Analysing the Control Software of the Compact Muon Solenoid Experiment at the Large Hadron Collider

The control software of the CERN Compact Muon Solenoid experiment contains over 30,000 finite state machines. These state machines are organised hierarchically: commands are sent down the hierarchy and state changes are sent upwards. The sheer size of the system makes it virtually impossible to fully understand the details of its behaviour at the macro level. This is fuelled by unclarities that already exist at the micro level. We have solved the latter problem by formally describing the finite state machines in the mCRL2 process algebra. The translation has been implemented using the ASF+SDF meta-environment, and its correctness was assessed by means of simulations and visualisations of individual finite state machines and through formal verification of subsystems of the control software. Based on the formalised semantics of the finite state machines, we have developed dedicated tooling for checking properties that can be verified on finite state machines in isolation.Comment: To appear in FSEN'11. Extended version with details of the ASF+SDF translation of SML into mCRL

An analysis of the control hierarchy modeling of the CMS detector control system

The supervisory level of the Detector Control System (DCS) of the CMS experiment is implemented using Finite State Machines (FSM), which model the behaviours and control the operations of all the sub-detectors and support services. The FSM tree of the whole CMS experiment consists of more than 30.000 nodes. An analysis of a system of such size is a complex task but is a crucial step towards the improvement of the overall performance of the FSM system. This paper presents the analysis of the CMS FSM system using the micro Common Representation Language 2 (mcrl2) methodology. Individual mCRL2 models are obtained for the FSM systems of the CMS sub-detectors using the ASF+SDF automated translation tool. Different mCRL2 operations are applied to the mCRL2 models. A mCRL2 simulation tool is used to closer examine the system. Visualization of a system based on the exploration of its state space is enabled with a mCRL2 tool. Requirements such as command and state propagation are expressed using modal mu-calculus and checked using a model checking algorithm. For checking local requirements such as endless loop freedom, the Bounded Model Checking technique is applied. This paper discusses these analysis techniques and presents the results of their application on the CMS FSM system

An Analysis of the Control Hierarchy Modeling of the CMS Detector Control System

The supervisory level of the Detector Control System (DCS) of the CMS experiment is implemented using Finite State Machines (FSM), which model the behaviors and control the operations of all the sub-detectors and support services. The FSM tree of the whole CMS experiment consists of more than 30.000 nodes. An analysis of a system of such size is a complex task but is a crucial step towards the improvement of the overall performance of the FSM system. This paper presents the analysis of the CMS FSM system using the micro Common Representation Language 2 (mcrl2) methodology. Individual mCRL2 models are obtained for the FSM systems of the CMS sub-detectors using the ASF+SDF automated translation tool. Different mCRL2 operations are applied to the mCRL2 models. A mCRL2 simulation tool is used to closer examine the system. Visualization of a system based on the exploration of its state space is enabled with a mCRL2 tool. Requirements such as command and state propagation are expressed using modal mu-calculus and checked using a model checking algorithm. For checking local requirements such as endless loop freedom, the Bounded Model Checking technique is applied. This paper discusses these analysis techniques and presents the results of their application on the CMS FSM system

Data - The Unreasonable Efficiency of Total Rain Evaporation Removal in Triggering Convective Self-Aggregation

This repository contains the data, scripts, SAM codes and files required to reproduce the results of the manuscript "The Unreasonable Efficiency of Total Rain Evaporation Removal in Triggering Convective Self-Aggregation" submitted to the Geophysical Research Letters (GRL). Brief description of project This project aims to examine the impact of rain evaporation removal or reduction in the planetary boundary layer (PBL) on convective self aggregation (CSA). Non-rotating radiative-convective equilibrium (RCE) simulations were conducted with the System for Atmospheric Modeling (SAM) cloud resolving model. Rain evaporation in the lowest 1 km was progressively reduced and the effect on CSA was investigated. The physical processes underlying this type of aggregation (referred to in the manuscript as no-evaporation CSA, or NE-CSA) were analyzed and described. The default SAM code base (version 6.10.8) can be downloaded from here: http://rossby.msrc.sunysb.edu/~marat/SAM.html Repository structure /SAM6.10.8 directory: contains the SAM fortran code and run time files required to conduct the simulations described in the manuscript /SAM6.10.8/SRC: contains the modified fortran code to run the experiments where rain evaporation was removed or reduced /SAM6.10.8/RCE_MM: contains the run time files required to run the RCE experiments /data directory: contains the model output data, in csv format /data/pw: contains data for the spatial snapshot of precipitable water /data/cold_pools: contains data for the virtual potential temperature anomaly on 1st model level as proxy for cold pools /data/MSE_streamfunction: contains data for plotting the MSE and streamfunction plot /data/thetav_timeseries: contains the timeseries data for virtual potential temperature, potential temperature and water vapor anomalies (deviation from domain mean) for buoyancy analyses /data/qrad_profile: contains data for the vertical profiles of the steady state radiative cooling rates /data/wsub_timeseries: contains timeseries data for the subsidence (vertical velocity) in the dry regions, averaged over lowest 1, 2, 3 and 5 km /data/agg_index_timeseries: contains timeseries data for the aggregation index, measured using two indices: variance of precipitable water divided by mean ("org_pw"), and variance of column relative humidity ("crh_var") /data/thetav_profile: contains data for the vertical profiles of virtual potential temperature, potential temperature and water vapor anomalies averaged over five separate days /scripts directory: contains the scripts required to plot all figures in the manuscript plot_cold_pools_pw.py: script to plot Figure 1 (RCE mean state PW and cold pools) plot_MSE_streamfunction.py: script to plot Figure 2 (MSE and streamfunction averaged over final 5 days) plot_thetav_timeseries.py: script to plot Figure 3 ($\theta^{'}_{v}$, $\theta^{'}$and $q_{v}^{'}$ timeseries for aggregated and scattered cases) plot_qrad_profiles_wsub_timeseries.py: script to plot Figure 4 (steady state radiative cooling profiles and $\overline{w}_{sub,PBL}$ timeseries) SI_plot_agg_index_timeseries.py: script to plot Figure S1 (aggregation index timeseries) SI_plot_thetav_profiles.py: script to plot Figure S2 ($\theta^{'}_{v}$, $\theta^{'}$and $q_{v}^{'}$ profiles on five separate days) SI_plot_thetav_timeseries.py: script to plot Figures S3 and S4 ($\theta^{'}_{v}$, $\theta^{'}$and $q_{v}^{'}$ timeseries for additional $\alpha$values, for aggregated and scattered cases) /scripts/applications directory: contains the utilities script that include helper functions for the plotting script

