Rogério M Calazan

Orlando C Rodríguez Larsys

TRACEO3D Ray Tracing Model for Underwater Noise Predictions

Keywords: Shipping noise, monitoring, Underwater acoustic, Ray tracing, Smart systems

Shipping noise is the main source of underwater noise raising concern among environmental protection organizations and the scientific community. Monitoring of noise generated by shipping traffic is a difficult challenge within the context of smart systems and solutions based on acoustic modeling are being progressively adopted to overcome it. A module of sound propagation stands as a key point for the development of a smart monitoring system since it can be used for the calculation of acoustic pressure, which can be combined with estimates of the source pressure level to produce noise predictions. This paper addresses the usage of the TRACEO3D model for application in such systems; the model validity is addressed through comparisons with results from an analytical solution and from a scale tank experiment. The comparisons show that the model is able to predict accurately the reference data, while a full-field model (normal mode-based, but adiabatic) is only accurate till a certain degree. The results show that TRACEO3D is robust enough to be used efficiently for predictions of sound propagation, to be included as a part of a smart system for underwater noise predictions.

Introduction

Shipping noise is the main source of underwater noise raising concern among environmental protection organizations and the scientific community. Shipping noise can propagate at long distances (like tens or hundreds of kilometers), thus having the potential to mask and/or disturb biological relevant sounds, such as those vocalized by marine animals for mating, orientation or detection of prays and predators. Monitoring of shipping noise is a difficult challenge due to many factors like, for instance, lack of equipment standardization and the large extensions to be covered (as a reference, the Portuguese EEZ itself has an area of about 1,7 million km2). Recently, the Marine Strategy Framework Directive [START_REF] Van Der Graaf | European Marine Strategy Framework Directive -Good Environmental Status (MSFD GES): Report of the technical subgroup on underwater noise and other forms of energy[END_REF] proposed the adoption of shipping noise modeling to overcome such difficulties. Generally speaking, realistic estimates of noise require transmissions loss (TL) predictions in coastal zones with a complex bathymetry and/or a complex sound speed distribution. To ease the computation load of the predictions they can be based on the adiabatic coupling of modes and/or the so-called N × 2D modeling, in which the three-dimensional field is constructed using N slices of predictions on a vertical plane, produced with a twodimensional model. A shipping noise prediction tool based on this approximation, combined with data from an Automatic Identification System (AIS), is discussed in [START_REF] Soares | A Shipping Noise Prediction Tool[END_REF] and shows that the system is able to produce relevant estimates of shipping coastal traffic. However, it is well known that even in the simplest case a three-dimensional bathymetry, either by itself or combined with a sound speed field, can induce propagation not confined to a given slice, an effect known as out-of-plane propagation. Modeling of acoustic fields in three-dimensional waveguides, accounting for out-of-plane propagation, had been an active field of research for many years [START_REF] Tolstoy | 3-D Propagation Issues and Models[END_REF][START_REF] Jensen | Computational Ocean Acoustics, Second Edition[END_REF][START_REF] Sturm | Comparisons of laboratory scale measurements of three-dimensional acoustic propagation with solutions by a parabolic equation model[END_REF]. In this context the wedge problem has been an important reference given the availability of an analytical solution [START_REF] Buckingham | An analytical solution for benchmark problem 1: the 'ideal' wedge[END_REF][START_REF] Deane | An analysis of the three-dimensional sound field in a penetrable wedge with a stratified fluid or elastic basement[END_REF][START_REF] Westwood | Ray model solutions to the benchmark wedge problems[END_REF]. Despite the apparent simplicity of the wedge problem the corresponding analytical solution has revealed many interesting features of three-dimensional propagation, such as horizontal refraction, mode coupling and rays propagating up-slope before connecting a source to a receiver. Recent evidence from tank scale experiments fully supports the analytical predictions [START_REF] Sturm | Numerical investigation of out-ofplane sound propagation in a shallow water experiment[END_REF].

Of interest for the topics discussed here is the TRACEO3D ray tracing model, which is able to predict fields of acoustic pressure and particle velocity in environments with elaborate boundaries; the model is under current development at the Signal Processing Laboratory (SiPLAB) of the University of Algarve. This paper looks forward for a robust module of sound propagation for noise predictions, through the comparisons of TRACEO3D predictions with results from an analytical solution of the wedge problem, and with results from a scale tank experiment [START_REF] Sturm | Comparisons of laboratory scale measurements of three-dimensional acoustic propagation with solutions by a parabolic equation model[END_REF]; both cases are considered to provide a robust reference to test the model's accuracy. The comparisons show that the model is able to predict accurately the reference data, while adiabatic coupling is only valid for a small wedge.

The remainder of this paper is organized as follows: Section 2 identifies the relationship of this work to the issue of Technological Innovation for Smart Systems; Section 3 compactly describes the TRACEO3D model; Section 4 provides a brief description of the analytical solution for the wedge problem, and of the scale tank experiment; Section 5 discusses the comparisons; Section 6 presents the conclusions and future work.

Relationship to Smart Systems

Generally speaking, Smart Systems are technologies able to combine data processing with sensing, data exploration and communication, and capable to analyze complex situations in order to take autonomous decisions. Although the availability of sensors is increasing in terms of accuracy and specificity (thus providing a better adaptation to the system objectives) the capability to develop predictions simultaneously with the acquisition of data improves the ability of a given Smart System to deal with real word (mostly unpredictable) situations. Of particular importance for the system is to rely on environmental knowledge to compensate for a given lack of information, or to proceed to a given task given a certain type of previous information. For the specific conditions of underwater acoustics a fundamental component of the Smart System should be a module for predictions of sound propagation, which can be further processed in order for the Smart System to proceed accordingly. Particular examples of such modules can be found in the literature for the case of underwater noise monitoring [START_REF] Soares | A Shipping Noise Prediction Tool[END_REF], underwater communications [START_REF] Rodríguez | The TV-APM interface: a web service for collaborative modeling[END_REF] and source tracking [START_REF] Felisberto | Experimental results of underwater cooperative source localization using a single acoustic vector sensor[END_REF].

The TRACEO3D Ray Tracing Model

The TRACEO3D model is a recent three-dimensional extension of the TRACEO ray model [START_REF] Rodríguez | The TRACEO ray tracing program[END_REF][START_REF] Rodríguez | Seismo-acoustic ray model benchmarking against experimental tank data[END_REF]. Generally speaking, TRACEO3D produces a prediction of the acoustic field in two steps: first, the Eikonal equation is solved in order to provide ray trajectories; second, ray trajectories are considered as the central axes of Gaussian beams, and the acoustic field is calculated as the coherent superposition of beam influences. The model can take into account the environmental variability in range, depth and azimuth.

The Wedge Problem

The general geometry of the wedge problem is shown in Fig. 1, with the wedge apex aligned along the Y axis; in the given geometry α stands for the wedge angle, and the source is located at the position (0, 0, zS). Propagation along the positive/negative X axis is known as downslope/upslope propagation, respectively, while propagation along the Y axis is known as cross-slope propagation. Two-dimensional acoustic models can be used to predict accurately upslope and downslope propagation and the models accuracy had been properly confirmed through comparisons with experimental data. Cross-slope propagation on the other side leads to out-of-plane effects and requires a three-dimensional model.

The Analytical Solution

A detailed description of the analytical solution can be found in [START_REF] Buckingham | An analytical solution for benchmark problem 1: the 'ideal' wedge[END_REF][START_REF] Deane | An analysis of the three-dimensional sound field in a penetrable wedge with a stratified fluid or elastic basement[END_REF][START_REF] Westwood | Ray model solutions to the benchmark wedge problems[END_REF]. Overall, the solution is based on the method of images, where the contribution of each image can be represented in terms of a Bessel function expansion inside an improper integral; numerical implementation of the solution is generally intensive because the convergence of the series is slow, and worsens when small α are considered; in this case the image solution can be replaced with the much faster adiabatic-mode solution. The limits of validity of the adiabatic solution still remain a topic of intense discussion.

