dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions Summary and Conclusions The paper was aimed at modeling the electro-thermal behavior of an LMO/Graphite battery cell in order to estimate the heat generated within the cell during electrical charging and discharging. An equivalent circuit model (ECM), was adopted, and different orders of the model have been tested (Rint, Thevenin, DP model). The second aim was also to provide data from the real characterizations of cells; electrical and thermophysical data were measured or identified, and they constitute real and helpful information for academic studies. A special bench was built to control the operating conditions (current profile) and to measure the electro-thermal response of the cell (voltage, temperature). The testing data have been used to identify the ECM parameters (resistances and capacitances), considering their evolution versus the SOC and the temperature, providing a complete and original set of data, which constitutes one of the contributions of this work.
Using the social media tracker provided by Picodash, all #iLoveLondon posts were downloaded for the period May -July 2017. Picodash shows the date and time, number of likes, comments, account usernames and a URL link for each Instagram post (see screenshot in Appendix 1). All the posts consisted of three elements: digital photographs, textual captions and hashtags. Whilst all three elements provide us vital information to understand how people express their DBL, two separate studies were conducted to analyse the data. We used different research methods in order to validate the findings through data triangulation  (Johnson & Onwuegbuzie, 2004; Denzin, 2010). Triangulation is purposive in reducing bias resulting from a single method by validating the interpretation of data and the resulting findings using multiple and different sources of information  (Creswell & Miller, 2000).
Study Two Whilst study one focuses on exploring also the non-codified meanings present in photographs, study two mainly focuses on codified meanings expressed via textual comments, captions and hashtags in digital photographs. Hence, study two attempts to extend and validate the existing findings through data triangulation  (Creswell & Miller, 2000; Denzin, 2010)   2020). The software uses various algorithms that adopt nonlinear dynamics and machine learning for data analysis  (Smith & Humphreys, 2006). Leximancer combines natural language processing and text-mining, allowing us to analyze, visualize and interpret the content of the textual documents collected  (Smith & Humphreys, 2006). Leximancer is based on Bayesian statistical theory, where fragmented pieces of evidence can be used to predict what is happening in a system  (Smith & Humphreys, 2006)   Before converting the file for data analysis with Leximancer, we removed frequent irrelevant words such as ilovelondon, londonlove, Great Britain, England, and UK.
Key destination attributes -Study 2 Study two adopts text analytics and a dataset of 48,783 posts and the findings of the analysis partially confirm the findings of study one. Compared to study one, we can see similarities and differences in terms of the destination themes that are used in the declaration of love. For example, some of the destination themes that were relevant in the previous analysis (i. e. food, iconic public transport, weather, and celebrations) did not emerge back in study two. Table  3  below shows that there are some differences also in the destination attributes that are most frequently used in the declaration of love; these attributes were: Hyde Park, London Bridge, Regent Street, Borough Market, Tower Bridge, Notting Hill, Big Ben, and Covent Garden. Furthermore, houses were less frequently adopted in the love declaration in study two; while Hyde Park, Covent Garden, Tate Gallery, Kensington Gardens, Royal Palace, Shoreditch were not used in the love declaration in study one.
Methodological contribution Furthermore, there is a lack of mixed-methods research in travel & tourism research in general, and on brand love in particular. More specifically, there is limited studies integrating qualitative analysis and text analytics with a large pool of data. This study adopted a mixed-methods approach, by combining qualitative visual content analysis of a limited number of user-generated photographs and quantitative textual analysis using a large dataset. If the qualitative analysis' strength is to enable a more in-depth understanding of the emotions and feelings, linked to the declaration of love, as well as of the non-codified meanings of the pictures, it also informs the subsequent quantitative analysis, in which a ranking of the themes and concepts most frequently used to express the love towards a destination is produced.
Limitations and Future Research Directions The research suffers from the following limitations. Firstly, the data was collected from one platform (i. e. Instagram) and at a specific point in time. Although this is one of the most popular platforms to post travel experiences  (Fatanti & Suyadnya, 2015; Sheldon & Bryant, 2016)  Secondly, whilst the relationship between people and places is very much contextspecific, future studies are also encouraged to look at how people declare and communicate their love towards other destinations. While our findings prove that destination love is expressed through a combination of key attributes and characteristics specific to the destination, together with emotional attachment, future studies are recommended to explore how destination love is expressed towards other destinations in order to further validate our findings and provide specific advice to different DMOs.
