dataesr/scientific-paragraphs-categorization huggingface dataset:
11 standards. Wi-Fi networks are networks that comply with the 802. 11a-x specifications. The different frequency bands used by the different Wi-Fi implementations is presented in Figure  3. 25  [91]. The new Wi-Fi generation is represented by the Wi-Fi 6 protocol that enhances network bandwidth (i. e. <9. 6 Gbps) to improve data throughput per user in congested environments. Wi-Fi 6E brings the technology into 6 GHz and features more contiguous spectrum, wider channels, less interface, gigabits speed, very low latency and high capacity  [213]. The increased network capacity and the improved simultaneous communication between access points and multiple endpoints allows Wi-Fi 6 (IEEE 802. 11ax) to offer improved performance in crowded areas. Several technologies facilitate these improvements and the most important are  [214] ; firstly building upon the Orthogonal Frequency Divisional Multiple Access (OFDMA) technology, already available in previous Wi-Fi standards, gives access points the ability to divide channels into many sub-channels, i.
e. that the access points can communicate with multiple devices at the same time at a lower data rate; secondly, utilising the Multi-User Multiple-Inputand-Multiple-Output (MU-MIMO) which already are used by Wi-Fi 5 for downlinks. Wi-Fi 6 utilises MU-MIMO for uplinks and enable access points to simultaneously receive communication from multiple clients. The combination of these technologies and some others (e. g. 1024-QAM) results in more efficient networks and reduces the overall network latency. The power management for connected IoT devices is also improved. Wi-Fi 6 supports a so called "target wake time" feature, which allow the Wi-Fi access points to put IoT devices in sleep mode for a given period  [214]. The possibilities to reduce the power consumption of battery driven IoT devices can be very useful.
Wi-Fi HaLOW Wi-Fi HaLow wireless technology is based on the IEEE 802. 11ah protocol standard. The technology augments Wi-Fi by operating in the spectrum below one gigahertz (GHz) to offer longer range and lower power connectivity. Wi-Fi HaLow meets the requirements for the IoT to enable a variety of use cases in industrial, energy (e. g. smart metering), smart home/building, agricultural, and smart city environments  [91]. Some features and benefits are illustrated in Figure  3. 26. Wi-Fi HaLow utilises 900 MHz license-exempt bands to give extended range Wi-Fi networks, compared to Wi-Fi networks working in the 2. 4 GHz and 5 GHz bands. The protocol has low energy consumption, allowing for IoT implementations with large groups of stations or sensors that cooperate to share data. The protocol's low power consumption competes with Bluetooth and has the benefit of higher data rates and wider coverage range.
White-Fi White-Fi wireless technology is based on the IEEE 802. 11af protocol standard that supports WLAN operation in the he VHF and UHF bands between 54 and 790 MHz TV white space spectrum, designed for ranges up to 1 km. The data rate for IEEE 802. 11af per spatial stream reaches 26. 7 Mbit/s for 6 and 7 MHz channels and 35. 6 Mbit/s for 8 MHz channels. The maximum data rate can reach 426. 7 Mbit/s for 6 and 7 MHz channels and 568. 9 Mbit/s for the 8 MHz channels by using four spatial streams and four bonded channels. In order avoid that the system does not create any undue interference with existing television transmissions, the White-Fi can utilise cognitive radio to detect transmissions and move to alternative channels or geographic sensing by using a geographic database and a knowledge of what channels are available to avoid interference with used channels.
Li-Fi Light-based Li-Fi (light fidelity) is a wireless communication technology that uses light to transmit data and position between devices. The technology addresses some of the short comings of radio-based wireless communications and has applications for industrial IoT connectivity by improved solutions for security, scalability, bandwidth, interference, latency. Li-Fi uses the modulation of light intensity to transmit data at high speeds over the visible light (e. g. LED devices), ultraviolet, and infrared spectrums. Li-Fi can transmit at speeds of up to 100 Gbit/s. A unique feature of Li-Fi is that it combines illumination and data communication by using the same device to transmit data and to provide lighting  [92]. Figure  3. 27 shows the concept of a Li-Fi attocell network. The illumination in the room is provided by several light fixtures with each light driven by a Li-Fi modem that serves as an optical base station or access point (AP).
