dataesr/scientific-paragraphs-categorization huggingface dataset:
Conversely, in the "rotational grazing" modality, pasture areas are subdivided into smaller paddocks, and the livestock are moved from one paddock to another, allowing for grazing renewal. This is a more intensive system with higher productivity. The pasture of each paddock, depending on the grazing load, will have different heights and forage biomass amounts. Consequently, the vegetation indices or biophysical variables can vary significantly. To detect continuous and rotational grazing management modalities, two methods exist: based on the vegetation spectral response or based on the visual interpretation of paddock spatial arrangement. If different pasture heights or biomass amounts are found at the same time, it can be indicative of rotational grazing management. Time series of high spatial resolution data can address this goal by measuring, for example, pasture growth rates or post-grazing regrowth  [164] [165] [166].
Studies addressing linear features mapping from remote sensing data have shown that Very High Spatial Resolution (VHSR) satellite images are suitable for automatic hedgerow mapping  [200] [201] [202]. For example, Fauvel et al. [203]  developed a method based on the waveform recognition theory applied to VHSR optical images to detect hedgerows as linear landscape elements composed of trees, distinct from forest patches. Deng et al. [204]  conducted belt continuity recognition and determined that belt gaps less than 10 m were not reliably detected using SPOT 5 images. Complementary studies evaluated the potential of metric SAR data to extract linear hedgerow networks. Bargiel [201]  used textural features computed from the images of the backscattering coefficients, derived from a high-resolution satellite radar image, to detect semi-natural habitats including hedgerows.
In this scenario, only the intensity of the backscattering signal was considered. Betbeder et al. [205]  demonstrated the potential of TerraSAR-X polarimetric data to detect hedgerow networks (Figure  6 ). The emergence of remotely sensed data from improved sensors has created opportunities to better characterize agro-ecological infrastructures in a whole landscape. For example, LiDAR sensors acquire high-resolution 3D measurements, which are useful to estimate a variety of tree features, such as tree height, volume, and biomass  [207]. Nonetheless, LiDAR data is generally acquired at one moment in time because of cost considerations.
General Patterns First, the detailed analysis of the publications on cropping practices and remote sensing shows that these publications primarily addressed one cropping practice at a time. Given that crop type mapping was not part of this review, we observed that the most common practices studied using remote sensing data are mainly sequential multiple cropping, soil tillage, and irrigation. Studies on other practices are less widespread. Second, the reviewed studies generally show that one type of sensor is used for mapping one particular cropping practice. The use of a mono-date decametric multispectral image to obtain information at the field level is outdated, and we observe now: (1) very high spatial resolution images (metric and sub-metric resolutions), acquired in mono or stereo mode, to identify intercropping and mixed-cropping, tree crop and agroforestry plot structure, or agro-ecological infrastructures such as hedgerows or riparian forests; (2) high image acquisition frequency of one to three cloud-free images biweekly to identify practices whose recognition is based on the extraction of phenology-based The emergence of remotely sensed data from improved sensors has created opportunities to better characterize agro-ecological infrastructures in a whole landscape.
General Patterns First, the detailed analysis of the publications on cropping practices and remote sensing shows that these publications primarily addressed one cropping practice at a time. Given that crop type mapping was not part of this review, we observed that the most common practices studied using remote sensing data are mainly sequential multiple cropping, soil tillage, and irrigation. Studies on other practices are less widespread. Second, the reviewed studies generally show that one type of sensor is used for mapping one particular cropping practice. The use of a mono-date decametric multispectral image to obtain information at the field level is outdated, and we observe now: (1) very high spatial resolution images (metric and sub-metric resolutions), acquired in mono or stereo mode, to identify intercropping and mixed-cropping, tree crop and agroforestry plot structure, or agro-ecological infrastructures such as hedgerows or riparian forests; (2) high image acquisition frequency of one to three cloud-free images biweekly to identify practices whose recognition is based on the extraction of phenology-based features (multiple sequential cropping, fallowing, etc.
