dataesr/scientific-paragraphs-categorization huggingface dataset:
A case study In this section, we use a case study to illustrate the treatment done on the balloon and brightness temperature datasets. The wave event took place in the Western Pacific Ocean (∼ 170 • E, 5 • S) to the east of Solomon Islands, on January 15 th , 2020. Figure  3  displays maps of infrared brightness temperatures over 10 days centered on the event. The position of balloon 01 STR1 that recorded the event is displayed in the middle map. The maps illustrate the eastward propagation of a large-scale convective complex in the course of these 10 days. This complex is related to an active phase of a Madden-Julian Oscillation event  (Zhang & Mu, 2005; Madden & Julian, 1971 , 1972). The convection is particularly active on January 15 th , and balloon 01 STR1 is flying over the center of the convective area on that day.
Statistical results We now statistically examine the whole 30-s dataset recorded during the eight flights of the 2019 campaign. Timeseries associated with STR and TTL balloons have been treated altogether, after careful examination that the differences between flights were not attributable to physical factors (e. g. , different flight levels), but resulted from different sampling of the tropical area. This is illustrated by the summary statistics for each flight given in Table  2. Figure  6  shows boxplots of total momentum fluxes for WF and HF waves displayed with respect to the balloon distance to convection. The distance bins have been chosen to ensure a minimum number of 300 observations per bin to reduce the noise in the distribution tails, and with a finest resolution of 25 km. The most prominent feature exhibited by these boxplots is the monotonic decrease of gravity-wave momentum fluxes as distance to convection increases.
Last, we note that the intermittency values reported in this study agree fairly well with those obtained by  Wright et al. (2013)  and  Ern et al. (2014)  in the tropical lower stratosphere with satellite observations. Hertzog et al. (2012)  and  Wright et al. (2013)  reported on much larger figures at higher latitudes, where orographic waves represent a significant fraction of the gravity-wave momentum flux. There, the top 10% waves may contribute to more than 60% of the mean momentum flux. Our study therefore confirms that the intermittency associated with convective waves, while obviously present in the balloon dataset, is rather moderate.
The dataset from this first Strateole-2 campaign has allowed a first exploration of gravity wave momentum fluxes in the lower stratosphere and of their relation to the surrounding flow. Directions to pursue include consideration of a more integrative diagnostic for convection nearby, accounting for the time development of convection and for the direction of the shear between the convection and the balloons. Upcoming Strateole-2 campaigns, with 20 balloons each planned for 2021-2022 and 2024-2025, will allow us to investigate and quantify convectively generated gravity waves over an even wider sample of events and configurations.
Acknowledgments The balloon-borne TSEN data were collected as part of Strateole-2, which is sponsored by  CNES ,  CNRS/INSU ,  NSF , and  ESA. The Strateole-2 TSEN dataset is available at https://data. ipsl. fr/catalog/strateole2/. The merged IR satellite images were collected from the  NOAA/NCEP GPM  MERGIR product, available at https://disc. gsfc. nasa. gov/datasets/GPMMERGIR 1/summary. Martina Bramberger  and  Joan Alexander  are warmly thanked for thoughtful discussions and suggestions during the final stage of the paper. This study was supported by  ANR Booster. Milena Corcos  is funded through a  Sorbonne-Universite  and  CNES doctoral fellowship. No author declare any conflict of interest.
Overview of G4-Interacting Ligands The increasing evidence that DNA and RNA G4s can regulate a variety of physiological functions in cancer and neurological disorders has encouraged the design and development of new G4-interacting ligands that may act as therapeutic agents  [59]. However, besides acting as potential therapeutic agents, ligands can be utilized as molecular agents in biosensing and bioimaging for diagnostic purposes  [60]. To date, a significant number of ligands targeting G4s have been investigated and most of them have been deposited in the G4 Ligands Database 2. 1 (http://www. g4ldb. com/ (accessed on 3 August 2021))  [61]. Some G4 ligands are highlighted in Figure  2. Most of these well-known G4 ligands share common structural features such as an aromatic core, which permits π-π stacking interactions with planar G-tetrads, and one or more positive moieties that may interact with DNA or RNA backbone phosphate groups in grooves and loops  [62].
