dataesr/scientific-paragraphs-categorization huggingface dataset:
As the tasks are run asynchronously from the transmission task and the time slot, the data to be transmitted must be stored in a properly sized buffer. In the case of long-range transmission, such as LoRaWAN networks, where only a few tens of bytes can be transmitted at once, the QoS of the tasks must be set carefully to avoid overflowing this transmission buffer and losing data. Although this solution is optimal with regard to the priority ρ i and energy consumption E τ i C of the tasks, it does not take into account the minimal and maximal QoS of the tasks. Moreover, it does not compute the delay between two consecutive transmissions, as it is supposed that each time slot ends with a transmission. Thus, these computations have to be included in an algorithm that enforces the required QoS and computes an adapted D TX.
Experimental Validation All proposed energy allocators were implemented, tested, and compared on a real LoRaWAN IoT node, shown in Figure  3. This implementation was performed in an external library, and neither the base OS nor the tasks needed to be modified. This node was based on the Wi6labs platform and the multi-source energy-harvesting board described in Section 3. LQ-tracker was used as the EBE algorithm. All functions were implemented as a portable C library to help validation and to speed up the development. During the experiments, all of the nodes were connected to a commercial IoT network and reported the measured data to a real application server. Thus, the experiments were executed and validated in a real-world scenario. These experiments also validated the approach of multi-source energy harvesting to increase the QoS of an IoT node.
Experiments on intact EDL muscles The x-ray diffraction data obtained in relaxed demembranated fibers from rabbit psoas muscle were compared with analogous data from resting mouse EDL muscles in the same temperature range. Some of the EDL data were presented previously in the context of a comparison with those obtained during active isometric contraction at the same temperatures  (Caremani et al. , 2019). Here, we present additional analysis of the x-ray diffraction data from resting mouse EDL muscle obtained in those experiments. Male mice (Mus musculus, strain C57BL/6) aged 4-6 wk were sacrificed by cervical dislocation after inhalation of isoflurane according to both the Italian regulation on animal experimentation (authorization no. 956/2015 PR), in compliance with Decreto Legislativo 26/2014 and the EU regulation (directive 2010/63), and the protocols approved by the Illinois Institute of Technology Institutional Animal Care and Use Committees.
X-ray data collection Bundles of demembranated fibers from rabbit psoas muscle were mounted horizontally at beamline ID02  (Narayanan, 2014)  of the European Synchrotron Radiation Facility (ESRF), which provided up to 2 × 10 13 photons s -1 at 0. 1-nm wavelength, with beam size measured on the detector approximately 144 × 128 µm (horizontal × vertical, full width at half-maximum, 3-m camera length) or 155 × 130 µm at the detector (6-m camera length). The measured beam size is convoluted by the ∼85-µm point spread function of the detector. The beam was attenuated for bundle alignment. To reduce scattering from the solution, x-ray exposures were acquired while the bundle was in air in a small chamber at the test temperature. To minimize radiation damage, x-ray exposure was limited to the data collection period (10 or 20 ms) using a fast electromagnetic shutter (NM Laser Products, Inc.
Data analysis The x-ray diffraction data were analyzed using the SAXS package (P. Boesecke, ESRF, Grenoble, France), Fit2D (A. Hammersley, ESRF), and IgorPro (WaveMetrix, Inc. ). Two-dimensional patterns were centered and aligned using the equatorial 1,0 or 1,1 reflections, then mirrored horizontally and vertically. The equatorial intensity distribution was determined by integrating from 0. 0036 nm -1 on either side of the equatorial axis, and the intensities of the 1,0 and 1,1 reflections were determined by fitting four Gaussian peaks (1,0; Z line; 1,1 and 2,0; see Fig. 2, A  and B ). The distribution of diffracted intensity along the meridional axis of the x-ray pattern (parallel to the fiber axis) was calculated by integrating from 0. 012 nm -1 on either side of the meridian for the myosin-based M1 to M6 reflections and troponin T1 reflection.
