dataesr/scientific-paragraphs-categorization huggingface dataset:
Tremor is one of the cardinal symptoms of Parkinson's disease. The neurophysiology of tremor is not completely understood, and so far it has not been possible to distinguish tremor from voluntary hand movements based on local brain signals. Here, we re-analyzed magnetoencephalography and local field potential recordings from the subthalamic nucleus of six patients with Parkinson's disease. Data were obtained after withdrawal from dopaminergic medication (Med Off) and after administration of levodopa (Med On). Using gradient-boosted tree learning, we classified epochs as tremor, self-paced fist-clenching, static forearm extension or tremor-free rest. While decoding performance was low when using subthalamic activity as the only feature (balanced accuracy mean: 38%, std: 7%), we could distinguish the four different motor states when considering cortical and subthalamic features (balanced accuracy mean: 75%, std: 17%).
Participants In this study we used a subset of previously collected data  (Hirschmann et al. , 2013a). We selected patients with enough data for comparing rest tremor, quiet episodes, and two motor tasks (see below). Recordings from 6 patients met these requirements. Overall, we had 11 datasets (5 subjects in Med On and Med Off, one subject in Med Off only; see Tab. 1). All patients involved were diagnosed with idiopathic PD, and underwent DBS surgery the day before measurement. They experienced spontaneous waxing and waning of rest tremor during the recordings. Patient details are provided in Tab. 1 The study was approved by the ethics committee of the Medical Faculty of the Heinrich Heine University Düsseldorf  (Study No. 3209). It was carried out in accordance with the Declaration of Helsinki and required written informed consent.
EMG labeling The hand performing the voluntary movements was presenting intermittent rest tremor in all subjects. In order to label the data, EMG data was 10Hz high-pass filtered, rectified and smoothed with a 100ms box-filter. Quiet periods were identified semi-automatically. Epochs with both EMG channels (forearm flexor and extensor) deflecting less than their respective 40% quantiles (computed using the data from the entire recording) were labeled as candidate quiet periods automatically and then adapted manually. Tremor and voluntary movements were labeled manually. Epochs with uncertain labels, as well as postural and kinetic tremor were discarded. When possible, "safety offsets" were included to ensure that different behavioral states were separated by at least 1s to mitigate the risk of confusing states (Fig.
Feature engineering For feature engineering, the data were separated into 1s-long disjoint windows. Windows containing MEG or LFP artifacts were discarded. The number of windows per dataset averaged across subjects and medication conditions after artifact and uncertain EMG state rejection was 1112 (std = 172), whereas total recording duration (without artifact rejection) was 1638s (std = 168s). For each window, we computed the logarithm of Hjorth activity, equal to windowed signal variance  (Hjorth 1970). The logarithm served to bring the data distribution closer to normal. For the source-reconstructed data, we averaged Hjorth activity across all grid points belonging to the same cortical parcel to obtain one feature per cortical area and time window. Hjorth activity (x(t)) = var(x(t)) The use of Hjorth activity rather than spectral power or connectivity was motivated by the intention to compare decoding performance across brain areas while keeping the number of features as low as possible to avoid overfitting.
Materials and Methods The population studied in this paper concerns fishers targeting scallops in the eastern English Channel, i. e. , the ICES area 7. d (Figure  1 ). Database concerns all vessels using dredges (exclusive or polyvalent) and which catch more than 1 kg of scallops via fishing trips. In France, these vessels are structured in four fleet categories according to the Location map of the study area (zones from 1 to J represent the scallop fishing areas. Zones 1, 2, 3, 4 and 5 are called Bay of Seine, zones 6, 7, 8, and 9 are called Proche exterieur, zones from 10 to J are called Outside the Bay of Seine or offshore zones). Note: The delimitation of the scallop fishing areas was revised in 2021, and zones from 1 to J are therefore no longer current. However, the approach and analysis in this study are still applicable to the new delimitations.
