bluuebunny/crossref_metadata_2025_split huggingface dataset:
Solution of motion parameters of natural fragments based on monte carlo subdivision projection simulation
Authors: Liu X Y , Zhan R J , Li Y L 
Published: July 2020

Abstract
In order to get the average windward area of natural fragments of non-metallic materials more accurately and efficiently, and to further simulate the motion parameters, a simulation calculation model of the average windward area of natural fragments is established based on the Monte Carlo subdivision projection method. By the finite element modeling, coordinate translation, random rotation, plane projection and triangulation, the average windward area of natural fragments with arbitrary irregular shape is simulated. Then, based on aerodynamics, the dispersion model of natural fragments is established. The simulation results of the two-dimensional trajectory and the farthest distance of natural fragments are obtained by Runge-Kutta method and cubic spline interpolation, which are compared with the empirical formula of the average windward area of regular fragments. The results show that the value by the Monte Carlo split projection simulation are more consistent with the experimental value, compared with the empirical formula of the windward area of spherical fragment, cube fragment, short cylinder fragment, cylinder fragment, rhombus fragment and cuboid fragment. The error of the farthest distance is between 13% and 25%. It is proved that the result of the average windward area based on Monte Carlo Subdivision Projection Simulation is more suitable for solving the dispersion model of natural fragments of low-speed and non-metallic materials.
Numerical study on hydroballistic and flow field characteristics of projectiles ejected from a vertical launch system underwater
Authors: Xu H , Wei Y J 
Published: July 2020

Abstract
A numerical simulation was performed to study the hydroballistic and flow field characteristics of projectiles launched successively underwater through a vertical launch system. The evolution of the projectile turbulence wake flow and its influence on the trajectory of other projectiles were studied under various launcher transport speed and launch sequence. Two major vortex structures are discovered in the wake: the vortex ring and the counter-rotating vortex pair. The vortex rings are generated when the projectile angle of attack is relatively small, and the counter-rotating vortex pairs are generated when it becomes larger. These large scale vortices are highly directional before breaking up. The counter-rotating vortex pairs can impose significant influence on projectile attitude upon wake vortex encounter. The outcome of such an encounter is highly unpredictable due to the nonlinear nature of these vortices. Projectiles launched with a higher platform transport speed are more likely to be affected due to the strong counter-rotating vortex pairs generated by previous projectiles with a high angle of attack.
The combination of numerical simulation of strong dynamic loading effects and big data technologies
Authors: Deng G Q 
Published: July 2020

Abstract
Near the zones of impact, explosion and other strong dynamic loading, the stress peak is very high, action time is very short, and the medium distortion is very large. The involving scientific problems are strong nonlinearity, multi-physics coupling, and so on. Numerical simulation and experimental investigation are mainly research method for these effects. However, the difficulty of the numerical simulation is at the top of the pyramid of scientific computation, and the noise, simulation is not true, is the biggest trouble currently existed. The emergence of big data technologies provides a feasible way to solve the current dilemma and realize accurate numerical simulation. Combination the big data technologies, such as data acquisition, infrastructure, data mining, statistical analysis, model prediction and so on, the numerical simulation of strong dynamic load effects is possible to achieve precision by automation of solver, intelligence of material state description library, expertization of human-computer interaction interface, credibility of simulation result data, virtual reality of simulation image. The combination of big data technology and numerical simulation will be the key to research the strong dynamic load effects, which would realize the transformation from numerical simulation to digital experiment, and the noise, simulation is not true, would be disappear completely.
Modelling and Production of artillery firing-tables: case-study
Authors: Mady Mohamed , Khalil Mostafa , Yehia Mahmoud 
Published: July 2020

