dataesr/scientific-paragraphs-categorization huggingface dataset:
It has become a highly successful mechanism, but although allowed, requesting large data volumes with this service has the risk of causing connection timeouts. It is difficult to efficiently resume transfers following broken connections, which are increasingly likely with very large volumes. Also, maintaining acceptable performance can require significant software and system engineering by the data centers. One tool, IRIS's ROVER (see Data and Resources), has overcome the issue of dropped connections by using a smart client to manage the synchronous data transfer in miniSEED, and constructing the final dataset on the client side. This approach requires a relatively complex client, but is a robust and efficient method of requesting large datasets. Future distribution capabilities worth exploration are the use of GridFTP  (Allcock et al.
, 2005) , or rsync (see Data and Resources) for efficient bulk file transfer. But these methods can only be used with existing files and not arbitrary data selections. Another possibility is the extension of web service interfaces like fdsnws-dataselect to allow asynchronous, or batch, data transfer to a site (e. g. HPC center) identified by the requestor. In other cases and depending on the archive format, some middleware solutions such as THREDDS Data Server (Unidata, 2020) or HSDS (see Data and Resources) could potentially be useful solutions for sub-selecting and transforming large data volumes.
Using the Data Considering the classical approach of data usage by seismologists, some important requirements have been identified in the previous sections, in particular the need for standard data and metadata formats, and correct and complete metadata. Presently, only miniSEED is an FDSN standard within the community, but this format is inefficient and cumbersome for processing large datasets. Non-FDSN standard formats (e. g. HDF5 based) are presently being used by researchers working with medium and large datasets (20+TB) and they would like to get data in this format from the data center provisioning systems. The definition of an additional FDSN standard aimed at large datasets would spur the development of community processing tools, and would greatly further the cooperation and sharing of resources between data centers.
The user's need to process data in a remote environment (e. g. cloud computing, containers), means that other factors should also be considered by data centers. For instance, even if a data center can provide a container or Jupyter notebook (see Data and Resources) with direct processing of the data, solutions need to be developed to limit restricted dataset access to authorized users. This is not a minor issue, as most of these large datasets go under an embargo period of some years before being opened to the public. This means that depending on the user, only part of the archive should be accessible to any given user from the computing interfaces. A similar issue is related to the data organization (format and structure). The data center can expose (or let the user access) what exists in the storage system so the user will in principle be able to use only the storage format.
It will be difficult (and in some cases impossible) to stage a new dataset in another format to be accessed by the user. Therefore, the data format and organization needs to be known by the user. As each data center is free to organize the data in whatever way is optimal to them, data processing software cannot easily be used across different data center computational facilities. The transparent federation of data is probably the biggest challenge for data centers because it includes not only all the improvements and developments mentioned in this article, but also the edge case in which multiple large datasets are requested from different data centers. In such a case, even running the code on top of the data is not a solution. That would only save the transfer from that data center, but there is still the problem of requesting all the other data.
Conclusions Despite present initiatives trying to tackle the problem of big seismological datasets and how they can be archived, delivered, and processed, data centers are facing a situation similar to that from the mid-80's. Namely, the amount of data generated was much bigger than the data centers could safely and sustainably archive. At that moment, the temporary solution was to keep only time windows of interest (e. g. waveforms related to an event) and discard the rest of the data after some time. What has been learned from this experience is that discarding data is not the best option to consider, as illustrated by present day research in data mining, new types of seismic signals, and imaging techniques using seismic noise. With the present increase of data volumes come additional problems associated with the internal organization of data within the data centers and meeting the need for data formats that are well adapted for storing and slicing big datasets.
Along with these problems, there is a need for adapting present data request tools or developing new ones. At present, the IRIS, GEOFON and RESIF data centers recognize that it is not possible for them to permanently archive very large datasets such as DAS due to prohibitive costs of long-term archiving. Safe storage (involving multiple copies) of such large datasets available online is presently not possible either. Therefore, the seismological data centers should explore a strategy for delivering datasets through automatic asynchronous services to designated destinations such as computing facilities where data pre-processing or processing could occur. For very large datasets, a common strategy is needed for storing only standard data products such as spatially and/or temporally down-sampled data, and/or small windows of highly sampled data.
