dataesr/scientific-paragraphs-categorization huggingface dataset:
Study Design This study was an ancillary study from the FAMPISEP project (NCT02849782). This project was a single-center phase 4 trial. It was conducted between April 2014 and March 2019 at the University Hospital of Besançon (France) to investigate the effects of fampridine Sensors 2023, 23, 3407 3 of 14  (4-aminopyridine)  in PwMS with walking disorders. However, in this ancillary study, Fampridine action was not evaluated. The protocol was governed by French legislation concerning interventional biomedical research and had been submitted to the National Ethics Committee (#13/405). The study was approved by the French Health Products Safety Agency (#2013-A002305-56). After providing the participant with information about the procedure and ensuring this information was adequately understood, written informed consent was obtained from all participants of this study.
Walking Evaluation Participants performed two 6MWTs (test and retest) one week apart in our laboratory. Both tests were performed following the protocol of the American Thoracic Society in both groups  [21] , with the exception that the participants performed the test on a 24 m circuit rather than the recommended 32 m circuit due to local architectural constraints. During the 6MWT, foot contacts were recorded by a 6. 10 m by 0. 61 m GAITRite electronic walkway (CIR Systems, Franklin, NJ, USA) (Figure  1A ). At the same time, the software PKMAS (ProtoKinetics, Havertown, PA, USA) was used to process and export the following ST variables for each walkway passage: velocity (m/s), cadence (steps/min), stride length (m), stride width (m), and double support time (% of the gait cycle) (Figure  1B ).
The brightness measurements were done using a calibrated photodiode. 38  The electroluminescence (EL) spectra were recorded with an Ocean Optics USB2000 spectrometer. For single-crystal X-ray analysis, a single crystal of Cz4FS was grown from acetone solution by the slow diffusion method using cyclohexane as an antisolvent. The crystallographic analysis was performed employing the XtaLAB mini diffractometer (Rigaku) with a graphite-monochromated Mo Kα (λ = 0. 71075 Å) X-ray source at room temperature (295 K). The structure of Cz4FS was solved with the ShelXT structure solution program using Intrinsic Phasing and ■ RESULTS AND DISCUSSION Photophysical Properties. In the first stage, we investigated the photophysical properties of Cz4FS. mCP was used as a matrix component to investigate energy transfer efficiency from the host material to the emitter Cz4FS.
The IP PE value of Cz4FS agrees with its ionization potential (5. 91 eV) previously measured by cyclic voltammetry. 35  The value of the electron affinity (EA PE ) of Cz4FS was calculated by the formula EA PE = IP PE -1. 37 × Eg opt ,  41  where Eg opt is the optical band gap (2. 37 eV) taken from the low-energy onset of absorption spectra of the neat film of Cz4FS (Figure  1a ). The different energy positions of the HOMO (-5. 1 eV) and LUMO (-2 eV) are for 4,4′,4″tris[(3-methyl phenyl)phenylamino]triphenylamine (m-MTDATA). Such HOMO-HOMO and LUMO-LUMO barriers of 0. 87 and 0. 72 eV between the donor m-MTDATA and acceptor Cz4FS provide the ability to form an exciplex exhibiting TADF (Figure  3a ). In addition, Cz4FS is characterized by a high triplet level of 3. 0 eV, which is practically the same as that of mCP (Figure  1c, d ).
Statistical analyses All data analyses presented below were performed using different linear mixed models run with the MIXED procedure of SAS 9. 4 (SAS Institute, Cary, NC), and variance components were evaluated using the restricted maximum likelihood (REML) procedure. Normality of residuals was evaluated and confirmed graphically as recommended by  Kozak and Piepho (2018). For all these analyses, data considered for traits related to yield (CY, RS, FIB and EI) were restricted to data collected during the three years following the initial spread of orange rust throughout Reunion, i. e. data collected for the 2019, 2020 and 2021 harvest crops. Consequently, two, three, three and two crop-cycles were analyzed for yield traits for S7, S8, S9 and S10 series, respectively.
