dataesr/scientific-paragraphs-categorization huggingface dataset:
For this analysis, we calculated the PCC which reflects the quality and the spatial consistency of the simulations and observations. Moreover, we applied the Kling-Gupta Efficiency index (KGE), the distribution added value index (DAV), and the root-mean-square deviation (RMSD)  where: ρ is the Pearson correlation coefficient, σ is standard deviation, μ is the mean value, subscripts -m andobs mean the model and the observational time-series. KGE = 1 attests to the fact that there is a perfect mapping between the experiment and the control data. The KGE values lower than -0. 41 correspond to an underperformance with respect to the mean of the control (observational) data (Tölle and Churiulin, 2021 (https://www. frontiersin. org/articles/10. 3389/feart. 2021. 722244/full)). The DAV is is another statistical index applied for determining the benefit of applying the alternative experiment versions over the original version of COSMO-CLM when compared to observations.
Moreover, the DAV index allows to estimate the Perkins skill scores (S) between the experiment based on one of the alternative versions (subscript -exp), the control simulation based on the reference version of COSMO-CLM (subscript -ref) and the observations (subscript -obs). where: Z is the frequency of values in a given bin for experiments, control run, and observations. The DAV = 0 indicates that no gain is found, DAV < 0 there is a loss in performance for the alternative version, DAV > 0 attests to the fact that there is a beneficial impact in using the alternative experiment version compared to the reference with respect to the observations.
ACKNOWLEDGEMENTS AND FINANCIAL SUPPORT We greatly appreciate  Rüdiger Schaldach  (https://www. uni-kassel. de/forschung/cesr/personen/schaldach) support and assistance with our scientific project. We are very thankful to  Claudia Becker  (https://www. dwd. de/EN/research/observing_atmosphere/lindenberg_column/boundery_layer/gmfalkenberg_node. html),  Marius Schmid  (https://dataservices. gfz-potsdam. de/tereno-new/showshort. php?id=escidoc:1707890) and scientists from the " Linden Environmental monitoring and climate impact  research station (https://www. unigiessen. de/fbz/fb09/institute/mikrobiologie/recycling-prozesse/mitarbeiter/sg_folder/sg) " for observational data provision. Moreover, we would like to say thanks for developing the GLEAM (https://www. gleam. eu/) project, the scientific group under the leadership of  Dr.
Among those cryptosystems is the Elliptic Curve Integrated Encryption Scheme (ECIES), which can be found in several cryptographic standards. It offers mutual authentication and data integrity that has become highly employed in RFID applications. In this paper, we present a novel secure ECC-based RFID authentication protocol that meets the security needs of existing published protocols and ensures data confidentiality and privacy. Beforehand, we present an overview of some ECC-based RFID authentication protocols and highlight their security weaknesses against server spoofing, tracking, and impersonation attacks. After that, a comparative study with existing protocols in terms of computational performance and security strength is performed. Finally, our protocol is analyzed and verified with the Automated Validation of Internet Security Protocols and Applications (AVISPA) analysis tool after being modeled in High Level Protocol Specification Language (HLPSL).
1) INITIALIZATION PHASE: During this phase, the server chooses a random number SS as its private key and calculates PS = SS. P as its public key. The tag also chooses a random number t as its private key and calculates PT = ST. P. Assuming that PT is the tag identity information. At the end of this phase, the server keeps its private and public keys and the identity of the tag in its database { SS, PS, PT}. While the tag keeps its private key, its identity information, and the public key of the server in its memory { ST, PT, PS}.
1) INITIALIZATION PHASE: The server generates all the system parameters. It first selects the identity of the tag. Then, it chooses the value PrR as the secret key of the reader and calculates the point PuR= PrR. P as its public key. At the end of this phase, the server stores in the reader database the values {XT,PrR,PuR} and in the tag database the values {XT,PuR}.
