dataesr/scientific-paragraphs-categorization huggingface dataset:
The outdoor air quality is typically monitored by stationary monitoring units with a fixed location. These monitoring units have a high cost of installation and operation  [88]. However, real-time monitoring procedures applied outdoor can help the identification of specific locations where the air quality conditions are defective to improve public health and safety. Nowadays, mobile phones usage time in the western world has increased considerably. Smartphones are used for numerous daily routine activities and support several communication technologies but also significant processing power and data visualization tools. The majority of smartphones incorporate GPS  [120]. In total, 70% of the Netherlands population and 90% of adolescents have smartphones  [121] ; 40% of the German population use a smartphone  [122] , and in the United Kingdom, 51% of adults also have smartphones  [123].
Furthermore, mobile computing technologies have offered ubiquitous Internet connection which leads to numerous daily routine changes. Therefore, a mobile application was proposed to provide an effective method for the user to carry their air quality sensing data for continuous use. Figure  4a  presents the last data collected by the system. Thus, the user can consult the CO2 data collected but also check the BLE RSSI from the connection between the cyber-physical system and the smartphone. Figure  4b  shows the corresponding location from the data collected. Using this method, it is possible to measure and map the air quality sensing in real-time for ELE in smart cities. The proposed method supports numerous advantages regarding modularity, size, portability, cost-effective and easy installation.
The mobile application provides an easy and accessible method for data analysis and visualization. On the one hand, the proposed architecture provides a relevant dataset for environmental management. Using the proposed system, it is possible to detect defective air quality scenarios and plan interventions for ELE. Therefore, this mobile computing solution can be assumed as a relevant decision-making tool to define strategies to increase air quality conditions and to guarantee the efficiency of these methods. Figure  5  presents the mapping of the monitored locations in the tests conducted. The numbers of Figure  5  represented the exact location where the CO2 concentrations were measured. The details regarding the latitude, longitude, CO2 level and time of the data collection are presented in Table  3.
The proposed method supports numerous advantages regarding modularity, size, portability, cost-effective and easy installation. The mobile application provides an easy and accessible method for data analysis and visualization. On the one hand, the proposed architecture provides a relevant dataset for environmental management. Using the proposed system, it is possible to detect defective air quality scenarios and plan interventions for ELE. Therefore, this mobile computing solution can be assumed as a relevant decision-making tool to define strategies to increase air quality conditions and to guarantee the efficiency of these methods. On the other hand, real-time data assessment allows us to conclude that typically the air quality is under demanded standards. The data collected during the tests conducted states that under certain conditions and during some specific times of the day, air quality levels can be very different from healthy standards.
However, the proposed solutions available on the market require data extraction and classification procedures before can be analyzed. Accordingly, the design of a portable air quality monitoring methods based on state-of-the-art open-source technologies that provide real-time data sharing and spatiotemporal information is essential to promote public health. Therefore, the method proposed by the authors is a reliable and effective solution for real-time air quality systems which combine portability, scalability and modularity features as essential benefits related to current solutions. The selection of the sensor was conducted, taking into consideration the accuracy and cost of the system. The principal objective of the study was to validate the IoT approach. Consequently, the focus of the research was the design and development of portable real-time data acquisition system for air quality measurement and mapping.
The CO 2 sensor was chosen because this type of gas can be used for both indoor and outdoor air quality assessment. Moreover, the CO 2 data can support clinical analysis conducted by health professionals. In the future, air quality sensing will be incorporated in every living environment as it allows an accurate validation of healthy living environments for enhanced public health and well-being. The proposed method can be used to monitor the air quality inside vehicles on the move to create a dataset which will be relevant to identify poor air quality scenarios in particular locations and often to better understand the effect and implications of the outdoor air quality parameters on the vehicle's occupants. In particular, air quality monitoring is of utmost importance in public transports due to the number of occupants and also the ventilation methods used in this kind of vehicles.
The principal objective of the presented system is the creation of a practical tool kit to guarantee and cost-effectively promote air quality standards. This solution provides a relevant dataset to support interventions and can be used for clinical decision support. Air quality data can be used to support medical reports because it is possible to correlate the health complications of specific groups of people who share the same living environment with the location air quality data. Nevertheless, the main limitation of the proposed method is associated with the main challenge of the standard GPS systems and relies on the post-processing of the GPS data. In most indoor environments, it is not possible to collect GPS data when the satellite signal is down. However, the use of mobile technologies brings a significant advantage since it is possible to get using the Internet provider when GPS data reception fails.
In this context, it will be possible to monitor the city air quality continually considering the geographical coordinates to create a relevant dataset which can be used to regulate traffic at specific hours of the day and promote public health. It is also planned to perform additional tests to evaluate the accuracy of the sensors with different types of technologies. Air quality monitoring allows the detection of unhealthy behaviours in real-time and should be incorporated in all living environments and be a fundamental part of the daily routine to promote health and well-being. The authors believe that architectures like the one proposed will provide ELE in smart cities.
