dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Internet of Things (IoT) represent an important area of network research since they constitute various problematics but at the same time, multiple applications  [3] , therefore many opportunities. Some of the most recurring problematics of IoT are the mobility, the availability and also the limited resources of each node  [4]. These problematics affect multiple aspects of communications inside a network of IoT, e. g. , sending messages or collecting data efficiently. The mobility creates network fragmentation, which affects the connectivity of the nodes. This causes two or more subnetworks and each can have their own set of information. The availability makes uncertainty about which nodes in the network we can rely on to send a message. This uncertainty creates the need for robust mechanisms that can withstand the availability of the nodes.
If a node triggers the monitoring while the network is fragmented, it will not be able to reach all the nodes. Therefore this can cause incomplete results for the monitoring purposes. Another problem is the analysis and interpretation of the data by the network. Given the mobility and the nodes availability, it can be difficult to compute a function among all nodes. Therefore each node can reply for themselves, but aggregation is needed to provide an overall result. These problems affect the monitoring of the network and the existing solutions. Most of the known monitoring approaches in the literature state that accuracy is not reliable in a mobile IoT due to the dynamic properties. Network fragmentation has a harsh impact on the accuracy of a decentralized and distributed approach. Given that the information is spread through the network then if a subset of nodes of the network fragments, there is a probability that the information for the monitoring result is fragmented as well.
This means, sending conflicting information that oppose the general view of the system. One of the most known consensus protocols is Paxos  [24]. Although this protocol is considerably complicated and difficult to integrate, it became the foundation for consensus algorithms. Although the complications stated by Paxos, it was still implemented by multiple big tech companies in services like Amazon S3, Amazon DynamoDB, and Apache ZooKeeper. There are other companies like Google that developed their consensus algorithm  [25]. In the recent years, a novel approach was proposed based on the limitations of Paxos, this proposal is Raft  [26]. Companies like Hashicorp, support this new approach and it has gained enough popularity to have implementations in most of the programming languages like C, Java, GoLang, Python and more.
Classical flooding The transmission of data in a network is not only important but it can also be considered as an energy bottleneck, specially in IoT networks. For our approach, the communication sent by any node is intended to reach every node for availability purposes. Therefore, we selected a classical flooding  [27]  mechanism to transmit data over the network. Any node that wants to share any information with the network will start this process by broadcasting the message to its nearest neighbours. Then, each node receiving a broadcasted message checks if it has received the message in the past by relying on a message ID. If the message has not been received in the past, it broadcast it to the nearest neighbours, otherwise it discards the message. Although it can be argued that this mechanism has some flaws, the intention of our paper is not to deep dive in the information dissemination algorithm.
Finally, a block is assembled with the group of transactions and the results of the validation, and it is sent to the network. After this, the elected leader goes back to a sleep state, and all the nodes stop any consensus activity until it is again needed. When it is needed again, a new election is held, and a new leader is elected. This is an essential factor of our approach. Given the properties of the IoT, it is crucial that, at each round, any node can act as the validator. This makes the approach more dynamic, suitable for change and scalable. From the data perspective, it also makes it more democratized, fair and distributed, since all nodes can and would participate in the validation of the monitoring information. Relying on the definitions made by Raft  [26] , we define our leader election algorithm illustrated as an input-output finite state machine (Figure  1 ).
The other nodes, upon the reception of the block, verify the block packet to make sure that the information is congruent. The verification is done by evaluating the query with the data that the node has. This should have the same result as the one transmitted in the block (line 3). If the block passes this second verification (line 4), then it is added to the nodes ledger (line 5) and then sent to the network. Otherwise, the block is rejected by the node and announced to the network (line 8). With these procedures, we can define the block packet, as the tuple with a global property, the evaluation of the global property and the traces that satisfy the global property. It is important to mention that, given the inherent properties of IoT, there can be incongruent scenarios for some nodes. Let us look at the case of network fragmentation, so all nodes have the same information, but one of the nodes in the network went off range for a period of time, and it has one or more missing transactions.
This enables us to improve the overall throughput for our experiments, giving us more data in less time. Finally, the orchestrator parses the logs from the Docker containers per emulation, extracts the information and centralizes it in a MongoDB server for further analysis. Together they provide a highly scalable, replicable and robust environment to conduct experiments. The testbed consisted in an implementation separated in four repositories of our proposal in the language Go (v1. 9). Each repository represents a layer of the complete implementation, it is separated into ledger 2  , miner 3  , router 4  and raft  5. The implementation is separated into different layers to isolate the different processes and to allow parallel execution of all the tasks without any problem. The ledger layer refers to the entity in charge of storing the chain of blocks, generating the transactions and validating blocks.
