dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions The DQSEGDB software has been tremendously successful in serving the GW astronomy community. This set of database, backend, frontend and client software has provided rapid access to the DQ segments needed by the LVC for all GW detections made thus far. The speed and reliability of the database combined with its clean, RESTful API has resulted in the design of new tools that enable scientists to more rapidly and easily understand the IFOs in the GW detection network.
ter for Theoretical Physics South American Institute for Fundamental Research (ICTP-SAIFR), the Research Grants Council of Hong Kong, the National Natural Science Foundation of China (NSFC), the Leverhulme Trust, the Research Corporation, the Ministry of Science and Technology (MOST), Taiwan and the Kavli Foundation. The authors gratefully for computational resources provided by the NSF, STFC, INFN and CNRS, and supported by National Science Foundation Grants PHY-1626190, PHY-1700765, PHY-0757058 and PHY-0823459. The authors gratefully acknowledge the contributions of Larne Pekowsky and Ping Wei for the development, support and improvement of the original LIGO segment database and Leone Bosi for the development of the original Virgo database.
lists some metrics of the performance of the server in 2016 and 2020. The database has grown by a factor of O(100) over this time, and the results demonstrate that the performance of the server has not changed despite this increase in size. The server also responds to O(5) times more requests per day now than in 2016, averaging 7. 178 requests per second. Demonstrating performance stability of the DQSEGDB service. In 2020, the database contains O(100) times more data, and responds to O(5) times more requests per second with nearly identical performance compared to 2016 values.
Acknowledgements The authors gratefully acknowledge the support of the  United States National Science Foundation  ( NSF ) for the construction and operation of the  LIGO Laboratory  and  Advanced LIGO  as well as the  Science and Technology Facilities Council (STFC)  of the United Kingdom, the  Max-Planck-Society (MPS) , and the  State of Niedersachsen/Germany  for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. The authors gratefully acknowledge the  National Science Foundation  Grants  PHY-1700765  and  PHY-1104371  for the support of this project. Additional support for Advanced LIGO was provided by the  Australian Research Council. The authors gratefully acknowledge the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  French Centre National de la Recherche Scientifique (CNRS)  and the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , for the construction and operation of the Virgo detector, their support in all software developments, and the creation and support of the EGO consortium.
By M12, most of the participants had already received two doses of either vaccine and were invited to come for a cross-sectional visit 2 weeks (window 12-19 days) after the second dose was administered (N=342, BNT162b2 N=271, mRNA-1273 N =71). Finger prick blood was collected from the subset who visited at M9, and 10 mL of venous blood was collected from all participants at M12. Plasma was isolated and cryopreserved at -80ºC. We collected retrospective data on the vaccination dates, related adverse events (AEs) and COVID-19 infection and symptoms. We also collected information on new SARS-CoV-2 infection episodes in this cohort until 6 months after vaccination (M18) through the Occupational Health department at the HCB. We had previously collected demographic data and information on chronic medication and smoking habits.
Data for each participant were collected and managed using REDCap version 8. 8. 2 hosted at ISGlobal through a standardized electronic questionnaire as previously described  [36]. REDCap (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies  [39, 40] , providing 1) an intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for data integration and interoperability with external sources.
Neutralizing antibodies For feasibility reasons, we selected 165 samples from the study visit M12 with a balanced representation of BNT162b2 and mRNA-1273 vaccinees and non-vaccinated participants (previously exposed and naive individuals). We already had pre-vaccination neutralization data from 33 of the selected 165 individuals  [37]. Plasma neutralizing capacity was assessed as the percentage of inhibition of RBD binding to ACE2 receptor and was measured through a flow cytometric-based assay that correlates with a validated pseudovirus neutralization assay  [37]. Briefly, a murine stable cell line expressing the ACE2 receptor was incubated with RBD-mFc fusion protein, composed of RBD fused to the Fc region of murine IgG1, previously exposed to the different plasma samples at a 1:400 dilution.
Analysis of antibody levels after the second dose included only data from samples collected 12-19 days after vaccination, while for the first dose we included data from all samples collected 7 or more days after vaccination, as no previous visit window was established. Groups were compared using the Wilcoxon Sum Rank test for continuous non-parametric variables and with the Wilcoxon Signed-Sum Rank Test for paired continuous data. Correlations between continuous variables were analyzed using linear regression models and Spearman's rank test. Locally estimated scatterplot smoothing (LOESS) plots were used to visualize trends in antibody levels over days post vaccination, days post-symptom onset (PSO) or post rRT-PCR diagnosis. Univariable and multivariable linear regression models were fitted to assess factors associated with antibody responses to SARS-CoV-2 RBD and S antigens and their neutralization capacity (%) after vaccination among exposed and naive individuals, and overall.
