dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2012 , 2013) , and external forcing, in particular climatic conditions  (Faillettaz, Funk, & Sornette, 2011; Faillettaz, Sornette, & Funk, 2011). Here, we study a hanging glacier's icequake signals focusing on later arriving seismic "coda" phases to quantify fracture development behind the ice front before a break-off event. We use seismic data from a 4-station network deployed on the Eiger hanging glacier in Switzerland between April and August 2016. We find over 200,000 recurring icequakes with substantial coda out of which we compile catalogs of 30 clusters each comprising events with high waveform similarities. Focusing on coda changes, we use the clusters to monitor source displacements that can be interpreted in terms of damage evolution as a preparation phase toward break-off events.
However, station EIG2 recorded continuously for 4. 5 months and up to three seismic stations operated simultaneously. Seismic data availability is shown in Figure  S3  in Supporting Information S1. Our seismic stations recorded the main break-off on 25 August together with a precursory event on 23 August, the Amatrice earthquake of magnitude 6. 2, and abundant icequake activity consisting of up to 400 events per hour (Figure  1d ).
Acknowledgments This work was funded by the  Swiss National Science Foundation (SNSF)  project  Glacial Hazard Monitoring with Seismology (GlaHMSeis , Grant  PP00P2_157551 ). The authors thank Editor  Mathieu Morlighem , the anonymous reviewer, and  Florent Gimbert  for their helpful comments that allowed us to improve the manuscript. The authors thank  John Clinton ,  Roman Racine , and  Stefan Wiemer ; the  Swiss Seismological Service  and its electronic laboratory ( ELAB)  for technical support and data archiving. The authors thank  Geoprevent Ltd  for allowing us to use their high-quality radar data and images. The authors thank  Dominik Gräff ,  Amandine Sergeant ,  Andrew Curtis ,  Aurélien Mordret , and  Guillaume Jouvet  for helpful discussions.
Data Availability Statement Obspy Python routines (www. obspy. org,  Beyreuther et al. , 2010)  were used to download waveforms and pre-process seismic data. REDPY can be downloaded from: https://github. com/ahotovec/REDPy/ (Hotovec-Ellis & Jeffries, 2016), and Fast Match Filter from https://github. com/beridel/fast_matched_filter/  (Beaucé et al. , 2018). For source displacement, we used a modified MATLAB code package from  Singh et al. (2019). The original MATLAB code package is available at https://github. com/JonathanSingh/cwi_codes/. Seismometer data from stations EIG1, EIG2, EIG3, and EIG4 of the 4D local glacier seismology network  (SED, 1985) , http://networks. seismo. ethz. ch/networks/4d/ are archived at the Swiss Seismological Service http:// www. fdsn. org/networks/detail/4D_1985/.
The archeological application Once it was verified that the methodological frame developed from phytolith assemblages of modern millets could work to indicate watering levels, it was decided to apply the model to archeological samples to test its validity and applicability and finally providing an answer to the primary archeological question on water management in past agricultural societies. To this purpose it was decided to exploit a C₄ phytolith dataset from layers dated to the Mature Harappan period (2500-1900 BCE) coming from four different archeological sites: Harappa, Kanmer, Shikarpur and Alamgirpur. Using millet phytolith samples from Mature Harappan period layers seemed interesting because, to date, there is an open debate on the consumption and spread of such crops throughout the Indus civilization.
Modern samples We selected five landraces of pearl millet (Pennisetum glaucum L. R. Br), four of finger millet (Eleusine coracana L. Gaertn) and five of sorghum (Sorghum bicolor L. Moench) from the gene bank ICRISAT collection (Hyderabad, India) (Table  1 ). Traditional landraces were preferred to observe the variability of phytolith production in un-improved crops, which should exclude any possible recent modern change in the genetics of biosilica accumulation. Landraces were selected according to the area and climate of origin. The specific landraces were chosen using the Climatic Research Unit TS3. 10 Dataset, a Google Earth Pro application that divides the entire world into high-resolution climate grids  (Harris et al. , 2013)  combined with climate data from Climate-Data. org (https://en.
