dataesr/scientific-paragraphs-categorization huggingface dataset:
used the i-HOGA software to analyze the optimal size combination for a remote village in Malaysia in terms of minimizing the amount of cost of energy and CO 2 emission. TRNSYS software can be used to simulate the behavior of transient systems. In Reference  [87] , Behzadi and Niasati examined a hybrid system consisting of PV, battery, and FC. In this system, TRNSYS software was used to conduct the performance analysis. The system sizing could be carried out by using the GA via i-HOGA, manual calculation, or HOMER. Authors used three different energy management strategies to dispatch energy in this hybrid system. Firstly, the value of excess energy is checked. If it is positive, the excess energy will be directed to charge the battery to its maximum SOC. The excess energy is then directed to produce more hydrogen.
Hardware Components Arduino is an open-source hardware platform supportive of electronic prototyping. It has been used in different projects and had several applications. In Reference  [39] , authors used the Mega 2560 model to measure the temperature of PV modules by means of a network of digital temperature sensors. Arduino microcontroller has been used to verify the smart grid features  [105]. In this study, a multi-agent approach was used to address the complexity issues in the microgrid network. Raspberry Pi (RPi) is an open-source and series of small single-board computers (SBCs) developed by the Raspberry Pi Foundation of the University of Cambridge in 2012. It provides huge computation resources and wide connectivity options. Raspberry Pi features a Broadcom system on a chip with an integrated ARM cortex A72 processor and on-chip graphics processing unit (GPU).
Energy Management Strategies Analysis In different control frameworks, problems can be formulated in different manners according to the objective functions and the underlying controls. Numerous energy management strategies have been discussed in the article, but few of them relate to the DC microgrid system for residential applications. Most adopted energy management strategies, such as linear programming-based EMS, artificial intelligence-based EMS, and hardware and software tool-based EMSs, such as SCADA and HOMER, are chosen with the system configuration (PV/wind/fuel cell/storage) and control objectives, and are summarized in Table  4. The EMS-integrated DC microgrid structure is presented previously in Figure  8 , consisting of PV panels and a WT connected with a storage system (e. g.
Generalizability and Limitations We propose a general framework, SmartParcels, for the cross-layer IoT planning problem with relatively static demands. The proposed modeling method of information and infrastructure flows supports a range of applications, such as (i) simple flows from data/sensor to the analytic model/processing unit and (ii) complex flows containing a database, sensor fusion, and multiple various data sources. Moreover, by decoupling the applications and communities, urban planners can easily apply the modeled flows to other communities requiring the same applications. However, SmartParcels does not support IoT planning in dynamic scenarios, where information and infrastructure flows change over time, such as mobile IoT devices and in wireless sensor networks with cluster head changes over time.
There are two ways to achieve auto-dimming: (i) imagebased analysis and (ii) motion sensor detection. The sets of service sites and candidate locations are identical, including the smart and regular streetlights. The details of streetlight locations and installed devices are added to the publicly available OpenStreetMap (OSM) database  [26] , which is queried in our evaluations. In the smart city, we consider the following communities: (i) A, Irvine Spectrum, which is an outdoor shopping center, (ii) B, Quail Hill, which is a residential area close to a highway, (iii) C, Shady Canyon Open Space Preserve, which is a wildland, and (iv) D, Shady Canyon, which is a residential area located next to a wildland. C and D are identified as very high fire hazard severity zones by the Orange County fire authority  [5].
We query the OSM database to get all road segments for identifying intersections, which are shown in Fig. 3(b). Table  1  lists the number of public facilities and intersections in each community. Last, we uniformly sample the candidate location from these locations across the communities so that communities have similar numbers of candidate locations. Information and infrastructure flows. Fig. 4  shows three sample flows used in our evaluations. We use a colon to indicate the corresponding infrastructure/information unit for a flow, e. g. , {sensing data:sound}. Moreover, we use parentheses to indicate multinetwork choices, e. g. , {network device:(wifi,lora)}. In addition, we use: (i) sd for sensing data, (ii) a for analysis model, (iii) s for sensors, and (iv) n for network devices. With the notations, we can write the information/infrastructure flows in a concise manner, such as the three sample flows in Fig.
This results in an accuracy level of 86%. We notice that our experiments give similar accuracy levels to those in the literature. Hence, we apply the accuracy levels in the prior work  [27, 38]  in our evaluations. Smart city. Because the monitoring applications merely compute the aggregated sensor readings without complicated analysis, we set their accuracy levels to 100%. For the gunshot detection, prior reports indicate an 89. 6% accuracy  [23]. For wildfire detection, we use image-and emission-based detection. We gather data from two real datasets: HPWREN [3], containing surveillance videos of several wildland fires, and use World Air Quality Index (WAQI)  [1] , for air quality. Specifically, we select six wildfire events that occurred in California, USA  [2]. Then, we retrieve 5538 images capturing the wildfire events and 5844 images without a wildfire from HPWREN.
