dataesr/scientific-paragraphs-categorization huggingface dataset:
Conceptual data model The goal of a document-oriented database is the representation of more or less complex information that satisfies the needs of flexibility, richness of structure, etc. Modeling of a big data-coding warehouse is a function of the hospital stay's structuring elements. The hospital stay is a document represented by a pair (key, value) and has a tree-shaped structure. The stay entity is the root of the tree. The entities (key) and values for coming up with a conceptual model (Figure  1 ) were designed from the base of HSR. The main entities were defined as an object class that consisted of stay, patient, movement of the patient between the different clinical unit entities, and terminology. The medical benefit entity is a sub-document of stay entities in which the related act and diagnosis are sub-entities.
Evaluation To evaluate our model, framework MongoDB was deployed in only one data node. The system resources used were (8Go RAM, processor i5-4 heats 2 Terabytes hard disk). MongoDB is a key-value system using document-oriented storage. The volume of data received by one node for the test is 1. 6 million documents representing 1 year of the hospital stays. The volume of documents can be worm at 40 times the initial volume. These data are divided into two major groups: encoded and rejected data. The interest of the rejected data is to expand the database, introduce the noise, and have a case of associations of diagnostic code to avoid. Two evaluations were performed. The first one was performed by calculating the model performance (multi-criteria request (Query #2), mono's (Query #1) elapsed time), and the second one was performed by calculating the precision and recall of the model.
The initial step was to create two main groups of requests arranged by dimensionality and selectivity validated by the business process, which can be used in a real context. Dimensionality is the value of different keys of the entities ("typediag" and/or "typeact"). Selectivity refers to the degree of data elimination through an aggregate function on the search attribute (code = "CCMA code or ICD-10 code"). Query #1 is expressed as follows: for a given diagnosis or act codes, find all associated code and their corresponding type (typediag, typeact). Three functional use cases were used. The first one concerns the specific case of Z codes as part of the entire ICD-10 code set. Z codes are diagnosis codes used for situations where patients Do not have a known disorder and required a related code to precise the real medical.
Results The document-oriented model of the big data-coding warehouse was implemented in the MongoDB database. Ten separate single-criteria queries were executed with an elapsed time between 75 and 90 milliseconds (ms), while an elapsed time between 80 and 110 ms was obtained during the execution of 10 separate multi-criteria queries. Query #1 requests the data warehouse to display all association codes, in which the diagnosis code is equal to "Z092" in the ICD-10 coding system, corresponding to "the pharmacotherapy for other conditions. " Associated codes obtained are the associated diagnostic code "E780" used to code "pure hypercholesterolemia" and the act code "EBQM002" used to code "Doppler ultrasonography of extracranial cervicocephalic arteries, with Doppler ultrasound of lower extremity arteries.
Query #1 and Query #2 were used to compute the precision and the robustness of the model (Table  3 ). Based on the observation, the least selective (more lines selected) queries required a long execution time. According to our evaluation, we observed that the system is bijective and corresponds to the reality of the coding of clinical activity of HEGP. This suggests that we can, from the document-oriented model, recover the initial encoding data and vice versa. In this regard, it is apparent that everything that has been set in the big data warehouse corresponds to the reality of the patient. The data warehouse gives the possibility of being more aware of the coding performed in the previous year. Based on the requests defined above and executed using the learn/control database, Table  1  shows the results of the evaluation provided by the big data.
Discussion and conclusion This study investigated the process for implementation of a big data-coding warehouse for coding support in a document-oriented NoSQL system. We observed that flexibility is the particularity of this model as it allows inserting redundancy into the database. A stay with four ASD codes and one PD code is split into four documents. The duplicated line is high when there are more associated diagnoses and medical acts. Therefore, presenting one entity is easier in the entire document. The case of "stay" with only a primary diagnosis, one or more associated diagnosis, and/or without a medical act can be easily inserted in the database without the need to implement a generic code to replace the missing one. In most cases, the addition of a generic code is meant to let the physician understand that there is no need of associating a diagnostic code used with the medical act.
