dataesr/scientific-paragraphs-categorization huggingface dataset:
This holistic knowledge is built upon the CIDOC/CRM protocol and it extends the standard to include historic documentation, geometric survey, material survey, as well as critical environmental parameters. Furthermore, iMARECULTURE project will extent BIM protocol, which is a process involving the generation and management of digital representations of physical and functional characteristics of places and objects, in order to describe and manage knowledge about underwater sites and CH assets. Finally, diagnostic schemes for site formation analysis will be bullied by incorporating (i) underwater changes profiling, (ii) non-invasive/non-destructive analysis methods while (iii) documenting the sensor acquired spatio-temporal data and (iv) supporting data aggregation strategies. Protocols developed in EU projects will form the basis for the iMARECULTURE protocols and will be adapted according to specifications.
-100 GHz: α T B = 0. • 01 ± 0. • 21; α EB = 0. • 46 ± 0. • 17. -143 GHz: α T B = 0. • 11 ± 0. • 16; α EB = 0. • 29 ± 0. • 13. -217 GHz: α T B = 0. • 56 ± 0. • 27; α EB = 0. • 23 ± 0. • 28. The beam mismatch parameters are not constrained by the MCMC, but they have little impact (i. e. , < ∼ 1σ) on the values of α quoted above. Whatever the channel and spectra, the angle is never significantly different from zero and always smaller than the average errors on PSB orientation (∼ 1 • ) measured on the ground prior to launch  (Rosset et al. 2010). Comparison with the polarization orientation of the Crab nebula was discussed in section 3. 5. 2 of (Planck Collaboration XXVI 2016) and shown to be consistent with the above numbers. We conclude that the uncertainties of the instrumental polarization parameters as set up in the data processing pipeline have no significant effects on the low-EE and BB cross-spectra and are also in broad agreement with preflight determinations.
-γ b,h is the amplitude of the transfer function template for each harmonic bin h. -C b,i,p,l are the templates of foreground components l. The dust component is fitted at all frequencies, the CO component is fitted at all frequencies except 143 GHz, and the free-free component is fitted at 100 GHz only. The templates for the components, without bandpass mismatch leakage, are taken initially from the Planck 2013 results and then iterated with the maps from previous iterations. The polarization of the foreground is taken into consideration only for the dust. -L b,l is the amplitude of the bandpass mismatch leakage. For all l in the range 1-n comp , we set n bolo b=1 L b,l = 0. -O b,i is the 1/ f noise effect modelled as one offset per pointing period. We set n bolo b=1 n period i=1 O b,i = 0, since the Planck data provide no information on the monopole.
B. 1. 3. HPR computation The SRoll method is based on HPRs as for the Planck 2015 data release (see section 6. 1 of Planck Collaboration VIII 2016). This procedure reduces the amount of data, and in the limit that the spin-axis pointing, noise, and systematic effects are constant during a pointing period, the information loss is negligible. Thus, each detector's TOI for each period of stable spin-axis pointing is binned into HEALPix pixels at N side =2048, compressing the data. Furthermore, all sky-map templates fitted by SRoll are converted from TOI to HPR (dipoles, CO maps, dust map, and free-free map). The residual transfer function templates (see  Sect. B. 4. 3 ) are computed at the timeline level and then projected into HPRs.
B. 2. Masks The solution for the map parameters is done with the sky partly masked. This masking has two goals, firstly, to avoid regions of the sky with time variable emission, and secondly to avoid regions with a strong signal gradient. Thus, the brightest point sources are removed (e. g. , the flux of 3C273 is strong and it changes during the mission). The Galactic plane is also masked, due to the strong signal gradients there. Nevertheless, a relatively large sky coverage is needed to properly solve for the bandpass mismatch extracted from the Galactic signal. We start from simulated TOI data sets after TOI processing, including the total CMB dipole (solar and orbital), the CMB anisotropies, Galactic and extragalactic foregrounds, and noise. These are processed to simulate all systematics relevant for mapmaking.
