dataesr/scientific-paragraphs-categorization huggingface dataset:
Figure7. Computed erosion rate distributions obtained by applying the method to a Himalayan dataset. For each distribution, the box extends from the lower to upper quartile values, the line corresponds to the median and whiskers extend from the box to show the range of the erosion rate estimates, excluding outliers. Outliers are indicated by small circles past the end of the whiskers. Erosion rate values are normalized such that the mean is 1. The gray circles connected by a dashed gray line are the product of the imposed area and mineral concentration factors, F i = A i α i , at each site. Site names refer to locations shown in Fig. 6.
This paper deals with an application of Genetic Algorithm (GA) tools in order to perform and optimize the settings phase of the Guidance, Navigation, and Control (GNC) data set for the endgame phase of a Kinetic Impactor (KI) targeting a medium-size Near Earth Object (NEO). A coupled optimization of the GNC settings and of the GC-oriented design of the Divert and Attitude Control System (DACS) is also proposed. The illustration of the developed principles is made considering the NEOShield study frame.
INTRODUCTION The collision probability with the up-to-now listed NEOs is assessed to be small, but not null, and the impact with a small to medium-class asteroid (50 to 100 m in diameter) could be catastrophic. Even if such event is not highly likely in a near future, preparing the technologies aiming at mitigating such event appears obvious when looking back, for example, at the damages generated by a 20-meter diameter meteor that exploded over Chelyabinsk. Within the frame of the NEOShield activities of the 7th Framework Programme for Research and Technological Development (FP7) funded by the European Council, a consortium of scienti¦c and industrial partners has been set to assess di¨erent mitigation techniques: destruction by a surface or an underground nuclear blast or de §ection using ion-beam propulsion systems, gravity, or solar sail tractors but also kinetic impactor.
NEOShield FRAMEWORK One of the objectives of the NEOShield program was to investigate di¨erent NEO mitigation solutions. Among them, KI appeared to be the most realistic one because of the potential heritage of exoatmospheric missile defence activities. Namely, the major di¨erences between missile defence and planetary defence endgame tasks that are given to the interceptor/impactor are essentially a matter of scale factor: the acquisition by the onboard sensor of a NEO can be made up to 24 h before impact but no more than a few hundreds of seconds for an incoming warhead; the expected ¦nal miss-range lays in the range of 25 to 50 m for a mediumsize NEO, but only a few tens of centimeters for a warhead (direct hit objective). For a missile defence kill vehicle, the objective of the endgame phase is to destroy its target, whereas for a KI, it is only necessary to push it, the kinetic energy transfer acting directly on the orbital parameters of the NEO interplanetary path.
Because of the various conditions at endgame handover, the optimization of the GNC data will be performed scenario per scenario at this stage of the project. If improving the performance goes naturally through the increase of the hit probability, one will try to minimize the fuel budget that was initially not constrained in the previous part of the study. This additional constraint helps in di¨erentiating potential solutions for the Guidance and Control data set, the objective of the time evolution of the scaling factor k G as well as the size of the tracking submatrix aiming to limit the divert and ACS fuel budgets. The performance of the Guidance is directly linked with the divert fuel budget and the ¦nal miss-range at impact. The performance of the Control that will partially drive the performance of the Guidance is de¦ned by the limitation of the fuel budget and the ability to keep the image of the target well inside the tracking submatrix all along the endgame phase.
The retained 5 comparison criteria are: (1) the 99. 7 percent (or 3σ value) ¦nal miss-range; (2) the divert fuel budget; (3) the ACS fuel budget; (4) the 99. 7 percent (or 3σ value) ¦nal time-to-go (one recalls that the objective of the guidance is to null the miss-distance at T = 0 s); and (5) the failure rate expressed with respect to the number of simulated cases (1000 MTCL runs are considered) yielding a ¦nal miss-range over 50 m (the 25-meter hit probability is only a secondary objective not retained at this level of comparison). It is clear here that the GA process is able to optimize the GNC performance acting on the GNC data set even if in some cases, the maximum divert consumption may be larger than with the handmade tuning. But if the 50-or 25-meter hit probabilities obtained on the ¦nal 1000 runs MTCL runs are analyzed, one gets the results summarized in Table  7.
COUPLED GNC SETTINGS AND DACS DESIGN OPTIMIZATION One of the baselines of the NEOShield program was to consider at least existing propulsive solutions for the DACS, hence the choice for the 10-, 200-, and 3500-newton divert options. Nevertheless, such choice was maybe not the most adequate, the analysis of some failure cases highlighting a DACS de¦nition issue, for instance, underestimation of the ACS needs for the 3500-newton divert and a 20-hertz image-based relative navigation. Thus, the driving here is to add the main features of the DACS, i. e. , thrust level and ¦ring period (still considering a pulsed-mode operating DACS) to the set of GNC data to be optimized by the GA process. In the end, the number of genes of an individual is raised to 20 (2 for the divert and ACS ¦ring periods and 3 for the divert, yaw-pitch, and roll ACS thrust levels).
