dataesr/scientific-paragraphs-categorization huggingface dataset:
(2007) , Figure  1 ). Moreover, the burst mode provided waveforms of all six electromagnetic components measured with a sampling frequency of 2500 Hz, corresponding to a Nyquist frequency of 1,250 Hz. With these waveforms, we can perform a detailed analysis of wave polarization and propagation properties using the singular value decomposition (SVD) methods described by  Santolík et al. (2006). In the burst mode data gathered over the 6. 5 years of DEMETER operations, we found 46 EN emission events, 29 of them on the dayside and 17 on the nightside. These events were picked manually by identifying the line structure in the electric and magnetic spectra. The processed spectra of an example dayside event recorded on 2004-11-09 between 08:19:15 and 08:21:15 UT are presented in Figure  1 , similar to the case analyzed by  Santolík et al.
Methods and Models To simulate the propagation of whistler-mode waves in a multicomponent cold plasma, we numerically solve 3D ray tracing equations with the 4th/5th order Dormand-Prince Runge-Kutta method from the SciPy Python library. The overall implementation is similar to the one found in  Santolík et al. (2009) , Section 2. 3; the code and a quick guide can be downloaded from the link provided in the data availability statement. We assume that the traversal of the plasmapause happens very early during the wave propagation so that the plasmapause can be excluded from our ray simulations. Previous ray tracing studies have shown that despite the Landau damping and other hot plasma effects, unducted low frequency whistler waves propagating inside the plasmasphere have long lifetimes  (Bortnik et al.
However, we notice that the spread of θ k is considerably larger than in the experimental data. We quantify the trend by a linear fit (green dashed line), which now has a slope of -1. 81, a much larger value compared to the slope of -1. 08 calculated for the DEMETER data. The simulated Poynting vector angle θ S also has the same general behavior as in the observational data, but most of the data points are concentrated near the parallel and antiparallel direction (more precisely, near 160° and 20°), with only 17% of rays falling into the range from 45° to 135°. Similarly, the magnetic field ellipticity obtained through SVD methods  (Santolík et al. , 2003 ) also follows the trend set by the DEMETER data, but with a higher preference toward values above 0. 5. The increase in E B values happens at very low altitudes, while the distribution of ellipticity in the source region always remains concentrated near zero, in agreement with spacecraft observations  (Santolík et al.
For the purpose of the fitting procedure, the experimental data are rebinned to λ Bmin bins of 0. 5°. The optimal parameters were found with the Levenberg-Marquardt algorithm (as implemented in SciPy,  More (1978) ). Because it does not ensure that global minima will be found, we tried five different guesses of σ θ , increasing from 1° to 5° with a step of 1°. The frequency distribution normalization factors were set to a 1 = 1. 0, a 2 = 1. 0, a 3 = 0. 25 and a 4 = 0. 0625, approximately following the distribution obtained from the DEMETER data. The resulting best fit parameters are: σ θ = 2. 01°, a 1 = 0. 59, a 2 = 0. 37, a 3 = 0. 0 and a 4 = 0. 040 (sum of a i has been normalized to 1). The histograms from Figure  4  are recalculated with the obtained weights and presented in Figure  6. The quantitative agreement between simulation and experiment has clearly improved, as shown by the decrease in the slope of the linear fit of θ k data from -1.
Discussion In the analysis of electromagnetic data from DEMETER, we decided to remove all frequencies below the local proton gyrofreqency. That way, we completely excluded the event studied by  Santolík et al. (2016)  from our dataset. In this case study, the reported equatorial noise emission occurred within 10° of the equator, with θ k very close to 90°, and at frequencies extending below f cp /2. Dispersion properties at these frequencies strongly depend on the ion composition, which would increase the number of parameters in our analysis. Of all the frequency-time intervals with EN in our dataset, after applying the thresholds on power density and planarity, only 27. 6% fall under f cp , and therefore we have chosen not to include this smaller, special part of the dataset in our analysis.
