dataesr/scientific-paragraphs-categorization huggingface dataset:
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Acknowledgements We acknowledge the free use of tropospheric NO2 column data from the OMI sensors from http://www. qa4ecv. eu(last access: 2 December 2021). We thank all research and agency teams who provided ozone sonde data to the WOUDC and SHADOZ networks. We also thank all the actors at  NOAA  and  NASA  that created and made public the CO flask observation and AERONET datasets, and the  Finish Meteorological Institute (FMI)  for the merged AOD product.
Grey tracks (n g ) correspond to lower momentum charged particles with an ionization energy loss much greater than that of minimum ionizing particles. In the context of this paper, we are studying them as a proxy for the interactions of the energetic parton and/or forming hadron with the nuclear medium. The factorization theorem states that the hadronization of a quark or gluon with high momentum is independent of the target, projectile, and subprocess  [2]. By performing detailed modeling of grey track production with the BeAGLE event generator enhanced by the upgrade of the PyQM module, we can frame the problem of getting microscopic information on such processes from leptonuclear scattering such as the data from the E665 fixed-target experiment at Fermilab. This work lays the foundation for future studies at fixed-target experiments at Jefferson Lab and other electron-beam facilities, but more importantly, at future lepton collider experiments such as the proposed electron-ion colliders  [15] [16] [17] [18] [19] [20].
II. THE E665 DATA The E665 data being described by the simulations in this paper consisted of muon-xenon scattering compared to muon-deuterium scattering in deep inelastic scattering (DIS) kinematics. The experiment achieved nearly 4π steradians of acceptance for charged particles by using a streamer chamber immersed in a 15 kG dipole magnetic field as a vertex detector. This enabled the study of grey tracks in combination with the high-energy forward tracks, which were analyzed with a downstream spectrometer system. Scattered muons were identified with a downstream iron absorber followed by tracking devices. An electromagnetic calorimeter was used for photon detection to identify muon bremsstrahlung. The experimental definition of grey tracks used by E665 starts with identification of the scattered muon in the downstream muon detector with track matching to the trajectories measured upstream.
Features not shown in the figure include: the falloff of the nuclear density at the boundary of the nucleus; other quarks and gluons produced in the process; possible fission of the nucleus; and neutron evaporation from the nuclear fission fragments. knocked-out protons and neutrons, among others. BeAGLE is able to simulate such processes, and it has been employed in evaluations of the effectiveness of determining geometric features from various experimental signatures, such as those just mentioned, for collider experiments  [24]. That work follows earlier studies with the DPMJET3 code for forward neutron tagging  [25]  in which it was found that the evaporation neutrons could give information on the event centrality. The impact parameter b is a well-known feature of the scattering process.
The quark with fixed momentum may be considered to be part of the target wave function. Thus this process corresponds, in the IMF, to the lowest order parton model γ * + q → q shown on the right-hand side of Fig. 1. There is a smooth connection between cases 1 and 2. At low x Bj the quark with Q-independent momentum nevertheless has a large longitudinal momentum ∝ 1/x Bj , and starts to interact via gluon exchange. Such quarks correspond to sea quarks in the IMF, generated by gluon splitting at higher orders of α s. In the following section we will make use of the PYTHIA6 module within BeAGLE to quantify the relative contributions of these processes in the E665 kinematics.
IV. MONTE CARLO SIMULATION: BEAGLE The Monte Carlo code "Benchmark eA Generator for LEptoproduction" (BeAGLE)  [40]  version 1. 02 was used to simulate the E665 grey tracks in this work. BeAGLE is a general purpose FORTRAN program for simulating electronnucleus (eA) interactions. We are testing and using the PyQM module which we updated in the current version of BeAGLE to explore the sensitivity of the grey track observables to partonic energy loss in the cold nuclear medium. BeAGLE is a hybrid model that uses the DPMJet  [41] , PYTHIA6  [42] , PyQM, FLUKA  [43, 44] , and LHAPDF5  [45]  codes to describe high-energy leptonuclear scattering. The geometric density distribution is provided primarily by PyQM while the quark distributions within that geometry are provided by nPDF EPS09  [46]. The parton-level interactions and subsequent fragmentation is carried out by PYTHIA6.
Hadronic formation and interaction with the nucleus is described by DMPJet, the impact of the scattering on the nucleus is described by FLUKA, including nucleon and light fragment evaporation, de-excitation by photon emission, nuclear fission, and Fermi breakup of the decay fragments. The PyQM module implements the Salgado-Wiedemann quenching weights to describe partonic energy loss  [47]. BeAGLE includes a variety of options to control the phenomena included in the simulation, some of which are mentioned here. Nuclear shadowing is described via two different approaches. The hadron formation time is accounted for in the DPMJet intranuclear cascade. Fermi motion of the nucleons in the nucleus can be described with several different mechanisms, or turned off completely. The nuclear geometry parameters from PyQM can be overridden, and deformed nuclei can be described.