Ideally, in the long term, international standards and products should be developed within the framework of the FDSN. As a preparatory phase, we suggest to undertake the following actions: • Encourage a wide and continuous international collaboration on DAS data. This would include dedicated workshops and special sessions in conferences involving data centers and scientific users who could define a limited set of standard products for DAS data. One natural forum would be a strong international presence in the Distributed Acoustic Sensing Research Coordination Network (DAS RCN), which involves both scientific users and data center operators/managers. • Extend the present discussion between our three data centers to other interested data centers. One option is to set up dedicated technical workshops on very focused subjects, including solutions to keep the raw data safely for future use.
Zarr ZARR (2020) Zarr is a format and a python library to manage numerical arrays. Data subsetting is allowed and it is a good design for HPC. Zarr has not been used much in the geophysical community and is only for python. ADIOS2  (Godoy et al. , 2020)  ADIOS2 is a framework for data IO management; it is not broadly known or used in seismology. Figure Captions.
Acknowledgments The authors thank the users and data providers for their continuous feedback, and in particular the users who participated in the survey. Javier Quinteros  collaborated in this work as part of the  EOSC-Pillar project , which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  857650 , as well as the  RISE  project, also supported by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  821115. GEOFON operates the GFZ seismological data archive, where own and third party data are curated alongside with data from passive GIPP experiments. The seismological facility for the advancement of geoscience (SAGE) is operated by  IRIS  on behalf of the  National Science Foundation (NSF)  through  Award   EAR-1851048.
The RESIF Data center is operated on behalf of the  RESIF  Consortium, a national Research Infrastructure coordinated by  CNRS  and which receives funding, personnel and other from all the Consortium members. Résif-SI is also supported by the  System of Observation and Experimentation for Research and the Environment (SOERE)  and by the  Ministry of Ecological Transition. Résif-SI  has benefited from the  Résif-CORE  project ( 11-EQPX-0040 ), funded by the national  French funding program Investissements d'Avenir  and managed by the  French National Research Agency (ANR).
Data and Resources The SEED Manual can be downloaded from http://www. fdsn. org/pdf/SEEDManual_V2. 4. pdf. Information about HSDS can be read from https://www. hdfgroup. org/solutions/highly-scalabledata-service-hsds/. Documentation of Zarr can be found at https://zarr. readthedocs. io/. Information about the DAS Research Coordination Network (DAS RCN) can be found at https://www. iris. edu/hq/initiatives/das_rcn. Specifications of the FDSN web services can be found at http://fdsn. org/webservices/FDSN-WS-Specifications-1. 2. pdf. Information on the Norwegian Seismic Array Network (NO) can be found at http://www. fdsn. org/networks/detail/NO/. Details of the International Registry of Seismographic Stations can be read from http://www. isc. ac. uk/registries/registration/.
On the other hand, they support the auditory display community in advertising and promoting sonification in general, and as a core element of software functionality. A both informative and entertaining sonification app is the Amino Acid Synthesizer  [14]  that makes amino acid sequences audible on android devices. Even though the target group for amino acid sequences seems rather narrow, the Google Play store already counts over 5000 downloads. Quite related is the DNA sonification web app  [15]  that allows users to explore random, pre-or self-defined DNA sequences aurally. To date the videos the author's YouTube channel have over 700 views. The multi-platform CURAT Sonification Game  [16]  is available for Android, Mac, Linux and Windows. It contains five mini-games in which players find targets by interpreting and interacting with sonification.
Gamification is leveraged to motivate users play conscientiously over a long period of time. To date, the game counts over 500 downloads. The Sonification Sandbox  [17]  is a cross-platform software that allows the user to load data from CSV-files, plot them and sonify them on a MIDI basis. The app has a Graphical User Interface (GUI) multiple pre-defined mappings and several options to tune the sonification and to export it as a MIDI-file.