When the plants had grown to c. 20 cm, about three weeks after planting, they were watered to field capacity and then the soil surface was covered with a plastic sheet and 2 cm of low-density polyethylene granules, which prevented about 90% of evaporation from the soil  (Vadez et al. , 2011b). After this, the lysimeters were weighed weekly to calculate plant water loss from only transpiration  (Vadez et al. , 2011a). Data on temperature and relative humidity were collected every 30 min by 2 recorders (Gemini Tinytag Ultra 2 TGU-4500 Datalogger) placed in the crop canopy. Temperature maintained at 32. 28 ± 0. 10 °C and relative humidity at 42. 57 ± 0. 23% RH. We harvested WW plants when the panicles were mature (when at least ⅗ replications reached maturity) and the WS plants when their transpiration rate dropped below 10% of the initial value, indicating stomatal closure (cuticular transpiration)  (Schuster et al.
, 2017). Panicles, leaves and stems were first dried in ICRISAT at 60-70 °C for one week and then brought to the Laboratory for Environmental Archaeology of the University Pompeu Fabra (Barcelona, Spain) where samples were processed and phytoliths extracted. Further and more detailed information about the experimental cultivation can be found in D  'Agostini et al. (2022). Table  1  Selected landraces from the ICRISAT genebank with a) the acronym used to identify them and b) their accession number.
io (dx. doi. org/10. 17504/protocols. io. q26g74mb8gwz/v2). We counted an average of 619 cells for each slide (raw data is available in the Supplementary Information-File S1). The applied protocol ensures reaching the minimum statistically meaningful number of silica cells per sample by counting more than 300 phytoliths per slide, as suggested by  Strömberg (2009). Additionally, it takes into consideration both disarticulated and conjoined cells, respecting richness and evenness distribution. Classification of morphotypes follows the available literature (especially  Barboni and Bremond, 2009; Gu et al. , 2016; Mercader et al. , 2010). Nomenclature follows the International Code for Phytolith Nomenclature (ICPN) 2. 0 (International Committee for Phytolith Taxonomy (ICPT) et al. , 2019). Fig.
In order to obtain a dataset comparable with the experimental one, all morphotypes uniquely belonging to C₃ species and all morphotypes produced in inflorescences were excluded from the archaeobotanical dataset. The remaining data include some redundant morphotypes that are present both in C₄ in and C₃. Hence, the phytoliths selected are: elongate entire, elongate sinuate, bulliforms (both blocky and flabellate), stomata, and all the short cells (bilobates, crosses, polylobates, rondels, saddles) except for globulars. All silica skeletons formed by elongates entire and sinuate were also included for analysis. The full selection dataset used for this work is available in the Supplementary Information (File S1). Ubiquitous morphotypes were included because they are present in C₄ and should they derive from C₃ the analysis would not be compromised as this methodology has already been proved successful in C₃ species.
Statistical analysis Statistical analyses were performed with R (version 3. 5. 1) using standard functions of base, ggplot2 (version 3. 3. 5) (Wickham, 2016) , vegan (version 2. 5. 6) (Oksanen et al. , 2020)  and MASS (version7. 3-51. 5) (Ripley, 2022)  packages. Scripts are available in Supplementary Information (File S3 and on GitHub github. com). The tests conducted can be summarized in three steps: (1) phytolith assemblages of modern plants were tested by analyzing individual morphotypes separately to understand which morphotype is most efficient as a proxy for water availability; (2) concentration and ratios (sensitive/fix, skeleton size) were tested as proxies for water availability; (3) finally, once the model based on the phytolith assemblage of modern crops had been constructed, the archeological application was carried out.
(1) After calculating each morphotype concentration, generalized linear models (GLM) with Gaussian distribution (the dataset is composed of non-normal variables where the output is a continuous nonnegative variable) were used to evaluate which explanatory variable better predicts phytolith composition. Total phytolith and single morphotype concentration were tested as dependent variables while the treatment (WW, WS) and the species (finger millet, pearl millet and sorghum) were considered as independent variables. The p-value and the Akaike information criterion (AIC) were used to assess the validity of the models and to identify the best fitting model  (Burnham et al. , 2011). (2) Total sample concentration, ratio of sensitive to fixed morphotypes and the silica skeleton size ratio were tested as possible proxies for water availability.
