAS. PanDA DB is a database system serving PanDA. It registers the comprehensive historical and operating meta-information about all physics analysis tasks, jobs being executed within the distributed computing environment of the ATLAS experiment. Additionally, PanDA DB registers metadata about input and output datasets of the computing tasks. Popularity of a dataset in the PanDA DB can be measured as the number of tasks executed within a certain period of time with this dataset as an input, that refers to the number of accesses to the dataset. Besides the popularity of input datasets we can study the execution process of each task, i.
Consistency Check of Data Popularity Metrics Between Rucio Traces and PanDA DB For this evaluation we collected data (production tasks with input AOD 3 datasets of the Monte-Carlo type) from Rucio Traces and PanDA DB in a defined time period (01/09/2020 -20/10/2020). Then we generated two data samples with pairs of (Dataset Name, taskID)  4  from Rucio Traces and PanDA DB. The results showed that these two samples are consistent. From PanDA DB we took tasks (with input datasets) that were started or finished within the specified time period, from Rucio Traces we selected tasks with input datasets that were transferred within the same time period. 2 We are not going to study users statistics. For the popularity measurements we just take the number of different users who executed tasks with ATLAS datasets  3  Analysis Object Data -these datasets are used for physics analysis tasks  4  taskID is a task that used the dataset as an input.
Analysis of the ATLAS EOS instance at CERN Data Center using EOS Report Logs We analyzed EOS Report Logs for the CERN Data Center for a period of three consecutive months (01/01/2020 -31/03/2020), which is not a data-taking period and the tasks performed at the site mainly consisted of Monte Carlo production jobs and data analysis. As seen in the description of the EOS Report Logs, most of the storage system activities logs, except for deletions, have the same format. They include all the possible file operations (reads, writes, updates and so on). The original set of log metrics does not indicate the operation type. Nonetheless, having this information is important to understand which operations a file undergoes during its lifetime. We use the existing metrics (size of the file on opening and closing and the number of bytes read and written) to classify operations into five categories: "Create", "Read", "Update", "Empty" and "Abnormal", where "Empty" operations have no bytes read or written and "Abnormal" are all the operations that cannot be classified as any of the previous categories.
The relative impact of the operations other than "Create" and "Read" is very small (less than 1% of all operations), which confirms the assumption that most of the data are immutable. Figure  1  gives an overview of the read/write processes happening at the EOS instances during the considered three months and shows how actively the provided disk volume was used by ATLAS and two other LHC experiments. The plot indicates that the access patterns differ from one experiment to another. ATLAS, in comparison to the other experiments, has the biggest EOS instance volume and had the most intense workload. Its total turnover (the sum of all the bytes read and written) is over 480% of the instance volume at the time. During the examined period, all the experiments read more data than they wrote, but not by a large margin.
Rucio Access Metrics for Detector and Monte-Carlo Data As we already saw, Rucio traces provide a very convenient way to record every time that a file has been accessed as input for a job or written as output, or downloaded/uploaded by a user 5  using the Rucio client tools, and this information is stored indefinitely in a Hadoop cluster at CERN, allowing all sorts of data popularity studies. The most useful attributes in traces are the file name, its size, the dataset name, the remote site, the access time and the access event type. Another important source of information generated by Rucio are the logs, which record events like file transfers and file deletions and they can be used, for example, to study the effectiveness of data management policies. In the vast majority of cases, ATLAS jobs process files from their local storage, possibly after they have been transferred from a remote site.
In some cases, for example for sites without local storage, files will be directly copied from a remote site to the job worker node. The first approach attempted was to examine file and dataset access patterns to a few representative ATLAS sites belonging to different categories: Tier-0/1, nucleus Tier-2  5  and non-nucleus Tier-2  6. Only traces related to PanDA jobs were considered, and only files belonging to AOD, DAOD  7  and HITS 8 datasets, as they constitute the vast majority of the data accessed. Besides the evaluation of the number of traces in this study we decided to investigate how accesses are spread over time or concentrated, that can have a big impact on data management. Additionally, we measured the fraction of weeks with accesses to a given dataset. The distributions over datasets in Figures  5(a, b ) show that DAOD dataset accesses are very few and concentrated in time during a year.
Differences in distributions across different sites are small, consistent with the fact that access patterns depend primarily on the file type independently of the location at which datasets are processed. The distributions for the same quantities were derived at a global scale, looking at accesses independently of the site, and very similar distributions where obtained (Figures  5(c, d )) for AOD datasets; it is worth stressing though that only datasets that are accessed at least once contribute to the distributions. There is therefore a strong indication that ATLAS datasets, even globally, were accessed very few times during a year. Therefore, ideally they would need to be accessible from disk only for a short time. Rucio logs can be used to measure the time between a file's last access and the time it is deleted from a site.