Rare lung diseases affect 1. 5-3 million people in Europe while causing bad prognosis or early deaths for patients. The European Reference Network for Respiratory Diseases (ERN-Lung) is a patient centric network, funded by the European Union (EU). The aims of ERN-LUNG is to increase healthcare and research regarding rare respiratory diseases. An initial need for cross-border healthcare and research is the use of registries and databases. A typical problem in registries for RDs is the data exchange, since the registries use different kind of data with different types or descriptions. Therefore, ERN-Lung decided to create a new Registry Data-Warehouse (RDW) where different existing registries are connected to enable cross-border healthcare within ERN-Lung. This work facilitates the aims, conception and implementation for the RDW, while considering a semantic interoperability approach.
We created a common dataset (CDS) to have a common descriptions of respiratory diseases patients within the ERN registries. We further developed the RDW based on Open Source Registry System for Rare Diseases (OSSE), which includes a Metadata Repository with the Samply. MDR to unique describe data for the minimal dataset. Within the RDW, data from existing registries is not stored in a central database. The RDW uses the approach of the "Decentral Search" and can send requests to the connected registries, whereas only aggregated data is returned about how many patients with specific characteristics are available. However, further work is needed to connect the different existing registries to the RDW and to perform first studies.
These ERNs include 23 rare or low prevalence complex diseases, conditions and disease-groups, including more than 300 hospitals  [4]. ERN-Lung currently includes 60 healthcare providers in 12 different countries, addressing rare respiratory diseases  [3]. ERN-Lung is divided in core networks, which include for instance research and clinical trials, registries and biobanks, cross-border care and continued medical education  [3]. However, to support clinical research for RDs, patient's registries and databases are needed. The small number of patients affected by RDs make registries even more important than in the normal population or in common diseases as they enable cross-border gathering of data to achieve sufficient sample size for any purpose in clinical research and or public health issues  [5, 6].
A typical problem in registries for RDs is the data exchange  [7]. Due to the disease specific and heterogeneity of data, registries do not provide any semantic interoperability to exchange data  [5, 8]. A system is considered as semantic interoperable if systems can exchange data with an unambiguous, shared meaning  [9]. To enable data exchange as the basis for clinical research in ERN-Lung, the consortia have developed a new Registry Data-Warehouse (RDW) to enable cross-border healthcare through using semantic interoperability, while considering common standards, ethical and data protection regulations. The objectives of this work are to present the aims, a conception and software-implementation of the RDW, as well as an interoperability approach between existing ERN-Lung registries. For reporting our work, we follow the ISCIL Schema (Introduction -State of the art -Concept -Implementation -Lessons learned).
Aims of the Registry Data Warehouse The aims of the RDW are to allow data exchange as the basis for clinical research in ERN-Lung. Existing and new registries will be brought together in a comprehensive registry landscape with interoperability across possible facets. The RDW provides appropriate software systems and components to achieve these goals. Clinical care registries will be able to exchange data with epidemiologic data registries as well as with healthcare utilization or health economic data registries. To enable the RDW in ERN-Lung, we conducted a conception and implementation phase. In both stages, we considered needs and ideas from all registries stakeholders, like clinicians and technical experts. In the conception phase, we involved all stakeholders in the development of a common dataset (CDS).
By this option we hope to get in contact with new patients who self-register in the ERN-Lung Population Registry for closing the gap of knowledge concerning the number of patients affected. This will help them to actively contribute to RD research and thus the ERN-Lung Population Registry can contribute to patient empowerment. The ERN Patient Portal will allow patient recorded outcomes once the patient is registered with the system (5). The RDW will allow bidirectional semantic interoperability (6) between the ERN-Lung Registry, the Population Registry and diseases specific registries (CF, PCD, non-CF BE and PAH)  (7). Interoperability between existing registries with a CDS is a prerequisite  (8). If data is to be given to a third party, an anonymised copy of the relevant data is generated  (9).
This is a one-direction export functionality (10). Data sharing with the RDW (data copy export only) upon request might be asked for by the European Commission's Joint Research Centre (JRC) of the European Commission in Ispra, Italy (11). Furthermore, to make data semantically interoperable, an ERN-wide Meta Data Repository (MDR) will be established. A MDR is a dictionary of data-elements, describing the clinical data in a registry. The functionality will be increased by a Registry & Metadata Repository (RMDR), which stores information about which registries are connected and which are using which kind of data (13). To further support collaborations between researchers, a central ERN ID-Management System (14), described in section 4. 3, will be provided to create unique pseudonyms in an untraceable manner based on the patients' identifying data.
