dataesr/scientific-paragraphs-categorization huggingface dataset:
This research has made use of  NASA 's Astrophysics Data System. Many of the acknowledgements were compiled using the Astronomy Acknowledgement Generator. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France. The original description of the SIMBAD service was published in Wenger et al. (2000). This research has made use of the VizieR catalogue access tool, CDS, Strasbourg, France (DOI: 10. 26093/cds/vizier). The original description of the VizieR service was published Ochsenbein et al. (2000a). Some of the data presented in this paper were obtained from the  Mikulski Archive for Space Telescopes (MAST). STScI is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS5-26555. Support for MAST for non-HST data is provided by the  NASA Office of Space Science via  grant  NNX13AC07G  and by other grants and contracts.
This research has made use of the  NASA Exoplanet Archive , which is operated by the  California Institute of Technology , under contract with the  National Aeronautics and Space Administration  under the  Exoplanet Exploration Program. The  Pan-STARRS1 Surveys  ( PS1 ) and the PS1 public science archive have been made possible through contributions by the  Institute for Astronomy , the  University of Hawaii , the Pan- STARRS  Project Office, the  Max-Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg  and the  Max Planck Institute for Extraterrestrial Physics, Garching , The  Johns Hopkins University, Durham University , the  University of Edinburgh , the  Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under Grant No.
Objectives: Preparation of injectable anticancer drugs in hospital pharmacies, in particular of cytotoxics, is a highrisk activity. We used Preliminary Risk Analysis (PRA) to analyse the risks in the different steps of our anticancer drug circuit, including the preparation step (PRA1). Then, to prepare an important change in management of the circuit with the software Chimio ® (pooling of three databases for subcontracting), we repeated the analysis of preparation step (PRA2). PRA is known to be time and resource consuming. To overcome this, we developed a strict organisational framework to perform the analysis within a reasonable amount of time. We present the PRA method including its practical implementation, and its application to the anticancer drug preparation process, before and after pooling of Chimio ® databases.
PRA2 was extremely useful to plan the organisation that would result from the new Chimio ® database, while involving the team and winning its commitment. It allowed an exhaustive and structured anticipation of this major change. Practical aspects of PRA method implementation we have adopted facilitate its application and can help to deploy it on many areas in our hospitals. Indeed, besides an exhaustive analysis of the risks, this approach promotes collaboration, develops a quality culture and is an excellent tool for team and project management, as well as communication.
Then, in 2016, a third study evaluated the risks during anticancer drug administration. Finally, a fourth study was performed in 2017 to manage the risks associated with anticancer drug preparation. In our unit, all of the steps of the anticancer drug process, from prescription to administration, are computerised with the software Chimio ® (Computer Engineering) using a local database that collect all of the specific settings for chemotherapy regimens prescribed in our hospital. At the beginning of our subcontracting activity, three different Chimio ® databases were used to manage production for each hospital. A single shared Chimio ® database was used after September 2019 which optimised and secured the process, but required reorganisation of production. Thus, on the occasion of implementation of the new shared Chimio ® database and to prepare for this reorganisation, we repeated risk analysis of the preparation step in 2019, as an update of the 2017 study.
Materials and methods PRA of injectable anticancer drug preparation step (PRA1) and PRA updated in relation to the new shared Chimio ® database (PRA2) were performed in 2017 and 2019 respectively, in the CPU of our university hospital. Our CPU produced approximately 33,600 anticancer drug preparations in 2019, including nearly 19,400 for our hospital and 14,200 for two outside hospitals. Preparations are produced according to an aseptic distribution process in two overpressure isolators with two workstations each (iso 5), installed in a controlled atmosphere area under negative pressure (iso 7) which is accessible via an overpressure airlock (iso 8). A team of seven pharmaceutical assistants, three caregivers and four pharmacists ensures production from Monday to Friday. PRA includes the PRA "system" and PRA "scenario" stages, after first creating a multidisciplinary working group  [15, 16].
