dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This work has been partially supported by  Proyecto de Excelencia   P12-FQM-1492  from  Junta de Andalucía , Project  PGC2018-096540-B-I00  from  Ministerio de Ciencia, Innovación y Universidades (Spanish government ) and Project  US-1254251  from  Consejería de Economía y Conocimiento  ( Junta de Andalucía ).
The nuclear p ⊥ -broadening of Drell-Yan and quarkonium (J/ψ, Υ) production in πA and pA collisions is investigated. The world data follow a simple scaling from SPS to LHC energies, once the process-dependent color factors are properly taken into account, which allows for the extraction of the transport coefficient in cold nuclear matter. We find that q(x) ∝ x -α with α = 0. 25-0. 30. The magnitude of the transport coefficient at x = 10 -2 is q0 = 0. 051 GeV 2 /fm and q0 = 0. 075 GeV 2 /fm, whether Q Q pairs are assumed to be produced as color octet or color singlet states, respectively. The relation between nuclear broadening data and the (CT14) gluon density is also investigated.
Data set q0 (GeV On top of the global fits, the values of q0 have been extracted from each data point using (2. 3) and (2. 6). Within each experiment, the different values correspond either to different nuclei (E772), different collision energies (NA3, NA10), and to different rapidity bins (PHENIX, ALICE, LHCb), see Table  2. Results plotted in Figure  4  show a remarkable consistency (as could be anticipated from Figure  2 ), pointing to a common value for the transport coefficient q0. The weighted average of q0 is also consistent with the estimates above. It is interesting to note that the /fm)  Drell-Yan measurements by NA10 lie slightly above the average. The largest tension is observed with the LHCb measurement at backward rapidity (-4 < y < -2), which might be partly attributed to FCEL or nPDF effects, as discussed in Appendix A.
7 GeV at RHIC at mid-rapidity and Q s = 1. 1 GeV at LHC at mid-rapidity (using here x 2 M J/ψ / √ s). The relation expected in perturbative QCD between the transport coefficient and the gluon distribution in a proton is explored further using CT14 LO gluon distribution. Good agreement is reported provided the hard scale entering xG(x, Q 2 ) and α s (Q 2 ) is Q 2 2. 5 ∆p 2 ⊥ , that is slightly above the BDMPS prescription Q 2 = ∆p 2 ⊥  [12]. The analysis has also been carried out assuming that Q Q pairs turn color singlet on a short timescale. In this case, the full dataset would favor a naturally larger transport coefficient, q0 = 0. 075 GeV 2 /fm, albeit with a similar small-x exponent, α = 0. 3. The present picture could be further tested with additional measurements. At the LHC, Drell-Yan data at backward/forward rapidity by LHCb and at midrapidity by ATLAS and CMS would be extremely valuable.
(B. 2) should be used only when the highest p ⊥ value reached in the experiment, p max ⊥ = p N bins ⊥ , is large enough so that p 2 ⊥ bins is independent of this upper cutoff, within the experimental uncertainty. Whenever available, we use in the present article the values of ∆p 2 ⊥ published in the experimental analyses. This is the case of all fixed-target experiments. The specific case of PHENIX data is specifically discussed hereafter. At the LHC the values of ∆p 2 ⊥ in minimum bias pPb collisions have not been published, neither by ALICE nor by LHCb. The extraction of ∆p 2 ⊥ from these data is discussed below.
The temperature records from the TCs above the subassemblies used for this study are presented in Section 3. These records provided to us by the CEA, Cadarache. Next, an explanatory parametric model describing the subassembly outlet temperature is developed in Section 4. This parametric model is non-stationary. An adaptive parameter estimation algorithm is proposed in Section 5. The reliable detection of transient changes, the nuisance parameter rejection and the design of the FMA test combined with an adaptive estimator are discussed in Section 6. The statistical properties of the detector and its evaluation with real data are also discussed in Section 6. Finally, some conclusions are drawn in section 7.