Communicating space science on social media: A study of engagement and trust in science

There is concern that a crisis of trust may be looming between society and scientists, as evidenced by the display of considerable public distrust in important issues such as climate change and childhood vaccinations. This perceived erosion of public confidence in science is one of the impetuses behind the public engagement with science movement, whereby scientists are called upon to engage in dialogues with public audiences to help improve trust in science and scientists. Thanks to their built-in architecture for participation, social media has been hailed as useful tools that scientists can use to engage directly with public audiences. Despite growing interest in social media to communicate science, evidence for the effectiveness of social media in influencing public attitudes towards science is scant. This thesis aims to fill this gap. In particular, two overarching questions were addressed: (1) does communicating with space scientists on social media have any effects on public trust in science and scientists? and (2) what are the factors that affect audience engagement and trust in science among space science audiences? Three studies were conducted, focusing on the communication of space science on social media. A combination of manual and automated content analysis methods was used. More than 500,000 social media messages were collected. The studies revealed three important findings. First, results indicate that talking to space scientists on social media causes a boost in trust in science and scientists. Second, the factors associated with audience engagement and trust in science are very different: visual elements stimulate audience engagement, while similarity is the biggest determinant of trust. Third, authenticity is the only feature that impacts both engagement and trust. This suggests that science audiences like and trust messages that are personal, honest, and genuine. The world increasingly needs scientists to also be advocates of science. The findings of this thesis represent concrete evidence that could help guide social media science communication efforts. Further, the world in which surveys worked well has been changed by the digital revolution and this thesis demonstrates the potential of using advanced computational techniques such as machine learning for science communication research

Assessing memory in convection schemes using idealized tests

Two assumptions commonly applied in convection schemes—the diagnostic and quasi-equilibrium assumptions—imply that convective activity (e.g., convective precipitation) is controlled only by the large-scale (macrostate) environment at the time. In contrast, numerical experiments indicate a “memory” or dependence of convection also on its own previous activity whereby subgrid-scale (microstate) structures boost but are also boosted by convection. In this study we investigated this memory by comparing single-column model behavior in two idealized tests previously executed by a cloud-resolving model (CRM). Conventional convection schemes that employ the diagnostic assumption fail to reproduce the CRM behavior. The memory-capable org and Laboratoire de Météorologie Dynamique Zoom cold pool schemes partially capture the behavior, but fail to fully exhibit the strong reinforcing feedbacks implied by the CRM. Analysis of this failure suggests that it is because the CRM supports a linear (or superlinear) dependence of the subgrid structure growth rate on the precipitation rate, while the org scheme assumes a sublinear dependence. Among varying versions of the org scheme, the growth rate of the org variable representing subgrid structure is strongly associated with memory strength. These results demonstrate the importance of parameterizing convective memory, and the ability of idealized tests to reveal shortcomings of convection schemes and constrain model structural assumptions

An information system for sustainable materials management with material flow accounting and waste input–output analysis

Sustainable materials management focuses on the dynamics of materials in economic and environmental activities to optimize material use efficiency and reduce environmental impact. A preliminary web-based information system is thus developed to analyze the issues of resource consumption and waste generation, enabling countries to manage resources and wastes from a life cycle perspective. This pioneering system features a four-layer framework that integrates information on physical flows and economic activities with material flow accounting and waste input–output table analysis. Within this framework, several applications were developed for different waste and resource management stakeholders. The hierarchical and interactive dashboards allow convenient overview of economy-wide material accounts, waste streams, and secondary resource circulation. Furthermore, the system can trace material flows through associated production supply chain and consumption activities. Integrated with economic models; this system can predict the possible overloading on the current waste management facility capacities and provide decision support for designing strategies to approach resource sustainability. The limitations of current system are specified for directing further enhancement of functionalities

Distributed error and alarm processing in the CMS data acquisition system

The error and alarm system for the data acquisition of the Compact Muon Solenoid (CMS) at CERN was successfully used for the physics runs at Large Hadron Collider (LHC) during first three years of activities. Error and alarm processing entails the notification, collection, storing and visualization of all exceptional conditions occurring in the highly distributed CMS online system using a uniform scheme. Alerts and reports are shown on\-line by web application facilities that map them to graphical models of the system as defined by the user. A persistency service keeps a history of all exceptions occurred, allowing subsequent retrieval of user defined time windows of events for later playback or analysis. This paper describes the architecture and the technologies used and deals with operational aspects during the first years of LHC operation. In particular we focus on performance, stability, and integration with the CMS sub\-detectors