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	diff --git a/apps/ccam/ccam.cpp b/apps/ccam/ccam.cpp
	index 36f5bee..0679f54 100644
	--- a/apps/ccam/ccam.cpp
	+++ b/apps/ccam/ccam.cpp
	@@ -1,532 +1,536 @@
	//===-- apps/ccam/ccam.cpp --------------------------------------- C++ --===//
	//
	// The RoSA Framework -- Application CCAM
	//
	// Distributed under the terms and conditions of the Boost Software
	/// License 1.0.
	// See accompanying file LICENSE.
	//
	// If you did not receive a copy of the license file, see
	// http://www.boost.org/LICENSE_1_0.txt.
	//
	//===----------------------------------------------------------------------===//
	///
	/// \file apps/ccam/ccam.cpp
	///
	/// \author Maximilian Goetzinger (maximilian.goetzinger@tuwien.ac.at)
	/// \author Benedikt Tutzer (benedikt.tutzer@tuwien.ac.at)
	///
	/// \date 2019
	///
	/// \brief The application CCAM implements the case study from the paper:
	/// M. Goetzinger, N. TaheriNejad, H. A. Kholerdi, A. Jantsch, E. Willegger,
	/// T. Glatzl, A.M. Rahmani, T.Sauter, P. Liljeberg: Model - Free Condition
	/// Monitoring with Confidence
	///
	/// \todo Clean up source files of this app: add standard RoSA header comment
	/// for own files and do something with 3rd party files...
	//===----------------------------------------------------------------------===//
	#include "rosa/agent/Abstraction.hpp"
	#include "rosa/agent/Confidence.hpp"
	#include "rosa/agent/FunctionAbstractions.hpp"
	#include <iostream>

	#include "rosa/config/version.h"

	#include "rosa/agent/SignalStateDetector.hpp"
	#include "rosa/agent/SystemStateDetector.hpp"
	#include "rosa/app/Application.hpp"

	#include "rosa/support/csv/CSVReader.hpp"
	#include "rosa/support/csv/CSVWriter.hpp"

	#include "rosa/app/AppTuple.hpp"
	#include <fstream>
	#include <limits>
	#include <memory>
	#include <streambuf>

	#include "configuration.h"
	#include "statehandlerutils.h"

	using namespace rosa;
	using namespace rosa::agent;
	using namespace rosa::app;
	using namespace rosa::terminal;

	+float GLOBAL_OUTERBOUND = 0.1;
	+
	+
	const std::string AppName = "CCAM";

	int main(int argc, char **argv) {

	LOG_INFO_STREAM << '\n'
	<< library_string() << " -- " << Color::Red << AppName
	<< "app" << Color::Default << '\n';

	//
	// Read the filepath of the config file of the observed system. The filepath
	// is in the first argument passed to the application. Fuzzy functions etc.
	// are described in this file.
	//
	if (argc < 2) {
	LOG_ERROR("Specify config File!\nUsage:\n\tccam config.json");
	return 1;
	}
	std::string ConfigPath = argv[1];

	//
	// Load config file and read in all parameters. Fuzzy functions etc. are
	// described in this file.
	//
	if (!readConfigFile(ConfigPath)) {
	LOG_ERROR_STREAM << "Could not read config from \"" << ConfigPath << "\"\n";
	return 2;
	}

	+ GLOBAL_OUTERBOUND = AppConfig.SignalConfigurations[0].OuterBound;
	//
	// Create a CCAM context.
	//
	LOG_INFO("Creating Context");
	std::unique_ptr<Application> AppCCAM = Application::create(AppName);

	//
	// Create following function which shall give information if the time gap
	// between changed input(s) and changed output(s) shows already a malfunction
	// of the system.
	//
	// ____________
	// /
	// /
	// __________/
	//
	std::shared_ptr<PartialFunction<uint32_t, float>> BrokenDelayFunction(
	new PartialFunction<uint32_t, float>(
	{{{0, AppConfig.BrokenCounter},
	std::make_shared<LinearFunction<uint32_t, float>>(
	0, 0.f, AppConfig.BrokenCounter, 1.f)},
	{{AppConfig.BrokenCounter, std::numeric_limits<uint32_t>::max()},
	std::make_shared<LinearFunction<uint32_t, float>>(1.f, 0.f)}},
	0.f));

