dataesr/scientific-paragraphs-categorization huggingface dataset:
In particular, including the frequency weighting filters in the optimization scheme introduces extra complexity to the planning problem. To improve computational efficiency, learning-based approaches could be an attractive alternative as they can effectively shift the bulk of the computational burden offline and are hence far less demanding on onboard resources. They have been successfully applied to a plethora of engineering problems including object detection and classification, speech recognition, content recommendation, etc. For our application though, the more popular branch of supervised learning techniques is not applicable. Because they require labeled training data in large quantities that are difficult to collect. To do so, human subjects should be exposed to sickening driving regimes and their responses should be recorded.
The state is expressed as x = [p T v T ] T. The acceleration vector is taken as the IMU measurements but converted to global coordinates using yaw orientation. The process noise is assumed to be a result of the noise in IMU measurements. The acceleration data has been low-pass filtered beforehand to remove the high-frequency noise using FFT-based techniques. We assume the system to be fully observable with all state measurements z k available from the GPS sensor: z k = p v + η k η k ∼ N (0, R) ( 23 ) Using the system dynamics given by (  22 ) and the measurement model given by (  23 ), and assuming normally distributed Gaussian noise for all sensors, the Kalman filter could obtain a better estimation of vehicle trajectory and velocity profile over the test run. The desired portions of the trajectory of RB1 and RB2 were extracted using the estimated vehicle position.
Settings We use, in principle, the version of the NSGA-II given by Deb (Revision 1. 1. 6), available at https://www. egr. msu. edu/ ∼ kdeb/codes. shtml, except that, as in our theoretical analysis, we do not use crossover. We re-implemented the algorithm in Matlab (R2016b). When a sorting procedure is used, we use the one provided by Matlab (and not randomized Quicksort as in Deb's implementation). As mentioned before, we utilize the first rank then crowding distance strategy for the binary tournament selection as in the recent pymoo platform (https://pymoo. org/algorithms/moo/nsga2. html) by Blank and Deb. Our theoretical analysis above covers three parent selection strategies and two mutation operators. For reasons of space, we concentrate our experiments on one representative setting, namely binary tournament selection (as proposed in  (Deb et al.
Finally, we conduct experiments with the population size N = n + 1, which is large enough to represent the full Pareto front, but for which we have proven the NSGA-II to be ineffective (on ONEMINMAX and when letting each parent create an offspring via one-bit mutation). of 2 for LOTZ, but by slightly less for ONEMINMAX. The data for the two population sizes smaller than N * indicates that also for these parameter settings the NSGA-II performs very well.
We ran the NSGA-II for 3000 generations for ONEMINMAX and 5000 generations for LOTZ and measured for each generation the ratio by which the Pareto front is covered. This data is displayed in Figure  2. We see clearly that the coverage of the Pareto front steeply increases at first, but then stagnates at a constant fraction clearly below one (around 80% for ONEMINMAX and between 50% and 60% for LOTZ) and this in a very concentrated manner. From this data, there is no indication that the Pareto front will be covered anytime soon. Back-of-the-envelope calculations (similar to the rigorous proof of Theorem 10) suggest that, regardless of the variant of the algorithm we consider, when P t covers only a constant fraction of the front, then the probability that R t covers the full front is exp(-Ω(n)).
Acknowledgments This work was supported by a public grant as part of the  Investissement d'avenir  project, reference  ANR-11-LABX-0056-LMH ,  LabEx LMH. This work was also supported by  Guangdong Basic and Applied Basic Research Foundation  (Grant No. 2019A1515110177 ),  Guangdong Provincial Key Laboratory  (Grant No. 2020B121201001 ), the  Program for Guangdong Introducing Innovative and Enterpreneurial Teams  (Grant No. 2017ZT07X386 ),  Shenzhen Science and Technology Program  (Grant No. KQTD2016112514355531 ).