Materials and Methods The population studied in this paper concerns fishers targeting scallops in the eastern English Channel, i. e. , the ICES area 7. d (Figure  1 ). Database concerns all vessels using dredges (exclusive or polyvalent) and which catch more than 1 kg of scallops via fishing trips. In France, these vessels are structured in four fleet categories according to the typology defined by the IFREMER's Fisheries Information System (French national monitoring network for the observation of marine resources and the monitoring of professional fishing fleets), which is adapted to the framework of the European programme of fleets economic performance (Table  1 ). The objective of this typology is to manage the heterogeneity of fishing vessels and to classify them into groups or segments having similar fishing techniques and strategies as well as homogeneous economic characteristics  [34, 35].
Exposure describes then the fact that fishing fleets are confronted with closures of production areas due to scallops' contamination by phycotoxins and refers to the magnitude of these closures. Here, the exposure is measured based on an indicator defined from the rate of lost working days related to fishing bans due to HAB events. This indicator was calculated using data on administrative closures (legal decrees) and scallop fishing periods, provided by the DIRM-MEMN (Direction Inter-régionale de la Mer Manche Est-Mer du Nord: administration in charge of the implementation of management measures regarding HAB events in the eastern English Channel). Data on area closures contain information about the date and duration of closures in number of days structured by production zones and fishing seasons between 2012 and 2019.
Sensitivity category f (zone z ), (season s ) = Scallop production (zone z ), (season s ) Value of total landings (zone z ), (season s ) Data used to calculate the sensitivity indicator were obtained from the Fisheries Information System of IFREMER. These data represent landings in volume and value, structured by year, week, fishing fleet category, species, and fishing zone (Table  2 ). As scallop fishing is a seasonal activity in France (from October to May), the variable "fishing season" was added to the data table, based on the fishing calendar of scallops provided by the management administration.
Potential immpact category f (zone z ), (season s ) = Exposure × Sensitivity (4) Vulnerability category f (zone z ), (season s ) = Potential impact -Adaptive capacity (5) The construction of the vulnerability composite index involves two steps. First, the indicators of exposure, sensitivity and adaptive capacity were calculated and normalised using Min-Max method. This method allowed us to transform resulting values into a normalized range between 0 and 1 in order to use common scale for all indicators (Equation (  6 )). X normalised = (X -X min )/(X max -X min ) ( 6 ) X normalised is the standard indicator, X is the original value of the calculated indicator, and X min and X max are the minimum and maximum values of the dataset for each vessel category, fishing season and zone. The second step concerns the aggregation of dimensions and the construction of the composite index of vulnerability using Equation  (5).
In this study, it is assumed that all indicators are equally important, and equal weighting is used. The resulting vulnerability index was normalised between 0 and 1, with higher values indicating greater vulnerability. Table  3  summarises the indicators chosen to characterise each dimension of the vulnerability index, their construction and aggregation. The processed indicators of exposure, sensitivity, and adaptive capacity as well as the composite index of vulnerability have been published as an open-access dataset (https://doi. org/10. 35110/c12ca075-e2aa-41b9-962c-99111897b86e)  [49] , accessed on 10 June 2023.
The objective is to translate a general risk of HAB occurrence into a vulnerability score adapted to the different categories of fishing fleets and provide a socioeconomic information in addition to the environmental aspects of this phenomenon. Spatial data on scallop production zones were provided by Ifremer. All data processing and production of the shape files were performed using R software (version 4. 1. 2) and QGIS (version 3. 10. 10). The spatialisation concerns not only vulnerability index but also its three components. This allows us to better understand factors influencing the vulnerability dynamics, explore the spatial patterns and correlations between the vulnerability components. An interactive Web application was developed using R programming language and Shiny package. This interactive visualisation aims to have a holistic view of the vulnerability and its components and to present all the dimensions of the dataset including the fishing fleet categories as well as the spatial and temporal aspects.
Currently, there is no consolidated database or system gathering all these historical data, which led to a loss of memory regarding past events and their associated crises. Nonetheless, this temporal and spatial analysis over seven fishing seasons (from 2012 to 2019) provides a better understanding of the associated impacts and highlights the periods and areas where fishing fleets are most susceptible to adverse consequences. Such an approach proves more operational than the commonly found monetary assessments that focus on lost income or profits  [23, 53]. Monetary impact assessments quantify the magnitude of the pressure, but do not provide sufficient information on the necessary actions for effective management. On the other hand, vulnerability indicators, allow for the characterisation of each fisherman's activity during specific periods.
