dataesr/scientific-paragraphs-categorization huggingface dataset:
A variable roughness could also produce false negatives, as high roughness will increase the return signal  [39] , and the difference ∆σ • (σ • with freezing and high roughness -σ • with thawing and low roughness) could be well lower than the 3 dB threshold. Finally, the study was carried out with data during the winter season, and over surfaces with lower vegetation cover; therefore, it would be interesting to develop an algorithm that works over different vegetation types. In this study, we propose a refined algorithm based on Baghdadi et al. [20]  that is capable of detecting frozen/unfrozen episodes in near real-time, with a temporal resolution of at least once each six days, at plot scale, and over different vegetation types. To overcome some of the limitations encountered during the previous study, two additional datasets will be used:  (1)  in situ temperature data using the measured temperatures from a weather station network for the refining of the detection results and (2) land cover classes.
However, as the aim of this study is to evaluate the potential of S1 SAR data product to detect in near real-time frozen/unfrozen events, comparison to ERA5-Land can identify areas where our algorithm does not perform well. The study sites and the used datasets are described in Section 2, followed by a detailed description of our algorithm for mapping frozen areas at the plot scale in Section 3. The main findings and the discussion are presented in Sections 4 and 5, respectively. Finally, Section 6 presents the main conclusions.
SAR Sentinel-1 Time Series Sentinel-1 is a constellation of two polar-orbiting SAR satellites (S1A and S1B) operating at Cband (ƒ = 5. 406 GHz), which were launched in April 2014 (S1A) and April 2016 (S1B). Sentinel-1 products are available free of charge, and can be downloaded one day after their acquisition from the Copernicus website (https://scihub. copernicus. eu/dhus). Over our two study sites, two Sentinel-1 time series data were used. The first S1 time series, from 1 September 2017 to 31 May 2018, was used for threshold calculation. This S1 time series contains 288 S1 images acquired from the Sentinel-1A (S1A) and Sentinel-1B (S1B) satellite constellation in both ascending (afternoon ~18h00 UTC) and descending modes (morning ~06h00 UTC). Of these 288 images, 133 images were acquired over study site A (88 images in ascending mode, and 45 descending mode), and 154 images acquired over study site B (87 images in ascending mode, and 67 in descending mode).
SAR Sentinel-1 Time Series Sentinel-1 is a constellation of two polar-orbiting SAR satellites (S1A and S1B) operating at C-band (ƒ = 5. 406 GHz), which were launched in April 2014 (S1A) and April 2016 (S1B). Sentinel-1 products are available free of charge, and can be downloaded one day after their acquisition from the Copernicus website (https://scihub. copernicus. eu/dhus). Over our two study sites, two Sentinel-1 time series data were used. The first S1 time series, from 1 September 2017 to 31 May 2018, was used for threshold calculation. This S1 time series contains 288 S1 images acquired from the Sentinel-1A (S1A) and Sentinel-1B (S1B) satellite constellation in both ascending (afternoon ~18h00 UTC) and descending modes (morning ~06h00 UTC). Of these 288 images, 133 images were acquired over study site A (88 images in ascending mode, and 45 descending mode), and 154 images acquired over study site B (87 images in ascending mode, and 67 in descending mode).
8°. This pattern was repeated every 12 days (Figure  3b ). The acquisition pattern for the images in the descending mode was two images (24 h difference) at incidence angles of 36. 6° and 45. 2°, respectively, followed after 5 days by an image with an incidence angle of 36. 6° (Figure  3b ). This pattern was also repeated every 12 days for a total of 16 images in both acquisition modes. The time difference between the acquisition of images in ascending and descending modes varied between two and three days. The entirety of the S1 SAR images database was acquired in the Interferometric Wide swath mode (IW) in both VV and VH polarizations. These images were generated from the high-resolution Level-1 ground range detected (GRD) product, which were acquired at a spatial resolution of 20m × 22m (range × azimuth), and then resampled to produce the final product at 10m x 10m pixel spacing.