Acknowledgments Henk L. Granzier  served as editor. We thank  Pauline Bennett  and  Thomas Kampourakis  for insightful comments on the manuscript; the  European Synchrotron Radiation Facility (ESRF)  and the  Advanced Photon Source  ( APS, Argonnne, IL ) for provision of synchrotron beam time;  Mario Dolfi , the staff of the mechanical workshop of the  Department of Physics and Astronomy (University of Florence ), and  Jacques Gorini  ( ESRF ) for electronic and mechanical engineering support. This work and some investigators were supported by  Fondo per gli Investimenti della Ricerca di Base (Futuro in Ricerca  project grant  RBFR08JAMZ  to  E. Brunello, Italy) ;  Progetti di Rilevante Interesse Nazionale-Ministero dell'Istruzione, dell'Università e della Ricerca, Italy and Telethon (Italy) ;  Consorzio Nazionale Interuniversitario per le Scienze Fisiche della Materia (Progetto d'Innesco della Ricerca Esplorativa 2007 ; Italy),  Fondazione Cassa di Risparmio di Firenze  ( 2016-2018 ; Italy);  UK Medical Research Council  (grant  MR/M026655/1 ); and the  European Synchrotron Radiation Facility.
This research used resources of the  Advanced Photon Source , a  U. S. Department of Energy Office of Science User Facility operated for the Department of Energy Office of Science by Argonne National Laboratory  under contract no. DE-AC02-06CH11357. The project was also supported by  National Institutes of Health/National Institute of General Medical Sciences  grant  P41 GM103622  (BioCAT). M. Caremani was funded by  University of Florence  (competitive project  marcocaremani_rictd1819 ; Italy). E. Brunello was funded by a  British Heart Foundation   Intermediate Basic Science Research Fellowship   FS/17/3/32604. L. Fusi was funded by a  Sir Henry Dale Fellowship  awarded by the  Wellcome Trust  and the  Royal Society  (fellowship  210464/Z/18/Z ). The authors declare no competing financial interests.
Clinical characterization We collected the following: demographic data (age, sex), clinical data (final diagnosis, Expanded Disability Status Scale [EDSS] at nadir, EDSS at 12 months, EDSS at last followup, duration of follow-up, the presence of relapse, as well as acute and long-term treatment), cerebrospinal fluid data (oligoclonal bands, pleocytosis, IgG index) and serum samples (AQP4-IgG, MOG-IgG), from medical files or the Lyon NOMADMUS database. Patients were diagnosed with MS according to 2017 McDonald criteria,  [12]  NMOSD according to the 2015 IPND criteria,  [2]  Sjogren syndrome according to the criteria defined by Vitali et al. ,  [13]  acute disseminated encephalomyelitis (ADEM) according to the criteria defined by Krupp et al. ,  [14]  ITM according to 2002 criteria,  [15]  neurosarcoidosis according to the criteria defined by Stern et al.
Statistical Analysis We studied and compared a group of NMOSD AQP4 + patients and a group of patients with other causes of myelopathy (OM group). The diagnostic performances of axial-BSLs and sagittal-BSLs were evaluated by comparing the data of the NMOSDAQP4+ group with those of the OM group. Demographic, neuroradiological and serological data were compared between the NMOSDAQP4+ group and the OM group using the Fisher's exact test or the Mann-Whitney U test for categorical or continuous variables, respectively. For the evaluation of the presence of axial-BSLs and sagittal-BSLs on the T2 sequences, interobserver agreement was assessed using Cohen's Kappa coefficient. In the interpretation of the Kappa coefficient, the standards for strength of agreement reported by Landis and Koch were followed.
[23]  Univariable logistic regression analysis was performed, and baseline variables with p ≤ 0. 10 were included in the multivariable logistic regression analysis. Odds ratios (ORs) with 95% confidence interval (CI) were used to measure the effect of predictors. Statistical significance was set at p < 0. 05. Statistical analyses were performed using the STATA-12 software (64-bit, StataCorp, College Station, TX, US) and figures using GraphPad Prism (version 5. 0, GraphPad Software, La Jolla, CA, US).
Data confidentiality and safety are ensured according to the recommendations of the French Commission Nationale Informatique et Libertés (CNIL). NMOSD cohort is part of the OFSEP (Observatoire de la Sclérose en Plaques) cohort. OFSEP has received approval for storing clinical, biological, and imaging data for research purpose. Patients give informed consent for their data to be stored in the database and used for research, in France and abroad (www. ofsep. org/en/cohort/ofsep-consent). The cohort has been registered to clinicaltrials. gov under the number NCT02889965. Patients Patients from the Lyon University Hospital who presented with a first acute spinal cord syndrome between 2005 and 2018 were evaluated for inclusion. Patients were identified in two databases: i) the radiology department's database (DXImage-RIS, version 5.