II. RELATED WORK The work in  [7]  presents the use of the IoT message protocol MQTT for transmitting accumulated and live V2X data from vehicles to a centralized big data platform. The usage of MQTT introduces a small overhead regarding network bandwidth and works on top of the standard TCP/IP protocol. Messages are transferred via an MQTT broker. The MQTT broker component is running next to the global database. According to the publish/subscribe mechanism, the component (i. e. , vehicle) periodically publishes location and network performance related topics, which are then received by registered MQTT clients of 2D and 3D visualization applications. Several research projects (e. g. , 5G-DRIVE  [8] , 5G-Carmen  [9] ) are introducing MQTT brokers in the cloud in order to collect all traffic information in an efficient manner to deploy geographical services with minimal overhead.
III. VEHICULAR MESSAGE GENERATION This section describes a custom experimental OBUs software application, which has been built with the idea of implementing in-vehicle OBU functionalities, including vehicular message generation and reception. The OBU application can be deployed on a general-purpose computer with external connections to a Global Navigation Satellite System (GNSS) receiver and to the Controller Area Network (CAN) bus of the vehicle by means of an On-Board Diagnostic (OBD) interface adapter (e. g. , ELM327 OBD-II). The OBU application can readily be configured onto a Linux system and is based on OpenC2X  [11] , which is an opensource software implementation of the Cooperative-Intelligent Transport System protocol stack (i. e. , ITS-G5) defined in the ETSI ITS specification  [12].
The interoperability of OpenC2X with a commercial OBU from Cohda Wireless has been demonstrated in  [13]  by sending and receiving CAM and The software architecture of the OBU is depicted in Figure  1. As it can be observed, it is composed of several independent software modules that communicate with each other using an asynchronous messaging library. In what follows, the core functionalities of each module are described. The Web Interface module is a graphical user interface (GUI) that acts as the Human Machine Interface (HMI) of the vehicle, facilitating the interaction between the driver and the OBU application. Through the Web Interface, the driver is able to: initiate the registration and authentication process with the MEC-hosted back-end service; configure the subscription to the back-end service in order to receive DENMs associated to hazards located within a certain geographical area; and emulate the detection of a roadside event by triggering the transmission of a DENM to the back-end service.
In addition, the Web Interface shows the current position of the vehicle, the location of road hazards and the position of other vehicles on a Local Dynamic Map (LDM). Furthermore, the Web Interface displays a notification message every time that a new DENM is received. The HTTP server module facilitates the communication of the Web Interface with the rest of the modules of the OBU. The HTTP server receives HTTP requests from the Web Interface and sends commands to the corresponding software modules, which execute the required operations. As it can be observed in Figure  1 , the HTTP server communicates with: the Registration & Authentication module, to initiate the registration and authentication process with the back-end service; the Subscription module, to configure the subscription parameters and initiate a new subscription to the back-end service; the DENM/JSON module, to trigger the transmission of a new DENM to the back-end service; and the LDM module, to query the positions of the vehicle, road hazards and other vehicles.
The DENM Service module is in charge of encoding and decoding DENM messages. In the current implementation, this module generates a new DENM when it receives a command from the Web Interface through the HTTP server. The DENM Service module fills the fields of the DENM with the geographical position (i. e. , latitude and longitude) provided by the GNSS module and other in-vehicle sensors information provided by the OBD2 module. The DENM message is passed to the V2X Message Manager to add the GeoNetworking and BTP headers. In addition, the generation of DENMs could be triggered by external modules such as smart sensors for collision avoidance. Regarding the reception of DENM messages sent by the back-end service, the DENM Service module extracts the GeoNetworking header, decodes the fields of the DENM and forwards the relevant data of the event (type, position) to the LDM module.
The CAM Service module is in charge of encoding and decoding CAM messages serving as location updates for vehicle tracing at the back-end service. The CAM messages are triggered periodically. The position of the vehicle is provided by the GNSS module and the speed is provided by the OBD2 module. The CAM Service module fills the fields of the CAM message at the facilities layer, as defined by ETSI-ITS specification  [14]  and passes it to the V2X Message Manager where GeoNetworking and BTP headers are added. The CAM Service module also receives CAM messages sent by other vehicles and forwards the relevant data to the LDM module. In order to communicate with entities such as Central Traffic Managers or additional back-end servers, an additional communications interface and protocol has been included in the OBU.