The Scale Tank Experiment

The experimental data was obtained from an indoor shallow-water tank of the LMA-CNRS laboratory in Marseille. The tank experiment is described in detail in [START_REF] Sturm | Comparisons of laboratory scale measurements of three-dimensional acoustic propagation with solutions by a parabolic equation model[END_REF][START_REF] Korakas | Scaled model experiment of longrange across-slope pulse propagation in a penetrable wedge[END_REF], therefore a compact description is presented in this section. The inner tank dimensions were 10 m long, 3 m wide and 1 m depth. The source and the receiver were both aligned along the across-slope direction, as shown in Fig. 2. The bottom was filled with sand and a rake was used to produce a slope angle α ≈ 4.5°; sound speed in the water was considered constant and corresponded to 1488.2 m/s. The bottom parameters corresponded to cp = 1655 m/s, ρ = 1.99 g/cm 3 and αp = 0.5 dB/λ. The source was located at 8.3 mm depth and bottom depth at the source position corresponded to 44.4 mm. The ASP-H (for horizontal measurements of across-slope propagation) data set was composed of time signals recorded at a fixed receiver depth and at several source/receiver distances starting from r = 0.1 m until r = 5 m in increments of 0.005 m, providing a sufficiently fine representation of the acoustic field in range. Three different receiver depths were considered, namely 10 mm, 19 mm and 26.9 mm, corresponding to data subsets referenced as ASP-H1, ASP-H2 and ASP-H3, respectively. Acoustic transmissions were performed for a wide range of frequencies; however, comparisons are presented only for data from the ASP-H1 subset with the highest frequency (180.05 kHz); this is due to the fact that the higher the frequency the better the ray prediction. It is important to remark that a scale factor of 1000:1 is required to properly modify the frequencies and lengths of the experimental configuration; that implies that the following conversion of units is adopted: experimental frequencies in kHz become model frequencies in Hz, and experimental lengths in mm become model lengths in m; for instance, an experimental frequency of 180.05 kHz becomes a model frequency of 180.05 Hz, and an experimental distance of 10 mm becomes a model distance of 10 m. Sound speed remains unchanged, as well as compressional and shear attenuations.

Comparisons

The TRACEO3D and KRAKEN models were used to perform TL predictions, which were compared with results from the analytical wedge solution and with measurements from the scale tank experiment. The KRAKEN model is based on normal mode theory [START_REF] Kuperman | Rapid computation of acoustic fields in three-dimensional ocean environments[END_REF]; as in the case of TRACEO3D, KRAKEN 3D calculations can be done in two steps: first, modes can be calculated on a two-dimensional grid; second, modes can be coupled along different directions over the grid to produce a 3D prediction. For smooth bathymetries one-to-one exchange of modal energy (i.e. adiabatic coupling) can provide accurate and computationally efficient 3D predictions.

Waveguide parameters for the analytical solution and for the experimental data are shown in Table 1; it is important to remark the difference in frequencies: for the analytical case the frequency is much lower than for the experimental data; such low value of frequency is important to test whether the ray approximation can be still valid for the parameters of the analytical solution. Comparisons for the analytical solution and for the experimental data are shown in Fig. 3. In Fig. 3(a) one case see clearly that the two models produce accurate predictions, although KRAKEN's prediction is smoother, a fact that can be attributed to the low value of frequency. On the other hand, in Fig. 3(b) KRAKEN's prediction is only accurate at the initial ranges, and quickly starts to diverge due to the failure of the adiabatic prediction to account for the exchange of energy between modes of different orders. TRACEO3D on the other side is able to produce an accurate prediction in both amplitude and phase along the entire cross-slope range, thus showing the capability of the model to deal with an arbitrary wedge slope.

Conclusions

The discussion presented in the previous sections demonstrated the feasibility of using TRACEO3D as a module of sound propagation for noise predictions, through the comparisons with results from an analytical solution of the wedge problem and measurements from a scale tank experiment. The comparisons show that the model is able to predict the reference data, while adiabatic coupling is only valid for a small wedge. Despite the low frequency limitation, typical of ray theory, TRACEO3D was able to provide an accurate prediction for the analytical (low-frequency) case. The results also indicate that TRACEO3D can be able to deal with arbitrary bathymetries, a feature of fundamental importance for the development of a Smart System for the monitoring of shipping noise. Future work will be dedicated to optimize the current version through parallel computing, allowing decreasing the computational time, and enabling the model to provide fast predictions in an environment with a fine grid. Further improvements will also look for efficient solutions of 3D eigenrays search and fast 3D calculations of particle velocity.

Fig. 1 .

 1 Fig. 1. Geometry of the wedge problem; the star indicates the source position; the dots indicate an array in the cross-slope direction.

Fig. 2 .

 2 Fig. 2. Indoor shallow-water tank of the LMA-CNRS laboratory of Marseille (from[5]).

Fig. 3 .

 3 Fig. 3. Comparisons for the (a) analytical solution and (b) experimental results.

Table 1 .

 1 Parameters for the wedge problem.

			Analyti	Scale
	Parameters	Units	cal	tank
			solution	experiment
	α	°	0.5	4.5
	Frequency	Hz	50	180.05
	Sound speed	m/s	1500	1488.7
	Source depth	m	10	8.3
	Depth at source position	m	90	44.4
	Bottom compressional speed	m/s	2000	1700
	Bottom compressional density	kg/m 3	2	1.99
	Bottom compressional attenuation	dB/λ	0.5	0.5

Acknowledgments. This work received support from the Foreign Courses Program of CNPq and the Brazilian Navy. Thanks are due to the SiPLAB research team, FCT,

University of Algarve and MarSensing. The authors are also deeply thankful to LMA-CNRS for allowing the use of the experimental tank data discussed in this work.


Jeferson Souza
email: jeferson.souza@usp.br

Diolino J Santos Fo
email: diolinos@usp.br

Reinaldo Squillante
email: reinaldo.squillante@usp.br

Fabrício Junqueira

Paulo E Miyagi
email: pemiyagi@usp.br

Jose Reinaldo Silva

Jeferson A L De Souza

Fabricio Junqueira

Safety Active Barriers Considering Different Scenarios of Faults in Modern Production Systems

Keywords: Modern Production Systems, Safety Instrumented Systems, Risk Analysis, Faults, Scenarios of Faults, Safety Barriers, Petri nets

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

In this first decade of the century XXI many studies have indicated that automation processes are undergoing transformations that have been strongly influenced by the advance of technology and computing resources, becoming increasingly complex due to their dynamic and needed to address issues such as global market competitive production and technology used, among other factors [START_REF] Chen | Design and high-level synthesis of hybrid controller[END_REF], [START_REF] Santos Filho | Aspectos do Projeto de Sistemas Produtivos[END_REF], [START_REF] Wu | Service-oriented communication architecture for automated manufacturing system integration[END_REF]. Given this new scenario, industrial processes and their control are becoming more and more complex. Additionally, organizations have focused on policies to achieve and to demonstrate people's safety and health, environmental management system, and the capability in risk management.

