dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction In-situ measurements can provide a great deal of information about the atmospheric environment that impacts almost all aspects of human life. Such measurements provide essential input for evaluating satellite retrievals  [1, 2] , establishing long-term datasets for monitoring atmospheric aerosol and gas levels, and assessing atmospheric models by giving insight into the localized air quality  [3] , long-range transport of pollutants, radiative forcing, cloud formation, etc. [4]. Ground-based in-situ observations performed regularly worldwide are extensively used in various studies focusing on air pollution and climate  [5]. Although a number of routine balloon-borne measurements (e. g. , based on radiosondes and ozonesondes) already take place, most airborne measurements are sporadic and campaign-based, and do not meet the needs of long-term monitoring of the upper levels of the atmosphere.
In addition, the Battery Management System (BMS) along with a 'Throttle-Kill' feature allows the UAV to use its energy with maximum efficiency, probing the atmosphere at the highest possible altitude (e. g. , 3. 5-4 km AGL) without compromising the safety of the flight. Workshops: USRL is composed of a team of ca. 10 experts with a wide range of complementary skills (electrical/electronic/mechanical engineering, software development, science, composites material fabrication and piloting). This diversity allows the facility to perform well in the design and development of the UAS, the integration of a variety of instruments as well as testing, operation, data acquisition and analysis. The USRL workshop (Figure  2 ) is located at the Cyprus Institute (CyI) premises (Athalassa campus, Nicosia, Cyprus).
The "Skywalker 2015" airframe, which is made by Expanded PolyOlefin (EPO) foam, serves as a low-cost solution ideal for high-risk applications (i. e. , in harsh environments or mobile shipborne operations, where there is a risk to lose or damage the UAV). Similarly, with the "Skywalker 2015", the main material of the "Skywalker X8" and "I-soar" airframes is EPO foam, but the particular airframes are designed to carry larger and heavier instrumentation without increasing the cost significantly. The MK8-3500 UAS is controlled through the Mikrokopter firmware and software, whereas the rest of the multirotors are controlled through the novel USRL firmware and software, fully developed by the USRL team. All the small-size fixed-wing UAVs are powered by Li-Ion or LiPo batteries. Medium-size fixed-wing UAV: The "Cruiser" has a payload capacity of up to 12 kg, which also includes the weight of the fuel to power the engine and the battery used for the instrumentation.
The Cruiser's payload bay, available inside the UAS, measures 1. 3 m × 0. 23 m × 0. 34 m (L × W × H). The UAS features a wingspan of 3. 8 m. It has been configured with an internal combustion two-stroke engine placed in a push configuration enabling an altitude ceiling of 4 km. Depending on payload and environmental conditions the Cruiser can reach a flying endurance of up to 8 h. During the flight, atmospheric sampling occurs at a velocity of 28 ± 5 m/s. Due to the Cruiser's size, a flat (ideally paved) runway is required for takeoff and landing. The Cruiser is controlled through the Micropilot Autopilot and Horizon Software. The Cruiser's engine uses gasoline as a fuel. Auto-Pilot (A/P) systems: The experience gained from commercially available and open-source A/P systems used for the different UAS led USRL to start developing an improved A/P system (software, firmware, and hardware).
Software and hardware of the commercial and open-source A/P systems are frequently incompatible with new atmospheric instruments and sensors. This has often led to the integration of intermediate systems and quick (but not optimal) software fixes. Additionally, maintaining a fleet of UAS with different types of A/P systems and avionics can be time-consuming and dangerous, leading pilots/engineers/programmers to deal at the same time with multiple complex systems. The purpose-built development of the novel USRL A/P system allows for easy adaptation of the scientific instruments on-board all types of UAS (fixed-wing and multirotor) owned and operated by the USRL. The direct communication of the scientific instruments without using any intermediate systems optimizes the overall weight; hence it improves the performance of the UAS, enabling a longer operation of the scientific instruments at greater distances and altitudes.
The coupling of the sensors with the avionics allows enhanced data-logging and remote-control through the UAS. This is mainly achieved due to the hard-wired communication of the A/P with the sensor controllers or dataloggers which allows the direct combination of scientific data with selected flight parameters in a way that the scientific instruments do not affect the A/P operation. As a result, the remote control of the sensors along with the real-time monitoring and visualization of the atmospheric data at the GCS is accomplished without compromising the flight safety or introducing unnecessary weighty components. The resulting homogeneity between the different unmanned platforms simplifies their operation. Flight automation: All the USRL UAVs can fly autonomously based on pre-programmed flight paths or by using more complex dynamic automation routines handled by a FC.
