dataesr/scientific-paragraphs-categorization huggingface dataset:
We also set L = 3. Bayesian variable selection-The function R2GUESS() is used to run the algorithm by calling the C++ executable and compiles inputs (including parameters detailed in Table  1 ), and outputs (detailed in Table  3 ) into an object of class 'ESS' that can be further analyzed using, among other functions, summary(), print(), and plot(). The object can also contain an annotation file describing the predictors (e. g. , SNP names, positions, etc. ). The following command lines give an example of how to run R2GUESS on the built-in example dataset. The model was run for 11,000 sweeps including 1,000 sweeps for burn-in. These commands will store results in the modelY_Hopx object, which can be visualized using summary() command to obtain a list of the best models (the number of output models is user-defined).
• Selection of covariates-As illustrated in Section 6. 1, R2GUESS offers the possibility to plot the MPPIs of all variables using the plotMPPI() function. By default plotMPPI() will use re-normalized MPPI estimates. Monte Carlo estimates can alternatively be plotted by using the useMC option and, in our example, produces highly comparable estimates. It is possible to assess significance of the findings by specifying an MPPI cut-off value which controls the empirical FDR at a specified level (FDR. permutation() function). This function will run R2GUESS for Npermut times on permuted datasets. Although the run can be parallelized on nbcpu processors, it may become time consuming when applied to large datasets. The function will provide a cut-off value for both re-weighted and Monte Carlo estimates of the MPPI.
on real genotype data (273,675 single nucleotide polymorphisms, SNPs) obtained in n = 3, 122 individuals from the Gutenberg health study (GHS; Zeller et al. 2010; Bottolo et al. 2013), we simulated outcomes (q = 3) for different population sizes and numbers of predictors. A sub-matrix X α was extracted from the original dataset by randomly sampling a subset of rows (individuals) and columns (SNPs).
Acknowledgments The authors wish to thank  Paul Newcombe  and  Aida Moreno Moral  for their extensive testing and precious comments on the package and documentation. Marc Chadeau-Hyam  acknowledges the European  FP7 projects Exposomics  (grant agreement  308610  to  P. Vineis ) and  Envirogenomarkers  (grant agreement  226756  to  S. A. Kyrtopoulos ). Leonardo Bottolo  was supported by  EPSRC Mathematics Platform  grant (grant agreement EP/.
A review is given of the impact that HF radars are having on the management of coastal hazards. Maps of surface currents can be produced every 10-20 minutes which, in real time, improve navigation safety in restricted areas commonly found near ports and harbours. The time sequence of surface current maps enables Lagrangian tracking of small parcels of surface water, which enables hazard mitigation in managing suspended sediments in dredging, in emergency situations where flotsam and other drifting items need to be found, and in pollution control. The surface current measurement capability is used to assist tsunami warnings as shown by the phased-array data from Chile following the Great Tohoku Earthquake in 2011. The newly launched Tsunami Warning Center in Oman includes a network of phased-array HF radars to provide real-time tsunami monitoring.
[6] ). HF radars are now being installed specifically to monitor the coastal ocean for approaching tsunamis, and Section 4 shows how this is being done in Oman. The capacity to plot maps of surface wind directions is being evaluated to feed in to the forecasting of the movement of cold fronts in the southern ocean in the Great Australian Bight. This is an area of sparse weather stations in a place where the movement of cold fronts is a key parameter, particularly in the management of bush fires in South Australia and Victoria. This is discussed in Section 5 of the paper. An example of the use of HF radar in routine management of a busy port is given in Section 6, using data from the WERA radar installed at the Port of Rotterdam in Netherlands (Van Heteren et al. [7] ). These initiatives in Europe as well as developments like The US Ports Infrastructure Projects recognise the need for operational use of current monitoring around ports.