5ff]. In contrast to PD, libpd has no audio drivers, no MIDI drivers, no user interface and no thread synchronization. In addition to PD as core library, libpd includes language bindings and utilities for different programming languages like Java, Objective-C and others  [20]. These bindings are used in order to make the functionality of libpd available in the corresponding target languages  [21, p. 43ff ]. In our application we use the Java 1  and Objective-C 2  API of the libpd library to interact with it and generate audio dynamically in real time.
Flutter To develop the app, Flutter was selected as framework, as it allows programming of cross-platform apps. This saves the additional work of developing native applications in different programming languages for both Android and Apple smartphones  [22, 23]. Flutter uses skia, its very own rendering engine to render views instead of relying on web wrappers  [24]  which provides for a very good performance compared to other solutions  [23]. Flutter was released in 2018 by Google and is still being further developed and improved  [22, 23]. This provides for stability and further support in the future compared to frameworks developed by other smaller companies. It also has hundreds of non-Google contributors who provide a big number of various useful libraries. Those make Flutter a good choice for a multitude of projects  [22].
Filtering Smartphone sensors continuously deliver analogue data that are converted to discrete digital values. Filters assist to receive a sensor signal that is as little noisy as possible. In a very simple form, high-and lowpass filters reduce the signal to a specified spectrum above or below a certain threshold. To reduce the effect of sudden changes, a sample rate can be applied to the signal. In Tiltification, a simple heuristic is used to filter the accelerometer data. It returns a weighted average of the previous estimate and the new incoming value of the sensor for all three axes. New values get weighted with 80 percent, the previous ones with 20 percent. The filtering is handled separately from the UI updates. Sensor events are processed at the maximum rate the phone offers, so there is no accurate sample rate limitation.
Conclusion and Future Work In this paper we introduced Tiltification, a free, sonification-centered spirit level app. Tiltification provides users with multi modal information about the tilt angle of the smartphone, which enables them to level furniture, take perfectly horizontal photos etc. We argue that more free and useful sonification apps should be distributed over mainstream channels in order to establish sonification as a part of the user interface besides visualization. We describe how we implemented the app, how we realized and examined accessibility, and how we advertise and distribute our app. Tiltification is released for iOS in the Apple App Store and Android in the Google Play Store. Apart from that, a free APK is available on the website. The source code will be made available publicly under an open license soon in order to support further development by the community.
Numerical and experimental tools 2. 1. 1 ADAMS Rigid body model ADAMS software  [32]  was used because it is relevant for realizing the rigid body model. The kinematic chain described by the model is shown in Figure  6. The different geometries of the parts as well as the assembly dimensions are taken from real engine data. The kinematics extracted is therefore representative of the real geometry of the valve train. The material data necessary for the ADAMS model are the Young's modulus of the components, the Poisson's ratio and their densities. These characteristics are given for the seat and the valve in Table  1. The ADAMS rigid body model uses the parameters given in Table  2. The plug-seat contact is considered a mixed contact, therefore a value of 0. 2 is taken for the dynamic friction coefficient m.
Sliding The impact is followed by a sliding phase of the valve against the seat. Based on the rigid body model, the relative displacement of an arbitrarily point on the valve determines the amount of slip between the valve and the seat along the contact plane. Misalignments are set between 0 mm and 240 mm in the ADAMS model (Fig. 11 ). For perfect alignment, the tilting dynamics of the valve train generates a sliding length of 13. 8 mm. For 100 mm of misalignment (maximum tolerance), the slip length is 33. 2 mm. Finally, for 240 mm of misalignment, the sliding length is 71. 8 mm. The increased misalignment results in increased valve sliding on the seat. Despite exceeding the alignment tolerance (100 mm in the specification against 240 mm in the ADAMS software), the slip length remains less than 80 mm.