In a globalized and competitive environment in which organizations are inserted, it is essential to adopt strategic plans and operational practices that ensure the ability to adapt rapidly and consequent change of the systems-productive but hitherto conceived. The expectation is that in addition to result in a process with effective cost reduction, high product quality and flexibility of production lines, and reduction of new products and delivery development times (Santos Filho, 2000) [START_REF] Chen | Design and high-level synthesis of hybrid controller[END_REF]) [START_REF] Wu | Service-oriented communication architecture for automated manufacturing system integration[END_REF], also causes the reduction of environmental impacts of the process. The results of this new scenario are Productive Systems (SPs) that perform highly complex processes [START_REF] Sampaio | Validação Visual de Programas Ladder Baseada em Modelos[END_REF] [START_REF] Ferreira | Challenges and Properties for Bio-inspiration in Manufacturing[END_REF]) that might not be achievable by conventional production methods [START_REF] Mazzolini | An Integrated Framework for Model-based Design and Verification of discrete Automation Solutions[END_REF]. Because of this complexity inherent in any modern production system, some states, though undesirable, can be achieved, it could be mentioned: the fault states of components, design flaws, or operational errors, including intentional, and environmental events that involve the system. Such occurrences could result, depending on the complexity of the SPs, serious risks to the physical integrity of people, the environment and economic losses resulting from damage to the equipment itself [START_REF] Sallak | A fuzzy probabilistic approach for determining safety integrity level[END_REF]. Although many studies have been presented both for diagnosis and for the treatment of faults [START_REF] Morales | Diagnosis and Treatment of Faults in Productive Systems based on Bayesian Networks and Petri net[END_REF] (Ru & Hadjicostis, 2008) [START_REF] Wang | Fault Detection and Diagnosis Based on Time Petri Net[END_REF] [START_REF] Zhang | Bibliographical review on reconfigurable fault-tolerant control systems[END_REF] [START_REF] Summers | Common cause and common sense, designing failure out of your safety instrumented systems (SIS)[END_REF] [START_REF] Sallak | A fuzzy probabilistic approach for determining safety integrity level[END_REF], [START_REF] Squillante | Safety in Supervisory Control for Critical Systems[END_REF] [START_REF] Souza | Mitigation Control of Faults in Critical Production Systems[END_REF] [START_REF] Souza | Coloured Petri Nets for Implementation of Safety Instrumented Systems in Critical Production Systems[END_REF] [START_REF] Peters | Risk Analysis and management based on the performance of safety barriers: case study: fixed platform Polvo A[END_REF] accidents continue to occur. In this context, according to specialists, the use of Safety Instrumented Systems -SIS is a solution to this problem in that aims to reduce the risks associated with SPs by successive risk reduction layers, which can be implemented by safety control systems that operate independently of the Basic System Process Control -BPCS. In general, the role of a SIS is to monitor through security sensors, critical events in the industrial process and indicate alarms or perform preset actions through security actuators, for the prevention of accidents or mitigation of the consequences generated by these events [START_REF] Goble | Control Systems Safety Evaluation & Reliability[END_REF]. SIS are referenced in standards such as IEC 61508 and IEC 61511, that lists the performance requirements and the life cycle for a design of a SIS. However, the standards make no mention of methods for SIS implementation. Another concept that promotes de risk reduction of a SP is that of safety barriers. Safety barriers are defined as any physical or non-physical means used in order to prevent, control or mitigate undesirable events or accidents [START_REF] Sklet | Safety Barriers: Definition, classification, and performance[END_REF]. It may be from human action to a complex logical system. This paper proposes a method for the implementation of SIS, in reference to IEC 61508 and IEC 61511, considering different scenarios of faults, making use of safety barriers both to prevent and to mitigate faults. By associating the occurrence of a fault to an event, and by the fact a fault promotes the change of the process state, scenarios can be modeled by Petri nets (PN). Several isolated models are obtained, representing a great difficulty to implement the control algorithm. The hypothesis lies in the interrelationship of the models that represent the different scenarios of faults, which can result in a considerable simplification both for the good properties verification and formal validation of the algorithm, as well as for control codes transcription. This paper is organized as follows: Section 2 presents the Relationship to Smart Systems, Section 3 the Fundamental Concepts of some Risk Analysis techniques, Petri nets and Fuzzy Logic. Section 4 presents the Proposal with partial results, obtained by the application of the method in a gas compression station. Finally, section 5 presents the Conclusion and References.

Relationship to Smart Systems

This work has close relationship with smart systems as it proposes a safety control system considering different scenarios of faults, by the use of safety barriers, in reference to IEC 61508 / IEC 61511. The work proposes that such barriers be fully implemented by programmable control sisyems, rather than passive physical barriers or exercised by human activity. The barriers interrelationship characterizes such a system as a "smart system", in line with the event proposal, contributing to the reduction of the inherent risk, presented in any modern production system, with the objective of protecting man, the environment and facilities.

Fundamental Concepts

Risk Analysis Techniques

This section introduces the fundamental concepts of some risk analysis techniques, such as FMEA, FTA, ETA HAZOP and the Bow Tie Diagram. The FMEA technique, described in IEC 60812 (IEC 2006), consists of a detailed and systematic study of possible failures of the components of a system. The failure modes of each component are identified, and a severity level is associated with its effect, and assess the likelihood of their occurrence. The FMEA also discusses actions to prevent, eliminate, mitigate and control the causes and consequences of failures. [START_REF] Lewis | Introduction to Reliability Engineering[END_REF]. Another technique used is the Fault Tree -FTA, deductive reasoning methodology described in IEC 61025 (2008) that part of a top event, which is the occurrence of a specific fault in a system, which aims to determine the relationship fault logic components and / or operational human errors that may be associated with the occurrence of the top event. The analysis is done from the construction of a logical tree. In this way, one obtains a graph which can be used to identify all possible causes for the occurrence of a fault [START_REF] Modarres | Reliability Engineering and Risk Analysis: a practical guide[END_REF]. The graph enables an analysis of the "top-down", which results in understanding how the event occurred. In the analysis "bottom-up" it has been "why" of the event. The advantage of FTA on the FMEA is that one can have a combination of several elements or multiple failure modes, the graph connected by logic elements such as "and" and "or". The study of operability and risks, or HAZOP (HAZard and OPerability studies) defined in IEC 61882 (IEC, 2001) was developed for efficient and detailed examination of the variables of a process having a strong resemblance to the FMEA. Hazop identifies the ways in which the process equipment may fail or be improperly operated. It is developed by a multidisciplinary team, being guided by the application of specific words -words guide -each process variable. Thus, to generate the deviation of operational standards, which are analyzed in relation to their causes and consequences. Event trees (DIN 25419, 1985-11) are frequently used in the analysis of sequences of events, including human activities, that can lead to disasters or undesirable events. The activity is sometimes called cause consequence analysis, and is used more frequently in safety studies (Leitch, 1995). In this analysis, a basic event, resulting from a specific failure of a human equipment or error, called the initiating event, is used to determine one or more subsequent states of possible faults (Modarres et al., 2009) (Rausand, 2011) (Villemeur, 1992). In this way, the ETA considers the action to be taken by the operator or the process response to the initial event. As in FTA, the study is performed through a tree, starting from the initiating event, in order to quantify the probabilities of failure in the system. Explore responses through a single initiating event and lays a path for assessing probabilities of the outcomes and overall system analysis. It may be applied to a system early in the design process to identify potential issues that may arise rather than correcting the issues after they occur (Clifton, 2005). A widely technique for modeling complete accident scenarios, based on the identification of the main "causes" and "consequences", given a top event (TE) or risk. Basically, the principle of this technique is to construct a tree type, called a bow-tie diagram, due to its special shape (Badreddine & Ben Amor, 2010). The bow-tie diagram is based on two parts, as shown in Figure 1. The left side of the diagram represents the fault tree (FT) that defines all the possible combinational logical relationships between the causes of the TE. In addition, the right side of the diagram represents the event tree (ET) that defines all the possible undesired consequences of TE.

Petri Nets

Petri net (PN) as a graphical tool and mathematics provides a uniform way for model, analysis and design of Discrete Event Systems -SEDs [START_REF] Adam | Modeling and analysis of workflows using Petri nets[END_REF][START_REF] Nassar | Modeling and Analysis of the Material Entry Flow System in a Pickling Line Process using Petri Net[END_REF][START_REF] Zurawski | Petri nets and Industrial Applications: a Tutorial[END_REF], being effective as a description of technical and specification processes [START_REF] Hamadi | A Petri net-based model for web service composition[END_REF][START_REF] Morales | Diagnosis and Treatment of Faults in Productive Systems based on Bayesian Networks and Petri net[END_REF]Yoo, et al., 2010). It provides a representation that can be used both as a conceptual model and functional model of a system that can analyze and validate the operation of the system at each stage of its development cycle. The PN can also be used as a design tool, allowing for easy interpretation and identification of processes and their dynamic behavior and / or systems being modelled [START_REF] Nassar | Modeling and Analysis of the Material Entry Flow System in a Pickling Line Process using Petri Net[END_REF]. The models based on NP can be used for qualitative and quantitative assessment involving the analysis of the behavioral properties and performance measure, respectively. Moreover, with the development of software simulators [START_REF] Zurawski | Petri nets and Industrial Applications: a Tutorial[END_REF], has provided tools for editing and analysis of these models. Enables the representation of the system dynamics and structure at various levels of abstraction, according to the withcomplexity system [START_REF] Nassar | Modeling and Analysis of the Material Entry Flow System in a Pickling Line Process using Petri Net[END_REF]. It is able to model synchronization process, the occurrence of asynchronous events, competitors and conflict operations, or resource sharing [START_REF] Adam | Modeling and analysis of workflows using Petri nets[END_REF][START_REF] Nassar | Modeling and Analysis of the Material Entry Flow System in a Pickling Line Process using Petri Net[END_REF].