Comparison of locations for orange rust infection The five locations used for the field trials corresponded to different agronomic and/or climatic ecologies of sugarcane production. To test for possible mean differences of natural rust infections between locations, we selected ORS data of the 36 varieties that were present in all locations in 2021 (n = 16, 7, 13 in the S8, S9 and S10 series respectively). This across-location balanced set of data was then analyzed using the following model: where Y ijls was the ORS value of a shared genotype i related to series s observed in replicate j at location l, S s the main random effect of series s, L l the main fixed effect of location l, R j(s,l) the random effect of replication j in the trial of series s in location l, GL il(s) the random effect of genotype × location interaction in series s and other terms were the same as in model 1.
Acknowledgements The authors wish to thank the technical staff members of eRcane (F. Poiny,  D. Vaitilingom ,  A. Corre ,  G. Morel ,  C. Sautron , and  E. Sautron ) for the cultural management of the field experiments and the laboratory analyses of sugarcane quality. We are very grateful to an anonymous reviewer who raised questions which helped to improve the manuscript.
Simulation Results In this section, a series of numerical simulations are conducted to validate the proposed control protocols. The simulations are performed using the PX4 Autopilot software-in-theloop (SITL) on the Gazebo simulator and the Mavros controller package integrated with the Robot Operating System (ROS)  [34, 35]. This simulation platform provides a highly realistic environment that closely emulates the behaviors and characteristics of real quadcopters equipped with the PX4 flight controller. The PX4 flight controller incorporates control input saturation in the low-level actuator controllers. The simulations are run on a computer with a Linux-based operating system, a 3. 2-GHz processor, and 16-GB RAM. Figure  2  displays a screenshot of the simulator. Two scenarios are examined in this simulation: coverage control with fixed topology and coverage control with switching topology.
, 2016; Inoue et al. , 2016; Kulawik et al. , 2016; Schepers et al. , 2016; Liang et al. , 2017a; Ohyama et al. , 2017; Velazco et al. , 2019) , TanSat  (Yang et al. , 2020) , the Orbiting Carbon Observatories (OCO-2 and OCO-3, e. g. ,  Liang et al. , 2017a, b; Wunch et al. , 2017; Kiel et al. , 2019) , and the Tropospheric Monitoring Instrument (TROPOMI, e. g. ,  Borsdorff et al. , 2019; Schneising et al. , 2019; Lorente et al. , 2021). The TCCON instruments are solar-viewing Bruker 125HR (high-resolution) Fourier transform infrared (FT-IR) spectrometers that record an interferogram once every few minutes. These interferograms are processed by the GGG software package to provide column average DMFs. Once the interferograms are converted to spectra, the core routine of GGG calculates the expected spectra from a forward model based on a custom linelist and a priori profiles of the absorbing gases with absorption lines in the fitting window.
The PV fields are of particular importance because they allow the GGG2020 priors to better represent latitudinal transport in the stratosphere, thus improving the stratospheric trace gas profiles. However, GEOS FP-IT data are only available from the year 2000 on, meaning that the GGG package retains the capability to use NCEP meteorology as input data. This capability has been further developed since GGG2014, though we do not include those changes in this paper. Here, we describe the algorithm used to compute the prior profiles of CO 2 , N 2 O, CH 4 , HF, CO, H 2 O, and O 3 for GGG2020. The algorithm is named "ginput" and is available through GitHub  (Laughner, 2022). We begin in this paper by describing the core parts of the algorithm that are common across many of the gases (Sect. 2). We then address elements specific to individual gases in Sect.
14a ). This results from removing a small but clear negative bias throughout the troposphere arising from an underestimate of the CO 2 secular growth rate in GGG2014. Using the MLO and SMO data eliminates that as a source of uncertainty for profiles before 2019 (2019 is the first year that the MLO and SMO trend is extrapolated for GGG2020 as we chose to use a static file to avoid the complications of updating the input data in a reliable, reproducible manner, as discussed in Sect. 2). In the stratosphere (above 200 hPa), the improved representation of stratosphere dynamics (Sect. 2. 3) better captures the gradient of CO 2 in the lower stratosphere, reducing the previous overestimate of lower stratospheric CO 2 in the GGG2014 priors. The CO 2 RMSE for the GGG2020 priors is still greater near the surface than at higher altitudes.