Furthermore, regional remote sensing studies require large training data sets that can be difficult to access. When very high-resolution images are used, the analyses are generally restricted to small areas, due to the large processing demands in terms of time and hardware capabilities. Finally, the conversion of the satellite data to information about a cropping practice is generally completed through usually supervised image classification, or statistical analysis, and often involves derived features such as spectral indices, and most commonly, vegetation indices. Some relationships are direct, but most are indirect, such as the link between the field location within the topo-sequence and the irrigation mode, and need local knowledge on the cropping practices, the environment, and the biological material to be correctly modeled.
Likewise, with increasing availability of combined optical and radar remote sensing data, research capitalizing the complementarity of both information sources should gain considerable momentum  [210]. Realizing its potential requires, at least, the development of robust data fusion techniques  [211]. However, the joint exploitation of several data sources has led to new data processing issues, such as massive data storage and computationally-intensive processing  [212]. Among the data sources that need to be leveraged, expert knowledge on the local agricultural systems, in particular the knowledge of crop types and crop calendars, plays an important role. The right use of expertise should permit the narrowing of the possibilities among the wide range of satellite data sources and data processing methods, and thus the processing time, while producing plausible results.
For example, the use of the crop rotation knowledge within a region can help with crop type mapping  [15] , or local knowledge on the irrigation practices can help specify the data and methods for irrigated land mapping  [92]. Finally, the use of coarse-to-moderate spatial resolution data has restricted the interest in the time series-based approaches to agricultural areas characterized by large fields. The recent launch of the Sentinels' constellation, providing Sentinel-1 SAR images and Sentinel-2 10 m optical images acquired every five days, is promising for studying the cropping practices for a large range of agricultural systems. In particular, Sentinel provides dense time series at decametric resolution which are especially valuable for smallholder agriculture monitoring. However, these data raise new issues, and techniques have to be developed to handle irregularly-spaced time series and to identify and remove cloud contaminated observations, as found in the Harmonic Analysis of Time-Series (HANTS)  [213] , or the temporal gap-filling and resampling method in the iota2 processing chain  [214].
Conclusions The review of the literature shows that the remote sensing community has focused on the detection and characterization of agricultural practices. The practices involved are diverse, using different sources of data and methods, but are mostly limited to case studies. This shortcoming is easily explained by the wide variety and variability of agricultural practices, which cannot be properly captured and described at the plot scale over large areas, due to the lack of suitable satellite data, such as dense time series of optical and radar images at decametric resolution. The increasing availability of remote sensing data, in particular the free European Sentinel-1 and 2 constellations data suitable for small to medium field size monitoring, and the emergence of new data processing techniques such as data mining and deep learning, should stimulate the research in this area, as well as the preparation programs of the next satellite generation with LiDAR and hyperspectral sensors on board.
Pending the next generation of satellite sensors, the research must focus on the current satellite technologies, and explore the combination of different satellite sensors (optical and radar, multi resolution, etc. ) and ancillary data (expertise, census data, etc. ), for homogeneous land units. Consequently, remote sensing is expected to improve spatial knowledge of agricultural practices and cropping systems, and thus enhance its role in the sustainable management of these complex systems. Acknowledgments: This study was conducted within the SIGMA (Stimulating Innovation for Global Monitoring of Agriculture) project, funded by European Union's Seventh Programme for research, technological development and demonstration, under grant agreement No. 603719, in support to GEOGLAM (Group on Earth Observations Global Agricultural Monitoring Initiative).
Indeed, the new eddy detection and tracking method gives access to an unprecedented assessment of the origin and fate of the Agulhas Rings and the Indo-Atlantic exchange of waters they carry out. Moreover, we will discuss their characteristics and variations along the trajectories in terms of various kinematic and dynamical properties that can be deduced from altimetry. The paper is organized as follows. In section 2, the data we have used are described and the methods we have developed are presented. Validation and comparisons of our eddy detection algorithm with a published databases are presented in section 3. Section 4 focuses on the Agulhas Rings. We discuss their origins, their disappearance from the altimetry field, their trajectories, and statistics on the different properties of Agulhas Rings.
