dataesr/scientific-paragraphs-categorization huggingface dataset:
Total recording of shark species occurrences. However, the lack of both research programs and systematic sampling protocols concerning Mediterranean and Black seas' large elasmobranchs does not allow the reliable detection of temporal and spatial trends, so far. In this sense, the MEDLEM database is likely to provide an underestimation of the real value of the incidental catches. In terms of species composition, the only recorded species before 1890 were the basking shark and the great white shark, with a sporadic presence of shortfin mako in 1870-1890. In the last decades, many other species were recorded (i. e. , thresher shark, bluntnose sixgill shark, devil ray, and shortfin mako), reaching a total number of 34 species plus 2 categories of mixed species (Fig. 1F ). Unfortunately, information on the fishing gears involved in the catches is often missing for most of the recorded specimens.
, 2012)  and northern Ionian Sea  (Carlucci et al. , 2014)  (Fig. 3B ). Data suggest seasonal hotspots of basking sharks around north Sardinia and Apulian coasts, where individuals were recorded mainly between January and March  (De Sabata & Clò, 2010; Carlucci et al. , 2014; De Sabata et al. , 2014. The shortfin mako (I. oxyrinchus) has been recorded along the Italian coasts, mainly in the northern Tyrrhenian and Adriatic seas, but also in Cretan waters (GSA 23) and south Levantine waters (GSA 26)  (Lteif, 2015) ; many records were reported also from Tunisian waters  (Bradaï et al. , 2002). The largest shortfin mako shark recorded in the Mediterranean Sea was caught off south-western Turkey, and it was ca. 580 cm in TL  (Kabasakal & De Maddalena, 2011 ) (Fig. 3C ). The bluntnose six-gill shark (H.
griseus) was reported in Maltese waters (20 specimens; GSA 15), in the northern Tyrrhenian Sea (45 specimens; GSA 9), in southern Adriatic Sea, northern Ionian Sea, south Sicily waters (21 specimens; GSA 18, 19 and 16 respectively), along the coasts of Tunisia (GSA 13 and 14)  (Capapé et al. , 2003a; Capapé et al. , 2004)  and in the Turkish waters (24 specimens). Kabasakal (2013b)  states that 150 specimens of H. griseus were caught by commercial fishing vessels in the seas of Turkey between 16 July 1967 and 4 February 2013, 90 of which were recorded in the Marmara Sea (Fig. 3D ). The bluntnose six-gill shark is also regularly captured along the coast of Lebanon  (Lteif, 2015. )  but currently not recorded in detail in the database. Also, there are some incidental catches of H. griseus mainly on deep bottom longlines as well as very rare incidental catches of A.
, 2002)  (Fig. 3H ). However, such data are not included in the MEDLEM database, since the bycatch of this species is not considered as an unusual event. Therefore, the map reporting the geographical distribution of the blue shark is not representative of the actual distribution of the species. The MEDLEM data provided some insights on the geographical distribution of other locally rare or uncommon species, such as Lamna nasus (Fig. 3I ), Carcharhinus plumbeus, Galeorhinus galeus, and S. zygaena (Fig. 3J ). A. superciliosus. I. oxyrinchus and G. altavela were regularly present in Lebanese waters, whereas in the last decade S. oculata and Sphyrna spp. seem to have completely disappeared in Lebanon, where M. mobular has never been recorded  (Lteif, 2015). The sandbar shark (C. plumbeus) has been mostly recorded and landed throughout the year in the Gulf of Gabes (Tunisia), and some authors suggest that this area might be a nursery for many species  (Bradaï et al.
1F ). On the other hand, the improvement of fishing technologies and the consequent increase in fishing effort may have played a role of primary importance, determining a significant rise in incidental catches. Some data from new scientific projects have recently been added to the MEDLEM database (e. g. , the catches recorded with the Elasmoit Project  (Relini et al. , 2010)  and Elasmostat Project  (Serena et al. , 2014). A larger effort to retrieve all the historic information not yet available but included in governmental and scientific institutions archives is needed. It is important to transfer into the database the information on large elasmobranchs that was collected through other monitoring activities such as EU programmes  (Reg. EC 1543 /2000; Reg. EC 1639 /2001; Reg EC 812/2004; Reg.
