dataesr/scientific-paragraphs-categorization huggingface dataset:
C. Scenario 2: Smart parking management -Service composition workflow using IoT visual programming tool The objective of this second scenario is to show that third party developersafter having identified, paid for, and received the necessary rights to access specific IoT data/services -can use their own preferred IDE to develop innovative applications. In this scenario, we consider the open source software tool Node-Red 11  , which allows developers to wire together devices, APIs, and other online services as part of the IoT. In this respect, the bIoTope consortium has developed and released a Node-Red's nodes  12  covering the O-MI and O-DF standard specifications (see bottom-left of Fig. 9 ). Overall, Fig. 9  provides an overview of the different flows and nodes used to create a new cross-platform service for citizen or visitors during the FIFA World Cup 2022 event.
schema. org, SSN, DATEX II, HL7, etc. ), as can be found on Linked Open Vocabularies (LOV) like repositories that make it possible to lookup vocabularies to annotate and parse messages. Finally, although this deliverable has not focused on security and privacy aspects, the bIoTope consortium is investigating and developing the necessary building blocks to make the ecosystem robust and resilient against cyber-attacks and/or failures (including identification and authentication, data protection and prevention against threats at both the device and system levels. The ecosystem must provide end-users (either developers who make use of the IoT service marketplace or Apps' end-users) with tools and supports to give them back control over of their personal data/services, e. g. to help them to (i) decide sharing (or stop sharing) personal data with third parties; (ii) know what personal data are exposed and its actual content (ii) audit who are accessing and processing personal data (iii) have automated vulnerability notification mechanisms to ensure that the ecosystem cannot be too much affected by harmful intent (malware, viruses, hackers), etc.
Compared to previous works, the proposed design methodology allows the achievement of robust LCL filter design with regard to large grid impedance variations without the use of any damping method. Also, it takes into account accuracy of capacitor standard values and proposes a simple design method for the converter side inductor that avoids saturation problems. An example of LCL filter design is presented and discussed. The obtained filter parameters were firstly tested using a Matlab-Simulink software tool. After that, they were tested through the development of an experimental set-up. The obtained simulation and experimental results show the reliability and efficiency of the proposed design methodology.
Then, in Section 3, the step-by-step LCL filter design methodology aimed to achieve robust tuning of the LCL filter parameters is detailed and discussed. After that, Section 4 presents a case study for LCL filter design with simulation results achieved under Matlab-Simulink software tool. Finally, in Section 5, the considered design methodology was verified through experimental tests. The obtained experimental results are quite similar to those of simulation and prove effectiveness of the used design methodology.
Simulation Results The designed LCL filter is tested through simulation using a Matlab-Simulink software tool. The simulation results are obtained with a three-phase PWM rectifier, a switching frequency equal to 10 kHz and without the use of any damping method. Figure  10a, b  show the simulation results of the converter current and grid current, respectively. It can be noted based on these figures that, using the designed LCL filter, the current harmonic components almost disappear at the switching frequency. The converter current THD is equal to 15%, while the grid current THD is equal to 3%. Hence, the THD in grid side has decreased and is well below 5% which meets the IEEE 519-1992 standard. It should be noted also, based on these figures, that the system is well stable without the use of any damping method.
The obtained filter parameters were tested using Matlab-Simulink software tool and through the development of an experimental set-up. The obtained simulation and experimental results show the reliability, efficiency and high filtering performances of the proposed design methodology. It should be noted that, although the system stability is guaranteed without damping by the proposed LCL design methodology, the stable region is small, and the resonance phenomenon must be considered. For further work, the robustness of the system can be improved by adding active damping methods.
g. degree, centrality measure, etc. ) or attribute-based (e. g. for a social network: age, gender, interests of the considered person). Berlingerio et al. (2009)  propose a method to identify graph evolution rules, i. e. association rules describing the evolution of local connections. Briefly, these rules state that if a certain local interconnection pattern is observed, the network will locally evolve in a certain way. For instance, in the case of an academic co-authorship network, such a rule could be that if a high degree individual is connected to four medium degree researchers, he is likely to get connected to a fifth one in the next time slice. Other data mining-related works have focused on pattern mining to describe dynamic networks. In the context of traditional data mining, a pattern is a set of descriptor values common to certain instances of the studied dataset, and possessing certain properties.