2) AUTHENTICATION PHASE: The Naeem protocol authentication phase is described in Figure  3. This phase is divided into four steps. The instructions executed during each step of this protocol are listed below:  Step1: The reader generates a random number r1 to calculate the point R1= r1. P. Then, it sends the point R1 to the tag.  Step2: The tag in its turn produces a random number t1 and calculates T1= t1. P. Then, it calculates C1= t1. R1 and C2 = XT + h(T1,R1,C1). After that, the tag sends the message {C1, C2} to the reader.  Step3: Using the two quantities C1 and C2, the reader calculates T1= (r1) -1 C1 and XT = C2 -h(T1,R1, C1) and it verifies the value of XT in its database. If the value of XT calculated is equal to the one stored, the reader authenticates the tag and then calculates C3= PrR.
A. OUR PROTOCOL EXPLANATION Our protocol consists of two main phases: the setup phase and the authentication phase. It is supposed that the reader to server and server to reader data transmission is done through a secure wired channel, while the reader to the tag and vice versa data communication is transmitted through an unsecured wireless channel. Setup phase: This phase is the same as Zheng et al protocol setup phase  [45] , it is dedicated to the generation of the private and public keys of the server and the tag. Firstly, the server chooses a random number s as its private key and then calculates its public key PS = s. P. Secondly, the tag chooses its private key t and calculates the public key PT = t. P. At the end of this phase, the tag has the following quantities (t, PT, PS,P), and the server stores on its database the following quantities (s, PS, PT, P).
1) MUTUAL AUTHENTICATION Our protocol ensures mutual authentication between the tag and the server. The first part of the authentication is the tagserver authentication, which allows the server to authenticate the tag thanks to its identity PT that is only known by the legitimate server. When the tag sends the quantity M1 to the server, during step 2, the server calculates the value r1. PT + s. R2, which must be equal to M1. In this way, the server verifies that the tag that queries is the tag corresponding to the value of PT stored in its database. The authentication of the tag by the server is therefore successful. On the other side, the server-tag authentication ensures the authentication of the server by the tag. At this end, the server sends the M2 = x. PT + s. R2 value to the tag in step 4, where the secret value x is known only to the server and cannot be obtained by an attacker due to the Elliptic Curves Discrete Logarithm Problem (ECDLP).
Step1The server randomly chooses a random number r1 and calculates R1=r1. P. Then, it sends R1 to the tag.  Step2: The tag selects a random number r2 and calculates R1=r1. P and the two quantities AT = PT + r2. PS and AT'= ST. R1 -r2. R1, then it sends the message M = {R2, AT, AT'} to the server.  Step3: After receiving the message M, the server calculates PT = AT -SS. R2 and searches for the tag based on the tag identity information PT stored in its database. The server then checks if AT'= r1. PT -r1. R2. If they are equal, the tag authentication is successfully performed, otherwise, the process stops.  Step4: The server generates the value AS = Ss. R2 -r1. R2 and sends it to the tag.  Step5: The tag checks if the received value AS is equal to AS'= r2. PS -r2. R1. If it was the case, the server authentication is performed, otherwise, the authentication does not pass.
: Step1The server randomly chooses a random number r1 and calculates R1=r1. P. Then, it sends R1 to the tag.  Step2: The tag selects a random number r2 and calculates R1=r1. P and the two quantities AT = PT + r2. PS and AT'= ST. R1 -r2. R1, then it sends the message M = {R2, AT, AT'} to the server.  Step3: After receiving the message M, the server calculates PT = AT -SS. R2 and searches for the tag based on the tag identity information PT stored in its database. The server then checks if AT'= r1. PT -r1. R2. If they are equal, the tag authentication is successfully performed, otherwise, the process stops.  Step4: The server generates the value AS = Ss. R2 -r1. R2 and sends it to the tag.  Step5: The tag checks if the received value AS is equal to AS'= r2. PS -r2. R1. If it was the case, the server authentication is performed, otherwise, the authentication does not pass.
