dataesr/scientific-paragraphs-categorization huggingface dataset:
To investigate previously reported furin inhibitors, we mined the ChEMBL database  (Mendez et al. , 2019). We downloaded 628 compounds that have been tested on furin and removed molecules that are too far away from the rule of 5. This rule was introduced in 1997, and suggests that to administrate a small molecule by oral route, the molecular weight (MW) of the compound should be less than 500 daltons, the lipophilicity (evaluated via computed logP) cLogP should not exceed 5, the H-bond donor count (sum of OH + NH groups) should be less than 5 and the H-bond acceptor count (sum of O + N atoms) should be less than 10  (Lipinski, 2000). As some of the compounds available in ChEMBL for furin have not been extensively optimized toward clinical candidates, we initially only applied a soft filter on MW and selected molecules with MW ~600 or less.
This simple filtering step led to the selection of 76 molecules. Then we focused on activity characterized by the so-called pChEMBL value. As defined at the ChEMBL website, the pChEMBL value allows a number of roughly comparable measures of half-maximal response concentration/potency/ affinity to be compared on a negative logarithmic scale. For example, an IC50 measurement of 1 nM would have a pChEMBL value of 9. This value is defined as: -log(molar IC50, XC50, EC50, AC50, Ki, Kd or Potency). It was possible to obtain a pChEMBL value for only 42 molecules. Then, removing duplicate compounds and clustering the molecules by structural similarities, we ended up with 11 drug-like molecules that inhibit human furin (Figure  4 ). To these compounds we manually added a molecule missing in the ChEMBL database named BOS-318 (Figure  3B ), this compound has been recently co-crystallized with furin and was shown to be selective and cellpermeable  (Douglas et al.
, 2022). Analysis of the molecules was performed with our software FAF-Drugs  (Lagorce et al. , 2017)  and the visualization carried out with DataWarrior  (Sander et al. , 2015). While potency is an important parameter for drug discovery, other metrics can be used to evaluate small molecules and help to select compounds for further developments. One such metric is called ligand efficiency or LE. This value normalizes the experimental activity to molecular weight, heavy atom count, or other molecular size quantifier (i. e. , an acceptable level for LE is around ≥ 0. 3)  (Hopkins et al. , 2004). LE metrics are thus considered as valuable indicator of compound quality in the early stage of drug discovery as they help focus on molecules that are relatively small and yet that have a significant target activity, suggesting that optimization of the molecules should be easier.
Indeed, in most cases, compound optimization usually deteriorates parameters such as MW and log P (e. g. , increasing these two parameters often lead to increased interaction with off-targets and anti-targets, render oral administration more difficult. ). In Figure  4 , we plotted LE as a function of MW (e. g. , to for instance see if compounds could grow in size) while color coding the molecules with the computed pChEMBL value. We note for instance a peptide-like molecule (CHEMBL569918) with a high MW that shows high potency with a reasonable starting LE, yet such molecule would require significant optimization before becoming a clinical candidate. Some molecules with a relatively small MW could be more interesting to optimize as they have a favorable LE but the starting chemistry is eventually not optimal.
Indeed, some structural alerts (i. e. , toxicophores, simple means to flag potential chemical hazards and problematic compounds) are often noticed in these molecules (please see details about , green means more potent). Next to each compound, the ChEMBL-ID (or name) is written. Some structural alerts and physico-chemical problems that may impede development are noted between parentheses. These data were generated with the FAF-Drugs server. More specific endpoints were computed with the interpretable-ADMET server, for some compounds no information could be obtained as outside the applicability domain of the method, for the others, the main warnings are as follow: compound CHEMBL403278, putative inhibition of hERG (cardiotoxicity), and inhibition of OATP1B3 organic anion transporting polypeptides (a critical transporter); CHEMBL1644908 or CHEMBL1644915, putative inhibitor of hERG; CHEMBL1644910 or CHEMBL449045, putative inhibitor of hERG and OATP1B3; CHEMBL1645025, putative hERG and Pgp-inhibitor and possible respiratory toxicity; CHEMBL1808931, putative inhibitor of OATP1B3, modulator of nuclear receptors and possible respiratory toxicity; CHEMBL1644912 putative inhibitor of hERG and modulator of nuclear receptors; CHEMBL569918, putative inhibitor of OATP1B3; BOS-318, CHEMBL3936612 and CHEMBL1644907, tend to be outside the applicability domain of the method and further computations will be required.