This has the advantage of dealing with data at the earliest point before any averaging or combining of data from two stations to form the surface current vectors on the Cartesian grid. The procedure consists of performing a series of tests on measurements in each grid cell in the latest radial current dataset obtained. The tests evaluate the measurements and analyze their consistency with past measured values. At the end, each grid cell will be given a quality level number from 1 to 5, where quality level 1 is the best quality and quality levels 4 and 5 are considered to be corrupted data. The processing chain may automatically exploit this option to obtain cleaner current velocity vector maps in eastward and northward components by combining radial velocities from two or more radars in which undesired quality levels are omitted.
Case Study: QC at South Florida. As reported by Gomez et al. [10] , the QC procedure was implemented on the current velocities measured by a pair of WERA HF radar systems of the University of South Florida (labelled in Figure  1  as Ft DeSoto and Venice) and tested for a period of 9 months. The QC parameters used in this experiment were empirically tuned using data for one month from both stations. The performance of the QC procedure was evaluated by comparing the data from Venice station with the radial projection of the current velocity measured every hour by an Acoustic Doppler Current Profiler (the red dot in Figure  1  labelled as C10) and calculating a correlation coefficient. The correlation coefficients of radar data compared to ADCP data were obtained excluding different levels of quality.
Case Study: Operational Tsunami Warning System in Oman. The newly launched Tsunami Warning Center in Oman is one of the most sophisticated tsunami warning system worldwide applying a mix of well proven state-of-theart subsystems while otherwise applying newest technologies. It serves as a benchmark for the integration of HF radar data into the tsunami warning procedure. The core of the tsunami warning system is the seismic real-time analysis system SeisComp3 described by Hanka et al. [19]  and used in most tsunami warning centers globally. The tsunami simulation and decision support system is based on the Tsunami Observation and Simulation Terminal (TOAST) system developed by gempa GmbH, which integrates a precalculated tsunami scenario database and a GPUbased based "on the fly" simulation allowing the system to react to atypical earthquakes that are not considered by the precalculated database.
Data Management System Structure. The new WERA data management system uses a MySQL database to store measured data. MySQL is a very powerful open-source relational database management system (RDMS) and allows subsequent tools in the WERA system to have a structured base from which data can be retrieved and stored. Using a database, in comparison with having data stored in text files, brings several benefits; some of them are as follows: (i) Data can be stored in binary form, not in ASCII characters, reducing space requirements. (ii) RDMS allows setting indexes, foreign keys, queries from simultaneous users, and other benefits which This allows users of WERA systems to develop their own routines or modify the existing structure of the database to satisfy particular needs. (iv) WERA users can access other types of open-source software (e.
g. , phpMyAdmin for MySQL databases). The WERA data management system includes several software tools which process and store the oceanographic data measured by reading the data files provided by WERA and storing the information to the database in an organized manner using SQL commands as summarised in Figure  12. In standard configuration, these tools are executed automatically without any human intervention. Additionally, the data management system includes a user-friendly web application that allows access to all the data stored in the database (please refer to Figure  2 ), as well as creating time series and exporting the data to different formats. The relatively easy access to the data provided by the database can be readily exploited to implement the raft of hazard reduction applications in coastal zones.
The integration of radar data into an operational tsunami warning system is demonstrated by the case study of the operational Tsunami Warning Center in Oman. The radar measurements can be used to confirm the timing and magnitude of the tsunami following prewarning from the seismic and buoy elements of the system. Based on actual (not simulated) tsunami measurements a set of requirements for the WERA ocean radar have been developed to enable its integration into the TEWS system. The requirements include a high range resolution, a narrow beam directivity of antennas, and a fast data update mode to provide a possibility of tsunami detection in real time. Routine real-time maps of wind directions from phasedarray HF radars in Australia can be used to identify the position and movement of meteorological fronts over the southern ocean.
Acknowledgments Archived data were sourced from  Rijkswaterstaat ,  Netherlands , and the  Integrated Marine Observing System (IMOS) ,  Australia. IMOS is a national collaborative research infrastructure, supported by  Australian Government. It is led by  University of Tasmania  in partnership with the  Australian marine and climate science community. Helzel Messtechnik GmbH  is the manufacturer of the WERA radar systems. gempa GmbH developed the  Tsunami Observation and Simulation Terminal (TOAST)  system.