This may be due to the simplified seasonal cycle (Sect. 2. 2). Comparing the priors to ATom and HIPPO observations in different seasons (Fig. S8 ) shows large differences near the Northern Hemisphere surface in spring and summer. As the seasonal cycle has latitudinal dependence, revising its parameterization will require adjustment to the distance function (Eq. 1) and the α https://doi. org/10. 5194/amt-16-1121-2023 Atmos. Meas. Tech. , 16, 1121-1146, 2023   S1  and S2. In each panel, both the GGG2020 and GGG2014 priors' RMSEs are shown. The number of profiles contributing to each panel is printed above the panel. FMI/RUG Sodankylä AirCore data above 20 km altitude are not included due to anomalously high mixing ratios in CO. CO 2 and CH 4 data above 20 km are also excluded for consistency.
and β coefficients (Table  3 ). This area will be revisited in a future version of the GGG priors. CH 4 shows a small improvement in RMSE throughout most of the troposphere (Fig. 14b , 800 to 200 hPa). Above 200 hPa, the RMSE shows a greater improvement, again due to the improved representation of stratospheric dynamics. However, near the surface (below 800 hPa) the RMSE increases somewhat in the GGG2020 priors compared to the GGG2014 priors. This increase in RMSE is driven by nearsurface CH 4 emissions not accounted for in the priors. Figure  15a  shows differences in the CH 4 priors versus AirCore data (which has frequent sampling of areas with high emissions), colored by which TCCON site the prior represents. The bias in CH 4 below 800 hPa is clearly due to underestimated CH 4 in the Lamont, OK, profiles.
2 ppm for CO 2 , 13 ppb for CH 4 , and 1 ppb for CO. For the standard TC-CON CO 2 , CH 4 , and experimental lCO 2 (CO 2 with stronger sensitivity to the surface) products the new priors produce an overall improvement compared to the in situ column averages. The CO and experimental wCO 2 (stronger sensitivity to the upper atmosphere) products compare slightly worse overall to in situ data using the new priors. For CO, this is likely due to overestimated anthropogenic CO emissions in the source model. Finding a way to correct this, either by using a different model run or by applying a geographically varying correction, will be a high priority for the next version of the TCCON priors. The reason for the slight worsening of the wCO 2 retrievals is not yet clear. An important guiding principle for the GGG2020 priors algorithm was to limit dependence on ongoing measurements or models as much as possible.
Doing so means that retrievals using these priors produce data that can be treated as statistically independent with most existing and future measurements and models. Only CO 2 , CH 4 , and N 2 O measurements from the Mauna Loa and American Samoa observatories and CO from the GEOS FP-IT model system are directly ingested, meaning that direct comparisons of TCCON GGG2020 or OCO-2/3 data with these data sources would not be fully independent. As latitudinal gradients from the HIPPO and ATom campaigns and correlations of N 2 O, CH 4 , and HF from the ACE-FTS instrument are used as well, comparisons between TCCON or OCO-2/3 and HIPPO, ATom, or ACE-FTS data should note that correlations of these spe- There are still clear areas for improvement. The age of air parameterization used in the troposphere is known to underestimate the age of air compared to SF 6 measurements, and anthropogenic emissions are not accounted for except in the CO priors.
Addressing these issues is planned for a future version of GGG; at that time, we will evaluate whether incorporating additional data from measurements or models produces worthwhile improvements in the priors' accuracy. Nevertheless, these new priors represent a significant improvement for the GGG2020 TCCON retrieval. Atmospheric Data Integration Project, 2020) were obtained from https://www. esrl. noaa. gov/gmd/ccgg/obspack/ (last access: 23 October 2020). NOAA AirCore data (v20201223) were provided by Bianca Baier and Colm Sweeney. Sodankylä AirCore data were provided by Huilin Chen and Rigel Kivi. Nicosia AirCore data were provided by Pierre-Yves Quehe. The CMAM model data used in the CO priors were downloaded from https: //climate-modelling. canada. ca/climatemodeldata/cmam/output/ CMAM/CMAM30-SD/mon/atmosChem/vmrco/index.
shtml (last access: 24 July 2019; Canadian Centre for Climate Modeling and Analysis, 2019). ACE-FTS v3. 6 data are available from https://doi. org/10. 20383/102. 0495 (Bernath et al. , 2021) ; access to these products requires registration. GEOS FP-IT data were downloaded from the Goddard Earth Sciences Data Information Services Center (GES-DISC) with a data subscription (see https://gmao. gsfc. nasa. gov/GMAO_products/, last access: 1 March 2023; GES-DISC, 2023;  Lucchesi, 2015). CAMS chemical forecast data were downloaded from https://ads. atmosphere. copernicus. eu/cdsapp#!/dataset/ cams-global-atmospheric-composition-forecasts?tab=overview (last access: 28 November 2022, CAMS, 202220). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/amt-16-1121-2023-supplement.