The rejected data were used to show that, in the optimization process of coding, we learn about as many accepted cases as rejected cases. The major interest in building the coding aid data warehouse is to use the huge volumes of coding information from a large number of hospitals because it is more exhaustive. The model that was implemented allows obtaining an optimal combination of codes (diagnosis, acts) for a given reason for care. Because of the way they are structured, relational databases usually scale vertically-a single server has to host the entire database to ensure reliability and continuous availability of data. This gets expensive quickly, places limits on scale, and creates a relatively small number of failure points for database infrastructure. It's why we propose our model to solve this problem.
Indeed, our coding aid data warehouse scales horizontally-several servers host the entire database, allow grouping of all the relevant data for the diagnosis and medical coding in a generic way, to enrich the coding data by crossing the coding information from other hospital sources and to allow for easier exploration of the coding code associations. It's a system that is subject to expertise. This fact Does not remove the richness of Clinical Data Warehouse (CDW). Our contribution consists of building a specific CDW-based document to propose an "in silico" test framework to enhance the efficacy of algorithms used to optimize coding as an example of algorithm based on manual decision-making paper  [15]  and various natural language processing (NLP) tools associated with the EHR in-/outpatient summary reports  [16].
Skyopener's goal is testing the use of RPAS for civilian applications to contribute to the roadmap for the integration of civil RPAS into non-segregated airspace. In this context, the use of EGNOS (European Geostationary Navigation Overlay System) and Galileo is a major opportunity to provide enhanced navigation services in terms of integrity and positioning accuracy. EGNOS is the first pan-European satellite navigation system. It augments the US GPS satellite navigation system and makes it suitable for safety-critical applications such as flying aircraft or navigating ships through narrow channels. Sometimes called the 'European GPS', Galileo provides accurate positioning and timing information using data from its 18 satellites. When the constellation becomes fully operational in 2020, it will provide high precision and reliability, and when used together with GPS it will allow receivers to determine position at few centimeter accuracy  (ESA, 2019).
Integration of Satellite Communication to increase the reliability of the RPAS communication systems. Radio communication systems on board RPAS usually provide 80-90% availability. SatCom systems provide much better availability, typically 99%. Skyopener is testing a system that will significantly enhance the reliability and the availability of the CNPC (Control of Non-Payload Communication). It will combine radio and satellite link systems with hot redundancy, thus increasing availability up to 99. 9%. Testing a UTM system dedicated to RPA VLL operations with interactive surveillance feature. Skyopener is testing the UTM system developed by one of its partners (Unifly) at U-Space level 2, and partly level 3, that allows management of flight authorization procedures and real-time monitoring of air traffic from the ground station, and will also demonstrate the capability for the ANSP to receive data from the RPAS to show the aircraft's position in real time.
Improvement of information processing and presentation on RPAS to reduce the risk of human error. Skyopener is testing the user interface at the level of the Remote Pilot in the RPAS, developing a more user-friendly interface for the pilot. Testing the RPAS mission payload and software package for e-TOD services and linear surveillance operations beyond line of sight. Skyopener is testing an integrated mission payload that includes remote sensing (LiDAR), a high definition visiblespectrum camera, an IR camera, and direct geo-referencing with satellite navigation. The goal is to provide accurate information about obstacles within the e-TOD (Electronic Terrain and Obstacle data) services geographical boundaries, as well as objects (trees, temporary structures, etc. ) that intrude into linear infrastructure corridors, and emergency or post-emergency surveys (landslides, flooding, collapse, short-circuits, etc.