Conclusion We analyzed electromagnetic wave data of low-altitudinal equatorial noise emissions measured during the whole mission of the DEMETER spacecraft and presented the statistics of wave propagation properties of those emissions, namely wave normal angle, polar angle of the Poynting vector, the ellipticity of polarization, latitudinal distribution and frequency distribution. The emissions are observed within about 20° of the geomagnetic equator with wave normal angles mostly between 60° and 120°, while the polar angle of the Poynting vector ranges across the whole range from 0° to 180°, with the very high and very low values being dominant farther from the equator. We have also confirmed that the occurrence of these emissions is accompanied by strong geomagnetic activity, as was indicated earlier by  Němec et al.
Acknowledgments This work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 870452  (PAGER). The work of MH and OS was supported from the  GA UK  project No. 64120. The work of FN was supported from the  GACR  project No. 21-01813S. The authors thank the engineers from  CNES and scientific laboratories (CBK, IRAP ,  LPC2E , LPP, and  SSD of ESTEC)  who largely contributed to the success of the DEMETER spacecraft mission.
Data Availability Statement DEMETER spacecraft data are accessible from https://sipad-cdpp. cnes. fr. The processed equatorial noise data used in this paper and related IDL procedures can be found at https://doi. org/10. 6084/m9. figshare. 19208646. v1. Kp indices can be downloaded from the World Data Center for Geomagnetism, Kyoto http://wdc. kugi. kyoto-u. ac. jp/dstae/index. html. Figure  S1  in the Supporting Information S1 shows data from the International Reference Ionosphere (IRI) model accessible through the online tool at https://ccmc. gsfc. nasa. gov/modelweb/models/ iri2016_vitmo. php. The ray tracing code and associated input files and Python plotting procedures can be found at https://doi. org/10. 6084/m9. figshare. 19181864. v1.
The observations and analysis are described in Section 2, and our main findings are discussed in Section 3. We conclude and describe future lines of work in Section 4. We assume a flat ΛCDM cosmology with Ω m = 0. 307 and H 0 = 67. 7 km s -1 Mpc -1 (Planck Collaboration et al. 2016); for this cosmology, 1″ = 5. 4 kpc at z = 6. 900. Images and data products are available on GitHub https:// github. com/spt-smg/publicdata with a copy preserved on Zenodo:10. 5281/zenodo. 6392003.
ALMA Observations ALMA observations of the dust and [C II] emission were carried out in projects 2016. 1. 01293. S and 2017. 1. 01423. S, designed to reach spatial resolutions ≈0 25 and 0 05, respectively, at 240 GHz. The 2016 data were presented in M18, and the new data are essentially identical in spectral setup. The spectral resolution of the sideband containing the [C II] line is ≈10 km s -1. The newly acquired data were obtained between 2017 November and 2018 October, comprising 40 minutes on source in a compact array configuration with 15 m-1. 4 km baseline lengths and 65 minutes in an extended array with 0. 1-8. 5 km baselines. The data were reduced using the standard ALMA pipeline and combined with the previous 2016 data. We performed a single round of phase-only selfcalibration with a solution interval equal to the scan length.
We imaged both the continuum and [C II] data using Briggs weighting with robustness parameter 0. 5; the resulting synthesized beam size is 0 07 × 0 08. The continuum image reaches 7. 5 μJy beam -1 sensitivity, and the [C II] data cube typically reaches 85 μJy beam -1 sensitivity in 40 km s -1 channels. The continuum image is shown in Figure  1 , together with an integrated [C II] (moment 0) map made by integrating channels from -600 to +1120 km s -1 with respect to z = 6. 900. As noted by M18, SPT 0311-58 consists of a western (SPT 0311-58W) and eastern (SPT 0311-58E) component, with SPT 0311-58W mildly lensed by a foreground galaxy and SPT 0311-58E essentially unlensed. The two components are separated by ≈700 km s -1 and are therefore distinct objects (M18, and see below) that our new data resolve in detail.