Predictor variables that had a P-value of 0. 2 in the univariable models were selected for stepwise multivariable models performed with the function stepAIC (R package MASS). The betas obtained in each model for each of the predictor variables were transformed into a percentage of antibody increase for easier interpretation. For continuous log 10 -transformed variables (log-log model) the beta transformed value (%) was calculated with the formula ((10^(beta*log 10 (1. 1)))-1)*100. This represents the effect (in percentage) on IgG levels of a 10% increase in the corresponding predictor variable. For categorical predictor variables (log-linear models), the beta transformed value (%) was calculated with the formula ((10^beta)-1)*100. This gives the difference (in percentage) in IgG levels between the reference and the study group.
Characteristics of study participants From the 578 participants recruited at baseline, 446 came to visits at M9 and/or M12, with 414 participants sampled at M12. We measured the levels of IgA, IgG and IgM to SARS-CoV-2 antigens in blood samples from both visits. Of the 414 HCW visited at M12, 360 (81%) had received one (N= 18, 1 BNT162b2 and 17 mRNA-1273) or two doses (N= 342) of the mRNA vaccines. Seventy-six percent of the 360 HCW received BNT162b2 and 24% mRNA-1273 (Table  1 ). Most of the study participants were females (73%) and had a mean age of 42. 7 (SD 11. 65) years. Around 20% had underlying comorbidities and 22% were under chronic medication (Table  1 ). Thirty-two per cent of all participants and 22% of those vaccinated had previously been infected by SARS-CoV-2 according to rRT-PCR or serology data (Table  1 ).
Acknowledgments We thank the participation of HCW who are committed to this study and are key personnel facing the pandemic. We are grateful to  Pau Cisteró ,  Chenjerai Jairoce ,  Selena Alonso ,  Rebeca Santano ,  Sarah Williams ,  Montserrat Lamoglia ,  Neus Rosell ,  Angeline Cruz ,  Eugénia Chóliz ,  Antía Figueroa-Romero ,  Silvia Folchs ,  Jochen Hecht ,  Mikel J. Martínez ,  Núria Pey ,  Patricia Sotomayor  and  Sara Torres  who participated in the field and/or laboratory work during previous visits. We also thank the administrative department in ISGlobal, and  Gemma Ruiz-Olalla , and  Sergi Sanz  for statistical advice. Maria José Molina  was registered in the  EMJMD LIVE (Erasmus+ Mundus Joint Master Degree Leading International Vaccinology Education ), cofunded by the  EACEA (Education, Audiovisual and Culture Executive Agency , award  2015-2323 ) of the European commission, and received a scholarship from the  EACEA.
This work was supported by  Institut de Salut Global de Barcelona (ISGlobal)  internal funds, in-kind contributions from  Hospital Clínic de Barcelona , the  Fundació Privada Daniel Bravo Andreu , and  European Institute of Innovation and Technology (EIT) Health  (grant number  20877 ), supported by the  European Institute of Innovation and Technology , a body of the European Union receiving support from the  H2020 Research and Innovation Programme. We acknowledge support from the  Spanish Ministry of Science and Innovation and State Research Agency  through the " Centro de Excelencia Severo Ochoa 2019-2023" Program  ( CEX2018-000806-S) , and support from the  Generalitat de Catalunya  through the  CERCA Program. L. I. work was supported by  PID2019-110810RB-I00  grant from the  Spanish Ministry of Science & Innovation.
Introduction The LHCb detector  [1]  is undergoing a major upgrade in preparation of the Run 3 data taking at the LHC, starting in 2021  [2]. The expected delivered instantaneous luminosity is L = 2 × 10 33 cm 2 s -1 , corresponding to an average of seven proton-proton interactions per bunch collision. The entire charged particle reconstruction (tracking) system of the LHCb detector is renewed as part of this upgrade. In particular, the tracker placed downstream of the LHCb dipole magnet is replaced by a scintillating fibre tracker (SciFi) described in detail in Ref. [3]. The SciFi consists of three stations (T1, T2, T3), each composed of four layers of stacked scintillating fibres. The layers within one station are separated from each other by an air-filled gap of 50 mm and they are oriented in a stereo configuration (x-u-v-x).
The HLT1 stage performs a partial reconstruction of the event in order to select general heavy-flavour physics signatures, such as tracks from displaced vertices, tracks with high transverse momentum or muons carrying large transverse momentum. The HLT2 reconstruction exploits the reduced event rate after the HLT1 selections to perform a full reconstruction of the event, adding offlinequality particle identification information and aiming at reconstructing all the tracks in the event. This reconstruction also benefits from the real-time alignment and calibration procedure developed for the Run 2 data taking  [5]. Within this scheme, and in its current version, the Hybrid seeding is designed to be executed within HLT2. The current goal is to run the HLT2 stage at a frequency of 1 MHz using around 1000 CPU nodes  [4].
