dataesr/scientific-paragraphs-categorization huggingface dataset:
Information system The choice of this project is to use a Pycom LoPy4 microcontroller and the Sigfox connectivity. Due to this, there are two possibilities to visualize the evolution of the sensor connected to the microcontroller: Using the Pybytes platform 8 which makes it possible to monitor and manage the Pycom microcontroller remotly. Deploying an entire back-end based on a database such as InfluxDB 9 and visualization tools such as Grafana 10. In order to control the lifetime of the data and have higher flexibility for data visualization, the second scenario has been preferred. The architecture of the information system is represented in Fig. 4. Each component in Fig. 4  has a specific role described as follows: The Sigfox Backend is a hosted service managed by Sigfox. The device must be registered on the platform.
Once registered, the data sent by the device are centralized on the platform. This data can be transferred to another platform through the Callback function. The Node-RED 11 service is a block-based web IDE that allows for the creation of API. The role of this service is to receive the raw data from Sigfox Backend, parse it and save it in Influx DB. The InfluxDB is a database specialized in time series. The role of this service is to store the data concerning the different devices in order to visualize and analyze it. The Grafana service is a visualization tool that has to be connected to a database. In this experience, Grafana is connected to InfluxDB and enables the creation of a dashboard for each device that shows the weight of plastic inside the collector. It can also show the state of the battery if the device is powered on batteries and even set up some alarms for maintenance action.
In order to simplify the development and the deployment of the information system, all the services have been containerized with Docker 12  and Docker-Compose  13. To make the information system accessible to the connected devices, it requires a server with public access. An alternative to a publicly accessible server is to install Docker on a computer and use the Ngrok  14 service, which creates a tunnel between the services on the computer and the internet. This tool is generally used for the development process and experimentation. For this article, we will consider the second scenario with the entire stack of services. Here are the steps to follow to start the information system: 1. Assuming Docker and Docker-compose are installed on the experimentation computer, the Information System has to be downloaded from OSF or Github.
2. Then go to the main folder that contains the docker-compose. yml and docker-compose. ngrok. yml. 3. Some configuration has to be done before starting anything. The first thing is to create an account on Ngrok and get your Authtoken. Once you have it, you have to go to the ngrok folder, duplicate the ngrok. template. yml and rename it ngrok. yml. Then edit this file and modify the authtoken field by replacing ${NGROK_AUTH_TOKEN} with the token you generated on the Ngrok website. 4. The second configuration is to duplicate the default. grafana. env and default. influxdb. env files that are in the main folder and rename them. grafana. env and. influxdb. env respectively. These two files will contain the login, the password, and other parameters that will be used to generate the database and the dashboard service.
Firmware and embedded program There are two distinct aspects related to programming the microcontroller of the smart collector: The firmware that handles the Micropython runtime, the bootloader and default libraries specific to Pycom microcontroller. The embedded program that is written in Micropython and that will do the measuring and communicating with the information system. The firmware is provided by Pycom and may require some updates according to the improvement and bug fix. The embedded program is the program that has been written by the author to collect data and send it to the information through Sigfox. To program the microcontroller, this would require three different software items: The Visual Studio code IDE The Pymakr  15  plugin for Visual Studio Code The Pycom Firmware updater  16 1.
Update the firmware of the microcontroller. Before uploading the program on the microcontroller, Pycom recommends updating the firmware. This update requires the Pycom Firmware updater. (a) Start the program; it will prompt a welcome dialog box (Fig. 13a ). You must click on continue. (b) The program gives you some instructions depending on the MCU or the expansion board you are using. A frequent source of issue is to keep VS code open when updating the firmware, so take care to close it before starting the upgrade (Fig. 13b ). If it is OK, click on continue. (c) Define the communication setting to do the update. You have identify the port where the MCU is connected. The default speed is set to 115200 baud. Take care to check the ''Show Advanced Settings" (Fig. 14a ). (d) In order to flash the proper firmware, it asks to validate the board used.
This would require calibration weights. An alternative, which has been used for this project, is to replace calibration weights with weightlifting weights. The drawback is the imprecision of the indicated weight. To mitigate this, each weightlifting weight was weighed ten times using a laboratory scale and then the readings were averaged. To simplify the later calibration tasks, the authors suggest putting a tag or anything that enables the identification of the weight and its real weight. Once the true value is known for each weight, the calibration procedure can begin. Up to now, the microcontroller has had the lastest firmware and is registered on Sigfox Backend to enable LPWAN connectivity. However, it does not have the expected functionality for the smart collector, which means doing a periodic weight measurement and sending data via LPWAN.