Geophysics-focused missions and improved geophysical data sets are critical for the future exploration of icy ocean worlds. Of particular interest is the exploration of the Galilean moon, Europa, and the Saturnian moons, Titan and Enceladus. These bodies likely have geologically active surfaces and may harbor habitable subsurface environments. Placing any candidate signatures for life in context requires further knowledge of the interior of these worlds. While the surfaces of these bodies have been mapped, their interiors remain poorly understood. Geophysical approaches such as geodesy, seismology, ice-penetrating radar and altimetry measurements, and electromagnetometry would provide critical information regarding the interior of these icy ocean worlds. The approaches described below would answer key science questions regarding ice shell and ocean dynamics, internal structure and interior layer thicknesses, near-surface structure, and how material from the deep interior might be exchanged with the surface.
Additional science questions regarding the astrobiological potential of these worlds can be addressed by using a geophysical instrument suite. Data provided through geophysical observations are key to fully understand the context of sampled materials and to properly interpret the habitability and any putative signatures of life. Here we describe a suite of geophysical approaches that could provide these data. Previous missions to icy ocean worlds returned data that suggest relatively young surfaces covering subsurface oceans  (Kargel & Pozio 1996; Carr et al. 1998; Porco et al. 2006; Schenk & Zahnle 2007; Kirchoff & Schenk 2009). The ice shells of these bodies range from a few kilometers to tens of kilometers thick, making direct sampling of the subsurface oceans difficult, except for Enceladus (and possibly Europa), where active plumes deposit ocean material onto the surface.
A lander that carries a seismometer (see Section 3. 5) could determine the timing and location of seismicity around the SPT. Combining multiple in situ geophysical data sets would allow for real-time monitoring and detection of active faults and fractures (see Section 3. 6). Enceladus is predicted to be less seismically active than Titan or Europa  (Hurford et al. 2020) , with only ∼10 13 Nm being released per ∼1. 4 Earth-day tidal cycle. However, because Enceladus is significantly smaller than Europa or Titan, any seismic event should be observable over a greater epicentral distance. Enceladus's relatively thin ice shell further increases the observed amplitudes of seismic phases compared to the thicker ice layers of Europa and Titan.
g. , urbanization dynamics, land use) to decide on which wetlands managers should prioritize. In 2017, the "wetland taskforce" ("Pôle Milieux Humides") of the Bourgogne-Franche-Comté region (France)-a team within a nonprofit environmental organization (Conservatoire d'Espaces Naturels)-was entrusted to elaborate a spatialized database on wetlands by a consortium of regional-tonational-scale institutions funding environmental actions. It was to focus on the whole regional scale based on a new prioritization methodology that would also need to be elaborated. (2) Relevant databases available for prioritizing wetlands are large and heterogeneous, and very often standard practices tend to conflate very different kinds of data indiscriminately. Some of the databases house quantitative scientific data such as the results of hydrological models or data on the abundance of a given species.
Others have political aspects and may include different forms of qualitative and quantitative information, such as zoning maps produced through political processes. Tsoukias et al. (2013)  emphasized the importance of taking into account the nature and design of data to provide relevant and legitimate decision support. The researchers involved in this case study therefore saw standard practices in wetland prioritization as an example domain in which policy analytics could make a difference, by developing methods that give importance to the nature of the data they use and their design. (3) The data used were the contents of the spatialized database elaborated by the wetland taskforce, and all the metadata corresponding to the methodologies used to capture these data, which we used to develop rules to aggregate parts of the information in the database using a rule-based approach  (Azibi and Vanderpooten 2002).
