dataesr/scientific-paragraphs-categorization huggingface dataset:
Certain directives (such as PARTITION and PIPELINE) are progressively added to the NFMO C/C++ code to improve the architecture. Thus, we build numerous NFMO hardware designs. Then, we preserve the optimal design, which strikes a balance between hardware cost and processing time. The hardware requirements for the various NFMO designs on the Zynq XCZU9EG FPGA in terms of Lookup-Table (LUT), Flip-Flops (FF), BRAM blocks, and DSP blocks are presented in Table  7. The cost of hardware grows yet remains significantly below the system's maximum capacity. In contrast, the number of clock cycles drops by 97. 5%. Hence, an image may be processed in 48 milliseconds. In order to further reduce the number of clock cycles, Solution 3 includes the PARTITION directive, which allows the filtering window (Figure  15 ) to be divided into tiny blocks and promotes parallel data access.
Data Download and Pre-processing 1. The latest available data from the MSWX-Past data product are downloaded. If necessary, these are completed with the MSWX-NRT (MSWX near real-time) product  (Beck et al. , 2022). Specifically, we download surface pressure, 2m temperature, total precipitation and 10-m wind speed data at daily resolution. As a gridded meteorological product, MSWX-NRT does not reflect extremes at spatial scales smaller than the grid's size. Moreover, the analysis data used for the near real-time extension might suffer from model errors. For these reasons, extreme values of temperature, precipitation and wind speed measured locally by meteorological stations and mentioned in our reports, may not be reflected in the MSWX-NRT data shown in our analysis. 2. The event is represented by a surface pressure pattern averaged over a certain number of days (≥ 1) and over a certain geographical region.
For example, a resurgent or transitioning La Nina is linked to significant shifts in patterns of convective outbreaks over the U. S. A. (Lee et al. , 2016) , yet these ENSO phases are not caught by the ENSO3. 4 index used in our analysis. Finally, in our analysis we always give equal weight to all three modes, even though depending on the geographical location of the event being analysed some of the modes may be more relevant than others. The ENSO and AMO data are freely retrieved from the Royal Netherlands Meteorological Institute (KNMI) Climate Explorer (https://climexp. knmi. nl/start. cgi), and the PDO time series from the National Centers for Environmental Information (NCEI) of the National Ocean and Atmospheric Administration NOAA (https://www. ncei. noaa. gov/), where the most updated version is available.
(d) If Q exceeds the 95th percentile for both the past and the present, we assign the maximum value to the gauge (95%). This means that the event is unique in our dataset. We choose relatively high percentiles of Q to determine the positioning of the dial since we analyse extreme events which, by their very nature, are not frequently observed. The dial should therefore be interpreted as referring to events which in any case are comparatively unusual, but which may not be unique. As for the other dial, we do not use 0% or 100% to acknowledge data and analysis uncertainties. 3. We display the event's average surface pressure anomaly, defined as the difference between the average surface pressure at each gridbox in the selected region for the duration of the event and the average surface pressure at each gridbox for the same calendar day(s) over the whole period 1979-2022.
Report Writing Protocol ClimaMeter has a structured protocol for writing reports that assess and contextualise extreme weather events relative to climate change. This is a living document, that can be updated based on input from the ClimaMeter core team. The latest version of the template at the time of writing, which is detailed in Appendix A1, encompasses all aspects of the report, including the formulation of the report title, homepage title (which appears on the ClimaMeter. org homepage when the report is released), press summary, event description, climate and data background, ClimaMeter analysis, and conclusion. The homepage title categorizes extreme weather events into those "strengthened by human-driven climate change", "mostly strengthened by human-driven climate change", "likely influenced by both human-driven climate change and natural variability," or "mostly driven by natural variability".
The report for a given extreme event is released as soon as possible after the event has occurred, if needed using the MSWX-NRT data (see Sect. 2. 1). When updated MSWX-Past data become available, we aim to update the report. Similarly, we update reports based on feedback from researchers and the general public. This means that we may update a given report several times. For every report, we indicate the first publication date, the date of the latest update, and the date when the report was finalised. Once the report is finalised, it cannot be changed further. We will provide a PDF version of the report as well as a DOI to cite the report. The finalisation of the report may happen several months after the occurrence of the extreme event being analysed.