Code and data availability. The code to generate GGG2020 prior profiles is the "ginput" package, available from GitHub (https://doi. org/10. 22002/D1. 20285; Laughner, 2022). GGG2020 TCCON data use ginput version 1. 0. 6, which is scientifically identical to the publicly archived 1. 0. 7 version (https://doi. org/10. 22002/D1. 1880; Laughner et al. , 2021). HIPPO data, provided by NCAR/EOL under the sponsorship of the National Science Foundation (https://data. eol. ucar. edu/, last access: 19 June 2019), were obtained from https://doi. org/10. 3334/CDIAC/HIPPO_010 (Wofsy et al. , 2017). ATom data were obtained from https://doi. org/10. 3334/ORNLDAAC/1925 (Wofsy et al. , 2021). Obspack aircraft data (CO 2 GLOBALVIEWplus v5: https://doi. org/10. 25925/20190812, Cooperative Global Atmospheric Data Integration Project, 2019; and CH 4 GLOBALVIEWplus v2: https://doi.
Software development process defines a scheme to structure and manage the various aspects of the development which are requirements elicitation, design, implementation, verification, maintenance. Software development processes are grouped into Structured Processes such as the waterfall or V-model and Agile Processes such as Scrum  [46]. With Structured Processes, specifications are fixed in advance and the software is developed and delivered thereafter. In the health domain, empirical research demonstrated that software developers experience difficulties when following Structured Processes  [31, 38, 44, 65]. In the particular case of epidemiological surveillance of diseases like Covid-19, it can be very difficult to capture all the requirements at such an early stage of a project. In addition, any change introduced once a project is underway can create cost and budget overruns.
Thus, our main contributions are: • to provide empirical data that contributes to giving a scientific answer to the following research question: how to combine Scrum and MDA to develop and maintain epidemiological surveillance software? • provides a framework based on MDA that can be used for the rapid development of any epidemiological surveillance system. The remainder of the paper is organized as follows: Section II presents the research context; Section III presents the MDA approach and Scrum Process; Sections IV and V present respectively the research design and the research results; Sections VI and VII present respectively the threats to validity and related works. We end with the conclusion in Section VIII.
II RESEARCH CONTEXT The EPICAM  1  (Epidemiology in Cameroon) project aims to improve Cameroon's health system by strengthening the Health Information System. Given the problem of failed software developed in the health domain  [44, 47] , the project of academic (University of Yaounde I in Cameroon 2  ), clinical (fifty hospitals in Cameroon), epidemiological (Centre Pasteur du Cameroun  3  -Epidemiology and Public Health department and NTCP), and industrial (MEDES  4  in France) partners were set-up. It aims to provide methodologies, methods, models and tools for improving the development of epidemiological surveillance of infectious diseases. Firstly, a pilot project consisting to set-up the epidemiological surveillance of Tuberculosis (TB) was put in place. In effect, given the burden of TB in Cameroon, the government has put in place the National Tuberculosis Control Program (NTCP).
Thereafter, the NTCP has set-up epidemiological surveillance. Even if some tools like Microsoft Excel are used, this system is mainly manually managed. Given the limit of the manual management  [13, 62] , the NTCP expressed the need for an electronic system. Thus, the purpose of the pilot project was to describe the methodologies, methods and tools for the development of epidemiological surveillance of TB and to investigate factors considered as important for its adoption. Given the limits of the code-centric approach  [39]  and the structured process  [31, 38, 44, 65]  for software development, Scrum as the process which aims to describe step by step the devel-opment of epidemiological surveillance software and the MDA approach which aims to define models and automatically generate the software were adopted.