An example of a rule in this context was "if there is no indicator in the database testifying that a given wetland plays a role in flood mitigation, then this wetland is assigned to the category "No information in the database suggesting that it is suitable, even poorly, to pursue this objective to conserve wetlands performing a flood regulation function". A rule-based approach consists in identifying a consistent set of such rules allowing information in the database to be aggregated. To design these rules, we worked with representatives of wetland managers, who collectively identified a series of management objectives that they deemed they had political legitimacy to choose. We then used a rule-based aggregation method and MR-Sort, a noncompensatory aggregation method  (Leroy et al. 2011) , to produce a framework that the wetland taskforce will be able to use autonomously.
(3) The case study involved an ex-post analysis of the functioning of collaborative environmental governance and the main factors explaining how collaboration relationships form, and an assessment of the heterogeneity and representativeness of the stakeholders involved, as recommended by WFD (Art. 14). Data collection used documentary sources to identify representatives of organizations and count their participation in four policy processes in the Gironde estuary (514 individuals representing 386 organizations). A two-mode network methodology and preliminary field survey was used to define the population of interest ("the actors who act"). Then a final survey of this population produced data covering their exchanges of information, expertise, and resources, as well as the names of the people who are members of their network, who were themselves interviewed afterward.
(3) The various steps of the process produced a large amount of data, including 85 initial questionnaires about citizens' perceptions of the river and of participation, 630 contributions to the citizens' diagnosis, 189 propositions of actions, 3 action plans, 1 final report, 5 thematic syntheses sent to the local water committee, and answers to 78 questions asked by citizens. In addition, the participatory process itself was monitored and evaluated to provide data about the composition of the participants' group, its representativeness, and the retention level of participants (whether participants stayed throughout the whole process or left part way through). Data were collected by researchers, facilitators, and participants themselves. A group of 16 citizen volunteers contributed to data framing and collection.
The general availability of numerous and sometimes large datasets that characterize our digital age means that large quantities of data can be easily accessed and computed. But information on the context that has led to the emergence of these data, the protocols used, their intrinsic limits, and the paradigms that should accompany their interpretation are often forgotten in this process. Devictor and Bensaude-Vincent (2016)  and  Jaric et al. (2019)  provide detailed examples of the problems that this can create for environmental policies, as data are computed and interpreted in questionable ways. Several of our case studies were motivated by attempts to master the whole process of data generation and analysis needed to overcome such problems. In "Case 1: Elaboration of a Wetland Prioritization Platform", data were specifically selected and aggregated in different ways, depending on how stakeholders understand them.
In contrast, one expects a signature of van der Waals forces on the fluctuation spectrum at low frequencies, below a few tens of Hz taking the same numerical values as above. We thus anticipate a significant contribution of intermolecular forces for thicknesses smaller than a few tens of nanometers and at low frequencies. Fig. 3  displays the experimental spectra of two different oil films with two different thicknesses: 12. 9 nm (viscosity 9. 3 mPa•s) and 30. 3 nm (viscosity 19 mPa•s). The data are obtained in the stationary regime and averaged over at least 20 spectra (the acquisition time of each spectrum being 50 s). The experimental spectra are in excellent agreement with the theoretical prediction obtained by numerical integration of the mode density Eq. (  4 ). Tabulated values of viscosity, surface tension, and refractive indexes were used, together with measured values of film height and beam radius.
In each experiment, the fluctuation spectra of a stationary thin film are measured at two different frequencies, respectively 4 Hz and 80 Hz. The corresponding values of the PSD, respectively averaged over a 0. 8 Hz-wide and 16 Hz-wide frequency range, are reported in Fig. 4  as a function of the film thickness. Note that for the thicker films, measurements are conducted before reaching the plateau as the film is thinning down. Only three spectra could then be recorded and the large error bars result from poor averaging. All data points are in excellent agreement with our theoretical predictions. Fig. 4  confirms that the effect of intermolecular forces is most significant for the lower frequencies and for smaller thicknesses. In contrast, intermolecular forces do not modify the spectra at the larger frequencies, even for film thicknesses below 10 nm.