Fuzzy Logic

Fuzzy logic is becoming useful in modelling of nonlinear systems, or when the use of differential equations becomes too complex, or even in processes whose knowledge of the dynamic behaviour is not yet fully understood (Lee, 1990). Fuzzy systems are based on the human knowledge or a set of rules that are designed to mimic human reasoning in control decisions (Zadeh, 1996). questions like "If ... (conditional) So ... (consequent)" are formulated process experts in analysis, and control actions are defined from the responses, and in its May-ria, systems multiple inputs to a single output (Zadeh, 1965) (Lee, 1990) (Stern et al., 2008). All rules are processed in parallel, with the consequent be active with its degree of membership in the system output. Unlike Boolean logic, fuzzy numbers are contained in a closed interval 0 to 1, and may take values within this range (Zadeh, 1965). The use of fuzzy logic in CPs is referenced in the IEC61131-7 standard, which deals with the conversion of fuzzy logic in implementable language in commercial CPs.

Proposal

The proposed method is outlined in the flowchart shown in Figure 2, and the steps for its implementation, described in the following items 4.1 to 4.5, are built from knowledge of independent experts and/or database obtained from field experiments, record of past operation or computer simulation of plant under study.

Fig. 2. Flowchart of steps of the proposed method.

To illustrate the proposed method, some results were obtained from a natural gas compression station.

FMEA -Determination of the Critical Elements

To determine the critical elements of the process under study FMEA is applied as a first evaluation insofar as it associates a level of severity to the occurrence of fault in an isolated and component-centered way, and proposes actions to prevent and mitigate the effects. Faulted components that pose risks to operators, the environment and equipment, besides violating the legislation, receives maximum severity. In the proposed method, it is part of the FMEA to associate sensors to detect the occurrence of fault of the critical element, and criteria to confirm the occurrence of failure, such as 2oo3 voting criteria. Based on the results, a cause / effect matrix is elaborated, in which the lines represent the initializing events of the sensors associated to the occurrence of faults and columns the respective actions proposed for prevention and / or mitigation. Such information is obtained with the assistance of a team of experts in the process under study, according to IEC 61508. Based on the concept of SIS, the Safety Instrumented Functions (SIFs) are listed. An example of cause-effect matrix is presented in Table 1.

Table 1 Example of a cause-effect Matrix of a SIF

Fault Tree and Event Tree Analysis -Bow Tie Diagrams

The next step consists in analyzing the logical combination of events (or faults) that can lead to the occurrence of a critical fault (or top event), making use of the FT technique. For each top event, the ETA technique represents the evolution of events that follow the occurrence of the top event and which can lead to a catastrophe. The junction of the FTA with the ETA results in the bow-tie diagram, which in turn can be understood as being a fault scenario. The results obtained in the cause-effect matrix certainly aid in the design of bow-ties.

Bow Ties to Petri net Conversion

The results from the previous step are isolated bow-tie diagrams, representing scenarios of faults. By associating the occurrence of a fault to an event, and the evolution of the fault to a succession of states, the bow-tie diagrams can be modeled by Petri nets, in which the editing, verification and formal validation properties of the model can be edited and simulated by software, such as PIPE2. An example of a bowtie diagram obtained in the application example is shown in Figure 3. It can be observed from the obtained models that places and transitions are common to different bow-ties, so that the graphical representation of PN can contribute to the simplification of the final model, integrating all obtained bow-ties. This step is fundamental to the implementation of the proposed method. After the simplification process, the properties for the formal validation of the obtained model must be verified again.

Insertion of Barriers / Fuzzy Logic

After the formal validation of the obtained PN model, the next step consists of associating external actions with the PN states, representing barriers to the evolution of fault states. In the proposed method, only the active barriers, associated with a control system, are considered, in which prevention and mitigation actions are associated with actuators. Such actuators can be defined in the initial stages of FMEA and FTA / ETA. N example of a PN model with barriers is show in Figue 4. The advantage of simplifying the models in the previous step is that actuators can be common to different bow-tie actions, which certainly minimizes implementation costs. Another advantage is the property of the encapsulation of functionalities, which represents a great advantage in the design process. In terms of the PN structure, the barriers have the function of inserting deadlocks. Fuzzy logic can help in this process since can define the parameters of the variables associated with the occurrence of a fault, as well as represent an anticipatory action of prevention / mitigation action insofar as the analysis of the temporal increment of the variable can be part of the control action. [START_REF] Souza | Mitigation Control of Faults in Critical Production Systems[END_REF] presents the contribution of fuzzy logic to the implementation of a SIS. An example of a fuzzy logic implementation is shown in Figure 5.

Control Codes Generation

The control codes can be obtained by transcribing PN to Ladder models (IEC-61131-3) or by means of IEC 61131-7, which deals with the conversion of fuzzy logic to Structured Text.

Conclusions

The paper presents a method for the implementation of a Safety Instrumented System in modern production systems, inserted in the context of smart systems, by the concept use of Safety Barriers, considering different scenarios of faults. The scenarios are modeled by PN, and the main and the main contribution lies in the interrelationship of the scenario models. It is possible to simplify those models by common initializing events and common actuators, resulting in a greater ease of implementation and control code generation. The proposed method is being applied in a gas compression station and in an oil extraction platform, and the results obtained so far have been quite satisfactory. A limitation of the method are the extensive graphs obtained from the interrelationship of the PN models of the Bow Tie diagrams. A possible solution would be the generation of the models in Colored Petri nets [START_REF] Jensen | Coloured Petri Nets. Basic Concepts, Analysis Methods and Paractical Use[END_REF] (ISO, IEC 15.909), , which would result in more synthetic models, without losing information or complexity of the models, and having as additional benefit the insertion of the fuzzy levels of the associated process variables.

Fig. 1 .

 1 Fig. 1. Example of a Bow-Tie Diagram

Fig. 3 .

 3 Fig. 3. Example of a Bow Tie to PN.

Fig. 4 .

 4 Fig. 4. Example of Safet Barrier in the PN model.

Fig. 5 .

 5 Fig. 5. Example of fuzzy logic implementation. Extracted from[START_REF] Souza | Mitigation Control of Faults in Critical Production Systems[END_REF]

Acknowledgments. The authors would like to thank the Brazilian governmental agencies CNPq, FAPESP, and CAPES for their financial support to this work.


Lorenzo Bottaccioli

Enrico Macii

Edoardo Patti

Abouzar Estebsari

Enrico Pons

Andrea Acquaviva

PVInGrid: A Distributed Infrastructure for Evaluating the Integration of Photovoltaic Systems in Smart Grid

Keywords: Photovoltaic, GIS, Distributed Infrastructure, Smart-grid, Realtime, Simulation

Planning and developing the future Smart City is becoming mandatory due to the need of moving forward to a more sustainable society. To foster this transition an accurate simulation of energy production from renewable sources, such as Photovoltaic Panels (PV), is necessary to evaluate the impact on the grid. In this paper, we present a distributed infrastructure that simulates the PV production and evaluates the integration of such systems in the grid considering data provided by smart-meters. The proposed solution is able to model the behaviour of PV systems solution exploiting GIS representation of rooftops and real meteorological data. Finally, such information is used to feed a real-time distribution network simulator.