Data Availability Statement: Data on indicators of vulnerability and its components are available in INDIGEO portal at https://doi. org/10. 35110/c12ca075-e2aa-41b9-962c-99111897b86e (accessed on 10 June 2023) and the visualization application is accessible at https://scallop-fishing-indicators. ifremer. fr/ (accessed on 28 May 2023).
TFCat (Time-Frequency Catalogue) is a data interchange format based on JSON (JavaScript Object Notation), which has been initially designed for exchanging low frequency radio events and features. It defines several types of JSON objects and how they are combined to represent data related to temporal-spectral features of a time spectrogram (a. k. a. , dynamic spectrum), their properties, and their temporal and spectral extents. This implementation is inheriting from the GeoJSON file format. The TFCat python library is implementing this specification and provides a software interface permitting to create, update and validate TFCat objects efficiently.
The TFCat (Time-Frequency Catalogue) specification has been developed with the aim of facilitating the recording, rese and processing of low frequency radio temporal-spectral events. In the past two years, several catalogues have been published using the TFCat format, and tools are now implementing interfaces on top of this specification, for labelling observations, displaying catalogues, or even using the catalogue shapes to select and process observational data  (Cecconi et al. , 2021a; Fogg et al. , 2021; Louis et al. , 2021; Taubenschuss et al. , 2021; Wu et al. , 2021 , Wu et al. , 2022b , Wu et al. , 2022a). In this paper, we will present the needs and use cases identified in the preliminary stages of the TFCat specification project. We then detail the proposed catalogue model and its implementation.
Finally in the Earth science community, feature geometries are using the GeoJSON format  (Butler et al. , 2016). This format is using geographic coordinates to describe features and proposes several classes of features (such as points, segments or polygons). In order to facilitate the adoption by catalogue providers (e. g. , authors of publications), the selected solution must be easy to implement and should avoid dependencies on too specific software. On the user side, the same statement applies with the other requirements that it should be easy for tool and science platform developers to implement the solution. Besides the description of the temporal-spectral feature geometries, it shall be possible to set additional properties for each feature (e. g. , a label, an identifier linking to the original observation, or any property or statistical quantity related to the observed feature).
Proposed catalogue model The TFCat model provides a way to describe a series of features referenced by their coordinates (or set of coordinates) in the temporal-spectral plane, as observed from a radio instrument (a ground based telescope or onboard a spacecraft), and identified by an agent (a scientist or a software). Each feature of the catalogue is defined by a set of temporal and spectral coordinates, and a geometry type. Features can have additional properties, depending on the science case, which content shall be defined in the catalogue. The temporal and spectral coordinate frame in use in the catalogue shall be defined uniquely in a catalogue file, or identified by unique coordinate system name. The catalogue can have general properties (e. g. , header type metadata). The TFCat base model elements are: • TFCat Geometry-There are 7 types of TFCat Geometry: Point, MultiPoint, LineString, MultiLineString, Polygon, MultiPolygon, with an additional GeometryCollection, which is a set of any of the six other geometry types.
Javascript object notation implementation The TFCat model has been implemented using the JSON format  (Bray, 2014) , reusing the structure of the GeoJSON specification. The TFCat JSON specification is available  (Cecconi et al. , 2022b). The specification is maintained on a git repository 5. When applicable the astronomy standards have been implemented (UCD (Preite  Martinez et al. , 2018) , VOUnit  (Derriere et al. , 2014) , and list of known keywords, like, e. g. , reference positions). The TFCat CRS specification aligns on GeoJSON CRS definition. Currently, the TFCat CRS must be defined in the TFCat object instance. However, a set of predefined time axis are available within the specification (e. g. , unix, jd, mjd, etc).