A solution has been developed and implemented that realizes these goals for the STEM field. On its sharing platform (see www. golab z. eu), the Go-Lab ecosystem offers learning resources (Inquiry Learning Spaces, or ILSs in short) that promote inquiry learning and that have online laboratories as their core component. In ILSs, these online labs are enriched with tools (apps) to support the inquiry process, such as apps to help students state hypotheses or design experiments, and with apps to train twenty-first century skills such as collaboration and reflection. Go-Lab also offers an authoring 1 platform (see www. graas p. eu) for teachers to design their own personalized ILSs. The Go-Lab ecosystem is being used in Europe and worldwide on a very large scale, with more than 37,000 registrations from 152 countries on the authoring platform as of the reference data of December 31, 2019 and more than 440,000 different visitors to the sharing platform.
As such, Go-Lab functions as a hub that brings together all types of initiatives worldwide (e. g. , it collects a large series of resources from third-party lab repositories, such as PhET, Concord, and Amrita, under one umbrella) and that serves as a bridge between educational providers, in our case often lab owners and the educational field. It also functions as a catalyst for teacher cooperation. In the current article, we describe the Go-Lab ecosystem from the perspective of the teacher/designer. We highlight the different design and customization options the system offers for creating online or digital inquiry-based learning spaces and explore selected usage data to investigate how these options have been used by teachers in the field. First, however, we present a brief introduction to how Go-Lab got started.
g. ,  Bell et al. 2005). In an experiment, students have to manipulate the values of variables and observe the effect of these manipulations on the values of other variables, and in this way infer how the underlying mechanisms work. They can do this to test and possibly adapt their existing knowledge and/or to generate new knowledge and create artefacts such as concept maps or hypotheses, which represents the active and constructive levels of processing from the ICAP framework. Inquiry learning is also very well suited for collaboration, thus also touching upon the interactive level of the ICAP framework. Traditionally, inquiry learning in STEM topics has centered on performing investigations in hands-on laboratories, but nowadays there is an alternative to this approach in the form of online (virtual and remote or dataset-based) laboratories (see e.
The Go-Lab ecosystem The Go-Lab ecosystem was and is being developed in a set of EU-sponsored (FP7: Go-Lab, H2020 Next-Lab and GO-GA) and national projects. Its development began in 2013. The Go-Lab ecosystem consists of two main online platforms. The sharing and support platform (Golabz) presents all digital resources 2  and is the main landing place, and the authoring and learning platform (Graasp) enables the co-creation and implementation of ILSs. The Go-Lab ecosystem is distinct (unique) in the sense that it not only offers under a single umbrella the possibility to access and use publicly available interactive resources supporting STEM education, but also enables their creation or co-creation with pedagogical templates and their implementation with colleagues and students, as well as ultimately, if desired, their public sharing under creative commons licenses with the whole STEM community.
The creation, the collaboration required for such creation, the personalization, and the sharing of digital resources are not common practices for the great majority of teachers. This paper discusses how the use of this complete ecosystem influences the instructional design practices of teachers, as well as their creation and sharing practices (see the section on the research questions for more details). The Go-Lab ecosystem has been developed following the participatory design, responsive design, and privacy-by-design methodologies. The public resources are accessible without registration, that is, no personal data are collected. Teachers are in full control of their resource creation and the data collection in the ILSs they offer to their students. Students do not need to register to use ILSs.
Students do not need to register to use an ILS. In Graasp, the teacher can determine whether students can use the ILS anonymously or need to log in with a nickname or a nickname plus password. Teachers can also share their ILSs with the larger world by publishing the ILS on the Golabz sharing platform, so that other teachers can re-use these ILSs, possibly after having first adapted them. To publish, teachers need to complete a form with metadata (e. g. , length of the ILS, subject domain, language, etc. ).