This demonstrates that our assumption of an SIE profile, while simple, is adequate in the absence of other data to constrain the lens mass profile. We expect these residuals to have minimal impact on our subsequent analysis. The reconstruction of the continuum and integrated [C II] emission is shown in Figure  1 , while Figure  2  shows a more detailed 3D rendering of the [C II] emission. Because SPT 0311-58E lies far from the multiply imaged region of the source plane and is essentially unlensed, its apparent morphology is reproduced basically exactly in the reconstruction. Similarly, much of the apparent image-plane structure in SPT 0311-58W maps simply to the intrinsic source plane, a consequence of the spatial offset between lens and source. Due to variations in the local magnification, the effective spatial resolution of the reconstructions varies from ≈60 pc (near the lensing caustics in the central regions of SPT 0311-58W) to ≈350 pc (across SPT 0311-58E and the southern end of SPT 0311-58W, which are not strongly lensed).
With some fine-tuning of the parameters we were able to recover all clumps consistently between the different algorithms, but we stress that our results are not strongly dependent on the details of the clump identification. We identify 12 distinct clumps in the [C II] data cube, 4 in SPT 0311-58E (labeled E1-E4 from north to south) and 8 in SPT 0311-58W (W1-W8). Ten clumps are also identified in the continuum image. Clump E1 peaks just below our minimum continuum S/N threshold, while clump W7 appears to be genuinely continuum-faint. For these two clumps, we "manually" measured continuum properties using apertures over the [C II]-emitting region; we verified that this method would also accurately recover the continuum flux density of the other clumps as well. All continuum clumps spatially overlap with a [C II] counterpart, and we conclude that the continuum clumps are physically associated with the corresponding [C II] clumps.
7-1. 3 kpc, measured from pseudo-moment-0 images that integrate the [C II] emission of each clump along   1. An interactive version that allows the cube to be zoomed, panned, and rotated is available in the online journal and at https://github. com/spt-smg/publicdata. Bottom: position-velocity slices along SPT 0311-58E and W, as indicated in the center panel, demonstrate that neither source is well-described by simple rotation curve models. Note that not all clumps are easily distinguishable in these 2D projections of the 3D cube. the spectral dimension. 23 Only two clumps in SPT 0311-58W, W3 and W4, show obvious internal velocity gradients that may suggest that the clumps themselves are rotating. Even for these clumps, it is unclear whether rotation dominates because these clumps are nearest to the center of SPT 0311-58W and are plausibly interacting.
The Nature of the Massive Clumps Finally, in this section, we seek to gain more insight into the internal structure of the SPT 0311-58 clumps from our ∼100-300 pc resolution data. Our results are summarized in Figure  5. Figure  5  (left) shows the size-line-width relation. We compare to a large sample of giant molecular clouds (GMCs) in nearby spiral galaxies from the PHANGS-ALMA survey  (Rosolowsky et al. 2021) , clumps identified in the lensed z∼1 "Cosmic Snake" galaxy (Dessauges-Zavadsky et al. 2019), and similar clumps in the lensed z∼3 IR-selected galaxy SDP. 81. The clumps in SPT 0311-58 are clearly physically larger and more turbulent than the lower-redshift clump-scale structures. The gray shaded regions in Figure  5  (left) illustrate the approximate limits of our data in size and line width, with size limits corresponding to best achievable resolution in high-magnification regions of the source plane as well as unlensed regions, and a line-width limit corresponding to FWHM = 40 km s -1 , consistent with our recovery tests described in Section 2.
J. S. S. was supported in part by  NASA   Hubble Fellowship  grant # HF2-51446  awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , for  NASA , under contract  NAS5-26555. M. A. acknowledges support from  FONDECYT  grant  1211951 ,  CONICYT + PCI + INSTITUTO MAX PLANCK DE ASTRONOMIA MPG190030 ,  CONICYT+PCI +REDES 190194 , and  ANID   BASAL  project  FB210003. K. C. L. , D. P. M. , K. P. , and J. D. V. acknowledge support from the  US NSF  under grants  AST-1715213  and  AST-1716127. D. N. acknowledges support from the  NSF  via grant  AST-1909153. This paper makes use of the following ALMA data: ADS/ JAO. ALMA# 2016. 1. 01293. S, ADS/JAO. ALMA# 2017. 1. 01423. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile.