ClimaMeter Website The website www. climameter. org comprises several pages, each serving distinct functions as listed below. -The Home provides real-time updates and summaries of the most recent extreme weather event reports, with links to the full reports. -The Event Dashboard provides an at-a-glance overview of all the extreme events that have been analyzed. Users can navigate the extreme events visually, based on the location and type of event -visualised with colour-coded icons on a world map. The dashboard also enables users to refine their search by filtering events according to location, time range, event category, or the sources of detected changes. A screenshot of the dashboard is provided in Figure  2. -The Hazard Database page provides the full list of extreme events that have been analyzed, divided by category.
At the time of writing, the categories are "Heatwaves", "Cold Spells", "Heavy Precipitation" and "Windstorms". principles, data sources, and analytical techniques. This serves as a reference for researchers and climate scientists seeking insights into the scientific basis of ClimaMeter's conclusions. -The About ClimaMeter page outlines the project's origins, goals, and the core team responsible for ClimaMeter's operation. -The Media Coverage page compiles news articles, interviews, and reports featuring ClimaMeter. It tracks the impact of ClimaMeter on public fora and climate-related decision-making processes. -The Peer-Reviewer Research provides a list of investigations from research efforts within peer-reviewed journals.
Shenzhen also recorded an unprecedented 469 mm of rain in a 24-hour period. Surface Pressure Anomalies show a weak negative pressure anomaly over the western part of the domain analysed, associated with Precipitation of over 100 mm/day (Figure  4 ). Climate and Data Background for the Analysis. According to the Intergovernmental Panel on Climate Change (IPCC) WG2 Chapter 10  (Shaw et al. , 2022) , Hong Kong has already been experiencing noticeable changes in its precipitation patterns, particularly those exceeding 100 mm of rainfall in a single day (R100). These changes are associated with a projected decrease in the annual number of rain days, alongside with a shift in the seasonality of rainfall, with more prolonged wet seasons. However, it is crucial to acknowledge that future projections exhibit substantial variability among different model and emission scenarios, underscoring the significant uncertainties that persist.
Acknowledgements. The authors thank  Bérengère Dubrulle  for useful suggestions. The authors acknowledge the support of the  INSU-CNRS-LEFE-MANU  grant (project  CROIRE ), the grant  ANR-20-CE01-0008-01  ( SAMPRACE ) and support from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 101003469  (XAIDA), and the  Marie Sklodowska-Curie  grant agreement No. 956396  (EDIPI).
The collective behavior of animals has been traditionally studied through observation, quantitative models of behavior, and devices of low intelligence. Nowadays, the advancements in the field of robotics allow for closed-loop experiments that occur in real-time, and for artificial agents that intelligently and autonomously blend into hybrid groups of animals and robots (i. e. , biohybrid groups). Such systems provide scientists with the opportunity to study the social interactions that govern collective behavior from within the group of animals, either by mimicking them or by introducing precise stimuli to elicit and study their response. In this work, we introduce an open source Biohybrid Observation and Interaction (BOBI) platform for small animals, which consists of 3 main components: 1) an experimental setup; 2) a wheeled mobile robot called LureBot; 3) their software.
Nonetheless, even in the latter approach, which combines many of the advantages of Cartesian and wheeled robots, more nuanced factors can impede performance. Specifically, mechanical, electronic, and software components may not readily translate their specifications into real-world performance capable of replicating the spontaneous movements of live fish. For instance, our previous robotic system, the FishBot v4. 4  [5] ,  [10] , featured a conductive power supply system comprising wires at the top and bottom of the robot, which connected to positive and negative poles, respectively. However, in practice, these wires tended to lose contact in prolonged experiments (approximately 1 h). Moreover, the motors of the FishBot v4. 4 lacked sufficient torque to reproduce the entire spectrum of fish speeds, effectively only approximating the average speed.