Indicator 3 for this species is 1. By emphasizing the importance of conservation genetic studies, and quantifying them, as a complement to the proxy-based indicators, this indicator could incentivize more countries to start implementing DNA-based monitoring; conservation genetic studies should work with stakeholders to ensure the data can inform management. These indicators more directly assess genetic erosion than the Red List Index or Living Planet Index. Neither Index focuses on the threshold of Ne 500 within populations; below Ne 500, genetic erosion is exponentially faster. Also, the Red List Index typically assesses the entire species, which may be safe from extinction but still suffer important losses of genetic diversity; wide-ranging species may lose entire populations, and their unique genetic diversity, without affecting Red List status.
Achievable Is there enough census or geographic data? Yes, many countries monitor priority species, maintain biodiversity databases or citizen science tools, and contribute Red List assessments which may have geographic and census data. Data collection can include local knowledge or expert consultation, or categorical or imperfect data. Genetic diversity indicator reporting is not necessary for all species; it would be for a relatively small, representative sample of species per country (we recommend ≥100)  (Frankham, 2022; Hoban et al. , 2021; Laikre et al. , 2021). Representative = from a diversity of ecosystems, taxonomic groups, rarity, and lifespan.
Data sharing Does reporting genetic indicators require reporting or sharing genetic data or Digital Sequence Information (DSI)? Reporting on genetic indicators does not involve sharing or reporting any DSI. Indicators 1 and 2 will typically be based on demographic and geographic data (e. g. , census sizes, population distributions), which do not use DNA data (DSI) at all. If DNA data were used to calculate Ne or define population boundaries, only the count of populations and estimates of effective size would be recorded; no DSI is reported or shared. Even raw census data and population data could be retained by the country, reporting only the proportions for the indicators Although indicator 3 does assess genetic studies in the country, it is only a count of studies. It does not assess DNA data or share DSI.
Indicating DNA data availability is at the discretion of each country  (Scholz et al. , 2022). ulation sizes are regularly assessed and stored, often in a gridded spatial database (common in some fish, plants, birds, and mammals), as with stock assessments, some National Red List databases, or forest inventories. Population presence can also be obtained from citizen science databases (e. g. , iNaturalist). Automated analysis can compare census sizes per population to the Ne (or Nc) threshold, and compare historic data, atlases, or range maps to current population presence or projections of habitat change to assess "populations maintained. " Examples from different countries illustrate the diverse options available (Table  2 ). Recovery plans for dozens to thousands of threatened species are mandated by national legislation (e.
Demographic & geographic information N e o r N c e s t im a t e s E x t i n c t / e x t a n t p o p u l a t i o n s N u m b e r o f D N A b a s e d s t u d ie s National reporting; policy setting (protection of distinct populations, harvest limits); planning management actions (genetic rescue, restoration) F I G U R E 2 Process of assessing indicators for a set of species, using data from different sources (manual, expert, automated). Indicators 1 and 2 are primarily obtained from demographic and geographic information. Note that genetic information is optional for indicators 1 and 2 as shown in grey dashed lines. For indicator 1, census sizes (Nc) can be converted to effective sizes (Ne) by applying a species-specific effective to census size ratio, and/or a ratio of Ne/Nc = 0. 1. Indicator assessment leads to reporting, policy outcomes, and management action.
from experts and diverse sources are collected in easy to access web-based portals (France-https://inpn. mnhn. fr/; Belgium-https://www. biodiversity. be/1767; Swedenhttps://www. artdatabanken. se/en/). In Colombia, the Biodiversity Information System (SIB) repository compiles species surveys from throughout the country (https:// biodiversidad. co/), which is mandated by many public and private organizations. These data are reviewed by national experts for validation and used to create freely available species distribution models (http://biomodelos. humboldt. org. co/) and for conservation prioritization.
CALCULATING INDICATOR VALUES AT NATIONAL LEVELS Genetic indicator calculation is straightforward. First, indicators are calculated for each species. When data are available as a range (e. g. , Nc is 10,000 to 20,000), the mean is used. Qualitative data such as "a few hundred" or "at least 5000" are often sufficient for comparison to the Nc 5000 threshold. In the case of differing estimates from multiple sources, either the most recent source is used, or a mean of values based on the different sources is taken (see Table  3 ). We add that in most practical applications, indicator 1 will typically be based on an Nc estimate and an Ne/Nc ratio, but Ne can be calculated directly from DNA-based genetic studies. In such cases the contemporary Ne value should be used, and reported with confidence intervals.
