dataesr/scientific-paragraphs-categorization huggingface dataset:
The language expression uses FuzzyTerms defined for FuzzyVariable and the operators "or" and "and", with a set of system or user supplied modifiers such as "very", "not" and "slightly" to form it. The FuzzySet in a FuzzyValue is the mathematical expression that represents the fuzzy concept of this FuzzyValue. For this work, to define four properties has_FuzzyVariable, has_FuzzyTerm, has_FuzzySet_FT and has_FuzzySet_FV, which represent the relationships between Fuzzy-Variable and FuzzyValue, FuzzyTerm and FuzzyVariable, FuzzySet and FuzzyTerm, as well as FuzzySet and FuzzyValue. As illustrated in Figure  4 , FuzzyVariable is divided into FuzzyVariable_GPM and Fuzzy-Variable_GSM, and FuzzyValue is classified into FuzzyValue_GPM and FuzzyValue_GSM, which are represented by the attributes has_FuzzyValue_GPM and has_FuzzyValue_GSM and correspond to Generic_Problem_Model and Generic_Solution_Model, respectively.
For this reason, the exact thickness of the protein corona cannot be reliably determined for these small particles. We first demonstrate here the multicolor analysis of STED image data with the aim to quantify the relative protein content at the single-particle level. From the images, we extracted the mean fluorescence intensities of single nanoparticles in each color channel. The origin of the varying level of intensities is the combination of the degree of labeling and the relative amount of protein on each nanoparticle, in combination with the different brightness for each dye. We used the extracted intensities, combined with calibration as described in the Supporting Information to estimate the relative protein content displayed on a single nanoparticle level. In Figure  4a , we show the three-color STED image of 400 nm smooth silica nanoparticles (MSN) with amine functionality.
It is clearly observed that fluorescent intensities collected from different color channels are vastly different on a single-particle level. From the line profile across a single particle in Figure  4b , we found that BSA with Star Red showed the highest intensity, while for IgG with Star Orange and Tf with Chromeo 494, a factor of 4 less intensity was detected from the same nanoparticle. Although less in intensity, these channels do not show a strongly reduced resolution due to the similar saturation intensity I sat and the same STED depletion intensity I for all dyes. We employed custom-made Matlab scripts (available in the Supporting Information) to perform the quantification of the relative protein content on single nanoparticles. The measured fluorescence intensity (sum of all pixel intensities on a single nanoparticle, corrected for background) from a single-color channel is proportional to the brightness of a single dye and the total number of fluorescent dyes in the excitation plane,i.
All samples were imaged with a 100× NA 1. 4 oil objective on an Olympus IX83 inverted microscope. Star Red labeled samples were excited with 640 nm, Star Orange with 561 nm, and Chromeo 494 with pulsed lasers (40 MHz). The power of excitation lasers ranged between 5 and 10 mW at the back aperture of the objective. To deplete the fluorescent signals from all three dyes, a pulsed STED beam of 795 nm at power ranging from 100 to 500 mW at back aperture applied depending on photophysics of different dyes was used. For all STED measurements, lateral and axial depletion was combined to realize approximately equal resolution in 3D. Deconvolution of the STED data was carried out using the deconvolution module in Abberior Imspector software. Estimated theoretical ideal PSF was generated, and Richardson-Lucy algorithm was used to perform deconvolution.
Individual force curves of AFM force maps were analyzed using JPKSPM data processing software. The baseline of the retraction curve was corrected using a linear fit on the last 20% of the retraction curve, allowing the positioning of the contact point. Unfolding distances were then manually measured between the contact point and the point of minimum force. These quantifications were initially validated by several observers on few similar AFM experimental recordings and gave similar results based on the established inclusion/exclusion criteria (Supplementary Fig. 2 ) (Inclusion: Adhesion forces >40pN; Exclusion: disturbances in the approach curve (Supplementary Fig. 2a ), tethers (Supplementary Fig. 2c ), unidentifiable baseline). Quantifications of the unfolding distances presented in the manuscript were conducted by two observers.
Conversion of the HTRF ratio of each sample into cAMP concentrations was performed on the basis of a standard curve to determine the linear dynamic range of the assay. Mixture Gaussian model analysis. The data were analyzed by a model of a mixture of heteroskedastic Gaussian populations. The analysis was performed by maximum likelihood optimization via the EM algorithm using the standard R routine normalmixEM from the "mixtools" package  34. Because of practical considerations, the number of subpopulations k was chosen in the interval 3-6. The selection of their size was performed using the Bayesian information criterion (BIC), which is favorable for small sizes. In some cases, two sizes had close BIC values, and the final decision was made by visual agreement between the histogram and the estimated density.