Minimal dataset of ERN-Lung registries The ERN-Lung Registry and Population Registry will contains a set of common data elements for RDs as well as specific data elements customized for ERN purposes like cross-border consultation. Both registries are making use of the same minimal data set as all other registry applications (CF, PCD, non-CF BE and PAH). The minimal dataset is based on the recommendation of the JRC of the European Commission. The ERN-Lung Registry, as well as all other registries (CF, PCD, non-CF BE and PAH) will implement these dataset to ensure semantic interoperability between the registries. The datasets contains 16 elements of the JRC minimal dataset, collecting basic information about the patients, e. g. diagnosis, diagnosis-history and age at onset. This data is expanded by six further data elements for ERN-Lung administrative reasons, e.
Using OSSE for the ERN-Lung Data Warehouse As stated in the previous section, two registries in context of the RDW were developed. The ERN-Lung Registry and the ERN-Lung Population Registry. Both registries were developed with OSSE, which allows patient organizations, researchers and physicians to create patient registries based on an open-source software. OSSE is currently used in various international and national registries, e. g. in the European lipodystrophy registry  [11]. OSSE was funded by the German Federal Ministry of Health in context of the National Plan for Rare Diseases in 2013. In order to reach semantic interoperability between registries, OSSE considered at an early stage the conception of a high level of interoperability. An important aspect for this is provided with the metadata concept  [5].
It is a central component of OSSE and will be described in the next section. There are several approaches to merge data from our local registries into the ERN-Lung Registry. First, the data can be uploaded to a central registry or remain decentralized at their locations. The first approach can be critical, since data protection laws did not allow to duplicate or upload the data into another system. OSSE supports the approach of decentralised data storage. This means that the data remains in the local registries. OSSE provides a component called "search broker" to connect the registries between each other, called "decentral search". A request, defined as a description of metadata can be send to one local registry, to check, if a sufficient amount of patients with specific characteristics are available.
Meta Data Repository & Registry & Metadata Repository (RMDR) Metadata is defined as data that describes the actual data concerning their structure and representation. This concept is used for the creation and documentation of data in OSSE registries and establishes the basis for data exchange between the registries. OSSE uses the software module Samply. MDR (abbreviated as MDR in the following) to describe the data to be stored in a registry. The MDR is a software to support the specification of data elements in a structured, formalized and standardized way. MDR was originally developed in the context of the German Cancer Consortium  [12]. The MDR uses the metadata standard ISO 11179 part 3 edition 3, which describes how metadata can be described  [13]. As an example for data element definition, we will show the definition of "blood pressure" as follows.
A data element called "blood pressure" is created in the MDR and a short description of the collected characteristic is stored. The data element contains two integer values: systolic blood pressure and diastolic blood pressure. It is stored which unit is used for the blood pressure measurement (mmHg). This is necessary, since different medical parameters can be described with different units (e. g. blood glucose with mg/dl or mmol/l). Furthermore, a validity check of each data element can be defined, to consider, that a value is in a certain value range or not. This means for the mentioned example, that a blood pressure value is never negative and therefore always above zero  [5]. Since the MDR is connected to the OSSE-registry, electronic case report forms (eCRF) can be created based on the MDR.
This enables the data input into the registry to be based on the data elements of the MDR  [5]. By using the MDRs in combination with a CDS, semantic interoperability between all ERN-Lung registries can be ensured. As the MDR only covers metadata about data elements and not about the registries collecting these, our initial approach was to extend the functionality of the MDR. However, the European Rare Diseases Registry Infrastructure (ERDRI) has been developed by the JRC in February 2019. ERDRI makes rare diseases registries searchable and findable. Core elements of ERDRI are the Central Metadata Repository (ERDRI. MDR) and the European Directory of Registries (ERDRI. DOR). The ERDRI. MDR contains all metadata used by participating registries with their specific designations and definitions.
With ERDRI. MDR metadata items can manually be entered using a graphical user interface. While establishing a new registry, a user can search for already existing data element specifications to select them instead of creating new data elements. The ERDRI. DOR gives an overview of all existing registries, including characteristics of these registries. In order to avoid parallel structures at European Level, we decided to use the ERDRI. MDR and the ERDRI. DOR for the RDW  [14]. To use the ERDRI-Infrastructure in the RDW, adaptations to various components of the registry software OSSE were necessary. The OSSE Electronic Data Capture and the Collaboration Client, which is part of the search broker, were adapted connect to the ERDRI-Infrastructure.
Therefore, the Identity Management must also be able to manage various pseudonyms and to merge them if necessary. Since pseudonyms preserve the linkage between medical data and identity data, the data remains personal in the legal sense, unlike with anonymization. The storage and processing of the pseudonyms must therefore be covered by a corresponding declaration of informed consent by the patient. To enable pseudonymization of patients, the OSSE architecture includes the local ID management/pseudonymization service for each national or regional OSSE registry. The ID management software used in OSSE is called Mainzelliste  [5, 15]. The software Mainzelliste was released as Open Source by the Medical Center of Johannes Gutenberg University Mainz and is already being used in various other projects.