Abstract
Projectile firing accuracy is an important artillery issue to increase killing probability with minimum number of rounds per target. This can be achieved through constructing a firing-table (FT) with both standard and non-standard conditions. Therefore, lot of rounds need to be shoot for different target ranges as well as meteorological conditions to increase such firing accuracy. Developing an accurate computational FT prior to shoots has the advantages of minimizing the cost and providing preliminary prediction of dispersion. In this study, a computational FT algorithm has been proposed based on the modified-point-mass projectile trajectory model for solving the ballistics problem. A flow-chart is proposed to illustrate the FT production levels and the corresponding procedures. A case study for 155mm-M107-HE projectile is utilized. In order to validate the proposed algorithm, a comparison has been implemented with results obtained from the well-known commercial package PRODAS. Finally, based on real firing data available including the corresponding meteorological conditions, a provisional firing table has been developed.
Research on explosive dust space ventilation scheme based on CFD simulation
Authors: Wang D , Jing Q , Qian X M , Yuan M Q , Liu S Q 
Published: July 2020

Abstract
Aluminum powder with high explosive sensitivity, strong detonation performance and low price has become the first choice fuel for fuel air explosives. Design three types of exhaust schemes for specific powder operation spaces, and use CFD simulation technology to study the flow field and dust removal characteristics of the three schemes. The results show that the bottom ventilation scheme has the best dust removal effect, the characteristic time of dust removal is 9.4s, and the maximum dust concentration is 85g/m3. And the characteristic flow field range of the bottom exhaust scheme is the smallest (4.12m×3.05m), which indicates bottom scheme has the least disturbance to the spatial flow field, so this scheme should be selected for ventilation and dust removal design. In addition, the function relationship between the wind pressure and the characteristic dust removal time is obtained by setting different wind pressures, which provides a basis for the design of the graded ventilation dust removal side according to the design.
A target detection algorithm based on gray distribution characteristics
Authors: Sun G B , Fan Q , Zou E B 
Published: July 2020

Abstract
The two-dimensional distribution of gray scale is an important feature of image target, but there is no effective description method for the two-dimensional distribution of gray scale. To solve this problem, a GRD operator is proposed to describe the two-dimensional distribution of gray scale. The design model and calculation steps of GRD operator are discussed. The experimental results of image data show that GRD operator is very effective to describe the features of image target. GRD operator can effectively detect and extract the target, which has a wide range of application value in image target detection, tracking, recognition and so on.
A Human Target Infrared Image Segmentation Approach Based on Convolution Neural Network
Authors: Liu Chao , Hu Qingping , Yao Yuan 
Published: July 2020

Abstract
In order to effectively segment the human target under complex background constraints, we present an infrared target segmentation method based on deep convolution neural network, and proposes the loss function based on the intersection-over-union for network optimization. Firstly, we design the network architecture which consists of a contracting path to capture the feature content and a symmetric expanding path that enables precise localization. And then rely on the powerful data amplification technology to effectively train the available sample data. The experimental results show that the network can make full use of the prior information of the data to study the characteristics of the human target, which can use less training data for end-to-end training in the human body target infrared image segmentation. And segmentation effect is superior to the traditional image segmentation algorithm. In addition, the network segmentation speed is very fast, 320 ×256 size image segmentation takes less than 0.2 seconds, to meet the human body target image segmentation of the effectiveness and real-time needs.
Modelling conditional probabilities with Riemann-Theta Boltzmann Machines
Authors: Carrazza Stefano , Krefl Daniel , Papaluca Andrea 
Published: July 2020

Abstract
The probability density function for the visible sector of a Riemann-Theta Boltzmann machine can be taken conditional on a subset of the visible units. We derive that the corresponding conditional density function is given by a reparameterization of the Riemann-Theta Boltzmann machine modelling the original probability density function. Therefore the conditional densities can be directly inferred from the Riemann-Theta Boltzmann machine.
reFORM: designing a new symbolic manipulation toolkit
Authors: Ruij Ben 
Published: July 2020