Greater attention to accidental catches occurring in the various parts of the Mediterranean should be given and a more systematic recording of these opportunistic events should be facilitated. Several initiatives are taking place in the Mediterranean Sea: integration and coordination among them is pivotal  (Bargnesi et al. , 2020). This would make the MEDLEM database a more comprehensive tool that would not only enhance a pro-active collaboration among the various research institutes, but also represents an important source of information contributing to the improvement of our knowledge on the biology of these fishes, enabling the assessment of the status of the elasmobranchs and, lastly, the promotion of better conservation measures.
Acknowledgements We would like to thank all our collaborators involved in the  MEDLEM  project and to all the people who provided information from all around the Mediterranean and Black seas. In particular, the  Italian Coast Guard  for helping us to record the information collected during their institutional programs and the  RAM (Reparto Ambientale Marino)  for sharing the archive of the official datasets. Thanks to the anonymous referees for their critical reading and valuable suggestions that allowed to obtain a more exhaustive and precise text. Finally, thanks to  Daniela Pace  ( Sapienza University of Rome, Italy ) for constantly providing us with useful information on sharks at sea.
realized on the first dataset and D val Nv. The results are: γ = 0. 01, γ s = 0. 3 and β w,i = 0. 7. These are then used in the 50 runs based MCs, which corresponds to Steps 4-9 in Algorithm 1 with these parameters fixed. The results are compared against a regular RKHS method using model (27) without sparse regularization and with optimized hyper-parameters γ = 1 and β w = 0. 7. Furthermore, in order to evaluate the performance of the proposed approach with respect to other sparse estimation methods, a LASSO estimator is also applied using a 6 th -order monomial basis based parametrization of each nonlinearity. The regularization parameter of the LASSO has been optimized using the grid search process. For a i (p k ), b j,1 (p k ) and b j,2 (p k ) estimated via the RKHS estimator (i. e. , with γ s = 0), its sparse version (S-RKHS) and the LASSO, the maximum absolute values a i , b j,1 and b j,2 of these functions are computed over P.
15,1 , b 4,2 and b 5,2 are plotted in Figure 2. It can be seen that the true nature of the nonlinear scheduling functions are well captured and accurately estimated by the RKHS model. 100 BFR [%] 80 90 70 Reg No-Reg Identification method The box-plots of the BFR on the validation dataset (used neither for training nor to tuning the hyper-parameters γ, γ s and β wi ) obtained with the RKHS and S-RKHS approaches are computed and reported in Figure 3. The obtained results clearly indicate that, by exploiting the sparsity structure, the LC-RKHS dramatically improves the model quality with respect to overparameterized one. 7 Conclusions In this paper, we have presented a sparse Reproducing Kernel Hilbert Space (RKHS) estimator for a gen-.
Benchmarking of CPU resources in WLCG has been based on the HEP-SPEC06 (HS06) suite for over a decade. It has recently become clear that HS06, which is based on real applications from non-HEP domains, no longer describes typical HEP workloads. The aim of the HEP-Benchmarks project is to develop a new benchmark suite for WLCG compute resources, based on real applications from the LHC experiments. By construction, these new benchmarks are thus guaranteed to have a score highly correlated to the throughputs of HEP applications, and a CPU usage pattern similar to theirs. Linux containers and the CernVM-FS filesystem are the two main technologies enabling this approach, which had been considered impossible in the past. In this paper, we review the motivation, implementation and outlook of the new benchmark suite.
Largely speaking, CPU benchmarks can be grouped into three categories  [10] : kernel, synthetic and application. Kernel benchmarks are based on libraries and small code fragments that often account for the majority of CPU time in user applications: an example is the LINPACK benchmark  [11] , based on the LINPACK matrix algebra package. Synthetic benchmarks are custom-built to include a mix of low-level instructions, e. g. floating-point or integer operations, resembling that found in user applications. Two examples are the Whetstone  [12]  and Dhrystone  [13]  benchmarks. Application benchmarks are based on actual user applications. From a user's perspective, benchmarking a machine based on the user's own application is clearly the best option, although in practice this is often impractical.