Tools developed by Moderna, BioNTech/P zer, and Oxford/Astrazeneca, among others, provide universal solutions to previously problematic aspects of drug or vaccine delivery, uptake and toxicity, portending new tools across the medical sciences. A novel method is presented based on estimating protein backbone free energy via geometry to predict e ective antiviral targets, antigens and vaccine cargos that are resistant to viral mutation. This method is reviewed and reformulated in light of the recent proliferation of structural data on the SARS-CoV-2 spike glycoprotein and its mutations in multiple lineages. Key ndings include: collections of mutagenic residues reoccur across strains, suggesting cooperative convergent evolution; most mutagenic residues do not participate in backbone hydrogen bonds; metastability of the glycoprotein limits the change of free energy through mutation thereby constraining selective pressure; and there are mRNA or virus-vector cargos targeting low free energy peptides proximal to conserved high free energy peptides providing speci c recipes for vaccines with greater speci city than the full-spike approach.
Materials and Methods See  [6, 7, 8]  for further details on this overview of materials and methods. The atoms in a protein peptide group lie in a plane containing the unit displacement vector of the peptide bond. A backbone hydrogen bond (BHB) from N-H to O=C, where carbon C and nitrogen N lie in the protein backbone, therefore determines a pair of such planes containing vectors, ordered from hydrogen bond donor to acceptor. There is a unique rotation of space carrying the rst plane and the displacement vector within it to the second and preserving the natural orientations on these planes. It follows that a BHB uniquely determines a rotation of space. A suitably unbiased and high quality subset called HQ60 (for "high quality at 60% homology identity") of the Protein Data Bank (PDB) therefore determines the histogram of all such rotations for all the constituent BHBs as in  [8].
This distribution on the space of rotations, i. e. , this histogram H (depicted in SI Fig. 1 ) representing the 1. 2 million or so rotations of BHBs of the proteins in HQ60, regarded as representative of all proteins, provides a fundamental database H, which allows the approximation of free energy, as follows. A standard tool in protein theory is the so-called Pohl-Finkelstein quasi Boltzmann Ansatz  [9, 10, 11] , which allows the estimation of BHB free energy (BFE) of a residue in any protein based upon this database, where in e ect low density in the histogram H corresponds to high free energy. By de nition, the BFE of a residue is the maximum of the free energy of all BHBs between the C=O or N-H nearest it along the backbone. This method of estimating backbone free energy is a new tool employed here that should be of general utility across structural biology.
2) INFORMATION EXCHANGE WITHIN PLATOON The information exchange within platoon can be divided into information exchanged between the platoon's vehicles via V2V (each vehicle has a unique label) and information received from the RSU to the platoon creator. The platoon creator (platoon manager), in turn, takes charge of sharing this information with platoon members as well as making real-time updates about the traffic data that platoon members have reported, and communicating it to the RSU via V2I. High-precision dynamic driving maps will be built based on the information that is shared within the platoon. In this use case we are talking about supporting a maximum latency of 10 ms for V2V communications and of 500 ms with a variable payload of 50-1200 bytes for sending two V2X messages per second between a UE and a RSU over another UE.
9 ). If the V2X UE supports multiple RATs (including LTE and 5G NR), the best technology to support the given application of interest should be chosen. The selection for the best RAT is based on several factors including the number of V2X UEs using a given technology, the presence of RSUs, the information configured by the network (e. g. , mapping between application ID and RAT), or QoS-related requirements. The use of multi-RAT to improve the V2X communication reliability is addressed in  [43] -  [45]. The authors in  [43]  evaluate different, independent and coordinated transmission schemes, where packets are transmitted through both the LTE-Uu and PC5 interfaces. While  [44]  introduces enabling efficient coexistence between DSRC and LTE. Reference  [45]  goes further by introducing an architecture and set of protocols for DSRC and C-V2X vehicular networks, addressing the problem of RAT selection, vertical handover, and data dissemination in a highway environment.