Abstract
Since modern-day multi-loop Feynman diagram computations often require manipulating billions of terms, taking up terabytes of memory, a powerful symbolic manipulation toolkit (SMT) is essential. The de facto solution is Form, but it has several shortcomings. In this work we present reForm, a new SMT in early development, that will handle the same workload as Form but does not have its shortcomings. We showcase some features of reForm, including Python and C APIs. Finally, we provide benchmarks for polynomial GCD computations, which show that reForm often outperforms its competitors. A link to the source code of the technical preview version is provided.
Numerical calculation of 5-loop QED contributions to the electron anomalous magnetic moment
Authors: Volkov Sergey 
Published: July 2020

Abstract
Preliminary numerical results of calculating the 5-loop QED contributions to the electron (and muon) anomalous magnetic moment are presented. The results include the total contribution of the 5-loop Feynman diagrams without lepton loops and the contributions of nine gauge-invariant classes that form that set. A discrepancy with known results is revealed. The contributions of the gauge-invariant classes are presented for the first time. The method of the computation is briefly described (with the corresponding references). The calculation is based on the following elements:
(i)a subtraction procedure for removing infrared (IR) and ultraviolet (UV) divergences point by point in Feynman parametric representation before integration;
(ii)a nonadaptive Monte Carlo integration method that is founded on probability density functions (PDF) that are constructed for each Feynman diagram individually using its combinatorial structure;
(iii)a GPU-based numerical integration with the help of the supercomputer “Govorun” (JINR, Dubna, Russia).
HepMC3 Event Record Library for Monte Carlo Event Generators
Authors: Verbytskyi Andrii , Buckley Andy , Grellscheid David , Konstantinov Dima , William Monk James , Lönnblad Leif , Przedzinski Tomasz , Pokorski Witold 
Published: July 2020

Abstract
We present the HepMC3 library designed to perform manipulations with event records of High Energy Physics Monte Carlo Event Generators (MCEGs). The library is a natural successor of HepMC and HepMC2 libraries used in the present and in the past. HepMC3 supports all functionality of previous versions and significantly extends them. In comparison to the previous versions, the default event record has been simplified, while an option to add arbitrary information to the event record has been implemented. Particles and vertices are stored separately in an ordered graph structure, reflecting the evolution of a physics event and enabling usage of sophisticated algorithms for event record analysis. The I/O functionality of the library has been extended to support common input and output formats of HEP MCEGs, including formats used in Fortran HEP MCEGs, formats used in HepMC2 library and ROOT. The functionality of the library allows the user to implement a customised input or output format. The library is already supported by popular modern MCEGs (e.g. Sherpa and Pythia8) and can replace the older HepMC versions in many others.
Computational challenges for MC event generation
Authors: Buckley Andy 
Published: July 2020

Abstract
The sophistication of fully exclusive MC event generation has grown at an extraordinary rate since the start of the LHC era, but has been mirrored by a similarly extraordinary rise in the CPU cost of state-of-the-art MC calculations. The reliance of experimental analyses on these calculations raises the disturbing spectre of MC computations being a leading limitation on the physics impact of the HL-LHC, with MC trends showing more signs of further cost-increases rather than the desired speed-ups. I review the methods and bottlenecks in MC computation, and areas where new computing architectures, machine-learning methods, and social structures may help to avert calamity.
Evaluating InfluxDB and ClickHouse database technologies for improvements of the ATLAS operational monitoring data archiving
Authors: Vasile Matei-Eugen , Avolio Giuseppe , Soloviev Igor 
Published: July 2020