4G (LTE) 3GPP specified technologies such as eMTC (enhanced Machine-Type Communication), NB-IoT, and EC-GSM-IoT. The eMTC brings some LTE enhancements for MTC such as a new Power Save Mode (PSM). Release 14 brings new eMTC feature enhancements such as support for positioning and multicast, mobility for inter-frequency measurements, and higher data rates  [131]. It brings enhancements such as lower costs, reduced data rate/bandwidth, and some other protocol optimisations. Release-14 delivers new enhancements for the NB-IoT technology such as support for multicast, power consumption and latency reduction, mobility, and service continuity enhancements, etc. EC-GSM-IoT delivered EGPRS enhancements, which in combination with PSM makes GSM/EDGE systems IoT ready. This technology brings improvements such as extended coverage, support for massive number of devices: at least 50 000 per cell, improved security compared to GSM/EDGE, etc.
5G Cellular next generation 5G with high-speed mobility support and ultra-low latency is positioned to be the future of autonomous vehicles and augmented reality. 5G is also expected to enable real-time video surveillance for public safety, real-time mobile delivery of medical data sets for connected health, and several time-sensitive industrial automation applications in the future. 5G networks are expected to support the new IoT technologies, enabling IoT/IIoT device producers to develop and deploy new IoT devices and systems across multiple industries and provide IoT/IIoT applications globally. The cell radius in 2 G systems is 35 km, in 3G systems 5 km, in 4G systems 100 m, and in 5G about 25 m to reuse the available RF spectrum more efficiently and to achieve higher data densities. To implement the ultra-low latency and very high bit-rate applications, the connectivity technologies require more sizable contiguous blocks of the spectrum than those available in frequency bands that have been previously used.
Above 24 GHz high frequencies -high bands are needed to ensure the ultra-high broadband speeds. The 26 GHz and/or 28 GHz bands have the most international support in this range. High-frequency bands are essential for providing additional capacity and delivering the extremely high data rates required by some 5G eMBB applications at specific locations ("hotspots"). The 400-800 MHz of contiguous spectrum per network operator is recommended from higher frequencies to achieve good return on investment and meet service requirements. The frequency bands 24. 25-27. 5 GHz (global), 37-43. 5 GHz (global), 45. 5-47 GHz (regional/multi-country), 47. 2-48. 2 GHz (regional/multicountry) and 66-71 GHz (global) were identified for International Mobile Telecommunications (IMT) for the deployment of 5G networks by the World Radiocommunication Conference 2019 (WRC-19), that took place in Egypt, 28 October to 22 November 2019.
g. , data rate, packet drop, and latency), end user's geographical distribution, etc. , in addition to per session/user/slice instance-based monitoring, etc. and control capability to enable the customers to utilise application programming interfaces (APIs) provided by the operator to control network service. Considering different applications where the network slicing concept is used, a slice encompasses a combination of relevant network resources, functions, and assets required to fulfil a specific business case or service, including operations support system/business support system (OSS/BSS), and DevOps processes. Internal and external slices are used. The internal slices are partitions used for internal services of the provider, retaining full control and management of them and the external slices are partitions hosting customer services, appearing to the customer as dedicated networks/clouds/datacentres.
32  [74]    Cellular IoT use cases have differing connectivity requirements and can be divided into four segments as presented in Figure  3. 33. Verticals using Massive IoT include utilities with smart metering, healthcare in the form of medical wearables and transport with tracking sensors. NB-IoT and Cat-M will be able to fully co-exist in spectrum bands with 5G NR. Broadband IoT includes wide-area use cases that require higher throughput, lower latency, and larger data volumes (e. g. peak data rates in the multi-Gbps range and radio interface latency as low as 10ms) such as smart watches, drones/UAV, etc. Critical IoT includes both wide-area and local-area use cases that have requirements for extremely low latency and ultra-high reliability such as such as interactive transport systems in the automotive industry, smart grids with real-time control and distribution of renewable energy in the utilities industry, and real-time control of manufacturing robots in the manufacturing industry.