Lessons learned (Discussion) The European Commission recommends to enable registries to improve research and healthcare in rare diseases  [16]. The ERN-Lung consortia follows this recommendation and have developed the ERN-Lung RDW. In this work we have shown the aims, a conception and software-implementation of the RDW, as well as a semantic interoperability approach between ERN-Lung registries. The use of OSSE, a metadata concept and a CDS, enables semantic interoperability between the RDW and the local registries. By using the CDS of the JRC of the European Commission, as well as the connection to ERDRI, makes the RDW usable and available on a European level. However, other ERNs are also developing similar structures within their networks. Using a CDS among all ERNs brings opportunity for interoperability.
This leads to a substantial pool of data collected by the system's LIDAR and subsequent cameras, where the data are stored both on the car's in-built computer and synchronized through centralized systems for processing. On this front, the stored data are divided into training and validation datasets and then learned by the ADS system which updates the ODD, thus increasing the safe state domain  [13]. This also improves the accuracy and confidence of the autonomous vehicle, particularly on curved lane detection, which is a complex domain requiring research attention. They have also claimed that the AV is operable in foggy conditions where it has been reported that it covered the entire Phoenix valley of 517 square miles (equivalent to 832 sq. Kilometers)  [14]. There has been much research and development in lane prediction, where vehicles may calculate possible trajectories of their neighbouring vehicles by using robust planning with continuous ambiguity  [15].
Therefore, all functional blocks and elements present in the ADS are responsible for overall vehicle safety, thereby probably eliminating all possible human error altogether. Currently there is no "State of the Art" or "Production Level capable" autonomous driving systems, mainly because of the ever-increasing complexity and difficulty of constantly updating databases and potentially simulating thousands of possible scenarios, requiring extensive computing power. Moreover the actuators and the processing system, required for full vehicle autonomy, are currently extremely space intensive, making it difficult to fit them into existing vehicles or into current vehicle forms that most customers are accustomed to. To dynamically analyse the various inputs being received by vehicles, a dynamic runtime safety assurance system is required.
• Use of neural networks (NNs): This is probably the most efficient method in predicting the range of accidents and their intensity and impact. The efficiency of this system is based on the amount of data which is fed. It also depends on the "cleanliness" of the data fed and the labelling of the data points accurately. This is usually done by dividing the data into LIDAR datasets and camera datasets, which are independently generated. Three-dimensional LIDAR data labelling is mainly done by providing 3D box labels which include vehicles, pedestrians, cyclists, and street signs. Each scenario can include areas which are not labelled, known as the no label zone (NLZ), represented as polygons in captured frames  [33]. [36]  using CNNs and SCNNs for detecting object and spatial features. These features are then analysed by the on board ADS and ADAS system to render a proper output.
• Path following: Calculating the margin of error and generating a virtual map of the path are the main functions of the path following system  [5]. Using recorded GPS waypoints and previously recorded human driving or data obtained from a web map, the path or the virtual map is generated. Then, there is a division of the routes into particular individual segments which are smaller segments of the road, and each segment contains an equal number of data points. Here, this paper makes use of a third degree polynomial equation: X i (λ) = a x i + b x i λ 2 + c x i λ + d x i (2) Y i (λ) = a y i λ 3 + b y i λ 2 + c y i λ + d y i (3) The path or the virtual maps which are mainly contained in the a,b,c, and d coefficients in the equation are all generated offline. Next, for the error calculation, these generated coefficients are used.
Case Studies on Lane Detection and Simulating Environments This section presents three cases studies on different methodologies in lane detection and vehicle simulation for autonomous vehicle safety. The first case study is the detection of straight lanes using Hough transforms. The second case study addresses curved lane detection using SCNNs and OpenCV. The third case study discusses the behaviour planning and safe lane change prediction system in highway and roundabout environments. Each of these case studies is discussed in the subsequent subsections. These methods and the solutions presented are geared towards small-scale businesses planning to test solutions for AVs who might lack the computing power (hardware and software) for test beds and the required sensors. Here, for example, in case study 1 and 2, the input sensor is the camera rather than the LIDAR, which has a higher setup and investment cost.
Case Study 1: Detection of Straight Lanes Case study 1 detects a straight lane using a Hough line transform, which involves the following steps: 1. Capturing and decoding video file: This captures the video object and decodes the video frame by frame (i. e. , converts video into a sequence of images). The following python code is used to capture the video and convert it into frames: cap = cv. VideoCapture ( ' ' video Input. mp4 ' ' ).