dataesr/scientific-paragraphs-categorization huggingface dataset:
In  [38]  conducted research on Match-on-Card (MOC) authentication for mobile devices using models for offline machine learning. The model used by the authors was calculated by simplifying the SC of the mobile devices, registering the users, and storing the data in vectors so as not to retrain the model, comparing the characteristics obtained by deriving a binary authentication decision. The authors point out that the advantage of their model is that it can be applied to different biometrics. The software used by the authors was Java Card in a 16/32-bit version for face detection. For the experiments used 8 face images from a template and 4 recently registered facial images. The ERR results were between 2. 4 and 5. 4. %. Authentication times are on the order of 1 second. In  [39]  developed a biometric authentication system for automobiles based on EEG, the recordings collected are transformed to create unique biometric identifiers based on the acquisition of unrepeatable physiological characteristics, the system is implemented in mobile devices, the work is based on obtaining data from brain waves, which are read by the system and measure the stability of the subject, if the subject is in an altered state the system does not allow the user to enter, due because it would be in a disturbed state and it would not be possible to establish the correct state with which the system was trained, although author's work has the defect that in a disturbed state of the user in which their security is not compromised it would not be possible to access the system.
DATA PROCESSING All exposures in DELVE DR2 were processed with the DESDM "Final Cut" pipeline  (Morganson et al. 2018)  as implemented for the processing of DES DR2 (DES Collaboration 2021). Data were reduced and detrended using seasonally averaged bias and flat images, and fullexposure sky background subtraction was performed  (Bernstein et al. 2018 ). SourceExtractor  (Bertin & Arnouts 1996)  and PSFEx (Bertin 2011) were used to automate source detection and photometric measurement. Astrometric calibration was performed against Gaia DR2 using SCAMP  (Bertin 2006 ). 4 We note that DELVE DR2 does not include the production of coadded images (e. g. , DES Collaboration 2018, 2021); however, we expect that coadded images will be produced as part of a future DELVE data release. Photometric zeropoints for each DECam CCD were derived independently for the DES exposures and the other DECam exposures included in DELVE DR2.
A b = R b × E(B -V ), 3. The radius for matching sources within and across bands has been reduced from 1 to 0. 5. This change was motivated by the excellent astrometric precision of the DELVE DR1 catalog (∼ 30 mas). The change, along with improvements in the algorithm for splitting pairs of closely separated objects, reduces the number of objects that are spuriously merged. 4. DATA RELEASE DELVE DR2 is derived from DECam data covering > 20,000 deg 2 in each of the g, r, i, z bands, while ∼ 17,000 deg 2 are jointly covered in all four bands (Figure  1 ). DELVE DR2 consists of a catalog of ∼ 2. 5 billion unique astronomical objects, with ∼ 618 million objects that have measurements in all four bands. This section describes the characterization of the sky coverage, astrometry, photometry, depth, and object classification of the DELVE DR2 catalog.
The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) do e-Universo (CNPq grant 465376/2014-2).
For both methods, the  value to be used is sampled randomly from the data  distribution, such that the ensemble of many events follows the  distribution of the data. If the detector signals were read out without any information loss, the two methods would give identical results. However, in reality some information loss occurs due to readout thresholds applied or custom compression algorithms designed to reduce the data volume. This can lead to differences in the reconstructed quantities used in physics analyses. While in most cases for ATLAS these differences were found to be negligible, in some cases corrections were derived to reduce the impact on physics analyses, as is discussed in Sections 5-8. Within the ATLAS Collaboration, a significant validation effort took place to ensure that this presampled pile-up simulation chain reproduces the results from the standard pile-up simulation chain accurately, so that there is no impact on physics analyses whether one or the other is used.
However, the pile-up emulation with data is not yet validated and not the subject of this article. The article is organised as follows. A description of the ATLAS detector is given in Section 2, highlighting the aspects that are most relevant for the pile-up emulation. Section 3 describes both the standard and presampled pile-up simulation chain, and Section 4 compares their CPU and memory performances. In Sections 5-8 the challenges in the inner detector, calorimeters, muon system and trigger are described and comparisons of the impact of the old and new methods are shown. For all studies presented in this article, unless otherwise stated, the distribution of the average number of events per bunch crossing follows the distribution observed in the ATLAS data in 2017, with an average  value of 37.