The TIB of a subassembly causes a rise in temperature of the heat transfer fluid of neighboring subassemblies while the temperature measured above the subassembly with a TIB is not representative for its detection because the inlet port blockage. The principle of the early TIB detection based on the core outlet temperatures is to detect an abnormal temperature rise with the TCs that monitor the neighboring subassemblies. This temperature rise is a consequence of the heating of adjacent subassemblies by conduction through the hexcans. It has been estimated that this heating resulted in an increase of the neighboring outlet temperatures of a few tenths of • C/s per second for 10 s (until the hexcans rupture). Up to our knowledge, the very first recursive filtering method to reduce the outlet temperatures uncertainties based on the statistical estimation theory has been proposed and tested with real data in  [5].
Real data used in this study The temperature measurements used in this study come from the French SFR "Phénix "(today at final shutdown). They were collected in 2009 during the end-of-life tests of the plant. These data were provided by the center of the CEA in Cadarache. The core outlet temperatures have been collected from the TCs, placed into the control plug for real-time observation of sodium outlet temperatures from individual subassemblies. The data acquisition system has been equipped with a 8-bit analog-to-digital converter. The spatial distribution of TCs in the core is illustrated in Figure  2(a). For this study, we have used the dataset containing the temperature measurements from 121 TCs located above the centers of the subassemblies shown in Figure  2(a). The TCs provide the temperature measurement every three seconds, i.
e. the sampling period is ∆T = 3 s. This dataset is composed of two samples covering two periods. The instantaneous temperature field measured at the positions (u i , v i ), i = 1,. , 121, is represented in Figure  2(b). First of all, it is worth noting that the spatial distribution of the temperature is not symmetrical. We can also notice that the temperature is more important in the center than on the edges. The average temperature in the center is approximately equal to 560 • C and 460 • C in the periphery. The temperatures do not vary in a homogeneous way over the whole core. Since the behavior of the temperature depends on the position of the TC, it seems appropriate to take into account the positions of the TCs to describe the evolution of the temperature. In the following, we will try to build a model that describes the variations of temperature signals in the absence of local temperature increase associated with a TIB.
Nominal model of the subassembly outlet temperature This section is devoted to a stochastic-dynamic model of subassembly outlet temperatures measured by a small group of neighboring TCs under normal operating conditions (without a TIB). The goal of this model is to define a projection of the observed data on a specially designed subspace, free from the normal operating variations of the outlet temperatures vector Y ∈ R n measured each ∆T seconds at the positions (u i , v i ), i = 1,. , n. From the preliminary data analysis, it has been established that an adequate description of the local temperature field can be done by the AutoRegressive Moving -Average model with eXogenous inputs, i. e. ARMAX (p, q) model, where p is the order of the AR part and q is the order of the MA part. In the sequel, our attention is restricted to a subclass of ARMAX (p, q) model, i.
e. all the TCs have approximately the same Standard Deviation (SD) σ i of instrumental errors; ii) the heteroscedasticity has no negative impact on the detection algorithm (see details in Section 6. 2). Hence, the LS estimation and its residual vector are given by using the batch of T data X = H T 1,T H 1,T -1 H T 1,T Y 1,T , Ψ 1,T = P H1,T Y 1,T = Y 1,T -H 1,T X (9) where Ψ 1,T = (ε T 1. ε T T ) T is the vector formed from residuals ε 1 ,. , ε T stacked together and P H1, T = I nT -H 1,T (H T 1,T H 1,T ) -1 H T 1, T is a projection matrix. Putting together equations (  7 ) -(  9 ), we get the covariance matrix of the residual ε t : Σ ε = cov (ε t ) = (I n -B) Σ, B = E H T (H T 1,T H 1,T ) -1 H T T (10) The outlet temperature model estimation should be causal because it will be used for the sequential detection of a TIB.
Rejection of nuisance parameters and adaptive detection A key issue in fault detection is to state the significance of the observed deviations (e. g. , a local temperature rise due to a TIB) w. r. t. random noises and nuisance parameters. Handling the presence of nuisance parameters is indeed an important issue in this framework. Distinguishing two subsets of components of the parameter vector, the parameters of interest and the nuisance parameters, is necessary because some parameters of no interest for monitoring. The nuisance parameters, if not of no physical meaning, may appear in the model for its flexibility or specification, or for data interpretation reasons  [34]. Let us consider a group of n TCs with a potentially accidental subassembly (TIB). We will propose a sequential test detecting a local increase in the sodium outlet temperatures of two subassemblies neighboring a potential TIB.
