dataesr/scientific-paragraphs-categorization huggingface dataset:
In most regions that show a high correlation between the datasets, there is good coverage of meteorological stations. On the contrary, the assessment of convection processes in humid equatorial regions in the ERA5 dataset is also affected by uncertainties  (Taszarek et al. , 2021)  and may affect the comparability between the datasets in these regions. In hyper-arid regions such as the Sahara or the Arabian Peninsula, the SPEI is driven mainly by changes in the AED. In these areas, land-atmosphere feedbacks related to the extremely warm land strongly control air temperature and vapour pressure deficit  (Brutsaert, 1986; Brutsaert & Stricker, 1979). It is possible, too, that the reanalysis is affected by uncertainties in assessing these processes. Also, wind speed assessment could play a role because the CRU dataset employs the 1961-1990 average monthly wind speed when calculating AED.
On the other hand, as a result of the complexity of the interaction between the relief and the dominant airflow direction, modelling ERA5 wind speed is significantly more uncertain than modelling other variables  (Deng et al. , 2021; Minola et al. , 2020). We assessed the degree of agreement in drought conditions between the two datasets. For this purpose, we compared the percentage of cases in which dry (SPEI < 0), mild (SPEI < -0. 84) and extreme drought (SPEI < -1. 65) occurred concurrently. To accomplish this, we calculated the proportion of cases in which drought conditions recorded in the CRU SPEI are well reproduced by the ERA5 dataset (Figure  4a ). Also, we looked for cases in which no drought conditions were recorded in the CRU dataset, while drought conditions were presented in the ERA5 dataset (i.
e. false positives). Considering dry conditions (SPEI < 0) the majority of the world regions exhibit high agreement between the two datasets, indicating that the occurrence of dry conditions with the CRU dataset is generally well-reproduced by the ERA5 dataset. When focusing on mild and extreme drought events, the agreement decreases, but large areas of the world agree on the occurrence of drought conditions. Moreover, there is a small percentage of false positives corresponding mainly to extreme and mild drought conditions, demonstrating that when CRU SPEI is greater than 0, the ERA5 SPEI showed similar patterns during the most anomalous drought conditions. Also, the percentage of false positives increases for dry conditions (SPEI < 0). Overall, it can be concluded that the ERA5 SPEI data is capable of identifying the dry conditions identified by CRU dataset, especially in regions of dense meteorological records.
Rather, more uncertainty is introduced in regions characterized by scarce and sparse network of observations. A careful look into the SPEI under specific periods shows that in areas with low meteorological station density, the CRU SPEI seems to be strongly determined by the stations' spatial distribution (Figure  5 ), which show important spatial and temporal differences (see Figure  1  in  Harris et al. (2020) ). The CRU SPEI maps present circular shapes (around available observations) that do not correspond to the expected smooth spatial variation of wet and dry conditions. A representative example is the areas of South America (Brazil) and central Africa. The ERA5 dataset, on the other hand, offers a much more coherent spatial distribution of the SPEI, with smooth, gradual transitions and no strange shapes.
In general, there is good agreement in the spatial distribution of the SPEI from both datasets, and they record similar large-scale drought periods. Nevertheless, the spatial correlation is not very high (Figure  6 ). Correlations vary between 0. 5 and 0. 6, which means a general agreement between the two datasets. Still, there are noticeable differences on the regional and local scales. Considering only the spatial structure of the SPEI values in particular months (see Figure  5 ), it seems that the ERA5 dataset is more coherent with the spatial variations in drought. Nevertheless, modelling precipitation and other variables involved in calculating the AED in the ERA5 dataset, particularly in areas with a low density of meteorological stations, still introduces a degree of uncertainty to this dataset.
As stressed above, the assessment of drought conditions seems to show agreement between both datasets, so for drought monitoring, particularly in areas characterized by high data availability, the use of the ERA5 SPEI appears to be highly recommendable. Nevertheless, for the longterm assessment of drought conditions, further research is required. Some studies have found divergent trends in annual precipitation between ERA5 data and observational datasets  (Shobeiri et al. , 2021; Tarek et al. , 2020). In particular, Nogueira (2020) compared the annual precipitation trends from the GPCC and ERA5 datasets and found that ERA5 shows ample precipitation declining trends in several regions of Africa and South America that are not observed with the GPCC. We found stronger increase in the surface area affected by drought using the ERA5 than the CRU dataset (Figure  7 ).