Atmospheric Instrumentation Deployed On-Board the UAVs A range of commercially available and in-house custom-made atmospheric sensors have been tested and integrated on-board the UAVs of USRL for scientific purposes. Table  3  provides an overview of the different atmospheric instruments that have been deployed. These instruments consist of meteorological sensors, Optical Particle Counters (OPCs), particle samplers, and atmospheric gas sensors. These are presented separately below. Relative Humidity (RH) and Air Temperature (T) data recorded during UAV flights are critical parameters to interpret vertical profiles performed with atmospheric sensors, providing information on the Planetary Boundary Layer Height (inversion layer), the altitude of specific atmospheric (dust) layers and clouds in the free troposphere, as well as the influence of water absorption onto ambient aerosols (RH).
The modified version of OPC-N2 deployed on-board our UAV flights integrates a pump (model: NMP830KNDC) and a critical orifice, to keep the flowrate constant at 1. 2 L/min. Additionally, a home-made lightweight silica gel dryer is deployed upstream of the OPC-N2 for humid environments (RH > 50%) for minimizing the influence of aerosol water absorption. Its dimensions are 24 cm × 4 cm. The dryer is made of carbon fiber and its total weight (including the silica gel) is 204 g. The volume and the internal surface of the silica gel is optimized in accordance with the drying requirements of the OPC and the flight duration. The miniaturized, lightweight DAQ system of the OPC-N2, based on an ARM-processor, includes a GPS receiver to synchronize the OPC measurements with the flight coordinates. The datalogging frequency is set to 1 Hz and the data (OPC measurements (16 bin counts), desired parameters of the NMEA GNSS sentences/or GNSS information, temperature and relative humidity from a lightweight Rotronic sensor) are logged on a micro SD-card.
Atmospheric Gas Sensors ECC ozonesonde: The atmospheric ozone concentrations are obtained with the ECC ozonesonde (EN-SCI, Westminster, CO, US) mounted in the upper section of the UAV's nose (forward to the wings). EEC ozonesonde working principle is described in Deshler et al. [55]. To reduce the size and weight of the οzonesonde, the system was disassembled and the aluminum casing of the sonde replaced by a single sheet, half the dimensions of the original footprint. A vibration dampening system was added between the ozonesonde and the part attaching to the UAV board. This is necessary as the sonde's pump creates intense vibration, which could be problematic to the UAV's electronics. Ozonesonde's weight was reduced from 260 g to 215 g, including full cells and a data logging system. SenseAir HPP CO2: Atmospheric CO2 observations are performed with a High-Performance Platform (HPP) non-dispersive infrared CO2 sensor from Senseair AB (Delsbo, Sweden)  [56, 57].
Profiling of Aerosol-Cloud Interaction Observations over Northern Finland In-situ observations of aerosol and cloud interaction are essential for improving our fundamental understanding of their role in the climate system  [59]. Properties of aerosolcloud interactions in the climate change Arctic hotspot are an important input to climate models, while the impact of clouds remains one of the largest uncertainties  [60, 61]. The 7th Pallas Cloud Experiment (PaCE) in Finland was organized in September 2017 and focused on cloud-aerosol interactions through in-situ and remote sensing methods. The UAS of USRL were deployed during this campaign (Figure  S3 ) and provided in-situ data of aerosol optical and microphysical properties inside/outside (below/above) clouds. These data were used in conjunction with ground-based remote sensing and in-situ observations performed at the Pallas-Sodankyla GAW station in order to enhance the understanding of aerosol activation and cloud processes.
Ozone Profiling over Cyprus Ozone (O3) is one of the most important chemical species involved in atmospheric chemistry, and has an impact on the environment through chemical oxidation capacity, health, greenhouse effect, and vegetation. Height-resolved ozone measurements are critical to better understand the role of gases in oxidation capacity of the atmosphere and radiative forcing  [19, 81]. As such, ozone soundings play a major role in the validation and evaluation of satellite measurements, particularly for their long-term stability. A widely used method to measure in-situ ozone profiles is through the use of compact balloonborne sondes (e. g. , SPARC-IOC-GAW, 1998). Up to an altitude of about 20 km, ozonesondes constitute the most important data source with long-term coverage for the derivation of ozone trends.