	//
	// Create following function which shall give information if the time gap
	// between changed input(s) and changed output(s) still shows a
	// well-functioning system.
	//
	// ____________
	// \
	// \
	// \__________
	//
	std::shared_ptr<PartialFunction<uint32_t, float>> OkDelayFunction(
	new PartialFunction<uint32_t, float>(
	{{{0, AppConfig.BrokenCounter},
	std::make_shared<LinearFunction<uint32_t, float>>(
	0, 1.f, AppConfig.BrokenCounter, 0.f)},
	{{AppConfig.BrokenCounter, std::numeric_limits<uint32_t>::max()},
	std::make_shared<LinearFunction<uint32_t, float>>(0.f, 0.f)}},
	1.f));

	//
	// Create a AppAgent with SystemStateDetector functionality.
	//
	LOG_INFO("Create SystemStateDetector agent.");
	AgentHandle SystemStateDetectorAgent = createSystemStateDetectorAgent(
	AppCCAM, "SystemStateDetector", AppConfig.SignalConfigurations.size(),
	BrokenDelayFunction, OkDelayFunction);

	//
	// Set policy of SystemStateDetectorAgent that it wait for all
	// SignalStateDetectorAgents
	//
	std::set<size_t> pos;
	for (size_t i = 0; i < AppConfig.SignalConfigurations.size(); ++i)
	pos.insert(pos.end(), i);
	AppCCAM->setExecutionPolicy(SystemStateDetectorAgent,
	AppExecutionPolicy::awaitAll(pos));

	//
	// Create Vectors for all sensors, all signal related fuzzy functions, all
	// signal state detectors, all signal state agents, and all input data files.
	//
	LOG_INFO("Creating sensors, SignalStateDetector functionalities and their "
	"Abstractions.");
	std::vector<AgentHandle> Sensors;
	std::vector<std::shared_ptr<PartialFunction<float, float>>>
	SampleMatchesFunctions;
	std::vector<std::shared_ptr<PartialFunction<float, float>>>
	SampleMismatchesFunctions;
	std::vector<std::shared_ptr<PartialFunction<float, float>>>
	SignalIsStableFunctions;
	std::vector<std::shared_ptr<PartialFunction<float, float>>>
	SignalIsDriftingFunctions;
	std::vector<std::shared_ptr<StepFunction<float, float>>>
	NumOfSamplesMatchFunctions;
	std::vector<std::shared_ptr<StepFunction<float, float>>>
	NumOfSamplesMismatchFunctions;
	std::vector<std::shared_ptr<PartialFunction<float, float>>>
	SampleValidFunctions;
	std::vector<std::shared_ptr<PartialFunction<float, float>>>
	SampleInvalidFunctions;
	std::vector<std::shared_ptr<StepFunction<float, float>>>
	NumOfSamplesValidFunctions;
	std::vector<std::shared_ptr<StepFunction<float, float>>>
	NumOfSamplesInvalidFunctions;
	std::vector<std::shared_ptr<
	SignalStateDetector<float, float, float, HistoryPolicy::FIFO>>>
	SignalStateDetectors;
	std::vector<AgentHandle> SignalStateDetectorAgents;
	std::vector<std::ifstream> DataFiles;

	//
	// Go through all signal state configurations (number of signals), and create
	// functionalities for SignalStateDetector.
	//
	for (auto SignalConfiguration : AppConfig.SignalConfigurations) {

	//
	// Create application sensors.
	//
	Sensors.emplace_back(
	AppCCAM->createSensor<float>(SignalConfiguration.Name + "_Sensor"));

