dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are thankful to  Yann Mathet  for his help on understanding his work on the γ-agreement. We also thank  Xuan-Nga Cao ,  Anne-Catherine Bachoux-Lévy  and  Emmanuel Dupoux  for their advice, as well as  Julien Karadayi  for helpful discussions and feedback. This work is funded in part by the  Agence Nationale pour la Recherche  ( ANR-17-EURE-0017Frontcog ,  ANR-10-IDEX-0001-02   PSL *,  ANR-19-P3IA-0001PRAIRIE 3IA Institute ) and Grants from  Neuratris , from  Facebook AI Research (Research Gift) ,  Google  ( Faculty Research Award ),  Microsoft Research  (Azure Credits and Grant), and  Amazon Web Service  ( AWS Research Credits ).
a b s t r a c t High-resolution X-ray imaging was employed at the JAEA MELT facility to visualize a kilogram-scale interaction between a jet of high temperature molten stainless steel and sodium, mimicking a sodiumcooled fast-neutron reactor in the aftermath of a core-disruptive accident. A novel software, SPECTRA, has been developed for the quantitative characterization of jet quenching and fragmentation. Tracking and 3D reconstruction of the melt phase traversing the imaging window enabled the detection of 72% of the debris mass recovered post-experiment. The rebounding of melt fragments confirmed a solid outer crust at the melt-coolant interface, while a thermal fragmentation event induced rapid vapor expansion. Jet fragmentation is best explained by the vaporization of coolant entrained within the melt jet generating an internal over-pressure sufficient for fragmentation of the crust.
, 2016). The rapid fragmentation resulted in a large increase in melt-coolant heat transfer area, accelerating melt cooling and inhibiting subsequent hydrodynamic fragmentation. The X-ray imaging presented with artefacts and limitations in pixel resolution which suggest that extensive post-processing would be required for a detailed and quantitative characterization of the interaction  (Matsuba et al. , 2016). A new experimental investigation of a kilogram-scale interaction between molten stainless steel and sodium is presented, using X-ray visualization to supplement ex situ debris analysis. To maximize these radiographic data, a new software program, SPECTRA (Software for Phase Extraction and Corium Tracking Analysis), is under development at the CEA for the processing of X-ray images acquired during FCI experiments in sodium and water-cooled environments alike.
1. The stainless steel was heated in the furnace crucible to 1973 K and then discharged, via an intermediate tip tray, necessary to isolate the furnace from the test section, through a centimetre-scale (< 50 mm) nozzle to the sodium pool beneath an argon-filled ullage. The majority of results will be presented in dimensionless form after normalization by the nozzle diameter, D 0 , initial jet velocity, v 0 , and the mass of debris recovered at the end of the experiment, m d : L ¼ L D 0 ; Ṽ ¼ V D 3 0 ; ṽ ¼ v v 0 ; m ¼ m m d ; t ¼ tv 0 D 0 ð1Þ In situ temperature data were monitored at eight thermocouples, mounted along the height of the test section on a steel support. The 110 mm internal diameter test section contains an interior debris catcher and an Al 2 O 3 thermal insulation cylinder, which reduce the effective working diameter of the interaction region to 78 mm.
Radiographic images of the test section were captured for a 1. 5 s duration at a frame rate of 1000 fps using the 1. 8 kW rated X-ray imaging device. The X-ray source was optimized to the radiodensity of the test section and its contents during preparatory tests performed using phantom models  (Singh, 2019; Berge et al. , 2018) , constructed from polyoxymethalene (POM) thermoplastic as an analogue for the sodium coolant (see  Fig. A. 1a ); optimum image contrast was obtained by operating the X-ray tube at 115 kV and 9 mA, corresponding to 1. 04 kW power. These X-ray images acquired during FCINa-30-1, along with post-experiment debris analysis, will be the focus of this work. The in situ temperature data is outside the scope of this study.
