dataesr/scientific-paragraphs-categorization huggingface dataset:
Identifying the best Spike Detection Algorithm The objective was to evaluate and identify the most suitable algorithm to be implemented on the FPGA. It is important to note that this step did not involve the HDL code generation directly. Instead, its primary focus was on algorithm evaluation and selection for the target embedded implementation, as reported in our previous work  [15]. Based on the results of the performance analysis and considering the remaining resources available in the FPGA, a meticulous evaluation of spike detection performance and implementation complexity led to the selection of a hard threshold algorithm. This algorithm effectively identifies local peaks in the signal, striking a balance between spike detection accuracy, implementation simplicity and chip area usage.
Architecture conceptualization and design The Intan RHS Stim/Recording controller is an open-source commercial device that enables recording and stimulation from 128 electrodes at the same time. The system's core is the Opal Kelly XEM6010-LX45, a USB 2. 0 integration module based on the Xilinx Spartan-6 FPGA. Opal Kelly provides a FrontPanel SDK for configuring communication and interfacing the FPGA with a PC, Mac, or Linux hardware, reducing the effort required to build functional prototypes. Intan Technologies offers a host computer application programming interface (API), written in C++, for multi-platform support (Qt), which handles communication with the FPGA and displays the graphical user interface (GUI). Additionally, they provide hardware description language (HDL) code, written in Verilog, that manages real-time communication with the RHS2116 digital electrophysiology stimulation/amplifier chips and the host computer.
The RhythmStim code, developed for the Opal Kelly board, enables streaming of up to 128 amplifier channels from multiple RHS2116 chips, data from up to 8 other ADCs, and signals from up to 16 digital inputs. It allows for setting stimulation protocols for all 128 channels, with settings such as amplitude, pulse width, and sequences configurable through the GUI. The data is synchronized, time stamped, and transmitted over a standard USB 2. 0 bus to the host computer at a rate exceeding 20 MByte/s. The core of the RhythmStim code is a Finite State Machine (FSM) that cycles through 140 repeating states to execute a single serial peripheral interface (SPI) cycle. Each SPI cycle involves executing a command like "convert(0)" to retrieve the actual value of channel "0" of all the connected headstages, and transferring the data to the FIFO within the same 140 states.
Custom signal processing The signal processing algorithm was developed using the Simulink® HDL library provided by MathWorks. After the model design, Verilog code was generated and integrated into the modified Verilog architecture. The input/output interface acts as a communication bridge between the Verilog code and the Simulink model. The custom computation developed in Simulink consists of three primary steps: data decoding, data processing, and data encoding (see Fig. 2  Panel A3): 1. Data Decoding: This initial step involves the interpretation and conversion of input data into a format that can be readily processed by the subsequent stages. If the input data is in a specific encoding or compressed format, it needs to be decoded and transformed into its original representation. In the custom computation developed in Simulink, the input data is first decoded using a Moore State Machine implemented with StateFlow.
StateFlow is a graphical language within Simulink that encompasses various tools such as state transition diagrams, flow charts, state transition tables, and truth tables. The purpose of the Moore State Machine is to determine the channel and headstage from which the input data was acquired. It operates by transitioning between different states based on the input data and current state. The output of a Moore machine is solely determined by the current state it is in. During each time step, the Moore chart computes its output, evaluates the input data, and configures itself for the next time step accordingly. By utilizing the StateFlow implementation, the input data is decoded and the relevant information regarding the channel and headstage from which the data originated is extracted. This approach provides a structured and organized manner to handle the decoding process, enabling the system to accurately determine the source of the input data.
Once the data is filtered, spike detection is performed using a hard threshold spike detection algorithm. In this algorithm, a local peak, specifically a negative one, is identified as an action potential (see Figure  3 ). The spike detection is based on a comparison of the current sample at time t, the sample at time t-1, and the sample at time t-2. To identify a spike, two conditions must be met: (i) the sample at time t-1 must be the lowest among the three samples, and (ii) the sample at time t-1 must be lower than a set threshold. If both conditions are satisfied, a spike is identified. Once a spike is identified, a refractory period begins. The refractory period is a specific duration of time during which the system ignores any potential action potentials that occur. This period is implemented to ensure that subsequent spikes are not erroneously detected in close temporal proximity to the initial spike.