	//
	// Create following function(s) which shall give information whether one
	// sample matches another one (based on the relative distance between them).
	//
	// ____________
	// / \
	// / \
	// __________/ \__________
	//
	//
	SampleMatchesFunctions.emplace_back(new PartialFunction<float, float>(
	{
	{{-SignalConfiguration.OuterBound, -SignalConfiguration.InnerBound},
	std::make_shared<LinearFunction<float, float>>(
	-SignalConfiguration.OuterBound, 0.f,
	-SignalConfiguration.InnerBound, 1.f)},
	{{-SignalConfiguration.InnerBound, SignalConfiguration.InnerBound},
	std::make_shared<LinearFunction<float, float>>(1.f, 0.f)},
	{{SignalConfiguration.InnerBound, SignalConfiguration.OuterBound},
	std::make_shared<LinearFunction<float, float>>(
	SignalConfiguration.InnerBound, 1.f,
	SignalConfiguration.OuterBound, 0.f)},
	},
	0));

	//
	// Create following function(s) which shall give information whether one
	// sample mismatches another one (based on the relative distance between
	// them).
	//
	// ____________ ____________
	// \ /
	// \ /
	// \__________/
	//
	//
	SampleMismatchesFunctions.emplace_back(new PartialFunction<float, float>(
	{
	{{-SignalConfiguration.OuterBound, -SignalConfiguration.InnerBound},
	std::make_shared<LinearFunction<float, float>>(
	-SignalConfiguration.OuterBound, 1.f,
	-SignalConfiguration.InnerBound, 0.f)},
	{{-SignalConfiguration.InnerBound, SignalConfiguration.InnerBound},
	std::make_shared<LinearFunction<float, float>>(0.f, 0.f)},
	{{SignalConfiguration.InnerBound, SignalConfiguration.OuterBound},
	std::make_shared<LinearFunction<float, float>>(
	SignalConfiguration.InnerBound, 0.f,
	SignalConfiguration.OuterBound, 1.f)},
	},
	1));

	//
	// Create following function(s) which shall give information whether a
	// signal is stable.
	//
	// ____________
	// / \
	// / \
	// __________/ \__________
	//
	//
	SignalIsStableFunctions.emplace_back(new PartialFunction<float, float>(
	{
	{{-SignalConfiguration.OuterBoundDrift,
	-SignalConfiguration.InnerBoundDrift},
	std::make_shared<LinearFunction<float, float>>(
	-SignalConfiguration.OuterBoundDrift, 0.f,
	-SignalConfiguration.InnerBoundDrift, 1.f)},
	{{-SignalConfiguration.InnerBoundDrift,
	SignalConfiguration.InnerBoundDrift},
	std::make_shared<LinearFunction<float, float>>(1.f, 0.f)},
	{{SignalConfiguration.InnerBoundDrift,
	SignalConfiguration.OuterBoundDrift},
	std::make_shared<LinearFunction<float, float>>(
	SignalConfiguration.InnerBoundDrift, 1.f,
	SignalConfiguration.OuterBoundDrift, 0.f)},
	},
	0));

	//
	// Create following function(s) which shall give information whether a
	// signal is drifting.
	//
	// ____________ ____________
	// \ /
	// \ /
	// \__________/
	//
	//
	SignalIsDriftingFunctions.emplace_back(new PartialFunction<float, float>(
	{
	{{-SignalConfiguration.OuterBoundDrift,
	-SignalConfiguration.InnerBoundDrift},
	std::make_shared<LinearFunction<float, float>>(
	-SignalConfiguration.OuterBoundDrift, 1.f,
	-SignalConfiguration.InnerBoundDrift, 0.f)},
	{{-SignalConfiguration.InnerBoundDrift,
	SignalConfiguration.InnerBoundDrift},
	std::make_shared<LinearFunction<float, float>>(0.f, 0.f)},
	{{SignalConfiguration.InnerBoundDrift,
	SignalConfiguration.OuterBoundDrift},
	std::make_shared<LinearFunction<float, float>>(
	SignalConfiguration.InnerBoundDrift, 0.f,
	SignalConfiguration.OuterBoundDrift, 1.f)},
	},
	1));

	//
	// Create following function(s) which shall give information how many
	// history samples match another sample.
	//
	// ____________
	// /
	// /
	// __________/
	//
	NumOfSamplesMatchFunctions.emplace_back(new StepFunction<float, float>(
	1.0f / SignalConfiguration.SampleHistorySize, StepDirection::StepUp));