The difference is likely due to the stronger decline in precipitation in ERA5 than in CRU (results not shown). However, further research is needed to determine the reliability of ERA5 data to assess longterm drought severity trends. Here, it should be noted that data temporal homogeneity is not a major concern for the real-time monitoring application, which is more concerned with establishing parts of the world are equally affected by this uncertainty. Though global SPEI trends in the CRU and the ERA5 datasets appear to be largely in agreement (Figure  8 ), there are notable differences between the two datasets in the regions where there is a limited number of meteorological stations and where any precipitation database suffers from significant limitations. In contrast, SPEI trends in Western Europe, North America and Australia are more closely aligned with each other, which strengthens the new system's ability to monitor drought in these regions.
| DATASET LOCATION AND FORMAT The drought monitoring dataset is available at https:// globa l-droug ht-crops. csic. es/. Figure  9  shows a screenshot of the system. In the top-right of the screen, there are two menus. The first one is a timeline bar that allows selecting any weekly timeframe between 1979 and the present. The second one is a menu organized by different SPEI time scales  (0. 5, 1, 3, 6, 9, 12, 24, 36 and 48 months). Under each time scale, a menu allows selecting the spatial mask of choice. The default option is to show the entire world (i. e. no mask applied), and then there are six options corresponding to the major global crop-growing areas. When a crop-growing area mask is selected, only the grid cells in which the particular crop is cultivated are shown. The map is fully navigable, so the user can zoom in and out and change the map centre.
There are two downloading options for that. The first one is to download the whole dataset from 1979 to the present, while the second one only downloads the last layer of the dataset. This last option is intended to suit the needs of particular users who already have the entire dataset and need to update it with the latest weekly data. The technical specifications of the netCDF format used are shown in Figure  13. It maintains the coordinate system (geographic) and the number of latitudes (361) and longitudes (720) of the original ERA5 dataset. The dataset times will vary with time, increasing as the dataset is updated with new values. In comparison with the SPEI global drought monitor (https://spei. csic. es/map) developed in 2011, the inclusion of the ERA5-based dataset has improved the SPEI monitoring system.
Since 2011, the system has been maintained live based on GPCC precipitation and CPC mean temperature data. The main improvements to the current system are the following: since every week new SPEI information is generated; and (4) the quality and reliability of the SPEI data included in the system has been improved significantly when considering the AED role, since the new system uses the physically based Penman-Monteith equation to calculate AED. With respect to the SPEI global drought monitor, the AED was calculated using the empirically based Thornthwaite equation  (Thornthwaite, 1948 ) that uses mean temperature data. This simple method was chosen because the Climate Prediction Center (CPC) mean temperature  (Fan & Dool, 2008)  was the only possible real-time input that allowed estimating the AED in quasi real time.
Nevertheless, it is widely known that empirical AED approximations based on temperature data alone show limitations under global warming, as is not the case with physically based approaches  (Sheffield et al. , 2012; Vicente-Serrano et al. , 2020). Although the SPEI global drought monitor will be maintained given its high accessibility (more than 3,000 visits per month), we recommend that current and future users of real-time SPEI data migrate to the new system based on ERA5 input given its higher temporal frequency, spatial resolution and physical consistency in AED calculation.
| CONCLUSIONS This study describes the generation of a global drought dataset based on the Standardized Precipitation Evapotranspiration Index (SPEI) using the ERA5 reanalysis dataset. The dataset is maintained in near real time and updated weekly and is integrated into a user-friendly interface with several functionalities. These characteristics make it an effective global drought monitoring system. The system provides information on a global scale, but users interested in crop-growing areas may apply spatial masks to show only the areas where specific crops are grown. The associated long-term dataset, covering the period between 1979 and the present, is potentially helpful in assessing the possible effects of global warming on drought given the role of the AED. Although this information is helpful in determining the severity of particular drought events, further research is necessary to.