This encoding step is crucial for maintaining the integrity and structure of the processed data during transmission. To enable the storage of filtered data and timestamps of the identified spikes, the dataflow to the host PC was customized. While this customization provides the advantage of retaining this valuable information about the filtered data and spike timestamps, it came at the expense of the raw data. By following this sequence of data decoding, processing, and encoding, the custom computation in Simulink can effectively transform the input data into a processed output that is appropriately formatted, ready to be transmitted to the host PC. It is important to highlight that each block, especially the signal processing block, has been designed to be as independent as possible from one another.
Graphical user interface customization Fig. 4. Customized GUI. An item called "Load Configuration File" in the File windows has been added to allow for the loading of a configuration file which includes the threshold value and the refractory period to apply. A custom tab called "Custom Architecture" has been added with a flag to enable the dataflow redirection to the custom path. If this flag is not enabled, the system maintains its original behavior. To enable the rerouting of the dataflow through the custom path, and the delivery of custom parameters to the FPGA, the manufacturer's original open-source C/C++ code has been modified (see Figure  6 ). These modifications allow for the integration of the custom computation within the existing codebase. The original graphical user interface (GUI), which was developed using Qt 5.
The text file contains the customized threshold and refractory period values for the spike detection algorithm. It is structured in a way that facilitates easy loading of the custom parameters. However, at the current state of the project, the same threshold and refractory period values are applied to all channels. This feature provides flexibility for users to adjust the spike detection parameters according to their experimental conditions. The software, including the modified GUI and the custom computation code, has been compiled using Qt Creator community edition. It has been specifically compiled for the Windows 10 64-bit operating system, ensuring compatibility with the target environment. These modifications and adaptations enable the integration of the custom computation within the existing software infrastructure, allowing for the seamless execution and control of the custom architecture through the modified GUI.
Results A thorough comparison between offline and real-time implementation of the spike detection algorithms was conducted for each of the 32 channels across the 6 animals in the study. The offline hard threshold algorithm was applied to the dataset and the results compared to the output of the real-time implementation as shown in Fig. 7. In Fig. 7  Panel A and B, exemplifying plots demonstrate the offline behavior versus the real-time behavior. For both areas, RFA and S1, have been compared the number of spikes identified by the implementation. In each case, the exact number of spikes and the exact same spikes in the signal are detected for the different thresholds. To quantify the performance of the real-time spike detection, two metrics have been calculated: i) percentage accuracy and ii) percentage error.
Acknowledgment The authors wish to express their gratitude to all the colleagues who directly or indirectly contributed to the making of this project. A special thanks to the colleagues  Vijay Iyer  and  Akshay Rajhans  from  MathWorks , who mentored the work. V. R. C. was supported by  Marie Skłodowska-Curie Individual Fellowship MoRPHEUS , Grant Agreement no. 101032054 , funded by the  European Union  under the framework programme  H2020-EU. 1. 3. -EXCELLENT SCIENCE. This received financial support from  MathWorks  and was carried out within the framework of the project " RAISE -Robotics and AI for Socio-economic Empowerment " and has been supported by  European Union -NextGenerationEU.
g. , prices, wages, and production levels) for optimizing the next period (i. e. , t + 1). After the model optimizes the last period (determined by the user), it provides the projection of the endogenous parameters, such as prices, household income, GDP, and employment rate, over the whole horizon. Moreover, ThreeME requires several exogenous parameters: the social accounting matrix (SAM) of the base year, population growth forecast, economic growth forecast, and substitution elasticities. SAM is a comprehensive and economy-wide database recording data about all transactions between economic agents in a specific economy for a specific period  (Ferrari et al. , 2023). The population and economic growth forecasts determine labor availability and productivity projection. Elasticities define the substitution proportion of production factors in production functions.
