dataesr/scientific-paragraphs-categorization huggingface dataset:
However, the data recorded and transmitted to the Earth are not in digital electronic form in an open access database. Therefore it was not possible to include this satellite into the creation phase of the IWS. Open (e. g. internet) access to the recorded data, the original ''raw'' data of a given satellite was very important in selection a satellite during this first creation step. Next, further selection criteria were applied in the case of electromagnetic wave (EMW) data. These are the following: according to the orbit the recorded electromagnetic signal is ionospheric one ( 800-900 km in the moment of or during the measurement) and it has no ''magnetospheric'' character. It is important, that the time domain waveform is present, the data is real broadband ELF-VLF ''burst''-type data in digital electronic form with the required accuracy, i.
Beside this the sufficient time resolution along the orbit provided by the onboard instrumentation permitting AGW and TID registration over available data sets is also important. A criterion is the existence of prolonged observations (large data sets) with the comparable set of onboard instrumentation for various satellite projects. By the application of the selection criteria for the satellite listed in Table  1  the following satellite missions were selected for the creation of the IWS: Atmosphere Explorer-C, Atmosphere Explorer-E, Dynamic Explorer 2, GPS system, Variant (Sich-1M), DEMETER, and Compass-2. By applying the original (levels 1 and 2) databases of these missions it was possible to produce the level 3 data sets and to create the IWS. After the completion of the basic IWS configuration it is possible to include other satellite databases -archived or provided by operational satellites, e.
Ionosphere transients, wave-like phenomena in the ionosphere As it is well known the investigation of the atmosphere-ionosphere transients is an actual problem in the geo-and Space Weather research. However, a catalog-like overview of these phenomena especially of the transients appeared in satellite records is a task often subject of updating with the measurements of the operating ionospheric satellites, today e. g. by the data actually coming from the Chibis-M satellite. But to create an IWS database an actual, state-of-the-art summary of our knowledge about the satellite recorded ionosphere transients was necessary. In the first phase of the planning of IWS we made this summary. It was possible to define two main classes of the wave-like phenomena in the ionosphere satellite records: electromagnetic wave-like phenomena (EMW) and non-electromagnetic, atmosphere wave-like phenomena.
Wave recognition and extraction methods It is clear that after the levels 1 and 2 data processing of the (ionosphere) satellite raw data flow the transient, wave-like phenomena listed in Section 4 are not selected from the data flow, i. e. these phenomena are there somewhere in the measured data mass. However, before starting of the research work of a given   task (Space Weather research, earthquake investigations, research of the generation or propagation processes of these phenomena in details, etc. ) it is necessary to find these wavelike phenomena in the whole data mass. Therefore it is essentially important to produce the level 3 processed data base, the IWS. However, to find the wave-like phenomena in the total data flow is possible only with automatic or at least semi-automatic methods.
This means the application of intelligent data processing methods and therefore a minimum requirement is the application of the up-to-date computer techniques. Earlier the development and application of these methods was limited by the non-adequate level of the computer techniques. As a consequence of this situation the transient-searching techniques are really very new and we have no such tool for the whole set of the important and interesting transients. It is necessary to overview of the existing methods and after the necessary adaptation and/or development of these methods it is possible to create the IWS.
The time base accuracy is good, however, it is exactly not known. The practical details of the method were not reachable during the construction of the IWS. 5. 1. 3. Bandpass filtering of TEC time series computed from bi-frequency GPS receiver's RINEX data TEC time series are extracted from GPS phase measurements for each receiver-satellite link. The geometry free combination is computed to provide relative value of TEC, a bandpass filtering is applied  (Lognonneet al. 2006; Crespon et al. 2007 ) and the data forms a web-service produced by Noveltis. So we obtain filtered TEC time series in which Large Scale TIDs (MSTID) and Medium Scales TIDs (LSTID), depending to the filtering band, can be observed. The wave recognition was usually done by human expertise  (Afraimovich et al. 2000 (Afraimovich et al.
