dataesr/scientific-paragraphs-categorization huggingface dataset:
However, if in one hand experimental data is expensive to obtain, it can provide, on the other hand, different insights and conclusions about the cutting process. Moreover, quite often, machining tests have to be performed before launching manufacturing in order to select suitable cutting parameters for a given machining process. While in some cases the selection of cutting parameters is based solely on catalog information, trial and error, or human experience, better results are often obtained by following standardized experimental approaches such as design of experiments or Couple Tool-Material (COM) protocol  [25] , with process evaluation based on quantitative sensor measurements rather than qualitative judgments. Considering this context, in this work, dataset acquired throughout the execution of the COM protocol is used to develop models for prediction of workpiece surface roughness while machining.
The proposed models use as inputs cutting parameters as well as cutting forces, temperature and vibration data acquired by robust sensors installed on the cutting tool. The machining operation considered in this work is cylindrical turning of Z38CDV5 tool-steel instrumented with a triaxial dynamometer, a triaxial accelerometer and Actarus® system for in-process measurement of, respectively, cutting forces, vibrations and temperature. Predictive models are developed using two machine learning algorithms: Random Forest (RF) and Gaussian Process Regression (GPR). The performance of the models is then evaluated in terms of the errors of their predictions. The rest of the paper is organized as follows. The "Experimentation" section describes experimental setup and procedures employed to acquire a dataset for training and evaluation of ML models.
A tool life test involves the execution of cutting experiments with a same cutting edge till the end of its life. Tool life is evaluated using flank wear measurements. It is considered that when Vbb reaches 300µm or edge breakage happens, the cutting edge reaches the end of its life. Tool-life tests allow the determination of the coefficients of the Taylor's tool life equation, which provides better comprehension of the cutting process behavior. In this work, five tool-life tests are carried out accordingly to the ISO standard 3685:1993. Additionally, and out of the COM scope, one tool life test was performed using different cutting parameters across experiments. This additional toollife test is performed to generate data in a diverse cutting condition. The resulting six tool-life plots are shown in Fig.
Model training and evaluation For training and evaluation of the models, the dataset acquired from the 92 experiments previously described will be used. Cutting parameters as well as cutting forces, accelerations and temperature measured by the instruments described previously are the inputs of the models, which will provide, as output, an estimation of the workpiece surface roughness. The performance of the models is evaluated in terms of root mean square error (RMSE) metric and K-fold crossvalidation technique. RMSE can be thought of as some kind of normalized distance between the vector of predicted values and the vector of observed values. This metric is calculated as 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = � � � ∑ (𝑦𝑦 � � -𝑦𝑦 � ) � � ��� , where  � � is a predicted value,  � is the corresponding observed value, and  is the sample size.
K-fold cross-validation is a resampling method that uses different portions of the data to test and train a model on different iterations. It generally results in a less biased estimate of the model performance than other methods, such as a simple train/test split since training and testing is performed on several different parts of the dataset. As defined in  [33] , in k-fold cross-validation, the data is first partitioned into k equally (or nearly equally) sized segments or folds. Subsequently k iterations of training and validation are performed such that within each iteration a different fold of the data is held out for evaluation, while the remaining k -1 folds are used for learning. The influence of the training size on the performance of the models is evaluated, in this work, by varying the number of folds used for cross-validation: 2, 3, 4, 5 and 10 folds are used.
The smaller the number of folds, the smaller the percentage of the dataset that is used for model training in each iteration of the cross-validation process. Fig. 3  shows the average and 95% confidence interval of the RMSE obtained with the two machine learning algorithms for the different number of folds tested. It is found that GPR performs better and more consistently (lower average RMSE and narrower 95% confidence interval) than RF for training sets representing 67 to 90% of the dataset. For visualization of the quality of the predictions provided by the models, the graphs on Figs. 4  and 5  presents the scatter plot of observed Ra versus the corresponding Ra predictions obtained in the 5-fold cross-validation for RF and GPR models respectively. A line corresponding to the equality between predicted and observed Ra, as well as error lines of ±0.