Event Detection We now need to identify all the evolution events taking place between each pair of consecutive time slices, for each node. This procedure is described as Algorithm 1, and outputs a database noted S. It takes the form of a set of n sequences, each one containing the ordered events underwent by the neighborhood of a given node (sequence-related concepts are described in more details in Section 3. 2. 2). Each node is processed iteratively (first loop), as well as each pair of consecutive time slices (second loop). We first extract the ego-networks of the considered node v for both considered time slices t and t + 1, using the function extractEgoNetwork (lines 3 and 4). This function performs a breadth-first search (BFS) restricted to v and its neighbors. Let us note note k t (v) the degree of v at time t.
Finally, all detected events are added to the database thanks to function insertSequence (line 11), which operates in constant time. The total computation for a node v and a pair of consecutive time slices is in O(k t (v) 2 ). Instead of k t (v), let us consider the degree averaged over all nodes in all time slices k = 2m/(θ n), where m = ∑ t |E t | (n being the number of nodes in each time slice). The complexity can then be rewritten O(m 2 /(θ n) 2 ). By taking both loops into account, the Algorithm 1 Finding Evolution Events Require: G Ensure: S 1: for v ∈ V do 2: for t ∈ {1. (θ -1)} do 3: G t (v) ← extractEgoNetwork(G t , v) 4: G t+1 (v) ← extractEgoNetwork(G t+1 , v) 5: G t (v) ← removeNode(G t (v), v) 6: G t+1 (v) ← removeNode(G t+1 (v), v) 7: C t (v) ← identi f yEgoComponents(G t (v)) 8: C t+1 (v) ← identi f yEgoComponents(G t+1 (v)) 9: M t,t+1 (v) ← processEventMatrix(C t (v), C t+1 (v)) 10: s v ← detectEvents(M t,t+1 (v)) 11: S ← insertSequence(S, s v ) 12: end for 13: end for expression becomes O(m 2 /(θ n) 2 • θ n) = O(m 2 /(θ n)).
Sequential Pattern Mining As mentioned before, we see evolution events as categorical variables describing the nodes. Once our database is constituted, we can analyze them through sequential pattern mining tools. Sequential pattern mining is the data mining task aiming at discovering frequent subsequences in sequentially structured databases  (Mabroukeh and Ezeife 2010). This approach was previously used in network science for interpreting communities in dynamic attributed networks  (Orman et al. 2015). In this work, we use it as a descriptive tool to find out general trends of node evolution. Here, we shortly explain the related concepts in the perspective of our work and how we use them. In our work, an item l i is an evolution event which can be birth, death, merge, split, expansion or contraction.
, a µ ⊆ b i µ. This is noted α β. It is also said that β is a super-sequence of α,which is noted β α. The node sequence s v of a node v is a specific type of sequence of size θ -1. We have s v = (l 11 ,. , l k1 ). (l 1(θ -1) ,. , l k(θ -1) ) , where l it is the item representing the occurrence of a i th evolution event in the neighborhood of v between time slices t and t + 1. The sequence database S built in the previous subsection is the collection of node sequences s v for all nodes in the considered network. The set of supporting nodes S (s) of a sequence s is defined as S (s) = {v ∈ V : s v s}. The support of a sequence s, Sup(s) = |S (s)|/n , is the proportion of nodes, in G, whose node sequences are equal to s, or are super-sequences of s. Sequences can be mined based on a number of different constraints, as illustrated in the sequential pattern mining literature  (Mabroukeh and Ezeife 2010).