X-ray image processing SPECTRA has been developed using the App Designer platform in Matlab 2018b (Mathworks, USA). Its functionality encompasses a number of processing steps for the suppression of X-ray imaging artefacts, the segmentation or isolation of a studied phase, the tracking of discrete objects through time and space, the reconstruction of objects in 3D and the characterization of objects' size and shape factors. X-ray imaging, particularly when operated in conjunction with image intensification, can be subject to significant Gaussian noise and other imaging artefacts including, but not limited to, vignetting (the decay in intensity towards the image border)  (Berge et al. , 2018)  and pincushion distortion  (Cowen et al. , 2008). The characterization and suppression of these artefacts is fundamental to the accurate segmentation of the melt and vapor phases from the raw images.
Acknowledgements The authors wish to thank everybody at the  MELT facility  for their contributions throughout the  FCINA experimental program , as well as  Hidemasa Yamano ,  Shigenobu Kubo ,  Frédéric Serre  and  Frédéric Payot  for their management of this JAEA-CEA collaboration. The authors also would like to express their gratitude to  Mr. S. Sato  of  NESI Inc. for his technical support to the measurement work during the FCINA experiment. Thank you to the  CEA  for their support of this work through a post-doctoral research grant, to  Claude Brayer  and  Magali Zabiego  for lending their expertise in the field of severe accidents and to  Laure Schneider ,  Théo Hipault  and  Shifali Singh  for their contributions to the development of SPECTRA.
Some apps include coaching in the form of telemanagement and 2-way consultations with health care providers, who can remotely follow up and provide recommendations based on patient-generated health data collected by the diabetes management app and system  [12]. More than 10 systematic literature reviews of studies on the use of mobile apps for self-management of diabetes have been published in the past 5 years  [3, 11, 12, [14] [15] [16] [17] [18] [19] [20] [21] [22]. Overall, 57 primary studies were included in this review. These studies found that using diabetes self-management apps can significantly improve the health outcomes of patients with T2D. In 18 of the 25 reviews, Greenwood et al  [18]  found that HbA 1c (average blood glucose) levels were significantly reduced through the use of technology-based self-management solutions.
System Design We analyzed and identified the preferred features of a mobile app diabetes management system, as discussed in prior study. The analysis informed the design of the mobile app GLOOK!, which was designed and developed by an academic research team and tested at the Box Hill Hospital. The research team comprised endocrinologists, information technology and knowledge management experts, developers, interaction designers, and health specialists. The team was supported by diabetes nurse educators at the Eastern Health Clinical School. GLOOK! was designed as a diary-style tracking and feedback-delivery system. The system integrates 3 components: a smartphone-based app for data collection and analysis, a smartwatch linked to the Apple Health App for activity recognition, and a wearable device-the Medtronic Guardian Connect (Medtronic Pty Ltd) continuous glucose monitor-for continuous interstitial fluid glucose monitoring.
Although 2 of the 3 components were off the shelf, the third-the GLOOK! app that integrated all the data-was specifically designed and developed at an Australian university. In this paper, the app is referring to the GLOOK! app (Figure  1 ). The system combines user input and sensor data to track patients' behavior and food intake data and medication and insulin use and record patients' daily activities. Other personal data were tracked using the mobile device's built-in sensors; these included insulin use, number of steps, heart rate, and glucose levels. The uploaded data were integrated into the smartphone app, which also enabled participants to record their activities and food intake. The study physician received a report on these integrated data, which allowed the creation of personalized feedback for each study participant.
User Interface Design The GLOOK! mobile app's graphical user interface uses a communication-driven design process to simulate intuitive communication between patients with T2D and physicians. User experience expert Everett McKay  [27]  regards the user interface as a mode of conversation between users and technology. The user interface enacts tasks so that users can achieve their goals through the language of the user interface instead of natural language. The communication-driven design process (Figure  2 ) underpins a clear understanding of users' needs, tasks, and goals. The top-prioritized needs, tasks, and goals for both patient and physician user groups in the design of the GLOOK! app were determined using a user story mapping method.