Biases Ideally, the 100+ species assessed by a country reflect diverse ecosystems, taxonomic groups, rarity categories, and life history traits (e. g. , lifespan). In reality, biases exist due to a country's habitat types, biogeography, latitude, and investment priorities in biodiversity monitoring and thus data availability. The Red List Index and Living Planet Index also have such biases  (Bachman et al. , 2019; Puurtinen et al. , 2022). Weighting by taxonomy or ecosystem can help address bias (previous section). Additionally, biases should be noted by displaying counts per category in a matrix, as is common in Red List summary tables.
ROADMAP We have presented the purpose and straightforward methodology of each indicator, addressed concerns of policy makers, showed that data are available and usable, and described ongoing work in nine countries. We demonstrated that genetic diversity indicators are ready for use in biodiversity conservation and reporting, with existing data. Increased uptake of these indicators by Parties to the CBD and other users (environmental agencies, wildlife managers, national legislation, etc. ) will require further successful demonstrations, published step-by-step workflows, and training workshops, ideally in multiple languages  (Kershaw et al. , 2022). We have created an online data collection form using Kobotoolbox (www. kobotoolbox. org/) and a guidance document (Supplemental Material, and https://github.
com/ AliciaMstt/GeneticIndicators) for anyone to use. Kobotoolbox is a free open source tool for data collection in a standardized structured format, with connections to analysis in Excel, R, Python or GIS software. Our GitHub encourages interactive engagement with stakeholders who can offer suggestions or ask questions. Future development of online resources can enhance data storage, managing, and sharing. An online portal could accept and store submitted data such as Ne or Nc values, population designations, and references over multiple reporting cycles at global, national and subnational levels, which would make compilation of the indicators easier and increase transparency. Other cyber infrastructure would be connections to resources like species distribution models  (Jetz et al. , 2012) , which could help with defining populations, calculating Ne based on area and density, loss of distinct populations etc.
Finally, as noted by others  (Garner et al. , 2020; Thurfjell et al. , 2022) , it would be advantageous for Red List assessment workshops to include gathering raw data and information needed for calculation of the indicators, including population boundaries. Red List assessors scour literature and consult experts for demographic and geographic information, and indicators could be recorded systematically in the Red List database. Lastly, we note that the indicators presented here complement other useful indicators for tracking genetic diversity which other authors have suggested. For example, the "genetic scorecard" assesses genetic diversity threats such as hybridization and low reproduction  (Hollingsworth et al. , 2020) , while another indicator assesses the extent to which geographic ranges are protected in situ or ex situ  (Khoury et al.
On top of each Raspberry Pi, a Grove Base Hat is installed to wire sensors. For the Raspberry Pi Zero WH, its hat sensors are three identical ±5 amperes DC/AC current sensors. Each of these sensors is linked to an electric wire: the two Raspberry Pi's power supplies and the wire that goes from the solar panel to the battery. For the Raspberry Pi 3b+, a temperature and humidity sensor (SHT31) are used. In addition to hats' sensors, the Raspberry Pi 3b+ collects sound through three USB microphones (range: 20 hertz to 16 kilohertz) and images from a Raspberry Pi camera module 2 The temperature and humidity sensor, the gas sensor, and the microphones are placed inside the beehive on the queen excluder. The camera is placed at one end of the entrance of the beehive and faces the other end to take pictures of the whole bees' takeoff and landing area.
IV. ENERGY CONSUMPTION OF THE DEPLOYED SYSTEM In this section, we aim to calibrate the frequency at which the Raspberry Pi 3b+ wakes up and performs a set of tasks at regular intervals. Data collection routines: The Raspberry Pi Zero, which records the current, is always switched on. At regular intervals, it sends a signal through GPIO to wake up the other Raspberry Pi, the beehive data recorder. Once the Raspberry Pi 3b+ wakes up, it collects data from all its sensors, including three 10-second audio samples collected at the same time and five 800×600 pixels images spread over five seconds, and it transfers the data through Wi-Fi to a remote data storage cloud server. In addition, at regular intervals, the Raspberry Pi Zero WH transfers the latest consumption data. Sizes of samples and arrangement of steps for the data collection and transfer are chosen thanks to an analysis presented in  [18].