Conclusions In this paper, models for prediction of workpiece surface roughness in cylindrical turning operations were developed using RF and GPR machine learning algorithms and a dataset containing 92 experiments obtained from the execution of a COM protocol. Cutting parameters as well as cutting forces, accelerations and temperature measured during cutting operations are the inputs of the models, which provides, as output, an estimation of the workpiece surface roughness. The developed models were evaluated in terms of its predictions Root Mean Square Error (RMSE). The experimental results have shown that GPR performs better and more consistently than RF for training sets representing 67 to 90% of the dataset. Furthermore, both models, when trained with 80% of the dataset, were capable of providing Ra predictions with an RMSE lower than 0.
0 Hi-Speed, 480 Mbit/s) for the communication with a PC and the power supplies (including the DC/DC convertor, which delivers the high voltage to the SiPMs). The digitization of the analog signals takes around 12μs for the 256 channels. The data transfer through the USB lasts 8 μs and generates most of the dead time. The count loss at a frequency of 1 kHz is 2% and follows a non-parallelizable behavior as a function of activity. The response uniformity of the SiPM arrays is improved by an 8-bit DAC (0-2. 5 V) embedded at the ASIC input, which allows each SiPM polarization voltage to be individually adjusted. The four thermal sensors are connected to the FPGA, which adjusts in real-time the DC/DC converter as a function of the mean temperature in order to maintain a constant overvoltage on the SiPMs (accuracy of 35 mV) and, thus, to accurately offset the influence of temperature variation on SiPM performance, including intrinsic gain and photodetection efficiency.
Acknowledgments This work was supported by  IN2P3. The authors thank  A Forbes  and  L Ammour  from the  Nuclear Medicine department of Hôpital Bicètre , for their help with the evaluation of the mobile camera on the radioactive phantoms. They also thank  Olivier Caselles  from the medical physics department of the  Claudius Regaud Institute  who provided the images of the thyroid phantoms acquired with the standard gamma camera.
Grit, a non-cognitive skill that indicates perseverance and passion for long-term goals, has been shown to predict academic achievement. This paper provides evidence that grit also predicts student outcomes during the challenging period of the Covid-19 pandemic. We use a unique dataset from a digital learning platform in the United Arab Emirates to construct a behavioral measure of grit. We find t hat c ontrolling f or b aseline a bility, s tudents w ho were grittier according to this measure before the pandemic, register lower declines in math and science scores during the coronavirus period. Using machine learning, behavioral data in the platform prior to the pandemic can explain 77% of the variance in academic resilience. A survey measure of grit of the same students, on the other hand, does not have significant predictive power over performance changes.
Setup, Data and Research Questions Our data come from a widely used digital learning platform in the United Arab Emirates. 1  In the pre-pandemic period, the platform was used for blended learning in K-12 public school classrooms, delivering core curriculum. After the pandemic, with the switch to complete distance learning, the platform became the sole environment for teaching and learning in the country, in conjunction with communication tools such as Zoom. The data used in the paper is partly coming from diagnostic performance tests and a survey conducted by Alef Education on a sample of middle school students in the United Arab Emirates, as part of their research and evaluation activities. 2  The data-set we use in the paper comes from a subset of students, for whom survey measures of grit and performance data from out-of-platform diagnostic tests before the pandemic period are available, in addition to behavioral data coming from the digital platform.
Data We use data coming from 1920 students, in 17 schools and 229 classes, out of which 1279 are female and 641 are male. 8 The students are middle-and high-schoolers, and the data include five grades: grades 5 to 9. 9  We have access to objective scores from a diagnostic test on math and science as well as a survey, which were all run before the pandemic, in Fall 2019. In addition, we have platform data from the period January 10 to March 5, 2020 (8 weeks), which we define as the pre-Covid period. The post-Covid period goes from 22 March, 2020 to June 13, 2020 (12 weeks). 10 In additional analysis of revealed preference data on non-cognitive skills, we implement an elastic regression using 400 variables from the digital platform. These variables come from "click data" recorded on the platform, and include a detailed set of observables about students' platform activity, such as accessing different types of instructional content for each subject (e.