Introduction and Motivation

Thanks to the commitment of the participating countries to Kyoto Protocol and to Paris Agreement, the presence of distributed Renewable Energy Sources (RES) is increasing in our electricity systems. In this transition from a centralized to a distributed system, converting passive buildings rooftops of our cities into active Photovoltaic rooftops becomes crucial. However, the deployment of PV system cannot be left to chance but needs to be planned with specific tools able to i) estimate PV production in time and ii) to assess the capabilities and requirements of the networks [START_REF] Jakubiec | A method for predicting city-wide electricity gains from photovoltaic panels based on lidar and gis data combined with hourly daysim simulations[END_REF]. This research aims at creating PVinGRID, a distributed infrastructure composed by software and hardware components. PVinGRID integrates: i) Geographic Information Systems (GIS) data and algorithms; ii) sensor, such as weather station and smart meter; iii) real-time hardware electricity grid simulator. Exploiting GIS tools and weather station data, PVinGRID estimates the availability of deployable area for PV system on rooftops and simulates sub-hourly PV generation loads in real-sky conditions. By merging data coming from smart meters [START_REF] Bahmanyar | Emerging smart meters in electrical distribution systems: Opportunities and challenges[END_REF] with simulated PV generation loads, PVinGRID evaluates the integration of PV system in the distribution network exploiting a real-time grid simulator. The rest of the paper is organized as follows. In Section 2, the relationship of this research with smart system is highlighted. Section 3 presents literature methodologies and services for PV energy simulation and integration into the grid. Section 4 presents the specifications and methodology to develop our solution. In Section 5, the results obtained with PVinGRID are presented. Finally, Section 6 provides the concluding remarks.

Relationship to Smart Systems

Information and Communication Technologies (ICTs) are a key factor in the development of smart systems for Smart Energy management. They can provide useful tools for planning and monitoring the deployment of RES systems. Furthermore, the spread diffusion of heterogeneous and pervasive sensors in our houses, district and cities, is increasing the smartness of our distribution grid. With such sensors, it is possible to monitor the electricity behaviours of the users. Information on electricity consumption are needed in both planning and monitoring phases for evaluating: i) energy management actions; ii) management of electricity distribution networks; iii) integration of renewable sources in the city. PVinGRID is a tool useful for both planning and monitoring phases of PV systems, spanning all scales starting from single building up to block, district and city. Our solution can be used for different purposes depending on the needs of different stakeholders. For instance, a Single citizen uses PVinGRID for estimating the economic benefits that he/she can achieve by installing a new PV system. End-users want to be aware on the avoided CO2 emission and virtuous actions to perform. Energy aggregators and Energy Communities can use our solution for scheduling the consumption pattern of their clients for maximizing self-consumption and minimizing energy bills. In particular Energy Communities can exploit such infrastructure to perform feasibility studies as proposed by [START_REF] Bottaccioli | A tool-chain to foster a new business model for photovoltaic systems integration exploiting an energy community approach[END_REF]. Distribution system operators (DSO) can use PVinGRID for analysing the reliability of their grids after the deployment of PV system and plan, if needed, retrofits actions and/or extensions of existing distribution grids. They can exploit our solution to avoid network congestions exploiting load balancing. Lastly, Energy and City planners can use PVinGRID for evaluating the benefits of large PV systems installations in cities or monitoring the performance of already deployed PV systems. Furthermore, if accurate weather forecasts are available, PVinGRID provides also information on generation profilse of existing PV systems to: i) DSO for estimate possible unbalances on the grid and to evaluate load balancing actions (e.g. Demand Response and Energy Storage); ii) Market Operators for creating local or dynamic prices; iii) Smart Energy management systems for evaluating next actions in smart energy management (e.g. usage of storage and load scheduling).

State of the Art

Geographic Information Systems (GIS) tools have been widely applied in the estimation of renewable potential as reported in [START_REF] Domínguez | Geographical information systems applied in the field of renewable energy sources[END_REF] in particular in urban contexts for solar applications [START_REF] Fielding | Architectural styles and the design of network-based software architectures[END_REF].

New methodologies exploiting GIS for simulation with spatial and temporal domains of PV system energy production are recently emerging [START_REF] Camargo | Spatio-temporal modeling of rooftop photovoltaic panels for improved technical potential assessment and electricity peak load offsetting at the municipal scale[END_REF][START_REF] Freitas | Modelling solar potential in the urban environment: State-of-the-art review[END_REF][START_REF] Keane | State-of-the-art techniques and challenges ahead for distributed generation planning and optimization[END_REF]. A spatiotemporal simulation of PV production integrated with network topology and users consumption load profiles allows to estimate the real PV potential by evaluating the effects of the penetration of PV systems into the grid [START_REF] Camargo | Spatio-temporal modeling of rooftop photovoltaic panels for improved technical potential assessment and electricity peak load offsetting at the municipal scale[END_REF]. Available web solutions such as [START_REF] Lukac | Buildings roofs photovoltaic potential assessment based on lidar (light detection and ranging) data[END_REF][START_REF] Patti | multiflex: Flexible multi-utility, multi-service smart metering architecture for energy vectors with active prosumers[END_REF] are limited in the analysis of the time domain and do not integrate electricity consumption and network topology data. Hence, there is the need to develop frameworks that integrate consumption, production and network topology in order to evaluate the integration of PV system, or more in general RES. Real-time simulator, such as Opal-RT®, are applied in the prototyping phase of the development of system or manufacture. This because real-time simulations allow testing solutions that are still not physical prototypes [START_REF] Bompard | A multi-site real-time co-simulation platform for the testing of control strategies of distributed storage and v2g in distribution networks[END_REF][START_REF] Dufour | Very-high speed control of an fpga-based finiteelement-analysis permanent magnet synchronous virtual motor drive 7[END_REF][START_REF] Suri | Geographic aspects of photovoltaics in europe: contribution of the pvgis website[END_REF]. With respect to RES, real-time simulations have been applied for PV generation [START_REF] Pak | Real-time simulation of a wind energy system based on the doubly-fed induction generator[END_REF] and for wind-farms [START_REF]Mapdwell Solar System[END_REF][START_REF] Vamsidhar | Hardware-in-the-loop simulation based design and experimental evaluation of dtc strategies[END_REF]. In work we propose a simulation framework called PVinGRID that couples PV simulator with a real-time grid simulator, such as Opal-RT®. PVinGRID considers the electric behaviour of the distribution system taking into account both information on electricity consumption and network topology. In this Section, we describe PVinGRID our distributed infrastructure for evaluating the integration of PV systems in a Smart Grid environments. PVinGRID is composed by both hardware and software components. The hardware components are: i) realtime grid simulator, ii) weather station and iii) smart meters. The software component is a distributed infrastructure for estimating PV systems energy production (see Fig. 1(a)).

Contributions and Innovative Aspects

Real-Time Grid Simulator

Real time simulation (RTS) is a highly reliable method based on electromagnetic transient simulation which serves a platform to test new control strategies or technologies on a virtual environment emulating the real world system. It provides very reliable real-like information on impacts and benefits of new strategies or devices. RTS could support decision makings from real-time operation and control phase to long-term planning. Regarding electricity systems, RTS is being widely used in protection and control system development and testing. In particular, for distributed generation modelling of RES integration (e.g. PV generation penetration), and intelligent grids development.

The purpose of using RTS in our work is to model a realistic distribution network to support investigations in terms of PV power penetration impacts in real-world situations. The objective is to simulate the behaviour of prosumers, and the set-up of a Software-In-the-Loop (SIL) platform for laboratory validations of new controls, operations, and planning algorithms for smart grids management [7].

Photovoltaic System Simulator

In this section we present the distributed software infrastructure for simulating PV generation profiles (see Fig. 1(a)). The development of this solution is needed to model and estimate PV system behaviours in both spatial and temporal domain. Thus, evaluations of their impact in the grid can be assessed. Our solution has been designed following both microservice 1 and REST [START_REF] Estebsari | An iot realization in an interdepartmental real time simulation lab for distribution system control and management studies[END_REF] approaches. This due to the intention of developing a simulation tool with an easy maintenance and to allow integration of third-party software. Our solution is composed by three layers described in the following:

The Data-source Integration Layer, the lower layer, is in charge of integrating the following data-sources: i) the Digital Surface Model (DSM) of the area in study, which is a raster image that represents terrain elevation considering the presence of manufactures; ii) the Linke Turbidity coefficients that express the attenuation of solar radiation related to air pollution; iii) the Weather Data coming from the nearest weather station provided by third-party services; iv) electricity consumption data provided by external software platforms(eg. [START_REF] Park | A novel real-time simulation technique of photovoltaic generation systems using rtds[END_REF][START_REF] Patti | Distributed software infrastructure for general purpose services in smart grid[END_REF]).