To complete the data, the weather data from both locations is collected at regular intervals. Figure  2a  displays the activity of the Raspberry Pi 3b+ of one beehive together with the in-hive temperature and humidity, and the meteorological data at the same moment. At this moment, the colony of bees was yet to be introduced inside the beehive, hence the abnormally low inside temperature. The data is shown over one full week to emphasize energy consumption dynamics. In this graph, the gray background shows time periods after sunset, whereas the white background shows daytime periods. This graph also shows moments when the system is not running due to the lack of light at night. We suppose that the low luminosity takes the solar panel's output voltage to uncontrolled values, thus affecting the batteries and the electronics.
For example, for a service tracking the temperature of the beehive, collecting data every 60 or 120 minutes suffices. On the other hand, in a period of collection of large datasets, collecting data every 5 minutes becomes reasonable. The experiment is designed so that each setting is active for at least nine hours in a row in order to collect substantial energy data. This duration is chosen because it corresponds to daylight time, where our energy node is more stable and able to provide constant power to the electronics. Doing so, the collected energy is a sum of the active power (when the Raspberry Pi boots, performs tasks, and shuts down) and the sleep power of the sleep state, which consumes nonnull resources: the cost of being able to receive wake-up calls. The data collected during this experiment covers 319 routines of the system.
All routines are identical. They follow the steps presented at the beginning of this section. It is then possible to compute average costs for one routine. On average, the Raspberry Pi 3b+ is turned on, performs its tasks, and shuts down in 1 minute and 29 seconds, with an average power of 2. 14 watts. This gives an average energy cost of 190. 1 joules from the boot to the shutdown. The standard deviation for the lengths of routines is 3. 5 seconds. This relatively high variability can be explained by the variance of the duration of the data transfer correlated to the unstable network throughput. The standard deviation for the average power of routines is 0. 009 watts, which shows stability. The average consumed power for on/off cycles at different wake-up frequencies is presented in Figure  3.
At the highest frequency, when the system wakes up every 5 minutes, the average power values reach their maximum: 1. 19 watts on average. When the duration between two consecutive wake-ups increases, the average power decreases and converges toward a value close to 0. 62 watts, which is the consumption of the Raspberry Pi 3b+ in a sleep state. This shows that the sleep state is significant when considering long-term consumption. Fig. 3 : Average power used for several durations between two consecutive wake-ups of the smart beehive's system V. ORCHESTRATION OF SMART SERVICES As observed in 2a, there are moments where the edge is not able to fully capture and process the data because of limited available energy. Since collecting data has to be performed at the edge, gaining flexibility in terms of costs can be achieved during the processing step of the data thanks to a cloud server.
Fig. 4 : Task diagram for one cycle of data collection, processing, and transfer for edge+cloud scenario Our selected cloud server has an Intel i7-8700K processor and an Nvidia RTX2070 GPU. This allows to train the deep learning models considered in this paper in few minutes. Compared to the average bare-metal cloud server, this is a less energy-intensive option. Our selected edge device is the previously introduced Raspberry Pi 3b+, capable of performing numerous tasks: pollen detection, counting bees, and swarm prediction, among others. Most of these tasks include AI models. In this paper, we focus on the energy consumed by classification models for the presence of the queen inside a beehive, thanks to sound data. In practice, in addition to the previously introduced data collection routine, an audio sample is used to predict the presence of the queen bee.
For this paper's connected beehive architecture, the prediction is performed thanks to the sample from the microphone placed in the middle part of the queen excluder grid. The selected task of queen detection has already been studied with deep learning (convolutional neural network, CNN) and classical machine learning (support vector machine, SVM) methods  [19]. The latter is an adapted choice because it allows acceptable performances for reasonably small datasets (less than tens of thousands of samples). We choose to follow the same methodology by selecting one classical machine learning option and one deep learning option. For the training of both models, 1647 audio samples labeled with the presence of the queen are used. The selected training features are mel-scaled spectrogram features computed from 10-second audio recordings of bees sampled at 22050 hertz.