Corrective actions that reduce the level of probability are called "preventive". Actions that reduce the level of severity are called "protective". Data generated by the working group are recorded throughout the study in a specific Excel ® table. After the PRA scenario is completed, this table is processed by the software Statcart APR ® which provides a representation of data analysis in several forms to help interpret and communicate the results. This includes the Farmer's diagram which shows, by danger and phase, whether the level of criticality is due more to severity or probability. The diagram shows three areas that correspond to the three levels of criticality (C1 in green, C2 in yellow and C3 in red). Drafting and structuring the action plan: Formal risk reduction measures must be drafted as "action forms" for scenarios with an initial criticality 2 or 3.
Revision of PRA1 to accommodate a major change in the production organisation Use of the new shared Chimio ® database will significantly change the production organisation and thus the analysis performed during PRA1. To perform PRA2, the PRA1 working group was reunited except for the caregiver and the quality and risk management director, whose methodological expertise was provided by the managing pharmacist. PRA "system" then involved identifying DS that had become more or less safe as well as those that had not changed, were no longer present, or were new, in order to build a new DS mapping. For PRA "scenario", PRA1 assessment and decision tools were kept and only DS that were new or had become riskier were analysed as scenarios. This analysis was used to draft a new action plan.
PRA2 to prepare implementation of a new software tool Two DS were removed and the security of 10 DS was improved with the new shared Chimio ® database. Five DS evolved towards less security and six new DS appeared. The shared Chimio ® database affected phases 4 (verification and delivery of the finished product) and 2 (preparation of the trays and sterilisation transfer) the most, with respectively 6 and 4 DS (Figure  6 ). The 11 DS described in PRA2 mainly concerned human (n=8) and organisational (n=3) dangers (Figure  7 ). This led to an analysis of 11 scenarios, including three of criticality 3, four of criticality 2 and four of criticality 1 (Figure  8A ). Among the C2 and C3 scenarios, two were due to the reorganisation of the product storage. The shared Chimio ® database would allow a unique storage location instead of 3, with a risk of accident due to the heavier quantities of products to manage and a risk of error due to practise changes.
However, whatever the level of security of the process, the human factor will always be present and must be considered. In the book To err is human, Linda T. Kohn says that human error is inevitable and must be accepted  [23]. The goal is to build systems where the compliant and secure task will be easy to accomplish and, in contrast, the error will be hard to make. PRA2 shows that overall, the shared Chimio ® database reduces the risks for our process. However, it also generates new risks which were identified and managed upstream. Human danger was once again the most critical source of risk, followed by organisational danger, with organisation of the production being transformed by the use of this new tool. Those results could be interestingly considered by other teams in their institution.
If these requirements can be met, performing risk analysis in our production units, whose activity is sustained, and more generally in the healthcare units or medico-technical services of our hospitals, is feasible and relevant. Although the method applied for PRA2 was an adaptation of the original methodological framework of PRA, it still respected the essence of the approach described by Alain Desroches  [15, 16]. It was particularly easy to perform since the working group was already trained in the method and the scales were validated. It permitted an a priori thinking on the coming change, in an exhaustive and structured manner that favoured team commitment. Besides the analysis of the risks associated with the new shared Chimio ® database, it was a powerful tool to project into the new organisation of production, anticipate changes and plan modifications while involving the team.
More generally regarding our global anticancer drug circuit, the PRA method allowed an analysis of future organisations, which had to be set up, as we did before starting subcontracts and before implementation of the new Chimio ® database, but also of ongoing processes such as preparation and administration of chemotherapies. To analyse the steps of prescription, administration and delivery, the working groups included prescribers, nurses and logistic units. Discussions within these groups allowed all actors of the circuit to meet and strengthen their collaborative relationship for the purpose of optimal patient care. Our risk analysis was the starting point for larger projects, and it provided consistency to their implementation within the unit. It is also a solid tool to defend projects at the hospital management and support funding requests.