Note, that a Polygon geometry requires that the last coordinate point of the input sequence should be the same as the first one (closed polygon condition). It also requires that the segments forming the polygon are not crossing over (no cross-over condition). The polygon must also be curled counter-clockwise. We use the shapely package to check the latter condition. Each properties must be defined at the main level of the feature collection object, using a set of TFCat Field objects, as shown below. The feature collection can also include collection-level properties, listing global metadata for the collection, such as the author, the instrument, etc. The next code snippet shows a set of collection level properties. We recommend to use keywords compliant with the EPNcore (Europlanet core) metadata dictionary  (Erard et al.
Listing 7: Validating a TFCat collection Writing the collection into a file is straightforward, since the TFCat module implements the same interfaces as the json python module for file system interactions (i. e. , dump(), dumps(), load(), loads() methods): The TFCat module also provides capabilities to load TFCat JSON files from the local file system, or from a remote location: In the following example, we load TFCat file available in  Taubenschuss et al. (2021). The script first displays the coordinate reference system (using the crs property), and the geometry type of the first feature. It then plots the two first features of the catalogue (see Figure  2 ), and then shows the use of the temporal and spectral converters, which can be used to load the time-frequency coordinates as an astropy.
Module installation and dependencies The TFCat module is distributed under MIT licence. The module dependencies are limited to a small set of external modules: astropy (Astropy  Collaboration et al. , 2022) , matplotlib  (Hunter, 2007) , shapely  (Gillies et al. , 2007)  and jsonschema  (Berman et al. , 2022). It is available on PyPI 13  and can thus be installed with pip: 1 pip install tfcat Listing 11: Install TFCat with pip.
Applications TFCat has already been used in several published data collections  (Cecconi et al. , 2021b; Fogg et al. , 2021; Louis et al. , 2021; Taubenschuss et al. , 2021; Smith et al. , 2022; Wu et al. , 2021 , Wu et al. , 2022b , Wu et al. , 2022a). In order to showcase the capabilities of the TFCat format and its python implementation, 3 Jupyter notebook tutorials have been prepared, and are available in  Cecconi et al. (2022a)    The TFCat model and python library are used in the SPectrogram Analysis and Cataloguing Environment (SPACE) Labelling Tool  (Louis et al. , 2022). TFCat features can also be loaded and displayed in Autoplot  (Faden et al. , 2010). Full support is not currently available, but an example Jython script is provided 15  by the Autoplot team.
Conclusion and future prospects The TFCat format and its associated Python library are providing the low frequency radio astronomy community with a novel tool enabling interoperability and reusability of temporalspectral features. The published catalogues are demonstrating its usefulness through various use cases. The proposed notebooks are showing its usability. Furthermore, the accessibility is guaranteed by the open access policies of the current TFCat catalogue publishers. The findability of the TFCat catalogues can be implemented using existing community data discovery interfaces (such as EPN-TAP  (Erard et al. , 2022)  for Solar System sciences). In order to enable the findability of the features, an implementation using the TAP Table Access Protocol,  (Dowler et al. , 2019) , is under study.
The TFCat format is now used in publications and tools to store, share and process temporal-spectra features. The GeoJSON inherited specification allows to reuse python based libraries (such as shapely) for processing the geometries. At Observatoire de Paris, the MASER team is integrating TFCat in their machine learning pipelines related to planetary and solar radio emissions, as well as in their radio data flagging pipelines (for excluding radio frequency interferences in the data processing). The TFCat collection properties are currently loosely defined: (a) the list of keywords is not defined in the TFCat specification; and (b) there is no metadata associated to them. This will be improved in a future major version, since this would require to refactor the already published collection. The specification foreseen for the TFCat collection properties would have the same definition as the TFCat Field items, with an additional value attribute, containing the value of the property.