A ROBOTIC SYSTEM FOR DEMANDING EXPERIMENTS Nearly a decade after the initial iteration of the Fish-Bot v4 and other similar platforms  [33] ,  [35] ,  [59] , advancements in algorithms and precision construction methodologies have enabled significant improvement. This progress also extends to the ancillary hardware and software that supports the robot's operation, such as cameras and communication protocols. In this work, we introduce a novel Behavioral Observation and Biohybrid Interaction (BOBI) framework, comprising: 1) an experimental setup; 2) a wheeled mobile robot, the LureBot; 3) a lure-building methodology, primarily designed to study fish interactions, as well as small animals or insects in general; 4) an open source distributed codebase utilizing Robot Operating System (ROS) 1  [57]  packages to manage various aspects of the setup and robot.
This new experimental framework facilitates and considerably extends continuous experiments (exceeding 3 h), at robot speeds that can reproduce or even surpass the motion profiles of multiple fish species (e. g. , Rummy-nose Tetra -Hemigrammus rhodostomus -and zebrafish -Danio rerio), with potential for applications to other small animal species. Additionally, BOBI incorporates significant upgrades over the FishBot v4. 4  [5] ,  [10]  and the Control and Control And Tracking Software (CATS)  [6] , in terms of real-time robot control, individual detection and identification algorithms, and hardware components such as cameras for capturing more precise data. In addition, in the context of the present work where the LureBot is interacting with rummy-nose tetra fish (Hemigrammus rhodostomus), the LureBot is commanded by a data-driven model which was shown in simulations to faithfully reproduce the collective behavior of this species  [12] ,  [38].
III. LUREBOT A. MECHANICS A full depiction of the LureBot's mechanical design 1  is presented in Fig. 2 , and can be generally summarized as a two-part design that consists of its chassis and cover. The robot's outer dimensions are 49 mm×50 mm× 42. 7 mm (w ×d×h), not including the magnets (Fig. 2a ) and ball casters (Fig. 2i ) or 49 mm × 78 mm × 46. 7 mm (w × d × h), otherwise. For reference, the FishBot v4. 4's No identities assigned at this point. (h) Trajectory assignment module. Given the tracked positions of robots (bottom camera) and individuals (top camera), and using past observations, this module assigns unique trajectory IDs to individuals. (i) Behavioral control module. High-level decision making process that generates target vectors (positions or velocities) for the robot to follow.
IV. BEHAVIORAL OBSERVATION AND BIOHYBRID INTERACTION FRAMEWORK The experimental setup and robot/lure is operated by the Behavioral Observation and Biohybrid Interaction (BOBI) software framework, for which, modules are implemented as ROS 1 packages 2. We use ROS because of its: 1) widespread use and support in the robotics community; 2) package-based architecture that allows modules to be easily replaced, maintained, adapted, or extended; 3) distributed architecture that allows programs (nodes) to be run on different machines to balance the computational load; 4) support of many common programming languages, e. g. , C++ and Python, which makes it easier to alter high-level operational aspects (e. g. , the behavioral model generating trajectories) for people with limited knowledge or interest in systems programming.
In comparison, our previous Control And Tracking Software (CATS)  [6] , was packaged as a single C++ software suite, with explicit dependencies to external libraries (e. g. , GStreamer 0. 1  [60] ). These choices shortened its life span and proved rather limiting: 1) for experimenters with little programming know-how, as it required good knowledge of C++ programming; 2) for maintenance, as GStreamer has since moved to a newer version that is not backwards compatible; 3) for software and hardware upgrades, because newer operating systems present dependency conflicts with CATS' external libraries which have been considerably updated and often do not support newer hardware (e. g. , GStreamer 0. 1 with USB Basler cameras). As depicted in Fig. 1 , BOBI is currently comprised of 4 main packages for vision, control, logging tasks, and interfacing with the robot (orange, green, blue, and red color, respectively).