Services, payload and demo applications While the focus of Skyopener is on demonstrating a complete system and environment for BVLOS operations, based on secure and reliable communication, and on integration with UTM, the demo flights of the project will also demonstrate feasibility and economic advantages of long-range/long-endurance unmanned missions. To this purpose, two significant applications shall be implemented, namely corridor mapping (survey of a stretch of high-voltage powerline), cfr. Matikainen et al. , 2016 , and airport area survey for e-TOD database population (survey of obstacles -ICAO, 2018). Based on requirements of such applications, a suitable set of sensors was selected, including:  a Yellowscan Surveyor Ultra LiDAR, based on a Velodyne VLP-32C sensor, capable of obtaining, with the typical flight profile employed (80km/h, 70m AGL), about 30 points/m 2 on ground, with two echos and 0.
g. when a direct visual check is required by the operator. The IR camera is mounted for the powerline survey application, in order to detect hotspots denoting possible failures. Optics for the cameras were chosen considering flight profile requirements (minimum safe operating speed, minimum safe altitude) and LiDAR characteristics and requirements, and optimizing the trade-off between swath and definition, also considering that some data are more relevant at shorter distance from the sensor, in particular power lines are closer to the RPAS during flight than ground. Exploitation of data obtained in the demo surveys is performed by DIET, La Sapienza University of Rome, Italy (www. diet. uniroma1. it) with the support of Oben srl, Sassari, Italy (www. oben. it). The objective of corridor mapping for powerlines, is basically identifying any object (normally trees), that intrudes into a given distance from wires, depending on local regulations and practice, and on powerline voltage.
DIET has developed a proprietary algorithm for automatic pylons and wires recognition and modeling, and detection of obstacles within a given distance from the wire (Fig. 3 ), tested on data provided by Oben and by Groupe, Fribourg, Switzerland, i. e. the Swiss energy distribution utility. Commercial solutions were also assessed. The project demo shall be performed in Switzerland in collaboration with Group-e. For the e-TOD exercise, the object is detecting and accurately mapping any object rising higher than given thresholds in the airport area and its neighborhood. Such survey is mandatory, and updated periodically. Drone-based survey is especially useful in the inner areas inside and around the airport, where detail requirements are higher, and considerable savings may be obtained thanks to automation of the process, and operation within the airport during down times of air traffic.
Methods: The QUANARIE study is an interventional, prospective, multicentre trial. Patients diagnosed with mRCC initiating sunitinib or pazopanib treatment will be invited to complete the EORTC QLQ-C30 questionnaire, nine additional questions from the EORTC items library, and the EuroQoL EQ-5D, prior to each visit with the physician. Questionnaires will be completed by patients using tablets and/or computer terminals via the e-PRO software. The physician will have real-time access to a visual summary of the HRQoL evaluation. The primary objective is to assess the proportion of patients having good compliance with Routine Electronic Monitoring of HRQoL (REMOQOL) during the first 12 months. Physicians' satisfaction with REMOQOL will be assessed as a secondary objective. We hypothesise that 80% of patients having good compliance with REMOQOL would be meaningful.
Key trial objectives The primary objective of the QUANARIE study is to evaluate the proportion of patients having good compliance with REMOQOL during the first 12 months of treatment. The key secondary objectives are the following: -To report the number of patients to whom the study was proposed divided by the number of patients initiating sunitinib or pazopanib treatment in each centre; -To report the number of patients who agreed to participate in this study as compared with the number of refusals divided by the total number, by centre and by molecule type (sunitinib or pazopanib); -To estimate the rate of missing data defined as the numbers of completely or partially filled items by HRQoL questionnaire and for each measurement time. We will consider a questionnaire as filled out if at least 10 items are completed; -To Describe the toxicities collected by the physician according to the NCI CTCAE V4 and reported by the patient according to the eight symptomatic dimensions of the QLC-C30 questionnaire and the six supplementary symptomatic dimensions; The exploratory objectives are the following: -To estimate OS, defined as the time from the start of treatment to death; -To estimate PFS, defined as the time from the start of treatment to progression or death; -To estimate Time to deterioration of HRQoL (TTD): TTD will be defined as the time from the start of treatment to the first significant deterioration (either increase or decrease depending of the scale) in a HRQoL score as compared with the baseline score with no further significant improvement as compared to the baseline score, or death  [29, 30].