General algorithm for transient signal detection in time domain data series The method was developed for and successfully applied in ground-based narrow-band VLF recordings  (Steinbach 2006). Time domain data sequences often exhibit transients in various characteristic time ranges. These variations may cover large dynamics in amplitudes, usually also overlap in time. Without known physical source mechanism they are considered as ''noise''. One typical shape is e. g. quick change and slower relaxation in recorded signal amplitude and/or phase data. With appropriate training process this algorithm is also possible to apply to other deterministic type of transients, appearing in time sequences recorded on board of satellites.
Wave recognition methods actually applied in the creation of IWS Seeing the development goals, the application results and the actual state of the automatic or semi-automatic wave identification methods it was possible to select the methods which are applicable for satellite recorded data sets directly or after a goal-oriented development. See the 5th column of Table  2. Only three methods remain after this selection because the two years long running time of the POPDAT FP7 project for creation of the IWS. However, it is clear that applying these methods the creation of a unique and practicable, goal-oriented IWS database is possible. Further expansion of this IWS is feasible with new wave phenomena and new satellite databases, e. g. with the CHIBIS-M database. The causes of selection were first the fact that in the case of the Automated Chorus and Hiss detection method only few information is known from the method, and in the case of the General algorithm for transient signal detection in time domain data series is applicable in satellite data records only to solve special tasks and with further and      In the case of satellite recorded signals this method is applicable only after an adaptation to the differences of the contents of the ground-based and satellite records.
In the case of more electromagnetic wave-like phenomena this description is not known, it is an actual task in fundamental research and therefore it was necessary to exclude such kind of UWB phenomena from the creation of the IWS. The classes which were omitted from the POPDAT project and from the creation of the IWS are: -Hiss and chorus detection (the method is in experimental phase). -Triggered emissions (no idea is known for the automatic detection). -Strange signals (no definition of these signals is known). The investigation of these signals is possible in the traditional way by researchers. By applying an advanced AWDSat software system it is possible the automatic detection and selection of the whistlers and whistler-like phenomena and the incorporation of these data and the derived metadata into the IWS: -The AWDSat-WHF for the automatic detection of the normal (typical single hop) whistlers in the satellite recordings.
This affects the minimum temporal separation between independently detected events, the detection efficiency. New detector settings had to be found and set so that frequent sequences of short path whistlers to make it possible to detect them as separate events. Further, whistlers detected in space-born datasets have dispersion values falling in a rather wide range, due to the diversity of the possible propagation paths. These waves may appear overlapped according to the variability of their possible signal dispersions. As a completely new concept, applied and tested first in satellite recordings within the POPDAT IWS database preparation, after one above-mentioned detection turn on a given type, values representing the detected events are nullified from the spectra, lowering the noise level and allowing a better detection in its next loop turns.
This is best performed if the most dominant type, determined also automatically is processed first. This kind of data processing is running in the creation of the IWS electromagnetic wave-like phenomena goal-oriented database. The investigation of other electromagnetic phenomena is also possible as ''sub-classes'' of the four classes listed above by the direct investigation of the automatically detected whistler-like events by researchers. These are: -Spiky whistlers (SpW), swallow-tailed whistlers (STW), and X-shaped whistlers between the ''normal'' and longer path fractional hop whistlers, but without a second step automatic selection. -Ion whistlers between the short path fractional hop whistlers, but without a second step automatic selection. The automatic selection of these sub-classes is a further task in the future.
Thus, system of equation (  7 ) is formed and solved by extended Kalman algorithm  (Quanrong 1993). Solution provides conjointly an estimation of VTEC on a regular grid and an estimation of the electronic biases IFB and TGD for each receiver and satellite by solving: d 1. N ¼ G VTEC 1. P IFB 1. R TGD 1. S 2 6 4 3 7 5; ð7Þ where N is the number of GPS observations, P is the number of grid points, R is the number of GPS receivers, S is the number of GPS satellites, and G is the interpolation operator. The complete logic scheme of the processing chain dedicated to the detection of TIDs from GPS networks is presented in Figure  20. By the application of these tools mentioned above the production of a useful database for the IWS and the creation of the IWS itself is possible.