We show that in the educational setting, the non-cognitive skill of grit predicts performance declines. This finding suggests that in addition to explaining performance, grit is also relevant for response to negative shocks in the educational setting, for instance distance learning during the Covid-19 pandemic. Our behavioral measure of grit is based on identifying performance declines, and utilizing the unique advantage offered by the data recorded in voluntary after-school hours activity on the platform to measure the revealed effort response. In this sense, our measure captures the "perseverance of effort" aspect of the construct of grit. Our results are therefore consistent with  Credé et al. (2017)  and  Ponnock et al. (2020) , who show that it is the perseverance facet of grit (in contrast to consistency of interest) that has explanatory power over academic performance.
Lessons Learned The shape modeling tests reported here illustrate the challenges of modeling the shape of an asteroid when less than half of the body is both visible to the camera and illuminated, the highest-resolution images possess essentially no stereo information, and the sole data set that could provide some stereo is so coarse in comparison to the Figure  26. Mean tilt directions of facets in the impact site DTMs. Tilt directions are computed with respect to the spacecraft impact vector, thereby providing information about the relative impact angle between DART and the asteroid. The figure shows tilts at two different radii. The 0. 9 m tilt radius provides information on tilts at the length scale of the core of the DART spacecraft. The tilt direction is the direction projected on a clock, with a 0°tilt direction pointing to the north pole of the asteroid.
4. The reliability of the model degrades as you move toward the terminator. This is not surprising in the case of DART, where areas that are in shadow in one DRACO image are in shadow in all DRACO images, and SPC has no image data to work with. However, mismatches between the terminator in images versus the rendered shape model are one of the only constraints on the shape into and out of the image plane. Terminator data will be used to constrain the broader-scale curvature of a starter model. A starter model that better matches the terminator in the image should lessen the degradation in topography as you approach the terminator. 5. Internal SPC metrics are not as reliable with far-fromoptimal images as they are with images optimized for SPC. The formal uncertainty metric breaks down without meaningful stereo, and the vertex sigma and rms residuals per maplet indicate much smaller uncertainties than those revealed by comparisons with the truth shapes.
This work was supported by the  DART mission  under  NASA  Contract  80MSFC20D0004. This work was supported by the  Italian Space Agency (ASI)  within the  LICIACube  project ( ASI-INAF  agreement AC n. 2019-31-HH. 0 ). This work used the Small Body Mapping Tool (sbmt. jhuapl. edu). JMTR is funded by  FEDER, Ministerio de Ciencia e Innovación and Agencia Estatal de Investigación of Spain. Erik Asphaug ,  Steve Schwartz , and  Masatoshi Hirabayashi  provided valuable comments on the manuscript.
Background Phase and step annotation in surgical videos is a prerequisite for surgical scene understanding and for downstream tasks like intraoperative feedback or assistance. However, most ontologies are applied on small monocentric datasets and lack external validation. To overcome these limitations an ontology for phases and steps of laparoscopic Roux-en-Y gastric bypass (LRYGB) is proposed and validated on a multicentric dataset in terms of inter-and intra-rater reliability (inter-/intra-RR). Methods The proposed LRYGB ontology consists of 12 phase and 46 step definitions that are hierarchically structured. Two board certified surgeons (raters) with > 10 years of clinical experience applied the proposed ontology on two datasets: (1) StraBypass40 consists of 40 LRYGB videos from Nouvel Hôpital Civil, Strasbourg, France and (2) BernBypass70 consists of 70 LRYGB videos from Inselspital, Bern University Hospital, Bern, Switzerland.