The accent in this paper is put on the design on a standardized monitoring platform. By surveying the literature, we can find also several works that have been done to address the verification of global properties in a distributed manner. Formal tools or formal languages can be used for that purpose. In  [45] , they use the GOANNA model checker  [46]  to prevent threats of IoT implementations, the model checker is based on static program analysis. The security aspects is out of the scope of our paper and it is not clear how this approach can be used to verify global properties in a distributed manner. Other approaches more close to our work are based on publish/subscribe principles such as  [12, 13]  which are topic-based pub/sub middleware, that are limited to topic-based routing. The middleware  [47]  is also too heavy for IoT sytems.
In particular, hadrons composed of strange quarks and hadrons with explicit gluon degreesof-freedom are abundantly produced as demonstrated at a multitude of previous experiments at LEAR, CERN  [20]. Hadronic reactions can be divided into two classes: formation and production. In formation, the initial systems fuse into one single state. The line shape of such a state can be determined from the initial system, using a technique called resonance energy scan. The beam momentum is changed in small steps thereby varying the centre-of-mass energy in the mass region of the state of interest and the production rate is measured. Each resulting data point is a convolution of the beam profile and the resonance cross section according to Fig. 2. The true energy-dependent cross section (green dashed line) is determined by the effectively measured cross section (solid blue line) based on the measured yields (markers) and the beam momentum spread (red dotted line).
Data acquisition The PANDA data acquisition concept is being developed to match the complexity of a next-generation hadron physics experiment. It will make use of high-level software algorithms for the on-line selection of events within the continuous data stream. This so-called software-based trigger system replaces the more traditional hardwaredriven trigger systems that have been a common standard in the past. In order to handle the expected Phase One event rate of 2 MHz, every subdetector system is a selftriggering entity. Signals are detected autonomously by the sub-systems and are pre-processed in order to transmit only the physically relevant information. The online event selection occurs in computing nodes, which first perform event-building followed by filtering of physical signatures of interest for the corresponding beam-target settings.
The simulation and analysis framework The feasibility studies presented in this paper have been carried out using a common simulation and analysis framework named PandaROOT  [37]. This framework provides a complete simulation chain starting from the Monte Carlo event generation, followed by particle propagation and detector response, signal digitisation, reconstruction and calibration, and finally the physics analysis. PandaROOT is derived from the FairROOT framework  [38]  which is based on ROOT  [39]. FairROOT offers a large set of base classes which enables a straight-forward customisation for each individual detector setup. It offers an input-output manager, a run manager, database handling, an event display and the Virtual Monte Carlo (VMC) interface which allows to select different simulation engines.
PANDA simulations thereby achieve a momentum resolution of about 1%. At the particle identification stage, the information from the dedicated PID detectors and the EMCs are associated with a charged track based on the distance between the predicted flight path and the hit position in the detector. Hits in the EMC without a corresponding charged track are regarded as neutral particles. The probabilities for various particle types of the different subdetectors are then combined into an overall probability of a given particle species. The selection of events for partial or complete reaction channels, referred to as Physics Analysis, is performed based on the combined tracking, PID and calorimetry data using the Rho package, an integrated part of Panda-ROOT. With Rho, various constrained fits such as vertex fits, mass fits and tree fits are available.
State of the art So far, worldwide experimental efforts in baryon spectroscopy have been focused on N * and ∆ resonances. Most of the known states have masses smaller than 2 GeV/c 2 and were discovered in πN scattering experiments. In recent years, many laboratories (JLab, ELSA, MAMI, GRAAL, Spring-8 etc) have studied these resonances in photon-induced reactions  [143, 144]. As a result, the data bank on nucleon and ∆ spectra has become significantly bigger and a lot has been learned. However, there are several puzzles that remain to be resolved. One example is the so-called missing resonance problem of Constituent Quark Models (CQMs): many states that are expected from these phenomenological-driven models have not been observed experimentally. This is in contrast to the Dyson-Schwinger approach whose predictions agree almost one-to-one with the experimentally measured light baryon spectra below 2 GeV/c 2  [145, 146].
Acknowledgement We acknowledge the support of the  Theory Advisory Group (ThAG) of PANDA  and we value the various discussions that took place with the ThAG sharpening the physics programme of PANDA. We appreciate the comments and feedback we received from  Gunnar Bali ,  Nora Brambilla ,  Stan Brodsky ,  Alexei Larionov ,  Horst Lenske ,  Stefan Leupold ,  Ulf Meißner ,  Simone Pacetti ,  Mark Strikman , and  Lech Szymanowski. We acknowledge financial support from the  Bhabha Atomic Research Centre (BARC)  and the  Indian Institute of Technology Bombay, India ; the  Bundesministerium für Bildung und Forschung (BMBF) ,  Germany ; the  Carl-Zeiss-Stiftung   21-0563-2. 8/122/1and 21-0563-2. 8/131/1 , Mainz , Germany; the  CNRS/IN2P3  and the  Université Paris-Sud , France; the  Czech Ministry (MEYS)  grants  LM2015049 ,  CZ.