3) Robot Interface We currently offer support for 3 robots in BOBI, namely, the LureBot, the FishBot v4. 4 used in our previous studies  [6] ,  [9] ,  [42]  and the Thymio II  [41]. In the remaining of this section, we focus on the LureBot, our latest hardware addition that is presented here for the first time. To interface with the LureBot we use the BLE protocol, which offers a publish-subscribe communication model  3. More specifically, the high-level interfacing routine, depicted in Fig. 1k , is responsible for three tasks: 1) to send the motor commands to the corresponding BLE characteristic; 2) to react to notifications from the LureBot which include information about the robot state (e. g. , current motor speeds, IR sensor values, etc. ), and to communicate those back to the motion control module (see Fig.
VIII. DATA FILTERING We use idtracker. ai (v4)  [51]  with a high-resolution recording (1500×1500 pixels) of the experiment to track the movements of all agents and extract their 2D trajectories offline. The software reports an average tracking success rate greater than 99. 5%, a result that is further validated by manual inspection. Additionally, we run a post-processing procedure that checks for and corrects any remaining instances where agents' identities are misclassified or missing (e. g. , when idtracker. ai cannot detect all individuals at a specific video frame). Analyses of later sections focus on the interaction of the lure with the fish, therefore, long periods during which the fish are barely moving are removed. More specifically, given the 35 mm average body length of H. rhodostomus, we remove intervals during which the fish or the lure speed is less than 1 BL/s.
This procedure also removes instances where the magnetic coupling between the robot and lure is lost, and the lure is stationary. The percentage of the experiment that corresponds to those periods is further discussed hereafter, and is referred to as the "inactivity percentage", for brevity. Finally, all trajectories are resampled with a timestep of ∆t = 0. 1 s instead of the original 1/30 s. The new timestep is carefully selected to reduce the random noise introduced between subsequent frames and the dimensionality of the dataset, but is still small enough to study the interactions of fish and lure.
First, we demonstrate that the LureBot can express the entire motion profile spectrum of the species under study (e. g. , H. rhodostomus), when interacting with the tank wall alone. Next, we present the results of experiments involving one fish and a lure, showing that the fish reacts to the robot similarly as when swimming with another fish. Notably, the biohybrid experiment consistently exhibits high activity, indicating that the robot can continuously engage in interactions. This finding holds significant potential for experiments requiring the collection of large, reproducible datasets. We also conduct experiments with larger groups, consisting of one lure and four fish, and show that the biohybrid group behaves comparably to fish-only experiments. However, in the five-agent experiments, the robot moves faster than individual fish in either the biohybrid group or fish-only experiments.
ACKNOWLEDGMENT The authors thank  Dr. Frank Bonnet  and Bionomous (https://bionomous. ch/)for their involvement in the early conception of the LureBot and the behavioral setup. We would also like to thank  Léa Pereyre  for hand-painting the H. rhodostomus patterns on multiple plastic lures and  Guillaume Valentin  and the  EPFL fish facility  for handling the equipment transportation to the  Université de Toulouse. This EPFL-Université de Toulouse collaboration was initiated thanks to the  Germaine de Staël  project no. 2019-17.
This work was supported by the  Swiss National Science Foundation  project " Self-Adaptive Mixed Societies of Animals and Robots ", grant no. 175731. The work of  Guy Theraulaz  and  Clément Sire  was supported by the  French National Research Agency  under Grant  ANR-20-CE45-0006-01. This work involved animals in its research. Approval of all ethical and experimental procedures and protocols was granted by the local ethical committee for experimental animals and were performed in an approved fish facility ( A3155501 ) (under permit APAFIS# 27303-2020090219529069 v8 , and performed in line with the French legislation.
2018)  using a parametric study and extending to other known directly imaged planets. The paper is organized as follows: Sect. 2 presents the data simulation and reduction for the MRS. Section 3 introduces the molecular mapping method we implemented. Section 4 provides a parametric study and Sect. 5 the application to a few known directly imaged systems. Section 6 presents a more in-depth atmospheric study for the target GJ 504 b and Sect. 7 a discussion of the results.