Abstract
The Trigger and Data Acquisition system of the ATLAS experiment at the Large Hadron Collider at CERN is composed of a large number of distributed hardware and software components which provide the data-taking functionality of the overall system. During data-taking, huge amounts of operational data are created in order to constantly monitor the system. The Persistent Back-End for the ATLAS Information System of TDAQ (P-BEAST) is a system based on a custom-built timeseries database. It is used to archive and retrieve any operational monitoring data for the applications requesting it. P-BEAST stores about 18 TB of highly compacted and compressed raw monitoring data per year. Since P-BEAST’s creation, several promising database technologies for fast access to time-series have become available. InfluxDB and ClickHouse were the most promising candidates for improving the performance and functionality of the current implementation of P-BEAST. This paper presents a short description of main features of both technologies and a description of the tests ran on both database systems. Then, the results of the performance testing performed using a subset of archived ATLAS operational monitoring data are presented and compared.
ELisA: the ATLAS logbook facility extensions
Authors: Corso-Radu Alina , Avolio Giuseppe 
Published: July 2020

Abstract
Information concerning the operation, configuration and behavior of the ATLAS experiment needs to be reported, gathered and shared reliably with the whole ATLAS community which comprises over three thousand scientists geographically distributed all over the world. To provide such functionality, a logbook facility, Electronic Logbook for the information storage of ATLAS (ELisA), has been developed and actively used since the beginning of the LHC Run 2 period. The facility includes a user-friendly web interface to browse activity logs and to report on system operations with a configurable email notification system; a RESTful API used programmatically by other tools and services of the data acquisition infrastructure, and a set of client API libraries and utilities to help user’s interaction with the REST API. Given its generic configuration capabilities, the ELisA facility has been recently deployed as a stand-alone logbook for other projects such as the commissioning of different sub-detectors and the offline assessment of data-quality. To ease this operation and to potentially extend ELisA usage to other projects, an extension of the database backend support is being implemented to reduce the dependency on the ORACLE database for the logbook deployment. Also, the deployment process of the logbook is being improved. This contribution will present the status of the logbook facility as well as the extensions and improvements implemented to ease the logbook portability to other projects.*
CMS Software and Offline preparation for future runs
Authors: Boccali Tommaso 
Published: July 2020

Abstract
The next LHC Runs, nominally Run III and Run IV, pose problems to the offline and computing systems in CMS. Run IV in particular will need completely different solutions, given the current estimates of LHC conditions and Trigger estimates. We report on the R&D process CMS has established, in order to gain insight on the needs and the possible solutions for the 2020+ CMS computing.
High-performance, deep neural networks with sub-microsecond latency on FPGAs for trigger applications
Authors: Nottbeck Noel , Schmitt Christian , Büscher Volker 
Published: July 2020

Abstract
Modern high-end FPGAs, as they are often used for hardware-level trigger applications, offer enough arithmetic performance to include artificial neural networks of considerable size into such systems. Yet, there are only very few examples of the inclusion of ANNs into high-performance hardware triggers, which is especially due to the complex and time-consuming development for FPGAs, and the need for an optimized design in order to make efficient use of the FPGA capabilities. We developed a library that provides three types of layers: Fully-connected dense layers, as well as 2D multi-channeled convolution and maximum pooling layers. For maximum design control, these were designed with VHDL and optimized for the specific data flow and control requirements of each layer type. By that, it was possible to obtain multiple hundred MHz processing frequency and have only little resource overhead beyond what is required for the actual computation for the individual layers. Furthermore, we created a Python-based toolkit that builds on these layer implementations to make it possible to take a trained network from the Keras framework and create the FPGA firmware and initialization data without requirement of in-depth understanding by the user. The resulting (deep) network designs can process data coming in at multiple ten MHz at multiple hundred MHz processing frequency and latencies ranging from tens to few hundreds of nanoseconds, depending on the network size.
A new scheduling algorithm for the LHCb upgrade trigger application
Authors: Govorkova Ekaterina , Hasse Christoph , Matev Rosen , Nolte Niklas , Ponce Sebastien , Raven Gerhard , Stahl Sascha 
Published: July 2020