Funding: This work has been supported by a grant provided by the  French State  and handled by the  "Agence Nationale de la Recherche ," within the framework of the  "Investments for the Future" program , under the reference  ANR-10-COHO-002. Acknowledgements: This work has been done by using data from the  Observatoire Français de la Sclérose en Plaques (OFSEP)  which is supported by a grant provided by the  French State  and handled by the  "Agence Nationale de la Recherche ", within the framework of the "Investments for the Future" programme, under the reference  ANR-10-COHO-002.
In this context, the absence of the direct link between the chemical space of the generated fragments and the pool of available BBs makes tools like RECAP and BRICS appear as rather theoretical, reality-disconnected approaches, distant from downto-earth practical library design based on the reagents present in the laboratory drawers. 18 Some methodologies of library de-novo designs considering BBs availability have been previously reported, including both commercial/proprietary software  [19] [20] [21]  and methodologies used mostly by the authoring academic group  22. Therefore, here we report a new open-source toolkit for synthons-based library design, called Synthons Interpreter (SynthI). In chemoinformatics synthons were first introduced by R. D. Cramer et al in 2007 as structures with one or more open valences each having a defined reactivity  23.
General description of SynthI SynthI is a python3 RDkit-based  9  (2021) library that generates synthons from larger molecules via fragmentation or from small reagents via functional group transformations. Being a knowledge-based tool, SynthI is based on the extensive library of SMARTS, defining each reagent class and SMIRKS that specify the reaction rules for synthon generation from BBs, pseudo-retrosynthetic bond disconnections, or synthon recombination. SynthI consists of four modules (Figure  1 ), each being responsible for a particular task. In the following chapters, you can find a detailed description of each of them.
22 monofunctional BB classes were considered, like acyl halides, boronics, ketones, primary amines etc. Almost each of them incorporates subclasses, totaling up to 100. For example, class "Alcohols" includes three subclasses that would have different reactivity -"Heterols", "Aliphatic alcohols" and "Phenols". In addition, there are 28 bifunctional and 19 trifunctional classes. All of them concern only reagents for coupling reactions as soon as the first version of SynthI does not include heterocyclization reactions. From the library design point of view, their usage would lead to the destruction of the privileged scaffolds that contribute significantly to the exhibited activity. Therefore, in the first implementation of SynthI heterocyclization reactions were not taken into consideration. For more detailed retrosynthesis, however, those reactions are highly important, therefore we are currently working on the implementation of the SynthI-Heterocyclization module, that would allow the user to select whether they want to include cycle bonds disconnection.
SynthI-BBs The same BB can be assigned to several classes followed by the generation of synthons, corresponding to each class using SynthI-BBs module. In each synthon, the special labels are placed at the former position of the leaving groups (Figure  2  and Table  1 ). They define the type of the bond disconnection and reaction center -electrophile, nucleophile, radical, etc. The full list of unique synthons generated from the user-provided BBs library produces a chemical space of available synthons. In the case of a compound, containing protective groups it is up to the user to decide whether to keep protected synthons or not (keepPG option). The list of all synthons generated from each BB class is provided in the Supporting information.
Scheme 2. Example of RECAP disconnection of the bond that can be formed via different reactions. The reaction rules behind SynthI were collected based on the analysis of current literature and our experience in medicinal chemistry synthesis. It included various reactions, leading to: • several ways of disconnection of the same strategic bonds that were already considered in RECAP and/or BRICS (Buchwald-Hartwig amination  26  , Cumediated C-N/O coupling  27  , umpolung crosscoupling  28  , Chan-Evans-Lam coupling  29  , olefin metathesis  30  , non-classical carbonyl olefination (like Julia-Kocienski)  31, 32  , C-H activation  33  , sulfonyl fluorides chemistry  34  , Suzuki C Ar -C Ar cross-coupling, novel methods for C Ar -C sp3 couplings). • disconnection of the new strategic bonds absent in the previous implementation (Heck C Ar -C sp2 , Sonogashira C Ar -C sp and Suzuki C sp2 -C sp2 couplings, imines, oximes, hydrazones and semicarbazones synthesis, sulphinic acid salts alkylation and their Cu-catalyzed arylation) Also, the set of new radical chemistry, as well as new methods of late-stage functionalization (Baran diversinates  35  , Minisci-type reaction  36  ), were included in SynthI.
