dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. S. Damsted  has received funding from the  Finnish Emil Aaltonen Foundation. Funding for the  Sloan Digital Sky Survey  IV has been provided by the  Alfred P. Sloan Foundation , the  US Department of Energy Office of Science , and the Participating Institutions. SDSS-IV acknowledges support and resources from the  Center for High Performance Computing  at the  University of Utah. The SDSS website is www. sdss. org. SDSS-IV is managed by the  Astrophysical Research Consortium  for the Participating Institutions of the SDSS A127, page 14 of 23 Damsted, S. , et al. : A&A 676, A127 (2023) Collaboration including the  Brazilian Participation Group , the  Carnegie Institution for Science, Carnegie Mellon University , the  Chilean Participation Group , the  French Participation Group ,  Harvard-Smithsonian Center for Astrophysics ,  Instituto de Astrofísica de Canarias , The  Johns Hopkins University ,  Kavli Institute  for the  Physics and Mathematics of the Universe (IPMU)/University of Tokyo , the  Korean Participation Group ,  Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP) ,  Max-Planck-Institut für Astronomie (MPIA Heidelberg) ,  Max-Planck-Institut für Astrophysik (MPA Garching) ,  Max-Planck-Institut für Extraterrestrische Physik (MPE) ,  National Astronomical Observatories of China ,  New Mexico State University ,  New York University ,  University of Notre Dame ,  Observatário Nacional/MCTI , The  Ohio State University ,  Pennsylvania State University ,  Shanghai Astronomical Observatory ,  United Kingdom Participation Group ,  Universidad Nacional Autónoma de México ,  University of Arizona ,  University of Colorado Boulder ,  University of Oxford, University of Portsmouth ,  University of Utah ,  University of Virginia ,  University of Washington ,  University of Wisconsin ,  Vanderbilt University , and  Yale University.
Participants and data collection Twelve children with SNHL followed in our audiology and otolaryngology department were included (mean age = 6 years and 2 months; range 5 years and 6 months to 9 years and 6 months) (Table  1 ). The inclusion criteria were children from 4 to 13 years old with peripheral Keywords Serious game, Children, Physiotherapy, Vestibular rehabilitation, Vestibular hypofunction, Gaze stability exercises unilateral or bilateral VH, participants able to understand the terms and purpose of the study, and the non-opposition of children and parents. The exclusion criteria were cervical spine disorder and vision impairment (uncorrected refractive error or eye movement disorders other than VOR impairment). The local ethics committee approved the study protocol; the study project has been registered on ClinicalTrials.
gov (NCT04353115). Written informed consent and assent were obtained. Demographic and clinical data collected were as follows: age, sex, hearing status, cervical vestibular evoked myogenic potentials (cVEMPs), and VOR gain using the Video Head Impulse Test (VHIT). The cVEMPs were obtained in bone conduction (Neuro Audio system, Collin®, France); the response was rated present (+) if a reproducible wave P13-N23 of at least 50uV in amplitude was present; if not, it was rated as absent (-) (Table  1 ).
Secondary outcome measures The secondary outcomes were the assessment of vestibular and motor function parameters. VOR gain was measured for all canals using the Video Head Impulse Test (VHIT, Synapsys®, Marseille, France), which uses a remote video camera detection to measure head and eye movements at high velocities (> 120-150°/s)  [24]. VHIT evaluation had a twofold purpose: first, to determine a VOR deficit, defined in this study as a gain below 0. 6 according to previous data  [25, 26] ; second, to evaluate VOR gain changes after the training. For VHIT evaluation, as of 5-6 years of age, children become more compliant; the procedure used is thus relatively similar to that used in adults  [25]. DVA was assessed using a DVA commercialized system (Framiral®, Grasse, France). Static condition is measured by reading letters or symbol optotypes flashed for 50ms on a computer screen with the head stationary.