Therefore, using 100 by 100 pixels images for the CNN model is the optimal choice when considering the accuracy and the energy cost of the model's execution at the edge. Thanks to this focus on the energy consumption of one service, the upcoming paragraphs will generalize the results on complete edge and edge+cloud scenarios that include this service. We observe the edge+cloud scenario in real conditions. First, the Raspberry Pi 3b+ and the cloud server are in sleep and idle mode, respectively. The edge device is then turned on and starts collecting data. Then, the edge transfers the collected data to the cloud, and the edge starts shutting down as the server executes the service's tasks. Finally, the server switches to idle mode. The energy consumption graph of the Raspberry Pi during this routine shows a spike during the data transfer step.
This step is more energy-intensive than the data collection because the network components have a larger energy cost than the sensors. We then focus on energy consumption measures performed during both scenarios and for both queen detection AI models. Each of the two models (SVM and CNN) is tested in the edge and edge+cloud scenarios. Thanks to the measures of the energy consumption of the edge and the cloud for the four options, we aim to select the best scenario for each service. Table  I  and Table  II  show the duration and the energy consumption for all cases when 5-minute cycles are considered. For each scenario, we show the collected time and energy consumption step by step, in chronological order. Since the queen detection model's execution in the cloud takes less time than the shutdown of the Raspberry Pi 3b+, the shutdown is divided into two lines in the edge+cloud scenario, and their sum adds up to the 9.
Modelling the photometric evolution The blackbody parameters obtained as described in the preceding sub-section appear to evolve smoothly in time. Encouraged by this, we fitted the simple SN model described in Appendix C to the entire photometric dataset. The model consists of an ejecta shell of mass M ej and UVOIR grey opacity κ expanding at a constant speed v ej and heated by γ-rays emitted by a central radioactive source of mass M Ni , initially composed entirely of 56 Ni. The ejecta luminosity is computed in the diffusion approximation and its photosphere is assumed to simply track the expansion, R ph = v ej t, where t is the time since the explosion, which was assumed to take place at a phase t 0 from our reference time MJD 58835. 1. The resulting model has five free parameters (M ej , κ, v ej , M Ni , t 0 ) plus the f sys parameter described in the previous sub-section, which is included as it avoids datapoints with very small formal uncertainties dominating the likelihood.
MEGARA IFU spectra taken on 28 Jan 2020, at +40 d were examined to determine the local metallicity at the location of SN 2019wxt. The reduction of these data is discussed in Appendix B. 7. We extracted a one-dimensional spectrum from both the LR-B and LR-R datacubes using a 5 pixel (1 , corre-   Appendix C ) fitted to the photometric dataset. The grey shaded area corresponds to times when the ejecta are formally optically thin (nebular phase). The inset in the top panel shows the model samples plotted against the time since explosion, with a logarithmic x-axis to better display the agreement with the upper limits. The latter are derived from our z-band upper limits assuming the blackbody spectrum corresponding to the best-fit model. sponding to 0. 73 kpc at the distance of KUG 0152+311) radius aperture centred on the position of SN 2019wxt (Fig.
Global host properties We retrieved science-ready coadded images from the Galaxy Evolution Explorer (GALEX) general release 6/7  (Martin et al. 2005)   5  shows the measurements in the different bands. We also used the data from the Atacama Large Millimeter Array centred at the frequency of the CO(1-0) line. The data cube has a resolution of 1. 66 × 0. 9. The channel width is 16 MHz, corresponding to 44 km s -1. We modelled the UV to mid-IR spectral energy distribution with the software package prospector version 0. 3  (Leja et al. 2017). prospector uses the flexible stellar for a discussion of the population synthesis Figure  12  shows the observed SED and its best fit. The median values of the marginalised posterior probability functions and their 1σ confidence intervals are shown in the same plot.