The SRoll mapmaking is then applied to these TOI to obtain the so-called E2E simulated frequency band I, Q, and U maps and associated T T , EE, and BB power spectra. For testing purposes, one can choose specific subsets of input signals, systematic effects, and processing modules. Building a simulation data set with 100 noise and 100 CMB realizations was not feasible for this paper. We thus test if the CMB can be simulated separately from the noise and systematics and for this purpose two sets of TOIs are simulated, the first including all the inputs, and the second including all inputs except for CMB anisotropies. Maps are made with SRoll from both sets of TOIs. In the second case, the CMB anisotropies are added at the map level after the mapmaking. The difference between these two tests shows that we can avoid including the loop over many CMB cosmological signals at the TOI level.
The Planck maps, at 100 and 143 GHz, are each ILC-cleaned using 30 and 353 GHz maps as foreground tracers, as described in Sect. 5. 2. The low dimensionality of the problem renders the likelihood analysis amenable to a scheme based on computing P(τ | C data , Ω), where Ω stands for the other cosmological and non-cosmological parameters. We formally have some freedom in the choice of Ω, but typically use best-fit values from the Planck cosmological parameter analysis (Planck Collaboration XIII 2016). More explicitly, we follow these steps. The simulation set C sim is built from HFPS1 (or HFPS2) for the noise and systematic effects, to which we add for each 100 CMB realizations. We then use the simulations described above to construct the sampling distributions P( C sim | , τ, Ω), for a grid of τ values from 0.
In order to quantify the significance of this result, we perform a Gaussian fit to the histograms (dashed lines), showing that the t value from the data is incompatible with these simulations at approximately the 3. 5 σ level. 2 shows an illustration of the method for the 100 × 143 cross-spectra. The top panel shows, for white noise only, the joint probability distribution in the ( t, τ) plane, assuming a uniform prior on τ. For a given value of t the red curve shows the probability distribution of τ, which peaks at a value close to the value of t by construction of the definition of t. The bottom panel is built using the HFPS1 simulations (with systematic effects) and 100 CMB simulations. We note that in this case, for low values of τ, the maxima of the probability distributions shifts slightly from the diagonal, because of the inclusion of the systematic effects.
The pixel-pixel covariance matrices (QML1 and QML2) are built using three different QML estimators. For QML1 (or QML2), the pixel-pixel matrix has been computed directly with the HFPS1 (or HFPS2) data set. For QML3, in order to minimize the effect of overfitting, the off-diagonal terms are reduced to the first four eigenmodes of a principal component analysis from HPFS1. SimBaL1, 2, 3: three posterior distributions for τ. They differ in the choice of the power spectrum estimator and the data set simulation used. SimBaL1 (or SimBaL2) uses QML1 (or QML2) as power spectrum estimator and HFPS2 (or QML1) as the simulation data set. SimBaL3 uses QML3 as power spectrum estimator and the full set of simulations (HFPS1+HFPS2+HFPS3) as the simulation data set. SRoll: a new polarized destriping algorithm, which removes residuals via template fitting, using HPR to compress the timeordered information.
Acknowledgements. The Planck Collaboration acknowledges the support of:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MINECO , JA, and  RES (Spain) ;  Tekes, AoF , and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ;  ERC  and  PRACE (EU). A description of the Planck Collaboration and a list of its members, indicating which technical or scientific activities they have been involved in, can be found at http://www. cosmos. esa. int/web/planck/planck-collaboration.
Samples In addition to the dataset already presented in  Millet and Dauphas (2014) , we have analysed 34 terrestrial samples reflecting a global coverage from a diverse range of geodynamic contexts. We have also analysed 8 primitive lunar mare basalts covering the range of TiO 2 concentrations observed in lunar rocks. Mid-Ocean Ridge Basalts (MORB): Seven MORB samples were analysed in order to constrain the composition of the upper mantle. The sample suite includes glasses from the mid-Atlantic ridge, the East-Pacific Rise  (Batiza and Niu, 1992) , the Pacific-Cocos-Nazca triple junction  (Puchelt and Emmerman, 1983)  as well as the South-West Indian Ridge  (Escrig et al. , 2004) , including a single MORB sample displaying Sr, Pb and Os isotope compositions characteristic of the Dupal anomaly (MD57 9-1).