We uniformly divide the dataset into 80% training and 20% testing sets. We train a CNN model, which achieves an accuracy  level of 94. 41%. Besides, we collect the emission data (pm 2. 5, O3) for one day from the stations closest to the wildfire events in WAQI database. We label the data during wildfire events as true using the open data from the government [2] and the daily emission data on other days as false. We get 576 days with wildfire and another 576 days without wildfire and divide them into 80% training and 20% testing sets. We use SVM (Support Vector Machine) to predict wildfire events, which achieves an accuracy level of 62. 62%. Last, we note that the accuracy levels of different applications carry diverse meanings. Therefore, using absolute accuracy levels leads to biased service utility and IoT plans.
Scalability Studies We study the scalability of SmartParcels since the previous simulations are conducted by assuming that an event occurs only at the service site. We implement a simulator in python that: (i) generates events uniformly within the boundary of the testbeds, (ii) determines whether an event is captured by a planning graph, and (iii) calculates the service utility for the captured events using Eq. (  1 ). We first generate 10000 events for each application and compare the planning graph generated by ENUM+MR and ENUM+MU in both testbeds: the smart campus and smart city. Fig. 10 (a) shows sample clean-slate results in the smart campus. We observe that MU derives higher overall service utility with fewer candidate locations, which can be attributed to the more than sufficient budgets (since the campus is relatively small).
In particular, we adopt a queueing network for our lightweight SmartParcels platform in two parts: (i) modeling the infrastructures (transmission and processing capability) and (ii) modeling variability (network and data burstiness). We exploit methods for rapid analysis of well-known queueing models to impose end-to-end analytical models that provide approximate performance metrics, such as latency, throughput, and utilization. Our lightweight SmartParcels provides prompt analysis on variability in a generated plan, and urban planners can react accordingly. Two theoretical extensions further explore: (i) the complexity analysis in Theorem 1 to determine approximation ratios of SmartParcels for performance guarantees and (ii) providing formal guarantees in placement  [33]. Finally, we are exploring the dynamicity of the system in the following topics: (i) how Software Defined Network (SDN) can help for the effective management in the multinetwork data flows  [28]  and (ii) the ability to dynamically choose edge analytics  [10, 34].
Large scale cultural heritage datasets and computational methods for the Humanities research framework are the two pillars of Digital Humanities (DH), a research field aiming to expand Humanities studies beyond specific sources and periods to address macro-scale research questions on broad human phenomena. In this regard, the development of machine-readable semantically enriched data models based on a cross-disciplinary "language" of phenomena is critical for achieving the interoperability of research data. This paper reports on, documents, and discusses the development of a model for the study of reading experiences as part of the EU JPI-CH project Reading Europe Advanced Data Investigation Tool (READ-IT). Through the discussion of the READ-IT ontology of reading experience, this contribution will highlight and address three challenges emerging from the development of a conceptual model for the support of research on cultural heritage.
Introduction The combination of digital sources and computational methods is at the centre of a change of paradigm and of research breakthroughs on cultural heritage. Firstly, the discoverability of sources described and enriched through the Semantic Web enables the construction of integrated datasets of sources based on different archives. Secondly, data integration, as well as quantitative and qualitative studies, complement the in-depth analysis of individual sources. The use of large-scale datasets and computational methods applied within a Humanities research framework is the pillar of the revolution of the Digital Humanities (DH). The current challenge for the Digital Humanities is how to scale up from the established paradigm of focused studies of specific sources and periods, to macro-scale research addressing broad human phenomena over the longue durée, as represented in cultural heritage  [1].
In addition, a summary of methods for validating conceptual models is included. Finally, we describe at the end of this section some notions that will help to define the READ-IT model of reading. Fig. 1. The READ-IT database is going to collect sources and annotations produced through case studies (outcomes) and their reuse in the development of new case studies. The READ-IT database will provide a large pool of evidence from different types of reading and readers in Europe, supporting the macroscale READ-IT research programme.
Previous Projects The design of the READ-IT ontology is based on the experience and limitations of previous projects focused on cataloguing experience recorded in literature. In this section, we present and discuss the case of the UK Reading Experience Database (UK-RED) and the Listening Experience Database (LED). We consider these two specific projects as they share their focus on the experience of contents (text or music performances) and the overall methodology grounded on the analysis and annotation of sources of experience. Furthermore, these two projects represent two significant steps towards finding a common operational definition of experience for supporting interdisciplinary and multidisciplinary research.