ATLAS detector The ATLAS detector  [14]  at the LHC covers nearly the entire solid angle around the collision point. It consists of an inner tracking detector surrounded by a thin superconducting solenoid, electromagnetic and hadronic calorimeters, and a muon spectrometer incorporating three large superconducting toroidal magnets. A two-level trigger system is used to select interesting events  [15]. The first-level (L1) trigger is implemented in hardware and uses a subset of detector information to reduce the event rate from 40 MHz to 100 kHz. This is followed by a software-based high-level trigger (HLT) which reduces the event rate to an average of 1 kHz. At the LHC, typically 2400 bunches from each of the two proton beams cross each other at the ATLAS interaction point per beam revolution, with one bunch crossing (BC) taking place every 25 ns.
Overview of simulation chain As is described above, the ATLAS simulation chain  [6] , used to produce MC samples to be used in physics and performance studies, is divided into three steps: generation of the event and immediate decays, particle tracking and physics interactions in the detector, based on G 4 (G4), and digitisation of the energy deposited in the sensitive regions of the detector into voltages and currents to emulate the readout of the ATLAS detector. This simulation chain is integrated into the ATLAS software framework, A  [18]. Finally, a series of reconstruction algorithms is applied in the same way as for the data, where final physics objects such as jets, muons and electrons are reconstructed  [14]. Each step can be run as an individual task, but in order to save disk space the digitisation step is usually performed in the same task as the reconstruction step, such that the intermediate output format from the digitisation step only needs to be stored locally on the computing node and can be discarded after the reconstruction step is finished.
The G4 simulation step is run by itself and, since it is independent of the detector readout configuration, the trigger and the pile-up, it is often run significantly earlier than the digitisation and reconstruction, which depend on these aspects. The G4 simulation is the most CPU intensive and thus it is desirable to run this as rarely as possible. The ATLAS digitisation software converts the energy deposits (HITS) produced by the G4 simulation in the sensitive elements into detector response objects, known as digits. A digit is produced when the voltage or current of a particular readout channel rises above a preconfigured threshold within a particular time window. Some of the subdetectors read out just the triggered BC, while others read out several bunch crossings, creating digits for each.
For each digit, some subdetectors (e. g. SCT) record only the fact that a given threshold has been exceeded, while others (e. g. Pixel or LAr) also retain information related to the amplitude. The digits of each subdetector are written out as Raw Data Objects (RDOs), which contain information about the readout channel identifier and the raw data that is sent from the detector front-end electronics. For any given hard-scatter interaction, the additional pile-up interactions must be included in a realistic model of the detector response. For this purpose, minimum-bias events are generated using the P event generator with the NNPDF2. 3LO [19]  parton distribution function and the A3  [20]  set of tuned parameters, then simulated and stored in separate files. In the current standard pile-up simulation chain, the simulation files of both the hard-scatter event and the desired number of minimum-bias events are read in concurrently at the digitisation step and the HITS are combined.
After the energy deposits in the trigger BC due to all contributing BCs have been combined, the detector response is emulated. This workflow is illustrated in Figure  3. The new presampled pile-up simulation chain is illustrated in Figure  4. Rather than digitising the minimumbias interactions each time a hard-scatter event is produced, a large sample of pile-up events is produced by pre-combining the simulated pile-up interactions, according to the  distribution of the data campaign, during a separate digitisation step, termed presampling  4. Here, the sampling is done exactly as for the standard pile-up, the only difference being that there is no hard-scatter event. These presampled pile-up events are written out in RDO format as pile-up RDO datasets and typically contain several million events.
Each simulated hard-scatter interaction is then digitised and combined with an event sampled from these pile-up datasets (step 3 in Figure  4 , called overlay). Here, instead of HITS for each channel, the signals of the RDO or digit (depending on the subdetector) in the hard-scatter event and the presampled event are overlaid. Since the digitisation, presampling and reconstruction steps are typically combined into a single task in the production workflow, the output is written locally to an RDO file that is then input to the reconstruction software; this local RDO file is subsequently discarded. The pile-up RDO datasets necessary for a given digitisation task are about five times smaller than the many minimum-bias HITS required in the standard pile-up simulation chain. The main benefit of the presampled pile-up simulation chain is that the CPU and I/O requirements of the digitisation are significantly lower and have a much smaller dependence on , as is discussed in Section 4.