A resolved resonance evaluation was performed for 16 O in the energy range 0 eV to 6 MeV using the computer code SAMMY resulting in a set of resonance parameters (RPs) that describes well the experimental data used in the evaluation. A RP covariance matrix (RPC) was also generated. The RP were converted to the evaluated nuclear data file format using the R-Matrix Limited format and the compact format was used to represent the RPC. In contrast to the customary use of RP, which are frequently intended for the generation of total, capture, and scattering cross sections only, the present RP evaluation permits the computation of angle dependent cross sections. Furthermore, the RPs are capable of representing the (n, a) cross section from the energy threshold (2. 354 MeV) of the (n, a) reaction to 6 MeV.
The main objective of the CIELO 16 O evaluation is to investigate issues in connection with the thermal elastic scattering cross section, elastic scattering in the energy 100 keV to 1 MeV, and the (n, a) cross section. The purpose of this paper is to briefly describe the procedures used in the evaluation of the 16 O cross section using the computer code SAMMY  [2]  from 1. 0 À5 eV up to 6 MeV. The results of the evaluation are a set of RP that reproduces well the experimental data and an RPC that reflects the data uncertainties and correlations. The motivation for representing the 16 O cross-section data with RP came about the time when a SAMMY evaluation of the silicon isotopes was taking place  [3]. Among the data used in the silicone evaluation there were data from measurements of enriched silicon samples for 28 Si, 29 Si, and  30  Si in the form of silicon dioxide, that is, 28 SiO 2 , 29 SiO 2 , and 30 SiO 2.
Consequently, there existed the need for RP for oxygen to complete the RP evaluation for silicon. Since no Reich-Moore RP for 16 O were available, a provisional set of RP for 16 O was derived for the evaluation of the silicon dioxide data up to 1. 8 MeV. As the oxygen RP replicated the experimental total cross section for 16 O rather well, a full evaluation with the Reich-Moore formalism appeared to be within reach. Therefore, a decision was made to extend the  16  O resonance evaluation up to the energy threshold of the first inelastic channel at about 6. 049 MeV. However, since it was observed that an (n, a) channel opens about 2. 35 MeV, it was required modifying the code SAMMY to account for charged particle penetrability. Sayer  [4] , together with the author of the SAMMY code, made that option available for fitting charged particle reactions.
In addition, no existing ENDF processing software such as NJOY  [11] , AMPX  [12] , and PREPRO  [13]  could calculate charged particle penetrability and consequently would not be able to process the evaluation. Therefore, the evaluation did not receive much attention as an option for the ENDF cross-section representation of 16 O. Existing evaluations rely entirely on a tabular representation of the data including the angular distribution. Later on the ENDF format was updated to allow the inclusion of more channels and a new resonance format was developed and the cross section processing codes were updated. The ENDF option for representing the RP is named LRF = 7 which is often referred to as the R-Matrix Limited (RML) format. As part of the RP evaluation, a RPC was generated with the code SAMMY.
Evaluation methodology 2. 1 Experimental database Differential data measurements were used in the SAMMY evaluation of the 16 O RP covering the energy range 0-6 MeV. The experimental data used in the evaluation are displayed in Table  1. Four total cross sections were used in the SAMMY evaluation. The SAMMY resonance evaluation of 16 O yielded a set of RPs that fit the total, capture at thermal, and the (n, a) cross section in the energy range 0 to 6 MeV. There are 34 resonances in the range 0 to 6 MeV with 3 bound levels and 16 energy levels above 6 MeV for a total of 53 resonances. Up to the (n, a) energy threshold (2. 354 MeV) each resonance level is represented by the energy of the resonance E r , gamma width G g , and the neutron width G n. Above the threshold an additional channel to represent the (n, a) reaction is added to each energy level with the width G a.