The program can be considered as behaving correctly since February 2022, so outliers are mainly due to issues in the field, such as people putting object on the collector. Even if it is only five minutes for a chat during a coffee break, this is enough to produce an outlier if the MCU schedule is reached. Due to the difficulty in obtaining electronic components, we have had to deal with different microcontrollers for prototyping smart collectors. Some have both LoRa and Sigfox (LoPy4 19 microcontroller) connectivity, whereas others have only Sigfox connectivity (SiPy 20 microcontroller). The original design included a LiPo battery in order to position the collector wherever the user wants to increase the collection. However, in practice, the autonomy was confusing in comparison to the common ESP32 21 microcontroller.
Acknowledgements This study was partly funded by the  European Commission  through the  INEDIT E. U. innovation project  (Grant agreement  n869952 ). The authors wish to thank the  European Commission , the  Lorraine Fab Living Lab Ò  ( LF2L ), in particular  Benjamin Ennesser-Serville ,  Cristian Andres Caceres Mendoza  and  Thibaut Boutevillain , for providing the tools and helping to develop the different prototypes and  Fedoua Kasmi  and  Catalina Suescun  for the help in the deployment experimentation. The LF2L benefited from the support of  ENSGSI, ERPI and Métropole du Grand Nancy.
Acknowledgments We kindly acknowledge the  Hellenic Public Power Corporation (HPPC)  and, in particular  Tryfon Barbas ,  Melina Andreadi ,  Marina Tentsoglidou ,  Aris Azas ,  Christos Papadopoulos , and  Agni Patra  for their support with personnel and equipment during the field campaigns and for providing all necessary information for the completion of this work. We are also thankful to  Stamatios-Rafail Tzaferidis ,  Dimitris Manolopoulos  and  Florian Wilms  for their assistance in field work and sampling.
C. Hyperspectral imaging in rock art studies The "reading" of the decorated walls is often made difficult by the alterations they have undergone, which have partially or totally erased the rock art. The advent of digital technologies has thus allowed the use of image enhancement software for the last twenty years, and in particular the use of the DStretch plug-in of the Image J software  (Harman 2005; Quesada Martínez 2008; Le Quellec et al. 2013 , 2015; Domingo et al. 2013). This software uses the decorrelation stretch algorithm, an image enhancement technique first used on aerial photos, and highlighting colour differences that are barely visible to the naked eyes. Statistical processing allows the colour histogram to be stretched. DStretch is one of the most efficient tools to decipher faint paintings while being cheap, fast and easy to use.
D. Why use hyperspectral imaging? The use of hyperspectral imaging at Otello has two main objectives: (i) to overcome the shortcomings of one of the most widely used pairs of tools in rock art archaeology today, RGB cameras and DStretch software, and to allow a better recording of the rock art (exhaustive inventory of paintings and separation of the superimposed figures); (ii) to overcome the lack of representativeness of point analyses of colouring matter by adding a spatial dimension to the study and, (iii) to help the taphonomic analysis of the painted wall, inseparable from the study of the colouring matter. If the use of DStretch software had made it possible to double the number of figures identified between the initial recording of the shelter and the recording carried out in 2014  (Hameau 2011; Defrasne et al.
C. Data processing Before doing any in-depth analysis of an image, the data has to be converted into reflectance (i. e. a normalised percentage of the incident radiant flux). First, right after acquisition, the raw sensor digital counts of the data cube are converted into radiance energy. This step is done in the NEO software as it involves the factory calibration of the instrument. We then proceed to convert this radiance into reflectance. For each wavelength, we calculate a mean radiance value on some pixels selected in the Spectralon™ area. Then all the radiance spectra of the image are divided by this mean reference radiance spectrum, and the resulting reflectance spectra are stored in a data cube (Fig. 5 ). Different mathematical transformations are generally used in space exploration and Earth's remote sensing to analyse hyperspectral image cubes.
g;  Schmitt, et al. , 2017). Schmitt et al. (2022, in review)  recently compared DStretch, PCA, ICA and MNF algorithms in the case of rock art painting and found that ICA was clearly the most efficient algorithm for revealing faint or very localised figures in the images and separating them and from the rock wall. In addition, a first test of extraction of the pixels containing pigment and their classification has been performed. We run it on the VNIR-016 image. Using ENVI™ hyperspectral toolbox software we first selected the nine ICA layers containing information on pigments and rock wall and then we selected 1 or 2 ROI (Region Of Interest) of end-member pixels (about 75 pixels each) for each of the six ICA layers displaying information on painted figures. A total of 10 ROI has been used in a SAM (Spectral Angle Mapper) classification run on the 9 selected ICA layers (threshold angle: 0.
A. Identification of new painted figures The first notable contribution of VNIR and SWIR hyperspectral imaging consists, in addition to an improvement in the readability of the paintings, in the highlighting of painted figures invisible to the naked eye or in RGB camera images despite systematic image processing such as with the DStretch plug-in of the Image J software. This applies to figures that have been almost completely erased, to figures that are superimposed or completely covered by concretions. In the case of the Otello shelter, this not only makes it possible to enrich the graphic corpus with numerous new figures, but also with new themes hitherto undocumented in this shelter, and relatively rare on the scale of Mediterranean France. Figure  7  illustrates such a contribution of hyperspectral imaging with the VNIR-017 image.