Notwithstanding, sorghum phytolith assemblages contributed to obtaining statistically significant results to discriminate treatments. Specifically, although taken separately sorghum seems to show no correlation between watering and phytolith production, the total database comprising the three millets (including sorghum) shows that phytolith production can be correlated with watering and that robust predictive models can be created with them. Sorghum was not removed from the model both because there was evidence that sorghum was present in the archeological record at the sites of interest (through independent proxies e. g. seeds) and because there was an intention to produce an inclusive model, containing phytoliths of as many species as possible, in order to approximate the archeological assemblage.
Indeed, it is often impossible to reach full identification to genus/species level in archeological assemblages and these can be formed by plants that are more responsive (such as finger millet and pearl millet in our study) and plants that are less responsive (such as sorghum in our study). Thus, including sorghum in the model makes it stronger when applied to an unknown archeological dataset. In finger millet and pearl millet, the concentration of phytoliths is actually positively related to watering, as was also discussed in our previous work regarding biosilica accumulation, where we hypothesize a biosilica accumulation driven by mostly transpiration  (D'Agostini et al. , 2022). The ratio of sensitive/fixed morphotypes does not seem to show positive relationships for millets, underlining a great variability between landraces (Fig.
5 ) in the relationship between the different morphotype classes. We can therefore conclude that fixed morphotypes are useful to distinguish the three species; phytolith concentration and total composition predicts the treatment while sensitive/fixed morphotype ratio is not a good proxy for water availability. We can deduce that since the biosilica accumulation trends are different for each species and since the production of fixed shapes is not constant between species, a common ratio for the three millet is not efficient in highlighting environmental changes in a mixed dataset. The full assemblage, on the other hand, which includes all morphotypes, allows for a model sensitive to water changes.
Archeological assemblage and interpretation Results from the model applied to the archeological phytolith dataset indicate that most of the cereals grew in water scarcity conditions (Fig. 6 ). All sites gave the same result without any particular differences, despite the fact that the water and climatic conditions are somewhat different among settlements. Harappa and Alamgirpur were possibly located near watercourses, with extensive doabs where it was very easy for agricultural practices to take place, perhaps aided even by irrigation systems  (Kenoyer, 1998). In contrast, Kanmer and Shikarpur stood most likely on drier sites with no alluvial plains to exploit for agricultural purposes  (Lancelotti, 2010). In spite of these differences, and considering that the presence of domesticated C₄ cereals has been reported at each of these sites (Harappa: Panicum L.
Conclusion Firstly, we constructed a methodological framework using phytolith assemblage to observe changes in water availability. Albeit the  miscellaneous assemblage of landraces highlighted different trends in phytolith production, the complete dataset allows us to make predictions on the water availability with a good fitting. Even if sorghum, in all likelihood, is an active biosilica accumulator which does not respond only to water availability for phytolith production, it contributed to construct a solid database for archeological comparison if considered together with the other millets. Bulliforms and stomata, specific sensitive morphotypes are well predicted by the treatments and gave sensible results when tested as explanatory variables in the archeological dataset. Fixed morphotypes on the contrary are better associated with the species.
These findings confirm the validity of leaf phytoliths to identify past water availability for the three species under study when considered together. Nevertheless, further experimental cultivations have been set to assess whether phytolith production are influenced by different growing conditions, where environmental variables as relative humidity, soil composition and silica availability, light intensity and exposition could lead to a variation on sorghum, pearl millet and finger millet biomass production and transpiration rate which in turn could influence phytolith production. Secondly, we aim to answer an important archeological question about water management in the Indus Valley. Through the study of the dataset of phytoliths from the four archeological sites Harappa, Kanmer, Shikarpur and Alamgirpur, we can hypothesize the presence of millets grown under water stress regimes, leaving open the hypothesis of double cropping/opportunistic management, where millets were possibly planted in marginal areas with little access to water.
Acknowledgments We would like to thank all the  GEMS team (ICRISAT)  who helped during the fieldwork, particularly  Rekha Baddam  and  Suresh Naidu  as well as  Dr. Krithika Anbazhagan  and  Dr. Sunita Choudhary  whose suggestions have been invaluable and benefited the success of the study. Thanks are due to  Alessandra Varalli  for her support and guidance and to  Carolina Jiménez-Arteaga  for the fruitful discussions on millets. This work is part of the  RAINDROPS  project founded by the  European Research Council  ( ERC-Stg-2017 ) under the grant agreement  759800. Universitata Pompeu Fabra health and safety measurements have been followed during both the fieldwork and the laboratory work. RAIN-DROPS  has received ethical approval from the  Institutional Committee for Ethical Review of Projects (CIREP)  at  Universitat Pompeu Fabra  (ethics certificate n.