The entirety of the S1 SAR images database was acquired in the Interferometric Wide swath mode (IW) in both VV and VH polarizations. These images were generated from the high-resolution Level-1 ground range detected (GRD) product, which were acquired at a spatial resolution of 20 m × 22 m (range × azimuth), and then resampled to produce the final product at 10 m × 10 m pixel spacing. The images were calibrated using Sentinel SNAP toolbox, developed by the European Space Agency (ESA). The calibration aimed to convert the image pixel values into backscattering coefficients (σ • ) in linear units, while the geometric correction was used to ortho-rectify the SAR images using the 30 m Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM). The radiometric accuracy of the S1 SAR backscattering coefficients is approximately 0.
Temperature Data In situ temperatures acquired from the nearest weather station network to each study site (approximately 3 and 17 km from the study sites A and B, respectively) were used to help derive the thresholds for the freezing detection algorithm. For each S1 SAR image, over the two study sites, we matched air temperatures from the time of the S1 SAR acquisition and up to three hours before the acquisition. Then, these temperatures were averaged because freeze/thaw events could have happened at the same time, or a few hours before the image acquisition. In addition, the ERA5-Land dataset, provided by the European Centre for Medium Range Weather Forecasts (ECMWF) was also used. ERA5-Land is obtained by the modelling of in situ and satellite observations, and provides a large number of meteorological and oceanographic variables  [36].
ERA5-Land data are available at a resolution of 9 km globally, in hourly intervals, and it is produced in near real time (three months after acquisition), with quality-assured monthly updates, and preliminary daily updates published within five days of acquisition. For the validation of our approach of frozen area mapping, we used the soil temperature and the 2 m air temperature. The ERA5-Land soil temperature products are composed of four layers, and go to 289 cm in depth. In this study, we used the upmost layer, which goes up to 7 cm in depth (C-band microwaves can penetrate the soil to only a few centimeters). The spatial matching between our plots and the ERA5-Land grid has been achieved by a nearest neighbor approach.
Comparison with ERA5-Land Dataset The assessment of accuracy for our algorithm to detect frozen/unfrozen plot surface states is a challenging issue, as direct ground measurements of the actual plot surface states are not available. Therefore, it is necessary to resort to proxy variables, such as the air temperature  [60]  or soil temperature  [28]. However, proxy variables might not necessarily represent the surface state and may sometimes introduce uncertainties  [16]. In addition, the proxy variables (soil and air temperatures) should be acquired in the same timeframe as Sentinel-1 acquisitions, as any interpolation of temperature data to Sentinel-1 acquisition time would inevitably lead to uncertainties. In this study, hourly soil and air temperature data from ERA5-Land were used. ERA data products have been utilized in a multitude of studies for the validation of freeze/thaw products  [15, 16].
Strengths and Limitations The results of our algorithm show that freezing episodes could be successfully detected using the high-resolution Sentinel-1 SAR images, with, at worst, a temporal resolution of six days. However, the implementation of the proposed method in a near real-time scenario primarly depends on the delivery time of S1 images. S1 images are usually delivered by ESA in a "fast 24 h" delivery mode. This mode ensures that S1 images are available for download 24 h after the satellite acquisition. Considering that the pre-processing of S1 images and applying the proposed algorithm is automatically performed with minimum human intervention, frozen plots could be detected in a few hours after the reception of S1 images based on the the size of the studied area. To detect a freezing episode, we rely on a simple but effective algorithm that requires only the calculation of two thresholds (to distinguish between unfrozen, mild-to-moderately frozen, and severely frozen) for each land cover class, and two additional datasets (land cover classes, and in situ air temperatures).
Using these three datasets (Sentinel-1 SAR, land cover classes, air temperatures), our algorithm provided near real-time results of plots' frozen states, which distinguishes between unfrozen, mild-to-moderately frozen, and severely frozen. The strength of our algorithm is multi-faceted. First, it is computationally inexpensive, and has the potential to detect freezing episodes at near real-time. In general, S1 SAR images are available 24 h after acquisition; therefore, frozen plots could be detected a day after the event. Secondly, over agricultural areas, vegetation growth, tillage, and irrigation can cause large fluctuations in the backscatter response, which has the adverse effect of causing uncertainties when detecting freezing episodes, as witnessed in previous studies  [15, 16]. In this study, we do not rely on the direct value of the σ 0 to detected freezing, but rather on the difference between the acquired σ 0 and the mean of the previous three calculated max_σ 0.