Indeed, the bound levels will guide, in the data evaluation process at low energy, the determination of the thermal scattering cross section, the effective scattering radius R, and the coherent scattering length a coh. Although the derivation above was done on the basis of the SLBW formalism, it is perfectly valid for low mass nuclide at low energy since the resonances interference effects are absent due to the large level spacing. The fitting of the coherent scattering data has not yet been formally implemented in the SAMMY code. However, the experimental scattering length data were fitted with a tool developed outside the SAMMY code environment. It is interesting to note that the spin coherent and incoherent scattering length, as a function of the spindependent scattering lengths a À and a + can be written as a coh ¼ I þ 1 2I þ 1 a þ þ I 2I þ 1 a À ; ð6Þ and a incoh ¼ I I þ 1 ð Þ ½ 2I þ 1 1=2 a þ À a À ð Þ ; ð7Þ where I is target spin, and that for 16 O for which the target spin is zero (i.
The RPC format used to store the information in ENDF was the LCOMP = 2 for which 30% less computer storage is required in comparison with the LCOMP = 1 option with no loss of information. An example of the RPC for the total cross section is shown in Figure  6  for which the relative uncertainty and correlation are displayed. The results are obtained on calculations done with the PUFF module of the AMPX code  [12]  with 44neutron groups. As can be seen below 100 keV the uncertainties in the total cross section are about 1. 2%. Above 100 keV, where the contribution due to the resolved resonances starts, the fitting of the experimental data leads to group uncertainties that oscillate reaching out as high as 6%. The 6% uncertainty occurs at the energy corresponding to a minimum of the total cross section meaning that a small cross-section infers a higher uncertainty.
Benchmark studies The 16 O scattering cross section at thermal energy derived in the present evaluation at room temperature is lower by 2. 5% compared with the values in existing nuclear data libraries. In this session the impact of the low scattering cross-section in benchmark calculations is investigated. Moreover, comparisons of benchmark results using two sets of the 16 O (n, a) cross-section values corresponding to the fitting of two experimental data, that is low and high, are also presented. Prior using the evaluated RPs in benchmark calculations the SAMMY RPs were converted into the ENDF LRF = 7 format. The ENDF/B-VII. 1 16 O evaluation was used as the base library. The 16 O ENDF LRF=7 RPs were inserted in the ENDF/B-VII. 1 for calculation of the cross section in the energy range of 10 À5 eV to 6 MeV.
One should bear in mind that in addition to the energy dependent cross section angular data are also retrieved from the RPs. Above 6 MeV, the ENDF cross section values are used. The evaluations were processed with the NJOY code, the NJOY2012. 50 adapted to retrieve angular data from RP, and the benchmark calculations were done with the MCNP code  [31]. Three benchmarks, namely two light-enriched and light-water moderated and one light-enriched and heavywater moderated systems, extracted from the International Criticality Safety Benchmark Evaluation Project (ICS-BEP)  [32]  named LEU-MET-THERM-015 cases 15 and 16 and another from the International Reactor Physics Experiments Evaluation  [33]  named Zero Energy Deuterium Reactor first case, were used in the MCNP calculations. The heavy water critical benchmark systems was chosen since the sensitivity to 16 O cross sections is enhanced due to the small step of the neutron energy slowing in the heavy water.
The k eff results are shown in Table  5  including the statistical error in connection with the Monte Carlo sampling. The experimental benchmark values and experimental uncertainty are listed in the far right column. The nuclear data for the remaining isotopes present in the benchmark were that of the MCNP library based on the ENDF/B-VII. 1. The MCNP results corresponding to the ENDF/B-VII. 1 data are shown in column A whereas the results for the low and high (n, a) cross-sections are shown in column B and C, respectively. It is noted a considerable decrease in the k eff values from column A compared with values indicated in column B and C. The decrease in k eff from column A to B is due to a decrease on the elastic scattering cross section. This result is in agreement with the suggestion made by Lubitz's  [25]  that the scattering cross section should be lowered for about 3% from the existing values in the evaluated nuclear data files.