The Services Layer is the core of the simulation infrastructure and is composed by independent services that communicates trough REST-API. Suitable Area service is in charge of identifying suitable real rooftop surfaces for deploying PV system. Clear-Sky condition service, exploiting the r.sun tool of GRASS-GIS 2 , produces a set of maps with the incident solar radiation every 15 minutes in the area of interest. Solar radiation decomposition service is used if the nearest weather station provides only global horizontal radiation (GHI). In its core, it has a set of solar decomposition models present in the literature used to estimate direct normal radiation (DNI) and diffuse horizontal radiation (DHI). Real-sky condition service produces real-sky maps for each time interval of the simulation. PV energy estimation service, using the maps of real-sky condition and suitable area service, estimates the size of the deployable PV system and its production. It considers the cell temperature that affects the PV efficiency. PV system Integration service is in charge of correlating the simulation of PV energy production with data coming from the Smart Grid (e.g. energy consumption). This service provides information on the level of self-sufficiency (i.e. the share local demand fulfilled by PV energy production) and the level of selfconsumption of the produced energy (i.e. the share of produced energy that is instantaneously consumed) for the area of interest. Furthermore, it enables the communication with the real-time grid simulator (see Section 4.1) as described in the following Section 4.3.

The Application Layer provides applications that allows end-users to interact with the simulation results exploiting Web-Map interface and Dashboards.

Smart-Grid Simulation Tool

As mentioned in Section 4.2, the PV system Integration service in Fig. 1(a) correlates the output of the PV system energy simulation with information coming from the Smart Grid with the same geographic area. In addition, it enables the communication with the real-time grid simulator through the Communication Software Adapter (see Fig. 1(b)). The adapter integrates two sub-modules: i) REST adapter and ii) UDP module. The REST adapter parses the simulation request from the RTS to the PV system simulator by translating them into REST calls to remote web-services. The UDP module pushes the simulated PV energy production data in to the RTS. Furthermore, it receives and processes the information coming from the RTS.

Results and Critical View

To demonstrate an application of the proposed integrated simulation platform in PV penetration assessment, we present some results of simulating the distribution grid behaviour in a real case-study considering a high PV penetration. The area under analysis is a city district with nearly 2200 residential buildings and 43 MV/LV substations. For this purpose, a summer sunny day where there is the maximum production from PV system has been selected to show the maximum level of both self-sufficiency and self-consumption and highlighting possible violation of MV/LV transformers. The simulation process identified 944 suitable areas in the building rooftops for deploying PV systems with a maximum nominal power equal to 14.21 MW. Fig. 2(a) shows the distribution of energy production for each substation in a summer sunny day, which for the whole area is equal to 28.41 MWh. In Fig. 2(b) and Fig. 2(c) the levels of self-sufficiency and self-consumption are reported. Fig. 2(b) shows that the sub-station with less production has less than 10% of self-sufficiency. However, sub-stations 17,18, 23, 24 and 25 have a level of self-sufficiency over 39%. Fig. 2(c) revels that almost every sub-station absorbs all the produced energy from PV systems. Only sub-station 17,18, 23, 24 and 25 feed the MV distribution grid and are the one with the highest level of self-sufficiency. Looking at the transformer capacity map (Fig. 2(d)), integrating PV generation would cause violations in substation 17 and 25. The maximum net consumption in these two sub-stations exceeds the transformers capacity due to an high amount of PV generation and low consumption. This highlights that in cases where PV generation is higher than local demand the installation of PV systems should reduced or grid infrastructures, in terms of transformers (and also cables/lines), should be enhanced to tolerate reverse power injection from substations to the grid.

One of the other challenges due to high penetration of PV generation relates the voltage control: in our case study, we observed that in two feeders which are derived from the same transformer, local generations with respect to local consumptions are not equally/closely distributed. Substations 22 and 24 with high self-sufficiency are connected to one feeder, and substations 7 and 9 with lower self-sufficiency are connected to the other feeder. According to the voltage profiles of these sub-stations (Fig. 3), any changes in the level of transformer voltage to correct over/under voltage in one feeder would result in more deviation in the other feeder.

Conclusions and Future Work

In this work PVinGRID a simulation infrastructure for evaluating PV integration into smart grid has been presented. In addition, motivations and relationships of the research with smart system has been highlighted. The results show how PVinGRID can be used for planning the deployment of PV systems and to evaluate the impact of such deployment in distribution grids. As future work a service for economic evaluation will be developed and included in the proposed solution. In particular, the simulation infrastructure will be integrated with a market simulator for evaluating dynamic prices and Demand Side Management policies. Furthermore the overall infrastructure will be used for evaluating Smart Energy actions as storage management, Demand Response events, Automatic Voltage Regulation, Network Reconfiguration and Load Balancing.

Fig. 1 .

 1 Fig.1. PVinGRID infrastructure

Fig. 2 .Fig. 3 .

 23 Fig.2. Mapped simulation results

Fowler, M.: Microservices (2014), http://martinfowler.com/articles/microservices. html

GRASS GIS, Open Source Geospatial Foundation, http://grass.osgeo.org


Andreia Artífice

Fernando Ferreira

Elsa Marcelino-Jesus

João Sarraipa

Ricardo Jardim-Gonçalves

Student's Attention Improvement Supported by Physiological Measurements Analysis

Keywords: eLearning, Heart Rate Variability, emotional arousal, attention

The focus of the most recent theories of emotional state analysis is the Autonomic Nervous System. Those theories propose that sympathetic and parasympathetic nervous systems interact antagonistically accordingly to each emotional state implying variations of interbeat intervals of consecutive heart beats. Emotional arousal and attention can be inferred based on the electrocardiogram (ECG) specifically through Heart Rate Variability (HRV) analysis, including the Low Frequency (LF), High Frequency (HF), and ratio LF/HF. The aim of this study is to analyze the impact of classic background music, in students' emotional arousal and attention, and performance in the context of e-Learning training courses. As a result, it is foreseen the development of a system integrating wearables to smoothly gather the mentioned biosignals, which will be able to sense user's emotions to further automatically propose recommendations for better learning approaches and contents, aiming student's attention improvement.

Introduction

The present research work analyses the role of classic background music in the students' attention, emotional arousal, and performance in the e-Learning environment. The proposed approach is based on ECG readings with the aim of feeding a smart system able to perform analysis and propose recommendations for improved learning approaches.

In the context of Internet based teaching, E-learning is becoming an innovative approach for delivering well-designed, learner-centered, interactive methodology. In that context E-learning is providing a learning environment to anyone, anyplace, anytime by utilizing the attributes and resources of various digital technologies, and for that it is growing its importance in the market [START_REF] Khan | Managing E-Learning: Design, Delivery, Implementation and Evaluation[END_REF]. The near future of E-learning will, most probably, be supported by the integration of sensing, diagnose, describe and qualify a given situation. Such exercises will be related with the specification of its related Smart Systems. The aimed characteristic of that Smart Systems would be the potential to sense using biosensors able to measure various physiological parameters such as heart rate (HR), galvanic skin response (GSR), electroencephalogram (EEG) and electrocardiogram (ECG) [START_REF] Kaufmann | ARTiiFACT: a tool for heart rate artifact processing and heart rate variability analysis[END_REF][START_REF] Appelhans | Heart Rate Variability as an Index of Regulated Emotional Responding[END_REF].

The hereby-presented research work follows the traditional research method and encloses the following research question: Does eLearning training courses with classic background music influence emotional states and will it have an impact in attention and performance?

Concerning the proposed research question, aforementioned, we argue, by hypothesis that if we define a prototype, able to conduct analysis of arousal and attention based on biosignals, then the process to determine the emotional arousal and attention of learning students could be seamlessly executed. Such development would be able to proof the usefulness of background music in eLearning materials.

This paper is organized as follows: next section is dedicated to Smart Systems and e-Learning. The state of the art, concerning background music and Emotional State and Attention detection using Electrocardiogram is presented in section 3, followed by the experiment in section 4. Section 5 reveals the results and finally section 6 presents the conclusions of the study.

Smart Systems and e-Learning

Smart systems are understood as systems able to sense, diagnose, describe and qualify a given situation as well as be capable to mutually address and identify each other. They are also able to interface, interact and communicate with their environment and with other smart systems [START_REF]European Commission ICT Work Programme[END_REF]. Sensors and actuators are part of the Smart Systems Ecosystem that will become, most of the times, its central component. Different approaches have been proposed in the context of E-learning, among those we highlight the development of systems based on context and emotional sensing.