1 step, until completion. Second, we take advantage of the TKS algorithm  (Fournier-Viger et al. 2013) , in which the notion of FS is expressed in terms of k most supported patterns. This algorithm allows putting constraints on the minimal/maximal length of the detected sequences. We look for the longest frequent sequences (LFS) by setting the minimal length parameter to the longest value treatable by our hardware. The length of a sequence gives us an idea about the duration of a trend, but longer patterns have also smaller support. In our case, nodes following LFSs may have interesting topological properties or a specific role in the network. Depending on the homogeneity of the considered dataset, it may not be possible to find interesting patterns when   1990-1996 1992-1998 1994-2000 1996-2002 1998-2004 2000-2006 2002-2008 2004-2010 2006 -2012 • • • • • • • • • • • • • • • • • • • • • • • • • • • 0 • • • • • • • • • • • • • • • • • • • • • • • • ••••• ••••••• • •• • • • •• • • • • • • • • • • • • • • • • • • • • • •• • • •••••••••••••••••• ••• •• • • •• • • • • •• • • • • •• • ••••• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • birth death merge split expand shrink Figure  3 : Number of nodes undergoing each type of event, per time slice, for all three considered dynamic networks considering the whole network.
It ranges from -1 (worst fit) to +1 (best fit). When averaged over a dataset, it allows assessing the quality of the considered partition. We then look for both types of patterns in each cluster separately. In order to select the most relevant ones, we take advantage of an additional criterion called the growth rate. The growth rate of a sequential pattern s relatively to a cluster C is Gr(s,C) = Sup(s,C)/Sup(s,C). Here, C is the complement of C in V , i. e. C = V \C. The growth rate measures the emergence of s. It is the ratio of the support of s in C to the support of s in C. Thus, a value larger than 1 means s is particularly frequent (i. e. emerging) in C, when compared to the rest of the network. The cluster analysis we perform can be considered as complementary to the pattern mining method.
Data Description We have studied 3 dynamic networks based on three different datasets: DBLP, LastFM and Enron. Both the DBLP and LastFM networks were previously used in the literature: the former to detect evolution patterns  (Desmier et al. 2012) , and the latter to interpret communities through sequential pattern mining  (Orman et al. 2015). The Enron network was specifically extracted for this study, as described below. A summary of the networks can be found in Table  1 : number of nodes, number of nodes undergoing at least one event, number of time slices, and time span of the considered data. When extracting slowly evolving dynamic networks from raw data (by opposition to streaming networks, as described in the introduction), one of the most important point is the time scale used to discretize or bin the original data.
In other words, it is the duration represented by each time slice. This point has been discussed in the literature, and it has been shown this parameter can have significant effect on the network topological properties  (Clauset and Eagle 2007; Krings et al. 2012; Sulo et al. 2010). The DBLP and LastFM networks have been the object of previous studies, so we assumed the problem of selecting the most appropriate time scale had already been solved for them, and used them as is. For the Enron network, we had access to the clean raw data and took advantage of this to apply the method proposed in  (Clauset and Eagle 2007)  in order to identify the most appropriate time scale.
DBLP Network The DBLP dynamic network considered in this work was first used in  (Desmier et al. 2012). It is a co-authorship network extracted from the DBLP database. Each one of its 2145 nodes represents an author working in one or several Computer Science subfields among: Databases, Data Mining, Knowledge Discovery, Information Retrieval and Artificial Intelligence. Two nodes are connected if the corresponding authors have published an article together at the considered time, in a selected set of 43 conferences and journals. Only authors who have at least 10 publications between 1990 and 2012 are included in the network. Each time slice corresponds to a period of five years. There are a total of 10 time slices ranging from 1990 to 2012. The consecutive periods have a three year overlap for the sake of stability.
Enron Network Enron is a well-known dataset, which has been widely studied in network science and text mining. In particular, it appears in the literature under the form of several distinct static and dynamic networks. In this work, we perform another extraction based on the original dataset 2  , in order to get a network appropriate to the application and evaluation of our analysis method. As mentioned before, we used the approach of Clauset & Eagle to identify the best time scale  (Clauset and Eagle 2007). Put briefly, in order not to leave the scope of this article: we compared the networks obtained with one-(without overlap), two-(without overlap), three-(without and with a 1-month overlap), six-(without and with 1and 2-month overlaps) and twelve-month time slices (without, with 1-, 2-and 3-month overlaps) and identified 1 month without any overlap as the most appropriate time scale.