The PyQM actions available include specification of the q transport coefficient to adjust the degree of interaction between energetic partons with the nuclear environment. Some details of the partonic energy loss process in PyQM can also be selected, such as the fraction of the recoil in PyQM transferred to the nucleus, the modeling options for quark/hadron transverse momentum generated by the medium, the multiplicity and energy of the generated gluons, and other options. The main program is DPMJet, which uses PYTHIA6 to handle elementary interactions and fragmentation. PyQM handles this directly after elementary interactions in PYTHIA6, while DPMJet handles nuclear geometry and, after fragmentation by PYTHIA6, DPMJet takes care of the nuclear evaporation by FLUKA. As described earlier, at high x Bj the process γ * + q → q dominates, and at low x Bj the process γ * + g → q q dominates.
V. MEDIUM-INDUCED GLUON RADIATION: PyQM In nuclear DIS processes, the partons emerging from hard scattering will, for a time τ c , travel as a colored particle  [48] , and experience multiple rescatterings on the nucleus remnant. The parton energy degradation-or parton energy losscaused by medium-stimulated gluon radiation is an important process to understand the suppression of large momentum hadron production observed in semi-inclusive nuclear DIS processes at various facilities  [49]. In BeAGLE, the PyQM module is used to simulate medium-induced gluon radiation and quark energy loss. For each quark and gluon generated by PYTHIA6, the amount E of energy radiated by a parton traveling through a nucleus is stochastically generated using "quenching weights" calculated by Salgado and Wiedemann  [47] , that provide the probability distribution P( E ; ω c , R) in the energy E.
Process x of the nuclear medium is taken into consideration by using a Woods-Saxon density distribution  [50]  to randomly generate the struck quark production point, and calculate its average inmedium path length d. The energy loss E is then calculated using quenching weights with L = d. The partonic final state generated by PYTHIA6 is then modified by removing the appropriate amount of energy from each of the partons generated in the hard scattering, and furthermore adding gluons to the final state, or letting these be absorbed by the nucleus remnant as discussed below. Finally, hadronization of this modified final state is handled as usual by PYTHIA6's implementation of the Lund string fragmentation model. A more detailed description of PyQM can be found in  [51]. The in-medium interactions of non partonic states generated by PYTHIA6- especially soft hadrons-are handled by BeAGLE's DPMJET module.
VI. RESULTS: COMPARISON TO E665 EXPERIMENTAL DATA The analysis  [33]  made by the E665 experiment compares μXe and μD data and pXe scattering from the NA5 collaboration to study nuclear effects in the hadronic system with the grey tracks in an event being the main tool in the comparisons. They reported that the average multiplicity n g of the grey tracks is significantly lower with μXe scattering than with pXe scattering, consistent with expectations due to the absence of initial state interactions between the incident particle and the nucleus in the muon scattering data. The E665 measurements were performed with a muon beam with an average energy of 490 GeV. Since particle identification detector information was not available, a partial identification of protons was used based on the physics expectation, considering that in μD scattering with E μ = 490 GeV and W > 8 GeV about 50% of the positive hadrons with x F (m π ) < -0.
At large n g , depletion of hadrons in the projectile fragmentation region is seen, presumably due to energy loss in projectile or cascade interactions. In the μXe scattering, the events with large n g account for only a tiny fraction of all events. Compared to the pXe data, the μXe data showed a stronger depletion of fast hadrons than would be expected from the number of projectile collisions. In Fig. 2  we can also see the multiplicity ratio simulated as a function of the number of grey tracks n g for q = 0. 5 GeV 2 /fm. The data consider up to seven grey tracks, but in BeAGLE we predicted up to 12. The different colors indicate the different gluon radiation options in the PyQM module. Red represents no gluon radiation option, pink represents a hard gluon whose energy is E = ω hard , green represents the radiation of a hard gluon with at least energy ω hard [Eq.
VII. CONCLUSIONS We find that the grey track production is dominated by interactions with in-medium hadrons in the backward region, where grey tracks are quite numerous. We have improved the PyQM module in BeAGLE to offer four options implementing partonic energy loss. Using a comparison of BeAGLE simulation to E665 grey track data we find that grey tracks are unaffected by such modifications for the forward production. We see a strong correlation between the number of grey tracks and the in-medium path length for lower values of n g , an FIG. 4. In-medium path length calculated by BeAGLE from the interaction point to the edge of the nucleus as a function of the number of grey tracks, n g. This plot corresponds to BeAGLE with the option no gluons and transport coefficient q = 0. 5 GeV 2 /fm.