SWOT is a satellite mission that will produce very unique global data on water over land and ocean that is qualitatively different from what the community has. The development of early engagement protocols and necessary training materials via the EAP was therefore recognized as essential to get the broader community ready for such a novel and vital mission for surface water. Our hope is that such a commentary will be of value to other satellite Earth-observing missions that are currently in service, scheduled for launch or prioritized for development in the near future according to the 2018 National Academies on Science, Engineering and Medicine Decadal Survey for NASA (NASEM, 2018).
The Uniqueness of the SWOT Mission The SWOT mission  (Andreadis & Schumann, 2014; Biancamaria et al. , 2016; Durand et al. , 2010; Fu et al. , 2009)  is jointly developed by NASA and CNES with contributions from the Canadian and UK space agencies. It is designed to provide, for the first time, spatially extensive, high resolution measurements of water elevation for the hydrology and oceanography communities  (Morrow et al. , 2019). Even though the sampling frequency of SWOT is latitude-dependent (lowest at equator, highest over poles), a large fraction of the Earth's ungauged surface will benefit from multiple SWOT observations in a 21 day repeat cycle (Figure  1 ). By virtue of its stated scientific goals, SWOT satellite data is expected to have beneficial societal impact in various sectors that involve water, including but not limited to, disaster management, reservoir operations, water management, flood management, ecosystem services planning, wetlands monitoring, hydropower and navigation, fisheries (freshwater and marine), coastal protection, and marine shipping.
Readers interested in knowing more about the application potential of the SWOT mission are referred to the SWOT website at http://swot. jpl. nasa. gov or at http://www. aviso. altimetry. fr/swot. We anticipate SWOT will play a pioneering role in building the world's space-based observational network for surface water similar to the Global Precipitation Measurement (GPM) mission with its constellation of precipitation sensors  (Skofronick-Jackson et al. , 2017). Current literature indicates that SWOT data will be able to increase knowledge of surface water fluxes, detect ocean features significantly better than current technologies  (Fu et al. , 2009) , improve global water budget calculations in conjunction with other models and satellite observations (see e. g. ,  Allen & Pavelsky, 2018; Andreadis et al.
, 2007; Durand et al. , 2016 Durand et al. , , 2021;; Garambois & Monnier, 2015; Lee et al. , 2010; Lin et al. , 2019)  and estimate unobserved hydraulic parameters (see e. g. ,  Brêda et al. , 2019; Emery et al. , 2020). SWOT data will also be a pathfinder to discover the human footprint in surface water storage variability associated with the development of water resources  (Biswas et al. , 2021; Bonnema & Hossain, 2019; Wongchuig-Correa et al. , 2020)  and bring benefits for the oceanographic community and marine applications  (Eldardiry et al. , 2022). A brief summary of the scientific measurement requirements of the SWOT mission can be found in Table  1  of  Fernandez et al. (2017). While the SWOT mission has all the ingredients to become an enduring mission for scientific discovery and societal applications due to its open-access and unique capability to observe water surface topography, a key challenge is the structure and format of its data products that are probably unfamiliar to many water scientists, practitioners and decision-makers.
Using Synthetic Aperture Radar (SAR) interferometry, SWOT will provide data on water surface elevations in the form of point clouds, that will then be packaged into other more widely used geospatial formats such as feature polygons and raster data  (Altenau et al. , 2021; Grippa et al. , 2019 ; see also-https://podaac. jpl. nasa. gov/SWOT?tab=datasets). However, there remains a sizable community of water scientists and practitioners who are more accustomed to point-based data from in-situ gauges which have a longer heritage and record of availability compared to remotely sensed data. For them, such spatially distributed formats may present some initial challenges for uptake. This is particularly relevant for hydrology applications while the challenge for ocean applications may be more on the use of the image information from SWOT.
This data format challenge is, however, not insurmountable, as a promising precedent already exists for SWOT and is further mitigated by the adoption of Earth science and Open Geospatial Consortium (OGC) data interoperability standards for SWOT mission data. The history of SAR data usage in general has remained confined to users with high literacy in data science. Recently, this challenge was mitigated for proliferating forest biomass applications by creating the SAR Handbook by NASA SERVIR program through a range of SAR workshops for stakeholders (NASA SERVIR, 2019). The handbook, which is open-access, builds on a comprehensive overview of the SAR remote sensing theory to provide detailed step-by-step tutorials on various application areas, such as forest stand height estimation, forest biomass monitoring and deforestation mapping, to name a few.