Computing performance comparison In this section the performances of the two simulation chains are compared in terms of CPU time, memory usage and I/O. The validation in terms of physics performance is presented in subsequent sections. The main computing performance benefit of the presampled pile-up simulation chain stems from the fact a pile-up dataset is only created once per MC production campaign, and then the individual events within that dataset are used for multiple hard-scatter MC samples, as opposed to being created on demand independently for each MC sample. An MC production campaign happens typically once per data-taking period and comprises billions (B) of hard-scatter events and thousands of individual samples. A sample is defined as a set of MC events generated using the same input parameters, e.
g. a sample of  t events produced by a certain MC generator with a given set of input parameters. The same presampled pile-up event can thus be overlaid on many different hard-scatter events from different MC samples. In doing so, care needs to be taken to ensure that no undesirable effects on physics analyses occur due to reusing the same pile-up events, as is discussed below. In ATLAS, typically 70% of the CPU resources are devoted to MC production via the simulation chain; the remainder is used for data processing and user analyses. At present, with the Run 2 pile-up profile, the simulation chain CPU usage is broken down into about 15% for event generation, 55% for G4 simulation, 20% for digitisation and 20% for other tasks (reconstruction, trigger, event writing). The presampled pile-up scheme decreases the digitisation time to a negligible level and thus reduces the overall CPU resources required for MC production by about 20%, as is discussed below.
The average CPU time per event in the standard and presampled pile-up simulation chains as a function of  is shown in Figure  5. As can be seen, both depend linearly on  but the slope is about 50 times larger for the standard pile-up than for the presampled pile-up simulation chain. For the standard pile-up simulation chain, the CPU time required at  = 70 is 7. 5 times larger than for  = 10, while for the presampled pile-up method, the corresponding increase in CPU time is only a factor of 1. 2. Extrapolating this to  = 200, the CPU time is 20 times greater than for  = 10 for the standard method and < 2 times higher for the presampled pile-up method. However, this comparison does not account for the CPU time required for the production of the presampled pileup dataset, which is needed to assess the overall CPU benefit in a realistic campaign, as is discussed below.
g. if  other ≈  digi /2, a CPU saving of 63% would be realised. These are illustrative examples that confirm the intuitive expectation that performing the digitisation just once per campaign is much more effective than doing it for each simulated hard-scatter event, as the number of presampled events needed is by construction smaller than the number of hard-scatter events. From the memory usage point of view, the presampled pile-up load is similar to the standard pile-up and well below the (soft) production limit of ∼ 2 GB per core (see Figure  6 ) for the  values observed during Run 2 and expected for Run 3. However, compared to the standard pile-up, the presampled pile-up simulation chain puts less stress on the I/O system both because, as is mentioned above, the presampled pile-up dataset files are about a factor of five smaller and because they can be read sequentially.
The sequential reading is possible because the random access necessary to combine the minimum-bias input files in the standard pile-up is now performed only once at the presampling stage. Hence, the presampled pile-up RDO production, with its heavier requirements, can be performed on a limited subset of ATLAS MC production sites designed to cope well with such workloads; the subsequent presampled pile-up simulation chain will then run on all resources available to ATLAS, utilising sites that have previously been excluded for reconstruction due to insufficient I/O or disk resources. The smaller I/O requirements from the presampled pile-up simulation chain jobs simplify the production workflow, and make it possible to transfer the pile-up datasets on demand to the computing node at a given production site, where they are needed.
If network speed is further increased in the future, it might even be possible to access them directly via the network during the job from a remote storage site. The Analysis Object Data (AOD) event size written to disk is the same for both methods, i. e. there is neither advantage nor disadvantage in using the presampled pile-up simulation chain in this regard. However, the many simulated minimum-bias events do not have to be distributed as widely any more throughout the year as they only need to be accessed once for creating the presampled events. These presampled events need to be made available widely though. It is expected that these two effects roughly cancel out but operational experience is needed to understand how to distribute the presampled sample in the most effective way.
The shaped signals are sampled and digitised by 10-bit ADCs at a frequency of 40 MHz. The sampled data are temporarily stored in a pipeline memory until an L1 trigger signal is received. Seven time samples, centred around the pulse peak, are obtained. A gain selector is used to determine which gain information is sent to the back-end electronics for event processing. By default the high-gain signal is used, unless any of the seven time samples saturates the ADC, at which point the low-gain signal is transmitted.
This then allows the unmodified trigger simulation to be performed on the merged data, and it avoids any possible bias due to merging data that have been quantised on a relatively coarse scale. The merging is performed by an additional algorithm, which is run during the pile-up merging prior to the trigger simulation to create a set of merged calorimeter towers. The merging itself uses the calorimeter object identifiers to match corresponding towers in the hard-scatter and pile-up event collections, and the amplitudes of the signals of the same towers in both events are summed. A new collection of objects containing the summed amplitudes is then created and written to the output stream. Figure  15  shows the L1Calo  T distributions in isolation regions around electrons in  →  +  -events, which are sensitive to the pile-up  T deposits close to the electrons.