Theoretical HA-GPCR contour lengths. Assuming that one amino acid is, on average, 0. 4 nm long  35  , we expect a single HA-β 2 -AR unfolding at approximately 169 nm, HA-mGlu3-R at 355 nm, HA-2xβ 2 -AR chimera at 315 nm, HA-µOR at 163 nm and HA-AT1a-R at 147 nm. Statistics and Reproducibility. Statistical analysis was carried out using GraphPad Prism 9. 1 software (GraphPad Software Inc. ). Statistical tests and the number of independent experiments performed are indicated in the figure legends. A p value < 0. 05 was considered as significant. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements We thank  Anne-Marie Caminade  and  Jean-Pierre Majoral  ( LCC-CNRS UPR 8241 ,  Toulouse ) for providing us with the dendrimer used in this study for the AFM tip functionalization, and  Marine Drapier  together with  Adrien Schaffner  ( INSA school, Toulouse ) for their participation in the mathematical analysis of the AFM data. This work was supported by the  Agence Nationale de la Recherche  ( ANR-17-CE11-0023-01  to C. G. ) and the  IDEX UNITI  for the Transversality grant SMiRCH (UFTMIP:  2016-106-CIF-D-DRDV  to E. D. ).
It is interesting to note that this report identified the LCDP segment as specific to the production of business applications, such as software for enterprise resource planning, customer relationship management, business process management, and other productivity-enhancing applications. In 2016, Forrester detailed the successful application domains for LCDPs in four specific application scenarios, i. e. database, requesthandling, process, and mobile-first  [49]. The definition has evolved, and in 2017, Forrester provided a more detailed version, characterizing LCDPs as "products and/or cloud services for application development that employ visual, declarative techniques instead of programming and are available to customers at low-or no-cost in money and training time to begin, with costs rising in proportion of the business value of the platforms"  [45].
The year 2017 noted the start of a series of acquisitions for LCDP vendors  [46]. Appian started an initial public offering in May 2017, and in 2018 its market valuation nearly reached $2 billion. In July 2018, OutSystems received investments of $360 million. In August 2018, Siemens announced the acquisition of Mendix for $730 million  [46]. In 2017, Forrester estimated a global market size for LCDPs of $3. 8 billion. Forrester also periodically surveys developers about LCDP usage 1  : In 2018, 23% of developers reported using low-code platforms, and another 22% planned to do so within a year  [48]. In 2019, 37% of developers were using or planning to use low-code products  [32]. In 2021, most large cloud providers offer LCDPs within their cloud-based solutions. Microsoft was among the first to embrace the trend by releasing its Power Apps LCDP in November 2016.
In January 2020, Google acquired the LCDP provider AppSheet and made it its flagship lowcode solution. In June 2020, Amazon released Honeycode, a LCDP for web, and mobile application development. No-code development platform (NCDP) is a related term used for platforms that eliminate the need for programming using visual languages, graphical user interfaces, and configuration. While the term is widely used in marketing, market analysis firms currently oppose using it to identify a clear market segment  [47]. For the context of this paper, we consider NCDP and LCDP interchangeably, and consequently, hereafter, we use the term LCDP only.
Overview of low-code development platforms In this section, we present an overview of the most significant LCDPs by considering the typical steps that are performed when using them and by relying on a refined taxonomy originally presented in  [51]. LCDPs support the development of applications that can be web-only or also native for the target deployment environments. Thus, they can natively support both desktop and mobile devices and integrate with existing workflows developed with popular Software-as-a-Service (SaaS) applications, including Zapier, Amazon AppFlow, and Trello to mention just a few. Appian [2] is among the most long-lived LCDP, whereas Amazon Honeycode  [1]  and Google App-Sheet  [15]  are among the most recent approaches. Particular characteristics that distinguish existing LCDPs pertain to the user experience of advanced Graphical user interfaces (see Fig.
Domain modelling In this phase, users are provided with modelling constructs to represent concepts and relationships underpinning the application being developed. Figure  3  shows a simple domain model specified with the Mendix platform to describe training courses. In particular, a training Course is defined in terms of its title, description, duration, and price. The data entity ScheduledCourse defines when a given course is scheduled in terms of start and end dates. Moreover, to schedule a course, it is necessary to specify its Trainer and the Location where it is held. The Registration entity is for defining registrations of Trainees that want to attend ScheduledCourses Pre-built templates can be exploited as starting point when defining domain models, and interactive application analytics are provided out-of-the-box.