	//
	// Create following function(s) which shall give information how many
	// history samples mismatch another sample.
	//
	// ____________
	// \
	// \
	// \__________
	//
	NumOfSamplesMismatchFunctions.emplace_back(new StepFunction<float, float>(
	1.0f / SignalConfiguration.SampleHistorySize, StepDirection::StepDown));

	//
	// Create following function(s) which shall give information how good all
	// samples in a state match each other.
	//
	// ____________
	// / \
	// / \
	// __________/ \__________
	//
	//
	SampleValidFunctions.emplace_back(new PartialFunction<float, float>(
	{
	{{-SignalConfiguration.OuterBound, -SignalConfiguration.InnerBound},
	std::make_shared<LinearFunction<float, float>>(
	-SignalConfiguration.OuterBound, 0.f,
	-SignalConfiguration.InnerBound, 1.f)},
	{{-SignalConfiguration.InnerBound, SignalConfiguration.InnerBound},
	std::make_shared<LinearFunction<float, float>>(1.f, 0.f)},
	{{SignalConfiguration.InnerBound, SignalConfiguration.OuterBound},
	std::make_shared<LinearFunction<float, float>>(
	SignalConfiguration.InnerBound, 1.f,
	SignalConfiguration.OuterBound, 0.f)},
	},
	0));

	//
	// Create following function(s) which shall give information how good all
	// samples in a state mismatch each other.
	//
	// ____________ ____________
	// \ /
	// \ /
	// \__________/
	//
	//
	SampleInvalidFunctions.emplace_back(new PartialFunction<float, float>(
	{
	{{-SignalConfiguration.OuterBound, -SignalConfiguration.InnerBound},
	std::make_shared<LinearFunction<float, float>>(
	-SignalConfiguration.OuterBound, 1.f,
	-SignalConfiguration.InnerBound, 0.f)},
	{{-SignalConfiguration.InnerBound, SignalConfiguration.InnerBound},
	std::make_shared<LinearFunction<float, float>>(0.f, 0.f)},
	{{SignalConfiguration.InnerBound, SignalConfiguration.OuterBound},
	std::make_shared<LinearFunction<float, float>>(
	SignalConfiguration.InnerBound, 0.f,
	SignalConfiguration.OuterBound, 1.f)},
	},
	1));

	//
	// Create following function(s) which shall give information how many
	// history samples match each other.
	//
	// ____________
	// /
	// /
	// __________/
	//
	NumOfSamplesValidFunctions.emplace_back(new StepFunction<float, float>(
	1.0f / SignalConfiguration.SampleHistorySize, StepDirection::StepUp));

	//
	// Create following function(s) which shall give information how many
	// history samples mismatch each other.
	//
	// ____________
	// \
	// \
	// \__________
	//
	NumOfSamplesInvalidFunctions.emplace_back(new StepFunction<float, float>(
	1.0f / SignalConfiguration.SampleHistorySize, StepDirection::StepDown));

	//
	// Create SignalStateDetector functionality
	//
	SignalStateDetectors.emplace_back(
	new SignalStateDetector<float, float, float, HistoryPolicy::FIFO>(
	SignalConfiguration.Output ? SignalProperties::OUTPUT
	: SignalProperties::INPUT,
	std::numeric_limits<int>::max(), SampleMatchesFunctions.back(),
	SampleMismatchesFunctions.back(), NumOfSamplesMatchFunctions.back(),
	NumOfSamplesMismatchFunctions.back(), SampleValidFunctions.back(),
	SampleInvalidFunctions.back(), NumOfSamplesValidFunctions.back(),
	NumOfSamplesInvalidFunctions.back(),
	SignalIsDriftingFunctions.back(), SignalIsStableFunctions.back(),
	SignalConfiguration.SampleHistorySize, SignalConfiguration.DABSize,
	SignalConfiguration.DABHistorySize));

	//
	// Create low-level application agents
	//
	SignalStateDetectorAgents.push_back(createSignalStateDetectorAgent(
	AppCCAM, SignalConfiguration.Name, SignalStateDetectors.back()));

	AppCCAM->setExecutionPolicy(
	SignalStateDetectorAgents.back(),
	AppExecutionPolicy::decimation(AppConfig.DownsamplingRate));
	//
	// Connect sensors to low-level agents.
	//
	LOG_INFO("Connect sensors to their corresponding low-level agents.");