Industrial automation IoT includes very specific use cases, with the most demanding requirements coming from the manufacturing and industrial sites. The deployment of 5G New Radio (NR) will increase data rates and the broadband IoT that includes wide-area use cases that utilise higher throughput, lower latency, and larger data volumes. Critical IoT that is used for timecritical communications in wide-and local-area use cases requires guaranteed data delivery with specified latency targets, will be introduced in 5G networks with the advanced time-critical communication capabilities of 5G NR in 2021. The use cases for critical IoT include cloud-based AR/VR, robotics, IoRT, autonomous vehicles, edge computing, advanced cloud gaming, and real-time coordination and control of machines and processes.
Communication Architecture The IoT based communication architecture adopted for this case considers each meter and the respective equipment to which is connected as a "thing". The data generated from all the things is stored in the database of a central AdTA server. The communication between the things and the server is done through the private AdTA wide area network for security reasons. From the universe of the IoT communication protocols  [13] , we have considered that Wi-Fi  [14]  and LoRa  [15]  would be two appropriate standard communication technologies to employ in this use case. Wi-Fi is a well-known technology, having low cost communication modules for the meters, a low-cost Access Point (AP), and high data rates (Mbps). As low cost is a key objective, the choice of Wi-Fi as one of the selected technologies looked appropriate.
Communication Architecture 191 There might be several Wi-Fi APs installed in a WRRF. The WRRF energy meters are deployed in different WRRF equipment, e. g. , recirculating pumps, equalization pumps, ventilators, etc. The meters communicate to the nearest Wi-Fi AP, sending a message containing the meter identification, followed by the electrical measurements. The data is forwarded from the Wi-Fi AP to an AdTA Server located at the Control Centre of AdTA Headquarters (HQ) via the AdTA private communication network. The data is uploaded into the database via Java Script Object Notation (JSON) format commands. The users can access the data either via a direct connection to the database or indirectly through a Web server. For the communication system with LoRa at the physical layer, the communication architecture is similar to the one indicated for the Wi-Fi case, having as a main difference the use of a Gateway instead of the Wi-Fi AP.
Associated with each measurement, at the bottom of each box, we can see a timestamp showing the instant in which the measurement was made. This is especially important for the LoRa based meters where due to bandwidth limitations it is not possible to send all measurements in a single message. In the database, only a subset of the electrical measurements is stored, namely the total power of the three phases, the total accumulated energy of the three phases and the service time, together with the meter id and time stamp. Based on those values, we can create different graphics to show the consumption of the different meters. In Figure  4. 13 we present an example of the consumption (in kWh) of the meter MED19 in July 2019. In addition to the meter web platform, an application was also developed to configure the different meters, namely the measurement period, the energy threshold to activate the service time counter, the current transformer ratio, and the electrical parameters or internal registers that can be read on demand.
Besides the referred functionality, this application allows also to display, in graphical form, the relation between the voltage and current of the three phases, which is very useful in the meter installation, as it allows an easy detection of a wrong configuration and correct it on the spot. Another interesting functionality of the developed application is to allow the calculation of the Discrete Fourier Transform of the voltage and current of a specific meter in order to calculate the respective harmonics. In the example presented in Figure  4. 15, we can see a low harmonic content of both the voltage and current, still with significant higher values for the current. At a next step, the data provided from the energy meters will be integrated in the AQUASAFE platform. The data is stored in the database of the AdTA server in real time as described and, automatically, the AQUASAFE imports all the energy meter information from the data files to the AQUASAFE  database recurring to an importer already installed in the AQUASAFE platform.
In the context of MONICA, Keycloak 3  is used as the Identity Provider. Authorisation rules are maintained as user or group attributes in Keycloak and can be defined by administrators based on path/topic and methods, e. g. to limit permissions for certain users to perform only HTTP GET requests on a certain REST API or to allow only MQTT subscriptions on certain topics. The rules are provided by the OpenID Connect provider as a custom claim in the access token to the Border Gateway. To improve performance, Border Gateway caches the access tokens during the span of their lifetime in a Redis database.
