dataesr/scientific-paragraphs-categorization huggingface dataset:
Generic device & sensor dashboard For the SHFs, the WaziGate's main component is the dashboard to visualize sensor data. Fully integrated and offering generic device & sensor management, the dashboard offers an intuitive interface with a simple data visualization module. Figure  5  illustrates the dashboard and also shows the responsive web interface allowing SHFs to use a smartphone to interact with the dashboard. The INTEL-IRRIS starter-kit come with a pre-configured dashboard where the soil moisture sensor device is already registered into the dashboard. After powering on the INTEL-IRRIS WaziGate, new data will be received and displayed in the dashboard without any additional configuration needed.
INTEL-IRRIS Irrigation WaziApp On top of the generic dashboard, INTEL-IRRIS develops a dedicated WaziApp for the project, referred to as the INTEL-IRRIS Smart Irrigation WaziApp. The purpose of an additional WaziApp on top of the generic sensor dashboard is to offer additional features specific to a particular application domain: better data visualization charts, additional data filtering & processing tasks, configuration of application-dependant parameters, etc. This approach appears more flexible and simpler to maintain than a separate mobile app. Running locally on the WaziGate itself, the WaziApp will have access to all collected sensor data. As shown in Figure  6 , all WaziApps are listed in the dashboard and user can select which waziApp to launch and display. The proof-of-concept of the INTEL-IRRIS Smart Irrigation WaziApp allows the user to select a particular device that they want to link the WaziApp with, using the device id.
The Smart Irrigation WaziApp will be continuously improved during the project's duration to include agricultural knowledge as well as a multiple knowledge streams framework for more accurate sensor calibration procedures leading to more reliable irrigation recommendations. The INTEL-IRRIS Smart Irrigation WaziApp is built as a Python microservice with Docker. It primarily includes a Python program with HTML/CSS and JavaScript for the user-interface. It accesses to sensor's data using the well-defined WaziGate REST API.
Edge-AI processing framework The Edge-AI framework is a new component of the WaziGate's ecosystem and provide both online and offline AI training capability. It consists in an environment for developers/users to conduct artificial intelligence and machine learning tasks. A dedicated system-oriented WaziApp with the full Jupyterlab functionality has been packaged as a Docker container where various machine learning packages with several regression models are available for developers. Again, several programming languages can be used and Python being pre-installed. There are also numerous pre-cooked notebook files available, for a variety of applications, to choose from. As it is considered as a normal WaziApp, it is straightforward to query the WaziGate' sensors data from inside the developed application to conduct different data processing tasks.
To demonstrate the Edge-AI features, we implemented a separated soil moisture prediction WaziApp using the results of the EU H2020 WAZIHUB Soil Moisture Prediction Challenge held on 2019 on Zindi platform  [15]. The objective of the competition was to create a machine learning model capable of predicting the soil moisture for a particular plot in the next few days, using data from the past. A part of the challenge was to design algorithms that are resilient and can be trained with incomplete data (e. g. missing data points) and unclean data (e. g. lot of outliers). Resulting models will enable farmers to anticipate water needs and prepare their irrigation schedules. The model proposed by Hamadi (2nd place)  [16]  was chosen because of its overall good performance and easy integration into the WaziGate Edge-AI framework based on Python and Jupyterlab.
Conclusions The INTEL-IRRIS low-cost and full edge-IoT/AI system targeting the smallholder farmers communities was presented in this article. We show how all the Edge features can provide the intelligent irrigation "in-the-box" concept that is key for SHFs adoption of these advanced technologies. Without the need of Internet access, cloud servers nor lockdown with proprietary software platforms, the proposed solution can be quickly deployed with a minimum of technological infrastructure, even in very remote and rural areas. Interaction with the control system is realized through an intuitive, simple and locally hosted web-based interface accessible with a simple smartphone. With the possibility to develop and host third-party applications on the edge-IoT/AI gateway itself, dedicated applications with specific data processing chain and user interface can offer a higher level of customization for a particular application domain.