Zarrad et al. [START_REF] Zarrad | Building a dynamic context aware approach for smart e-learning system[END_REF] propose a smart system with an architectural approach in which modalities are selected dynamically based on the operating context. The system considers context from different features including lighting, noise level, network connectivity, communication costs, communication bandwidth, and the social and medical situations.

Emotions have an important role in learning, and are significantly related with students' motivation, learning strategies, cognitive resources, self-regulation and academic achievements [START_REF] Pekrun | Academic Emotions in Students' Self-Regulated Learning and Achievement: A Program of Qualitative and Quantitative Research[END_REF]. Several contributions were developed based on the identification of a user's emotional state with the goal of improving the eLearning experience. The Framework for Improving Learning Through Webcams And Microphones (FILTWAM) [START_REF] Bahreini | Towards multimodal emotion recognition in e-learning environments[END_REF] was developed with the aim of interpreting the emotional state of users in an e-learning environment by using webcams and microphones. This study focused only on facial mood detection and presents a structured framework composed by the following layers: learner, device, application and data with the most complex application layer on top of it and including an affective computing tool. In that same direction, Santos et al. [START_REF] Santos | Toward interactive context-aware affective educational recommendations in computer-assisted language learning[END_REF] conducted a research in computer assisted language learning, using an Arduino based infrastructure that could sense the learners' affective state and deliver interactive recommendations. For that it was developed the Ambient Intelligence Context-aware Affective Recommender Platform (AICARP), a platform similar to the one that will be developed in the context of this study. A particularity of the platform is the sensor module that receives biosignals from the learner and information from the environment and a recommendation module that takes advantage of the intelligence of the environment using internal and/or external actuators to offer interactive personalized support to the learner. This overview brings the relevant smart systems related to the work developed in the scope of the proposed research question. In particular we look to design a smart system that analyses data from bodily-connected sensors, analyses such data and by reasoning over selected algorithms identifies a person's emotional state. Such systems, while connected with other similar smart systems, can produce markers that give indications of common properties of different people while listening to music and performing a learning task (e.g. attending an Elearning course).

State of the Art

Background Music

The "Mozart effect", i.e. the scientific finding that listening to Mozart's piano sonata enhanced spatial reasoning [START_REF] Steele | The mystery of the Mozart effect: failure to replicate[END_REF], was supported by McCuntheon [START_REF] Mccuntheon | Another failuree to generalize the Mozart effect[END_REF], while other studies found no support [START_REF] Ransdell | The effects of background music on word processed writing[END_REF][START_REF] Kallinen | Reading news from a pocket computer in a distracting environment: effects of the tempo of background music[END_REF]. Despite that, the study conducted to new research and subsequent hypothesis in the field. A study conducted by Thompson et al. [START_REF] Thompson | Arousal, mood and the Mozart Effect[END_REF] suggested that the "Mozart effect" performs a sort of manipulation of arousal and mood that conducted to enhanced performance. According to Day et al. [START_REF] Wilson | Reexamination of the effect of Mozart's music on spacial-task performance[END_REF], the effect of background music is rather specific and thus, needs to be tested on a selected target situation [START_REF] Wilson | Reexamination of the effect of Mozart's music on spacial-task performance[END_REF].

Concerning computer based environments, it was visible that, when reading news from a pocket computer in a distracting environment, the reading rate and efficiency was improved in the presence of fast music than slow music [START_REF] Zarrad | Building a dynamic context aware approach for smart e-learning system[END_REF]. A study by Thompson et al. [13] revealed that background music disrupted the ability of writing fluently.

In order to study the effect of background music in virtual environments for learning Richards et al. [START_REF] Richards | An investigation of the role of background music in IVWs for learning[END_REF], it was created a specific environment for a history lesson. Results revealed that listening to the soundtrack of 'Oblivion' conducted to an improvement in the number of facts remembered by the participants.

It is important to notice that the majority of the reported findings about background music are not representative of computer-based environments. Thus, considering the actual impact of technology in daily life it is crucial to continue progressing on that specific research field.

Emotional State and Attention Detection Using Electrocardiogram

Physiological measurements, along with face recognition, or other measurements (e.g. posture and body movements) are the indirect clues about a person's emotional states since those cannot be assessed with some sort of direct measurements. Because of that, biosignals, such as electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), galvanic skin response (GSR), blood volume pressure (BVP), heart rate (HR) or heart rate variability (HRV), temperature (T), respiration rate (RR), become the most important vital signals of the human body for an emotional assessment. Although the majority of the studies use different biosignals in conjunction with ECG in order to detect the emotional states [START_REF] Kim | Emotion recognition based on physiological changes in music listening[END_REF][START_REF] Schaaff | Measuring Emotional Arousal for Online Applications: Evaluation of Ultra-Short Term Heart Rate Variability Measures[END_REF][START_REF] Kreibig | Autonomic nervous system activity in emotion: A review[END_REF], there are studies focused only in relating ECG with the expression of human emotions [START_REF] Kater | Towards Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach[END_REF][START_REF] Appelhans | Heart Rate Variability as an Index of Regulated Emotional Responding[END_REF].

Emotions and Autonomic Nervous System (ANS) are interrelated. ANS activity is viewed as the major component of the emotions in many recent theories about emotional assessment [START_REF] Mccorry | Physiology of the autonomic nervous system[END_REF]. ANS is composed by the sympathetic and parasympathetic nervous system components [START_REF] Mccorry | Physiology of the autonomic nervous system[END_REF]. The sympathetic nervous system is stimulated by changes in external environment, reacting mostly to any situation perceived as an emergency [START_REF] Mccorry | Physiology of the autonomic nervous system[END_REF]. The primary function of parasympathetic nervous system is for example to coordinate activities related to the restoration and conservation of the body [START_REF] Freeman | Autonomic nervous system interaction with the cardiovascular system during exercise[END_REF]. Both systems interact antagonistically causing the variations in interbeat intervals of consecutive heart beats [START_REF] Appelhans | Heart rate variability as an index of regulated emotional responding[END_REF]. Parasympathetic nervous system is more active in calm periods decreasing heart rate (HR) [START_REF] Schaaff | Measuring Emotional Arousal for Online Applications: Evaluation of Ultra-Short Term Heart Rate Variability Measures[END_REF]23]. However sympathetic activity is predominant when arousal increases conducting to the acceleration of HR [START_REF] Schaaff | Measuring Emotional Arousal for Online Applications: Evaluation of Ultra-Short Term Heart Rate Variability Measures[END_REF]23].

HRV is a measure of the continuous interplay between sympathetic and parasympathetic influences on heart rate that yields information about autonomic flexibility and thereby represents the capacity for regulated emotional response [START_REF] Appelhans | Heart rate variability as an index of regulated emotional responding[END_REF].

Several HRV parameters can be measured resulting from time domain analysis or frequency domain analysis. Among those parameter is the ratio between LF/HF, which is known as the active indicator of the sympathetic nervous system activity. High values of LF/HF correspond to the predominance of sympathetic activity and thus high arousal in opposition to parasympathetic activity for which the ratio LF/HF is lower, corresponding to a low arousal [START_REF] Fruhata | Doze driving prevention system by low frequency stimulation and high density oxygen with fragrance of GF (Grape Fruit)[END_REF].

Progressing further towards the relevance of such parameters for the current study, it is important to value the physiological signals from ECG as a good source to analyze and predict the lack or presence of cognitive attention in individuals during task execution [START_REF] Appelhans | Heart rate variability as an index of regulated emotional responding[END_REF] that can achieve similar performance than EEG. Scientific evidence shows that there is a close relation between variability in heart rate and attention in adults [START_REF] Porges | Respiratory an heart rate components of attention[END_REF], infants [START_REF] Richards | Heart rate variability during attention phases in young infants[END_REF] and children [START_REF] Porges | Heart rate indices of newborn attentional responsivity[END_REF]. It also has been shown that during tasks that require attention, HF and LF parameters of HRV have a decrease [START_REF] Tripathi | Attentional modulation of heart rate variability (HRV) during execution of PC based cognitive tasks[END_REF].