These new images also complete the chronological sequences of the painting phases, the global vision of which now requires systematic acquisitions on the scale of the shelter. We also noted that hyperspectral imaging makes it possible, when the crusts are not too thick, to identify figures that have been covered up and that are totally invisible to the naked eye. A good example can be seen in the hyperspectral image VNIR-016 where a grid or crest shape figure, covered by calcite crusts, and not distinguishable with DStretch enhancements, can now be easily recognized and recorded (Fig. 10 ). In the same part of the painted wall, data obtained with VNIR and SWIR hyperspectral cameras reveal different and complementary new paintings. This difference can be explained by differences in the pigment composition.
The penetration depth of the light being essentially inversely proportional to the square root of the absorption coefficient (Clark and Roush 1984), it is generally larger at wavelengths with higher reflectance in the spectrum, i. e. outside absorption bands. Most of the red or orange-yellow chromophore pigments containing iron-bearing minerals (hematite, goethite, …) very strongly absorb in at least part of the visible range but become more reflective in the near-infrared above about 900 nm, outside their absorption bands (see e. g. (Chalmin et al. 2021) , Fig. 7 ). In the SWIR range, the penetration depth of the radiation is thus stronger and the hyperspectral data probes deeper layers. They should therefore be more relevant to distinguish superimposed paintings. By evidencing these new images, hyperspectral imaging makes it possible (i) to approach the exhaustiveness of the rock art recording and thus complete our knowledge of the graphic repertoire used by prehistoric societies; (ii) to complete areas of the rock wall that we thought were empty; (iii) to help study the superimposition of the pigments (complemented with other techniques) and thus the relative chronology of the graphic acts; (iv) to document the graphic practice by highlighting repaints or the mobilisation of a previous shape in the structure of a new figure; and finally (v) to allow a better knowledge of the spatial distribution of the figures at the scale of the wall, and of their organisation.
13 ). The large penetration depth (up to a few mm) of the radiation inside these infrared transparent materials (outside the main absorption bands) may further allow us to probe the wall composition under thick crusts. For the North Otello wall we extracted an average spectrum corresponding to the end-members (group of pixels among those having the highest values) of three ICA components (#1, 2, 9) depicting the wall crusts in image SWIR-004 (Fig. 12 ) and we compared them with laboratory near-infrared reflectance spectra of relevant minerals measured at IPAG (Institute of Planetology and Astrophysics of Grenoble) and available in the GhoSST@SSHADE database  (Schmitt et al. 2018). This allows us to identify the high values of ICA component #1 as depicting the presence of fine-grained calcite (with some water), component #2 as gypsum (with some calcite and Fe-oxides), and component #9 as a mixture of calcite and gypsum (Fig.
During some of our acquisitions we also got some fog which entered inside the shelter. Some spectral bands are sensitive to the presence of water and acquisition during this kind of weather is unreliable. A good outside weather is therefore primordial for our study. The addition of a second light source with a different position and illumination angle, during a second field session, will improved the homogeneity and stability of the illumination, in particular by removing gradients and shadows and by decreasing the contribution of the outside indirect illumination of the shelter walls and its fluctuations. A significant increase of the light flux will also reduced the integration time and thus, the acquisition time. It will also greatly improve the signal to noise ratio of the hyperspectral dataset and thus provide better results.
More advanced tools need to be tested to estimate whether better classification results can be obtained and at what complexity cost. The presence of specific minerals has been established from the SWIR data, but this identification still needs to be done for the pigments, a more complex task that will need the combined use of the VNIR & SWIR data together with complementary in-situ and laboratory analyses. Also, higher resolution VNIR+SWIR spectra (point field spectrometer) to be acquired at the specific locations of both the ICA end-members and the in-situ analyses will allow to link these analyses with the hyperspectral data and extrapolate them to the entire set of measurements of the painted wall.
Acknowledgments We would like to thank the municipality of  Saint-Rémy-de-Provence  and the  Service Régional d'Archéologie de Provence Alpes Côte d'Azur  for granting us permission to work on the Otello site. We would also like to thank the people who helped us with the fieldwork that led to the results presented here, and in particular the porters who made it possible to transport the material.
| Eliciting expert knowledge Here, we elicited information from experts experienced in MPAN research, design, implementation, monitoring, and/or evaluation. Expert elicitation is an approach commonly used in conservation science to inform decisionmaking  (Krueger et al. , 2012; Martin et al. , 2011)  and help improve the process of conservation programs and policies  ( Álvarez-Fern andez et al. , 2017; Whitney & Ban, 2019). Experts, including MPAN managers, researchers, and field technicians, shared information on the MPAN objectives, attributes of the four dimensions considered in any stage of the MPA planning, implementation, and evaluation process, and the perceived importance of each attribute to the overall effectiveness of MPANs they are familiar with. Expert elicitation was conducted through an online survey in English, Spanish, and French using the Qualtrics software (v.