It is based on the use of JavaScript Object Notation (JSON) text format to serialize CAM and DENM messages, and sent to other interested parties using the MQTT protocol. The main concept is that clients connect and authenticate to a MQTT broker, susbcribe to the topics of interest (topics can be pre-defined based on geographic ROI, ITS protocol, etc. ) and publish messages to the corresponding topic. We use a direct translation of CAM and DENM message mapping ASN. 1 objects to JSON fields. Consequently, the MQTT Client module facilitates the communication via the MQTT protocol among the CAM module and DENM module and servers hosting back-end services. The MQTT module registers into an MQTT broker allocated in the distributed edge cloud, and it publishes on specific predefined topics the CAM and DENM messages generated by the CAM and DENM modules.
The MQTT Client module subscribes to several topics in order to receive CAM and DENM messages published by the back-end service and forwards the incoming CAM and DENM messages into their respective CAM and DENM module. The JSON encoding follows closely the ASN. 1 specification for DENM messages. In the example detailed in Listing 1, we show a simplified version of the DENM message in JSON format. Listing 1: Simplified JSON DENM message { "message":{ "management_container":{ "detection_time":503253332000, "event_position":{ "altitude":1340, "confidence":{ }, "latitude":486263556, "longitude":22492123 }, "reference_time":503253330000 }, "message_id":1, "protocol_version":1, "situation_container":{ "event_type":{ "cause":1, "subcause":6 }, "information_quality":4, "linked_cause":{ "cause":94, "subcause":2 } } }, "context":"etsi", "message_base64":"AQEAAA.
", "origin":"on_board_application", "source_uuid":"", "timestamp":"1580723993599", "type":"denm", "version":"0. 3. 0" } The V2X Message Manager module facilitates the communication via the UDP protocol among the CAM Service and DENM Service modules and the back-end service. The V2X Message Manager adds the GeoNetworking and BTP headers, encapsulates the CAM and DENM messages into UDP packets and sends them to a UDP server allocated in the back-end. The V2X Message Manager receives UDP packets from the back-end service, decapsulates the CAM and DENM messages, extracts the GeoNetworking and BTP headers and sends them into their respective CAM Service and DENM Service modules. The LDM stores in a SQL-based database the information contained in incoming CAM and DENM messages. This data is read from the Web Interface module for real-time monitoring and can also be accessed for off-line analysis.
In order to improve the performance, the latest data is stored in a cache. The GPS Service module connects via Universal Serial Bus (USB) to an external GNSS receiver to collect periodic position updates (i. e. , latitude, longitude, altitude) and forwards these data to the CAM Service, DENM Service and JSON/MQTT Client modules. In order to perform reproducible experiments, previously recorded GPS data can also be fed into the application in form of a trace. Instead of acquiring real positions from the GNSS receiver, the OBU application can read the position of the vehicle from a trajectory file that contains a sequence of geographical coordinates with associated time-stamps. The OBD2 Service module communicates with the vehicle's on-board network (i. e. , CAN bus) by means of an OBD interface adapter connected to an USB port of the OBU.
ACKNOWLEDGMENT This work is part of  5GCroCo  project that has received funding from the  European Union   H2020   Research and Innovation Programme  under grant agreement No. 825050. It has also been partially funded by  SPOT5G  ( TEC2017-87456-P ) and AURORAS ( RTI2018-099178 ) and by  Generalitat de Catalunya  under Grant  2017 SGR 891  and  FEMIoT  project, which is funded by the  European Regional Development Fund (ERDF). EURECOM acknowledges the support of its industrial members -BMWGroup, IABG,  Monaco Telecom ,  Orange , SAP, ST Microelec-tronics and Symantec.
They used the ILOG CPLEX software package, with the aim of minimising the amount of investment and the operational cost of the system. District planning included studies on the influence of the number and kind of buildings served by the cooling network. Their model considers the demand of the user at each period but does not consider the dynamics of temperature in the system pipes. As already stated, one of the least explored subjects of study in the field of energy systems is the dynamic optimisation and control of these systems. Recent advances in this field include the study by Schweiger et al. [17]  dealing with optimal production planning in district heating systems. They presented a framework to represent on-grid energy systems and performed a dynamic thermo-hydraulic simulation of energy systems.
The framework was based on the Modelica® modelling language, performing the continuous optimisation tasks with the OPTIMICA compiler toolkit, and the discrete optimisation in the Python open-source environment using the Pyomo module. They decomposed the resulting mixed-integer-optimal control problem into a Mixed Integer Quadratic Constrained Programming (MIQCP) problem (a particular form of MINLP problem) and a continuous problem. The results of the former provided the status and heat production of each unit. The discrete variables representing the status of each unit were thus fixed by this solution from the MIQCP, although the real heat production was calculated in the continuous problem which was transformed into a Nonlinear Programming (NLP) problem using a direct collocation method, then solved using the interior point algorithm IPOPT.