6 Popularity of ATLAS User Analysis Data Central production tasks are typically planned in advance and executed sequentially as a bunch of tasks, as can be seen at the bottom of the scatter plot in Figure  6. User analysis tasks, on the contrary, can be created and started at any time. Start points of the analysis tasks are almost randomly distributed over time. For this reason we decided to distinguish the popularity analysis of datasets utilized for user tasks. We collected data from two main data sources: Each data source provides access to a specific group of metadata for a particular task, dataset, computing center and user. PanDA database is used to access metadata of individual user analysis tasks. These metrics allow us to identify delays in tasks brokerage, datasets usage density, total duration of succeeded and failed tasks and many more.
The WLCG infrastructure topology and experiment-specific configurations are described in CRIC. With the help of CRIC we are able to identify task execution hardware specifications and geolocations of computing centers. These metadata are useful for anticipation of task execution time [10]. The CERN Phonebook Directory is exploited to address deep study on user analysis tasks. The information provided is used to identify the user's home institution and its country with geolocation coordinates. This information is potentially important as it allows us to discover institutes and countries that carry out more physics analysis tasks of particular types of ATLAS data. The Rucio API provides us with the detailed information about datasets and replicas. The popularity is measured with four parameters: N tasks -number of tasks, N usersnumber of users who carried out these tasks, N institutes -number of users' home institutes, N countries -number of countries where the home institutes are.
Number of tasks refers to the number of accesses of a dataset. Other user-specific parameters allow us to measure how a datasets usage is distributed around the world, across institutes and among users [11]. Four further metrics allow us to estimate the efficiency of the execution process of datasets and correlate it with popularity metrics: Replication factor -average number of dataset replicas, Computing factor -average number of computing sites, Duration -task execution time from start to finish, Delay -task run delay time. These metrics can be applied for datasets and for groups of datasets. The Tables  1  and 2  are built on data aggregated for the   10  , and represent popularity and task execution process measurements. The most popular by the number of computing tasks are datasets of the "mc16_13TeV" 11 project.
The projects "data18_13TeV", "data17_13TeV" 12 and "mc15_13TeV" 13 are less poopular by the number of tasks, but still popular among users from different institutes and countries as expected. Computing factor is correlated with dataset popularity: more popular datasets are processed by far more computing sites than unpopular. Replication factor for popular datasets fluctuated from 1. 5 to 4. 7, but at the bottom of the table we can observe that non popular datasets have 3-5 replicas, that is clearly excessive. Delay time in most cases depends on the replication factor: it decreases with the increasing of the number of dataset replicas. For example, the diagram shows that datasets belonging to the project "mc15_13TeV", that are not popular, have 4. 76 replication factor and, consequently, not long execution time (Duration = 143088 s) and delay (19518s).
Conclusion At each step throughout the processing and data management stack, Rucio and PanDA keep detailed logs on the operations on files, tasks and datasets. By combining this information with the access records produced by the EOS file system, CRIC and the CERN Directory, it is now possible to understand the complete life cycle of data from the global distribution down to the fraction of files that are read, covering managed and individual use. While it is highly desirable  10  Project identifies the particular physics or computing context of a set of datasets  11  Monte Carlo production at 13 TeV in 2016 year  12  Real data at 13TeV in 2017-2018 years  13  Monte Carlo production at 13 TeV in 2015 year to expand the work to cover longer time periods, additional storage systems and more details, this level of insight will serve nevertheless as the foundation on which further analysis can be based to optimise the management and usage of storage.
In addition, the concepts developed are sufficiently general that they can be adapted with relative ease for wide use beyond ATLAS. The main outcome of the studies covering AOD and DAOD datasets is that the vast majority of data is accessed only a few times, that most accesses take place within short intervals, and that a significant fraction of the data occupies disk space for extended periods without seeing active use. These findings, while still preliminary and not necessarily representative for all phases of the experiment, indicate that the usage of caches, more aggressive data deletion policies and changes between different levels of quality of service have the potential to optimise the overall cost of storage. However, before gains can be realised in practice, more work is required.
Acknowledgements We thank our ATLAS Distributed Computing colleagues, ATLAS sites, Tier-1 ATLAS centers, CERN Tier-0 operations. The work at Plekhanov University (Section 6) is funded by the  Russian Science Foundation  grant (project No. 19-71-30008 ). The work at  Brookhaven National Laboratory  is funded in part by the  U. S. Department of Energy, Office of Science, High Energy Physics  and Advanced Scientific Computing contracts.