Abstract
During Run3 of the LHC the LHCb detector will process a 30 MHz event rate with a full detector readout followed by a software trigger. To deal with the increased computational requirements, the software framework is reviewed and optimized on a large scale. One challenge is the efficient scheduling of O(103)-O(104) algorithms in the High Level Trigger (HLT) application. This document describes the design of a new algorithm scheduler which allows for static-order intra-event scheduling with minimum complexity while still providing the required flexibility.
Federation of compute resources available to the German CMS community
Authors: von Cube R F , Giffels M , Heidecker C , Quast G , Sauter M B , Schnepf M J 
Published: July 2020

Abstract
The German CMS community (DCMS) as a whole can benefit from the various compute resources, available to its different institutes. While Grid-enabled and National Analysis Facility resources are usually shared within the community, local and recently enabled opportunistic resources like HPC centers and cloud resources are not. Furthermore, there is no shared submission infrastructure available.
Via HTCondor’s [1] mechanisms to connect resource pools, several remote pools can be connected transparently to the users and therefore used more efficiently by a multitude of user groups. In addition to the statically provisioned resources, also dynamically allocated resources from external cloud providers as well as HPC centers can be integrated. However, the usage of such dynamically allocated resources gives rise to additional complexity. Constraints on access policies of the resources, as well as workflow necessities have to be taken care of. To maintain a well-defined and reliable runtime environment on each resource, virtualization and containerization technologies such as virtual machines, Docker, and Singularity, are used.
The ATLAS EventIndex and its evolution towards Run 3
Authors: Villaplana Perez M , Alexandrov E , Aleksandrov I , Baranowski Z , Barberis D , Dimitrov G , Fernandez Casani A , Gallas E , Garcia Montoro C , Gonzalez de la Hoz S , Hrivnac J , Alexander I , Kazymov A , Mineev M , Prokoshin F , Rybkin G , Sanchez J , Salt J , Vasileva P T 
Published: July 2020

Abstract
The ATLAS experiment has produced hundreds of petabytes of data and expects to have one order of magnitude more in the future. This data are spread among hundreds of computing Grid sites around the world. The EventIndex is the complete catalogue of all ATLAS events, real and simulated, keeping the references to all permanent files that contain a given event in any processing stage. It provides the means to select and access event data in the ATLAS distributed storage system, and provides support for completeness and consistency checks and trigger and offline selection overlap studies. The EventIndex employs various data handling technologies like Hadoop and Oracle databases, and it is integrated with other parts of the ATLAS distributed computing infrastructure, including systems for data, metadata, and production management. The project has been in operation since the start of LHC Run 2 in 2015, and it is in permanent development in order to satisfy the production and analysis demands and follow technology evolution. The main data store in Hadoop, based on MapFiles and HBase, has worked well during Run 2 but new solutions are being explored for the future. This paper reports on the current system performance and on the studies of a new data storage prototype that can carry the EventIndex through Run 3.
CernVM-FS Container Image Integration
Authors: Mosciatti Simone , Blomer Jakob , Ganis Gerardo , Popescu Radu 
Published: July 2020

Abstract
Linux containers have gained widespread use in high energy physics, be it for services using container engines such as containerd/kubernetes, for production jobs using container engines such as Singularity or Shifter, or for development workflows using Docker as a local container engine. Thus the efficient distribution of the container images, whose size usually ranges from a few hundred megabytes to a few tens of gigabytes, is becoming a pressing concern. Because container images show similar characteristics than scientific application stacks, unpacking the images in CernVM-FS can remedy the distribution issues provided that the container engine at hand is able to use such unpacked images from CernVM-FS. In this contribution, we willl report on recent advances in the integration of Singularity, Docker, and containerd with CernVM-FS. We show improvements in the publishing of container images from a Docker registry that rely on new means of directly ingesting image tarballs. Well also show a repository file system structure for storing container images that are optimized for storing both container engines using flat root file systems (Singularity) as well as container engines using layers (containerd, Docker). To evaluate the benefits of our approach, we show concrete use cases and figures for production and development images from LHC experiments stored in the recently created unpacked.cern.ch repository.