02. 1. 01/0. 0/0. 0/16  and  013/0001677 , the  Deutsche Forschungsgemeinschaft (DFG) ,  Germany ; the  Deutscher Akademischer Austauschdienst (DAAD) , Germany; the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  824093. the  Forschungszentrum Jülich, Germany ; the  Gesellschaft für Schwerionenforschung GmbH (GSI) ,  Darmstadt, Germany ; the  Helmholtz-Gemeinschaft Deutscher Forschungszentren (HGF) ,  Germany ; the  IN-TAS, European Commission  funding; the  Institute of High Energy Physics (IHEP)  and the  Chinese Academy of Sciences, Beijing, China ; the  Istituto Nazionale di Fisica Nucleare (INFN), Italy ; the  Ministerio de Educacion y Ciencia (MEC)  under grant  FPA2006-12120-C03-02 ; the  Polish Ministry of Science and Higher Education (MNiSW)  grant No.
Then, we create simulation scenarios in the framework AMoDeus  [9]  that replicate the conditions of the real world accurately. We serve the same demand under the same conditions with a hypothetical coordinated mobility-on-demand system. For this, we use state-of-the-art fleet operational policies and different fleet sizes and show the Pareto front of operating points in comparison to the taxi system. Our contribution is the quantification of performance and efficiency gains resulting from the implementation of fleet coordination in existing mobility-on-demand systems. We complete this assessment for the cities of San Francisco, Chicago and Zurich for which we have taxi data available. The used simulation scenarios and the source code are available online. Paper Organization: in Section II we first provide a detailed summary of the work related to our paper, covering contributions from both the economic and engineering research domains.
Studies presenting recommender systems, e. g. ,  [21] ,  [22]  and  [23] , typically focus on the actions of one single taxi. Instead, we consider the coordinated operation of the entire fleet. Furthermore, recommender systems often focus on one particular metric, e. g. , vehicle availability. Instead, we evaluate jointly different metrics for the entire fleet. Existing studies in agent-based simulators such as  [26] -  [29]  are not based on actual taxi trip data and do not provide a comparison between a simulated coordinated taxi system and a real existing taxi system. Empty vehicle relocation (rebalancing) as well as vehicle miles traveled aren't considered in these studies. Instead, our results do not only focus on customer service quality (wait time) but contrast this metric with the operators viewpoint, i.
III. METHODS Among the publicly available taxi data sets, there is no common standard format. Some contain the traces of the taxis, encoded with data objects of varying scope and recording frequency. Other data sets only contain information on a trip basis: each taxi trip is logged and additional information is recorded, e. g. , origin, destination, distance or payment information. However, all analyzed taxi data sets allow to extract the trips on a certain day including their origin, destination, submission time and duration. Furthermore, they all allow to estimate the number of taxis that were used to serve the trips and to determine a lower bound of the vehicle miles traveled by the fleet. The methods that have been used to create transportation simulation scenarios out of these data points are presented in this section.
Finally, we compare conservatively, i. e. , we always favor methods that evaluate the real taxi system positively. Therefore, our results show a lower bound of the efficiency and service level improvements possible with fleet coordination. These guiding principles lead to a number of design decisions: we use the queuing-based traffic simulator of MATSim  [25] , which is a publicly available open-source software that has been well-tested. Its setup offers both accuracy and fast simulation speeds, which allows to conduct parametric studies. The fleet operating policies that we use are also publicly available, they are part of the open-source software package AMoDeus  [9]  that we have presented in previous work. Then, we read out the travel demand served by the taxis directly from the taxi data and serve the same trips in the simulation environment, with identical origin, destination and time of submission.
C. Estimation of Network Speeds The network generation captures different types of roads with different freeflow speeds, e. g. , 30 or 50 miles per hour. However, the taxi data sets were recorded under real operating conditions with congestion and reduced speeds. These network conditions present at the time of the data recording must be replicated in the simulation environment in order to make meaningful comparisons. In many studies, the problem of network calibration is viewed from a different angle. MATSim studies focused on dynamic demand take as input a population with specific activities and travel plans and personal utility functions that value time, travel time, punctuality etc. Then, based on the co-evolutionary algorithm of MATSim, the Wardrop equilibrium  [35]  of the scenario is estimated, and from it the traffic speeds can be derived.