Island Arc Basalts. The island arc basalts studied here mainly comprise basalts sampled from oceanic arcs in order to limit the potential for crustal contamination and assimilation processes. In addition to the New Britain basalt data published by  Millet and Dauphas (2014) , 3 arc lavas from the Mariana arc were studied that were previously analysed for major and trace elements by  Yi et al. (2000). A single basalt from the Izu-Bonin arc, rock standard JB-2, was also analysed. Intraplate basalts. The intraplate basalts mainly comprise ocean island basalts (OIBs) from four different localities. Single samples from Hawaii (BHVO-2 rock standard) and Easter Island (sample 17678,  Baker et al. , 1974)  were analysed as well as samples from the Cape Verde (São Nicolau island;  Millet et al. , 2008)  and Azores archipelagos (São Miguel island:  Turner et al.
, 1997; São Jorge island: Millet et al. , 2009). In addition, a single continental flood basalt (the USGS rock standard BCR-2) was also analysed. Eclogites. The three eclogites measured here were collected from the Zermatt-Saas Fee ophiolite, Switzerland. They consist of 2 basaltic eclogites and 1 gabbroic eclogite, based on their mineralogy  (Dale et al. , 2007). Estimates of peak metamorphic conditions for these samples range between 2-3 GPa and 550 to 630 ºC  (Barnicoat and Fry, 1986; Reinecke, 1991). Major and trace element data  (Dale et al. , 2007)  show that all the samples selected suffered loss of volatile and fluid-mobile elements during metamorphic dehydration. Mantle samples. The mantle-derived samples are of two types. First, a single orogenic peridotite from the Beni Bousera massif was analyzed (GP13,  Pearson et al.
Isotope ratio measurements were carried out on Neptune Plus MC-ICP-MS installed in the Arthur Holmes laboratory at Durham University and at the Origins Laboratory of the University of Chicago. The samples were injected into the plasma torch using an Aridus II desolvating nebuliser and the isotope measurements were performed in medium resolution mode. Despite being double-spiked, all sample measurements were bracketed by measurements of double-spiked standards (OL-Ti) measured at the same concentration level in order to account for small unresolved polyatomic interferences on 47 Ti and 48 Ti produced in the mass spectrometer. Raw data is then processed offline using in-house double-spike deconvolution codes written in Mathematica ®. All data is expressed as  49 Ti, which is the deviation of the 49 Ti/ 47 Ti ratio of samples relative to that of the OL-Ti standard  (Millet and Dauphas, 2014).
Results Data for all standards and samples analysed during the course of this study are given in Table  1. For samples measured once only, quoted errors are internal errors, whereas for samples measured multiple times, 95% confidence intervals were calculated using the Isoplot software  (Ludwig, 2003). Repeated digestion and analysis of BCR-2, BHVO-2 (HF or flux fusion digestion) and JB-2 reference materials, previously analysed by  Millet and Dauphas (2014)  at the Origins Laboratory (University of Chicago), were conducted in order to certify data quality and the absence of inter-laboratory bias (Fig. 1 ). The average Ti isotope compositions of standards analysed at Durham University is in excellent agreement with that of the Origins Laboratory data and demonstrates that the 2 reproducibility of our  49 Ti measurements is ca.
Ti stable isotope fractionation during magma differentiation A striking feature of our terrestrial sample dataset is the significant enrichment of differentiated magmatic rocks in isotopically heavy Ti isotopes relative to more primitive magmatic rocks. This enrichment is correlated with SiO 2 content, suggesting a relationship with fractional crystallisation processes (Fig. 2 ). Titanium is highly to moderately incompatible in most of the silicate minerals typically involved in magma differentiation (pyroxenes, olivine, plagioclase, micas, quartz) regardless of the composition of the melt. Mass balance considerations therefore suggest that it is unlikely that the crystallisation of silicate mineral phases could have modified the Ti isotopic composition of the remaining melt. Titanium behaviour during fractional crystallisation is mainly controlled by the crystallisation of Fe-Ti oxides that can occur at all stages of magma differentiation.