The structure of the Ionosphere Waves Service -IWS The wave extraction methods were carried through effectively on a mass of data, and an easy accessible data set is now ready for scientific investigations. This database contains level 3 data which are comfortable e. g. in Space Weather research. The goal orientation of this level 3 data processing was the extraction of the wave-like phenomena listed above from the raw and firstsecond level processed, i. e. original satellite records. However, to use effectively these level 3 data a complete data service is necessary. This data service is the IWS which is an online tool. As it was described above the datasets in the IWS are grouped by the main types of phenomena into four topical catalogs: By the problem oriented processing of satellite and groundbased data, these catalogs are constructed to provide queriable data fields via Internet-based open access Ionosphere Wave Service.
The NetCDF data are of course stored and can be referenced immediately for analysis or extraction, but not for conducting searches. Therefore, the NetCDF data file exist in IWS archives containing the processed wave data, but also there exists a metadata representation in a relational database allowing fast searches that tells which data file where can be found and what type of event does it contain. The next principal reason provided in IWS is to provide users with the ability to distinguish between the two cases of: -an event that was not recorded (no data exists that can be targeted by this query), -an event that was found not to have occurred (relevant data exists and the query returned showing no event occurred). The general architecture of the IWS concept is presented in Figure  21. The Service Topical Catalogs are created and are submitted to the IWS system.
The files are produced by a process specific for each contributor and data type. Typically they store a number of variables that change with each time step along a satellite orbit track. The exact list of variables is defined for end users in IWS. Events are phenomena detected by the processing algorithm in each Data File, see earlier in Section 4. 2. Events are recorded as individual XML files containing specific information for that event, and importantly, all the information that will ever needed to search for that event. The IWS inner data flow is presented in Figure  23. The architecture design and technical component of the Ionosphere Waves Service system are in Figure  24. In this system for the end users the web interface is the most important. The web interface is constructed in JavaScript using the ExtJS toolkit, providing support for rich UI controls, windows, grids, and other utility functionality.
Since the IWS contains mass volume of wave-like events by their metadata system this new database is proven to be an effective tool in the statistical investigations of the event generation and/or appearance. An example of this type of applications is the statistics of the fractional hop whistler signal pairs (trapezoid whistler pairs) identified by AWDSat-WHPF software in DEMETER records and the events stored in IWS. In Figure  26  the statistical distribution D 0 dispersion values of the signal pairs are present as a function of the geomagnetic latitude of recording, composed by IWS yielded whistler parameters of nearly 15 millions of whistler pair occurrences. Detailed characters, e. g. temporal behavior of latitude dependent ionospheric propagation is thus possible to investigate on a statistical base, unlike to low efficient manual data analysis  (Hughes & Rice 2002).
Application example in the investigation of thunderstorm activity Another option in IWS application is e. g. the investigation of the electromagnetic manifestation of thunderstorms. The relation between the thunderstorms and the ionospheric electromagnetic transient activity is also part of the Space Weather research. In this case it is necessary to have simultaneous satellite and ground-based recorded data. The ground-based data (e. g. E-field records, lightning detector network data) sets are not a part of the IWS. However, this example suggests that it will be important in the future to create a new higher level database in which the satellite and ground data will be incorporated. (There was a proposal elaborated in the FP7 dedicated to this topic, but it was not selected. ) Whistlers are electromagnetic waves that can be generated by lightning discharges and are observed in the VLF range.
de) in Europe, or WWLLN (wwlln. net), serve vital auxiliary data with lightning locations, time, and intensities. On the other hand there are satellite electric field registrations. For the purpose of this investigation we present two whistler events that were recorded on DEMETER on 20th July 2007. At the same time SPARTAN Sprite-Watch Team form IMGW Meteorological Observatory located on S ´nie_ zka Mountain in Poland recorded two sprites. Figure  28  presents the two sprites. The distance between the location of the event and the satellite position was around 1200 km. The wave forms were analyzed and as a final output the spectrogram in the ELF and VLF ranges were computed. Figure  29  presents the ELF spectrogram of DEMETER records  (Błe ˛cki et al. 2009 ). Complex analysis of large amount of corresponding events is thus possible utilizing the capabilities of the IWS.