Conclusion This paper addresses the adaptive detection of an abnormal temperature rise due to a TIB in a single subassembly in the core of a SFR. This problem has been reduced to the reliable detection of transient changes in the residuals of a subassembly outlet temperature parametric model. The proposed adaptive recursive algorithm is composed of two parts : i) an adaptive RLS algorithm, which estimates the ARX (p) model of outlet temperatures measured by a local group of TCs installed at the top of the fuel subassemblies and computes the residuals of this model; ii) the FMA test for the reliable transient change detection, which uses these residuals to calculate the LLR in a moving window and to compare the LLR to a predefined threshold. The statistical properties of this algorithm have been studied by using the asymptotic upper bounds for the probabilities of missed detection and false alarm and by using the real data provided by the center of the CEA in Cadarache.
VRR Drilling All samples drilled on VRR were collected and analyzed using the FED-uP and FEST techniques. Data from the reconnaissance phase of the campaign were used to prioritize what samples to collect while being mindful of finite rover and instrument resources. Drilling under FED/FEST is a particularly time-intensive activity because Curiosity is precluded from using its arm for contact science activities or driving while the drill sample is held within the drill bit assembly. This means the rover has to stay parked at the drill location until sample delivery to CheMin and, if desired, SAM is complete. The Curiosity team decided a minimum of three samples were needed to characterize the diversity of VRR rocks. VRR divides into two stratigraphic members defined by changes in lithology, shown in Figure  3  and discussed in more detail in results section 5.
, 2020). A stratigraphic column placing Curiosity's results at VRR in the context of the rest of Mount Sharp is shown in Figure  3  and is discussed in detail by  Edgar et al. (2020). In brief, rocks composing VRR are divided into the Pettegrove Point member and the overlying Jura member. The Pettegrove Point member is fine grained (mudstone to fine sandstone) and composed of parallel thin laminations. The Jura member is also a thinly laminated mudstone to fine sandstone. The Jura is distinguishable from the Pettegrove Point member by its darker color, tendency to erode consistently into centimeter-sized clasts, and occurrence of local decimeter-to meter-scale inclined strata that dip in multiple directions. Although the Blunts Point, Pettegrove Point, and Jura members are defined from in situ data, these member boundaries are observable in orbital data as distinct changes in texture, color, and topography.
Gray Patches: Due to Diagenesis, Not Facies Variation The majority of gray VRR rocks Curiosity encountered occur within the Jura member, although the rover did observe an outcrop of gray rocks in the Pettegrove Point member on Sol 2128. Detailed observations of strata characterized by gray areas and surrounding rocks support the hypothesis that the color changes result from diagenetic processes rather than facies changes. Notably, sedimentary structures and textures do not change between the gray and red rocks within each member; sedimentary features in both are consistent with deposition by lacustrine processes  (Edgar et al. , 2020; Horgan, 2020). In many locations, the spectral transition from gray to red material is also gradational rather than discrete  (Horgan, 2020 ). At least one example of a sharp color transition from red to gray is exposed on the vertical face of the Pettegrove Point member in an area informally named Red Cliff, and multispectral data show absorptions consistent with red and gray hematite  (Horgan, 2020).
, 2020; Rampe et al. , 2020). In summary, active DAN investigations at VRR are consistent with results from other payload instruments that show Cl and, to some extent Fe materials, are heterogeneously distributed within the Jura member. DAN data are also consistent with a heterogeneous distribution of neutron absorbing elements (i. e. , Cl, B, and/or Fe) at the meter scale and are especially variable within gray Jura bedrock patches. Additional colocations of active DAN footprints with APXS and ChemCam measurements are necessary to further pinpoint the exact source of large-scale variability in neutron absorbers across the ridge and thus characterize the relative mobility of Cl versus Fe species in diagenetic events.
(2020) and McAdam et al. (2020)  describe in detail the CheMin-and SAM-derived compositions, respectively, of VRR drilled samples. CheMin data show all three VRR samples contain feldspars, pyroxene, hematite, calcium sulfates, phyllosilicates, and X-ray amorphous material. The Highfield sample from the gray Jura has a very similar crystalline mineralogy as the Stoer red Pettegrove Point sample. The red sample has the most hematite of any sample drilled to date (~15 wt % of the bulk), although the hematite abundance is not significantly greater than rocks in the underlying Murray formation. Hematite (~9 wt %) is present in the gray sample, which, when combined with the color and spectral properties of this material (Figure  12 ), is interpreted to imply gray rather than red hematite (>5 μm crystals) is present  (Morris et al.