The BBU handles the remaining network function processing, and then a disaggregated RAN architecture is formed. 1  In this regard, data are transported over the FH link according to the applied functional splits, such as the options defined by third Generation Partnership Project (3GPP) and by CPRI initiative as enhanced CPRI (eCPRI). This also creates an opportunity to transport Radio over Ethernet (RoE) as a cost-effective alternative  [8]. In this sense, the point-to-point FH link can evolve into a multi-segment FH mesh network requiring extra routing and switching functionalities  [9] , and thus the extra multiplexing gains over the FH network are presented. To exploit the above multiplexing benefits of BBU pooling and FH networking, we can partition all the RRUs into several RRU clusters  [10].
B. NETWORK TOPOLOGY The initial C-RAN topology features a dedicated point-topoint FH link between the RRU and the BBU. However, FH networks have evolved to support more complex topologies (e. g. , tree, mesh)  [18]. In this work, we focus on a multi-segment FH network that transports data in a two-tier FIGURE 2. Network topology example: A two-tier disaggregated RAN over multi-segment FH network. disaggregated RAN. An example is depicted in Figure  2  with three RRU clusters and two BBU pools. First, a portion of the RAN processing, depending on the functional split, is performed at one type of disaggregated RAN node, that is, the RRU and BBU for the uplink and downlink directions, respectively. Then, the intermediate data are transported over the multi-segment FH network toward another type of disaggregated RAN node, that is, the BBU and RRU for the uplink and downlink directions, respectively.
In this research, a flood control operating strategy is developed based on a simulation-optimization model to reduce flood damage downstream of multi-reservoir systems by using spillway gates. For this purpose, an optimization algorithm is introduced, in which the maximum water level of the downstream control points and hydropower potential generation in the flood season are objective functions, and the level of the stages of spillway gates are the decision variables. A global optimization tool, Shuffled Complex Evolution (SCE) algorithm implemented in the AUTOCAL software was coupled with the Mike 11 from DHI simulation model for optimizing stages level of spillway gates. As a case study, the Vu Gia Thu Bon rivers catchment including multi-reservoirs of A Vuong, Song Tranh 2, Dak Mi 4 and Song Bung 4 is analyzed.
The popular Shuffled Complex Evolution (SCE) global optimization method that is used the AutoCal software, is coupled with the hydrodynamic Mike 11 model for optimizing the FLWL boundary. The SCE algorithm is one of the techniques that are robust optimization techniques to find the global optimum solution of complex problems with many functions. The proposed model is applied to the four large multi-purpose reservoirs in the Vu Gia Thu Bon catchment using the hourly inflow data series for representative hydrological years.
Optimization model The above objective functions are used to build a simulation-based optimization model with decision variables of FLWL. Fig. 2  shows the framework of the simulation model coupled with the optimization model. The optimization of FLWL, a multi-reservoir system, can be formulated as a combination of a simulation model and an optimization algorithm. In this method, the SO module of the hydrodynamic model Mike 11 is adopted for the simulation of the operation multi-reservoir considering the physical constraints of the system as well as operation policies. The SCE algorithm is applied to determine the best set of decision variables, such as FLWL. In this study, the SCE algorithm, as implemented in the AutoCal  [17]  software, is adopted for optimizing FLWL of the multi-reservoir system in the case study.
86 GHz GMVA+ALMA Observations of J1924-2914 at 86 GHz (λ3. 5 mm) were obtained with the GMVA+ALMA on 2017 April 3 in conjunction with observations of Sagittarius A * published in  Issaoun et al. (2019). The array was composed of eight VLBA antennas, the Green Bank Telescope, the Yebes 40 m telescope, the IRAM 30 m telescope, the Effelsberg 100 m telescope, and ALMA. The data were recorded with a bandwidth of 256 MHz at a data rate of 4 Gbps over a 12 h track, of which 8 h included ALMA. The total recorded time on J1924-2914 was about 2 hr. In the left panel of Figure  3  we show the (u, v) coverage of these observations. These observations yield images with a beam size of (122 × 88) μas at 36°. The data reduction, processing, and imaging followed a similar pathway to the EHT data, and are described in more detail in  Issaoun et al.