UK-RED The UK-RED 2  (UK Reading Experience Database) ontology was developed to support Digital Humanities research on reading experience. The RED ontology and database were the result of an incremental rationalisation of research data produced in multiple projects and through the engagement of Humanities students and volunteers. The RED ontology 3  used concepts from the linked event (event), Friend-of-a-Friend 4  (foaf) and DBpedia 5  (dbo) ontologies, and classified entities using three classes: foaf:Person, foaf:Document and red:Experience. The pillar of RED is the concept of reading Experience, connecting (a) a foaf:Person in the role of reader or listener, (b) the place and date of the reading, and (c) a document (object of reading). The RED ontology is supported by 10 years of use. One of the earliest crowdsourcing projects in the field, RED was based on sources collected directly by researchers and stored in its database.
LED Similarly to UK-RED, the aim of the LED 6  (Listening Experience Database) project is to support research on listening experience. The LED strategy is two-fold: 1) reconstructing the context of the listening experience (linking places, performers, events, musical works, etc. ), and 2) supporting the collaborative distributed incremental curation of sources about listening experience. The LED ontologies (led) revolve around the concept of (listening) experience as "a documented engagement of an individual in an event of one or more pieces of music being performed"  [7]. In this regard, LED core connects four main notions: Listening Experience, Source, Agent and Music. LED considers the listening experiences as events and, therefore, it uses an event ontology existing in literature  7. As a result, LED regards participants as agents, listening events as source documents and reports, and music as a factor of the event.
Sources are described from a bibliographical point of view. LED uses the BIBO ontology 8 , together with its own additional controlled vocabulary of source categories typically not formalised in structured datasets (such as oral history or official papers). The musical performances and works are represented using the FRBR-aligned Music Ontology 9 , and so are the roles of participants in an experience modelled after that ontology, combined with BIBO. This makes it possible to query the datasets to find e. g. authors who reported on experiences where they played the music themselves. As an evolution of RED, LED structures include a formal description of the curation process of sources as part of the database and approaches the representation of the experiences as an abstraction of external repositories (result of the curation process), not limited to first-hand collected sources.
Related Ontologies In the field of cultural heritage, it is worth mentioning the LAWD (Linking Ancient World Data) Ontology 10. LAWD provides a notion of reading as a "word, phrase, or larger chunk of text from a witness (or any observation of variance concerning the text, such as an omission or interpolation)". In addition to LAWD, the RED ontology is used in datasets that represent the history of reading in Britain from 1450 to 1945. This ontology, described in Section 2. 1. 2, represents knowledge about reading tastes and habits. Reading can imply a direct consequence in the reader related to a severe and temporary mood disturbance, pleasant or painful. This effect is known as emotion and is central to reading experiences  [25]. In order to represent emotions, the Emotion Ontology (EMO)  11  was developed.
However, CQs can also be considered as criteria for technical validation as much as conceptual, if regarded as having the same role as unit tests for software. Lastly, the ability to translate CQs into formal queries is a crucial factor to an ontology's potential for community adoption, therefore a case can be made for them as external validity factors. The manifold validity of CQs and their adoption as a tool across several methodologies were influential in the decision to incorporate them in the validation of our model. With the progressive growth of the Web of Data, methods for validating ontologies against datasets and text corpora, as originally introduced by Brewster et al. [22] , have also been gaining continued attention. These methods attempt to respond to a need for objective measures of ontology quality beyond those mandated by the underlying logical framework.
The idea behind data-driven ontology evaluation is "to determine how appropriate [an ontology] is for the representation of the knowledge of the domain represented by the texts"  [22] , although it has been argued that such evaluation metrics would not be exempt from at least temporal or category bias  [23]. Although instances of data-driven ontology evaluation methods still form a checkered pattern, some insightful implementations have attempted to compare the model of an ontology to the features extracted from corpora using machine learning and text mining  [24]. Though still in its infancy as a proper validation methodology, a corpus-driven approach provides several elements of interest for the domain at hand. However this aspect is deferred upon completion of the READ-IT dataset construction.
Ontology Requirements READ-IT builds on the experience and limitations of previous projects. These include, for example, their limited geographical range and their lack of integration of multimodal digital sources of evidence (images, texts, audio sources, and computer-mediated communications). An additional limitation is the focus of their data models on the context of experience (e. g. , who, where, when, and how), with only partial attention paid to its mental aspects (why and with what result). The READ-IT information architecture, see Fig. 3 , is an evolution of previous projects, taking into account their limitations and the new requirements related to the envisioned interoperability of research data and scaling up of Digital Humanities research. In the READ-IT framework, the model of reading has the role of informing the design of 1) the database that should integrate research data produced through case studies and 2) the tools that will support research activities (e.