Experimental approaches to acquire MS/MS data are generally referred to as Data Dependent or Data Independent Acquisition (DDA and DIA, respectively). In the most widely used DDA acquisition workflow, MS/MS spectra are automatically collected for precursor ions whose MS intensity exceeds a pre-defined threshold. In this mode, precursor ions are selected using a small isolation window (typically ≤1 Da wide), which leads to high-quality and higher purity MS/MS spectra. Nevertheless, the selection of precursor ions is a (semi)stochastic event suffering from low analytical reproducibility and favoring selection of the most abundant but sometimes biologically irrelevant ions. As a consequence, some biologically relevant precursor ions may not be selected for fragmentation if they do not match the defined selection criteria or if they are coeluted with more intense species.
Although some software solutions for handling MS/MS data obtained from DIA as well as DDA workflows have been published for metabolomics such as MS-DIAL  [24]  or MetaboDIA  [25] , no comparative study has been carried out to objectively assess the advantages and limitations of these different tools. While DIA strategies such as SWATH are often used in proteomics, very limited work has been reported on small molecules  [26]. This particularly holds true when considering the use of the Orbitrap mass analyser, with very sporadic examples published  [27, 28]. In this context, the aim of this study was to evaluate the performance of DDA and SWATH-type DIA acquisition workflows when implemented on an Orbitrap Fusion TM Tribrid TM mass spectrometer. We especially exploited the versatility of this mass spectrometer, comprising of quadrupole, linear ion trap and Orbitrap mass analyzers, in order to conciliate multiple modes of analysis and experimental flexibility.
Ion transfer parameters were set as follows: mass range at "normal", S lens RF level at 60%. Scan parameters applied were as follows: acquisition time from 0 to 20 min, positive ionization mode, micro scan at "1", data type at "profile", AGC target at 5 × 10 4. For acquisition in MS mode only, the data were acquired using the Orbitrap analyzer, at a resolution of 240,000 at m/z 200 (FWHM, a spectrum collected at this resolution requires ~0. 5 s); maximum injection time was set at 400 ms, and the scan range from m/z 85 to m/z 1000. More detailed information regarding experimental conditions is given in the Supporting Information (Tables  S2-S4 ).
Data Processing and Evaluation of the Quality of MS/MS Spectra Raw instrument data (. RAW) were processed using Xcalibur v. 2. 2. 44 (ThermoFisher Scientific) processing methods. The genesis algorithm was used for detection (nearest RT, minimum peak height (S/N) = 3) and integration (smoothing point = 1, S/N threshold = 0. 5, enable valley detection = no, constrain peak width = no). Accurate [M+H] + m/z, fragment ion m/z and expected retention time (min) are shown in supplementary data (Supplementary Material, Table  S1 ). MS-DIAL software  [24]  (version 4. 12) which is freely available (http://prime. psc. riken. jp/compms/ msdial/main. html), was used to calculate MS/MS similarity between the evaluated and reference spectra. We merged all the reference MS/MS spectra from our in-house spectral library  [29]  into a single.
msp file for further mass spectral matching, using the dot product (DP) scoring method included in the MS-DIAL software. For both DDA and DIA data analyses, MS-DIAL parameters were set as follows: MS1, tolerance, 0. 01 Da; MS2 tolerance, 0. 025 Da (0. 5 Da for ion trap detection); retention time begin, 0 min; retention time end, 100 min; minimum peak height, 1000; mass slice width, 0. 05 Da; smoothing level, 3 scans; minimum peak width, 5 scans; sigma window value, 0. 5.
Implementation of a First "HCD-only" DDA Acquisition Protocol on Authentic Standards as a First Step toward the Collection of Meaningful MS/MS Data The set of 47 authentic standard molecules was first used as a well known and calibrated mixture in order to optimize experimental conditions (e. g. , precursor isolation filters and collision energy applied) and preliminarily evaluate the relevance of a DDA method. Prior to MS detection, metabolite separation was achieved on a C 18 UHPLC column. Under these conditions, chromatographic peaks are typically less than 10 s wide. In order not to downgrade the quality of MS1 quantification, sufficient data points (at least 8-10) should be obtained per chromatographic peak. Resolutions of the MS1 survey and MS/MS scans were thus kept at 240,000 and 30,000 (both at m/z 200, FWHM), respectively.