Acknowledgments: The authors gratefully acknowledge the USRL team of the Cyprus Institute:  Neoclis Hadjigeorgiou ,  Fotis Manellides ,  Evagoras Nearchou ,  Charalambos Neocleous ,  Constantinos Photiades ,  Constantinos Savvides ,  Michalis Strouthos , and  Panos Vouterakos. We would also like to thank all the people who were involved in the preparation and the conduct of the scientific campaigns mentioned in this paper.
in some cases for the positron production processes. These have been estimated based on a new simulation software developed for LUXE. These facts impose strong requirements/high demand on the detection system (in terms of radiation hardness, response-linearity, background discrimination etc. ). The goal of this conceptual design report is to present the physics case for the experiment and to explain how it can be technically realised so that the physics goals can be achieved. The work presented here builds on the previous Letter of Intent  [21]. While the basic principle and physics goal of the experiment remain largely unchanged, a significant number of details have been updated, in particular to ensure that conceptually the experiment can be built on the proposed timescale, and to successfully complete the physics programme suggested.
Monte Carlo Event Generation A custom-built strong-field QED MC computer code, named PTARMIGAN  [202] , is used to simulate the strong field interactions for LUXE for the relevant physics processes using the LMA for cross-section calculations as described in Sec. A. 1. The task of MC generator is to simulate the physics processes in a realistic scenario with the laser focusing and electron beam sizes taken into account properly. Several cross-checks have been made to ensure the validity of the programme  3. Details on the simulation are given in App. B. The MC has been used to generate datasets for all foreseen LUXE experimental configurations. These include the non-linear Compton process, the two-step trident process, and the non-linear Breit-Wheeler pair production process. The results of the MC are validated by comparing with analytic distributions (see also App.
The digitised QDC signal is transferred via a VME bridge with an optical link to a computer where it is processed by the central data acquisition (DAQ) system. A CAEN multi-channel high voltage power supply for the photodetectors is steered from the detector control software via the VME bridge. Furthermore, one pressure sensor per gas volume and several temperature sensors provide data to the detector monitoring system. One important property that has to be quantified for the Cherenkov counting detector is the linearity of both the photodetector and the QDC. For the available Cherenkov prototype, extensive studies measuring the differential and integrated non-linearity of the QDC and different photodetectors were performed  [209] , where in a larger light-intensity regime, non-linearities of (1.
Performance The objective for the Cherenkov detectors is to measure the Compton edge position as a function of the laser intensity parameter ξ. It is expected that the Compton edge is shifted to larger values with a functional behaviour of E edge (ξ ) E beam = 1 - 2η 2η + 1 + ξ 2 ( 17 ) where η(16. 5 GeV) ≈ 0. 192. The performance of the LUXE Cherenkov detector is estimated using a fast detector simulation based on a package developed for polarimetry  [210]. It uses as input the particles from the IPSTRONG MC (see Sec. 5) or from beam-target interaction simulation in GEANT4 (see Sec. 5. 2). The package emulates the propagation of charged particles through a uniform magnetic dipole field, as well as the production of Cherenkov photons in the medium. The package furthermore emulates the photodetection and the QDC digitisation step.
A sketch of the structure of the detector plane is shown in Fig. 6. 17 (left). An new idea for the signal fanout is also under consideration at present. In that scenario, the sensors will be covered with another layer of metallic traces that will be used for signal fanout. This will remove the need for an external Kapton layer as well as the need for fragile wire-bonding between the sensor pads and fanout, reducing the module thickness by about 150 µm and making it more durable and easier to handle in the detector assembly. This idea will be explored on GaAs sensors to which the LUXE team has an easy access through the facilities existing in the Tomsk State University. If successful, GaAs sensors will be implemented instead of the silicon-sensors. Prototypes of GaAs pad-sensors were investigated by the FCAL collaboration in a test beam  [225]  and the response was found to be very similar to the one of silicon pad-sensors.