Observations were recorded in right-hand circular polarization mode only, at a data rate of 2 Gbps. For further information on observation and data calibration, refer to  Hunt et al. (2021). The calibrated data were imaged using the CLEAN algorithm implemented in the DIFMAP software package  (Shepherd 1997 (Shepherd , 2011)). The beam sizes are (9. 58 × 3. 58)mas at -5°and (2. 45 × 0. 94)mas at -3°for the 2. 3 and 8. 7 GHz observations, respectively. We iterated the reconstruction process using a hybrid imaging loop, consisting of CLEAN and a phase self-calibration cycle, with a stopping criterion of obtaining three times the noise floor of the residual image. The final images are presented in Section 4. 1 as part of the multifrequency analysis.
6 Gλ, a single SMT-LMT baseline in the range 0. 6-1. 5 Gλ and no coverage between 1. 5 and 2. 8 Gλ. Hence, constraining structures larger than ∼100 μas is extremely challenging with EHT data. Monitoring by the SMA 149  shows that the total compact flux density at 1. 3 mm remained in the 3. In Table  2 , we show the reduced χ 2 calculated for the lowband data sets averaged in 120 s bins, as a metric of the fit ) for the final representative (or "fiducial") images from all methods and all observing days. The best reconstructions from the imaging methods based on least-squares fitting to closure quantities and the mean images from the posterior-exploration methods were chosen as the fiducial images. The eht-imaging and DMC models exhibit the best values in the χ 2 metric with consistently good performance for all days, and hence we focus on those two software packages in the subsequent quantitative analysis.
Hence, if the effect is systematic, it appears to be related to the imaging or parameter extraction algorithms, rather than to the data calibration. In any case, we conclude that a < 20 deg consistency between the ALMA-only data and the sum of the VLBI components is overall reassuring and constrains the contribution from the systematic errors. In Figure  12 , we present the relative distance of the individual total-intensity and polarization component centroids from the core C0 across the EHT observing campaign. There is no systematic offset between centroids of C0 and P0, indicating that most likely they correspond to the same physical core component. However, the peak brightness location of P0 is shifted to the east with respect to the peak brightness of C0 (see also the discussion in Section 4.
Brightness Temperatures We measure the observer frame brightness temperatures T B of the VLBI core by performing Gaussian model fitting in DIFMAP at each frequency, and estimating the core size and flux density. The results are presented in Table  3 , with T B calculated as ( ) n q = ´n T F 1. 22 10 , 4 B 12 2 2 where the units of F ν , ν, and θ are as given in Table  3. Apart from that, nonsimultaneous MOJAVE data at 15 GHz give a core brightness temperature ∼ 10 12 K in 2012, and an apparent brightness temperature in excess of 10 12 K was found at 1. 6 GHz  (Shen et al. 1999). The reported brightness temperatures are generally lower limits if the cores are not resolved. Nevertheless, the trend of the brightness temperature decreasing with the frequency seems robust and consistent with signatures of an accelerating (sub)parsec-scale jet  (Lee et al.
043. 513 ) and  Spinoza Prize SPI   78-409 ; the  New Scientific Frontiers  with  Precision Radio Interferometry Fellowship  awarded by the  South African Radio Astronomy Observatory (SARAO) , which is a facility of the  National Research Foundation (NRF) , an agency of the  Department of Science and Technology (DST) of South Africa ; the  Onsala Space Observatory (OSO)  national infrastructure, for the provisioning of its facilities/observational support (OSO receives funding through the  Swedish Research Council  under grant  2017-00648 ) the  Perimeter Institute for Theoretical Physics  (research at  Perimeter Institute  is supported by the  Government of Canada  through the  Department of Innovation, Science and Economic Development  and by the  Province of Ontario  through the  Ministry of Research, Innovation and Science) ; the  Spanish Ministerio de Economía y Competitividad  (grants  PGC2018-098915-B-C21 ,  AYA2016-80889-P ,  PID2019-108995GB-C21 ); the  State Agency for Research of the Spanish MCIU through the "Center of Excellence Severo Ochoa " award for the  Instituto de Astrofísica de Andalucía  ( SEV-2017-0709 ); the  Toray Science Foundation ; the  Consejería de Economía, Conocimiento ,  Empresas y Universidad of the Junta de Andalucía  (grant  P18-FR-1769 ), the  Consejo Superior de Investigaciones Científicas  (grant  2019AEP112 ); the  US Department of Energy (USDOE)  through the  Los Alamos National Laboratory  (operated by  Triad National Security, LLC , for the  National Nuclear Security Administration of the USDOE  (Contract  89233218CNA000001 ); the  European Unionʼs   Horizon 2020 research and innovation program  under grant agreement No 730562  RadioNet ;  ALMA North America Development Fund ; the  Academia Sinica ; Chandra  DD7-18089X  and  TM6-17006X ; the  GenT Program (Generalitat Valenciana)  Project  CIDEGENT/2018/021.