Ensuring a high level of vessel traffic surveillance and maritime safety is determined by exploiting innovative ICT technologies and international cooperation among maritime authorities. Therefore, initiatives for maritime surveillance, global and regional integrations are realised through a collaborative, cost-effective and interoperable Common Information Sharing Environment (CISE). Consisting of the institutional network of maritime authorities that cooperate on various domains like safety, border control, environmental and rescue missions at sea, CISE enables the efficient transfer and economic exchange of maritime data and information via different interoperable systems using modern digital technologies. The ever-increasing amount of data received from heterogeneous data sources requires specific processing through the adoption of a Big Data framework which hosts, manages and distributes data to maritime users, contributing with great overall benefits to the CISE network core functionality.
Specifically, this paper analyses the advantages of the Data Lake infrastructure, including its processes, techniques, tools and applications used to enhance maritime surveillance and safety across the CISE network. This part contains the deployment and interoperability achieved through the components of the participating command and control (C2) systems. Last, as a case study, an overview of the EU project EFFECTOR is provided which aims to demonstrate an end-to-end interoperability framework of data-driven solutions for maritime situational awareness at strategic and tactical operations.
Also, the term "maritime big data" is related to a vast volume of various data received by maritime sensors with specific relevance to maritime authorities/companies (  [2] ,  [3] ). Referring to maritime safety and surveillance, the exploited assets provide essential information about ships and vessel types, exact positions, navigational routes, weather etc. , by exploiting essential data sources such as: static and dynamic data of Automatic Identification System (AIS), Satellite AIS (S-AIS), Long-range Identification and Tracking (LRIT), coastal radars, CCTV/thermal cameras, electrical and optical devices and weather data, collected from their own or international platforms. Specifically, in order to integrate the abovementioned datasets and provide a general maritime picture of the area of responsibility, the maritime authorities have introduced the IMO directive for Vessel Traffic Monitoring and Information Systems (VTMIS), designed according to the needs of a particular authority  [4].
Therefore, the CISE structure comprehends the different information layers, determined by international platforms (SafeSeaNet, Thetis, LRIT) and participating agencies' particular ICT systems/sensors. All these assets are integrated according to the specific CISE Data and Services Model, which can fill existing information gaps across the EU member states, and it is able to provide a Common Operational Picture (COP) to all networked users ([4],  [5] ). Having defined the operational needs of the User Community, the European Maritime Safety Agency (EMSA), as a governing EU body for CISE, has provided the technical framework for implementing CISE infrastructure at member state level. There are several governance models for participating in CISE stakeholder group  [6] : 1. One CISE nodeone adaptor, 2.
One CISE nodemore than one adaptor, 3. One country with more than one CISE node, 4. National node connected to the CISE node. Specifically, concerning on model 4, as represented in Figure  1  (adapted according the  [6] ,  [7] ,  [8] ), the CISE technical infrastructure comprises five basic building blocks: the member state Legacy Systems (LS), EU/national/regional CISE Node, CISE Adaptor, CISE Node/Gateway and CISE Network. Other blocks related to Data Lake are developed within EU project EFFECTOR, with more detailed description in the respective Case study chapter. To begin with LS, these ICT systems represent the existing assets used by national maritime authorities, e. g. VTMIS, National Maritime Single Window (NMSW), Unmanned Aerial Vehicles (UAV or UxV), Command, Control and Communication Systems (C2/C3i), able to share the data and information received by maritime surveillance sensors (AIS, radars, VMS, METOC, LRIT, etc.
). In general, to join the national/regional/EU node, a maritime authority needs to link its own LS with the national Node via specific LS-to-CISE adaptor, which translates the data in IVEF or NMEA communication protocols to Node component. Furthermore, the CISE Adaptor translates CISE data and services models to LS specific data format and intermediates between authority and CISE Node/Gateway. Finally, stakeholders are Figure  1. General CISE Hybrid Architecture with integrated Maritime Big Data Framework (authors adapted according the  [6] ,  [7] ,  [8] ) enabled to access the CISE network (fully secured by VPN and IPSEC) via CISE Node/Gateway core, advanced and common communication services (web and application security, auditing, collaboration and administration console for status tracking).
Specifically, the services are defined by attributes Service ID, Service type, Profile, Capabilities, and the most significantly, all provided services delivered by participating authorities' LS are listed in CISE Service Registry in order to inform all stakeholders about services which are provided by a particular agency to the CISE Network. For instance, mostly exchanged services include collaboration services, vessel details, location and voyage, intervention assets, incident history/event notification, identified risks, cargo and distributed search services. The CISE Data & Services Model is based on XSD (XML Schema Definition) accompanied by UML diagrams (Unified Modelling Language), providing the models for classes and subclasses, related to enumeration of authority and message and service core entities (  [11] ,  [12] ,  [13] ).