In addition, divert and ACS fuel budgets are reduced. If it is quite direct to derive a relation between the endgame conditions (maximum miss-distance and timeto-go at endgame handover), the divert fuel budget and the divert thrust, the ACS consumption which is mainly linked to the duration of the endgame phase does not evolve as expected: scenarios 2 and 3 have similar ACS values, but the maximum fuel consumption for the scenario 2, which is twice shorter than scenario 3, is 30% higher. This overconsumption that was already observed when only the GNC data were optimized is essentially the result of a di¨erent GA sizing of the camera tracking zone but the fuel budget remains limited. Last but not least because this is the most important result with respect to the mission objective, the better adaptation of the DACS to the GNC data enables an important improvement of the GNC performance with 25-or 50-meter hit probability equal or very close to 100%.
CONCLUDING REMARKS Among the di¨erent mitigations techniques investigated within the frame of the NEOShield program funded by the European Commission (FP7 framework), the kinetic impactor appeared the most promising to deviate a threatening asteroid far away from the Earth. The analysis performed during the NEOShield activities demonstrated that a kinetic impactor with a medium (200 N) to high (3500 N) thrust liquid-fuelled divert or even a very low thrust level (10 N) for a satellite-like KI was able to impact an asteroid with a ¦nal miss-range below 50 m, or even 25 m, in most of the cases, even for very degraded conditions at endgame handover. Nevertheless, such performance level that was established for a given de¦nition of the divert and ACS (driving idea being to consider existing propulsive system) left some room for improvement of the GNC performance, only time-consuming handmade settings of the GNC data set having been done.
Limiting the analysis to the medium to high thrust divert KI and to each endgame scenario (from very optimistic to very pessimistic conditions at endgame handover), it was then decided to replace the classic handmade setting of the GNC data set (15 parameters to tune) by an optimization process relying on GA. As usual with such optimization method, the most challenging aspect is the de¦nition of the objective function and the GA parameters (number of chromosomes, individuals, epochs, mutation, reinsertion, selection, etc. ). But once this preliminary step is completed after some iterations, the optimization allows an improvement of the GNC performance in addition of shortening the setting phase. Discarding the possible reuse of existing DACS solution that may be undersized for some degraded endgame conditions and considering 5 additional genes, it is possible to perform a coupled GNC-DACS optimization which yields a much larger improvement of the performance.
Study design We will conduct hospital-based evaluative research to evaluate the PPA intervention. We will use the "Theory driven evaluation"  [32] , which is recommended for evaluation of interventions that are not under the control of the evaluator-as is the case for PPA. The "Theory driven evaluation" states that the theory of the intervention must be elucidated in order to evaluate whether the results obtained can be explained by the intervention, or are due to other factors. The study design is a case study, the case being PPA intervention. We will use mixed methods  [33]  of research, primarily quantitative, with qualitative components integrated into the data collection and data analysis  [34]. We plan to develop both the quantitative and the qualitative components in two phases. In the first phase, we will evaluate the degree of implementation and the effect of the intervention in a sample of hospitals.
Theoretical and practical training of research staff We developed electronic forms using the application REDCap  [35]. Electronic questionnaires enable internal reviews, decreasing the number of typing and filing errors, such as blank or non-applicable spaces, as well as filing invalid numbers (such as dates, age, gestational age, etc. ). Moreover, online access to the database allows real-time monitoring of the fieldwork. Interviewers, supervisors and coordinators participated in theoretical and practical training that lasted 5 days and covered supervisor and interviewer duties, the selection of puerperal women, form filing, electronic data submission using the REDCap platform, ethical considerations, conducting of interviews, and confidentiality of information. We devised instruction manuals for the form filing, which also included the definition of all variables.
Data collection At the beginning of fieldwork in each hospital, the supervisor in charge interviewed the hospital director or the head of obstetrics or nursing at the obstetric centre. The interview focused on the structure and processes of the hospital (Additional file 1), taking into account the four PPA driving components. Trained interviewers, 90% of whom were nurse-midwives or midwives, conducted the data collection. They approached all women who were admitted for birth after the beginning of fieldwork and who met the eligibility criteria to participate in the study, until 400 participants were enrolled in each hospital. We included births that took place on weekends and public holidays. We interviewed women face-to-face-at least 6 h after vaginal births and 12 h after CS births-after they had read and signed the free and informed consent form.