IWS as Ionosphere Dynamics Virtual Observatory IWS is powerful tool for the visualization and study of the ionosphere dynamics. To demonstrate IWS possibilities let us consider Figure  30 , which simply shows a few AGW waveforms taken from IWS database and superimposed on a geographical map. This figure visualizes the global picture of the wave activity of upper atmosphere. It turns out that AGW wave field has specific morphological structure, including: (1) areas of polar caps, where AGWs are continuously generated and their amplitude reaches maximum values; (2) mid-latitudes, where the intensity of AGWs decreases as the distance from the auroral ovals increases; and (3) latitudes below 40°-50°, where the wave activity reaches minimum but does not fully disappear. Moreover, on this global wave back-ground the isolated wave bursts (distinctively localized wave packets) are sporadically observed.
The value of analyzing NMR spectroscopy data guided by a conformational ensemble has long been recognized  (14, 15). Conformational diversity for RNA ensemble analyses is often provided by sophisticated molecular dynamics simulations  (16, 17). Long trajectories with specialized force fields on dedicated supercomputers are required to adequately sample conformational space, limiting ensemble analyses to modestly-sized RNA molecules  (18). Here, we present an efficient conformational sampling procedure, Kino-geometric sampling for RNA (KGSrna), which can report on ensembles of RNA molecular conformations orders of magnitude faster than MD simulations. KGSrna represents an RNA molecule with rotatable, single bonds as degrees-of-freedom and groups of atoms as rigid bod-. , θ n ) and vertices (circles) encode rigid bodies.
Sampling procedure An overview of the sampling procedure is shown in Figure  2. KGSrna takes as input an initial conformation θ init , an exploration radius r init and a set of canonical WC pairs to identify hydrogen bonds A(N3)-U(H3) and G(H1)-C(N3) Next, a pool of conformations is initialized with the input structure and then grown by repeatedly perturbing a randomly selected conformation from the pool with a rebuild or null-space perturbation at a 10/90 rate. If no clashes between atoms were introduced in the perturbed conformation, it is added to the pool. The procedure is repeated until a desired number n struct of conformations is obtained. as distance constraints. WC pairs are obtained from the RNAView program  (37). Next, it grows a pool of conformations by repeatedly perturbing either θ init or a previously generated seed conformation, θ seed , in the pool that is within r init C4 root mean square deviation (RMSD) of θ init.
Back calculating NMR properties from ensembles Observed chemical shifts were obtained from the BMRB. Chemical shifts were back calculated using the software NUCHEMICS  (40). A flowchart of the procedure used to obtain back calculated chemical shifts is shown in Supplementary Figure  S1 (a). RDC data were back calculated using the program PALES  (41) , with command line option -inD loopshort. tab -bestFit -pdb $file -H, where loopshort. tab contained measured RDC data for nucleotides 30-35 published by Dethoff et al. (42).
Fitting 1 D CH residual dihedral coupling data We calculated 20 000 Monte Carlo samples each starting from models 1 to 10 in the NMR bundle of wild type HIV-1 TAR with pdb id 1ANR. The sampling included the full, 29 nucleotide long models. Sampling was biased toward generically pairing the C30 and A35 bases and the U31 and G34 bases using a Metropolis criterion (Supplementary Figure  S1(b) ). A newly sampled structure was accepted into the pool of samples with a probability min (1, e -d ), where d is the change from the seed to the new structure, i. e. d = min p∈P  (30, 35)  |2A -|p|| + min p∈P  (31, 34)  |2A -|p|| and P(A, B) is the set of all vectors between any charged hydrogen in base A and any hydrogen acceptor in base B or vice versa. The seed selection was also modified. Instead of selecting a seed from the pool based on RMSD to the starting model it was based on d from the starting model.