5D problem  [39]. While the appeal of machine learning is strong for performing point cloud processing in the case of complex geometries as encountered in heritage objects, the main bottleneck remains the generation of the training dataset  [25]. In this paper, an algorithmic approach is considered in order to provide a fast result which may eventually be used to help generate training data for future machine learning techniques. Indeed, manual labeling of heritage objects also present a particular difficulty since objects in the same class may have many variations.
Nature of Available Datasets The research described in this paper utilizes several datasets which are mainly heritage sites (Figure  2 ). The main datasets both involve multi-sensor and multi-scalar data. The multi-sensor aspect is due to the fact that the final point cloud is a result of the combination of several 3D sensors, in most cases photogrammetry (aerial and close range drone as well as close range terrestrial photos) and laser scanning (terrestrial, but also aerial LIDAR in the case of the St-Pierre dataset). The multi-scalar aspect is achieved by the recording of not only one particular building of interest, but also the heritage complex or the neighborhood around it. This is done in order to have a complete documentation of the heritage site within the context of its geographical location.
The method of 3D data integration follows the existing workflow as described in  [21, 46]. In order to create a common coordinate system, each of the available 3D data was georeferenced separately into the same geodetic coordinate system. In order to do this, topographical surveys were conducted in parallel to photogrammetry and laser scanning in both of the main datasets. Artificial targets were measured and thereafter integrated in the absolute orientation phase for photogrammetry and the georeferencing process of a TLS point cloud. The chosen coordinate system corresponds to the respective national mapping projection system of each entity, therefore ensuring that future projects may also be integrated easily. The multi-scalar aspect of the datasets is directly linked to the multi-sensor aspect.
In general, both the main datasets were recorded in different scales in order to have different levels of details; i. e. , the neighborhood scale level comprising the heritage complex was recorded using either drone photogrammetry or aerial LIDAR, the building scale level comprising individual heritage building exterior was recorded using TLS and close range photogrammetry, while the interior scale level as well as the architectural elements were scanned using TLS and in some particular cases also photogrammetry. In addition, two supporting datasets were also used to augment the research and serve as an objective experiment on the developed algorithm's performance. These datasets are generally specific in nature, and does not possess the multi-sensor and multi-scalar attribute of the two main datasets.
They are, however, useful in order to give another perspective and test the capabilities of the algorithm. The two supporting datasets comprise of one dataset dedicated for the pillar detection algorithm (Section 4. 2) and another for the beam detection part (Section 4. 3). The main datasets used in this research are as follows: 1. Kasepuhan Royal Palace, Cirebon, Indonesia ("Kasepuhan"): This historic area dated to the 13th century and includes several historical buildings within its 1200 m 2 brick-walled perimeters. A particular area of the dataset called Siti Inggil is of particular interest to the conservators as they represent the earliest architectural style in the palace compound. In this paper, the Siti Inggil area is used as a focal point, with one its pavilions (the Central Pavilion) used as a case study for the more detailed scale level.
The neighborhood around the church was used as a case study in the research along with its interior. The church's surroundings was scanned by aerial LIDAR in 2016 by the city's geomatics service; the point cloud data have since been published as open data (https://data. strasbourg. eu/explore/dataset/odata3d_lidar, retrieved 24 January 2020). The exterior of the church was also recorded using drones in May 2016 to get a larger-scale and thus more detailed data, while the interior was scanned using a TLS in April 2017. The supporting datasets are as follows: 1. Valentino Castle, Turin, Italy ("Valentino"): The Castle of Valentino is a 17th century edifice located in the city of Turin, Italy. It was used as the royal residence of the House of Savoy and was inscribed into the UNESCO World Heritage list in 1997.
Today, the building is used by the architecture department of the Polytechnic University of Turin. The particular "Sala delle Colonne" or Room of Columns inside the castle was used in this study. This point cloud has been graciously shared by the Turin Polytechnic team for our experimental use. The Valentino dataset is used exclusively for the pillar detection part of the research. 2. Haut-Koenigsbourg Castle, Orschwiller, France ("HK-Castle"): The Haut-Koenigsbourg is a medieval castle (dated to at least the 12th century) located in the Alsace region of France. Badly ruined during the Thirty Years' War, it underwent a massive, if somewhat controversial, restoration from 1900 to 1908. The resulting reconstruction shows the romantic and nationalistic ideas of the German empire at the time, the sponsors of the restoration.
The castle has been listed as a historical monument by the French Ministry of Culture since 1993. In this research, only a part of the timber beam frame structure of the castle scanned using a TLS was used to perform tests on the beam detection algorithm. The beams are mostly oblique and distributed in the 3D space. The beams are of very regular shape and relatively unbroken  [55]. The HK-Castle dataset is used exclusively for the beam detection part of the research.