Empirical User Study A technology package was prototyped for the study, assembling 3 applications to gather data and provide 1-way communication between the physician and participants (Figure  3 ). The iOS app (GLOOK! app developed by Monash University) was installed on an Apple iPhone provided to the study participants. Participants also wore an Apple Watch linked to phone-recorded data on heart rate and steps. The Medtronic Guardian Connect continuous glucose monitor was applied to the skin according to the manufacturer's instructions, and it provided 24-hour continuous glucose monitoring for 6 days. At the end of 6 days, another continuous glucose monitor was applied, thus providing a total of 12 days of data. The glucose data were scrubbed from the CareLink website. All sensor data were combined for display in the GLOOK! app (Figure  4 ).
Recruitment A total of 15 patients with T2D were recruited for the pilot study from a larger cohort of registered outpatients attending a large public hospital in Melbourne, Australia. Participants were preselected from the public hospital database and associated clinics based on their T2D diagnosis (none were recently diagnosed), basic computer skills, and access to digital media. In addition, the recruited participants had no disabilities or health conditions that could interfere with their activity levels. Of the 15 participants, there were 4 (27%) women and 11 (73%) men. Patient ages ranged from 42 to 65 years. Participants aged >65 years were excluded because of presumed unfamiliarity with the use of smartwatches, smartphones, and SMS text message communications. It was important that participants had a reasonable familiarity with using smart technology (smartphones) and the ability to adapt to any smart gadgets and devices provided in this study (Figure  5 ).
Data Analysis Patient characteristics were compared from baseline to the final visit using the Student t test (2-sided with equal variance). In addition, activity levels (steps), average blood sugar, glucose variability, glucose time in range, and resting pulse rate were compared for the first 4 days of the study with those for the final 4 days of the study using the mean of 4 days' data for each participant compared by Student t tests. Statistical analysis of the collected biometric data was performed using SPSS software (IBM Corporation). All audio recordings from participant interviews were reviewed by 2 design researchers using a deductive framework approach for data analysis  [29, 30]. A Microsoft Excel spreadsheet was developed for the thematic synthesis of interview data, where themes were drawn from the study's hypotheses and aims.
Clinical Outcomes Our study's participants were on a wide range of antidiabetes medications (Tables  2  and 3 ), and these were not changed during the 2 weeks of the study apart from variations in insulin doses at the patient's discretion. Specific advice regarding medication changes was not provided in the feedback. Electronic data collected by wearable arrays were incomplete for a variety of technical reasons. The original data set contained 30,000 data points for each patient. It was estimated that approximately 79. 86% (2300/2880) of the glucose trace and 76. 19% (4320/5670) of the activity and pulse rate data were available for analysis. There were sufficient data for a feedback response on 83% (10/12) of the days of the study. As a pilot or feasibility study, this study was underpowered to detect changes in HbA 1c , nor did it have a control group that did not receive feedback.
Previous research on the use of diabetes self-management applications has shown increased efficacy in interventions that allow more patient customization and choice over the frequency of text-based message delivery  [6]. The timing of received messages can also influence patient acceptance of and adherence to the program; enabling patient choice over what time of day they receive messages reduces the effect of them feeling nagged and may allow more reflection and interpretation  [5]. An ideal protocol may comprise a personalized text-based message per day at a time chosen by the patient, with the option of receiving additional generic messages related to diabetes education and health. The diabetes self-management app studies we reviewed deployed both generic (not based on individual data inputs) and personalized tailored messages.
Acknowledgments This work was supported by a seeding grant from the  Monash Institute of Medical Engineering  granted to CG. Contributions were also provided by the  Monash Faculty of Information Technology and the Special Purpose Fund of the Endocrine Department of Eastern Health. The authors would like to thank  Flavia Xie-Ku  of  Monash University Art, Design , and  Architecture  and  Yuxin Zhang  and  Farid Raisi  of  Monash University Faculty of Information Technology  for their contributions to the GLOOK!app user interface design and development. The authors would also like to  Vanessa Viola ,  Gabrielle Garner , and  Kerrie Peacock  from the  Eastern Clinical Research Unit  for providing GLOOK! app pilot study participants with app training, the initial setup of the glucose monitoring sensor, and technical support.