For each of the 10 sets of 20 000 samples, we calculated RDCs with the software PALES. For each set, we fitted an optimal ensemble with a new algorithm we developed, called rdcFit, using the following quadratic program: min w 1 D o CH - i w i 1 D c CH,i 2 s. t. w i ≥ 0 for all i 0 ≤ i w i ≤ 1, where 1 D o CH is a vector of observed RDCs and 1 D c CH,i a set of vectors of back-calculated RDCs for nucleotides 30-35. The vector w T is the fitted variable that simultaneously determines the optimal size of the ensemble and the relative weights of its members, under the constraint that the weights sum to unity. Unlike stochastic or heuristic optimization procedures, a constrained quadratic fit deterministically identifies the global optimum of the fitted parameters, i. e. both the size and weights of the ensemble.
KGSrna recovers proton chemical shifts Chemical shifts are time-averaged measurements on conformational ensembles at sub-millisecond timescales  (23). Non-exchangeable 1 H chemical shifts (CS) predicted directly from RNA 3D structural models are generally in excellent agreement with those reported from experiments in the BMRB. Experimental 1 H CS are widely available, are sensitive to conformational changes and have aided in structurally characterizing conformational substates  (47). Researchers have combined measured 1 H CS for proteins with structure prediction algorithms that use a database of structural fragments to determine atomically detailed de novo conformations (48). Das et al. recently established that proton chemical shifts can aid structure prediction algorithms in distinguishing decoys from a native state in RNAs  (26).
Here, we regard measured 1 H CS on a benchmark set of 3D RNA structures as time-and ensemble-averaged distributions over the conformational landscape. We examined the ability of KGSrna to sample native dynamical ensembles that recover sugar (H1 ) and nucleobase (H2, H5, H6 and H8) CS distributions for unconstrained (non-helical) and WC paired (helical) regions. We used the well-calibrated program NUCHEMICS  (40)  to predict 1 H CS from our 3D RNA structures. KGSrna enables broad sampling to identify sparsely populated substates, while maintaining conformational distributions similar to those measured. Figure  4a  shows the distribution of measured and predicted 1 H CS for helical (top row) and non-helical (bottom row) regions for each proton type over the whole benchmark set. Figure  4b  shows the location of the probes.
To test if KGSrna can structurally characterize conformational substates of the loop guided by RDC data, we calculated 20 000 samples each starting from the full, 29 nucleotide models 1 to 10 in the NMR bundle with pdb id 1ANR of free HIV-1 TAR. To enable structural characterization of the dynamics leading to the ES, we biased our sampling toward broad, non-specific conformational pairing of C30-A35 and U31-G34. A Metropolis criterion skewed the sample set to include favorable interactions of any charged hydrogen in base A with any hydrogen acceptor in base B (Materials and Methods). For each of the 200 000 samples, we back-calculated RDCs with the program PALES  (41). PALES accurately calculates the overall alignment of the RNA molecule  (18). From each batch of 20 000, we then determined a weighted ensemble that optimally explained the experimentally observed RDCs using a new constrained quadratic fit algorithm (rdcFit) that we adapted from an application we previously developed for X-ray crystallography applications (qFit)  (54, 55).
The differences between measured-to-KGSrna, measured-to-initial and measuredto-NMR are shown in the bar plot. A negative value indicates better agreement of the ensemble of KGSrna 3D structures with measured values than its comparison 3D structures. (c) Predicted 1 H chemical shifts calculated from the 3D structures from the KGSrna ensemble and the NMR bundle compared to the measured values of the 32nt P2a-J2a/b-P2b (helix-bulge-helix) of human telomerase RNA (pdb id 2L3E). The data points are expected to lie along a 45 • line if measured 1 H CS are accurately predicted.