Data Considering the limited number of studies carried out on urban air pollution with mobile sensors, the number of public datasets is limited. In this paper, we used the data from the OpenSense project to answer the research question. The ozone concentration was selected as the first pollutant to be examined in this study, and the methodology remains the same for any other pollutant categories. The OpenSense project  [53] , is a Swiss project aiming to integrate air quality measurements from heterogeneous mobile and crowd sensed data sources in order to understand the health impacts of air pollution exposure and to provide high-resolution urban air quality maps. This project deployed several mobile air quality sensors on the trams' roofs in the Swiss city of Zurich and Lausanne's buses, collecting the measurement of ozone concentrations and counting Ultra Fine Particles (UFP).
More information about the data as well as the data collection methodology can be found in these studies  [37, 54]. Even if these data show drawbacks, especially the sampling only on static trajectories of the city, they remain, nonetheless, very valuable for the application and the evaluation of new approaches to model the spatio-temporal variability of pollution in the urban environment. In this paper, our study was carried out using the measured ozone concentration provided by the mobile sensors deployed on the top of the Zurich trams. The trajectory of the trams can be seen on Figure  1. Since the objective is to predict the concentration on a very detailed temporal resolution, this paper restricted the data used for a single week (from 28 February to 5 March 2016) containing data from five sensors on lines number 4, 7, 8, 12, and 13, resulting in a dataset of 40,000 observations.
The Opensense data provide the ozone concentration in parts per billion (ppb) in a given volume (volume of gaseous pollutant per 10 9 volumes of ambient air). In order to convert it to µg/m 3 to match the unit of the data from the fixed monitoring station, we applied the following formula: µg/m 3 = (ppb) • (12. 187) • (M)/293 where M is the molecular weight of the ozone pollutant (M(O 3 ) = 48). An atmospheric pressure of 1 atmosphere and a temperature of 20 • C is assumed. Reference data for fixed stations was obtained from www. ostluft. ch, the official air quality monitoring network in eastern Switzerland, which manages several fixed stations in the country. The data used here is the ozone concentration, available as hourly averaged. Since it is needed at a high temporal resolution, a linear interpolation was performed.
The estimation of a i (additive bias) and b i (multiplicative bias) is needed to get the calibrated data. The estimation of a i and b i involves F(t): F(t) = a i + b i • X i (x, t) + (2) The estimation of a i and b i from Equation (2) was made using ordinary least squares, that minimized. This calibration was done for each sensor individually, using all sensor i data from all days in the dataset and fixed station data. There were as many estimates of a i and b i as there are sensors.
Results In this chapter, different results from the application of the methodology on the dataset are presented. Starting with the variographic study, we show the two different directional variograms, as well as the experimental variograms and their respective theoretical variograms considered for the different models. Then, a prediction with the three models was carried out for the day of 04/03 from 5 a. m. to 10 p. m. The result of the cross validation procedure in each of the scenarios is shown for the three performance indicators, for the three spatio-temporal geostatistical models as well as the IDW method. Last, the prediction of ozone concentration as well as the associated uncertainty via the KED model is displayed, using all the data available for one day.
However, several limitations in this study must be detailed: The trams did not go through all types of streets and, therefore, only measured a specific type of urban pollution. Furthermore, the methodology described above was not used to identify the best model to estimate the ozone concentration but rather the concentration measured by sensors similar to those used in this study. In this dataset, we do not have access to the real value of the ozone concentration from reference sensors, and it is, therefore, impossible to carry out a cross validation for this purpose. Moreover, the data from the mobile sensors were considered independent conditionally on the ozone concentration, and this study did not take into account the autocorrelation of data from the same tram. Ordinary kriging does not use the fixed station data in its prediction.
This paper proposes a method for Rapid Control Prototyping (RCP) targeting microcontrollers. The methodology relies on a Matlab/Simulink interface which makes the target configuration and coding easier. Developing low level embedded code is bypassed by a high-level implementation which is straightforward for control system engineers. This article is intended for students, engineers or researchers looking to validate the effectiveness of their control algorithms on industrial targets. The design procedure is illustrated by testing various speed and current feedback loop on a DC motor.
INTRODUCTION Digital controllers are increasingly present in industrial applications (automotive, aeronautics, space, industry. ). However, manufacturers seek solutions to reduce global cost while maintaining good performances. For example, one can replace a high precision mechanics for a pointing antenna with a simpler mechanical design but with an advanced control algorithm. Control engineers are dedicated to design these advanced controllers in order to guarantee that specifications are verified. Developing embedded code for microcontrollers requires skills that are out of scope of the curriculum of control system engineers which is typically limited to high level tools like Matlab®/Simulink® and RCP tools like dSPACE® or Speedgoat. This task is often left to embedded system engineers who are specialists of hardware architecture but have little knowledge in control theory.