Satellite Altimetry Data This study is based on more than 24 years (January 1993 to May 2017) of daily maps of delayed time absolute dynamic topography (ADT) and derived geostrophic velocity fields in the South Atlantic and Southeast Indian oceans [70 ∘ W to 65 ∘ E; 55 ∘ S to15 ∘ S] (see Figure  1 ). These maps are produced by Ssalto/Duacs and distributed by the Copernicus Marine Environment Monitoring Service (http://marine. copernicus. eu/) in the version released in April 2014 (DT14; ; Duacs/AVISO+, 2014;  Pujol et al. , 2016). They correspond to the gridded sea surface height (SSH) above the geoid calculated by combining all the data recorded by the satellites available among the 12 altimetric missions (Topex/Poseidon, ERS-1 and ERS-2, Jason-1, OSTM/Jason-2, SARAL/Altika, Cryosat-2, Envisat, Geosat, Haiyang-2A, Jason-3, and Sentinel-3A).
Validation of the Eddy Detection and Tracking Algorithms In the following we summarize the main results of the cross validation between LU16 and the different eddy satellite altimetry databases listed in Table  1 , as well as the different threshold parameters and a thorough comparison with the META2017 atlas. Details of validation and comparisons are discussed in Appendix A. All data sets tested (Table  1 ) show both a decrease in error and an increase in detection efficiency for LU16 eddies with large radii (see Table  2 ). This is most likely due both to the limited spatial resolution of satellite altimetry and its limited ability to capture small structures (e. g. ,  Chelton et al. , 2011)  and also to the lower probability that drifting buoys are captured in small eddies rather than in large eddies.
A2. Validation of the Eddy Detection Algorithms The results of the cross validation between LU16 and the different eddy satellite altimetry databases listed in Table  1  are discussed in detail below. Table  2  shows the number of eddies identified in each data set and their detection efficiency expressed as percentages of the total number of collocations with LU16 eddies. To assess the skill of the method and provide quantitative comparisons between the different eddy data sets, a matching percentage is computed. It represents the proportion of each polarity of the LU16 eddies that 10. 1029/2018JC014270 were successfully cross detected with eddies of the same polarity in each data set (Table  2 ). Cross-detection errors are also defined as mismatches in eddy polarity or when several eddies detected by altimetry have been assigned to the same LU16 eddy.
This difference is also noticeable in META2017, but less marked. In TOEddies, fewer than 1% of the eddies have a radius greater than 140 km, while it corresponds to 5% of the structures for META2017. To compare the size of eddies detected by satellite altimetry with an independent variable related to mesoscale ocean dynamics, we estimated the first Rossby baroclinic radius (L R ). L R characterizes regionally the size of long-lived eddies in the open ocean. The average value of L R was calculated using the definition of  Chelton et al. (1998)  and the 7-year average (i. e. , 2005 to 2012) of the World Ocean Database  (Boyer et al. , 2013). The resulting value is represented by the vertical dotted line in Figure  7. The shaded area represents L R percentiles 10 and 90. This figure shows that TOEddies identifies structures whose size is comparable to L R (around 60% of TOEddies radii are in the percentile range L R 10 -90), whereas this is not the case for META2017, for which less than 20% of radii are in this interval.
Materials The Virtual Equipment The virtual equipment was composed of a computer-generated 3-D model of an artificial environment. This environment was built with Virtools Dev 3. 0 software and the novel EditoMem and SimulMem softwares developed in the lab. The environment was run on a PC laptop computer and explored using a video-game steering wheel, a gas pedal, and a brake pedal. It was projected with a video projector onto a screen 85 cm high and 110 cm wide. The participants were seated in a comfortable chair. The VE was projected 150 cm in front of them.