Method The explorations described above were based on the traces and products generated in Graasp. 6 To provide a solid dataset, several filters were used to select the relevant ILSs for our study (see Fig. 7 ). First of all, the period of analysis spans from July 2015, when user actions started to be systematically logged, up to and including December 2019. In this paper, we will call this period (July 1st, 2015-December 31st, 2019) our reference period. Second, since we are interested in tracing teacher practices, only those ILSs with teachers involved in their creation were considered in the analysis (i. e. , ILSs created exclusively by project members were discarded). Third, some assumptions were also made regarding implementations. An ILS was considered implemented in the classroom when more than 10 students logged on to it (this empirical rule of thumb stems from conversations with teachers using the platform).
We assume that including only ILSs with more than 10 different students logged on filtered out tryout ILSs and ILS in initial development. After taking these considerations into account, out of the 46,169 ILSs created during the reference period, 2414 ILSs (co)created by teachers and potentially implemented in the classroom were selected as our dataset. We started the analysis of the dataset by identifying the composition of the design team, using the following (co)creation categories: single teacher; group of teachers; teacher(s) and project member(s). These categories reflect whether teachers worked individually or collaborated with either their peers or project members. Then, we analyzed several parameters per (co)creation category, for instance: design process characteristics (iterations, reuse of ILSs, number of students logged on), design effort (number of authors, design time, design actions), and structural parameters as to how ILSs were populated (number of phases, Golabz apps and labs, other apps/labs, and external resources such as links, images, videos, etc.
). Because our data were not normally distributed, we used Fig. 7  Filters applied in selecting ILSs for the dataset non-parametric statistics to examine main trends in the data; specifically, we used likelihood ratio chi-square tests for nominal data, Mann-Whitney and Kruskal-Wallis tests for comparing between two or three (co)creation categories, respectively, and Spearman's rho indices for correlations between parameters. We also examined several parameters focused on ILS usage, including the number of created vs implemented ILSs, the number of students reached, and the number of published vs not published ILSs.
Design process characteristics Although the starting point was not traceable for a small number of ILSs in the dataset (3. 23%), the analysis did reveal the different paths followed by teachers while designing and creating an ILS. Out of the 2414 implemented ILSs, around 20% started from the Golabz sharing platform (13. 46% were based on published ILSs and 7. 76% were based on a published lab). This means that the majority (75. 56%) of the implemented ILSs originated in Graasp, starting from an empty ILS that the teacher populated with materials. These figures show that while teachers often used the material available in the portal (published ILSs or labs) as a starting point to create their ILSs, they more often started from the structure provided by an empty ILS and populated that with materials available at Golabz (e.
The Go-Lab ecosystem was created to give teachers the facilities to use or create these types of learning environments, which in the case of Go-Lab are called Inquiry Learning Spaces (ILSs). In the current paper, we report an analysis of teachers' use of the Go-Lab ecosystem over a period of 4. 5 years. This data collection started roughly one year after Go-Lab became available online in its very first version. In the analysis, we focused on characteristics of the process of designing ILSs and the structure of the resulting ILSs. This paper, therefore, provides an overview of the different paths followed during the design process in the Go-Lab ecosystem (e. g. , in terms of how to start the design process, with whom, and how to exploit its results), illustrating how the ecosystem supports the decisions made by teachers.
Thus, this may slow down the implementation process. In addition, the Go-Lab ecosystem has seen large improvements after its first online introduction, which could have hindered early adoption. In this context, it is important to note that the implementation figures increased drastically over the years, from 68 classroom implementations in 2015 to 971 in 2019. Our data show that, perhaps contrary to what might be expected, teachers prefer to start from empty pedagogical structures instead of starting from ready-made ILSs or available labs. This may show that even when teachers have in mind the domain that they want to include in the ILS, they also have a more or less clear idea of the approach they would like to use, and later fill that in with domain content. It may also indicate that teachers have idiosyncratic ideas about what they would like to do, and they do not want to make the effort to change all kinds of elements in existing ILSs.