Results Our proposed system was successfully used by an increasing number of users mainly in the context of use cases and field trials of M-Sec Project. The Blockchain Marketplace, integrating very efficient and scientific sound solutions for sensor management, data handling and identity management as described in previous sections, was utilized as a central point for data collection during field trials in use cases. The data and user created content were available for trading in both Japan and Europe while ensuring security on all layers through blockchain and other mechanisms. Different stakeholders participated such as citizens sharing photos, plus using the smart contracts coupons system, and members from universities and research institutions, cities, and companies. During dedicated events, users were able to register and use the functionalities of the platform, exchange information, and provide and purchase sensor data and user-created content as well.
We should note that a lot of effort was given to operate the Blockchain Marketplace in a secure environment while considering the requirements imposed by GDPR (https:// gdpr. eu/, accessed on 2 September 2021) and APPI (https://www. ppc. go. jp/en/, accessed on 2 September 2021). Indicatively, we have conducted a lot of research on coupling encrypted databases with blockchain technologies, thus making the synergy of off-chain and on-chain storage and processing of data possible, a characteristic which enhances security considerably, while still ensuring data reliability and users' privacy. The system was evaluated in five different cross-border pilots in different cities in EU and Japan (https://www. msecproject. eu/use-cases/, accessed on 2 September 2021). Users were able to interact with the proposed distributed system, by registering and accessing the different user interfaces.
End-users, having the option of being both buyers and sellers, could upload user-generated content (photos, videos etc. ), purchase content, sensor data, datasets and access to past, current and future sensor data as well by using dedicated created blockchain-based Tokens. These Tokens were also used as motivation mechanism and as a means to participate in contests. As a result, more than 10,000 units of virtual Tokens were exchanged for virtual goods trades, regarding thousands of actual resources and data, by verified users of the Blockchain Marketplace.
In the area of smartwatch IoT devices, they propose a solution based on a permissioned Hyperledger Fabric blockchain, which uses smart-contracts to handle data ownership and trading between all stakeholders of this IoT ecosystem, smartwatch owners, manufacturers, and data analytics companies. Thus, the proposed solution addresses the issues of trust and data management and they agree that future research should explore issues of dataprivacy and the tradeoff between public verifiability and privacy, as well as effective model delegation. A great amount of research has focused on the privacy issues that arise, regarding the storage of sensitive personal user data on the blockchain, due to their irreversibility and transparency  [15]. They propose the use of the decentralized peer-to-peer repository, InterPlanary File System (IPFS), as the off-chain storage of the personal data, and they handle the access to this data through a private smart contract, only for administrator users.
In the context of this research work, we treat several information and topics as well, so using SOXFire has many advantages and allows us to manage those data effectively. However, it is difficult to implement an advanced data commerce mechanism based only on SOXFire. The main reason lies in the fact that while Pub/Sub messaging model is good at handling data for many clients at the same time, this model does not guarantee enough security regarding the integrity of the data. In addition, in a Pub/Sub model-based platform, a sender cannot directly connect to a receiver, so while the receiver purchased the data of the sender, the sender cannot receive notifications about it. To this direction, in this research work, we are implementing a data-secure marketplace by collaborating SOXFire and another information technology like blockchain, as will be described in detail in the sections that follow.
Blockchains at the same time can provide a tamper-proof framework for data to be exchanged between smart city platforms, while forming the underlying technology for building the internet of value that can make a marketplace of sensors in smart city context a reality. An example of such an approach for smart cities is the M-Sec (https://www. msecproject. eu/, accessed on 2 September 2021) project. M-Sec in particular makes use of today's key technologic enablers by applying the blockchain concept to a highly decentralized IoT. A smart object, part of smart city IoT infrastructure, can be registered to a blockchain and the object remains a unique entity within the blockchain throughout its life. This gets even more apparent with plans for creating a disruptive way IoT will work in the next years, proposing new the business models and usage patterns of IoT infrastructures, providing the way where economic value can be generated from the devices for both the devices and humans.
). (iv) define, design, and implement a novel marketplace where smart objects can exchange, information, energy, and services through the use of virtual currencies, allowing real-time matching of supply and demand, enabling the creation of liquid markets with profitable business models of the IoT stakeholders. In order to enable the M-Sec paradigm, the project defines and implements a platform (comprising middleware, protocols, and tools) for the implementation of applications involving peer-to-peer decentralized interactions between objects and people in a hyperconnected smart city context. The M-Sec project brings together two smart cities (Santander, Fujisawa) and five use cases, such as securing IoT devices to enrich strolls across smart city parks in Santander, cross-border handling of heterogeneous user data (e.