7) MmWave MmWave is one of the 5G enabling technologies that plays an important role in meeting capacity requirements by providing a larger bandwidth from around 30 GHz to 300 GHz, i. e. , a shorter wavelength between 10 mm and 1 mm, narrow beams, in addition to increasing security and reducing interference  [124]. Despite the advantages of this technique, challenges and limitations are discussed in the literature. When relying on contiguous bandwidths of up to 800 MHz, mmWave is expected to provide extremely high data rates of up to 20 Gbps. At these extreme data rates, 5G NR mmWave technology improves the V2X experience for use cases such as 4K/8K video, augmented reality, extended sensors, autonomous platooning, remote driving, and advanced driving. 5G mmWave can also play an important role in improving the reliability of the cellular primary procedure in massive V2X communication scenarios  [125].
In terms of data sources, some programs, such as ITS DataHub  [17] , may facilitate access to some datasets created using ITS technologies, but data 107732 VOLUME 9, 2021 sources remain scarce. Some solutions depend on training the proposed methods through the retrieved results from the network environment simulation. While other methods rely on distributed or federated learning, but it also requires that the equipment at network edge have larger computational and storage capacity. From our point of view, the majority of research in the field of vehicular communications is built on three main axes, i. e. , utilizing more spectrum resources, increasing the possibility of reusing these resources, and finally improving spectral efficiency. But the complexity of the used algorithms, additional CAPEX and OPEX, and maintenance stand in the way of further developments.
Light Dark Matter (DM), defined here as having a mass between 1 MeV and about 1 GeV, is an interesting possibility both theoretically and phenomenologically, at one of the frontiers of current progress in the field of DM searches. Its indirect detection via gamma-rays is challenged by the scarcity of experiments in the MeV-GeV region. We look therefore at lower-energy X-ray data from the Integral telescope, and compare them with the predicted DM flux. We derive bounds which are competitive with existing ones from other techniques. Crucially, we include the contribution from inverse Compton scattering on galactic radiation fields and the CMB, which leads to much stronger constraints than in previous studies for DM masses above 20 MeV.
With all the ingredients above, we are therefore in a position to compute the full spectrum of photons from DM annihilation. The final step consists in integrating the contributions in  Left: The hard X-ray and soft γ-ray spectrum produced by a 150 MeV DM particle annihilating into µ + µ -. We show the different components in color and the total flux in thick black. The spectrum cuts off before reaching Fermi's data (taken from  [182]  and reported here just for reference, as they are not the focus of our work), but produces a signal in X-rays that can be constrained by Integral (taken from  [121] , Fig. 7 ). The Final State Radiation (FSR, blue dashed) and Radiative Decay (Rad, blue dot-dashed) contributions yield signals that pass well below the X-ray data. However, the inclusion of the DM-induced Inverse Compton Scattering (ICS) contribution on the different components of the Galactic ambient light (starlight (SL, green dotted), dust-reprocessed infrared light (IR, brown dotted)) and the CMB (not visible in the plot), leads to a flux which is orders of magnitude larger, thus producing stronger constraints.
Integral X-ray data and analysis We use the data from the Integral/Spi X-ray spectrometer, as reported in  [121] , which follows previous work in  [183, 184]. The data were collected in the period 2003-2009, corresponding to a significant total exposure of about 10 8 seconds, and cover a range in energy between 20 KeV and a few MeV. They are provided both in the form of a spectrum of the total diffuse flux in a rectangular region of observation centered around the GC (|b| < 15 • , | || < 30 • , Fig. 6  and Fig. 7  in  [121] ) and in the form of an angular flux in latitude and longitude bins (Figs. 4  and 5  in  [121] ). In Fig. 2 , for illustration, we showed the former set of data. In the following, however, we will use the latter set of data, from which we cut out the Galactic Plane (GP).