Results The HP1 Gene Family Recurrently (But Selectively) Amplifies across Diptera To investigate the diversification of the HP1 gene family across Diptera, we performed a tBLASTn search of 64 curated genomes or transcriptomes that span 250 My of evolution (see Materials and Methods; supplementary tables S1 and S2 and fig. S1 , Supplementary Material online). Canonical HP1s are characterized by the presence of both a chromodomain ("CD") and chromoshadow domain ("CSD"); however, abundant CD-only and CSD-only HP1s have been described across Drosophila (fig. 1A ;  Levine et al. 2012). While the CSD is diagnostic of HP1 gene family membership, many other euchromatin-and heterochromatin-packaging proteins encode a CD. We therefore considered true HP1 family members only those genes encoding a CD and a CSD ("full"), a CSD only, or a sole CD that forms a monophyletic clade with canonical "full" HP1s.
A small subset of deeply diverging, CD-only HP1-like candidate genes required an additional reciprocal best BLAST analysis to confirm membership (see Materials and Methods). In any given Diptera genome, we inferred that our search for HP1 orthologs and paralogs was exhaustive when our HP1-only query (supplementary table  S3 , Supplementary Material online) recovered three non-HP1 genes that encode closely related CDs. These "HP1-relatives" are Polycomb (Pc), Chromator (Chro), and Suppressor of variegation 3-9 (Su(var)3-9). We also identified in most Diptera genomes a fourth, more phylogenetically labile HP1-relative gene, CG8289, which we used only for phylogenetic delineation of true HP1 family members (supplementary fig. S2  and table  S4 , Supplementary Material online). The CDs of proteins that also have a helicase domain, like CHD1 and Mi-2 ("HP1distant" genes), were too distantly related to offer additional HP1 phylogenetic resolution (supplementary fig.
D. miranda encodes six independently duplicated HP1 paralogs, all of which are predominantly testis-expressed. Finally, the distantly related T. dalmanni (stalk-eyed fly, 70 My diverged) harbors three ubiquitously expressed and four testis-restricted young HP1 genes. These data are consistent with convergent germline expression evolution across independently derived HP1s. We also identified scores of lineage-specific HP1s in many Diptera species for which soma-versus germline-transcriptomes are not publicly available. To investigate gene-and tissue-specific expression patterns in two such species-Glossina morsitans and Scaptodrosophila lebanonensis-we performed RT-qPCR on dissected male and female heads and reproductive tissues (fig. 4 ). For both species, we detected expression in all tissue types for the ancient HP1A, HP1B, and HP1C genes.
We then verified domain identification with NCBI Conserved Domain Search  (Marchler-Bauer et al. 2015). We inferred that our HP1 search was exhaustive in a given genome when we identified a complete CD sequence of "HP1 relative" genes: Pc, Su(var)3-9, and Chromator. These three genes encode CDs that are closely related to the HP1 CD, in contrast to the CD and helicase domain-encoding genes, like CHD1 and Mi-2 (supplementary fig. S2A  and table S4, Supplementary Material online). When available, we defined intron/exon boundaries based on transcriptomic sequence assembly. If a transcriptome was not available for gene model prediction, we aligned the predicted Helleu and Levine. doi:10. 1093/molbev/msy128 MBE HP1A, HP1B, HP1C, or HP1 relatives (see below) to orthologous exons identified in closely related species to determine intron/exon boundaries using MAFFT  (Katoh and Standley 2013).
HP1 Gene Phylogeny and Family Membership Delineation We aligned paralogs and orthologs using MAFFT nucleotide alignment v7. 222 with default parameters, then realigned with MAFFT as a translated alignment with default parameters in Geneious (v. 9. 1. 5). We checked the final alignment by eye and we removed all codon positions from the alignment that contain a gap in >50% of the sequences. We built CD and CSD gene trees with MrBayes v3. 2 implemented in Geneious (> 2, 500, 000 trees, 500 sampling frequency, and 25% burn-in). We generated trees until the average SD of split frequencies was <0. 025 for all trees but the two extremely large data sets in supplementary figure  S2B  and C , Supplementary Material online, for which we used a 0. 05 cut-off  (Guindon et al. 2010; Ronquist et al. 2012).