This specification is then easily exposed to the rest of the system, making the newly arrived data available. The major advantage of a Data Lake, contrary to databases, is the facility of integration of new data sources as there is hardly any or no transformation of it at all. The drawback associated to this is the difficulty of querying the different data stored. To support the Big Data technologies, an aggregation layer  [15]  brings the capability to get a variety of data from very different data sources, it admits analysis of large quantities of heterogeneous data related to maritime surveillance. Furthermore, this huge data, thanks to the aggregation layer will ensure data quality, avoiding data redundancies. After the acquisition phase, the storage phase follows. In Figure  2. the Data Lake storage subsystem could be composed of three modules in which the data can be stored depending on use cases: the Operational Database module, serving transactional operations; the Data Warehouse module, serving analytical operations; the Search Engine module, indexing the data for search operations.
The need of multiple storage system comes with the need of managing data at any scale and it is the same for the Big Data. With sufficient volume, no database can provide sufficient performances in both throughput and latency, leading to the need of multiple databases solutions  [16] : online analytical processing database (OLAP) and online transactional processing database (OLTP) sometimes complemented with a hybrid transactional analytical processing database (HTAP) to fill the gap and enable analytics on the fast data. In the maritime domain, datasets traditionally used are well structured. Even semi-structured data sources are far away from being that heterogeneous as Web data would be. This eases the data integration process as datasets are homogeneous. Although the datasets are generally homogeneous, they are actually heterogeneous referring to each other.
Multiple data sources produce more consistent, accurate and useful information than any other provided by any individual data source. Thanks to the data fusion and analysis layer, it is possible to integrate the data that comes from multiple data sources continually; to catalogue the available systems, assets and sensors to be used, and finally to identify which of those produce data. Some of a data provided from data fusion and analysis module are the result of analysis over a raw dataset. In such cases the time needed to analyze the raw data can cause a time delay between the acquisition of the raw data and the availability of the processed data. The aim of this module in the architecture is to send enhanced data to the application layer (interactive and interoperable C2 system). Big Data Analytics characterize processing unstructured information from sensors generated content which can be transformed into valuable border security information, using computational techniques to unveil trends and patterns between datasets.
B. Support of Data Lake for enhancing Command and Control capabilities Operators of Command-and-Control systems require easy and quick access to data from their sensors, surveillance systems, external third-party databases and operational information created during their operation  [18]. As technology evolves, new sensors along with their protocols are available to enhance the maritime picture of an agency. Additionally, users want to be able to keep historical data and to query this data when needed. Managing these needs and catering for future expansion of storage and new data types are the requirements that can be successfully met by the implementation of a multilayered Data Lake technologies, as described in the previous section. Data Lakes can provide an infinite pool of raw and processed information, enabling operators of C2s to obtain quick access to current information and to perform extensive analysis on historical information.
Incidents can be propagated up to higher levels, while incoming data from the CISE network can be pushed down to the specific department, whose responsibility is a certain area where e. g. , the vessel is located or information can be used operationally (documents, risks, etc. ). The segmentation allows for deployments to be distributed and not centralized. Each regional command structure or even a local command structure can have its own installation and information is moved automatically or by a user-action. If connectivity is broken, information is queued and once connectivity is restored, this queued information is propagated. As time passes, historical information is accrued from sensors, external databases and from the day-to-day operation of a maritime agency. Access to this information is needed for various reasons.
IV. CASE STUDY: EU PROJECT EFFECTOR Following the goals of the European Commission for achieving maritime CISE network's full operability and building on outputs of previous projects related to surveillance and CISE topic, the project EFFECTOR (an End to end interoperability Framework For maritimE situational awareness at strategiC and Tactical OpeRations) is focused on exploitation of significant potentials of maritime surveillance assets and systems with enhanced interoperability framework implementation. The project assumes deployment of multilayered data lake platform with incorporated Big Data analytics, techniques and tools. Its aim is to improve decision support and information sharing process in order to reach high interoperability between maritime information systems (at national level, or cross-border) and cooperation among partners at strategic and tactical level  [8].
Using the maritime surveillance assets and C2 systems (SeaMIS, ENGAGE and MUSCA) of the end users involved in the project and deploying Data Lakes, ontology design and data alignment, the project EFFECTOR provides improved solutions for data fusion and analytics, maritime anomaly -suspicious vessel behavior detection, and semantic trajectories fusion, as well. The set of software tools are also supported by AI modules (for alert and anomaly detection), Uniform Communication Gateway, as well as Early Collision Notification system based on fuzzy logic. The project gathers relevant partners from the IT industry, academia and end-user community of maritime authorities, which jointly work on provision of enhanced collaborative framework for maritime surveillance and control. Three national Data Lakes will be installed, tested and validated through 3 operational trials (respectively run in Portugal, France and Greece) covering the large spectrum of coastguards' missions.