Application generation and deployment The next step of the process consists of generating and deploying the modelled application by means of provided Application build mechanisms. Several execution environments can be supported, as for instance, in the case of zAppDev  [61] , which provides users with different code generation facilities. Once the desired system has been specified and built, a dedicated Deployment support is available to deploy the system in private or public environments. Deployments are typically done on cloud infrastructures with a few clicks, as shown in Fig. 7. In particular, Out-System  [37]  provides developers with quick mechanisms to publish developed applications, connect different services, and create real-time dashboards. Application maintenance The last step of the process is monitoring and maintaining the developed system by means of dedicated features, e.
g. via code generation or interpretation) but without offering deployment or lifecycle management capabilities for the produced system. Examples of this class of approaches are JHipster  [20]  and its JDL  [21]  domain-specific language, Google Protocol Buffers  [16] , or the OlivaNova model execution system  [39]. 3. This region contains the platforms that use models to facilitate the development of software applications with reduced code and offer built-in deployment and lifecycle management facilities for the produced application. Examples include the Codebots  [7]  and Judo [22]l o wcode platforms, both of which are based on technologies of the Eclipse Modelling ecosystem  [56]. 4. This region and the next one contain approaches that cannot be considered model-driven. In particular, region 4 contains the platforms that facilitate the development of software applications with reduced code.
Such approaches offer built-in deployment and lifecycle management facilities for the produced application. However, they do not use models that conform to explicitly defined languages/metamodels (e. g. they use data stored in a relational database or schema-less XML/JSON documents). 5. These approaches aim to reduce the amount of code required to implement a system without offering deployment or lifecycle management capabilities for the produced system, and-like region 4-without using models that conform to explicitly defined languages/metamodels. Examples of this type of approach include databaseschema-driven generators like Phreeze  [41]  and one-off generators such as those provided by Ruby on Rails  [44]. Next, we elaborate on other aspects that differentiate model-based and low-code approaches, based on Fig.
8 : Platform Low-code application platforms (regions 3 and 4 in the figure) are mostly cloud-based: they can be used from the web browser and host the defined applications. This frees the user from both installing the development platform itself and from deploying the defined applications. This approach simplifies the adoption of low-code by newcomers. While MDE solutions can be cloud-based (falling in region 3)  [8] , this is not the norm today. Instead, many solutions are based on the desktop, for example, those using the Eclipse Modelling Framework (EMF)  [56] , or meta-modelling tools like MetaEdit+  [30]. These approaches would fall under region 2-and may be considered low-code development approaches-if their aim is automating application development, otherwise they would fall in region 1.
Please note that not all low-code software development approaches are cloud-based; in particular, those in regions 2 and 5 are not. Users LCDPs mainly target end-users, so-called citizen developers. Therefore low-code platforms tend to be easy to use for people with a non-technical background. This means that frequently (but not always), users of tools in regions 3-4 are citizen developers and non-professional programmers. For example, while low-code platforms like OutSystems target citizen developers, others like Judo target teams of business analysts, software architects, and programmers. In their turn, MDE solutions can target end-users, but many of them are directed to professional software developers since they are expected to be used within development processes. Therefore, typically, users of approaches in regions 1 and 2 have a more technical background.
Domains As mentioned in Sect. 2, the first wave of lowcode targeted business applications. Recently, we are witnessing proposals for low-code tools in other domains, like IoT/event-driven applications (e. g. Node-RED  [34] ), chatbots (e. g. Google's Dialogflow  [10] , Amazon's Lex  [25] , IBM's Watson Assistant  [59] ), or Machine Learning (e. g. Google's AutoML  [14]  or RapidMiner  [42] ). MDE solutions (in regions 1-3 of the figure) can target those domains but frequently also target more technical areas, which require specialized engineers. These include domains like automotive  [11] , power engineering  [13] , or cyber-physical systems  [33]  in general, among many others.
Low-code development: why now? In terms of their core ambition to expedite the delivery of software systems, LCDPs are not very dissimilar to previously tried approaches like 4GLs, CASE tools, etc. , as already mentioned in Sect. 2. Essentially, they provide an environment for specifying the structure and behaviour of a software system at a high level of abstraction. Such an environment shields developers from low-level concerns (e. g. specific databases, object-relational mappers, services, messaging, and security middleware). They then generate executable code that realizes the specified software system. Given the broad consensus that 4GLs and CASE tools were not wildly successful, why should low-code environments fare any better? There are multiple reasons why this may be the case, which we analyse next.
Thus, the availability of documentation, support, consultancy, reusable components, etc. , is of major importance. In MDE, such an ecosystem was triggered by Eclipse, i. e. a large and active ecosystem around the Eclipse Modelling Framework was established from the industrial and academic sides. It has to be further explored how such ecosystems will develop for LCDPs, as most current platforms are single vendor efforts. This issue also concerns the academic area, where scientific community efforts are required to stimulate research on topics related to low-code  [57]. For instance, a current example is the low-code workshop  [27]  hosted with the MODELS conference since 2020, which provides a forum to discuss low-code development and MDE. Managing software evolution Notably, one of the most crucial stages of the software lifecycle is the maintenance of a software product after its release.