The CERN Unit had been designed to be based, as much as possible, on real HEP applications, rather than on kernel or synthetic benchmarks. McIntosh made this clear in his 1992 paper, where, however, he also commented that new approaches may be needed for the future, as at the time he considered it virtually impossible to capture a modern event processing program involving over 100k lines of code, several external libraries and one or more databases or data sets. For reference, the CERN Unit was shipped as a tarball that required less than 50 MB of disk space in total, for unzipping, building and executing all included programs, using the compiler and operating system found on the machine to be benchmarked.
By construction, in fact, a benchmark based on a HEP application is guaranteed to give a score and a CPU usage pattern that are the most representative of that application. This is precisely the approach followed in the new HEP-Benchmarks  [31]  suite, which we are building within the HEPiX BWG to make it the successor of HS06, as described in the next section. The central package of this project, hep-workloads, is a collection of workloads from the four LHC experiments, covering all of the GEN, SIM, DIGI and RECO use cases. In retrospective, this is the same approach on which the CERN Unit was based. As discussed in the previous section, the CERN Unit was eventually discontinued because in the early 1990's it seemed no longer possible to capture a complex HEP application with all of its software and data dependencies.
A problem which seemed impossible to solve 30 years ago, however, can be much more easily addressed using the technologies available today. The reason why it is now possible to encapsulate HEP workloads in the hep-workloads package, in particular, is the availability of two enabling technologies: first and foremost, Linux containers  [32] , which allow the packaging and distribution of HEP applications with all of their dependencies, including the full O/S; and, in addition, the cvmfs shrinkwrap utility  [33, 34] , which makes it possible to selectively capture which specific software and data files are needed to execute a HEP workload in a portable and reproducible way, out of the much larger LHC experiment software installations on the cvmfs (CernVM-FS) filesystem  [35].
The hep-workloads package: HEP reference workloads The hep-workloads package is the core of the HEP-Benchmarks suite. It contains all the code and infrastructure, both common and workload-specific, to build a standalone container for each of the HEP software workloads it includes. Individual images are built, tested and versioned in the package's gitlab CI and are then distributed via its container registry  [37]. Images are built as Docker containers  [32] , but they can also be executed via Singularity  [38]. A single command is enough to download a specific benchmark and execute it using the embedded pre-compiled libraries and binaries. A benchmark summary file in json format and more detailed logs are stored in a results directory. For instance, to download the latest version of the LHCb GEN/SIM image, execute it using either Docker or Singularity, and store results in the host /tmp directory, it is enough to run one of the two following commands: The main result in the json file is the benchmark score for the given workload, measured as an absolute throughput of number of events processed per wall-clock time.
This is essentially derived from the total wall-clock time to complete the processing of a predefined number of events. The json file also contains all relevant metadata about how the benchmark was run, as well as more detailed results, including memory and CPU time usage. Some of the HEP workloads, like ALICE and LHCb GEN/SIM, are single-process (SP) and single-threaded (ST); others use parallelism to save memory on multi-core CPUs, via multi-threading (MT) techniques like CMS RECO  [39] , or via multi-process (MP) techniques involving forking and copy-on-write, like ATLAS RECO  [40]. For MT/MP applications, the number of threads/processes is fixed to that used by the experiment in production, and every image is executed in such a way as to fill all available logical cores on the machine that is benchmarked, by launching an appropriate number of identical copies of each application.
When more than one copy is executed, the benchmark score is the sum of their throughputs. The number of logical cores, derived from the nproc command, is equal to the number of physical cores for machines configured with hyper-threading disabled, but it is higher if this is enabled. On a machine with 16 physical cores and 2x hyper-threading, for instance, by default 32 copies of the ST/SP LHCb GEN/SIM and 8 copies of the 4xMT CMS RECO benchmarks are executed. All of these parameters are, in any case, configurable. The design of the hep-workloads package relies on the fact that all four LHC experiments install and distribute their pre-compiled software libraries using the cvmfs file system. To add a new workload, experiment experts just need to prepare an orchestrator script, which sets the runtime environment, runs one or more copies of the application, and finally parses the output logs to determine the event throughput, which is used as benchmark score.