Among the seven end-user participants spanning from Portugal and Bulgaria (and including European navies, coast-guards or police corps), Administration for Maritime Safety and Port Management of Montenegro (AMSPM), being the EU candidate country, contributes with provision of necessary AIS information for implementation of operational scenario related to the French Maritime Trial, shared in specific format respecting CISE Data and Service Model  [8]. The CISE hybrid architecture, referred to Figure  1 , assumes the specific interface between participating authorities substantiated in the component of mentioned interactive and interoperable C2 platforms for execution of actions and services and information exchange according to the provided entities in CISE Data Model. These C2 software provide full COP visualization, the entire control over actions taken by users, and services exchanged in joint missions based on communication patterns, as designed in CISE architecture.
Moreover, in order to process a huge amount of data received by maritime authorities' sensors, it was necessary to involve the concept of Maritime Big Data Framework, in the form of Data Lake, which covers all necessary operations to enable the relevant data flow into C2. Through this interface the data are further transferred to EU/regional/national CISE Node, then to CISE adaptor, and over CISE Node/Gateway to CISE Network and Environment. To be more specific, the repository databases of Data Lake, previously filled with data in raw formats, e. g. CSV, JSON, or RDF received from national maritime authorities' LSs over specific adaptors, consist of several operational layers which process the ingested data transferred over IVEF/NMEA protocols. After structuring and refining the data, the capabilities such as Data Fusion & Big Data Analytics, Situational Awareness & Decision Support Tools involving some artificial intelligence engines with Service Management, transfer the data to harmonization layer, which delivers the information in standardized format to the mission and reporting entity.
Finally, the C2 platform receives processed and structured information as an output of Big Data Frameworkdata lake, in the form of straightforward actions, services, missions and reports, which are then transferred to other agencies in CISE Network, as described. In detail, data is ingested from different data sources and communication protocols into the Data Lake, transformed, filtered, stored and routed where necessary to the various operational layers of involved maritime agencies (e. g. , national, regional, local). Any associated metadata is also propagated throughout the various processes to provide context to the data item. This allows for data to be stored in its raw format and in other formats. Many third-party services use different data formats (e. g. , AIS or IVEF or ASTERIX etc.
V. CONCLUSION The paper provides an analysis of current collaborative environment for exchange of relevant maritime information based on CISE concept, according the communication patterns and services designed under CISE Data and Services Model. The highly developed interoperable platform, in the form of multi-layered Data Lake, that has capacity to fuse, structure, manage and store vast amount of data received by heterogeneous maritime sensors is here presented as an essential part of CISE network. For the purpose of providing greater decision support for maritime safety/security agencies, Data Lake deploys processing layers with big data techniques for analysis of vessel movements, behavior, risks, various maritime activities and other events at sea. Following the provided system design, the Big Data component and its interaction with C2 capabilities, represent key feature which enables higher level of situational awareness at sea.
As a case study, it is presented the EU project EFFECTOR, that aims to further deploy interoperability framework of national agencies competent for maritime surveillance and foster collaboration among them on tactical and strategic level. Therefore, the integration of Maritime Big Data applications into information-sharing process significantly contributes to enhance CISE capabilities bringing many benefits to collaboration among member states. Finally, they are reflected in a higher quality of maritime COP, detailed coverage of monitoring areas, efficient and economic process of information distribution among stakeholders, more comprehensive and quicker responsiveness in surveillance and operations at sea, ensuring safer and more secured EU maritime domain.
They predicted that, in 2015, the business would be service-advantages-oriented with flexible business models, changed customer requirements and changed OEM abilities. They also expected standards to be available on at least contracting, access to machines, monitoring conditions, and quality assessment and best practices would be shared widely in areas such as knowledge to design, to use machines. As of 2022, we are still far from real-life implementation of these concepts. Even though Meier et al. predicted the following to come true by that very year: "Result-oriented business models evolve as an industry standard. Complex development processes are simplified by automatic configuration by Plug&Play product and service modules. Service will be provided globally by service supply chains based on modularized service processes.
IPS 2 and smart products and services With the ubiquity of Internet, computational intelligence and network technologies, smart product-service systems (SPSS) have become an important research area and a source for potential innovative value propositions  [29]. In the original definition proposed by Valencia et al. , SPSS were defined as "the integration of smart products and e-services into single solutions delivered to the market to satisfy the needs of individual consumers"  [221]. Huge research effort has been devoted to propose conceptual frameworks for capturing the aspect of the systematic co-digitalization of products and services  [88, 242]. In  [83] , Kuhlenk€ otter et al. analyse the specific challenges of SPSS design to exiting engineering methodologies. Keys are intrinsically high levels of complexity through horizontal interconnectedness of many components and disciplines, as well as closed-loop engineering design activities having to cover the entire SPSS life cycle, including the business model.