We report in supplementary table S6, Supplementary Material online, that only 10 of the initial 53 CD-only hits satisfied these criteria. HP1A, HP1C, and HP1A-like genes formed well-supported monophyletic clades. HP1B genes from outside Brachycera (supplementary fig. S1 , Supplementary Material online) frequently branched from the root (supplementary fig. S4B  and C and table S12, Supplementary Material online). To confirm that these full HP1s are HP1B orthologs, we conducted a reciprocal best BLAST analysis with three well-annotated genomes that encode unambiguous HP1B genes. All 17 predicted HP1B genes met the reciprocal best BLAST criteria (supplementary table S12, Supplementary Material online). For the non-Diptera arthropod HP1 phylogenomic analysis referenced in the Discussion, many HP1 genes did not form monophyletic clades with the well-supported HP1A and HP1C clades and instead form a polytomy on both the CD and CSD trees.
Acknowledgments The authors thank  C. Leek  for assistance with RT-qPCR and  L. Kursel ,  G. Lee , and four anonymous reviewers for their comments on earlier versions of the manuscript. The authors are also grateful to the  Aksoy lab (Yale University ), especially  G. Attardo  and  B. Weiss , for their generosity of time, tissue, and input. This work was supported by the  National Institutes of Health  ( R00 GM107351  and  R35 GM124684  to M. T. L. ). Helleu and Levine. doi:10. 1093/molbev/msy128MBE.
96, without running), and 3 × 10 -9. Since very small scales that are not explored by current CMB anisotropy data are relevant in this context, a broad set of primordial spectral indices needs to be explored. A wider range of chemical potentials is found by  [90] , allowing also for variations in the amplitude of primordial perturbations at very small scales, as motivated by some inflation models. Figure  7. Spectrum of dipole (in equivalent thermodynamic, or CMB, temperature) expressed as the difference between that produced by a distorted spectrum and that corresponding to the blackbody at the current temperature T 0. Thick solid lines (or thin three dots-dashes) correspond to positive (or negative) values. Left: the case of BE distortions for µ 0 = -2. 8 × 10 -9 (representative of adiabatic cooling; green dots, note the opposite signs with respect to the cases with positive µ 0 ), µ 0 = 1.
We found that the recovered uncertainty scales linearly with the map pixel linear size (i. e. , inversely with N side ). Although maps can be oversampled through a proper scanning strategy and by setting the sampling time of the data acquisition well below that corresponding to the beam resolution, it is clear that the experimental resolution plays a crucial role in this respect. Among CMB space missions proposed for the future, CORE has the best angular resolution. The dipole direction determination can be averaged over the various frequency channels, improving accuracy and providing cross-checks for systematics. With the assumption of a pure blackbody, the same holds for the amplitude. However, when searching for dipole spectral signatures, increasing the accuracy at each frequency (which results from a better sky sampling) turns out to be even more important.
Relative calibration accuracy is an important limiting factor in CMB anisotropy experiments in general and even more so for analyses based on the dipole. In current data analysis pipelines the dipole itself is in fact typically used for calibration, which raises the issue of a circular argument. However, for all-sky mapping experiments (like WMAP and Planck), the orbital dipole from the Earth and satellite motion is ultimately used for calibration, rather than the CMB dipole itself. Precise calibration is always challenging, and it is unclear what the limiting step will be for any new experiment. Nevertheless, in principle it will be possible to measure the spectrum of the dipole with an anisotropy experiment. In general, improving and extending calibration methods is crucial for these analyses.
Various approaches can be integrated into the data reduction design, ranging from a better instrumental characterization to cross-correlation between different CMB surveys and substantial refinements in astronomical calibration sources. We have carried out a large set of simulations, summarised in Table  11 , to parametrically quantify the accuracy required to achieve significant improvements with respect to FIRAS. We find that the importance of the impact of calibration errors decreases from BE distortions to Comptonization distortions and to the CIB amplitude, while the opposite holds for the impact of foreground contamination (in agreement with the increase with frequency of foreground level and of the relative amplitude of the imprints left by the three types of signal). Applying suitable masks also yields an improvement in parameter estimation.