The SWOT Early Adopter Program As mentioned earlier, SWOT has had an EA Program (EAP) since 2018  (Hossain, Elmer, et al. , 2020; Hossain et al. , 2017). In the vision of the EAP, each selected EA (i. e. , a potential stakeholder) proposes an activity for the use of planned satellite data using either proxy datasets or simulated data that mimics the anticipated mission during the post-launch phase. EAs are defined as those groups and individuals who will have a potential or clearly defined need for data from the planned mission, and who are planning to apply their own resources to demonstrate the utility of planned satellite mission data for their application, system, or model. Experience from satellite missions such as Soil Moisture Active and Passive (SMAP) indicates that the sooner this pre-launch engagement begins and is sustained, the better the outcomes for use of mission data in areas beyond the declared goals of scientific requirements and data generation  (Escobar et al.
Nevertheless, the articulation of an eventual press release title as an end-goal summarizing the success of each EA project was deemed a useful exercise to define the ultimate goal of the SWOT EAP. The reader is referred to  Hossain, Elmer, et al. (2020)  or the webpage at http://depts. washington. edu/saswe/swot for more information on these press release titles. Of the many recommendations that emerged from the first EAP workshop in 2019, the key one we wish to highlight here is as follows  (Hossain, Elmer, et al. , 2020) : "Close and more frequent mentoring support for EAs is needed as EA projects mature and EAs begin facing new challenges with data structure and processing. EAs will continue to require guidance, pointers on data access, and with queries on data structure/handling. Effective support of EAs will set a good precedent to maximize the user readiness of SWOT data after launch.
Where Do SWOT Early Adopters Stand Today? Since the first SWOT EAP workshop in 2018, there have been regular training workshops and hackathons organized for early adopters with continuous engagement year round. Today the SWOT EA program boasts of 25 organizations from public, private, national, regional and international stakeholder entities (Figure  2 ). The hackathons and workshops have helped SWOT EAs improve technical literacy on the SWOT mission, learn more about SWOT's data structure and explore how exactly SWOT data can add value or solve a previously unsolved societal challenge. This engagement process has led to many EAs making cross-connections with other EAs to facilitate greater collaboration in newer areas of application (see e. g. ,  Nair et al. , 2021 Nair et al. , , 2022  on water surface mapping and citizen science).
It has also contributed to the development of educational content and open-source tools for analysis  (Soman & Indu, 2022). Finally, EAs are currently preparing to develop the software and hardware infrastructure necessary within their organization to leverage anticipated SWOT data in their operations or decision making environment  (Hossain, Bonnema, et al. , 2020; Hossain, Elmer, et al. , 2020; Hossain et al. , 2022). Here it should be noted that the data distribution arm of space agencies, such as NASA PO. DAAC and Hydroweb-Next by CNES, provides data infrastructure required for processing, archiving and distribution. The EAs are responsible for any further complementary downstream capabilities needed for the specific user communities they serve. The societal challenges addressed by the EAs now cover diverse topics such as flood and reservoir management, fisheries and marine science, transboundary water resources, state-wide water supply, wetlands monitoring, marine navigation and coastal studies.
The EAs span the continents of North America, Africa, South America, Europe and Asia (Figure  2 ). The SWOT EAP has so far convened five workshops that include three virtual training hackathons. These workshops have led to the development of open-source EA-specific tools for SWOT data with more than a dozen peer-reviewed publications showcasing the expected impact of SWOT data on the EA's baseline operations  (Hossain et al. , 2022). With support from the SWOT science community, the SWOT EAP has been able to build a growing archive of freely available multimedia tutorials, education materials and self-help resources for any user interested in exploring SWOT applications. This archive of resources continues to expand because of the communal spirit of the EA program where fellow Early Adopters and the scientific community help each other to overcome their respective project hurdles in a "hack" manner  (Hossain, Bonnema, et al.