IV. TAXIS IN SAN FRANCISCO The still most studied data set of taxi traces  [38]  was recorded in the city of San Francisco and is publicly available at  [39]. It contains the traces of a total of 536 taxis that were recorded in the time interval May 17th, 2008, 03:00:04 to June 10th 2008, 02:25:34. Each trace is composed of measurements that contain a time stamp in Unix Epoch Format, the location latitude and longitude in WGS84 coordinates and the taxi status ∈ {available, occupied}. The sampling time is irregular, and mostly around 1  [min]. However, sometimes it is much larger. The data set contains a total of 464, 045 customer trips. Time stamps extend over a period covering 25 days. For the work at hand, we have selected the day June 4th. On that day, 18,813 trips were recorded and 495 different taxis were in operation.
VI. TAXIS IN ZURICH A taxi data set for the city of Zurich, Switzerland was provided to the research group by a local transportation company. The set is different from the other used sets as it contains actual trip bookings made in a taxi call center. As in the other taxi data sets, the data allows to extract the origin, destination, duration and start time of a taxi trip. Additionally, in this set also the time of the request submission and the time of the taxi assignment is known. Thus, the data set allows to compute the actual wait times experienced by customers. To protect privacy of customers and business interests of the company, the data set cannot be made publicly available. However, we are able to present aggregate results in the work at hand and relate them to the other assessed taxi systems.
Overall, the curves have a good fit. In the Zurich data set, the taxis belong to different operators or individual taxi owners that are independent of each other. The trips they get assigned to by the call center are only a subset of all their completed trips. However, other trips, e. g. , curbside pickups, are not accessible. Thus, some taxis on record have only 1 or 2 trips during the entire day, although they have probably completed more trips. This makes it harder to estimate a fleet size of the original taxi system for the 1,244 trips. As a conservative estimate, we thus assume that all taxis are as successful as the most productive taxi on record that served 13 trips assigned by the call center during the day. This allows to conservatively estimate the fleet size for the taxi system at 95 taxis.
, 2011; Riedel et al. , 2015). The clinical ATCC43255 strain of C. difficile, used to capitulate symptomatic infections in mouse models, encodes 4,117 genes and $327 putative TFs, Cell Host & Microbe 29, 1709-1723, November 10, 2021 Published by Elsevier Inc. 1709 ll of which $97% have orthologs in the CD630 strain  (Girinathan et al. , 2021). To address questions regarding the broader systemslevel interplay among genes in colonization and infection, we used computational modeling and network inference algorithms to construct an environment and gene regulatory influence network (EGRIN) model for C. difficile. This model leverages a compendium of 151 public transcriptomes that surveyed responses of CD630 in diverse contexts. The EGRIN model consists of modules of putatively co-regulated genes identified based on their co-expression over subsets of conditions, enrichment of functional associations, chromosomal proximity, and shared cis-acting gene regulatory elements (GREs) within their promoter regions.
Reconstruction of the environment and gene regulatory influence network (EGRIN) model for C. difficile 630 To investigate C. difficile's transcriptionally driven adaptive strategies, we compiled 151 public transcriptomes from 11 independent studies on CD630 (Table  S1 ). This compendium captures diverse transcriptional responses of C. difficile to commensals, in vitro and in vivo responses to nutrient conditions, and consequences of TF deletions. The transcriptome compendium together with functional associations information was analyzed with a suite of network inference tools (i. e. , cMonkey2 and the Inferelator) to infer an EGRIN model for C. difficile (Figure  1A ;  Arrieta-Ortiz et al. , 2015; Reiss et al. , 2015). The resulting EGRIN model organized 3,995 of 4,018 CD630 genes into 406 gene modules and inferred module regulation by 138 of 309 genomically identified TFs that putatively act through GREs discovered within gene and operon promoters.
Among the Inferelator implicated regulatory networks, 255 modules were controlled by more than one TF, and 120 were regulated by more than two TFs (Figure  S1 ). The TF module assignments support subsequent hypothesis-driven design of ChIP-seq and TF-deletion experiments to validate the regulatory network architecture under physiologically relevant environments. Residual scores reflect the coherence of gene co-expression patterns and evaluated the quality of modules within the EGRIN model  (Reiss et al. , 2006). The lower the residual score, the higher the quality of the module. Using an empirical approach, a residual cutoff of 0. 55 identified a functionally meaningful set of 297 high-quality modules (73% of the total 406 modules) based on the relative enrichment of related functions within modules that passed filtering (Figure  1B ).
This threshold was similar to the threshold used to identify high-quality EGRIN modules for Mycobacterium tuberculosis  (Peterson et al. , 2014). The high-quality modules captured transcriptional regulation of 3,617 genes (90%) in CD630, with average membership of 20 genes per module (Figures  1C  and 1D ). These metrics were consistent with models developed for other organisms  (Brooks et al. , 2014; Peterson et al. , 2014) , a remarkable finding given that the transcriptional dataset used to construct the C. difficile model was less than 10% the size of compendia used to construct models for other species.