The aim of Surgical Data Science (SDS) is to analyze data sources acquired during surgical treatment to improve patient safety and clinical outcomes  [1]. Opposed to traditional clinical research centering on preoperative patient characteristics and postoperative outcomes, SDS focuses on the whole data stream of surgical treatment. To unravel the "black box" of the operation room (OR) and the impact of the understudied intraoperative phase on patient outcomes, SDS particularly analyzes data streams captured in the OR during surgery. Since the introduction of video technology in minimally invasive surgery, video recordings of surgical interventions are easily recorded and therefore readily available. As analysing surgical videos is time consuming, costly, and often lacks objectivity, the full potential of video analysis was often not tapped in the past  [2].
In the last decades however, the evolution of computer vision (CV), which is the analysis of visual information by computer algorithms, boosted the potential of surgical video analysis. One of the most analysed surgeries in SDS is laparoscopic cholecystectomy (LCHE). Classical CV tasks in the analysis of surgical videos are phase recognition and tool presence detection. They were developed for LCHE  [3]. Moreover, safety feedback  [4, 5]  and surgical skill assessment algorithms  [6]  were trained on LCHE videos. However, the disadvantage of LCHE as a model intervention for SDS is that there are hardly any intraoperative events or postoperative complications to study given the limited size of datasets. Therefore, recent SDS research focuses on longer and more complex procedures like colorectal  [7, 8]  or bariatric surgery  [9, 10].
However, most datasets in SDS are small, monocentric, and lack external validation. To overcome these limitations, larger and multicentric datasets are warranted. Furthermore, variability of ontologies and its application on datasets limits generalization across centers  [18]. To ensure data quality and reliable algorithms procedure-specific ontologies need to be defined and validated multicentrically. This work is the first to propose a LRYGB ontology for phases and steps and to validate it on a multicentric dataset in terms of inter-and intra-rater reliability. The application of this ontology enables workflow analysis across surgeons and centers. Furthermore, it facilitates downstream applications of SDS, as the training of phase and step recognition algorithms using artificial intelligence for automated surgical video analysis.
Intervention Inter-rater reliability (inter-RR) defines the extent of agreement among observers, whereas intra-rater reliability (intra-RR) defines the consistency of observations of a given observer over time. To assess the inter-RR of the proposed LRYGB ontology ten randomly chosen videos of the StraBypass40 and BernBypass70 datasets were annotated according to the step and phase definitions as provided in the Supplementary Material (Tables  S1, S2 ) by both raters using the in-house video annotation tool MOSaiC. The annotations of both raters were compared. To assess the intra-RR of the proposed LRYGB ontology ten randomly chosen videos were annotated a second time  1 3  by the same rater after a wash out phase of 1 month. The two sets of annotations were compared.
Results For StraBypass40 the mean ± SD video duration was 108 ± 33 min An average LRYGB video consisted of 10 phases and 33 steps. For BernBypass70 the mean ± SD video duration was 75 ± 21 min An average LRYGB video consisted of 8 phases and 27 steps. Average phase and step durations in the StraBypass40 and BernBypass70 datasets are displayed in Fig. 2. Quantitative inter-RR results for StraBypass40, BernBy-pass70 and overall inter-RR results are shown in Table.
For laparoscopic cholecystectomy, one of the most analyzed surgeries in SDS, a systematic review identified 8 different phase definitions in the literature  [18]. Multiple phase definitions are a hindrance to comparison of results across datasets and institutions. Therefore, with the definition and multicentric validation of an LRYGB phase and step ontology we aim to prevent the use of multiple competing ontologies. To implement the use of the proposed ontology on a global scale, awareness for surgical video recording in general, and in particular, larger consensus among bariatric surgeons using the Delphi method must be created. Inter-RR and intra-RR metrics are higher for phase compared to step annotation. Comprising 12 phases and 46 steps the proposed ontology is less granular on the phase than on the step level.