Scatterplots of the average annual, January and July atmospheric evaporative demand (AED) (left) and precipitation (right), using the ERA5 and CRU datasets. The scale represents the density of points. F I G U R E 3 Spatial distribution of the Pearson's r correlation between the 3-month and 12-month SPEI between 1979 and 2020 using CRU and ERA5 datasets. Statistically significant trends are set at Pearson's r = 0. 11. spatial differences between areas affected by dry or humid conditions. It is evident that the metrics used to quantify the severity of drought could be affected by the lack of temporal homogeneity in the input data. Nonetheless, this level of uncertainty can be found in any precipitation dataset (Hassler & Lauer, 2021). In addition, not all F I G U R E 4 (a) Percentage of cases in which there is an agreement between the occurrence of droughts identified with the CRU SPEI, (b) percentage of false positives: Cases in which the CRU SPEI does not show drought conditions and the ERA5 SPEI suggests drought of different severity.
Evolution of Pearson's r spatial correlations at the global scale between the CRU and ERA5 SPEI at the 3-month (blue) and 12-month (red) timescales. F I G U R E 7 Evolution of the percentage of surface areas affected by mild (<-0. 84; yellow), moderate (<-1. 28; red) and extreme (-1. 65; dark red) drought conditions on the global scale. Results are presented for the 3-and 12-month SPEI based on the CRU and ERA5 datasets. To avoid the role of the strong temporal autocorrelation in the trend analysis, trends were calculated from the December SPEI at the time scale of 12 months and the average of March, June, September and December SPEI at the time scale of 3 months. Trend analysis was based on the Mann-Kendall test, while the magnitude of change was calculated by means of linear regression. The correlations between the CRU and ERA5 series are shown at the bottom of the figure.
(a) the new dataset multiplies by four the spatial resolution, as it goes from 1° to 0. 5°; (b) the temporal frequency of the new dataset increases from the monthly time scale available in the SPEI global drought monitor to the weekly frequency of the new SPEI monitor based on ERA5 data; (c) the new system has also been enriched with more updated information F I G U R E 9 View of the drought monitoring system based on the ERA5 data and the SPEI.
ACKNOWLEDGEMENTS This work was supported by projects  PCI2019-103631  financed by the  Spanish Commission of Science and Technology and FEDER  and  CROSSDRO  project funded by  AXIS (Assessment of Cross [X]-sectoral climate Impacts and pathways for Sustainable transformation ), JPI-Climate co-funded call of the  European Commission. F I G U R E 1 2 Example  of an interactive time series plot based on a user-defined coordinate. F  I G U R E 1 3 Specifications  of the netCDF SPEI data obtained from the  ERA5  dataset.
F 2 2 Scatterplots of the average annual, January and July atmospheric evaporative demand (AED) (left) and precipitation (right), using the ERA5 and CRU datasets. The scale represents the density of points. F I G U R E 3 Spatial distribution of the Pearson's r correlation between the 3-month and 12-month SPEI between 1979 and 2020 using CRU and ERA5 datasets. Statistically significant trends are set at Pearson's r = 0. 11. spatial differences between areas affected by dry or humid conditions. It is evident that the metrics used to quantify the severity of drought could be affected by the lack of temporal homogeneity in the input data. Nonetheless, this level of uncertainty can be found in any precipitation dataset (Hassler & Lauer, 2021). In addition, not all F I G U R E 4 (a) Percentage of cases in which there is an agreement between the occurrence of droughts identified with the CRU SPEI, (b) percentage of false positives: Cases in which the CRU SPEI does not show drought conditions and the ERA5 SPEI suggests drought of different severity.
F Evolution of Pearson's r spatial correlations at the global scale between the CRU and ERA5 SPEI at the 3-month (blue) and 12-month (red) timescales. F I G U R E 7 Evolution of the percentage of surface areas affected by mild (<-0. 84; yellow), moderate (<-1. 28; red) and extreme (-1. 65; dark red) drought conditions on the global scale. Results are presented for the 3-and 12-month SPEI based on the CRU and ERA5 datasets. To avoid the role of the strong temporal autocorrelation in the trend analysis, trends were calculated from the December SPEI at the time scale of 12 months and the average of March, June, September and December SPEI at the time scale of 3 months. Trend analysis was based on the Mann-Kendall test, while the magnitude of change was calculated by means of linear regression.