The serious game The KGR software was developed for this study; the video game was controlled and displayed on a classical laptop computer. The system used a head-mounted sensor to detect the angular speed of head movements in real-time (Fig. 1A ). The patients wore a lightweight cap, and the sensor (MPU 6050) was placed on the center of the forehead. The sensor and accelerometers measured head accelerations along the horizontal, anterior, and posterior axes. The lightweight sensor contains a 3-axis accelerometer and a 3-axis gyroscope. The MPU data (acceleration and angle) were collected by the Arduino Nano, a programmable board containing a microcontroller and a USB port. The data were then transmitted to the software installed on the computer via the USB port in serial communication. The system allowed to determine a maximum and minimum angular speed threshold for each patient.
Discussion This study tested the safety and efficacy of a 5-week rehabilitation program including a serious game dedicated to training gaze stability. Children with VH showed good tolerance and acceptability of the serious game KGR. After training with this rehabilitation system, their DVA significantly improved from the baseline assessment. In the future, interactive rehabilitation could be beneficial for children with VH. This approach may offer greater accessibility, delivery, and treatment compliance in young children with VH. Table 2 SSQ questionnaire scores in each section (nausea, ocular motricity, and disorientation) and children satisfaction questionnaire scores for each child. For SSQ, a score of 0 stands for an absence of side effects. For the satisfaction questionnaire, a score of 5 stands for the maximal level of satisfaction.
A new marine volcano is erupting offshore Mayotte since May 2018, generating numerous earthquakes. The population felt many of them and the stronger shaking of the ongoing sequence caused slight damage to buildings. Historical records also confirm that damaging earthquakes had occurred in the past in this region. Seismic damage scenarios are a key tool for supporting the decision-making process, the preparedness, and for designing appropriate emergency responses. This paper provides the outcomes of a work consisting in improving the seismic risk assessment as a part of disaster risk governance and exposes the scientific background of this workflow. It illustrates its use with two earthquakes. Related post-seismic surveys provide observations that are useful to check the validity of the reference dataset.
Introduction Mayotte is an island located in the Indian Ocean, East of Africa, in the Mozambique Channel. Since 2011, it got the administrative status of France's departments. The French seismic regulatory zonation (2010) classifies Mayotte's territory as a zone of moderate seismicity (Zone 3 out of 5). Nevertheless, most of the buildings date from before the enforcement of seismic regulation. The recent demographic evolu- buildings. Historical records also report that some destructive events have hit the island in the 20th century, for example in 1993 (M b = 5. 3, according to ISC, M w = 5. 5  [Bertil et al. , 2021] ) and in 1936 (see the historical database SisFrance Indian Ocean for example) (Figure  1 ). Consequently, prevention policies have to consider preparation for a destructive earthquake on the island as an important component of action plans.
The article focuses on the seismic risk in Mayotte, whether it results from the ongoing volcanic event or from the regional tectonics. It documents and releases a reliable dataset and an operational work-flow for rapid loss assessment, considering the latest developments about the geodynamical context. The dataset is ready to assess consequences of future events, as well as to build realistic seismic loss scenarios that can illustrate the potential impacts of earthquakes. Two past earthquakes are simulated, for which there is documentation about their impacts on buildings: 1993 December 1 earthquake and 2018 May 15 main shock. Simulation results describe the operational outcomes of the workflow and set a basis for comparison between simulation and field observations, that contributes to justify the reliability of the key parameters provided as a reference dataset for seismic risk assessments.
Approach Damage scenarios consist in simulating the damage on buildings resulting from a selected seismic configuration (also called event description or seismic scenario) and represent it in a way that can support decision-making process. Building realistic scenarios implies namely: (i) to consider historical and recent seismicity, (ii) to take into account most recent data regarding geology and site effects, (iii) to derive vulnerability indexes taking into account the characteristics of local building stock, (iv) to elaborate a new geospatial dataset describing the distribution of the building stock by different typologies, (v) to release results in a format that directly matches the needs of the decision makers. The procedure for damage assessment follows the framework developed in the European project RISK-UE  [Lagomarsino et al.