This interview included questions on maternal identification; socio-economic condition; previous obstetric history; maternal anthropometric data; prenatal care; illnesses and medication during gestation, labour and birth; and evaluation of care received by woman and newborn (Additional file 2). We also extracted data from medical records of the women and neonates following their discharge from hospital, including from prenatal cards and ultrasound exams. We collected information regarding prenatal care; hospital admission; labour, birth and infant care; and the use of medication and intervention from these records (Additional file 3). In the case of prolonged hospitalisation, we collected the data on the 28th day of infant hospitalisation, and on the 42nd day of the woman's hospitalisation.
In the case of hospital transfer, we collected data from medical records at the hospital from which the puerperal woman and/or the neonate were discharged. In a second interview, we contacted women by telephone between 43 and 60 days after birth, a process that took about 3 min, to obtain information on: a) the motherinfections, haemorrhage, re-hospitalisation, and death; b) the infantrespiratory problems, infections, jaundice, rehospitalisation, breastfeeding, and death (Additional file 4). The same team of interviewers who collected data at the hospital conducted the telephone interviews. Puerperal women answered the questions. In the case of either hospitalisation or death, questions were answered by a close relative or companion. All data collected during interviews and from hospital records were related to two PPA driving components "participation of women and families" and "reorganisation of care".
Missing data We expect a very low proportion of missing data of variables (< 5%) due to the real-time monitoring of the fieldwork, made possible by online electronic questionnaires for data collection. This strategy enabled internal (by the system) and external (by the supervisors) reviews that decreased the number of typing and filing errors, such as blank or non-applicable spaces, as well as the filing of invalid numbers (such as dates, age, gestational age, etc. ). Notwithstanding, missing data will be handled by means of multiple imputation using chained equations. We will apply the Fully Conditional Specification method to obtain five imputed datasets and then adjust our principal models based on these sets using Rubin's rules to combine effectiveness and standard error estimates. For women who did not wish to take part in an interview, we asked permission to consult their hospital record.
This allowed the data collection of relevant variables. To account for the loss in follow-up, such as the loss of contacts or refusal of telephone interview, we will apply a logistic regression model. This will estimate the probability that each woman who took part at the baseline (hospital interview) would answer the telephone interview, using a set of variables that differentiate the groups of respondents and non-respondents. Non-response adjustment factors attempt to compensate for the tendency of women of certain characteristics (such as being younger and of lower education) as being less likely to respond, affecting the probability of response in a specific stratum. We will calculate individual sample weights for the analysis of the follow-up interview. The rationale for applying non-response weights is the assumption that non-respondents would have provided similar answers, on average, to respondents from each stratum and adjustment category.
Qualitative stage In the qualitative stage, we evaluated the PPA intervention in a subsample of eight of the twelve hospitals included in the quantitative stage. The inclusion criteria were: regional location, institutional context, and effect on childbirth. We excluded four hospitals because of similarities in geographical location and management model. The qualitative stage will allow a more comprehensive understanding of how different context factors are linked with the degree of implementation. In addition, this stage should help to explain the influence of success or failure of the implementation of PPA on the CS rate. The first phase of qualitative research took place from July 2017 to October 2017. Our data collection methods consisted of systematic observation and interviews  [42].
The systematic observation plan was based on the following core concepts: organisation of work process; career prospects of health professionals; information register; communication vehicles; regulation and protocol; woman's care flow; and hospital ambience (Additional file 5). Researchers were immersed in the maternity hospital for 5 days. During that time, the observed data was gathered in field notes as a text file. We used the structure and process questionnaire answered by the hospital manager during the quantitative stage as a reference for the researcher responsible for conducting the systematic observation at the beginning of the activity. This allowed for important issues to be explored during this stage. Analytical treatment will follow the guidelines set out by Bardin's thematic content analysis  [43].
Subsequently, we asked interviewees to nominate colleagues who shared similar opinions regarding PPA, until the sample was exhausted and the data saturated. We held individual interviews using a structured guideline. The interview included questions about the decision-making process of the hospital in opting to participate in PPA; implemented strategies; participation of the healthcare team and the women in strategy design; barriers and facilitators; healthcare practice; monitoring; results; and any other relevant contextual factors related to the intervention. Interviews were face-to-face, digitally recorded and held in reserved rooms in the selected hospitals, with only the interviewee present. Each interview lasted approximately 45 min. Finally, we intend to interview 50 women in order to learn about their experiences of the PPA, their participation in care flow, and whether their expectation for birth has been fulfilled (Additional file 7).