In order to resolve co-selected or co-occurring isobaric precursor or fragment ions, which is a common phenomenon in metabolomics  [14] , and facilitate spectra identification, Orbitrap resolution was set at the highest value still compatible with the duty cycle. Acquisition of MS data was performed every 10 MS/MS spectra (i. e. , a so-called top-10 method) to maintain a total cycle time below 1 s. For comparison, a previously reported and carefully optimized top-5 protocol implemented on an Orbitrap Velos instrument involved mass resolutions set at 30,000 and 7500 in the MS and MS/MS modes, respectively  [17]. This highlights the substantial improvement in scan speed (or resolution) of the Orbitrap Fusion TM compared to previous generations of instrument, essentially due to the presence of a high-field Orbitrap mass analyzer.
These acquisition conditions often proved to yield the most informative MS/MS spectra, while maintaining the precursor ion detectable. This was also a mean to comparatively evaluate the NCE required to reach similar fragmentation level of given precursor ions. The 47 studied compounds have optimal NCEs varying from 10% to 100% (27% on average, Supplementary Material, Table  S1 ), while ~500 additional compounds from our chemical database and detectable in the positive ion mode overall show optimal NCEs in the 10-50% range (~40% on average, Supplementary Material, Figure  S4 )  [29]. Employing the stepped collision energy option represented a viable way to obtain information-rich MS/MS spectra by combining three different NCEs in a single fragmentation spectrum. A good compromise seems to set stepped NCE at 30 ± 20% (i.
The pool of 47 molecules was preliminarily analyzed at 100 ng/mL in triplicate by the implemented DDA method with a stepped NCE set at 30% ± 20%. At this rather high concentration, 43/47 compounds were detected in the MS1 survey scan. Limits of detection of 3'dephosphocoenzyme A, 6-hydroxymelatonin, p-coumaric acid, and dodecanedioic acid were much higher than 100 ng/mL under the conditions used here, thus precluding their efficient detection (Supplementary Material, Table  S1 ). All 43 compounds were efficiently fragmented in each of the three replicates, except ochratoxin A for which no MS/MS spectra were obtained in one replicate. More than 88% of these acquisitions returned a minimum of two MS/MS spectra per replicate for each compound, which highlights data consistency across replicate experiments.
Our results are in good agreement with those of Blazenovic et al. who found that a dot product score threshold of 400 gave the best results on a training dataset of more than 300 MS/MS spectra, within the frame of the Critical Assessment of Small Molecule Identification (CASMI) contest  [34]. Each 10-fold dilution of test mix concentration (from 100 to 10 and then 1 ng/mL) resulted as expected in a 15-20% drop of the MS/MS triggering (B) Schematic overview of the "HCD-only" DDA method (More detailed information regarding experimental conditions and filters is given in Supplementary Material, Table  S2 ). The pool of 47 molecules was preliminarily analyzed at 100 ng/mL in triplicate by the implemented DDA method with a stepped NCE set at 30% ± 20%. At this rather high concentration, 43/47 compounds were detected in the MS1 survey scan.
Limits of detection of 3'-dephosphocoenzyme A, 6-hydroxymelatonin, p-coumaric acid, and dodecanedioic acid were much higher than 100 ng/mL under the conditions used here, thus precluding their efficient detection (Supplementary Material, Table  S1 ). All 43 compounds were efficiently fragmented in each of the three replicates, except ochratoxin A for which no MS/MS spectra were obtained in one replicate. More than 88% of these acquisitions returned a minimum of two MS/MS spectra per replicate for each compound, which highlights data consistency across replicate experiments. Since precursor ion selection occurs in a stochastic fashion within DDA workflows, such a good reproducibility is particularly remarkable. DP scoring was used to evaluate spectral similarity under these DDA conditions. DP scores ranged from 296 to 999 (mean value: 692; median value: 699), with 91% of metabolites having DP scores above 500 (Supplementary Material, Table  S7 ).