Thereafter, using these spectrally deconvolved Stokes I continuum images, we directly measure for four mini-halos the flux density, radio power, size, and in-band integrated spectra. Further to that, we show the in-band spectral index maps of the mini-halo (with point sources). We present a new mini-halo detection hosted by MACS J2140. 2-2339, having flux density S 1. 28 GHz = 2. 61 ± 0. 31 mJy, average diameter 296 kpc, and α 1. 5 GHz 1 GHz = 1. 21 ± 0. 36. We also found a ∼100 kpc southern extension to the ACO 3444 mini-halo which was not detected in pre vious VLA L-band observ ations. Our description of MeerKAT wide-field, wide-band data reduction will be instructive for conducting further mini-halo science.
In this paper, we present the calibration and imaging of mini-halo results using MeerKAT archi v al L-band observ ations for a sample of five galaxy clusters. We provide a guideline on how to process MeerKAT radio data to impro v e image quality via facet-based 3GC and unambiguously study the diffuse emission after point source subtraction. We describe the sample in Section 2. Thereafter, in Section 3 , we describe our data reduction procedures, including 3GC, source subtraction and spectral imaging. The results of the MeerKAT and mini-halo data processing are presented in Section 4 , with a discussion thereof provided in Section 5. Finally, a summary and conclusions are given in Section 6. Throughout this paper, we adopt the physically moti v ated minihalo definition proposed by  Giacintucci et al.
The KATDAL (KAT Data Access Library) package was used during download to convert from MeerKAT Visibility Format (MVF) to the standard CASA  (McMullin et al. 2007  ) Measurement Set (MS) while applying flags generated by the telescope's control and monitoring system. A known issue with early MeerKAT observations is that of poor calibrator positions. 3  The sources J0408 -6545 and J1939 -6342 needed to be corrected by 0. 52 and 2. 00, respectively, which was done using CASA's fixvis task. Subsequent flagging, 1GC and 2GC were automated using the CARACal pipeline  (J ózsa et al. 2020 ). First, the pipeline copies all sources into separate 'calibrators' and 'target' MS files. The calibrator data was flagged for the known problematic ranges of the spectral window, autocorrelations, and any shadowed antennas using CASA's flagdata task.
Thereafter, Tricolour  (Hugo et al. 2022  ) -an automated flagging software specifically developed for MeerKAT data -was used to flag persistent RFI with a particularly strict default flagging strategy from the CARACal MeerKAT files 4  to ensure no erroneous data were carried into calibration. In order to impro v e image DR and the detection of faint diffuse emission, we perform 3GC on all data. Below we briefly describe our calibration process.
The bandpass solutions from the primary calibrator were applied to the secondary calibrator on-the-fly while its delay and complex-gain corrections were calculated. The deri v ation was repeated after a mild automated flagging. The final secondary gain solutions were scaled to that of the primary using CASA's fluxscale task. The complex-bandpass solutions of the primary calibrator and the delay and flux-scaled complex-gain solutions of the secondary were applied to the target data which was then frequencyav eraged o v er fiv e channels to sav e on storage space and future processing time. Finally, this cross-calibrated target data was flagged similarly to the calibrator data but with a slightly more relaxed flagging strategy. 7.
Riseley et al. ( 2022 )  applied a similar approach to MeerKAT L-band observations of the MS 1455. 0 + 2232 mini-halo. Additionally, we used the holographic MeerKAT L-band primary beam models generated via the eidos package to correct for the primary beam attenuation and rotation  (Asad et al. 2021 ; de Villiers & Cotton 2022 ). The kMS software performs 3GC on a tessellated DI image by exploiting Wirtinger complex differentiation (Tasse 2014b ) to directly estimate the physical DD RIME terms. We used its non-linear Kalman Filter algorithm (KAFCA, Tasse 2014a ) to solve for stable and wellconditioned estimates within a 5 min time and 10 channel frequency solution interval in as many directions as there were tessellation tiles. DDF is a facet-based imager that can take into account kMS solutions to perform wide-band, wide-field DD spectral deconvolution in the apparent and intrinsic (primary beam corrected) flux scale.