Experiment

The proposed experiment was designed to study emotional arousal and attention in the context of eLearning and was prepared as follows. Ten individuals where enrolled in the study (1 female and 9 males). Their ages were between 32 and 47. The participants have different educational backgrounds, all completed at least high school, and none was employed in the aquaculture business. This fact was not considered relevant since the aim was not the course contents but it was considered of relevance as they would be similar to an aquaculture candidate willing to have training in such matters.

The experiment was conducted in the scope of the AquaSmart project [29], which has the focus on data mining and big data technologies applied to the aquaculture sector. AquaSmart helps companies to transform raw data into knowledge via accurate business-driven analytical models in a seamless and efficient process. The AquaSmart project has a component of training and that leads to the relevance of such experiment in the scope of the project's training framework.

The materials for the execution of the experiment were adopted from AquaSmart training program and adapted for the hereby described experimental setup. The study comprises two e-learning courses: a) Introduction to AquaSmart (course 1); b) History of aquaculture (course 2). The second course was delivered with an extract from the beginning of the first part of Bach´s Brandenburg Concerto Nº 2 as background music, which was rated as expressing happiness [START_REF] Kallinen | Emotional ratings of music excerpts in the western art music repertoire and their self-organization in the Kohonen neural network[END_REF]. Both courses consisted of a learning component composed by 4 slides followed by a test with 5 questions about the learned materials. Previous studies already evaluated the impact of music in a person's mood, in this case it is aimed a step further in evaluating music's impact on attention [START_REF] Gião | Profiling Based on Music and Physiological State[END_REF].

During the sessions ECG measurements were executed using a Olimex SHIELD-EKG-EMG. In the end of the session a questionnaire was performed to quantify: a) the attention of the participants in each training course; b) the likability of each course; c) the difficulty of each course.

The sample was divided into two groups (Figure 1): a) a group that performed the training course 1 with no music followed by the training course 2 with music in an automatic form, i.e., with a pre-determined time interval between slides; b) a group that performed the training course 1 with no music followed by the training course 2 with music in an interactive form, i.e., requiring by the user to press the "Next" button of the interface to move to the next slide. The participants were informed about the procedures and while agreeing each signed a consent form. The procedural execution started by installing clinical electrodes to his/her left arm, right arm and right leg. Heart rate was measured 30 seconds before the start of each e-learning training course for baseline purposes and data was collected during the attendance to the e-learning courses. After the session, each subject rated the level of difficulty, attention and motivation towards each training course on a seven-point scale instantiated from "very little" (1) to "very much" [START_REF] Bahreini | Towards multimodal emotion recognition in e-learning environments[END_REF].

Heart Rate Variability was measured, according to specification, as beat to beat variation in the heart rate (R-R intervals) of ECG. Such measurement quantifies the interplay between sympathetic and parasympathetic system of ANS, and it was acquired using Artifact software [START_REF] Kaufmann | ARTiiFACT: a tool for heart rate artifact processing and heart rate variability analysis[END_REF]. Special emphasis was given to LF and HF of HRV, related with attention, and the ratio LF/HF indicator of arousal (more details in Section 3.2) that were measured while resting and along the proposed learning tasks.

Results

As an example, the results of participant 1 (P1), concerning training course "AquaSmart" (course 1) with no music, revealed that LF/HF ratio increased during learning phase, as expected. Since LF and HF values, calculated in milliseconds squared, decreased; it can support that such participant was more attentive during the learning stage (Table 1). The performance achieved during the test phase for training course 1 in an automatic form was 80%.

The next tables present LF and HF measurements for Course 1 (no music) and Course 2 (with music) respectively for the same person designed as P1. Participant P1 commented, after performing training course "History of Aquaculture" (course 2) with music, that the volume was excessively high and that he usually does not appreciate history related contents. Those remarks could explain the reason why HRV analysis revealed a decrease in the ratio LF/HF during learning phase (Table 2), i.e., the low level for emotional arousal indicator meaning that he didn't like the contents of the course. Parameters related with attention (LF and HF absolute values) decreased (table 2) revealing a high level of attention while comparing to the other course stages. The performance achieved by this participant during test phase in course 2, in an automatic form was 60%.

Additionally, from the collected information of all questionnaires participants it was concluded that some of them like to study with music and others not. From the data collected using the ECG, it was possible to infer the same. This means that music helps in the learning and test phases only of a part of the participants. Such conclusion is in line to the study made by of Gião et al. in [START_REF] Gião | Profiling Based on Music and Physiological State[END_REF], which says that a same music can result in different emotional states to each person. This is also in line to the standard deviation of the graph of Figure 2, where with music (Figure 2 -right graph) it has a σ= 4.67 and without music (Figure 2 -left graph) it has a σ= 2.48. This means that there is more variability of values in the first case (course with music) representing its tendency for amplified results, which implies also an amplified arousal (emotional intensity). That means that people feel the music reacting accordingly, independently if in a positive or negative way. In conclusion could be said that despite being in a positive or negative way, classic background music enhances intensity of emotion in eLearning experiences. When analyzed the engagement of students in the courses with music in relation to the one without can be verified that a higher commitment attention it is obtained in courses with music. That can be verified in Figure 3 by lower average LF and HF values in absolute terms in courses with music. Figure 4 presents the average LF and HF values of all the students, from which could be inferred that students were more attentive or engaged in the automatic course. This means, that since they had not the possibility of going back or forward to see the contents again. It indicates that in order do not fail in the test they tried to be more attentive to the contents. This is explained by the reducing in time of the LF and HF absolute values. The graph in the left of Figure 4 shows that in relation to the "automatic" course. On the other hand, the graph in the right part of Figure 4 shows the opposite.

Conclusions and Future Work

The use of wearables able to obtain LF and HF values from a ECG potentiates the creation of smart systems. This can be applied to the area of education, which will allow the system to sense the students' physiological signals enabling the possibility of inferring particular situations about the level of the attention of the student.

Through this kind of systems could be implemented specific recommendation services that will suggest particular contents topics to recover the students attention.

A smart system prototype able to conduct analysis of arousal and attention based on biosignals using wearables to measure ECG is proposed with incorporation of the analyzed HRV parameters. Thus, the process to determine the emotional arousal and attention of learning students can be facilitated and recommendations, such as a pause in the study, adjust the volume, presence or absence of the music could be suggested by the smart system.

As future work would be interesting to apply the study to a numerous population in order to have more reliable results since interesting results were found in the presented experimental setup.

Fig. 1 .

 1 Fig. 1. Experimental design.

Fig. 2 .

 2 Fig. 2. At Left) Average LF/HF ratio for course 1 with no music presented in both automatic and interactive way; At Right) Average LF/HF ratio for course 2 with music presented also in both ways.

Fig. 3 .

 3 Fig. 3. At Left) Average LF(ms 2 ) and HF (ms 2 ) for course 1 with no music; At Right) Average LF (ms 2 ) and HF (ms 2 ) for course 2 with music.

Fig. 4 .

 4 Fig. 4. At left) Average LF (ms 2 ) and average HF (ms 2 ), for courses presented in an automatic form; At right) Average HF (ms 2 ) and average LF (ms 2 ) for courses presented in an interactive form.

Table 1 .

 1 Training Course 1 with no Music for P1.

		Resting	Learning	Test
	LF (ms 2 )	8074.65	4899.53	1453.03
	HF (ms 2 )	6336.96	2479.01	4787.75
	LF/HF	1.27	1.98	0.95

Table 2 .

 2 Training Course 2 with music for P1.

		Resting	Learning	Test
	LF (ms 2 )	7377.99	2868.48	2996.58
	HF (ms 2 )	4995.74	2761.50	2341.79
	LF/HF	1.48	1.04	1.28

Acknowledgements. The authors acknowledge the European Commission for its support and partial funding and the partners of the research projects from ERASMUS+: Higher Education -International Capacity Building -ACACIA -Project reference number -561754-EPP-1-2015-1-CO-EPKA2-CBHE-JP, (http://acacia.digital); and Horizon2020 -AquaSmart -Aquaculture Smart and Open Data Analytics as a Service, project number -644715, (http:// www.aquasmartdata.eu/).