	AppCCAM->connectSensor(SignalStateDetectorAgents.back(), 0, Sensors.back(),
	SignalConfiguration.Name + "_Sensor ->" +
	SignalConfiguration.Name +
	"_SignalStateDetector_Agent-Channel");

	AppCCAM->connectAgents(
	SystemStateDetectorAgent, SignalStateDetectors.size() - 1,
	SignalStateDetectorAgents.back(),
	SignalConfiguration.Name +
	"_SignalStateDetector_Agent->SystemStateDetector_Agent_Channel");
	}

	//
	// For simulation output, create a logger agent writing the output of the
	// high-level agent into a CSV file.
	//
	LOG_INFO("Create a logger agent.");

	// Create CSV writer.
	std::ofstream OutputCSV(AppConfig.OutputFilePath);

	for (auto SignalConfiguration : AppConfig.SignalConfigurations) {
	OutputCSV << SignalConfiguration.Name + ",";
	}
	//OutputCSV << "StateFirstMean,";
	OutputCSV << "StateID,";
	OutputCSV << "Confidence State Valid,";
	OutputCSV << "Confidence State Invalid,";
	OutputCSV << "Confidence Inputs Matching,";
	OutputCSV << "Confidence Outputs Matching,";
	OutputCSV << "Confidence Inputs Mismatching,";
	OutputCSV << "Confidence Outputs Mismatching,";
	OutputCSV << "State Condition,";
	OutputCSV << "Confidence System Functioning,";
	OutputCSV << "Confidence System Malfunctioning,";
	OutputCSV << "Overall Confidence,";
	OutputCSV << "\n";

	// The agent writes each new input value into a CSV file and produces
	// nothing.
	using Input = std::pair<SystemStateTuple, bool>;
	using Result = Optional<AppTuple<unit_t>>;
	using Handler = std::function<Result(Input)>;
	std::string Name = "Logger Agent";

	AgentHandle LoggerAgent = AppCCAM->createAgent(
	"Logger Agent", Handler([&OutputCSV](Input I) -> Result {
	const SystemStateTuple &T = I.first;
	OutputCSV << std::get<0>(
	static_cast<const std::tuple<std::string> &>(T))
	<< std::endl;
	return Result();
	}));
	//
	// Connect the high-level agent to the logger agent.
	//
	LOG_INFO("Connect the high-level agent to the logger agent.");

	AppCCAM->connectAgents(LoggerAgent, 0, SystemStateDetectorAgent,
	"SystemStateDetector Channel");

	//
	// Only log if the SystemStateDetector actually ran
	//
	AppCCAM->setExecutionPolicy(LoggerAgent, AppExecutionPolicy::awaitAll({0}));

	//
	// Do simulation.
	//
	LOG_INFO("Setting up and performing simulation.");

	//
	// Initialize application for simulation.
	//
	AppCCAM->initializeSimulation();

	//
	// Open CSV files and register them for their corresponding sensors.
	//
	// Make sure DataFiles will not change capacity while adding elements to it.
	// Changing capacity moves elements away, which invalidates references
	// captured by CSVIterator.
	DataFiles.reserve(AppConfig.SignalConfigurations.size());
	uint32_t i = 0;
	for (auto SignalConfiguration : AppConfig.SignalConfigurations) {
	DataFiles.emplace_back(SignalConfiguration.InputPath);
	if (!DataFiles.at(i)) {
	LOG_ERROR_STREAM << "Cannot open Input File \""
	<< SignalConfiguration.InputPath << "\" for Signal \""
	<< SignalConfiguration.Name << "\"" << std::endl;
	return 3;
	}

	AppCCAM->registerSensorValues(Sensors.at(i),
	csv::CSVIterator<float>(DataFiles.at(i)),
	csv::CSVIterator<float>());
	i++;
	}