To better assess and visualize how spectral quality translates into DP score, Figure  2  presents eight MS/MS spectra with variable DP scores. The lowest DP score of 233 was obtained for a rather noisy spectrum of capryloylglycine, but this was the only score below 480. Then comes 4-pyridylacetic acid with a DP of 482 but with a good match between evaluated and reference spectra. Thus, despite fluctuations in the relative intensity of some fragment ions can be observed, a DP score > ~500 seems sufficient to retain high confidence in metabolite identification (Figure  2 ). Our results are in good agreement with those of Blazenovic et al. who found that a dot product score threshold of 400 gave the best results on a training dataset of more than 300 MS/MS spectra, within the frame of the Critical Assessment of Small Molecule Identification (CASMI) contest  [34].
In this method, we made use of the filters already optimized in the first DDA workflow described above (blue boxes in Figure  3 ). For metabolites present in our internal database, i. e. , for which the corresponding standards are available (red part of the decision tree, Figure  3 ), the main goal was to obtain high quality MS/MS spectra as close as possible to those present in the spectral database, i. e. , with fragments and precursor ion intensities around 20%. To better cover the optimal NCEs of the ~500 chemical compounds included in our chemical database and detectable in the positive ion mode (Supplementary Material, Figure  S4 )  [29] , two distinct stepped NCEs of 15% ± 10% and 45% ± 20% were applied for improved reliability of the annotated metabolites identification. For metabolites absent of our chemical library (green part of the decision tree, Figure  3 ), each corresponding precursor ion was fragmented both under HCD and CID conditions to obtain complementary structural information  [17, 35] , and using compromised stepped NCEs of 30% ± 20% for HCD and 22% for CID.
Of note, the multiplexed nature of this multi-event workflow prompted us to lower resolution of MS1 and MS2 events at 120,000 and 15,000, respectively. S3  for detailed information about the acquisition conditions. IT: Ion Trap, OT: Orbitrap. In this method, we made use of the filters already optimized in the first DDA workflow described above (blue boxes in Figure  3 ). For metabolites present in our internal database, i. e. , for which the corresponding standards are available (red part of the decision tree, Figure  3 ), the main goal was to obtain high quality MS/MS spectra as close as possible to those present in the spectral database, i. e. , with fragments and precursor ion intensities around 20%. To better cover the optimal NCEs of the ~500 chemical compounds included in our chemical database and detectable in the positive ion mode (Supplementary Material, Figure  S4 )  [29] , two distinct stepped NCEs of 15% ± 10% and 45% ± 20% were applied for improved reliability of the annotated metabolites identification.
More specifically regarding the endogenous metabolites monitored in the NIST plasma and belonging to the S2 group, valuable MS/MS spectra were obtained for 15/19 plasma metabolites, i. e. , 78% of precursor ions triggering MS/MS acquisition. Manual inspection of the data revealed that this method provided both relevant HCD and CID MS/MS spectra for 10 metabolites, while HCD or CID spectra were retrieved for three and two compounds, respectively. Altogether, these results emphasize the ability of the Orbitrap Fusion TM to perform high quality DDA for metabolomics. Further software improvements, as those implemented in the AcquireX data dependent software suite (ThermoFisher Scientific), would be useful to improve the presented workflow, for instance regarding automatic exclusion of background signals or for implementing Overall, MS/MS spectra obtained at low and high resolution were of high quality and proved comparable to the reference ones, with more than 80% of spectra characterized by a DP score > 500 (Figure  4B ).
Even though a single non-optimal NCE was used to fragment unknowns (i. e. , 30% ± 20%) while maintaining a suitable cycle time, relevant high-resolution mass spectra were still recorded. More specifically regarding the endogenous metabolites monitored in the NIST plasma and belonging to the S2 group, valuable MS/MS spectra were obtained for 15/19 plasma metabolites, i. e. , 78% of precursor ions triggering MS/MS acquisition. Manual inspection of the data revealed that this method provided both relevant HCD and CID MS/MS spectra for 10 metabolites, while HCD or CID spectra were retrieved for three and two compounds, respectively. Altogether, these results emphasize the ability of the Orbitrap Fusion TM to perform high quality DDA for metabolomics. Further software improvements, as those implemented in the AcquireX data dependent software suite (ThermoFisher Scientific), would be useful to improve the presented workflow, for instance regarding automatic exclusion of background signals or for implementing iterative DDA acquisitions in which fragmented metabolites are automatically included in an exclusion list to allow further acquisition of MS/MS data of minor metabolites.