Acknowledgements This work has received funding from the  Lowcomote  project under European Union's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agree-ment no. 813884. The work has also been partially funded by the  Spanish Ministry of Science  ( RTI2018-095255-B-I00 ) and the  R&D programme of Madrid  ( P2018/TCS-4314 ).
In this article, we present an experiment we conducted with discrete event simulations to analyze the effects of multi-step cyberattacks on the safety of cyber-physical systems. We show how to represent systems, their components (either software and/or hardware), communication links, security measures, and attacks from a malicious intruder. The latter are typically taken from the MITRE ATT&CK knowledge base. The discrete event simulation method makes it possible to represent any event affecting the system. We illustrate our approach by means of an illustrative example involving cyberattacks against the navigation system of an autonomous ship. We show how the formal modeling language AltaRica, primarily dedicated to safety analyses, can assess this illustrative example by representing the system and automatically extracting sequences of attacks, leading to a safety-critical situation, namely the deviation of the ship by the attacker.
To give insight into the applicability of the approach, we develop an illustrative example of the navigation system of an autonomous ship inspired by several articles  [4] [5] [6] [7]. These articles allowed us to gather the inputs of the modeling process, which are the architecture of the system and the threats. We demonstrate how to model the navigation system and how to generate sequences of attacks. The autonomous vessel is then modeled using AltaRica, a formal modeling language based on DES and dedicated to safety evaluations. With this model, we use an existing tool to automatically generate all the sequences of attacks leading to a safety-critical situation: the deviation of a vessel from its route. The remainder of this article is organized as follows. We first present the works related to our study in Section 2.
For instance,  Kang et al. [52]  used the Alloy language to model a water treatment system and to identify cyberattacks that jeopardize the system's safety. This work was supported by a testbed to play the attacks identified and by Alloy Analyzer to generate attack scenarios. Alloy Analyzer is built on a "small-scope" analysis, which assumes that small counterexamples are sufficient to demonstrate software flaws. Such a concept might be effective while creating software, but it seems less pertinent when dealing with cyberattacks on CPS, where larger architectures lead to longer attack sequences. Li et al. [53]  developed a security model based on SysML called SysML-Sec. It aims to model CPS and assess the communication protocol's security and relationship to safety. Then, since SysML is not a formal language, the tools ProVerif and UPPAAL are utilized to obtain the results.
In this work, we choose a compromise between the two and model attacks as described in the MITRE ATT&CK  [3]  database. Therefore, we use the database's (sub)techniques and model the behavior of the system when one or several of them are used. If a technique can be used by an attacker on a component of the system, we may model it. The pre-conditions shall be modeled as guards (g) and consequences as instructions (i), and the ATT&CK ID or name is the name of the event (e). The modeler can add any relevant variable in the guard or instruction or model events unrelated to ATT&CK but justified in the model (e. g. , attribute update). The next section will show how to model the vessel with DES and how we abstract cyberattacks as they are in ATT&CK.
Implementation in AltaRica The previous section highlights the need for a tool to create and analyze DES and to automatize attack scenario generation. As our experiment is based on DES, we searched for existing tools and (formal) languages to model and analyze them. Furthermore, we are interested in the impact of cyberattacks on the safety of CPS; thus, we first searched for formal languages used in safety analyses and based on DES. Therefore, in this paper, we chose the language AltaRica  [70]  to model the system and its security properties. The reasons for this choice are threefold. First, this formal modeling language is built on DES and is mostly used to model systems along with their safety properties. Thus, as the approach defined in this work uses a standard definition of DES, such a definition is already handled by AltaRica.
Second, some authors are working with AltaRica and are developing tools to model complex and interrelated systems along with their safety properties and to analyze them. Therefore, we chose this language and modeled the autonomous vessel in the third version, AltaRica 3. 0  [70]. This enabled us to use the tools implemented in the OpenAltaRica platform to automatically generate the sequences of attacks and to evaluate the validity of the model. Finally, AltaRica has been used in industry to model large systems with numerous components. For this reason, we assume that the formalism will be able to handle complex CPS. As we just explained, AltaRica is a modeling language used in safety, and the tools developed are made for safety analyses. However, as CPS have potential effects on the loss of human lives, their cybersecurity risk analyses need to perform qualitative and quantitative assessments evaluating safety impacts.
Then, if the security mechanisms are correctly modeled, OpenAltaRica can generate the sequences of attacks leading to a top safety event. The generator runs through the executions according to a target property (here, referring to one top event or the other) and the constraints of the model. Then, it generates all the executions leading to the target. One particularity of DES is the fact that the attacks can come from any component infected by the attacker. It results in a large number of sequences (i. e. , attack scenarios), with the atomic attacks (use of a single threat) being launched in any order. To model the system in AltaRica, the variables of the model (introduced in Section 4. 1) are defined as variables in AltaRica; events and transitions already exist in the language and are used.