Here we will investigate such links using the Van Allen Probes and PROBA-V data, as well as some models. We also study the links between the Magnetic Local Time (MLT) distribution of the plasmapause, the ionospheric convection boundary (using SuperDARN), the radiation belts for different energies from 10 keV to 8 MeV, and the auroral oval during two specific events: March 14, 2014 corresponding to a quiet period with a sudden Northward turning of the IMF, and August 26, 2018 corresponding to a geomagnetic storm. We start in Data and Models with the description of the satellite data and models used. In Long Term Variations of the Plasmapause: Comparison Between Observations and the Space Weather Integrated Forecasting Framework plasmasphere model Model, we compare the plasmapause observed by two satellites (Van Allen Probes and CRRES) with the plasmapause positions obtained from the dynamical SPM model  (Pierrard and Lemaire, 2004).
Satellite and Radar Data Van Allen Probes Data from the Van Allen Probes mission  (Mauk et al. , 2013)  are used in this study. The NASA Van Allen Probes mission, formerly called RBSP (Radiation Belt Storm Probes A and B) was launched in 2012, enabling unprecedented studies of the electron radiation belt variability in response to solar activity. The two spacecraft of the Van Allen Probes mission flew on a low inclination (<20 °) elliptical orbit ranging from 600 to 30,600 km. We will use three of the Van Allen Probes mission instruments, enabling us simultaneous observations of the radiation belts and the plasmapause. A review of recent radiation belt studies involving Van Allen Probes data is available in  Ripoll J. F. et al. (2020). The NASA Van Allen Probes have four magnetic spectrometers aboard each of the two spacecraft, one lowenergy unit (20-240 keV), two medium-energy units (80-1,200 keV), and a high-energy unit (800-4,800 keV), which compose the MagEIS (Magnetic Electron Ion Spectrometer) instrument used to measure the radiation belt flux.
PROBA-V/EPT Data We use the Energetic Particle Telescope (EPT) on board the ESA PROBA-V satellite to provide radiation belt electron flux. PROBA-V operated simultaneously with the Van Allen Probes from 2013 to 2019, so that both data sets allow direct comparison of the electron radiation belt variability in response to solar activity. The EPT instrument has been developed to obtain the best discrimination between the particle species and determine uncontaminated particle spectra useful for space weather predictions  (Cyamukungu et al. , 2014). It was launched on May 7, 2013 to a polar orbit at an altitude of 820 km onboard the ESA satellite PROBA-V with an inclination of 98. 73 °, an orbital rotation period of 101. 21 min and 10:30 AM as nominal local time at the descending node  (Pierrard et al.
Combined Release and Radiation Effects Satellite Data We make use of some of the data from the Combined Release and Radiation Effects Satellite (CRRES) (e. g. ,  Johnson and Kierein, 1992). The CRRES mission took place between August 1990 and October 1991. We use the  Moldwin et al. (2002)  CRRES database for nearly 1,000 orbits of the observed plasmapause positions sorted by the maximum geomagnetic index Kp during the previous 12 h. The plasmapause locations were identified as the innermost sharp density gradient of at least a factor of five within a radial distance of 0. 5 L.
Comparison With CRRES Satellite These new results can be compared with previous observations and fits. Moldwin et al. (2002)  selected in the database of CRRES observations all orbits where a sharp density gradient indicated the presence of a well-defined plasmapause. The L-value of the observed plasmapause positions were ranked according to the maximum geomagnetic index Kp during the previous 12 h. The bottom panel of Figure  1  shows these plasmapause positions (blue dots) observed for all local time sectors as a function of the maximum value of Kp. The scatter of the observed values of Lpp is rather wide but there is a clear trend confirming that the plasmapause forms closer to the Earth when Kp is large. The green diamonds symbols in Figure  1  correspond to the mean value of the observed plasmapause positions for Kp jmax between Kp j -0.