Funding The  Europlanet 2024 Research Infrastructure  ( EPN2024RI ) project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  871149. CKL's work at  DIAS  is supported by the  Science Foundation Ireland  Grant  18/FRL/6199. BC and MT's contributions for this study are supported by the  EPN2024RI  project. BC, XB, and AL acknowledge additional funding provided in France by the  Centre National d'Études Spatiales (CNES) , and  Action Spécifique Observatoire Virtuel (ASOV). Support from  Paris Astronomical Data Centre (PADC)  is acknowledged.
following code snippet, a CRS object is created with a time axis using the unix timestamp representation, a spectral axis (frequency in kHz) and a reference position set to the Juno spacecraft. 1 from tfcat import CRS 2 crs = CRS({ 3 "type": "local", 4 "properties": { 5 "name": "Time-Frequency", 6 "time_coords_id": "unix", 7 "spectral_coords": { 8 "type": "frequency", 9 "unit": "kHz", 10 }, 11 "ref_position_id": "juno", 12 } 13 }) Listing 2: Example CRS definition 6 TFCat JSON Schema v1. 0: https://voparis-ns. obspm. fr/maser/tfcat/v1. 0/ schema#. 7 STILTS website: http://www. starlink. ac. uk/stilts/. 8 TFCat Python library: https://gitlab. obspm. fr/maser/catalogues/tfcat. 9 GeoJSON: https://github. com/jazzband/geojson. 10 shapely: https://shapely. readthedocs. io/en/stable/. the.
• Enhancing the message forwarding scheme by prioritizing service traffic, that is, proposing a service-oriented dissemination scheme that enables multicasting messages based on the type of sender and receiver. • Providing an open-source implementation of our solution (in addition to state-of-the-art solutions used for comparison) that is fully compatible with the NS3  (Riley and Henderson, 2010). Our implementation, available on  Anon (2022) , is based on the LTE ProSe code provided by the NIST  (Rouil et al. , 2017). The remainder of the paper is organized as follows. In Section 2, we present an overview of relevant solutions for post-disaster communications that are based on IoT technologies. In Section 3, we present the system model and formulate the problem statement. In Section 4, we present the details of how we built a communication backbone for an emergency network with support for IoT devices.
Evaluation methodology and scenarios In this section, we present an evaluation of the proposed solution through simulation in the discrete event Network Simulator NS-3  (Anon, 2021). We used version 3. 22 of NS3 which we ran on a simulation station equipped with an Intel Core i7-7700K processor and 16 GB of RAM and operates on a 64 bit Linux Ubuntu 20. 04. We implemented our scenarios on top of the LTE D2D ProSe module provided by NIST  (Rouil et al. , 2017). Nodes were deployed in an area of 500 m*500 m randomly following a uniform distribution and divided into two groups, stations, and devices. Stations are mobile and have a variety of wireless interfaces,   et al. , 2010)  propagation model across all our simulation scenarios, which is considered one of the best-suited models for wireless communications in a disaster situation  (Hunukumbure et al.
Background: Infections by SARS-CoV-2 variants of concern (VOCs) might affect children and adolescents differently than earlier viral lineages. We aimed to address five questions about SARS-CoV-2 VOC infections in children and adolescents: i) symptoms and severity, ii) risk factors for severe disease, iii) the risk of becoming infected, iv) the risk of transmission and v) long-term consequences following a VOC infection. Methods: We carried out a systematic review. We searched the COVID-19 Open Access Project database up to 1 March 2022 and PubMed up to 9 May 2022 for observational epidemiological studies about alpha, beta, gamma, delta and omicron VOCs among 0 to 18 year olds. We synthesised data for each question descriptively and assessed the risks of bias at the outcome level. Results: We included 53 articles, of which 47% were from high-income countries and none were from low-income countries, according to World Bank categories.
Search strategy We searched: 1) the COVID-19 Open Access Project (COAP) database, which aggregates COVID-19 research from PubMed, EMBASE, PsycINFO, bioRxiv and medRxiv from 1 January 2020 to 1 March 2022 and 2) PubMed using MeSH terms from 1 August 2020 to 30 January 2022. 9  Owing to large numbers of records that did not fulfil the inclusion criteria, we revised the PubMed search strategy, including each VOC, identified by their Greek letter and Pangolin lineage terminology, and conducted two new searches between 31 January and 7 March and between 8 March and 9 May 2022. We also searched reference lists of systematic reviews and included studies, and we included studies suggested by experts in the field, but not identified by our search. Full search terms are included in the supplementary note 1.