, 2019 , Negulescu et al. , 2019] , is a tool that comprises risk computation modules, a data management system, and a convenient user interface. The computing module of the platform runs the algorithm of the Armagedom software  [Sedan et al. , 2013] , which crosses geospatial information (acceleration, site amplification, and building types) to assess and to map damage indexes (Figure  2 ). The classification of damage in five grades introduced in the European EMS-98 macro seismic scale  [Grunthal, 1998]  is the reference for ranking the level of damage for common buildings (housing) in our simulations. This facilitates comparison of the results of the simulations with field data collected by GIM (Macro seismic field survey group) in 2018 and pub-lished in their field survey report  [Sira et al.
, 2018]. Data and simulations are stored through the platform VIGIRISKS for future use. Thanks to web services, the calculation codes hosted by VIGIRISKS can also be used independently from the platform. For instance, the SEISAid tool developed by BRGM [see Section 4 and  Guérin-Marthe et al. , 2020]  uses the code Armagedom, one component of the platform VIGIRISKS. It assesses automatically the probable losses associated to shake-maps  [Gehl et al. , 2017 , Worden et al. , 2018]  for earthquakes that have just occurred on the French territory, and quickly produces reports for the local authorities. On the other hand, this damage assessment code makes it possible to pre-calculate credible scenarios for crisis management purposes and land use planning. They are also good communication supports to enhance risk-awareness: the French Ministry in charge of disaster prevention required such scenarios for the territory of Mayotte to BRGM.
Data collection The reference dataset takes into account the results of the latest studies concerning geology and ground classification in Mayotte as well as the outcome of a dedicated field survey for building classification and distribution. Field investigations have completed existing data about the structural characteristics of the local building stock. The key data for establishing the scenarios are: • The shake-map corresponding to the scenario earthquake, which gives the spatial distribution of intensity measurements (IM) expressed in terms of peak ground acceleration (PGA) or macro seismic intensity (MI). The calculation of this shake-map requires in particular the characteristics of the earthquake scenario (magnitude, location, and depth), regional models of attenuation (Ground Motion Prediction Equations-GMPEs) and conversion between PGA and MI (Ground Motion to Intensity Conversion Equations-GMICEs), and a local model to take into account site effects, taking the form of an amplification factors map.
Class A (rock) represents about 10% of the surface of the island and the remaining 90% lay on soil with amplification. Considering geological data and geophysical measures available in past studies, it appeared that class C reasonably represents the behaviour of these soils. Then, the corresponding site effects can be computed using soil related parameters in the GMPE used in the software ShakeMap 4. 0  [Worden et al. , 2020 ]. In the selected GMPE, the soil velocity (parameter Vs30 is the proxy for the type of   3  shows Vs30 values with a step of 100 m.
The PGA values for the 2018 earthquake coming from measures at the two local seismological stations that are on a rocky-type soil, YTMZ and PMZY, provide control points to correct the shake map (Figure  5b ). The software calculates a general correction factor for results coming from the model (GMPE). A slight underestimation of PGA by the model can be noticed, consistent with Bertil's conclusion stating that the selected GMPE underestimates accelerations for earthquakes of magnitudes around 6. The PGA calculated by the model differs only slightly from measured values (less than 5%, a PGA of 5. 3% g is measured on the YTMZ station, located on a rock-like soil, whereas the estimated intensity is V). A complementary set of shake-maps expressed in MI is produced using the GMICE from  Caprio et al.