AC power supply A complete block diagram of the powerbox is shown in Fig. 5. AMICA is powered only by a single-phase AC power supply line through connector J1 at the powerbox connector panel. Inside the powerbox, the AC current is passed through two filter modules (F1 and F2) to minimize conducted EMI interaction between AMICA and the aircraft power system. It is then distributed to supply power to four component groups: (i) an inlet heating, (ii) pump and fans, (iii) enclosure temperature control system, and (iv) ICOS measurement system. Prior to flowing in any components or power converters, the current is passed through thermal circuit breakers (B1-B5) to protect the system from currents exceeding their nominal values (e. g. due to component malfunctions or unforeseen short circuits). The breakers B1, B3, B4 and B5 are chosen with current limits of 2.
AC/DC conversion All other components in AMICA require DC power. This is generated by AC/DC converter modules V1-V3. These converters from Vicorpower have an auto-ranging AC input that automatically senses the AC supply voltage between 90 and 132 V, as well as 180 and 264 V, and the frequency over a range of 47-440 Hz, so AMICA can be operated inside a laboratory (115 or 230 V at 50 or 60 Hz) and inside aircraft (typically 115 V at 400 Hz) without any internal modifications. The outputs of V1-V3 are 24 V DC each. Self-made add-on boards are installed directly onto the output terminals of V1-V3 to sense the currents for monitoring by using special Hall ICs (integrated circuits). The boards also detect the output voltage and distribute the output power into several current-limiting paths by using small SMD (surfacemounted device) fuses specially designed for space-limited circuit boards ("nano fuses", Littelfuse, Chicago, USA).
Voltage, current and temperature signals are digitized by a LabJack T7 data logger powered from the embedded PC (C1) via a USB line passed through connection S2/P2. Through the same USB line, the set of 14 parameters is regularly read by the software on the embedded PC. The monitoring of these parameters is needed to observe smooth operation of each component in the power supply system, and the data can help in case trouble-shooting becomes necessary. In the enclosure, signals from the temperature and pressure sensors on the C13a board and from the temperature probes C14 and pressure gauges C4 of the two cavities are acquired by the embedded PC from analogue input channels of a data acquisition card (RedWave S310) in the embedded PC/104 stack. Parameters related to the lasers C7 (laser and heat sink temperatures) and their driver boards C6 (voltage, board and processor temperature) are communicated via UDP data stream together with the spectra from the C6 boards (see Communications section below and Sect.
Communications AMICA contains a self-contained and fully operational embedded PC (C1; note that a previous version of AMICA contained two independent embedded PC/104 stacks, cf. Sect. 5) equipped with a 64 GB SATA SSD (C2) and running under a Linux operating system (Lubuntu 18. 04). Communications ports are wired to appropriate connectors at the enclosure connector panel (details given in Table  2 ). Two USB ports (USB1/2) allow for connection of computer peripherals such as keyboard and mouse as well as the use of USB memory devices. An RJ45 connector (ETH1) allows for LAN connections to an external PC or network via the embedded PC's 1 Gbit Ethernet port. However, cable-based LAN connections are not always feasible, e. g. on the M55 Geophysica when the dome cowling is closed and the instrument cannot be accessed.
To still be able to communicate with AMICA, a wireless LAN module (RTD, WLAN18202ER) in the PC/104 stack is wired to an SMA connector (WIFIA) in the enclosure connector panel with a Wi-Fi antenna attached on the outside. With this setup, Wi-Fi connections between AMICA and a laptop or desktop PC are possible up to about 200 m distance. Another RJ45 connector (ETH2) is used for VGA extension via RJ45 cable that allows for the connection of an external screen to the PC's VGA port even when the enclosure is fully closed. This can be useful to directly work on the AMICA PC in the laboratory, or for troubleshooting if an external Ethernet connection can not be established. A PC/104 module with two additional Gbit Ethernet channels (RTD, LAN18222HR) is used for internal communication between the embedded PC (C1) and the LTC-1141l boards (C6a/b).