g. , production, employment, national accounts) and in other statistical domains  (Eurostat, 2008). Instead, an ESM is usually framed around energy supply and demand sources, and it is calibrated on energy balance statistics. For example, the IESA-Opt model divides the national energy use into five main sectors: built environment (i. e. , residential and services), agriculture, industry, transport, and energy conversion sectors. The Dutch database of this model is calibrated using the Dutch 2020 CBS (Central Bureau of Statistics) energy balance reports. We start the soft-linking procedure by aligning the sectoral definition of two models. In this regard, we aggregate the sectoral definition of ThreeME to match with IESA-Opt. We group the 65 macroeconomic activity sectors into 32 sectors, as shown in Table  1.
For the ThreeME model, the SAM of the Netherlands in 2015 is obtained from the National Accounts datasets of Eurostat  (Eurostat, 2015). Moreover, the population and GDP growth forecasts are obtained from the Dutch statistical agency (CBS). Although both models use a separate source of GDP and population forecasts, the assumed values are not considerably different (see Table  4 ). IESA-Opt data sources assume a GDP growth of 1. 45% (from 2020 to 2050), while ThreeME assumes a constant 1. 5% growth.
Acknowledgments The authors wish to acknowledge the feedback and support provided by  Paul Koutstaal ,  Jinxue Hu ,  Hettie Boonman , and the  ESTRAC Integrated Energy System Analysis  project financed by the  New Energy Coalition  (finance code:  656039 ). Moreover, the authors are grateful for the feedback provided by participants of IEW-2022 conference. The views expressed here are those of the authors alone and do not necessarily reflect the views of the project partners or the policies of the funding partners.
Computational and in-depth human-coded content analysis To answer RQ1, first we performed a quantitative content analysis of 8,855 messages sent by publishers on their WhatsApp channels over four months (exactly 120 days), between 9 November 2020, and 8 March 2021 (dataset COMP). Data were collected by exporting each chat on WhatsApp for iPhone, and analyze the text using Python  (Van Rossum, 1995)  for the dimensions of news snacking (frequencies, times of day, text formats, message lengths, adoption of multimedia formats, and use of emojis). To complement this, we conducted an in-depth, human-coded content analysis of 965 messages (dataset HUM) sent over two weeks within that period (from 11 January to 24 January, 2021) to examine the type of subjects addressed by the channels. Topics were manually coded based on 14 categories used by  Boczek and Koppers (2020) , namely politics, international affairs, economy, crime, disaster, weather, arts, sports, entertainment, lifestyle, science, transportation, prize competitions, and events calendars.
After a pretest on coding data, we added one topic (health) that seemed important in the context of the COVID-19 pandemic. The use of WhatsApp as an interpersonal communication tool to engage with audiences (RQ2) was determined by coding each message on dataset HUM for seven types of requests for audience engagement (survey, contact, share, promotion, feedback, questions, crowdsourcing). Categories were defined after coding pretest data and by refining the categories used by  Boczek and Koppers (2020).
Use of emojis Experts seemed divided on the institutional use of these icons in messages sent to readers via WhatsApp. Half of the 18 channels, on average, used not more than one emoji per message; four channels sent a moderate number of emojis (between one and two) per message; while another five channels could be considered frequent emoji users, with an average of more than five such icons per message. The average number of emojis sent by Brazilian publishers was 1. 65 per message, and the publisher that sent the most was Correio Sabiá, with an incredible average of 47 emojis per message (dataset COMP) (Figure  3 ). The main emojis used in the sample give a hint of the main topic in the sample: three of the top ten most popular icons (the needle, the masked face and the virus) were related to the COVID-19 pandemic (Figure  4 ).
Main news subjects Our human-coded content analysis showed that the subjects most frequently addressed by the cases in our sample in the selected period on both platforms were, indeed, public affairs: health (34. 9% of all the messages in dataset HUM), politics (34. 4%) and economy (19. 1%). We also noticed a high frequency of subjects such as crime (14. 1%) and disaster (10. 2%), but this was less a reflection of publishers' attempt to reduce the news gap identified by  Boczkowski and Mitchelstein (2013)  and more a finding related to the higher number of messages from local and regional publishers in the sample. In an attempt to compensate for this discrepancy, we also performed an analysis according to the reach of each publisher (local or national). As expected, we found that there was a very significant variation in the types of content prioritized by local and national publishers, with the exception of health, which remained a relatively constant topic of interest for all media in our sample (Figure  5 ).