This study aims to develop a telemedicine platform for self-motor rehabilitation and remote monitoring by health professionals, in order to enhance recovery in patients after hip replacement. The implementation of such a technology is justified by medical (improvement of the recovery process by the possibility to perform rehabilitation exercises more frequently), economic (reduction of the number of medical appointments and the time patients spend at the hospital), mobility (diminution of the transportation to and from the hospital) and ethics (healthcare democratization and increased empowerment of the patient) purposes. The Kinect camera is used as a Natural User Interface to capture the physical exercises performed at home by the patients. The quality of the movement is evaluated in real-time by an assessment module implemented according to a Hidden-Markov Model approach.
and a will to reduce the costs of the medical care. The concept consists of enabling patients to complete part of a rehabilitation program at home, using a Web application that permits them to share with health professionals the evolution of the recovery process  (Rybarczyk & Vernay, 2016). The current applications are mainly developed for the rehabilitation of cognitive disabilities  (Rybarczyk & Gonçalves, 2016). Due to a complex and relatively expensive implementation, the technological tools for physical therapy are rarer. Nevertheless, the recent emergence of Natural User Interfaces (NUI), such as the Kinect, allows for an affordable system to capture the human movement. Once the threedimensional coordinates of the body joints are automatically extracted by the device, motion data can be processed for applications in different fields, such as gaming  (Gameiro et al.
, 2014) , ergonomics  (Plantard et al. , 2016) , psychology  (Coelho et al. , 2014) , education  (Carrasco et al. , 2013) , health  (Brook et al. , 2014) , among other. This paper presents a Web-based platform for motor tele-rehabilitation applied to patients after hip arthroplasty surgery. This orthopaedic procedure is an excellent case study, because it involves people who need a postoperative functional rehabilitation program to recover strength and joint mobility. The development of a tele-rehabilitation system is justified by the condition of these individuals that makes difficult their transportation to and from the physiotherapist's office. The proposed approach considers two fundamental conditions for the development of a suitable tele-rehabilitation platform. First, the motion capture must be based on a low-cost device, in order to be economically viable.
Second, the platform should automatically detect the correctness of the executed movement to provide the patient with real-time feedback  (Kleine Deters & Rybarczyk, 2018). The development of such a tele-rehabilitation platform involves four steps, which are described in the rest of the manuscript. Section 2 summarizes the main stages of the therapeutic program. Section 3 is a presentation of the Web application that includes backend, frontend, user interfaces and database. Section 4 focuses on the assessment module, which automatically classifies the quality of the movement through a Hidden Markov Model approach. Section 5 consists of a description of an experiment to test the usability of the Website. Finally, the results of the assessment performance and platform usability are discussed in the conclusion of the paper.
| REHABILITATION PROGRAM A systematic literature review performed in MEDLINE, PEDro and ScienceDirect databases shows that the rehabilitation program should be divided into two stages. Each stage is defined by different therapeutic objectives and durations  (Peter et al. , 2014). Then, in stage 2, the transition from early to late Post-Acute Rehabilitation (PAR) depends on the clinical progress of each patient. The first stage is characterized by three aspects. The first one is an education of the patients, in order to prepare them to manage their rehabilitation at home  (Jäppinen et al. , 2016). The second one consists of completing a cryotherapy that aims to reduce pain and contain blood lost  (Ni et al. , 2015). Finally, an active workout involves a repetition in supine position of the exercises as follows: respiration, core stability training, and lower limb movement  (Peter et al.
ARCHITECTURE The rehabilitation platform is based on a clientserver architecture (Figure  1 ). The server side is divided into two parts, which are the application server and the database server. The functionalities of the application server depend on the type of user. The patients can access to the therapeutic exercises, which are presented as a game. A feedback of their performance is automatically displayed thanks to the movement assessment module. The health professionals (physiotherapist and physician) can carry out a remote monitoring of the patient's performance and update the rehabilitation program. The database stores an heterogenous source of information such as: demo videos of the exercises; joint coordinates of the performed movements; questions/responses to medical questionnaires; and exchange of messages between the stakeholders.
BACKEND AND FRONTEND The Website is based on the Django technology, a Web framework written in Python. For an effective development, Django CMS, the content management system version of Django, is used in order to simplify the implementation of the Web application. Node. js, a Javascript run-time environment, is used as a gateway between the Kinect and the Django Website to manage the motion capture data flow. It allows the creation of an application coded in JavaScript and HTML and a communication with the server. The backend manages the data from the Kinect and generate an HTML page for the browser. Node. js is chosen for its wide range of modules and, more specifically, the library Kinect2 which simplifies the communication with the Kinect. The library http is used to start the application.
Once the exercise selected, a demonstration video of the movement to perform is displayed on the screen (Figure  4 ). The movement recoding starts when the hand of the user's avatar is inside the red square of the patient's visual interface (see Figure  2 ). The 3D joint coordinates received from the Kinect and the assessment associated are saved into the database. At the end of the exercise, the patients can review their movements and have a detailed feedback on their performance. The same questionnaire as previously mentioned is asked to the user at the end of a practical session, in order to provide the therapist with information on the state of the patient after performing the physical exercises. Several functionalities are available for the health professionals. First, they have to create an account if they are not yet registered.