MIRISim The software MIRISim is designed to simulate representative MIRI data  (Klaassen et al. 2020) , incorporating the best A109, page 2 of 24 knowledge of the instrument. The simulation takes into account effects due to the detectors, slicers, distortion, and noise sources. MIRISim outputs are the detector images in the uncalibrated data format that can be used directly in the JWST pipeline. In this work, we used version 2. 4. 1 1. The simulations are parameterized using three configuration files that define the astronomical scene, the setup of the instrument, and the parameters of the simulator itself, as described in the following. Scene. For this study, the scene is composed of a host star and one or several planetary companions. Each object is simulated by attributing a spectrum and its position in the FoV, which is calculated based on the known astrometric positions.
These systems cover a broad range of temperatures, angular separations, and stellar types (see Fig. 9 ), and are therefore meaningful for testing our ability to characterize atmospheric parameters in the mid-IR, as compared with previous analyses in the near-IR. Furthermore, all of these planets will be observed in the GTO programs with coronagraphs, either with MIRI or NIRCam. Stellar spectra are defined with the parameters from Table  3  and normalized to the mean flux density values at 5. 03 µm 5  as measured in the M band of the Johnson photometric band and tabulated at the SIMBAD astronomical database  (Wenger et al. 2000). As we are studying young systems, we can expect unresolved inner dust rings to contribute to the mid-IR flux, which needs to be taken into account in the global stellar flux.
0, -2. 5) ′′. As it is a nearby system, the star is too bright for the MRS and the detector would saturate in a few groups. Offsetting the star allows longer integrations. Processing the simulated data with the molecular mapping method, we obtained the correlation maps shown in Fig. 10  As a test case, we ran the simulation without the star to assess the impact of the stellar contribution. We find an improvement of the S/N by a factor of three for NH 3 and HCN, respectively, and by a factor of two for CH 4. Other molecules are also easier to detect, and in particular, an S /N = 6. 8 is achieved for CO 2. These results argue for better stellar removal to improve the detections and possibly detect CO 2 in this system and other similar systems. The HR 8799 system harbors four young giant planets with similar characteristics in terms of temperature and luminosity.
HIP 65426 b was discovered by  Chauvin et al. (2017)  with VLT/SPHERE. The Y-to H-band photometry and low-resolution spectrum indicate a L6 ± 1 spectral type and a warm, dusty atmosphere. Petrus et al. (2021)  studied this system with different methods, including molecular mapping using VLT/SINFONI data and detected carbon monoxide and water vapor. The planet is detected in channel 1, and only the molecule H 2 O is detected. At this temperature, the detection of CO was expected from our parametric study. However, in the case of this system, which is almost four times more distant than the systems simulated in the parametric studies, the fainter flux of the planet explains why we do not have better detection of the planet and no detection of CO. 51 Eri b was discovered with GPI in the near-IR.
The comparison to a grid of models with a χ 2 approach provides reasonable results (Sect. 6) but for other targets that are too close to their star, the confidence regions in the χ 2 maps are too large to reduce the accuracy on the atmospheric parameters. Only planets with S/N larger than 30 in our sample (HR 8799 planets, 2M 1207 b at 1000 K) are amenable to atmospheric characterization with the MRS. We note that the extracted spectra of the others planets (with S /N < 30) are too contaminated by stellar residuals. Although well detached, HD 106906 b is a special case because Exo-REM is not well adapted to modeling a planet at this temperature; other atmospheric models would therefore be needed to characterize this planet. Moreover, having priors on the atmospheric parameters to correlate our data with models is necessary to reduce the A109, page 13 of 24 A&A 671, A109 (2023) explored parameter range.
In addition, CH 4 are less abundant in Exo-REM-based models with respect to models from petitRADTRANS, which explains why the detection of CH 4 is lower in our analysis of the HR 8799 planets. These differences in molecular composition mostly come from a different treatment of chemistry. In Exo-REM, chemical abundances are computed assuming chemical disequilibrium with quenching levels (generally between 1 and 10 bar) derived from a parametrization of the vertical mixing. In contrast, in  Patapis et al. (2022) , the quenching level is fixed at 10 -2 bar and assumes equilibrium chemistry. Also, we can expect a family of models that best fits the ground-based near-IR data to show large differences in the mid-IR regime. Depending on model assumptions, there is a large range of atmospheric parameter values that can reproduce the data.