According to The Intercept Brasil's former director of social media, Juliana Gonçalves, the physical process of sending messages via the organization's more than 150 distribution lists, which had around 40,000 registered users on the platform, took half the working hours of a communication intern every day. The example is particularly significant because the online publisher, with over 600,000 followers on Facebook, 950,000 on Twitter and 1. 2 million on Instagram, was the one that sent the least number of messages in our sample, with an average of only 0. 3 texts per day (dataset COMP). The more messages, the greater the workload. Because of its private nature, WhatsApp did not offer statistics about these messages, which is commonplace on open social media. Without analytics, uncertainties increased: respondents reported not being sure if all users subscribing to broadcast lists actually received the messages sent to them (the condition for users to receive them was to store the news organization's contact details on their devices).
2020] ) for each date, specified for 24 h of simulation. Finally, these data were preprocessed (wind speed and direction converted from polar to terrain-following cartesian components) and interpolated to the calculation grid using the PRESFC and PREUPR software  [Douglas et al. 1990 ]. We note that, by using the ECMWF/ERA5-DIAGNO-DISGAS workflow, our approach is identical to that proposed by the VIGIL software  [Massaro et al. 2021; Dioguardi et al. 2022; Viveiros et al. 2023 ].
9 and 2 m height [as described in  Allard et al. 2014; Tamburello et al. 2019; Moune et al. 2022 ]. Most commonly, the fumarolic emissions were flattened to the ground by strong trade winds (up to 20 m s -1 ), and their upper boundary stood at ca. 3-4 m above the ground with a maximum gas density centred at 1. 5-2 m above the ground (our visual observations consistent with  Gaudin et al. [2016]  and  Tamburello et al. [2019] ). Moune et al. [2022]. We interpolated the concentration measurements using a 2D spline function and then integrated them over the plume cross section to obtain integrated concentration amounts (ICAs) using RatioCalc software  [Tamburello 2015 ]. These computations were based on CO 2 as a volcanic marker, given that the IR CO 2 sensor reacts faster than the H 2 S electrochemical sensor and CO 2 measurements are not affected by high humidity conditions  [Tamburello et al.
2019 ]. Then, CO 2 fluxes were computed by scaling the obtained CO 2 concentration in plume cross sections with the average wind speed. Wind speed was measured with a portable anemometer at the same height as the MultiGAS inlet during all traverses. The major source of error in MultiGAS-estimated fluxes is wind speed uncertainty and we estimate that our gas flux relative standard error is of the order of 40 %  [Tamburello et al. 2019 ]. We took the average CO 2 flux for the duration of the study period (2016 to 2021) for each of the sources as the gas flux input in our simulations. This is justified as, considering the relative standard error (40 %), the gas flux data do not show significant variation and we may thus consider them to be constant (Figure  3C ). This is in keeping with  Tamburello et al.
Terrain models We used a 5 m resolution Digital Elevation Model obtained from the Institut Géographique National (IGN) airborne Li-DAR datasets for topographic calculations in both DIAGNO and DISGAS. This was downsampled to 25 m for the corresponding simulations. We saw no significant effect of topographical resolution on our results, except in how well the wind fields were resolved which is consistent with  Massaro et al. [2021]. Furthermore, the terrain surrounding La Soufrière consists of de-vegetated stony ground (summit and flanks of volcano), low-lying vegetation (flanks), tropical forest, and buildings and is evidently heterogeneous. In spite of this, we used a uniform terrain roughness of 0. 5 m for all simulations, though we also tested uniform values of 0. 05 and 5 m without significant impact to our results.