The impact on the magnitude of the (n, a) cross-section data in the k eff results can be seen on columns B and C. It seems that the impact of the low to high (n, a) cross-sections is not a very big improvement in the k eff results. More benchmark calculations should be performed with system sensitive to the (n, a) cross-sections to better understand the effect of the new RPs evaluation on integral benchmark calculations. However the results presented in this work demonstrate that the new evaluation is performing reasonably well. Comparisons of the total capture, scattering, and (n, a) cross sections processed with NJOY and the SAMMY code corresponding to the high (n, a) are shown in Figure  8. In general the percentage difference between the twoprocessed NJOY-SAMMY cross sections ranges around 10 À5 %.
By way of comparisons, Table 6 also illustrates the results of calculationsbased solely on the ENDF/B-VII. 1 data. The use of the new 16 O evaluation leads The effect of the 16 O nuclear data uncertainty propagated to the k eff results has been accomplished by using the TSUNAMI-IP sequence of the SCALE code system  [34] , which consists of combining the sensitivity and the covariance data to calculate the variance on k eff as spelled out in equation 10. The results are displayed on Table  7  for each of the four-benchmark cases. The first column is the k eff , which is also given in the third column of Table  6. Listed in the third column of Table  7  is the percentage standard deviation of k eff due the nuclear data uncertainties on the 16 O cross section. Note that the statistical error resulting from the Monte Carlo sampling is also listed.
Conclusions This paper depicts a certain degree of detail the work done in the resonance evaluation of 16 O cross section in the energy range 0 to 6 MeV using the reduced Reich-Moore formalism of the SAMMY code. The procedure used for performing the resolved resonance evaluation, generation of RP covariance, inclusion of the evaluation in the ENDFs, and the processing of the data for use in calculation of k eff is described. Double-differential elastic cross-sections were fitted based on the Blatt and Biedenharn formalism and RP covariance was generated in the fitting process of the experimental data. The evaluation addresses concerns with regard to thermal elastic cross section data and coherent scattering data. Thorough comparisons of the point crosssection generated with the code SAMMY, AMPX, and NJOY was carried out.
The paper discusses the issue on the normalization of the (n, a) cross sections from the viewpoint of simple benchmark calculations. The use of the ENDF data representation based on the LRF = 7 and LCOMP = 2 has been discussed. The impact of the nuclear data uncertainty propagation on benchmark calculations was presented based on four highly enriched critical benchmark systems. For the benchmarks analyzed we observe a systematic decrease in the calculated reactivity of 150 pcm due to the decrease of the elastic scattering cross section. The magnitude of the uncertainty derived from the RPC due to 16 O propagated to the benchmark calculation is about 150 pcm. Part of this work was supported by the United State Department of Energy, Nuclear Criticality Safety Program while L. Leal was an employee of the Oak Ridge National Laboratory.
The project iMARECULTURE is focusing in raising European identity awareness using maritime and underwater cultural interaction and exchange in Mediterranean Sea. Commercial ship routes joining Europe with other cultures are vivid examples of cultural interaction, while shipwrecks and submerged sites, unreachable to wide public are excellent samples that can benefit from immersive technologies, augmented and virtual reality. iMARECULTURE will bring inherently unreachable underwater cultural heritage within digital reach of the wide public using virtual visits and immersive technologies. Apart from reusing existing 3D data of underwater shipwrecks and sites, with respect to ethics, rights and licensing, to provide a personalized dry visit to a museum visitor or augmented reality to the diver, it also emphasizes on developing pre-and after-encounter of the digital visitor.
Approach Being an interdisciplinary project, the first step is the clear definition of targets, goals, test sites and wrecks to be used. Several decisions about the serious games, the age, the era and data to be gathered, will be taken. Discussions about existing VR and AR technologies  [1]  and implementation will clarify the roadmap towards milestones and goals. Data gathering & pre-processing phase: Is the fundamental pillar for the project. Both 3D as well as supporting data will be accumulated by partners, from open sites, published journals and books and partner's archives to support storytelling, narratives, 3D models of sites  [2, 3, 4] , ships  [5] , cargos, probabilistic geospatial analysis about ship routes, wreck site formation processes, etc. Having all the necessary information, iMA-RECULTURE will create and re-use a plethora of 3D models for both the ship wreckages and the amphorae, allowing people to examine the 3D underwater environment and objects on it.