European plethodontid salamanders (genus Speleomantes; formerly Hydromantes) are a group of eight strictly protected amphibian species which are sensitive to human-induced environmental changes. Long-term monitoring is highly recommended to evaluate their status and to assess potential threats. Here we used two low-impact methodologies to build up a large dataset on two mainland Speleomantes species (S. strinatii and S. ambrosii), which represents an update to two previously published datasets, but also includes several new populations. Specifically, we provide a set of 851 high quality images and a table gathering stomach contents recognized from 560 salamanders. This dataset offers the opportunity to analyse phenotypic traits and stomach contents of eight populations belonging to two Speleomantes species.
exploration, as for subterranean environments, where sampling and progression require considerable effort and specific technical skills  [25] [26] [27]. Subterranean habitats are not only hard to find or explore, but their peculiar environmental conditions (e. g. , cold temperatures, high moisture, narrow space) might challenge surveyor's stamina with a negative impact on the data recorded  25, 28. Another limit to data collection can be determined by the techniques used to collect information. For example, the old-fashioned research methods involving the sacrifice/harm of individuals are now widely condemned and avoided, especially when concerning protected species  29, 30. Therefore, there is an increasing trend in the use of new harmless alternative approaches  31, 32. We here describe a new database reporting data on two endangered Speleomantes species that can be handled only under specific national authorizations (see Acknowledgments).
This dataset includes information on the population structure, phenotypic traits and diet of individuals belonging to two mainland Speleomantes species: S. strinatii and S. ambrosii. The information gathered here was collected adopting methodologies that limit negative impact on individuals, and can be combined with the two previously published datasets on these species  9, 23  , extending the available information in space and time. In this work we gathered data from new populations to cover more area of the species' range, but we also repeated the surveys in previously visited populations, thus providing temporal series of information allowing long-term studies focusing on populations but also on single individuals as well  33, 34. Specifically, we here describe a dataset composed of two types of data: images and stomach contents.
Methods Surveyed sites. We surveyed eight subterranean sites, three artificial mines and five natural caves (Fig. 1 ); all these sites fall within the species' natural range  1. Surveys were performed in 2020, between 9 am and 6 pm during warm and sunny days, periods in which subterranean abundance of Speleomantes is the highest  42. All sites were surveyed in July, while for six of them surveys were also repeated in September (Table  1 ). We performed extensive research of Speleomantes within the subterranean sites  43  , covering areas where the exploration was possible without speleological equipment. Salamanders were captured and placed in drilled plastic boxes waiting to be checked. When salamanders sampling was finished, we proceeded with the data collection following this order: (i) assessment of the presence/absence of the mental gland for the identification of adult males (see Fig.
technical Validation This dataset provides data on two strictly protected amphibian species  13. Salamanders were sampled following protocols aiming to avoid the spread of potential pathogens  46  ; specifically, we used disposable gloves and disinfected with bleach equipment and boots before changing location. During each month, all surveys were performed within 4 days to limit the variation of environmental conditions which may alter the local ecological opportunity  38, 41. To limit pseudoreplication, each site was surveyed only once per month. On the other hand, the   1. Updating photos and stomach contents datasets for two mainland Speleomantes salamanders: data from summer 2020. Detailed information related to the photographed salamanders and to their stomach contents  45. two surveys performed on the same populations during different months (July and September) create the condition to test additional hypotheses, like the assessment of temporal variability on salamanders' trophic niche  41  , or even to employ specific software to individually recognize salamanders  47, 48  and focus future research on single individuals as well.
Usage Notes The first part of this dataset is composed of high quality images of individuals of Speleomantes strinatii and S. ambrosii from dorsal view. We suggest the use of the program ImageJ to extrapolate salamanders morphometrics and to estimate their snout-vent length  35  , a fundamental parameter to distinguish juveniles from adults  1. The images can be also used in R environment (http://www. R-project. org/) to perform analysis on multiple phenotypic traits  51, 52. Considering that the dorsal pattern of Speleomantes does not change through the time  33  , it can be used as natural mark to individually recognize salamanders  48, 53. The repeated surveys performed on the same locations were thought to test the efficacy of specific software to automatically recognize Speleomantes salamanders  47, 53.