This leads to lower variability in phase compared to step annotations. Therefore, the ontology performs better in terms of inter-and intra-RR on a phase than on a step level. As the two datasets are from different institutions and therefore represent different surgical techniques, the aim of this study is not to compare them. However, to understand the performance difference of the proposed ontology on StraBypass40 and BernBypass70 it is crucial to elaborate, how they differ. When comparing StraBypass40 with BernBypass70, there is a considerable difference in average video duration (108 vs. 75 min). This is also reflected by the greater average number of phases and steps in Stra-Bypass40 compared to BernBypass70 (10 vs. 8 phases, 33 vs. 27 steps). In StraBypass40 the creation of the gastric pouch (phase 2, 26 vs.
15 min) and the creation of the  Inter-RR metrics on StraBypass40 show better results when compared to BernBypass70. This is likely an effect of the difference in average video duration between datasets. Given the same number of phase and step transitions, the longer a video is, the less the metrics are influenced by a single transitional delay. Using application dependent metrics, a 0. 4-1. 4% boost in accuracy, precision, recall and F1-score can be observed. This is due to the relaxation of transitional moments by extension of the acceptable transitional delay. Setting the transitional delay threshold allows to tailor the metrics application dependent to the desired use case. To estimate the remaining time of an intervention based on a phase and step recognition algorithm a 5 s delay is reasonable.
Acknowledgements The authors would like to thank  Sanat Ramesh  for help with dataset statistics. Funding Open access funding provided by  University of Bern. Joël L. Lavanchy was supported by a grant of the  Swiss National Science Foundation  ( P500PM_206724 ). This work was partially supported by  French State Funds  managed within the  "Plan Investissements d'Avenir ", by the  ANR  (reference  ANR-10-IAHU-02 ) and through the  National AI Chair program  under Grant  ANR-20-CHIA-0029-01 (Chair AI4ORSafety ).
Funding " Investissements d'avenir" programme  supported by the  French National Research Agency  ( ANR ; Grant  ANR-10-LABX-14-01 ; AKM);  COACCH  project supported by the  European Union  (Grant 776479; JH); INSeaPTION supported by the  European Union (EU) & the Dutch Research Council (NWO , Grant  690462 ; EL, RSWW),.
Test Environment The performance of the proposed 5G-SOS is assessed in AnyLogic ® software. An emergency scenario in the Transverse city in Michigan, USA, with a number of devices varying between 5000 to 15,000 and 7 working gNBs covering an area of 16. 2 × 21 km 2 is considered. This scenario is illustrated in Figure  6. 2 ), the Cost231 propagation model for semiurban areas, given in  [42] , is used. P l (d, f ) of a signal transmitted by a working gNB varies according to the signal transmission frequency of the working gNB, f , and the distance between the working gNB and receiver UE, d, as given in  (8). Further, h g and h m represent, respectively, the height of the gNB and receiver UE. C m is a correction offset associated with the semi-urban environment. For the simulations, f is set to 885 MHz.
The provided results are all averaged over 100 random executions, which differ by the source devices at which the emergency occurs and emergency arrival times. Table  5  summarizes the parameters used in the simulation scenarios. The emergency data transfer takes T d2d seconds. During the emergency call transmission (D2D transmission or ordinary transmission) the UE cannot transmit other emergency requests. Extreme emergency scenarios are considered to assess the performance of 5G-SOS. In particular, success rate, EEL, φ, and β metrics under extreme emergency call duration, device density, data size, and emergency call number are studied.
To associate meteorological and snow conditions to each observed avalanche, we removed from the dataset observations with an uncertainty of more than three days on the release date. When the uncertainty is larger than one day, the last day of the period was defined as the day of the avalanche event. Moreover, aspect and elevation of the starting zone were not reported in a few cases (representing less than 5% of the total number of events) because the starting point was not visible from the observation point or due to a lack of time. In these cases, the starting zone was defined by the average elevation and aspect of the typical release area defined for each avalanche path. We applied this definition of release day and zone to the 2779 avalanche events in the studied spatio-temporal domain.