, 2020). CheMin, ChemCam, and APXS data all support the hypothesis that these deeper hematite-related spectral absorptions are primarily due to changes in grain size and/or a higher proportion of total hematite presenting as pigmenting hematite  (Frydenvang et al. , 2020; Jacob et al. , 2020; Horgan, 2020; Rampe et al. , 2020; Thompson et al. , 2020). Notably, the spectral observations at VRR do not reflect significantly greater abundances of ferric minerals at VRR, which does not support the original interpretation of VRR as being a site of substantial iron enrichment. Maps of multiple spectral properties (band depths, slopes) across the ridge show that variations in these properties crosscut the primary sedimentary stratification, indicating that diagenetic alteration is likely responsible for the large-scale spectral variability, including within regions on VRR that have remarkably deep hematite-related spectral absorptions  (Horgan, 2020).
, 2019). Calculated Gini index values for rocks in the Pettegrove Point and Jura members are similar to values from rocks in the recessive Blunts Point member rocks, suggesting only a slight coarsening upward from the Blunts Point member through the Jura  (Bennett et al. , 2018). Given this result, it seems likely that enhanced cementation played at least some part in VRR's relative resistance to erosion compared with underlying and adjacent strata. Increased cementation at VRR is also consistent with its higher thermal inertia compared with surrounding Murray formation in orbital data sets (~350-400 vs. ~200-250 J m -2 K -1 s -1/2 )  (Edwards et al. , 2018). What is the composition of the cement in VRR rocks? The association of the orbital hematite spectral signature with VRR was initially interpreted to suggest hematite as the cementing agent  (Fraeman et al.
, 2013). However, CheMin data do not show any correlation between qualitative rock strength and crystalline hematite abundance  (Jacob et al. , 2020). Furthermore, there is no obvious correlation between qualitative rock strength and abundance of any crystalline phases measured by CheMin, elemental compositions measured by APXS or ChemCam, or estimated elemental compositions of the amorphous material in the CheMin data  (Jacob et al. , 2020). Mechanical studies show that very small amounts of cement can increase the strength of granular materials  (Dvorkin et al. , 1994; Wang et al. , 2019; Yin & Dvorkin, 1994). Small changes in amount and composition of cement may therefore be undetectable by Curiosity's payload instruments but still contribute to the increased strength of VRR rocks. Below VRR, quantitative calculations of the compressive strength of Murray formation rocks drilled with nominal drilling techniques similarly showed no clear correlations with CheMin-measured crystalline mineralogy or APXS/ChemCam elemental compositions  (Peters et al.
, 2018). Based on the phases that were present,  Peters et al. (2018)  hypothesized either hematite, calcium sulfate, and/or phyllosilicates could be effective chemically derived cementing material in the Murray formation, and  Smith et al. , (2020)  investigated the possibility of early stage diagenetic silica. These phases may also be cementing VRR. Data collected during Curiosity's campaign at VRR do not support several hypotheses previously proposed about the ridge's origin that were proposed from orbital observations (summarized in section 1). If the ridge formed at a redox interface where dissolved Fe (II) was transported by near-neutral, anoxic waters that were later oxidized and caused precipitation of Fe (III) phases, we would expect to see either an increase in the total measured amount of iron at the ridge compared with surrounding regions or a substantial increase in the total wt % of ferric minerals.
Neither is observed, so the redox interface hypothesis in not supported by VRR in situ data. Curiosity data also definitively confirm that the ridge is not an area that experienced extensive, top-down oxidative weathering that would have left a lag of iron oxides and other insoluble phases, similar to a laterite deposit. If this process had occurred, APXS and ChemCam would have measured increases in iron, aluminum, and titanium with increasing elevation on VRR (i. e. ,  Nesbitt & Young, 1982). Similarly, the elemental enrichments and depletions caused by strong oxidative weathering would have been evident in increasingly higher calculated CIA values with increasing elevation on the ridge. Instead, APXS show CIA values that are constant across VRR, and ChemCam data even suggest that CIA values decrease toward the top of ridge, with values around 50-55 compared to the underlying Murray formation where values frequently reached 60 or more  (Frydenvang et al.