4. This final container is tested, by running the workload using both Docker and Singularity. If tests succeed, the image is pushed to the gitlab registry  [37]. A key element of this approach is reproducibility: repeated runs of each workload are meant to always process the same events and produce the same results. This is essential for resource benchmarking, to ensure that timing measurements on two different nodes correspond to the same computational load. It is also important during the build process, where tests of the final container may fail if they need different /cvmfs files from those identified while tracing the interim container. Strict reproducibility can be guaranteed for ST (including MP) workloads, but not for the CMS MT workloads, where the sharing of processing across different software threads may lead to small differences in execution paths; however, this is not considered an issue for benchmarking, and no errors have been observed during the build process either.
Workload images vary in size between 500 MB (ATLAS GEN) and 4 GB (CMS DIGI). GEN containers are generally the smallest because event generation is CPU intensive with almost no input data, while DIGI and RECO images are much larger as event digitisation and reconstruction are more I/O intensive and large reference data files must be shipped within the workload containers. Docker images are internally made up of layers (and this structure is maintained when they are converted to Singularity). Taking into account that bug fixing and feature improvements have often led to a rapid development cycle, the hep-workloads CI has been optimized to stack these layers in the order which makes them as cacheable as possible. The bottom layers contain what changes least often, like the O/S and data files, while the higher layers include experiment software and common and workload-specific scripts.
There are, however, many use cases where a single number is needed, and the accounting of WLCG resources is presently one of them. This is not a technical issue: it is a policy issue, and its discussion is beyond the scope of this paper. On a technical level, our design of the hep-score package takes this into account by allowing the definition of a highly configurable combined score, but also by the fact that the detailed scores of individual workloads are also stored in the report of any HEPscore execution. This is very important because it provides a mechanism to analyse a posteriori the performance of individual HEP workloads. The hep-benchmark-suite package: a toolkit for benchmark execution and result collection The hep-benchmark-suite package, finally, is a toolkit to coordinate the execution of several benchmarks, including not only HEPscore, but also HS06, SPEC CPU2017, KV and others.
Results are collected in a global json document that can then be uploaded to a database. This package is used for what are currently the main activities of our team, to demonstrate the readiness of HEP-Benchmarks as a replacement of HS06: first, for testing that individual HEP workloads are reliable and give stable results (typically, within 5% or better) on repeated run on the same system; second, for the study of their correlations to one another, and to HS06 and other benchmarks. To this end, a wide range of x86 worker nodes has been collected, similar to the lxbench cluster that had been used in the initial comparisons of HS06 and SI2K.
A combination of ultraviolet (UV)-Visible, high throughput (HT) -Raman and HT-FTIR spectroscopies were used to evaluate the spectral content of both haemolysed and non-haemolysed plasma. Significant spectral differences were observed in the infrared and Raman spectra of non-haemolysed and haemolysed plasma samples. The acquired FTIR and Raman spectra were also analysed by principal component analysis (PCA) and classical least squares (CLS) fitting analysis in the MATLAB environment. The CLS fitting analysis was employed to estimate the changes in the weighting of various analytes in non-haemolysed and haemolysed plasma samples.
Ethical approval Ethical approval was awarded by the Technological University Dublin Research Ethics Committee for the collection of blood samples from healthy donors. The prostate cancer patients used in this study were recruited from the Cancer Trials Ireland (formerly All Ireland Cooperative Oncology Research Group, ICORG) trial 08-17 which is entitled "A Prospective Phase II Dose Escalation Study Using intensity modulated radiotherapy (IMRT) for High Risk N0 (cancer has not spread to nearby lymph nodes) M0 (or elsewhere in the body) Prostate Cancer (NCT00951535)". The patients were recruited for a translational study on vibrational spectroscopy for monitoring radiation therapy response  [18]. The translational research study was approved by the St Luke's Radiation Oncology Network Research Ethics Committee and all research was performed in accordance with relevant guidelines and regulations.