4204 Kp max 12 + 5. 704 (3) As a comparison, taking the Probe B of the Van Allen Probes mission in 09/2012-07/2019 in the night sector (21-3 h), we find that the mean plasmapause position (defined as the 100 #/cc level of the EFW density) is: Lpp 2. 7 for all events with Kp > 7, Lpp 3. 6 for Kp in ]5,7], Lpp 4. 3 for Kp in ]3,5], and Lpp 5. 2 for Kp < 3. These values are represented by the black crosses in Figure  1  (bottom panel), and the linear relation Eq. 2 that was found with the Van Allen Probes for 2015 is illustrated by the black line. An empirical relationship between Lpp and Kp had also been deduced previously from ISEE one data for 0-15 LT  (Carpenter and Anderson, 1992) : Lpp -0. 46 Kp max 24 + 5. 6 (4) where Kp max24 is the maximum value during the preceding 24 h. This best fit linear relationship is shown by the gray dashed line in the bottom panel of Figure  1.
Discussion About the Long Term Plasmapause The linear fits of the equatorial plasmapause positions versus Kp that are shown in the bottom panel of Figure  1  (Van Allen Probes black Eq. 2, CRRES magenta Eq. 3, ISEE1 gray Eq. 4, SPM model red Eq. 6) are rather similar for the satellite observations and the model. The slope for the Van Allen Probes data is lower than the others. This is probably due to the lower number of observations and to the fact that the computation is not restricted to the night side. In comparison, the four mean plasmapause positions (black crosses) given as a function of Kp established over the whole Van Allen Probes mission and restricted to the night side are closer to the other observations and models. The large scatter of the experimental data indicates that the actual plasmapause is a rather irregular and dynamic surface (cf.
The bottom right map in latitude-longitude shows that the outer belt corresponds to the high latitude band located above Scandinavia in the Northern hemisphere. The fluxes are always artificially low above Europe with EPT due to the pause in the measurements during the transmission of the data to the Belgian station of Redu. One can see that in the Northern hemisphere, the radiation oval is located between L 3. 5 and 8. 5 represented by the black dots in the bottom map. The South Atlantic Anomaly SAA is visible in the Southern hemisphere (top right) and in the latitudelongitude map (bottom). Due to the characteristics of the orbit of the PROBA-V satellite that has a period of 101 min (thus about 14 orbits around the Earth per day), it is necessary to accumulate the observations during several days to obtain good coverage on the maps.
ACKNOWLEDGMENTS The authors thank the editors for their invitation to contribute to this special issue about on Coupled Feedback Mechanisms in the Magnetosphere-Ionosphere System. The authors thank the EFW and EMFISIS teams of the  Van Allen Probes mission  for their support. Van Allen Probes wave data used in this work are available from the EFW and EMFISIS team websites (which one can link to http://rbspgway. jhuapl. edu). The authors thank the  ECT team of the Van Allen Probes mission  for their support. VP thanks the team of  Center for Space Radiations  for the validation of the EPT data. JFR thanks  M. Cosmides  for her contribution to the ECT combined flux figures. The authors thank the  International Space Sciences Institute (ISSI)  and the participants in a 2020 ISSI workshop for the project " Radiation belts physics ".
DATA AVAILABILITY STATEMENT Publicly available datasets were analyzed in this study. The SPM model nowcast images are available on the ESA SSA (Space Situational Awareness) website (https://swe. ssa. esa. int/spaceradiation). Ovation model, developed at the Johns Hopkins University, Applied Physics Lab by Patrick Newell and coworkers, and validated by NASA Polar satellite data, is available on NOAA, Colorado: https://www. swpc. noaa. gov/products/ aurora-30-minute-forecast. EPT data are publicly available at the ESA website http://swe. ssa. esa. int/space-radiation. All RBSP-ECT data are publicly available at the Web site http://www. RBSPect. lanl. gov/. The EFW spacecraft potential is available from the University of Minnesota Van Allen Probes EFW site http://www. space. umn. edu/rbspefw-data/.
EMFISIS data can be obtained from http://emfisis. physics. uiowa. edu. The authors acknowledge the use of on-line plotting tools hosted by the Virginia Tech SuperDARN group at their website (http://vt. superdarn. org/tiki-index. php? page Conv+map+overview). The CRRES Plasmapause Data (Data set) have been put in repository by Moldwin, M. , O'Brien, P. , University of Michigan -Deep Blue: https://doi. org/ 10. 7302/mwy1-v326.