It has been observed that the ventral pattern of the mainland S. strinatii can be used to individually recognize salamanders, and that software can be employed to do it automatically  54. However, only the three mainland species have visible pigments on their ventral side 1 , thus alternative methods are needed for analyses embracing all Speleomantes (i. e. including the five Sardinian species). Furthermore, the recognition of Speleomantes using their dorsal pattern limits individuals' handling, a potential source of both stress and pathogens  50, [55] [56] [57]. The population S_strinatii2 in the previously published dataset  23   The second part of the dataset is provided in CSV format and is ready to be analysed with R. Populations are codified following  23. To combine the data on stomach contents with the previous dataset: 9 S_ambrosii2 = Cave_ ambrosii1, S_ambrosii3 = Cave_ambrosii3, S_ambrosii4 = Cave_ambrosii2.
included two nearby sites, the S_strinatii2 and S_strinatii5 shown here. To combine these data, the actual S_strinatii2 contains the previous 20 photos (1045074-1045113) and the actual S_strinatii5 the other 20 (1045114-1045142). Furthermore, also the population S_strinatii3 in the previous dataset 23 includes 2 different nearby sites; we therefore suggest to split it in S_strinatii3 with the first 33 photos (1045032-1045056) and in S_strinatii7 with the other 10 (1045057-1045069).
45 854 photos of salamanders from two mainland Speleomantes species: S. strinatii (S_strinatii4, 28 in July; S_strinatii1, 47 in July and 73 in September; S_strinatii2, 15 in July and 10 in September; S_strinatii5, 118 in July and 88 in September, S_strinatii6, 26 in July) and S. ambrosii (S_ambrosii2, 108 in July and 98 in September; S_ambrosii3, 21 in July and 51 in September; S_ambrosii4, 59 in July and 112 in September). The code adopted to label populations (name of the species + number indicating the population) enables to combine the present dataset with two previously published (see Usage Notes). Detailed explanation of dataset Photos and stomach contents of two mainland Italian Speleomantes salamanders: data from summer 202045is given in Table1.
acknowledgements This study was authorized by the  Italian Ministry of the Environment (Ministero dell' Ambiente e della Tutela del Territorio e del Mare)  with the PNM prot. n. 56097/T-A31 of 13/11/2017. This study was conducted under the auspices of the  Chinese Cavefish Working Group  and supported by a grant from  National Natural Science Foundation of China  ( NSFC-31972868 ). Enrico Lunghi  is supported by the  Chinese Academy of Sciences President's International Fellowship Initiative  for postdoctoral researchers.
45 854 photos of salamanders from two mainland Speleomantes species: S. strinatii (S_strinatii4, 28 in July; S_strinatii1, 47 in July and 73 in September; S_strinatii2, 15 in July and 10 in September; S_strinatii5, 118 in July and 88 in September, S_strinatii6, 26 in July) and S. ambrosii (S_ambrosii2, 108 in July and 98 in September; S_ambrosii3, 21 in July and 51 in September; S_ambrosii4, 59 in July and 112 in September). The code adopted to label populations (name of the species + number indicating the population) enables to combine the present dataset with two previously published (see Usage Notes). Detailed explanation of dataset Photos and stomach contents of two mainland Italian Speleomantes salamanders: data from summer 202045is given in Table1. 2. 565 salamanders stomach contents, subdivided in 313 individuals with empty stomach, 37 with non-recog- nizable contents, and 215 individuals with full stomach.
Therefore, the control signals are generated from the state variables and the feedback gains according to  (32)  and  (33). As mentioned, the   control component is only generated and used in the operation connected to the grid. This is achieved by including a selector on the current controller output and an enabling signal on the resonant control block. In the case of the voltage controller, the resonant controller (  ) is disabled and replaced with a feedforward component by means of the    gain. In the present work, the linear matrix inequalities for synthesis of the controllers were described using the Yalmip ® complement on the  MatLab ® platform, with the chosen solver being LMILab ®. Considering the parameters listed in Table  1 , as well as the procedure detailed in Section 5, the voltage controller gain   is: 𝐊 𝑣 = [-0.