[2015]. The historical database SISFRANCE (www. SisFrance. net), hosted in BRGM and extended to French overseas territories, contains information about the 1993 earthquake. It provides IM for each municipality, determined from post-seismic field survey reports and other documentation. The shakemaps in intensity (Figure  4b ) were updated by these observations. To do it, the software calculates back the PGA corresponding to the observed IM values from the model (GMICE, GMPE and amplification factors for site effects), and then calculates a correction factor. There is a difference of about 1 point between intensities calculated by the model and IM; the correction on PGA values reaches 30%. Comparison between shake-maps shows that: • The geological site effect zoning used in this study results in strong local amplifications of MI (and in particular of IM), especially for soil of type C, that is rather common in urbanized areas, • PGA values predicted by the GMPE from  Atkinson and Boore [2006]  fit well the acceleration recorded during the earthquake of May 15, 2018 (i.
ization in 1993. It is expressed as the number of buildings and the percentage of the total buildings stock of constructions, damaged at different grade, according to EMS-98 scale  [Grunthal, 1998]. A complementary scenario, called "optimized" scenario calculates back the vulnerability index for type 3 houses that best fits the post-earthquake data for the 2018 earthquake. For all the simulated cases, the number of severely damage buildings (level D4 or D5) is close to zero, and does not reach the level of statistical significance. The "optimized" scenario neutralizes the vulnerability of type 1 houses and effects a vulnerability index of Vi * = 0. 52 to type 3 houses. It uses the calibrated shake-map, considering it provides the most precise information about the ground motion. The dataset was prepared for future simulations and stored in the VIGIRISKS platform.
Consequently, the reference dataset keeps recommended values of the vulnerability index, since it use by tools dedicated to crisis management accommodates better overestimation than underestimation of damages. Recommended values benefit from larger support from field observations on a broader range of magnitude considered in the Risk-UE project, even if they are less specific to the territory. There is still a need to find an appropriate way to handle the behaviour of poor housing (type 1 buildings), which represent locally a significant part of the building stock. It is then important to note that the method used for rapid damage modeling intrinsically bears high reliability limits. Unlike damage scenario calculations, for which the user sets the seismic parameters with precision, rapid loss assessment tools are supposed to use the data available immediately after an earthquake, which is often tainted with high uncertainty.
Frequency-Selective Measurement System The frequency-selective measurement system used for continuous temporal measurements in this study consists of a real-time spectrum analyzer, i. e. , Tektronix RSA306B, switch, Arduino-based hardware, tri-axis electric field (E-field) probe, a PC that runs Tektronix SignalVu-PC™ RF signal analysis software and a graphical user interface (GUI) to control the measurements as shown in Figure  1. The tri-axis E-field probe, which is commercialized by Microwave Vision Group (MVG) as TAS-1208-01 antenna, is used to conduct measurement of RF-EMF exposure on the three orthogonal polarizations (X, Y, and Z). Our frequency-selective measurement system allows measurements from 9 kHz to 6. 2 GHz. The RF switch connects the spectrum analyzer and the tri-axis E-field probe to conduct measurements on the three orthogonal polarizations.
We have one measurement of the selected band at a time if we use only the Tektronix SignalVu-PC™ RF signal analysis software interface. Therefore, we developed a GUI that is synchronized with the SignalVu-PC software to control all measurement parameters and to fetch real-time measurement values continuously. Calibration was performed in the laboratory and an anechoic chamber to maintain the measurement system's accuracy. The frequency bands, that are under analysis, are the ones used by the network providers in France as given by ANFR  [17]. The resolution bandwidth (RBW) is set to 250 kHz for for all bands. For each measurement location, the frequency-selective measurement system recursively measures the E-field induced by 27 frequency bands (cellular bands used by all network providers in France) on a single axis before switching to the other axes for 20 records.
Conclusions This paper analyzes the indoor RF-EMF DL exposure with outdoor cellular antennas located at more than 200 m from the buildings. In the three buildings, 1176 measurements have been performed with a broadband probe at both corridors and offices on different floors. With the base station antenna far away, the exposure is well below 1% of the ICNIRP reference levels as expected. A statistical approach has been implemented to characterize and model the indoor RF-EMF DL exposure. The measurement data were analyzed and the p-values of the one-sample K-S test are above 0. 05. Therefore, it has been statistically proved that the indoor RF-EMF DL exposure on each floor over the length of a wing can be modeled by a Gaussian distribution when the size of the building is small compared to the distance to the base station antennas.