	//
	// Start simulation.
	//
	AppCCAM->simulate(AppConfig.NumberOfSimulationCycles);

	return 0;
	}
	diff --git a/include/rosa/agent/SignalState.hpp b/include/rosa/agent/SignalState.hpp
	index 73967bd..893b0ff 100644
	--- a/include/rosa/agent/SignalState.hpp
	+++ b/include/rosa/agent/SignalState.hpp
	@@ -1,534 +1,540 @@
	//===-- rosa/agent/SignalState.hpp ------------------------------- C++ --===//
	//
	// The RoSA Framework
	//
	//===----------------------------------------------------------------------===//
	///
	/// \file rosa/agent/SignalState.hpp
	///
	/// \author Maximilian Götzinger (maximilian.goetzinger@tuwien.ac.at)
	///
	/// \date 2019
	///
	/// \brief Definition of signal state functionality.
	///
	//===----------------------------------------------------------------------===//

	#ifndef ROSA_AGENT_SIGNALSTATE_HPP
	#define ROSA_AGENT_SIGNALSTATE_HPP

	#include "rosa/agent/FunctionAbstractions.hpp"
	#include "rosa/agent/Functionality.h"
	#include "rosa/agent/History.hpp"
	#include "rosa/agent/State.hpp"

	#include "rosa/support/math.hpp"

	+extern float GLOBAL_OUTERBOUND;
	+
	+#define MOORING_SIZE 10
	+
	namespace rosa {
	namespace agent {

	/// Signal properties defining the properties of the signal which is monitored
	/// by \c rosa::agent::SignalStateDetector and is saved in \c
	/// rosa::agent::SignalStateInformation.
	enum SignalProperties : uint8_t {
	INPUT = 0, ///< The signal is an input signal
	OUTPUT = 1 ///< The signal is an output signal
	};

	/// TODO: write description
	template <typename CONFDATATYPE>
	struct SignalStateInformation : StateInformation<CONFDATATYPE> {
	// Make sure the actual type arguments are matching our expectations.
	STATIC_ASSERT((std::is_arithmetic<CONFDATATYPE>::value),
	"confidence type is not to arithmetic");

	/// ConfidenceOfMatchingState is the confidence how good the new sample
	/// matches the state.
	CONFDATATYPE ConfidenceOfMatchingState;

	/// ConfidenceOfMatchingState is the confidence how bad the new sample
	/// matches the state.
	CONFDATATYPE ConfidenceOfMismatchingState;

	/// The SignalProperty saves whether the monitored signal is an input our
	/// output signal.
	SignalProperties SignalProperty;

	/// The SignalStateIsValid saves the number of samples which have been
	/// inserted into the state after entering it.
	uint32_t NumberOfInsertedSamplesAfterEntrance;

	public:
	SignalStateInformation(unsigned int SignalStateID,
	SignalProperties _SignalProperty) {
	this->StateID = SignalStateID;
	this->SignalProperty = _SignalProperty;
	this->StateCondition = StateConditions::UNKNOWN;
	this->NumberOfInsertedSamplesAfterEntrance = 0;
	this->FirstCnt = 0;
	this->StateFirstMean = 0.0;
	this->StateIsValid = false;
	this->StateJustGotValid = false;
	this->StateIsValidAfterReentrance = false;
	this->ConfidenceStateIsValid = 0;
	this->ConfidenceStateIsInvalid = 0;
	this->ConfidenceStateIsStable = 0;
	this->ConfidenceStateIsDrifting = 0;
	}

	SignalStateInformation() = default;
	};

	/// \tparam INDATATYPE type of input data, \tparam CONFDATATYPE type of
	/// data in that the confidence values are given, \tparam PROCDATATYPE type of
	/// the relative distance and the type of data in which DABs are saved.
	template <typename INDATATYPE, typename CONFDATATYPE, typename PROCDATATYPE>
	class SignalState : public Functionality {

	// Make sure the actual type arguments are matching our expectations.
	STATIC_ASSERT((std::is_arithmetic<INDATATYPE>::value),
	"input data type not arithmetic");
	STATIC_ASSERT((std::is_arithmetic<CONFDATATYPE>::value),
	"confidence data type is not to arithmetic");
	STATIC_ASSERT(
	(std::is_arithmetic<PROCDATATYPE>::value),
	"process data type (DAB and Relative Distance) is not to arithmetic");

	public:
	// For the convinience to write a shorter data type name
	using PartFuncReference = PartialFunction<INDATATYPE, CONFDATATYPE> &;
	using StepFuncReference = StepFunction<INDATATYPE, CONFDATATYPE> &;

	private:
	/// SignalStateInfo is a struct of SignalStateInformation that contains
	/// information about the current signal state.
	SignalStateInformation<CONFDATATYPE> SignalStateInfo;