12018). These languages were chosen to be more inclusive of many non-English speaking regions where MPANs currently exist. We used a combination of systematic sampling and snowball sampling to reach a broad suite of practitioners. We sent 320 invitations to participate in the survey to corresponding authors of peer-reviewed literature on MPANs, and to MPAN managers whose email addresses were publicly available. MPANs were identified through a search of the world database on protected areas (WDPA) (IUCN & UNEP-WCMC, 2017) for "networks" or "system" and a follow-up Google search of the MPANs found in the WDPA that matched our search criteria and "marine protected area network". We also promoted the survey via relevant mailing lists (Table  S1 ) and over social media (Twitter and Facebook). In the invitation, we encouraged invitees to share the survey invitation with other experts familiar with MPANs, helping reach a broad audience.
4. 0. 2 (R Core Team, 2020). Because respondents from a same MPANs are more likely to have similar responses, trends emerging from the data could be biased by a larger influence of few MPANs over others. To account for potential variation among respondents we treated the data differently among the overarching questions about attributes considered and respondent's perceptions of importance. We carried out our analyses about MPAN objectives and MPAN attributes with MPANs as the unit of analysis so that MPANs with multiple respondents did not skew responses. For our goal of understanding practitioner perceptions regarding the importance of attributes, we used individual responses.
To understand the factors that influenced what attributes were considered in each dimension, we first assessed the similarity of responses from practitioners representing the same MPANs using Cronbach's alpha (α). Cronbach's α measures whether several items measuring the same general construct produce similar scores  (Cronbach, 1951). We then collated these responses so that each response represented one MPAN. We then transformed the data from the number of times each attribute was selected (count) to binary (presence/ absence) format to ensure MPANs with more respondents did not have an outsized influence on the end results. This transformation also allowed for comparison between variables, such as the different objective types, that were not evenly distributed among responses. We created descriptive statistics, figures, and performed a permutational multivariate analysis of variance (PERMANOVA;  Anderson, 2001)  to test whether the attributes considered in MPA planning, implementation, and evaluation differed between MPAN objective types.
We further calculated a multilevel pattern analysis using the Indicspecies package  (De C aceres et al. , 2020)  to identify which attributes are found statistically more abundantly in one group versus another. To get a sense of the balance of attributes considered among participants associated with MPANs that have different objective types, we evaluated the evenness of the attributes selected for each dimension across the objective types. Figures were done using the R package 'ggplot2' (Wickham, 2016), and PERMANOVA.
| Perceived importance of attributes for MPAN management effectiveness To assess whether the suite of attributes associated with each dimension was considered in the MPAN process as being important for the overall performance, we collected information from respondents regarding their perceived level of importance using a Likert-type scale (i. e. , not important, slightly important, moderately important, very important, or extremely important) for each attribute. To preserve individual perception-based responses about the importance of attributes considered in the MPAN process, we assessed individual responses of the Likert-type questions. We summarized the Likert-type data using R 'Psych' package (Revelle, 2021). To evaluate if the MPAN objective type was associated with differences in the perceived importance of attributes across the four dimensions, we performed an ordinal Chi-square analysis using the Cochran-Mantel-Haenszel (CMH) test  (Agresti, 2007).
Here we used one independent variable with two levels (B and B&S), an ordered dependent variable (importance), and we stratified the analysis using the four dimensions. Stratification allowed identifying differences in perception among the attributes according to MPAN objective type within each dimension. We used count data (number of times a scale choice was selected) per dimension in R built-in package (R Core Team, 2020). We followed this test with groupwise CMH tests to determine which dimensions differed in importance levels between objective types. Finally, we performed a Chi-square analysis within each dimension with a Bonferroni adjustment for multiple testing of attributes, to determine the attributes that contribute to the results  (Agresti, 2007). We reviewed the Chi-square residuals to determine if there was an association between the responses from the different MPAN objective types (i.
Despite efforts to promote the survey through as many channels and individuals as possible, and in several languages, the geographic representation of responses for MPANs was highly skewed to the UK, USA, Canada, and Australia. This study did not have sufficient data across country incomes to test for an effect on attributes. While a survey is a useful tool to elicit responses from various individuals, it is not capable of reaching practitioners that speak other languages not included in the survey. Additionally, respondents were biased toward academics, our survey had fewer responses from project managers, facilitators, and monitoring specialists. Although we found no differences between responses based on participants roles, this could be an interesting are of additional research to examine whether participants' roles bias how an MPAN is measured.