First, we look for the most prevalent bar widths, and we examine the networks formed by filaments of different bar widths as well as their hierarchical organization. Second, we compare the filament orientations to the magnetic field orientation inferred from Planck polarization data and, for the first time, we study the statistics of the relative orientation angle as functions of both spatial scale and H 2 column density. Results. We find preferential relative orientations in the four Herschel fields: small filaments with low column densities tend to be slightly more parallel than perpendicular to the magnetic field; in contrast, large filaments, which all have higher column densities, are oriented nearly perpendicular (or, in the case of G202, more nearly parallel) to the magnetic field. In the two nearby fields (G210 and G300), we observe a transition from mostly parallel to mostly perpendicular relative orientations at an H 2 column density ≃1.
7 cm -2 (Planck Collaboration XXXV 2016b). A similar trend (low-N H filaments mostly parallel and high-N H filaments mostly perpendicular to the magnetic field) within individual clouds, with a similar transition column density, were also found by  Malinen et al. (2016) ;  Cox et al. (2016) ;  Soler (2019) , who compared the orientations of the plane-of-sky (PoS) magnetic field traced by Planck (at ≃10 ′ resolution) and the filaments traced by Herschel (36 ′′ resolution) in a sample of nearby molecular clouds. These results provide evidence that a coupling exists between magnetic fields at cloud scales and filaments at smaller scales  (Soler 2019). They also demonstrate the benefit of combining Planck and Herschel data sets despite their different angular resolutions. This benefit, in turn, calls for further statistical analyses based on Planck and Herschel data together to explore relative orientation trends towards star-forming regions in different Galactic environments.
The purpose of the companion paper  (Carrière et al. 2022 , hereafter Paper I) was precisely to develop such a method, which we dubbed FilDReaMS (Filament Detection & Reconstruction at Multiple Scales). Our purpose here is to apply FilDReaMS to a first selection of four Herschel fields and to study the properties of the detected filaments, with a special attention to their relative orientations to the magnetic field. In Sect. 2, we present the data used for our study. In Sect. 3, we review our new FilDReaMS method, giving just enough details for its application. In Sect. 4, we apply FilDReaMS to a sample of four Herschel fields selected amongst the 116 fields of the Herschel-GCC program. In Sect. 5, we provide a detailed discussion of our results. In Sect. 6, we summarize and conclude our study.
Data We will apply our new FilDReaMS method to a sample of four molecular clouds, for which we will exploit the combination of Planck-HFI and Herschel data. In brief, Planck-HFI observations at 353 GHz provide a whole-sky map of the dust polarized emission with an angular resolution of 4. 7 ′ ; from this map, one can infer (a dust-weighted line-of-sight average of) the orientation of the PoS component of the magnetic field. Meanwhile, Herschel observations provide dust emission maps at several wavelengths (with an angular resolution of 18 ′′ at 250 µm); based on these maps, column density maps have been constructed with an angular resolution of 36 ′′.
In a second step, we compared the filament orientation angles derived with FilDReaMS to the local magnetic field orientation angle inferred from Planck polarization data. This enabled us to study (for the first time) the statistics of the relative orientation angle as functions of both spatial scale and H 2 column density. We emphasize that combining Herschel and Planck data despite their widely different angular resolutions does really make sense. Indeed, the derivation of a filament orientation angle relies on a bar of length larger than (L b ) min = 3 (W b ) min = 15 px = 180 ′′ = 3 ′ , which is nearly half the 7 ′ resolution of Planck. This highlights the great potential of combining Herschel and Planck data. No firm, general conclusion can be drawn from our study regarding the preferential widths of filaments.
Consequently, the median of all K c values is computed to obtain K: K = 0 signifies regular dynamics, whereas K = 1 indicates the presence of chaos. It should be noted that the dataset N should be of a sufficient data length so that an asymptotic behaviour of D c (n) takes place. Although its few requirements in application, the 0-1 Test is however not always reliable. The used data should not be oversampled nor downsampled, otherwise false results can be obtained. Furthermore, the parameter c has to be tuned in a convenient range to avoid resonance that could yield to K c ∈ [0, 1].