System Overview and Requirements This paper introduces a distributed application that blends several technologies, including smart contracts deployed on the blockchain, web services, and databases to produce an Ethereum blockchain Dapp, enabling users to easily buy and sell IoT sensor data, by using a custom token as the payment currency. The proposed system acts as a widely used decentralized repository where sensor owners can gain value from their data. An indicative use case includes a potential sensor data buyer who compensates the data provided by paying with cryptocurrency. To accomplish this, our deployed contracts communicate with each other, verify that the buyer has sufficient funds, and perform the payment following the billing schema. M-Sec Tokens (our specifically created cryptocurrency) are transferred from the buyer's account to the owner's account and they both receive the transaction hash, as a proof of this deal.
Blockchain Marketplace The idea of data marketplace is to construct a marketplace where data integrity is present and tamperproof data can be securely distributed with secure multi-layer technologies. Marketplace's users can exchange IoT sensor data and media easily, securely and with complete anonymity. It allows the real-time matching of supply and demand, enabling the creation of liquid markets with profitable business models of the IoT stakeholders. IoT devices and humans using mobile applications and APIs are able to exchange data and value through the blockchain-based implementations, as shown in Figure  2. The implementation details of this novel marketplace, where smart objects can be exchanged through the use of virtual currencies is publicly available The implementation details of this novel marketplace, where smart objects can be exchanged through the use of virtual currencies is publicly available (https://github.
com/ MSec-H2020/IoT_Marketplace, accessed on 2 September 2021) and is based on three main parts: 1. Smart Contacts: implemented in Solidity (https://docs. soliditylang. org, accessed on 2 September 2021) programming language, a contract-oriented, high-level language whose syntax is similar to Javascript and supports inheritance, libraries, complexdefined types, and is designed to target the Ethereum Virtual Machine.
2. Web Application: The interactions among end-users, the Blockchain Marketplace and deployed smart contracts are handled by the developed web service. It is accessible through its web browser and API endpoints and the data exchanged can be distributed on off-chain and on-chain databases. We used Nodejs and Node-Red (https://nodered. org/, accessed on 2 September 2021) (a flow-based development tool) for the Back End. A relational database management system PostgreSQL (https://www. postgresql. org/, accessed on 2 September 2021) was used for optimizing system's performance and caching frequent requests.
3. User Interfaces: Implemented using HTML, Javascript, jQuery, Bootstrap to develop the different Front-End interfaces, Web3 Javascript API to handle the interaction with smart contracts. The Interfaces between the users and the blockchain provide functionalities helping users interact with the smart contracts deployed on blockchain, browse available media items and datasets from smart cities, sensors and other users, and access data they have bought. Owners are able to register their sensors to the marketplace, while a buyer browses sensors, specifies desired time period, and buys data using our dedicated created M-Sec cryptocurrency. Through the Marketplace sending transactions and reading data of transactions and smart contracts is also allowed, while users are "protected" from misreading or mistyping info when sending a transaction.
SOXFire and Data Collection In this research, SOXFire collects some information. We collect various data from Fujisawa city in Japan. First, we collect environmental data in the city. At this fieldwork, we focus on garbage trucks because they drive around the whole the city almost every day  [31]. We attached a sensor box to the garbage truck, which has some sensors measuring temperature, PM2, illuminance, and more. In addition, we installed other sensors in some restaurants in the city. Currently, checking CO 2 concentration in the restaurant is important because there is a concern by the public about this because of the increasing population density. Additionally, because of measures related to the virus, COVID-19, people keep distance among each other to prevent transmission and infection.
SOXFire and data collection In this research, SOXFire collects some information. We collect various data from Fujisawa city in Japan. First, we collect environmental data in the city. At this fieldwork, we focus on garbage trucks because they drive around the whole the city almost every day  [31]. We attached a sensor box to the garbage truck, which has some sensors measuring temperature, PM2, illuminance, and more. In addition, we installed other sensors in some restaurants in the city. Currently, checking CO2 concentration in the restaurant is important because there is a concern by the public about this because of the increasing population density. Additionally, because of measures related to the virus, COVID-19, people keep distance among each other to prevent transmission and infection.
Offchain data storage interacting with IPFS The integration of IPFS as a distributed off-chain data storage for blockchain solutions has already been applied in several works. In the field of healthcare, Kumar et al. [16]  propose the use of the IPFS as a means to preserve the security of sensitive data, by avoiding the use of a single centralized database that stores the entire information. Ali et al. [33]  combined blockchain with the IPFS to develop a decentralized effortlessly privacypreserving IoT-data storage solution. Additionally, Zou et al. [34]  propose a blockchainbased incentive anonymous reporting system that uses IPFS as an off-chain storage solution to achieve security, high-throughput, high-capacity storage, and high concurrent access, while at the same time preserving user anonymity.