The IRAM 30 m telescope on Pico Veleta, Spain is operated by IRAM and supported by  CNRS  ( Centre National de la Recherche Scientifique, France ),  MPG  ( Max-Planck-Gesellschaft, Germany)  and  IGN (Instituto Geográfico Nacional, Spain). The SMT is operated by the  Arizona Radio Observatory , a part of the  Steward Observatory of the University of Arizona , with financial support of operations from the  State of Arizona  and financial support for instrumentation development from the  NSF. Support for SPT participation in the EHT is provided by the  National Science Foundation  through award  OPP-1852617  to the  University of Chicago. Partial support is also provided by the  Kavli Institute of Cosmological Physics at the University of Chicago. The SPT hydrogen maser was provided on loan from the GLT, courtesy of ASIAA.
We present version 2 of SModelS, a program package for the fast reinterpretation of LHC searches for new physics on the basis of simplified model results. The major novelty of the SModelS v2 series is an extended topology description with a flexible number of particle attributes, such as spin, charge, decay width, etc. This enables, in particular, the treatment of a wide range of signatures with long-lived particles. Moreover, constraints from prompt and long-lived searches can be evaluated simultaneously in the same run. The current database includes results from searches for heavy stable charged particles, disappearing tracks, displaced jets and displaced leptons, in addition to a large number of prompt searches. The capabilities of the program are demonstrated by two physics applications: constraints on long-lived charged scalars in the scotogenic model, and constraints on the electroweak-ino sector in the Minimal Supersymmetric Standard Model.
SModelS is based on the concept of simplified models; it decomposes the signatures of full BSM scenarios into simplified model components (here called topologies) which are then confronted against the experimental constraints from a large database of results. Since this does not involve any Monte Carlo event simulation, SModelS is extremely fast and thus particularly well suited for model surveys, including large scans. The code and its vast database were also exploited recently in a new statistical learning algorithm that aims at identifying dispersed signals in the slew of LHC results  [11]. While very powerful, the SModelS approach to testing BSM theories involves a number of approximations. In particular, in the SModelS v1 series, the simplified model description involved only the structure of the topology (number of vertices in each branch, and number and type of SM final states in each vertex) and the masses of the BSM particles.
As mentioned above, this allows in particular for a better treatment of LLP signatures. Moreover, constraints from prompt and long-lived searches can be evaluated simultaneously in the same run. On the database side, SModelS v2 includes results from searches for heavy stable charged particles (HSCPs), disappearing tracks, displaced jets and displaced leptons, in addition to a large number of prompt searches. In the following, we describe in detail the technical novelties in SModelS v2 and demonstrate the capabilities of the program by means of two physics applications: the scotogenic model  [15, 16]  with either a scalar or a fermionic dark matter (DM) candidate, and the electroweak (EW)-ino sector of the Minimal Supersymmetric Standard Model (MSSM)  [17] [18] [19] [20]. The appendices give further details on the current database, the development of home-grown HSCP efficiency maps, and the interface to micrOMEGAs  [21] [22] [23].