These new reactions dramatically changed modern retrosynthetic thinking of the medicinal chemist  14, 37  , and the new more effective conditions for such reactions still actively investigating  38. In total SynthI contains 13 broad reaction types for the bond disconnections and 37 subtypes, that may lead to different synthons. For example, for "Olefination" type, there are two subtypes -"Knoevenagel-, Wittig-, Julia-Kocienski-type reactions" and "Olefin Metathesis". The first one is the example of polar bond disconnection resulting in bivalent electrophilic and nucleophilic synthons, while the second one produces neutral biradicals (Scheme 3). Obtained synthons can be traced back to the potential BBs for compound synthesis. The full list of reaction rules with some examples is available in the Supporting Information.
One may also navigate the disconnection hierarchy using several built-in functions. More details on the usage of SynthI and tutorial can be found on the GitHub page (https://github. com/Laboratoire-de-Chemoinformatique/SynthI). 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑟𝑎𝑡𝑒 = ∑ ℎ𝑒𝑎𝑣𝑦.
SynthI-Enumeration This last module is based on the list of the same reaction rules used for bond disconnection, allowing to generate the full combinatorial library of all compounds that can be synthesized using a given set of synthons (Figure  3 ). Users can control the maximum number of synthons that can be combined together. As well as the list of reactions for enumeration. If the maximal number of synthons has been reached but not all reaction centers were "closed" this product will be discarded. SynthI-Enumeration also allows to generate a focused library of the synthesizable analogs of the provided compound. The input molecule is first fragmented up to the smallest synthons. Their availability is checked using the BBs synthons library. The same library is used for the search of the analogs of generated synthons -synthons containing the same types of reaction centers (but not necessarily in the same positions), the same number of rings, and matching the constraints, adopted from the positional analogs scanning (PAS) strategy for lead optimization  39.
| Center-of-bay sampling To analyze variation in stickleback morphotype distribution in the central parts of the bays, we collected data on fish, vegetation, and substrate, as well as water temperature, salinity, and turbidity at 3-5 stations within each bay. Fish were sampled with 30 m long by 1. 5 m high Nordic survey gillnets consisting of twelve 2. 5 m long panels, each with a different mesh size:  43, 19. 5, 6. 25, 10, 55, 8, 12. 5, 24, 15. 5, 5, 35, and.
| Shallow sampling To analyze variation in shallow water along the shoreline, we collected data on fish, vegetation, and substrate. Fish were caught with 45 × 24 × 24 cm (length × width × height) minnow traps (KAYOBA, Skara, Sweden) with a mesh size of 3 mm and two 5. 5 cm diameter round holes on each short side. To standardize the vegetation measurements around the trap, so that one measurement was always in front of the opening and one to the left and right, we closed off one hole on each trap with a cable tie. We set five traps for a total of 135 traps across 27 bays at 0. 5-1 m depth. Traps were used instead of gillnets because of the dense vegetation and shallow depth range. Traps were deployed in the afternoon and retrieved the following morning. Traps were distributed such that they covered a gradient of vegetation cover.
| Data analysis We analyzed the distribution of stickleback morphotypes using mixed-effects logistic regression models with a binomial distribution and logit link function. We ran separate models for the center-of-bay and shallow data. In both cases, the dependent variable contained the numbers of completely plated and incompletely plated stickleback. For the center-of-bay data, we included net nested within bay as a random factor. We included the following fixed factors: coverage of habitat-forming vegetation, piscivorous fish CPUE, maximum depth of the bay, wave exposure, bay location (inner, mid, or outer archipelago), relative latitudinal bay position (north, middle, or south), and bay topographic openness. We evaluated models with all possible combinations of predictor variables except variables which were collinear.