g. olivine, pyroxenes, plagioclase, spinel, garnet) has been identified to preferentially incorporate the heavy isotopes of titanium relative to melt at equilibrium. As an arc basalt, JB-2 is expected to display anomalously low abundances of fluid immobile elements such as Ti and Nb relative to elements of similar incompatibility (Sm and Tb for Ti, Th and La for Nb;   * ⁄ normalised to the primitive mantle composition of McDonough and Sun, 1995) in normalised trace element patterns. For example, compiled data for the Izu-Bonin arc eruptives (obtained from Georoc database) show that at similar SiO 2 contents to JB-2 (53. 2wt%), Izu-Bonin magmas typically have Ti/Ti* of 0. 7 to 0. 85 and Nb/Nb* of 0. 16 to 0. 35. While JB-2 displays the strong negative Nb anomaly (Nb/Nb* = 0. 21) expected for island arc lavas, it does not display a negative Ti anomaly (Ti/Ti* = 1.
Acknowledgements MAM acknowledges financial support from  Durham University  and a  Marie Curie COFUND International Junior Fellowship  held at  Durham University. ND was supported by grants from  NSF  ( EAR 1144429 ,  1444951 ,  1502591 ) and  NASA  ( NNX12AH60G ,  NNX14AK09G ,  NNX15AJ25G ). NDG was supported by the  Swiss National Science Foundation  (grant  P2BEP2-158983 ). HMW acknowledges support from  NERC   Advanced Fellowship   NE/F014295/2  and the  ERC  ( Consolidator Grant "HabitablePlanet" 306655 ).
The composition of Dark Matter (DM) remains an important open question. The current data do not distinguish between single-and multi-component DM, while in theory constructions it is often assumed that DM is composed of a single field. In this work, we study a hidden sector which naturally entails multicomponent DM consisting of spin-1 and spin-0 states. This UV complete set-up is based on SU(3) hidden gauge symmetry with the minimal scalar field content to break it spontaneously. The presence of multiple DM components is a result of a residual Z 2 × Z 2 symmetry which is part of an unbroken global U (1) × Z 2 inherent in the Yang-Mills systems. We find that the model exhibits various parametric regimes with drastically different DM detection prospects. In particular, we find that the direct detection cross section is much suppressed in large regions of parameter space as long as the Standard Model Higgs mixes predominantly with a single scalar from the hidden sector.
Given the charges under the Z 2 × Z 2 symmetry and the mass relations of Eqs. (15, 18, 22) , different states can contribute to Dark Matter. The options are summarized in Table  2. In all cases, DM consists of 3 states. Since A 1 , A 2 are degenerate in mass, one may introduce a formal analog of the W ± bosons via the linear combinations A 1 ± iA 2 even though A 1 , A 2 have different parities. We find that such a redefinition facilitates numerical computations, in particular, what concerns the software Micromegas. This allows for the treatment of A 1 , A 2 as an effectively single (complex) DM component. A similar redefinition can be applied to another mass degenerate pair A 4 , A 5.
01 is required to keep the χ-annihilation efficient. While qualitative features of the plot can be understood semi-analytically, we have performed our numerical analysis using the software Micromegas  [39]  which is well suited for 2 component DM. In Fig. 6 , we show the contours of correct DM relic density in the (m A , g)-plane (left panels) and (m χ , g)-plane (right panels). The color coding along the contours indicates the value of f A : the red (blue) end of the spectrum refers to vector (pseudoscalar) dominance. Many features of the plots can be understood qualitatively. In the upper left panel, the dark annihilation process AA → χχ is important, yet the resulting χ states annihilate very efficiently through the h 1 resonance into the SM fields. As a result, DM is mostly vector (apart from the small region m A m h 2 /2).
Acknowledgements The authors thank  A. Pukhov  for his help with the package Micromegas. G. A. acknowledges support from the  ERC  advanced grant Higgs@LHC. C. G. is grateful to the  Mainz Institute for Theoretical Physics (MITP) , where a part of this work was done, for its hospitality and partial financial support. C. G. and O. L. acknowledge support from the  Academy of Finland , project The  Higgs Boson  and the Cosmos. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  674896. S. P. is supported by the  National Science Centre, Poland , under research grants  DEC-2014/15/B/ST2/02157 ,  DEC-2015/19/B/ST2/02848  and  DEC-2015/18/M/ST2/00054. T. T. acknowledges support from  P2IO   Excellence Laboratory (LABEX).