Extension of the topology description in SModelS v2 The most significant novelty in SModelS v2 is the introduction of particle objects, which allows for more flexibility when dealing with simplified models. In the SModelS v1 series  [4, 5, 7, 9] , only the masses of intermediate BSM particles were used for describing the simplified model topologies tested by the database or obtained from the decomposition of the input model. Furthermore all final states, whether they are SM particles or (meta-)stable BSM particles, were described by simple labels (strings). Hence, despite the handling of a large variety of simplified model constraints, SModelS v1 was not able to deal with width-dependent results, such as searches for displaced decays, or searches which depend on additional information concerning the BSM particles, such as their spin or color representation.
Particle class In the SModelS v2 series, the introduction of a particle class replaces the simple list of particle masses by full objects, which can carry any desirable number of properties, such as mass, width, spin, electric charge, etc. As illustrated in figure  1 , these objects are henceforth the fundamental building blocks for describing the experimental results in the database, and for decomposing the input model. When matching the signal topologies of the input model onto those in the database, the comparison is made at the level of particle objects; particle objects with the same properties are considered as equal, independent of their labels. At present, the following attributes (properties) are considered: • Z2parity: the Z 2 -type parity of the particle (-1 for odd particles and +1 for even particles).
JHEP08(2022)068 • spin: the particle spin (1/2 for fermions, 0 for scalars,. ) • colordim: the color representation (1 for singlets, 3 for triplets and 8 for octets) • eCharge: the electric charge (-1 for electrons, 0 for neutrinos,. ) • mass: the particle mass • totalwidth: the particle total decay width. Note however that the number of attributes is a priori not fixed; new ones may be introduced by the user if needed. In the database of experimental results, if a specific property has no assigned value, it is assumed to be "arbitrary". For instance, if a search is sufficiently inclusive to be, to a good approximation, insensitive to the spins of the BSM particles, the spins are left unspecified in the database entry. It is then understood that the corresponding result applies to particles of arbitrary spin.
Model input The input provided by the user is conveniently split into two files: one containing the definition of BSM particles and their properties (model specification) and one defining their masses, widths, branching ratios and production cross sections (model parameters). The list of BSM particles can be defined in two distinct ways: either by writing a simple file using Python syntax and instantiating the particle objects: 3  or by providing a SLHA-type file with QNUMBERS blocks for each BSM particle, as shown in figure  2. Note that in the QNUMBERS blocks, whether a BSM particle is even or odd is specified by a symmetry factor S, S = 0 or 1, in key 11. This is related to the Z 2 (-like) parity, P Z 2 , by P Z 2 = (-1) S. If key 11 is not defined, it will be assumed that S = 1 (odd particle).
We point out that such SLHA files containing QNUMBERS blocks for the model definition are generated automatically by the micrOMEGAs-SModelS interface  [23] , see also appendix C. Moreover, they can be generated by tools relying on the UFO format  [25] , such as MadGraph  [26, 27]. As in previous versions, the model parameters (masses, decays,. ) can be provided either as an SLHA file containing MASS, DECAY and XSECTION blocks, or an LHE file containing parton level events. The typical usage is to keep the BSM model fixed and test distinct points of its parameter space. In this case, one needs a single file with the list 2 This is a good approximation for many E miss T searches, apart from mono-X searches, analyses relying on ISR jets, and analyses explicitly using shape information. 3 A few specific model definitions, such as the MSSM, NMSSM and the Inert Doublet Model (IDM), are shipped with the code in the smodels/share/models folder and can be used as examples for defining new models.
Results description in the database In previous SModelS versions, the database of experimental results was limited to upper limit (UL) or efficiency map (EM) results parametrized as a function of the BSM masses (M i ) appearing in the simplified model topology. Consequently, only searches for promptly decaying BSM particles, or for BSM particles which are stable on detector scales could be included. In order to describe searches for non-prompt particles decaying inside the detector, in SModelS the ULs or EMs were extended to include both the mass and the JHEP08(2022)068 width (Γ i ) of the BSM particles: (M i ) → (M i , Γ i ) and σ UL (M i ) → σ UL (M i , Γ i ), (2. 1) where represents an efficiency 5  and σ UL an upper limit on the production cross section. However, most of the (prompt) results contained in the SModelS database have no explicit width dependence, since they implicitly assume all decays to be prompt and the last BSM particle in the cascade decay to be stable.