Phase-0 Options In order to perform energy measurements in the phase-0 early runs, an already available high granularity electromagnetic calorimeter from the CALICE collaboration  [226]  together with a partly instrumented LUXE ECAL may be used. This way both measurements for physics at low laser power as well as background will be performed. The CALICE calorimeter has a fiducial area of 18 × 18 cm 2 and sensor pads of the same size as foreseen for the LUXE ECAL. The silicon-sensor structure of the ECAL, five silicon-sensors per sensor-plane, allows to assemble the calorimeter in a modular way  [227]. At least one module of the LUXE calorimeter will be fully instrumented, extending the range of the CALICE calorimeter in the x-coordinate, and allowing to test the performance under realistic conditions.
It has been used extensively at DESY as well as CERN beam lines by a number of different detectors. The unit can accept signals from some detector and can generate a global trigger for all LUXE detectors to indicate the start and end of data taking. The unit can also act as a master clock unit, receiving synchronisation and trigger commands from the software DAQ, as well as a precise clock reference. Therefore the timing from the electron bunch and/or the electron-laser timing will be fed to the TLU which will then synchronise the LUXE detectors. The detectors can also send signals back to the TLU to indicate that they are busy and, for example, data taking to be paused until the busy is removed. Communication between the DAQ system and the TLU uses the IPBus protocol, a well-established and reliable protocol widely used in the CMS and ATLAS experiments at CERN.
A DAQ software will be required which could be used for individual components or, if they come with their own software, a central DAQ software will be needed to interface to the detector software. Many different DAQ softwares exist with different levels of complexity and scale, with some designed for specific experiments and others as generic developments. At LUXE, we propose to use the EUDAQ2  [240, 241]  software which has been developed for tests of high energy physics prototype detectors (in beam tests), a similar setup to LUXE. It can cope with different triggering and readout schemes and has been used by several different detectors and projects. Specifically, EUDAQ2 has been used in beam tests to read out several detectors like those to be used in LUXE, such as calorimeters and pixel detectors  [240] , including the ALPIDE sensors.
EUDAQ2 is also fully compatible with the TLU and the two have been used as a basis for DAQ systems in several situations. The software has been used extensively in DESY test beams, as well as other facilities. DESY is also the lead developer of the EUDAQ2 software and so we will have access to in-house expertise. The hardware, firmware and software designs of the TLU  [239]  and the EUDAQ2 software  [241]  are freely available and so work can start quickly on their implementation in LUXE.
In this section, the plan for the construction, installation and operation of the LUXE experiment will be presented. In all cases the procedure is designed to minimise as much as possible any impact on the XFEL. EU operations. As discussed in Sec. 3. 1, it is currently planned to install the experiment in the annex of the XS1 access shaft that is scheduled to host the future TD20 line for the 2nd fan of the XFEL. EU. 8. 1 shows a 3D CAD view of the XS1 shaft building and various components. The conversion of the experimental components from the GEANT4 simulation described in Sec. 5 to the XS1 CAD model was done using the software described in Ref. [246]. The experiment is built on two different floors. The experimental area where the electron-laser or photon-laser collisions will happen is located in underground level -3 (UG03), with all the other XFEL.
Thus the classical trajectory must be defined in terms of q and a cycle-averaged position r, which evolve according to the 'ponderomotive' force equations: q = -m 2 ∇ a 2 /(2q 0 ) and ṙ = q/q 0 , where dots denote differentiation with respect to lab time  [269]. Similar concepts were used to simulate SLAC experiment 144  [164]  (codes NUMINT  [270]  and MCSCAT  [271] ), and are also used in CAIN  [265]  and GUINEA-PIG  [266]. In Appendix B. 2 we discuss IPSTRONG, the Monte Carlo code that is used to simulate photon and pair creation at the LUXE interaction point.
Different configurations have been considered for the beam parameters. A custom built strong field QED Monte Carlo computer code, named IPSTRONG, was used to simulate the strong field interactions for LUXE. IPSTRONG implements the transition rates described in the previous section, namely Eqs. (  31 ) and  (32). IPSTRONG is a FORTRAN2008 code which implements a strong field Monte Carlo event generator, within a particle-in-cell approach that includes the full form of the laser pulse. Historically, this MC code was used for the development of the LUXE design; however, in the final CDR the results were based on PTARMIGAN as described in the 5. The task of IPSTRONG is to obtain an accurate representation of the strong field transition rates within the realistic collision of a charge bunch and a laser pulse.