Routine electronic monitoring of HRQoL duration Patients clinical and HRQoL data will be monitored for two years from the initiation of sunitinib or pazopanib. Patients will participate in REMOQOL until sunitinib or pazopanib is permanently discontinued due to toxicity, progression or death, and also according to a decision by the investigator, non-compliance with the protocol or loss of follow-up. All patients included in this study will be able to leave at any time, without having to justify the reason. After treatment discontinuation, all living patients can choose to continue REMOQOL during the subsequent therapy.
Follow-up assessments From treatment initiation, clinical, radiological, and biological data will be prospectively collected. Patients will fill out electronic EORTC QLQ-C30 and Euro-QoL EQ-5D questionnaires at each physician visit, regardless of the interval between visits. Patients will be monitored for two years from the initiation of sunitinib or pazopanib treatment, according to the schedule of usual medical care. Following cancer progression, the physician and patient have the choice to continue REMOQOL for the subsequent therapy. Any subsequent treatment will be recorded with the date and mode of progression.
0%, is α = 0. 045%. The probability of concluding inefficacy, when p = 80. 0%, is β = 9. 9%. Analysis of secondary endpoints Secondary endpoints will be described using the mean and standard deviation for continuous variables and percentages for qualitative variables. The rate of missing data at each HRQoL measurement time will be reported. HRQoL results within treatment groups and in all evaluable patients will be assessed using descriptive statistics. The adverse events occurring during the study, according to the NCI CTCAE V4, will be reported by group. There will be no comparative analysis between the sunitinib and pazopanib groups. Survival outcome (OS, PFS, TTD) will be estimated using the Kaplan-Meier method. Median survival will be described with confidence intervals (one-sided statistical test for significance (α: 5%)) in each of the treatment groups and in all evaluable patients.
Since the HAMSIQ questionnaire is not freely available, we used the EORTC Items Library to create new questionnaires based on the database of items used in fully and partially validated EORTC Quality of Life questionnaires. We added supplementary items to the QLQ C30 questionnaire that target the most frequent adverse events reported in sunitinib and pazopanib trials. The integration of HRQoL assessment into daily clinical care faces multiple barriers such as material and IT limitations and implementation of an intervention that may increase the workload of already busy physicians or a training physician who is not familiar with interpreting HRQoL measures  [23, [43] [44] [45] [46]. The QUANARIE trial is part of a larger project led by the Methodological and Quality of Life Unit in Oncology (UMQVC) with the aim of integrating HRQoL monitoring as a complementary tool into daily clinical practice.
Two other studies are currently recruiting patients: the GYNEQOL-Pilot [NCT02864797] and the QOLIBRY trial [NCT02844608]. To date, more than 360 patients treated for different types of cancer (gynecological, colorectal, lung, breast, and kidney) with different types of treatment (chemotherapy, TKI, and immunotherapy) have been included in these three clinical trials. The results of the QUANARIE study will demonstrate whether REMOQOL is feasible on a large scale and whether patients are receptive to this new practice. This study will also show how real-time multidimensional evaluation of patient perception can help physicians in their daily practice and how they can use it in conjunction with other clinical information to manage patient care. GM, JF, SPB, AP, IE, AM, DV, AA, and ATV. Authors who have given final approval of the version to be published.
An internal charging (IC) analysis of the active measurement box of electrostatic risk (AMBER) instrument onboard Jason-3 in polar earth orbit (PEO) has been performed with a new dedicated modeling chain. The ExtendeD Gdml Editor (EDGE) application is in charge of the definition of the computer-aided design (CAD) model. The Modelling Of Radiation (MoORa) front end helps to configure, control, and postprocess the ESA/GRAS Monte Carlo simulation based on Geant4 to model the transport of space radiation environment. Finally, using the radiation results, the IC analysis, including internal electric fields and risk of discharge, is performed with the Spacecraft Plasma Interaction Software (SPIS)-IC 3-D time-dependent simulation code.