This kind of solution uses sensors to measure environments and other factors. The sensed values from sensors will be sent via a wireless network to a data center periodically. Next, a back-end system will manipulate these values and propose solutions relating to collected situations of surrounding conditions. Such application is called bio-environmental surveillance network. There exists a relation between a bio-environmental phenomenon and its observation WSN in a surveillance network. Indeed, the WSN provides sampling inputs for the phenomenon and the phenomenon occurs in accordance with its rules based on these inputs. On the other hand, damage levels of bioenvironments caused by the phenomenon are sampling measured by sensor nodes of the WSN. The paper proposes a new approach to model a bioenvironmental surveillance network as synchronous networks  [14]  in order to emerge the relation between bio-environmental phenomena and their WSNs.
Related work Examples of bio-environmental surveillance network is variant such as light trap networks, flood surveillance, fire forest surveillance. Light trap network uses light traps to measure environmental factors and insect densities. Thanks to it, people can make suitable decisions to get better protection for their crops from insect attacks. Light trap method is one of solutions to prevent high densities of spruce budworm in Canadian forests [9]  [10]. It allows people to participate insect trapping by giving light traps to them and track their traps from June to end of August every year. Periodically, people only report estimated densities of insects via a website, an application or even with a paper and a pen. Finally, trap samples are collected and counted in a lab environment. Applications of data collections from these light traps are variant, for example, thanks to wing wear and body size measurements of adult spruce budworms captured at light traps in some previous years, some useful inference on seasonal patterns related to reproduction can be archived  [11].
Problematic Light trap network is one of solutions for insect management in Vietnam. The network uses light traps to attract insects (due to their phototactics) in order to classify and count insect densities. Thanks to these density values, people can know situations of their fields better and make decision if necessary. The Wireless Sensor Network approach applied to a light trap network (as proposed solutions in  [4]   [5] ), may help calculating Brown Planthopper (BPH) densities and measure environmental factors automatically. This kind of solution uses sensors, new automatic light traps, to measure environments and hopper behaviors. These sensed values from sensors will be sent via a wireless network to a data center periodically. Next, a backend system will manipulate these values and propose solutions relating to situations of collected data.
Nodes. Each cell in figure  5  represents a node in the graph G 1 =(V 1 , E 1 ). By the time passing, each node (or process) i composes a collection of states states i of which a state holds values of rice age, wind, and BPH density at the time t. Each node may consist a sensor node to sense above factors of a state. When the sensor node senses environment, it transmits the collected data to a gateway for storing and post processing. Edges. Edges in the graph G 1 are composed by links between a node and its neighbors (4, 6, 8 neighbors). Because G is a directed graph, there are 2 edges between the node and a neighbor of it (2 directions). Example. Figure  8  is an example of a graph of the BPH surveillance network in Phongdien district, Cantho, Vietnam. In this example, the map of this district is divided as hexagonal cells, each cell is almost a commune of the district.
HopperobservationWireless Sensor Network A WSN is created by distributing sensor nodes in some cells to sampling measure environmental factors and hopper density (sensor P1, P2,. in figure  5 ). Data collections from the WSN can be considered as indications for the operating of the whole BPH surveillance network as well as for post processing. The WSN can be considered as a synchronous network. The WSN represents a mesh network by being considering as a graph G 2 =(V 2 , E 2 ) where V 2 is a collection of nodes and E 2 is a collection of edges. A node in V 2 represents a sensor node while an edge is created between 2 sensor nodes if their distance is at most the transmission range (figure  5 ). In addition, each sensor node in the WSN has its own behaviors. Behaviors. Behaviors of the WSN depict the data dissemination inside the network.
Bio-environmental surveillance network data model A bio-environmental surveillance network plays important role in collecting data. Thanks to the current data collection and historical data, people can know better their solutions and can make suitable decisions. For example, in comparison with historical light trap data, the current trap data may indicate the peak season of BPH and people can propose a suitable cultivation time. The data model of the bio-environmental surveillance network consists of 2 parts: meta-data model and data collection model. Meta-data model depicts the meta structure of a bio-environmental surveillance network, includes of the physical system and WSN system. On the other hand, data collection model is the model of collected data; it depends on what types of data people want to collect or sense.