On the other hand, whether code-centric or MDA approaches are used during the development of a software, there is a need for a software development process  [31, 38, 65]. Given that epidemiological surveillance systems evolve faster, the recommended process is Agile. While several agile development processes have existed for some years, a large number of software organizations have implemented agile processes such as Scrum  [65]. Then, in this research we used Scrum. In the next subsections, Model Driven Architecture (Subsection 3. 1) and Scrum (subsections 3. 2) are presented.
Once the first objective is attained, we take a short break and adapt the path according to the situation of the moment, and so on, until we reach the final destination. This helps us to give more visibility, involving the client from the beginning to the end of the project and adopting an iterative and incremental process. In a broader sense, an Agile Process can be seen as a process consisting of an initialization step, iterative steps and incremental steps  [4, 7]. While several agile development processes have existed for some years, a large number of software organizations have implemented agile processes such as Scrum  [65]. The first reference of the term "Scrum" originates from Japan and refers to an adaptive, quick, and self-organizing product development. Scrum describes how the team members should be organized in order to develop a system using Agile principles  [9].
Stakeholders involved The research team consisted of two persons playing the role of researchers and software developers. Both had a strong background and experience in Scrum and Model-Driven Engineering Techniques and tools. The target population of the research was a big stakeholder group including: • The product owner: working at NTCP, he is the member of the Scrum Team responsible for the prioritization of the features to develop. He also plays the role of interchange belt between the software team and the others stakeholders; • The NTCP Staff: this is the technical staff at the NTCP who are going to participate in the development and the maintenance of the surveillance software. They assists to the Scrum meeting and will be trained on the update of the system; • End users: they are the health workers at the different levels of the health system.
As presented by the Fig. 2 , the action research we applied consisted of a set of aggregated interventions to develop epidemiological surveillance software of TB. These interventions involved a series of actions taken during the development of the epidemiological surveillance of TB in order to come up with an effective system. It is organized into the Pre-Intervention, the Intervention and the Post-Intervention phase. Pre-intervention phase. During the Pre-Intervention, we identified the problems and proposed solutions. These solutions are divided to give a list of interventions. Thereafter, we conduct an empirical investigation of the research environment by interviewing health workers at all the levels of the health system. This empirical investigation allows us to identify problems and issues related to the current system.
Pre-intervention. The data collection during the Survey was made using semi-structured interviews. The participants were health workers involved in the epidemiological surveillance of TB at the hospital, regional and NTCP levels. These interviews were conducted by the two researchers and one epidemiologist. Each interview lasted between 30 to 45 min and was recorded in a notebook. The text and conclusions were presented to the NTCP staff. The following open-ended questions were asked to participants: • How do you manage your data? • What are the main problems you generally have? What are the solutions you generally use to solve these problems? • What is your knowledge on the use of IT tools? • What do you expect from the use of a software for epidemiological surveillance? Alongside the data collected from the health workers, other sources of data used in the research were the supports that are used at NTCP for TB surveillance.
Intervention. During the Intervention phase, the data were gathered by watching how the combination of Agile and MDA is being used during the development of epidemiological surveillance of TB. Thus, data from daily observation of the implementation of the interventions was collected in order to gain an understanding of the software development phases and decisions. These observations were documented by note taking in notebook. In some cases, we used semi-structured interviews to investigate a phenomenon. For instance, we observed that only one health worker investigated himself in the use of the modeling tools. To understand this phenomenon, a semi-structured interview was conducted. Then, through self-assessment, we determined how things went and how they can be improved. To this end, after each Sprint, the following questions were answered: • What went well? -we determine if what has been done can be replicated in another context.
Model construction and transformation In this research, the PIM is constructed using UML class diagram. To construct UML class diagram, many tools are proposed. We choose to use Dia Diagram Editor 9 because we master its use to build UML models. On the other hand, Dia is used to build large diagrams spanning multiple pages that can be saved in XML format and be printed. The assessment of tools used for complete code generation in epidemiological surveillance (presented in Section 3. 1) reveals that Imogene were more close to the features expected by the NTCP. Our proximity with the MEDES, the company which developed Imogene motivated us to adopt it. Imogene is a platform to model and generate integrated data collection information systems. Thus, the class diagram (PIM) designed using Dia is transformed into the imogene model (Imogene).