FTIR spectroscopy The sample preparation and acquisition methodology for FTIR spectra was detailed in our previous paper  [18]. Briefly, plasma samples stored at -80° C were thawed at room temperature and were diluted threefold in physiological water. A volume of 4 μL diluted plasma sample was deposited on a 384-well silicon plate (Bruker Optics GmbH, Ettlingen, Germany), and air-dried at room temperature. For each sample, 10 spots were used giving 10 instrumental replicates. The plate was then inserted into a high-throughput module (HTS-XT, Bruker Optics GmbH) attached to an FTIR spectrometer (Tensor 27, Bruker Optics GmbH). FTIR spectra were acquired in the transmission mode using the OPUS v6. 5 software (Bruker Optics GmbH) in the wavenumber range from 4000 to 400 cm -1 , using a spectral resolution of 4 cm -1 and 32 co-additions.
Increased weighting of p-ethanolamine was observed in both FTIR and Raman spectral analysis. The FTIR spectral analysis showed an increased weighting of albumin with an increase in haemolysis but a slightly decreased level of albumin was observed in the Raman spectra of the moderately haemolysed plasma samples. A decreased weighting of uric acid, thymidine and interleukin-6 was observed in the FTIR spectral analysis with an increase in haemolysis. Conversely, an increased weighting of uric acid, thymidine and interleukin-6 levels were observed in the Raman spectral analysis. The differences in the CLS fitting analysis results of FTIR and Raman spectra may be due to the employment of different pre-processing and sample preparation methods. Second derivative FTIR spectra were used in this analysis, second derivative spectra allow more distinct identification of small and adjacent lying absorption peaks which are not clearly visible in the original spectrum.
Acknowledgements:  Cancer Trials Ireland  acted as sponsor for the  CTRIAL-IE (ICORG) 08-17 study , providing project management, protocol development and monitoring, patient information leaflet and CRF development, ethics application and amendments. The authors are very grateful to the patients who participated in the CT-IE 08-17 study and donated blood samples for this translational research study. Arachidonic acid Sigma Aldrich 10931 Liquid.
Funding: This research was funded by an  Irish Research Council   Postgraduate Award ,  COST Action  ( BM1401 )  Short Term Scientific Mission Award ,  Irish Research Council-Ulysses Award  (Grant Number  PJ29007 ),  EU   FP7 Network of Excellence DoReMi  (Grant Number  249689 ) and  Science Foundation Ireland  ( 11/RFP. 1/BMT/3317 ).
Water surface extents from L-band microwave radiometry The soil water fraction (SWAF) retrieved from L-band microwave radiometry is used to determine the open water surfaces  (Parrens et al. , 2017). The SWAF is obtained using a contextual model on the SMOS angle-binned brightness temperatures (MIRCLF3TA) data  (Al Bitar et al. , 2017). SMOS was launched in November 2009 by the European Space Agency (ESA) and is the first satellite dedicated to map soil moisture. SMOS is a passive microwave 2-D interferometric radiometer operating in the L-band (1. 413 GHz, 21 cm wavelength;  Kerr et al. , 2010). SMOS orbits at a 757 km altitude and provides the brightness temperature (TB) emitted from Earth over a range of incidence angles (0 to 55 • ) with a spatial resolution of 35 to 50 km. Parrens et al.
(2017)  showed the capability of SMOS to retrieve the water fraction under dense forests over the Amazon basin. One of the main upsides of SMOS is its sensitivity to the soil signal under vegetation in all-weather conditions thanks to the L-band frequency. The SWAF data were averaged each month over the sampling period  (2011) (2012) (2013) (2014) (2015)  within the Amazon basin. The SMOS satellite observes the Earth surface at full polarization (horizontal -H, vertical -V and cross polarization -HV) at multi-incidence angles. In this paper, the SWAF product was generated from the SMOS TB data at 32. 5 • and V polarization. Figure  2  outlines the common hydrological patterns observed in the Amazon basin as well as the dynamic of the inundations for the different floodplains. The contrasted seasonal peaks in flooded areas between the northern and southern floodplains are well depicted.