These background values have been removed from the dataset to obtain the real values of H 2 S peaks. A total of 28 reports of perceived odours related to volcanic degassing were filed with the OVSG by inhabitants of the towns surrounding La Soufrière, dating from September 2020 to the early 2022. Eighteen reports were made via online surveys, which were active in the second half of 2021. Ten additional comments were published on the OVSG Facebook page (Supplementary Material 2. D). The reported dates were compared with the Gwad'Air station data and we found three dates when the survey reports about sulphur odours coincided with the H 2 S peaks measured at the air quality station, namely 15-06-2021, 07-10-2021, and 10-11-2021 (see Figure  5  and Supplementary Material 2. D).
ACKNOWLEDGEMENTS The authors are grateful to the  Mésocentre Clermont Auvergne University  for providing help, computing and storage resources. We also thank  Thierry Hamel  for assistance with computing resources. This work benefited from the  French government initiative "Programme d'Investissements d'Avenir " managed by the  Agence Nationale de la Recherche (ANR)  under grant  ANR-20-SFRI-0003  project  CAP  GS. This work was supported by the  EU -funded project  IMMERGE  ( Impact multi-environnemental des retombées volcaniques et sahariennes en Guadeloupe; PI: Céline Dessert, IPGP ). This project has been financially supported by  European  ( FEDER   FSE PO 2014-2020 ) and  Région Guadeloupe  (agreement  GP0023419 ).
Acknowledgment This research was supported by the European Research Council through the FP7-IDEAS-ERC Starting Grant scheme, project 278011 STUCCOFIELDS. A Proofs of the preliminary Lemma 4. 1 and A. 2. Lemma A. 1. Let Π 1,h be the edge interpolant. For p > 2, the following inequalities hold. There exists C > 0 such that for all (F h , ζ h ) ∈ Y 1 h × Z 0 h (i) ∥F h ζ h -Π 1,h (F h ζ h )∥ L 2 (S) ≤ Ch 1-2 p ∥F h ∥ L p (S) |ζ h | H 1 (S) , (A. 1) Furthermore there exists C > 0 such that  (ii) K∈T h |A h | 2 H 1 (K) ≤ C ∥∇ × A h ∥ L 2 (S) + sup r h ∈Y 0 h |⟨A h , ∇ r h ⟩| ∥r h ∥ L 2 (S) , (A. 2) (iii) K∈T h |Π 1,h (A h ϕ h )| 2 H 1 (K) ≤ Ch -2 p ∥∇ × A h ∥ L 2 (S) + sup r h ∈Y 0 h |⟨A h , ∇ r h ⟩| ∥r h ∥ L 2 (S) ∥ϕ h ∥ L p (S) + +∥A h ∥ L p (S) |ϕ| H 1 (S). (A. 3) Proof. Proof of (i). Let Π be the projector on the reference domain K corresponding to Π 1,h on Z 1 h , and Fh , ζh the corresponding functions classically defined on the reference element K.
1. Social media has created new opportunities to map cultural ecosystem services (CES) related to biodiversity at large scales. However, using these novel data to understand people's preferences in relation to these CES remains a challenge. 2. To address this, we trained a deep learning model to capture people's interactions with selected flora and fauna on Flickr as a cultural service related to biodiversity and compared this with citizen science data on iNaturalist, with photos of individual species considered as human-species interactions. 3. After mapping the distribution of people's interactions in Great Britain on Flickr and iNaturalist, we find significant spatial differences in people's preferences on the two platforms. 4. Using a second, pretrained deep learning model, we were also able to identify different preferences for species groups such as birds on social media versus citizen science.
, 2021). Crowdsourcing constitutes an important part of these new techniques to capture a broad range of cultural preferences  (Ghermandi & Sinclair, 2019). Meanwhile, deep learning is being directly integrated into citizen science platforms to support species classifications  (McClure et al. , 2020) , enabling the release of pretrained models for species detection  (Van Horn et al. , 2018). These technological developments now enable large-scale CES analyses using social media. By utilising such large social datasets alongside citizen science data, large-scale CES assessments related to biodiversity can potentially include a greater diversity of individual preferences both due to demographic variations and the core purpose of different platforms  (Fox et al. , 2021; Scowen et al. , 2021).