ACKNOWLEDGMENTS The authors thank  Paul Hoyer  and  Stan Brodsky  for stimulating and informative discussions concerning the physics processes of the parton-level scattering and production in the target reference frame and in the infinite momentum frame. The authors thank  Elke Aschenaur  for hosting a visit of C. R. to  Brookhaven National Laboratory  in the first stages of this work, and for support throughout the project. C. R. thanks  Laboratoire de Physique des 2 Infinis Irène Joliot-Curie ,  CNRS -Université Paris-Saclay  for extended research visits, and support throughout this project. This work was supported in part by the  National Agency for Research and Development (ANID) /DOCTORADO BECA NACIONAL, Chile/2017-21171926, by  PUCV   DEA 1-2020 , by  UTFSM   DGIIP 2-2020-2021 , by  ANID PIA  Grant No.
The dataset includes previously published data  (18). Mechanical Turk Experiments. Behavioral experiments were conducted on the Amazon Mechanical Turk platform through psiTurk  (33). Initial versions of the experiments were conducted in the laboratory (five subjects) with eye tracking to obtain a baseline level of subject performance that could be compared with the performance on Mechanical Turk, where subjects could not be monitored (SI Appendix, Fig. S5 ). All participants provided informed consent and received monetary compensation for participation in the experiments. All experiments were conducted according to protocols approved by the Institutional Review Board at Boston Children's Hospital. Responses from a Mechanical Turk subject were included if they attained the minimum in-laboratory accuracy, even if the subject did not finish the whole experiment.
We conducted pairwise permutation tests by permuting image assignment to categories for 10,000 permutations. P values associated with Pearson's r (experiment 6) were calculated using the exact distribution for the null hypothesis that the two variables were drawn from a bivariate normal distribution with zero covariance, as implemented in the Python library "scipy"  (37). One or two tailedness of tests and other types of tests are noted in the text and were implemented using the Python library "scipy. stats. " P values for multiple comparisons were corrected to control false discovery rate at the level of 0. 05 using the two-stage Benjamini-Krieger-Yekutieli procedure  (38)  as implemented in the Python library "statsmodels"  (39). Data Availability. Psychophysics and neural recording data have been deposited on the lab website and are publicly accessible (https://klab.
The modern communications landscape requires reliable, high-speed, highthroughput and secure links and sessions between user equipment instances and the data network. The 5G core implements the newly defined 3GPP network architecture enabling faster connectivity, low latency, higher bit rates and network reliability. The full potential of this set of networks will support a set of critical Internet of Things (IoT) and industrial use cases. Nevertheless, several components and interfaces of the Next-Generation Radio Access Network (NG-RAN) have proven to be vulnerable to attacks that can potentially obstruct the network's capability to provide reliable end-to-end communication services. Various inherent security flaws and protocol-specific weaknesses have also been identified within the 5G core itself.
Methods In this paper we examine a set of attacks which are implemented inside the 5G core. The method used for testing and validating the identified set of attacks is purely experimental. As documented in detail in 6, we created a small-footprint 5G testbed to perform the attacks. Our methodology involved the formulation of the appropriate packets to implement nominal control-plane signalling for the control of subscriber sessions. Formulation of said packets was implemented using scapy. We assumed that an attacker has already gained access to the N4 interface of the 5G core. Moreover, in order to test our applied methods in a realistic scenario, we wrote a set of python scripts to simulate two swarms of UAVs. Said two swarms are interfacing via an established 5G tunnel. The attacks are considered successful when connectivity between the two swarms is effectively disrupted.
When utilising the proposed mitigation methodology, the authors observed that only minimal negative effects were identified. The authors conclude that while this inter-slice isolation approach is effective, if introduces measurable computational overhead. As is the case with the previously analysed related research, the key differentiating factor with our own work is the fact that for our research, we assume a compromised network function, which we exploit to cut off the communication tunnel between a specific subscriber session and the data network. Correspondingly, in  [10] , Yal et. al. present a 5G testbed and deployment framework with the purpose of interconnecting infrastructure in multiple sites so as to form a single 5G end-to-end facility. Saedi et. al. in  [11]  investigate Rogue Base Station (RBS) attacks against cellular networks and subscribers, mainly in the context of Vehicle-to-everything (V2X) ad hoc communications.
Unauthorised PFCP Session Deletion Request The first attack scenario involves the transmission of malicious PFCP session deletion control messages. The unauthorised PFCP Session Deletion Request is instantiated from the SMF. The target of this attack is the UPF, which handles processes and forwards user data to the DN. The goal of this attack is to disassociate a targeted UE from the DN. More specifically, the script targets the PDU sessions between the clients and the DN in a such manner that does not disassociate the UE from the 5G RAN or the Core network, but rather only severs their connectivity to the DN. This attack is implemented on the N4 interface, and the impact can be observed in the N3 interface. The only way to re-associate an affected UE is re-initiating the attachment procedure: the affected UE can either re-start its session or enter the range of another gNB, at which event a new SEID will be attached to the UE's PDU session and the attack's effect will be stopped.