III. PROPOSED CHAOS DETECTION APPROACH The proposed ConDN-based chaos-detection method is summarized in Algorithm 1. The inputs of this method are two vectors: p -representing a time series whose dynamics should be analyzed (regular or chaotic); and s -representing an ordered sampling vector whose elements s i , i ∈  [1, |s|]  provide sampling factors to use for the sampling of the initial time series p. Note that, |s| denotes the length of s. The first phase of this approach is to build the database of input signals needed to feed the ConDN network. This database is built by using the time series p and sampling factors s i (s i ∈ s, i ∈  [1, |s|] ). The first element of this ordered sampling vector is always 1 (s 1 = 1) meaning that the initial time series without sampling is always a part of the database of input signals.
B. ONE-SHOT SAMPLING We first evaluated the one-shot sampling method described in Algorithm 1 on the seven known attractors detailed in Section IV-A1. The set of parameters of the integer-order systems, as well as the set of parameters and orders of the fractionalorder systems, used to generate signals with chaotic and regular nature, are presented in Tables  1  and 2  respectively. The first component x(t) of each of the seven systems, for a given set of parameters and orders corresponding to chaotic or regular nature, is injected beside its sampled counterpart into a separate ConDN. The reservoir size N of all networks is arbitrary set to 1000. Figure  7  presents the variation of the trace values t i , i ∈ s =  [1, 2]  of the chaotic and regular input patterns as a function of their data length, where i = 1, 2 denote the raw p 1 and sampled p 2 patterns respectively.
VI. CONCLUSION In this paper, we investigated a novel methodology for chaos detection in time series using a reservoir computing (RC) paradigm called concept-driven network (ConDN). This chaos detection approach, that involves the sampling of the input series before being injected the network, can be applied in two ways: (1) one-shot sampling, and (2) multiple-shots sampling. The proposed approach has been validated on known chaos attractors driven by ordinary differential equations (i. e. Lorenz, Rossler, Chen) of both integer and Fractional order, as well as on a highly non-linear spintronic oscillator called NCVO whose data are collected by micromagnetic simulation. All considered case studies involve chaotic and regular behaviors, whereas some of them involve additionally a transient behavior between them.
Mineralogy Mineralogical composition of the samples (n = 42) was identified via X-ray diffractometry. A Philips PW3710 X-ray diffractometer was used with Cu-Kα 1. 54060 Å radiation generated at 40 kV and 30 mA. The samples were scanned at a rate of 3°per minute, over the range of 2-70°(2θ). The diffraction patterns were identified with the data from X'Pert HighScore Plus software using the data from the PAN-ICSD database, version 2. 3 and Rietveld polynomial for quantitative determination of the phases. The amorphous compound was determined using an external standard phase (NIST-676a) to determine an instrument intensity constant (K-factor) (O'  Connor and Raven, 1988).
Geochemistry X-Ray fluorescence (XRF) analysis was performed on the surface of the split sediment cores, which was covered with 4 μm thick Ultralene, at 2 mm intervals using an Avaatech core scanner (EDYTEM). The geochemical data were obtained with two tube settings: 10 kV at 0. 175 mA for 10 s for Al, Si, S, K, Ca and Ti, and 30 kV at 0. 2 mA for 15 s for Cu, Zn, Br, Rb, Sr, Zr, Mn, Fe and Pb  (Richter et al. , 2006). Three replicates were measured every 10 cm to estimate the standard deviation. Each individual power spectrum was deconvoluted into relative components (intensities), expressed in counts per second. Principal component analysis (PCA) was performed on the geochemical results using R software (R Core  Team, 2018)  to find element correlations between, and hence to identify principal sediment end-members, which were used to better constrain each sedimentological facies (e.
Non-palynological palynomorphs (Sporormiella;  Van Geel, 2002; Gelorini et al. , 2011) , stomata of conifers  (Hu et al. , 2016)  and charcoal particles were noted. The pollen diagram was made using Psimpoll program 4. 261, where zonation was determined with binary splitting by sum-of-squares  (Bennett, 2005).