Wu et al. used YOLO v3  model to extract the leg area coordinates of dairy cows in video frames, calculate the relative step length of the front and rear legs of dairy cows, and construct the relative step length feature vector, and then used LSTM model to classify lameness  [29]. Lu et al. proposed a maturity calculation method of winter jujube combining the YOLO v3 model and artificial features, and the accuracy of maturity classification was 97. 28%  [30]. He et al. proposed an analysis method of cow estrus in endoscopic images based on CNN  [31]. According to the characteristics of the sheep face dataset, Noor et al. used transfer learning method to train a CNN model and realized the classification of normal (no pain) and abnormal (pain) sheep images  [32]. In summary, we can see that deep learning technology can not only automatically obtain multi-dimensional and multi-level feature information of the target in the image, but also effectively solve the problem of manually extracting the target feature vector, which makes deep learning technology have a broad application prospect in the field of precision animal husbandry.
Among the deep learning techniques, the YOLO v3 model is a representative of target detection methods. YOLO v3 has the advantages of fast recognition speed, high recognition accuracy, and strong model resistance to interference, and has the potential to recognize ewe estrus crawling behavior in complex environments. Therefore, we chose YOLO v3 for the detection of ewe estrus. To address the shortcomings of manual detection methods and sensor detection methods in the detection of ewe estrus, we proposed a multi-objective detection layer neural network-based method for ewe estrus crawling behavior recognition. Firstly, we decomposed the ewe estrus videos obtained from large-scale meat sheep farms into pictures using the video keyframe decomposition technique. Secondly, we used LabelImg to manually annotate the images containing ewe estrus crawling behavior to obtain the sample dataset.
Then, we improved the model based on the dataset and YOLO v3 model structure characteristics. We mainly improved the model in terms of target detection layer, residual structural unit, anchor box size, and image resolution-keeping aspect ratio. Finally, we train, validate, and test the improved model using the PyTorch deep learning framework, the PyCharm development tool, and a high-performance computing platform. Briefly, the main contributions of this work are summarized as follows: (1) We established an ewe estrus dataset, along with the thorough analysis and findings on our dataset. (2) We propose to use the YOLO v3 model to detect the estrus crawling behavior of ewes, and improve the YOLO v3 model. (3) The advantages and disadvantages of our proposed model are compared with other models.
Data Sources The video of the estrus crawling behavior of ewes in this study was collected from the Xinao animal husbandry and mutton sheep breeding base in Lanzhou Bay Town, Manas County, Xinjiang Uygur Autonomous Region. Through a field investigation, we found that the estrus crawling behavior of ewes mainly occurred in the shaded area; therefore, this study chose to be in the shaded area for video capture. The shaded area is 33. 75 m long and 13 m wide. Two surveillance cameras with a resolution of 4 million pixels (Hikvision DS-2CD2346FWD-IS dome network cameras) (Hangzhou Hikvision Digital Technology Co. , Hangzhou, China) are installed in the shaded area, and the camera installation height is 2. 7 m. The installed camera and field of view are shown in Figure  1. The video of estrus behavior of ewes collected the activity records of 5 adult rams and 80 adult ewes.
Dataset Construction After watching the ewe activity monitoring video, the breeding staff selected 300 video clips with estrus climbing behavior from the video, with each clip ranging from 4 to 20s in length. We used Python script to decompose video key frames of video clips, and take one frame every three frames to obtain 3640 original images. In these original images, we found that each image contains not only the crawling behavior of ewes in estrus, but also other behaviors of ewes not in estrus, such as feeding, standing, walking, lying down, etc. In order to expand the sample size of the dataset and improve the generalization ability of the model, we performed data enhancement processing on the dataset. Firstly, in view of the uncertainty of the location of eweʹs climbing behavior in the shaded area, we performed horizontal flipping, horizontal moving, and vertical moving of the samples in the dataset.