Wave exposure was estimated from fetch and longterm wind data using digital nautical charts and GIS methods, where refraction/diffraction effects are simulated by a spreading algorithm  (Isaeus, 2004; Sundblad et al. , 2014). Bay location was categorized Ŷijk = Y jk -B jk L ik -50 based on the relative shortest water distance to the open sea estimated by GIS methods (e. g. ,  Eklöf et al. , 2020). Bay topographic openness (Ea) was calculated using the formula: where At is the cross-sectional area of the smallest connection to the sea, and a is bay surface area (e. g. ,  Håkanson, 2008). For the center-of-bay data, we only included sampling events where we caught at least 20 stickleback in the net which left us with 39 of the 101 nets. This threshold was chosen by first visually examining the proportion of completely plated individuals caught in the net plotted against the total number of stickleback caught in the bay, and then estimating where the variation in the proportion of completely plated individuals became consistent (Figure  S1a ).
Continuous variables were on different scales so all were centered and scaled before analysis. We checked models for collinearity by calculating variance inflation factors (VIFs) of each predictor variable. Maximum depth of the bay, bay location, and relative bay position were collinear (VIF > 2) so they were included in separate models. When analyzing the shallow data, we included trap nested in bay as a random factor and coverage of habitat-forming vegetation, Fucus presence, maximum depth of the bay, wave exposure, bay location, relative bay position, and bay topographic openness as fixed factors. We chose to include Fucus presence in the shallow data analysis as it is a perennial habitat-forming species and hence is often the dominant vegetation habitat in the spring before annual or semiannual species have gained substantial biomass.
, 2020). This is, however, conjecture and cannot be tested with our data. To begin testing these hypotheses, feeding preferences must be examined. For this, morphometrics of feeding structures and stomach content data of the different morphotypes are needed. Gape size, suction strength, and gill raker length and number have all been shown to differ between stickleback with different diets  (McGee & Wainwright, 2013; Robinson, 2000; Schluter & McPhail, 1992). We did not collect such data, with the exception of jaw length which did not differ between our morphotypes. Alternatively, our results could be driven, similar to the center-of-bay results, by the piscivorous fish community as there is typically higher piscivorous fish biomass in densely vegetated areas  (Eklöv, 1997). However, sampling with minnow traps does not give us information on the piscivorous fish community (we caught a single perch and no pike in the traps).
29 mm knot-to-knot (European Union standardized method EN 14757:2005). Nets were placed at 1. 5-3 m depth, F I G U R E 2 Map of sampling locations. Axis labels are latitude and longitude following a stratified randomization. Three to five nets were set in each bay, depending on the surface area of the bay, for a total of 102 nets across 27 bays. The nets were set in the afternoon and retrieved the following morning. Fish were identified to species, measured for total length (cm), and counted. Stickleback were counted, but when more than 30 were caught only a haphazardly chosen sample of 30 individuals were measured (total length, mm) and preserved in 95% ethanol for later morphometric analysis. Total length was used to calculate biomass for all species following length/weight conversion factors from the Swedish National Database for Coastal Fish (http:// www.
However, in CRC-ECCV for HEVC, we use Deblock+MVS when we cannot correct the packet. The Intact sequences correspond to error free decoded sequences, using JM and HM software for AVC and HEVC encoded video sequences, respectively. The Ice, Crew, City, Mobcal, Ducks and ParkRun video sequences were used to conduct the simulations. We chose these sequences because they have different visual characteristics: • Ice sequence (4CIF 704 × 576) represents ice skaters moving laterally over a fixed ice rink background. • Crew sequence (4CIF 704 × 576) represents crew members walking towards the camera, with no camera movement. Horizontal traveling follows the runner's movements. As we will show, it is crucial to take the characteristics of the transmission channel into account when deciding to apply CRC-based error correction.
V2I video transmission can also be used to monitor traffic conditions, by sending video from vehicles equipped with external cameras to a roadside unit (RSU), thus providing information on the traffic conditions. V2V channels have been implemented in Matlab from real-world data collected by  [38]  for different scenarios, such as urban and rural scenarios, considering that the vehicles are either in line-of-sight (LOS) or non-line-of-sight (NLOS) situations. The 802. 11p transmission and vehicular channel models used in our simulations are available in the Matlab WLAN toolbox  [39]. We tested our method over several channel conditions, which are described in each example. Fig. 6  presents the comparison, in terms of reconstructed video quality, at the receiver, between state-of-the-art methods and the proposed approach, for different channel SNRs, in a rural LOS scenario.