Our network is publicly available online 3. It contains 158 nodes representing Enron employees between 1997 and 2002. All the addresses in the From and To fields of each email are considered, resulting in a network of 28802 nodes representing distinct email addresses. Two nodes are connected if the corresponding persons emailed each other during the given time slice. Note that the networks extracted from such datasets are usually directed. As this information was not relevant to our studies at this time, we did not make any distinction between sender receiver, and thus produced an undirected dynamic network. Since we used a one-month long time slice, this dynamic network is composed of 46 time slices.
Although not the result of a merge, ICDM is one of the most important conferences on Data Mining, it has gathered numerous researchers from the whole world since its beginning. We believe the collaborations resulting from these academic events explain our observations regarding the neighborhood events in the DBLP network. Or, conversely, the events extracted from the network depict the historical evolution of the Data Mining field. Time slice 2004-2010 corresponds to a trend reversal: the numbers of births, expansions and merges plummet and are dominated by their counterparts (deaths, contractions and splits). Although it is a bit more far-fetched, this could correspond to the emergence of new domains relatively close to Data Mining, but possessing their own conferences. We think, in particular, about the large quantity of data produced by the Web 2.
LastFM Like for DBLP, the most frequent events are births/deaths, followed by expansions/contractions, and then merges/splits. However, for LastFM the measured values are much stabler, and the plot does not display any sudden increase or drop. When checking the data, we observed most of these births/deaths do not concern large ego-components, but rather single nodes arriving/leaving the neighborhood. Our LastFM dataset corresponds to a 1 year evolution of Jazz listeners, so the considered time span might not be long enough to uncover stronger changes in this network. Nevertheless, we obtained interesting results when characterizing local event evolution through sequential pattern mining, as shown in Section 4. 3.
Sequential Patterns of the Network As explained in Section 3. 2. 2, we use the SPMF framework (Fournier-Viger 2010) to identify the closed frequent sequences (CFS) and the longest frequent sequences (LFS) in the whole network. We now describe the main results obtained for the three considered datasets. Our goal here is not to be exhaustive, but rather to illustrate what can be done by combining our event-based approach and pattern mining methods.
Longest Patterns The LFSs identified for each network are listed in Table  3. In the case of Enron, the longest pattern spans 13 time intervals (out of 45). It is a sequence of births and deaths indicating the consecutive starts/ends of email conversations. There are a number of very similar shorter patterns alternating the same events (not represented in the table). They reflect the very unstable nature of the neighborhood in this dataset, as already noticed in the previous subsection. In DBLP, the longest pattern spans 8 time intervals (out of 9). The trend is: first births (for 2 time intervals), then expansions (3 times intervals) and finally contractions (3 time intervals). It concerns 1% of the nodes, which is low, but we have found many very similar (though slightly shorter) patterns with higher support (not represented in the table).
After having investigated in detail the nodes following this trend, we noticed all of them have one big ego-component including many neighbors, and several small ego-components, many of them containing a single node. Expansions, merges and contractions affect the big ego-component, whereas births and deaths occur on the single neighbors. Interestingly, these 14 nodes are the most active users on the LastFM service (for this dataset), as reflected by their degree: in average, it ranges from 42 to 50, while it is between 2. 7 and 3. 3 for the rest of the nodes. The patterns can be interpreted as follows: the users supporting it tend to listen to a relatively stable group of artists, together with their friends, which correspond to their core favorites. But they are also open to trying new artists, without necessarily sticking to them, as shown by the fluctuations observed in the pattern.
Patterns of the Clusters We obtain the patterns characterizing the behavioral classes by mining CFSs and LFSs in the corresponding clusters. For this purpose, we use the same methods as in Section 4. 3, but with a restriction to the considered cluster. Table  5  lists some representative results. We get many similar patterns, with small differences, not only in terms of sequence, but also support Sup(s) and growth rate Gr(s). For the sake of simplicity, we only give the most representative ones in the table. Like before, we observe certain similarities between the datasets. For the Clusters 1, the CFSs are exactly the same as the ones found for the whole graph (cf. Table2). Thus, despite their very high support, they are not characteristic of these clusters. This is confirmed by their growth rate, which is lower than 1 and therefore indicates that the patterns are frequent not only in Cluster 1, but also in Cluster 2.