A TCP/IP protocol is used for initialization of the boards at software start-up and for sending commands (e. g. to change the temperature set point) to or receiving stahttps://doi. org/10. 5194/amt-14-5271-2021 Atmos. Meas. Tech. , 14, 5271-5297, 2021 tus information from C6a and C6b. A data stream consisting of the acquired and averaged spectra as well as some additional parameters (cf. Sect. 3. 1) is transferred one-way from each LTC-1141 to the PC via UDP data stream.
Data acquisition In the current version, the AMICA software uses one continuously running data acquisition loop to consecutively read spectra and housekeeping data (a complete list of monitored parameters is given in Table  4 ): -Time averaged ICOS spectra from each LTC. At each time interval t int , the LTC sends an ICOS spectrum averaged from N ramps (cf. Sect. 2. 2) together with six parameters related to laser and LTC status (see Table  4  for details) to the PC via UDP stream in packages of 256 floating-point numbers each. The packages are read by a socket command inside a loop that puts the housekeeping parameters into corresponding named variables and the spectra into a netCDF type structure, which is synchronized to the SSD hard disk every 60th data acquisition cycle. -Cavity and enclosure pressures and temperatures.
Both pressure and temperature sensors in the enclosure and in each cavity are sensed as analogue voltages and converted to digital numbers by the RedWave S310 data acquisition card in the PC/104 stack. The six parameters (listed in Table  4 ) are read into named variables in the AMICA software via the S310 application programming interface (API). -Powerbox temperatures, voltages and currents. The AMICA software calls the LabJack T7 (C6) to transfer the readings on the 14 AD channels (given in Table  4 ) via USB connection. Actual temperatures, voltages and currents are calculated from the received voltage signals and stored in named variables. At the end of each data acquisition cycle, all housekeeping parameters listed in Table  4  are converted into a single string and added to an ASCII file on the SSD.
(  1 ) holds with the same fitting parameters when this is done. This is regularly checked and validated by testing if all peaks appear in the correct positions when measuring standards with many absorption peaks present in the spectral range. Using a relation with respect to I laser rather than points or time along the ramp in Eq. (  1 ) allows for the possibility of applying a curved ramp of I laser resulting in a quasi-linear wavenumber scale that makes spectral fitting easier. Spectral fitting following the method described by  Sayres et al. (2009)  has been implemented in Interactive Data Language (IDL) code and has been used to process the spectra recorded during the M55 Geophysica field campaigns described in Sect. 5. 1 and 5. 2 (note that proprietary ABB-Los Gatos Research software was used for instrument control and spectra acquisition during these campaigns, with two separate PCs handling the two ICOS channels).
Encoding of the full spectral fitting algorithm in Python is currently in progress and shall not only be used for post-processing of recorded spectra but also for real-time processing during measurement flights. In the current Python software actually running on the AMICA instrument, a less computationally demanding approach is used to fit trace gas concentrations: using an experimentally measured baseline (with the cavity filled with Argon at 99. 9999 % purity), the spectrum is transformed into absorption space and a Lambert-Beer fit of wavelength-dependent absorption A(λ) is solved for the concentrations c i of different absorbers with A(λ) = L 0 i c i α i (λ), (2) where α i (λ) is the wavelength-dependent absorption coefficient for each absorber based on the line parameters from the HITRAN2012 database  (Rothman et al.
, 2013). This method is only an approximation as it does not take into account the cavity broadening effects (cf. Sayres et al. , 2009) , which are small as long as L cav c i α i (λ) 1 -R. ( 3 ) When this condition is not met, the effective path length L eff is reduced compared to L 0 determined for the empty cavity and the sensitivity is reduced and the analysis is complicated, because the variation of L eff with wavelength over a broadened absorption peak leads to additional broadening in the observed spectrum compared to the Voigt profile calculated from the broadening coefficients in HITRAN. The simplified fitting method in absorption space has been used for the spectra recorded during SouthTRAC (Sect. 5. 1) and for the calibration experiments (Sect. 4). In theory, these spectral fitting procedures avoid the need for frequent calibrations and in particular an in-flight calibration system that would substantially add to the instrument dimensions and weight, complicate airworthiness and safety compliance certification, and lead to data gaps during calibration periods.