Reading Experience Ontology The design of the READ-IT ontology focuses on addressing the description of the human experience of reading. The aim of the ontology is to provide the common language to structure and share research data addressing aspects related to three main conceptual areas (see Fig. where and when reading takes place, in which material and social conditions and how a reading is carried out. The ontology will address the situated interactions between reader, medium and content, and the effects of reading during and after reading. In the following section, the concepts of the READ-IT ontology are described using local names, e. g. , Reader, Medium, and as prefixed QNames, e. g. , foaf:Person, and with terms from other third-party ontologies, specifically: Friend-of-a-Friend (foaf), CIDOC CRM (cdc) and FRBRoo (frbr).
Source and Annotation The Reading Experience Ontology is aimed at supporting the encoding of annotations, of a wide variety of sources, produced through different approaches (e. g. , trained annotators or volunteer-based work). In READ-IT, we represent the annotation process by using the W3C Web Annotation Data Model (WADM). This model connects a graph (wadm:body) to the metadata describing the annotation process (wadm:annotation), e. g. , annotation agent with the source of the annotation, wadm:target. The findings about a reading experience in a source, i. e. , the body of an annotation, is a sub-graph generated using the Reading Experience Ontology, see Fig. 6. Thus, a research dataset is a collection of WADM graphs, annotation, body and target, and relative graphs about reading experience.
In this scenario, the adherence of the ontology is assessed as the ability to represent the information content of sources and questions from research use cases  [14]. The functional requirements extracted from the sources guided the development of the ontology while the requirements concerning the research use cases are discussed in Section 7. 2 (Conceptual Validity). Lastly, the external validity of the ontology is assessed in relation to the READ-IT project. Specifically, we discuss the non-functional requirements concerning the project architecture 23 https://github. com/eureadit/reading-experience-ontology (System Requirements) and the research activities (Research Requirements).
Description Logic Consistency A basic metric for the validity of any ontology is its consistency with respect to the more expressive description logic (DL) by which it can be interpreted. This includes, at the terminological (TBox) level, whether it defines classes that subsume both the top (owl:Thing) and bottom concept (owl:Nothing), or at the level of facts (ABox), whether it defines individuals that are instances of disjoint classes or violate cardinality restrictions or property domain/range definitions. Assessing consistency requires that disjointness axioms be present, therefore class disjointness was formally defined between all sibling classes in the READ-IT model. The DL consistency of the READ-IT data model was verified by running the HermiT 1. 4. 3 reasoner 24  over the transitive closure of the imported ontologies (Erlangen CRM, FRBRoo, and CWRC).
The collection of the statements of the reader about these aspects of their condition outline a profile of the reader. The characterisation of habit will be addressed in a later stage of the project, 25 http://www. cidoccrm. org/crmsoc/sites/default/files/CRMsoc_20190326. pdf as part of the development of case studies and collection of research data in the READ-IT database. About occupation and social status, UK-RED and LED provide two different characterisations of these concepts. In READ-IT, we did not address concepts concerning the social and personal sphere of the person, but we reuse the upcoming CIDOC CRM module for social structures and social relations, CRMsoc 25 , and the definitions of previous projects. It is noteworthy that in the study of historical periods and sources, these concepts and their descriptions vary greatly with social values and structures.
, class lesson and reading during the class lesson. -My evidence reports of reading social media, blog posts or other contents which are not books: how should that be encoded in the data model (web contents)? -In case reading is between multiple contents connected through hyperlink, how do we represent references between content (web contents)? -How do we encode experiences in which we have information about fragments of text, but information about the title is missing (letters)? These questions concern the object of reading. Indeed, today, reading is a multi-modality activity (e. g. , beginning on a laptop and then switching to a smartphone) that can involve a wide range of types and combination of physical and digital contents, e. g. , blog posts, web novels, comics, social media comments.
In general, the lack of information about the effect of reading on the reader is a legitimate piece of information, for instance for the study of how readers report their reading experiences. The ontology can be used to represent an interaction without specific effects or effects of reading without details about the reader, the content or the process. -In the evidence, the reader is describing emotional aspects of the content and not of their personal reading experience. Is this information in the scope of the READ-IT data model (interviews)? This question points out that the content of a reported experience could be personal or impersonal and states an ambiguity about the content of the experience. In a broader sense, the ontology represents the orientation of a state of mind, e. g. , an emotion, which could be oriented toward the self, internal, or other entities and activities.