The UML of the OGC data model is reported in Figure  5. 3. The metadata generated by the SCRAL concerning "Things", "Sensors" etc. as well as the concrete "Observations" (i. e. the sensor data) are permanently stored in the backend using an open source PostgreSQL database. In addition, the OGC server API provides endpoints for maintaining the metadata and writing and retrieving the data over the web in JSON format. The OGC server can also subscribe to different MQTT topics in order to fetch observations from sensors and to store them. Most of the sensor data collected during the MONICA pilot events are stored in the OGC server. Some of the exceptions include raw camera measurements which are processed locally. In case of multimedia data, only aggregated information is stored in the OGC server.
Sound Monitoring Solutions for Large Open-Air Events 243 Input from HLDF-AD and GOST is received as crisp numerical values. A fuzzification module is then used, responsible for the mapping of such values to fuzzy sets. The resulting sets can be used in turn to activate all relevant rules by calculating their membership functions. The knowledge base is the process model of the system. It consists of a database that contains the rule structure for each different venue and event that the MONICA project is employed, constructed based on expert input. The fuzzy rules together with the input from the fuzzification module are combined to generate output by the decision-making module. The defuzzification module aggregates the rule consequents and selects the highest-rated one to produce a crisp output as the outcome of the Fuzzy Logic Device (FLD).
The application container also encases the functionality of data management, with the modules of local dynamic maps (LDM) and the pothole detector. LDM is a database that achieves integrated management of map and vehicle information (functional requirement of context awareness). It contains information on real-world and conceptual objects that have an influence on the traffic flow. The bundle of the pothole detector represents the implementation of the pothole detection algorithm. It is based on data fusion techniques in order to implement the concept of "virtual sensors". This module collects data from multiple sensors on the vehicle (IoT in-vehicle components or OEM in-vehicle components), processes the various data and sends the results of this elaboration to the cloud oneM2M platform, RSUs or other vehicles via the communication system.
Acknowledgements The  IoT European Research Cluster -European Research Cluster  on the Internet of Things (IERC) maintains its  Strategic Research and Innovation Agenda  (SRIA), considering its experiences and the results from the on-going exchange among European and international experts. The present document builds on 2010 , 2011 , 2012, 2013 , 2014 , 2015 , 2016 , 2017 and  2018 Strategic Strategic Research and Innovation Agendas. The  IoT European Research Cluster SRIA  is part of a continuous IoT community dialogue supported by the  EC DG Connect -Communications Networks, Content and Technology, E4 -Internet of Things Unit  for the European and international IoT stakeholders. The result is a lively document that is updated every year with expert feedback from on-going and future projects financed by the EC.
Acknowledgements The research leading to this work is being carried out as a part of the  InteGrid project (Intelligent Grid Technologies for Renewables Integration and Interactive Consumer Participation Enabling Interoperable Market Solutions and Interconnected Stakeholders) , which received funding from the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation 204 Real-time Management of Energy Consumption  under grant agreement No. 731218. The sole responsibility for the content of this publication lies with the authors.
Acknowledgements The content of this chapter summarizes the work of different projects, coordinated by Brazilian institutions, such as the  Centro Interdisciplinar de Tecnologias Interativas (CITI)  from the  University of São Paulo , and  LSI-TEC. Also, a number of institutions provided financial support for developing these projects, such as  SMART Modular Technologies 6  , the  Ministry of Science, Technology, Innovation and Communications of Brazil (MCTIC)   7  , the  Brazilian Development Bank (BNDES) , the  Coordination for the Improvement of Higher Education Personnel (CAPES)  8 , the  National Council for Scientific and Technological Development (CNPq)  9 , the  University of São Paulo (USP)  10 ,  Samsung   11  ,  LG Electronics   12  , and  Santander Bank 13. Further acknowledgements include: • SwarmOS:  Mr.
• Device layer: comprises the Fixed-Time Traffic Light Connected Controllers, which are able to receive remote updates to its local database of traffic light schedules. These devices can also notify the Control Center about any emergency or problem detected in the traffic light function;• Network layer: pursuing the flexibility of semaphore controllers manufacturers, the use of a standardized and open communication protocol is mandatory. The solution will use an event-oriented IoT messaging protocol and a long-range, low-cost, IoT-suitable communication, such as LoRaWAN, which uses sub-gigahertz frequencies for extremely energyefficient data transmission over distances of up to 10 km. One of the technical and scientific challenges faced in this project is the potential size of the configuration packages, which must be optimized to fit within the communication mechanism bandwidth requirements; • Application layer: comprises the Fixed-Time Traffic Light Control Center, which will be developed using open source software, commissioned by the municipality but not yet validated in the field.