This is at least partly caused by the issue with the strong CO 2 absorption affecting the absorption path length, but we also note that, unlike the HITRAN parameters used for OCS and CO, the parameters for the CO 2 line used are not based on experimental data but on theoretical calculations and are therefore associated with higher uncertainties. As a result, an additional calibration factor, determined from calibrations against known standards, needs to be introduced when deriving CO 2 mixing ratios from spectral fits. As a result of the spectral fitting issues and the additional uncertainty of the calibration factor, the error margins of the AMICA CO 2 data are currently of the order of a few parts per million (ppm), much larger than those of other CO 2 instruments used during the field campaigns.
For CO 2 , both curves exceed 1 ppm for all averaging times, which is another reason (besides the issues described above) for currently not releasing AMICA CO 2 data for scientific use. In the current setup, H 2 O can only be detected at mixing ratios above 100 ppm. Comparison of mixing ratios derived from spectral fits based on HITRAN parameters with a relative humidity sensor (MSR 165) placed near the AMICA https://doi. org/10. 5194/amt-14-5271-2021 Atmos. Meas. Tech. , 14, 5271-5297, 2021  inlet showed agreement better than 4 % under different conditions (4000-30 000 ppm H 2 O). Because OCS is one of the main scientific interests of our research group, the 2050-2051 cm -1 setup is typically used in AMICA in the cavity 1 position.
O 3 and NH 3 at 1033-1034 cm -1 A quantum cascade laser (QCL, Hamamatsu), operated at 29. 6 • C and ramped over the current range 775-925 mA, emits light over the wavenumber range of 1033. 21-1034. 36 cm -1. In this spectral window, absorption lines exist for O 3 (major absorption lines at 1033. 24, 1033. 35, 1033. 68, 1033. 86 and 1033. 93 cm -1 ) and NH 3 (major absorption lines at 1033. 32 and 1034. 01 cm -1 ). A spectrum measured by this channel during the 2019 SouthTRAC deployment (Sect. 5. 3) is shown in Fig. 10  together with a spectral fit in absorption space that uses HITRAN line parameters. The spectral fit does not closely reproduce the observed spectrum. First, significant absorption up to 0. 01 between peaks points to either a bias in the used baseline or trace gas absorption by lines or bands that are not included in the HITRAN database.
M55 Geophysica, Kalamata, 2016 The very first aircraft deployment of AMICA took place in Kalamata, Greece, with three flights in late August or early September 2016. A major aspect of this campaign, which was part of the European StratoClim project  (Stroh et al. , 2021) , was to test several new or modified instruments in the M55 Geophysica payload. At the time, two separate PC/104 stacks were used, one for each of the two ICOS systems. Acquisition and A/D conversion of the voltage signals from C9a/b was handled by a board inside each PC/104 stack, and averaging was done by the software running on the PC. The data acquisition boards were also used to generate voltage ramps that were passed on to the laser controllers used at the time (RedWave Labs) for current modulation. Temperature regulation of lasers, laser mounts and detectors was done by separate TEC controllers (Wavelength Electronics).
2 and 3. Cavity pressure regulation worked nominally up to the HALO ceiling altitude of about 15 km. Except for two short test flights in Oberpfaffenhofen in August, where the recorded spectra were not correctly stored to disk due to a software problem, AMICA operated and recorded data during all flights. Towards the end of the long transfer flights at the beginning of the campaign, some data gaps are present that were caused by incidental shut offs of the LTC-1141 boards C6a and C6b to prevent overheating. A close examination of this issue at the campaign base in Rio Grande revealed that the NTC sensors of the two large TEC boards (Fig. 2 ) were incorrectly connected, causing them to work against each other and not sufficiently cool the ICOS enclosure. By swapping the NTCs into their correct positions (as shown in Fig.