Homology modeling A homology model of NB2 (GIII. 2) VP1 was built based on the Norwalk virus (GI. 1, Fig 1B  and 1C ) capsid protein crystallographic structure (PDB: 1IHM)  [3]. Chain B of 1IHM was chosen as a structural template for the target GIII. 2 dimer sequence as it is the more complete, comprising residues 10-520 of the 530-residue GI. 1 VP1. The Modeller 9. 14 program  [10, 11]  was used to generate 100 structural models of the GIII. 2 monomer that were further evaluated based on their pseudo-energy ("DOPE") value  [12]  and the number of residues outlier on the Ramachandran plot  [13]  with the RAMPAGE webserver (http://mordred. bioc. cam. ac. uk/r apper/rampage. php). We selected the structural model with lowest DOPE value (8 Ramachandran plot outliers) and generated the dimer by aligning two copies on both A and B chains of the GI.
Clustering We developed in python a software to clusterize our trajectories  [21]. We used the MDTRAJ package  [22]  for the trajectory reading and RMSD matrix calculation. Numpy 1. 11  [23]  was used for the hierarchical clustering. The Ward method  [10]  was chosen and its graphical classification of the trajectory was used to select the clustering level. The representative frame is defined by the structure with the lowest RMSD against all the frames which belong to the same cluster. The source code is available at https://github. com/tubiana/TTClust.
1 ps. Then we performed a NPT equilibration of 500ps. The pressure of the simulation box was kept at an average of 1 bar using the isotropic Parrinello-Rahman barostat  [29]  with a time constant of 2 ps and a compressibility of 4. 5e -5 bar -1. After the equilibration, the position restraints were removed. All other simulation parameters were the same as during the equilibration. VMD 1. 9. 2 was used for RMSD calculations and Gromacs tool "rmsf" for the RMSF. Atomic model derived from the GII. 10 cryo-EM map An extended model was generated for the GI. 1 crystallographic dimer (pdb ID: 1IHM, chains A and B). The cryo-EM map used is the available one for the GII. 10 norovirus VLP to about10 Å resolution (EMDB ID: 5374)  [8]. The interstitial regions (residues 220 to 230) of the crystallographic dimer were removed and the P dimer and S dimer were fitted separately with Chimera program version 1.
Simulations for the P/S domain interaction study For this study, we used the same simulation protocol as for N-terminal arms study (see above) for replicas 1 and 2. The distance between the protein and the edge of the box was increased from 1. 3 nm to 2 nm for replicas 3-5. We initially set a pre-production time of 5 ns before a production period of 100 ns but finally included the whole 105 ns as production. The principal axis of the S domain was calculated with the Single Value Decomposition module of numpy  [23]. This method is adapted from the UCSF Chimera 1. 10  [30]. Only residues in beta sheets of the S domain (67-70, 79-84, 107-114, 122-128, 147-151, 158-162, 181-186, 202-210) were considered to minimize the noise due to the thermic fluctuation of the inter-strand loops. The height of domain S was calculated by aligning the P dimer axis on the Z axis.
Sequence conservation 29 norovirus capsid protein full sequences were retrieved, each one the representative sequence of a single genotype  [6]. Genbank accession codes for GI members are: M87661 (GI. 1, crystal structure), L07418, U04469, AB042808, AJ277614, AF093797, AJ277609, AF538679; GII members are U07611, X81879, U02030, X76716, AJ277607, AJ277620, AJ277608, AF195848 (GII. 10, cryo-EM map), AY038599, AF427118, AB074893, AJ277618, AY113106, AY130761, AY130762, AY502010, AY502009; GIII members are AJ011099, AF320625 (GIII. 2, TR-SAXS); GIV member is AF195847 and GV member is AY228235. An alignment of these 29 sequences was generated within Jalview software v2. 7  [33]  with the MUSCLE  [34]  webserver and default parameters. From this alignment, a sequence conservation score was computed at each position in Jalview.