II. PRESENTATION OF THE MODELING CHAIN FOR INTERNAL CHARGING STUDY The proposed modeling chain used for IC analysis is composed of several steps, as described in Fig. 2 , only partially handled, or through different tools, in previous IC works. To evaluate the IC risk, 1-D modeling tools, like DICTAT  [5]  or NUMIT 2  [6] , have been widely used. However, these models imply to reduce the studied systems to a one-dimension layer approximation, which is not relevant for most realistic systems. Moreover, these tools estimate the dose and charge deposition rates through analytical models or by fitting precalculated profiles, constituting another strong approximation, especially for electrons. Spacecraft Plasma Interaction Software (SPIS)-IC  [7]  was the first answer to address the 3-D dynamic evolutions of charges in dielectrics, but the primary charges and dose depositions remained delegated to external tools.
3) Radiation Transport Effects Analysis: There are two major approaches to model the radiation transport effects on devices. a) Sector shielding analysis: In an engineering context, the sector shielding analysis is a basic approach usually used to evaluate the dose on a device. This approach is based on a geometric ray-tracing method to compute the equivalent shielding in each direction around one specific point. Combined with a dose depth curve, it is then possible to compute the dose in a specific point. Such an approach presents the major advantage to be simple and fast. In the present modeling chain (see Fig. 2 ), the sector shielding analysis approach is addressed by tools like Sector Shielding Analysis Module (SSAM), available inside EDGE. However, depending on the application cases, the sector shielding analysis may not be accurate enough, especially for an IC analysis downstream.
In the space domain, the open code ESA/GRAS, based on Geant4, has become a standard  [17] ,  [18]  and, for this reason, has been integrated into the present chain. Until now, GRAS can only be used with command-line using a control script and lacks a GUI for nonexperts. In order to help in the use of GRAS, the MoORa tool  [19]  has been used. It is a GUI application, able to set up GRAS inputs, monitor the simulation, and postprocess the results in 3-D and in y = f (x) type plots. In this modeling chain, the sector shielding analysis is used to validate the parameter inputs for Monte Carlo simulation and GRAS/MoORa is used to compute the dose and charge deposition rates with better physic results regarding the sector shielding analysis  [15].
B. Internal Charging Analysis After the radiation transport analysis, the last part of the modeling chain presented in Fig. 2  is the IC analysis. This second part uses as inputs the 3-D maps of the dose and charge deposition rates resulting from the radiation transport simulation. Since 2001, Spacecraft Plasma Interaction Software (SPIS) is widely used for surface charging analysis  [20] ,  [21]. Open-source and available through the SPINE community  [22] , SPIS is based on an electrostatic 3-D plasma model, called SPIS-NUM, and a volume unstructured mesh. Over the span of several projects, ESA has encouraged the development of new numerical models to compute IC effects. These efforts, added to ONERA's internal research and development activities, have recently led to a dedicated branch of the SPIS software, called SPIS-IC, for IC simulations.
Input parameters for the radiation transport analysis (i. e. , energy spectrum, the geometry of space environment sources, and angular distribution) are fully defined through the MoORa GUI for GRAS, as illustrated in Fig. 7. The source geometry used for the GRAS simulation is the surface of a sphere with a radius of 13 cm. The electrons are launched inward the sphere and with a cosine law angular distribution to reproduce an isotropic flux in the whole computational domain  [26]. To reduce the simulation time of the direct Monte Carlo analysis, the input particle spectrum has been biased to only consider incoming electrons with enough energy to pass through the AMBER casing. A sector shielding analysis has been performed by the SSAM module of EDGE to determine the mean thickness of equivalent aluminum in each direction around the studied card.