Acknowledgment This paper has benefited from the support of the Chaire European Electricity Markets (CEEM) of the  Université Paris-Dauphine PSL  under the aegis of the  Foundation Paris-Dauphine , supported by  RTE , EDF, EPEX Spot and  TotalEnergies. The views and opinions expressed in this paper are those of the authors and do not necessarily reflect those of the partners of the CEEM.
70 mM Dox concentration as compared with that of the free drug (Fig. 4e ). The D-g-PAAan nanoparticles had a prolonged effect on the toxicity of Cis against human and murine LLC (Fig. 3c-f ). Thus, D-g-PAAan-Cis at 48 h decreased the viability of LLC and A549 cells on 26 and 21% as compared with the free Cis effect correspondingly at a 20 mM equivalent Cis concentration (Fig. 3d  and f ). Treatment of cells with drug-D-g-PAAan nanoparticles was followed by a signicant drop in IC 50 for the D-g-PAAan-loaded drugs (Table  2 ). Visual changes in cell quantity and morphology were also observed with phasecontrast microscopy. As shown in Fig. 4a  and b , complexation of Dox and Cis with D-g-PAAan nanoparticles caused a decrease in viable cells at 24 h. These data denote D-g-PAAan nanoparticles' ability to potentiate both Dox and Cis toxic effects against cancer cells.
Dynamic light scattering Particle size distribution was evaluated with a Zetasizer Nano S (Malvern Instruments, UK) equipped with a He-Ne laser (633 nm). Data were recorded at room temperature in backscattering modus at a scattering angle of 2q ¼ 173°. Zeta potential measurements were used for ascertaining the electrokinetic potential of particles in aqueous solution. Measurements were conducted on a Zetasizer Nano-ZS90 (Malvern, Instruments, UK) at room temperature. Drug release D-g-PAAan-Dox and D-g-PAAan-Cis nanoparticles (1000 mL) were added to a dialysis bag (MWCO 1 kDa, Spectra/Por® Float-A-Lyzer® G2 (Carl Roth GmbH + Co. KG, Karlsruhe, Germany). The dialysis system was suspended in a release volume of 100 mL buffer at 37 °C and rotated at 200 rpm (1 : 100 dilution between donor and acceptor compartments).
At scheduled intervals, 500 mL of the release medium was collected for the HPLC-ESI-MS/MS analysis. The same volume of fresh buffer at the same temperature was added immediately to maintain a constant release volume. Acetate and PBS buffers were used for pH 5. 0 and 7. 4, respectively. The obtained data were normalized with the buffer control and expressed as a % of the respective control sample, analyzed at 0 h. The Dox concentration was assessed with the HPLC-ESI-MS/MS analysis described before,  37  whereas Cis quantication required the establishment of a new method, described below. Elution and separation of Cis were performed using an Eclipse XDB-C18 column (4,6 Â 100 mm) with a 3 mm particle size under isocratic conditions with a mobile phase of methanol and a 0. 1% formic acid water solution.
Acknowledgements The authors are grateful to  Eckhart Kornejew  ( Technical University of Applied Sciences Wildau ) for his support of DLS measurements. It is a pleasure for us to thank  Catherine Foussat  and  Mélanie Legros  of the characterization group of  Institut Charles Sadron (ICS) , who performed polymer characterization through  Size Exclusion Chromatography and Refractometry. We are grateful for nancial support from the  European Fund for Regional Development (EFRE)  within the program " Strengthening technological and applicationoriented research at scientic institutions (StaF Directive)" (FullDrug, 85037298) in the federal state of Brandenburg, Germany ; the  French program PAUSE for emergency welcome of Ukrainian scientists. This work was also partly supported by the  German Science Foundation (DFG ,  RI/966/19-1 ).