Acknowledgements We thank the members of various DESY groups without whom this work would not have been possible: MVS (vacuum modification), MIN (kicker magnet, beam dump), D3 (radio protection),  MEA  (installation and magnets),  ZM1  (construction), MKK (power and water),  IPP  (CAD integration). We also thank the  DESY directorate  for funding this work through the  DESY Strategy Fund. The work by  B. Heinemann  and  C. Grojean  was in part funded by the  Deutsche Forschungsgemeinschaft under Germany's Excellence Strategy -EXC 2121 "Quantum Universe" -390833306. H. Abramowicz  and  B. Heinemann  would like to thank the  German-Israel-Foundation (GIF)  (grant number  1492 ). H. Abramowicz  would also like to thank the  Israel Academy of Sciences. The work by  A. Hartin  and  M. Wing  was supported by the  Leverhulme Trust Research Project  Grant  RPG-2017-143  in the UK.
The work of  G. Perez  is supported by grants from the  BSF ,  ERC-COG ,  ISF ,  Minerva , and the  Segre Research Award. A. Ilderton ,  B. King ,  S. Tang  and  A. J. Macleod  acknowledge support from the  Engineering and Physical Sciences Research Council (EPSRC)  (grant no. EP/S010319/1 ). G. Sarri wishes to acknowledge support from  EPSRC  (grant Nos:  EP/N027175/1  and  EP/P010059/1 ). T. Blackburn  acknowledges the use of resources provided by the  Swedish National Infrastructure for Computing (SNIC)  at the  High Performance Computing Center North  ( HPC2N ). The work of N. Tal Hod is supported by a research grant from the  Estate of Moshe Glück , the Minerva foundation with funding from the  German Ministry for Education and Research , the  Israel Science Foundation (ISF)  (grant No.
708/20 ), the  Anna and Maurice Boukstein Career Development Chair and the Estate of Emile Mimran. Y. Soreq  and  T. Ma  are supported by grants from the  ISF, BSF  ( NSF-BSF program ),  Azrieli Foundation  and the  Taub Family Foundation. A. Fedotov  and  A. Mironov  were supported by the  MEPhI Academic Excellence Project  (Contract No. 02. a03. 21. 0005 ) and by the  Russian Foundation for Basic Research  (under grants No. 19-02-00643  and No. 20-52-12046 ). B. Heinemann,  H. Harsh ,  X. Huang ,  R. Prasad ,  F. Salgado ,  U. Schramm ,  K Zeil  and  M. Zepf  thank the  Helmholtz Association  and the  Bundesministerium für Bildung und Forschung  for the support via the 2018 Helmholtz Innovation Pool. This work has benefited from computing services provided by the  German National Analysis Facility (NAF).
Data structures. We use a data structure TLM to store the triple of sets (S, S push , S pop ) and which is accessed with the following methods. TLM. create() creates the data structure with the three sets empty. TLM. add(q, Z, q , Z ) adds [(q, Z), (q , Z )] to S. TLM. addPush(q, Z, a, q , Z ) adds [(q, Z), a, (q , Z )] to S push. TLM. addPop(q, Z, a, q , Z ) adds [(q, Z), a, (q , Z )] to S pop. TLM. isNewRoot(q, Z) returns [false, Z ] if there exists some (q, Z ) ∈ S with Z ∼ q Z, and returns [true, Z] otherwise. TLM. isNewNode(q, Z, q , Z ) returns false if ∃[(q, Z), (q , Z )] ∈ S with Z q Z , and returns true otherwise. TLM. isNewPop(q, Z, a, q , Z ) returns false if ∃[(q, Z), a, (q , Z )] ∈ S pop with Z q Z , true otherwise. TLM. isNewPush(q, Z, a, q , Z ) returns false if [(q, Z), a, (q , Z )] ∈ S push , and returns true otherwise.
Experiments and Results We build on the existing architecture of an open-source tool for analysis of timed automata, TChecker  [18]. Our tool is freely available for download and use at https://github. com/karthik-314/PDTA_reachability. Currently, our implementation uses only the LUsimulation  [7]  and hence we only deal with diagonal-free timed automata. But we could also extend it to use G-simulations from  [16] , which would allow us to handle diagonal constraints as well.