Conclusions A new method for reproducing the impact of pile-up interactions on the ATLAS detector performance is presented, based on overlaying presampled pile-up events on the hard-scatter event of interest during the digitisation. The method is validated separately for each ATLAS detector system and the trigger. In all cases, it is possible to achieve good agreement with the standard pile-up simulation chain which has been used up to now. For a large variety of quantities, detailed comparisons are made between the two methods, and all the differences are found to be small, so that the impact on physics analyses is considered negligible. The presampled pile-up method is shown to use significantly less computing resources than the standard method used so far within ATLAS. For the Run 2 pile-up distribution and software, the CPU resources required for the entire MC simulation chain are reduced by around 20%.
1. To address the problem of poor AC of patients over their EHR, we suggest using a PRE-based blockchain to preserve the privacy of EHR and patients' fine-grained AC by re-encrypting the data by the patient's public key after each access by a third party (data researchers); 2. To address the problem of patient identity disclosure, we suggest storing the real identity in a private blockchain and using a unique blockchain address as a pointer to their identity; 3. To address the problem of preserving confidentiality in an EHR-sharing blockchain, we suggest using PEKS with a conjunctive keyword searchable scheme to store the encrypted index in the smart contract on a consortium Ethereum blockchain then storing the EHR in a private blockchain and periodically using a renewal key server technique to prevent compromising the key server and to solve the KGA problem.
Public blockchain network The data in a public blockchain network are accessible to the public where participants can be part of the consensus without the need for permissions. Transactions with some anonymities are available to all nodes  [18]. The participant, based on a consensus algorithm, may therefore verify a transaction and engage in the approval process, as in Proof of Stake (PoS) and Proof of Work (PoW)  [2]. The system is fully secure by repeating synchronous public blockchains with each network miner. This architecture is employed in cryptocurrency networks such as Bitcoin and Ethereum; however, it raises privacy issues. 2.
Private blockchain (permissioned) This kind of limited blockchain permits the return of an intermediary. Private blockchains strictly control the data access permission of a network. Any transaction in the network can only be verified and validated by companies or organizations with a high level of efficiency. The failure to provide a decentralized infrastructure for secure databases is a drawback of private blockchains, which are provided by public blockchains  [19]. Private blockchains can be recognized correctly as conventional centralized networks but with a strong cryptographic model for network transactions to be verified and validated  [2]. Each hospital keeps EHRs in its private blockchain, which offers the benefits of speed, privacy, cheap cost, and improved security  [20].
Blockchain-Based Searchable Encryption and Access Control Schemes This section presents how searchable encryption and access control can work together. To obtain strong conceptions of confidentiality, various technologies must be combined. The integration between PEKS and PRE schemes present in  [42]  can provide an efficient EHRs sharing protocol based on two types of blockchain: private blockchain for stored data and public blockchain for storing an EHR index. According to this research  [35] , the EHR data ciphertext is stored in the cloud, and the EHR data keyword index is stored on the blockchain. Fine-grained access control of cloud data is achieved via the attribute encryption approach, and attribute signature technology is employed to validate the authenticity of the EHR data source.
Phase 1: Registration Process In this phase, firstly, before seeing a doctor, patients must register with the hospital's blockchain network, and the system manager provides the patient with an Ethereum address which is unique and is equal to the visit cards of patients. Secondly, the system manager produces the pair key (public key, private key) for the patient and the doctor. Thirdly, the system manager generates new keywords from the key server. In this step, the preparation of the data samples was made by registering the patients, doctors, and data users (third parties) to produce the blockchain accounts for them. We use four entities in the system: medical service provider (doctor), system manager, data owner (patient) and finally data users (third parties). In this paper, the public key encryption with conjunctive keyword search features are used to permit any third party to search a document containing several keywords while using an encryption public key.
Phase 2: Data Storage In this step, firstly the doctor produces the encrypted patient's personal information and his/her EHRs with the patient's identity and sent it to the private blockchain which works as a medical server. Secondly, the doctor encrypts the index with a public key encryption using the ECDH algorithm and stores it in the smart contract-based consortium blockchain, then requests the keyword set from the key server and then encrypts the keyword set and the patient private key. Secondly, the doctor sends the encrypted EHRs to the private blockchain and the secure index (encrypted keyword and patient's private key) is stored in the smart contract-based consortium Ethereum blockchain. In this phase, the doctor first logs in to the Ethereum blockchain with the patient's address.