Unauthorised PFCP Session Establishment Flood The PFCP Flood attack is instantiated from the SMF of the 5G core network. The target of this attack is the UPF, which handles processes and forwards user data to the DN. The goal of this flood attack is the exhaustion of the UPF's resources to handle legitimate Session Establishment Requests and Heartbeat Requests. This will potentially hinder the capability of the 5G core to successfully formulate new PDU sessions between clients and DN. Algorithm 3 describes the procedure for the implementation of this attack in detail. Essentially, this attack is implemented on the N4 interface, and the impact can be observed in the intermediate interfaces. The SEID is randomised for each session establishment request. The script written to implement this attack receives the following input:.
Complementary to this, we implemented the Open5GS Webui functionality, to register UEs to the data network. Fig. 6  showcases the configuration we used for the subscriber registration. Alternatively, we were able to interface directly with the underlying mongodb database (also a running as a containerised process) and register the subscribers directly, using the open5gs-dbctl script available on GitHub [15], inside the mongodb container. The Command-Line Interface (CLI) tool to register the subscribers proved to be invaluable to automate the registration of numerous UEs. For our tests, the following parameters were used to register a virtualised UE: • IMSI: 208930000000001 • KEY: 0C0A34601D4F07677303652C0462535B • OPC: 63bfa50ee6523365ff14c1f45f88737d After registering the subscriber with the parameters listed above, we were able to instantiate UEs and set up PDU sessions, as well as the appropriate interfaces.
This attack was validated by generating the traffic with a Python script from the UPF and checking the incoming packets at the UPF's end. Lastly, in the case of the sixth attack of the list above (Unauthorised UPF Forwarding Rules Misconfiguration), the scenario involved gaining shell access directly to the UPF and modifying the forwarding rule, as specified in 5. 4. This attack was validated by checking whether the affected UEs still had access to the DN. The attack was successful since the UEs are not able to access the DN. Observing the logs of the UPF we can see that following the transmission of the illegitimate PFCP Session Deletion Request, the UPF indeed removes the targeted session. [nas] [debug] Selected integrity  [2]   We can deduct that radio-level signalling is completely unaffected, and from the perspective of the user, the UE functions normally.
Funding Intermob study is supported by the  French National Research Agency  in the framework of the " Investissement d'avenir" IDEX programme  ( ANR-15-IDEX-02 ) for the  Mobil'Air research program. It also received support from the funding partners of the  IResP (Institute  for public health research) in the framework of the 2018 General call for projects -Prevention and Health Promotion strand ( LI-CHALABAEV-AAP18-PREV-002 ), the  Ambition Research Pack 2019 of the Auvergne-Rhône-Alpes Region , the SMAAG and, the  Grenoble-Alpes Metropole. The funding sources had no role in determining the design nor the execution, analysis, or interpretation of the data.
The known background of few-electron S2s from photoionization of impurities or metal surfaces is expected to be observed within the maximum drift time. As such we search for few-electron S2s only in the orange shaded region in this data mode, before any photoionization S2s are expected. However, XENON1T used a real-time software trigger, described in Ref. [17] , to decide whether a particle interaction had occurred within the TPC. The trigger was able to distinguish S1 and S2 signals by recognizing the tight time coincidence of S1 signals relative to the broader S2 signals. In the absence of this software trigger signal, data were not stored. After classification, the trigger logic is made based on the number of pulses from individual channels that contribute to the signal. At the analysis threshold of 150 PE used during the main science run of XENON1T  [5] , referred to as SR1, the trigger accepts over 98% of valid S2s in the center of the TPC with a background interaction rate of ∼5 Hz  [17].
In the inset of Fig. 1  an example of the digitized data from this trigger is shown, where the trigger resulted from the S1 signal. Data is stored 1 ms before each trigger, which allows smaller S2 and S1 signals, which would not have resulted in a trigger, to be recorded. This data is then processed with the customdeveloped PAX data processor  [18] , which identifies clusters of digitized photon signals within the ∼2 ms of recorded data, and classifies them as S1 or S2 signals. We will refer to this dataset as triggered data. In order to exclude prompt photoionization S2s, we exclude any few-electron signals which occur within 1 ms after any S1, or any S2 greater than 150 PE. Additionally, we exclude 75 μs prior to any trigger, as we observe that for interactions that occur within the GXe region above the anode, the S1 is occasionally misclassified as an S2.