Publishing an ILS can be done simply by clicking the Publish button in Graasp and filling in a set of metadata. Before publishing an ILS, the Go-Lab team checks each offered ILS on a number of basic quality criteria (such as configuration of apps, language setting, etc. ). ILSs that were published had longer average design time and were richer in terms of apps involved. To summarize very briefly, our overall conclusion is that designers of ILSs start from the structure provided by the ecosystem (e. g. , the Go-Lab inquiry cycle,), but that quite often they are able to make adaptations when they feel it to be necessary. This is true, despite the fact that, at the start, a teacher using Go-Lab already faces many challenges to the adoption of educational technology. An implemented ILS as presented in this paper is the result of many adoption steps taken by teachers, including accepting the risk of relying on digital technologies and network infrastructures, accepting the change from traditional teaching to active learning practices, accepting the implementation of online labs and open educational resources, being willing to create personal learning material, and last but not least, being (potentially) willing to share their product with colleagues and teacher communities at large.
A second general conclusion is that collaborative design, either between peer teachers or when it involves experts, has advantages in terms of the richness of the resulting ILSs. The current analysis gives initial insight into how a rich online educational design environment is used in practice. This Go-Lab usage is very versatile: it covers many countries and languages, different levels of teaching and different capabilities of teacher/designers. Collecting these data in vivo and with data protection and privacy rules in place also means that we do not have a full view of all relevant variables involved. This clearly points to some limitations to the analyses presented here. Another major limitation is that the data we present cannot give a full view of the design features used by teachers, because Go-Lab is an evolving ecosystem.
For the analysis, this means that the ILSs in our sample came from different periods in Go-Lab history, with changes in the user interface and functionalities, available apps and labs, as well as support material and training. Thus, the general Go-Lab experience varied greatly over the ILSs analyzed, as did our capabilities to track user activity. Still, the data presented could be seen as valuable insofar as they open the black box of technology adoption that is normally kept closed. In any case, Go-Lab seems to be an exception to the rule that innovative instructional designs are only used in well-defined, closed, circumstances  (Spector 2014). By the time we were revising this manuscript (November 2020), the second wave of the COVID-19 pandemic was under way. The first wave had already impacted more than 1.
Apart from the current strengths of the Go-Lab ecosystem (collection of labs, apps, and ILSs offered in different languages and accompanied by meta-data; Graasp authoring tool; support page), there are two additional assets of great importance for teachers in order to address classroom management and adverse effects on the development of twentyfirst century skills for their students during the COVID-19 era. First, the Learning Analytics applications in the Go-Lab platform can allow teachers to track student performance and navigation in ILSs and to enact online formative assessment. Second, several labs and apps.
Recognizing the mechanisms underlying seismic activity and tracking temporal and spatial patterns of earthquakes represent primary inputs to monitor active volcanoes and forecast eruptions. To quantify this seismicity, catalogs are established to summarize the history of the observed types and number of volcano-seismic events. In volcano observatories the detection and posterior classification or labeling of the events is manually performed by technicians, often suffering a lack of unified criteria and eventually resulting in poorly reliable labeled databases. State-of-the-art automatic Volcano-Seismic Recognition (VSR) systems allow real-time monitoring and consistent catalogs. VSR systems are generally designed to monitor one station of one volcano, decreasing their efficiency when used to recognize events from another station, in a different eruptive scenario or at different volcanoes.
We propose a Volcano-Independent VSR (VI. VSR) solution for creating an exportable VSR system, whose aim is to generate labeled catalogs for observatories which do not have the resources for deploying their own systems. VI. VSR trains universal recognition models with data of several volcanoes to obtain portable and robust characteristics. We have designed the VULCAN. ears ecosystem to facilitate the VI. VSR application in observatories, including the pyVERSO tool to perform VSR tasks in an intuitive way, its graphical interface, geoStudio, and liveVSR for real-time monitoring. Case studies are presented at Deception, Colima, Popocatépetl and Arenal volcanoes testing VI. VSR models in challenging scenarios, obtaining encouraging recognition results in the 70-80% accuracy range. VI. VSR technology represents a major breakthrough to monitor volcanoes with minimal effort, providing reliable seismic catalogs to characterise real-time changes.