Note that we describe all images in which primary beam corrections were performed as being intrinsic. The image tessellation mentioned abo v e was determined using the MakeModel. py script 9 of the DDF software. Since 3GC is very 8 ht tps://skaafrica. at lassian. net/wiki/spaces/ESDK B/pages/1484128294/Dyn amic+range+considerations 9 ht tps://github. com/saopicc/DDFacet /t ree/mast er/SkyModel sensitive to the signal to noise (SNR) of the data, we experimented with the number of tessellation tiles in each cluster case. We found that 4-6 tiles each centred on one of the brightest field sources responsible for radial artefacts gave a good impro v ement in image noise after 3GC. Once the tessellation pattern was defined, the 3GC procedure was analogous to that of 2GC (i. e. imaging a rigorous model then deriving the calibration solutions and applying them in a further imaging round).
ACO 1413 Giacintucci et al. (  2017 ) classified ACO 1413 as a (modest) non-coolcore cluster using Chandra data (Observation ID: 5002). Govoni et al. ( 2009 )  first showed the presence of a candidate mini-halo in ACO 1413 using VLA 1. 4 GHz C-configuration data (maximum baseline 3. 4 km with L-band resolution 14 ), making it the first and only noncool-core cluster to host a candidate mini-halo. Savini et al. ( 2019 )  later used a 144 MHz LOFAR observation to confirm its detection. Our MeerKAT wide-FoV images of ACO 1413, shown in Fig. D1  of Appendix D , give a depiction of the improvement from 2GC to 3GC imaging. The DI image displays a significant snowflakelik e artef act around a bright double radio source located at (RA, Dec) =  (11:52:26. 5, 23:13:47. 3), and (11:52:26. 2, 23:12:54.
The PSF sidelobes and further artefacts have been reduced by the kMS gain solutions and are now almost at the level of the background noise. The global noise rms impro v ed by 8 per cent from 6. 4 μJy beam -1 to 5. 9 μJy beam -1. The inset of Fig. D2  shows a zoom of the third contaminating source mentioned earlier; its local rms impro v ed by just o v er a factor of two, from ∼52 μJy beam -1 to ∼24 μJy beam -1. Figs 2 a, b show the comparison between the 3GC versus 3GC SRC-SUB images. The embedded BCG (marked S1, optical counterpart 2MASX J10235019-2715232) and mini-halo are clearly visible. Also visible are five compact sources on the eastern edge of the mini-halo that obscure its boundary, identified from NED to be 11 The NASA/IPAC Extragalactic Database (NED) is funded by the National Aeronautics and Space Administration and operated by the California Institute of Technology.
We determine a flux density of S 1. 28 GHz = 2. 61 ± 0. 31, consistent with the difference in measurements from  Yu et al. ( 2018 ). We determine an average diameter of 296 kpc with LLS of 390 kpc. The k-corrected mini-halo radio power is P 1. 4 GHz = (0. 79 ± 0. 11) × 10 24 W/Hz. The measured BCG flux density is consistent with that given in  Yu et al. ( 2018 ). The integrated spectra gives α 1. 5 GHz 1 GHz = 1. 21 ± 0. 36 for the minihalo, and α 1. 5 GHz 1 GHz = 0. 72 ± 0. 31 for the BCG. The spectral index maps of the unsubtracted data are shown in panels (d) and (e) of Fig. 4. The BCG and western infrared source obscure much of the underlying mini-halo spectral distribution. Ho we ver, we can see that the spectral index monotonically increases radially outwards from ∼0. 75 to ∼1. 50.
As an active field of research and with the development of state-of-the-art algorithms to analyze EEG datasets, the parametrization of Electroencephalography (EEG) analysis workflows has become increasingly flexible and complex, with a great variety of methodological options and tools to be selected at each step. This high analytical flexibility can be problematic as it can yield to variability in research outcomes. Therefore, growing attention has been recently paid to understand the potential impact of different methodological decisions on the reproducibility of results. In this paper, we aim to examine how sensitive the results of EEG analyses are to variations in preprocessing with different software tools. We reanalyzed the shared EEG data (N = 500) from (Williams et al. , 2021) using three of the most commonly used open-source Matlab-based EEG software tools: EEGLAB, Brainstorm and FieldTrip.