• Land-use changes (e. g. , crop changes, reverting from arable to grassland, agriculture to forest, or other uses). The solution most appropriate to one landscape is not necessarily transferable to another. Targeting is therefore essential to ensure that the right interventions are taken up in the right place. This is not as simple as it seems, however, since most of the landscape, national or international scale decision-makers described above do not have direct control over resource use and management (including for example land, water, energy, and human resources). They need to apply policy levers [or Sustainable land management (SLM) approaches c. f. (WOCAT, 2019)] to encourage land management change. Maps, datasets and evidence to inform targeting often need to be compiled from many sources.
A PROPOSED INTEGRATED LANDSCAPE MANAGEMENT EVIDENCE FRAMEWORK AND TOOLKIT One way to make evidence more accessible and usable to policymakers is to provide guidance on which combinations of approaches can be used to answer particular types of policy question. In Table  3  and Figure  2 , we set out the blueprint for a "toolkit of toolkits. " This provides the structure for a future database of evidence sources, decision support tools and participatory approaches to inform decisions at each of the seven integrated landscape management stages set out in the section on Decision Making in Integrated Landscape Management. Each of the seven parts can act either as a standalone toolkit to address specific challenges or as part of a holistic toolkit that can be used in its entirety to guide decision makers through policy or programme design as a learning cycle.
Exclusion criteria were the following: hypercalcemia (> 2. 65 mmol/L) or any other calcium-phosphorus imbalance, hypertension (>140/90 mmHg) from the first trimester, renal insufficiency (creatinine > 120 µmol/L), bone disease, lithium therapy, bowel malabsorption or kidney stone disease. A bolus vitamin D dose (100 000 IU of cholecalciferol) was prescribed to the patients at the 7 th month of pregnancy according to current French recommendations. Written informed consent was obtained from each patient before inclusion in the study. The protocol was conducted in accordance with the Declaration of Helsinki and was approved by a local independent Ethics Committee (2011/13NICB). The study was sponsored by the Assistance Publique-Hôpitaux de Paris (AP-HP) and was funded by a grant from the Programme Hospitalier de Recherche Publique -PHRC national 2010 (Ministry of Health -AOM10113).
Selection of cases and controls We matched cases of preeclampsia to eligible controls randomly selected in a 1:4 ratio. Preeclampsia was defined as a blood pressure > 140 mmHg or a diastolic blood pressure > 90 mmHg and proteinuria > 0. 3 g/24 h. This definition is the one currently used in France and it was also used internationally at the time of the trial set up. Matching factors were parity (primiparous or not), season at conception, skin color (< 5 or ≥ 5 according to Fitzpatrick scale), body mass index (< 25 or ≥ 25 kg/m 2 ) and maternal age (< 35 or ≥ 35 years). In addition, cases were matched to controls with the closest age whenever possible. Skin color was preferred instead of the more commonly used variable ethnicity, because its use seemed more reliable. Patients were excluded from selection of controls if we could not be sure whether preeclampsia occurred or not (patients not followed up to delivery or no data on blood pressure or proteinuria), if pregnancy was interrupted (abortion, intrauterine fetal death) or if there were no data on delivery.
Demographic and clinical characteristics of cases and controls Pregnant women were included from April 2012 to July 2014 with the last delivery in February 2015. From this total cohort of 3093 women, we identified 84 cases of preeclampsia. Mean term at the onset of preeclampsia was 36. 2 ± 3. 8 WA, 9 cases occurring before or at 32 WA. A total of 296 patients were excluded and the prevalence of preeclampsia in the remaining 2797 women was therefore 3. 0% (84/2797). Finally, 83 cases of preeclampsia were randomly matched among the eligible controls. Only one case out of the 84 was excluded because of incomplete data at inclusion and over follow-up. For six cases with the least common profiles, we could not match the four controls required. Overall, the 83 cases were matched to four controls (n=77), three controls (n=1), two controls (n=3) or one control (n=2), leading to a total number of 319 controls.