	/// The FuzzyFunctionSampleMatches is the fuzzy function that gives the
	/// confidence how good the new sample matches another sample in the sample
	/// history.
	PartFuncReference FuzzyFunctionSampleMatches;

	/// The FuzzyFunctionSampleMismatches is the fuzzy function that gives the
	/// confidence how bad the new sample matches another sample in the sample
	/// history.
	PartFuncReference FuzzyFunctionSampleMismatches;

	/// The FuzzyFunctionNumOfSamplesMatches is the fuzzy function that gives the
	/// confidence how many samples from the sampe history match the new sample.
	StepFuncReference FuzzyFunctionNumOfSamplesMatches;

	/// The FuzzyFunctionNumOfSamplesMismatches is the fuzzy function that gives
	/// the confidence how many samples from the sampe history mismatch the new
	/// sample.
	StepFuncReference FuzzyFunctionNumOfSamplesMismatches;

	/// The FuzzyFunctionSampleValid is the fuzzy function that gives the
	/// confidence how good one matches another sample in the sample
	/// history. This is done to evaluate whether a state is valid.
	PartFuncReference FuzzyFunctionSampleValid;

	/// The FuzzyFunctionSampleInvalid is the fuzzy function that gives the
	/// confidence how bad one sample matches another sample in the sample
	/// history. This is done to evaluate whether a state is invalid.
	PartFuncReference FuzzyFunctionSampleInvalid;

	/// The FuzzyFunctionNumOfSamplesValid is the fuzzy function that gives the
	/// confidence how many samples from the sample history match another sample.
	/// This is done to evaluate whether a state is valid.
	StepFuncReference FuzzyFunctionNumOfSamplesValid;

	/// The FuzzyFunctionNumOfSamplesInvalid is the fuzzy function that gives
	/// the confidence how many samples from the sample history mismatch another
	/// sample. This is done to evaluate whether a state is invalid.
	StepFuncReference FuzzyFunctionNumOfSamplesInvalid;

	/// The FuzzyFunctionSignalIsDrifting is the fuzzy function that gives the
	/// confidence how likely it is that the signal (resp. the state of a signal)
	/// is drifting.
	PartFuncReference FuzzyFunctionSignalIsDrifting;

	/// The FuzzyFunctionSignalIsStable is the fuzzy function that gives the
	/// confidence how likely it is that the signal (resp. the state of a signal)
	/// is stable (not drifting).
	PartFuncReference FuzzyFunctionSignalIsStable;

	/// SampleHistory is a history in that the last sample values are stored.
	DynamicLengthHistory<INDATATYPE, HistoryPolicy::FIFO> SampleHistory;
	/// DAB is a (usually) small history of the last sample values of which a
	/// average is calculated if the DAB is full.
	DynamicLengthHistory<INDATATYPE, HistoryPolicy::SRWF> DAB;
	/// DABHistory is a history in that the last DABs (to be exact, the averages
	/// of the last DABs) are stored.
	DynamicLengthHistory<PROCDATATYPE, HistoryPolicy::LIFO> DABHistory;

	/// LowestConfidenceMatchingHistory is a history in that the lowest confidence
	/// for the current sample matches all history samples are saved.
	DynamicLengthHistory<INDATATYPE, HistoryPolicy::FIFO>
	LowestConfidenceMatchingHistory;
	/// HighestConfidenceMatchingHistory is a history in that the highest
	/// confidence for the current sample matches all history samples are saved.
	DynamicLengthHistory<INDATATYPE, HistoryPolicy::FIFO>
	HighestConfidenceMismatchingHistory;

	/// TempConfidenceMatching is the confidence how good a sample matches the
	/// state. However, the value of this variable is only needed temporarly.
	- CONFDATATYPE TempConfidenceMatching = 0;
	+ CONFDATATYPE TempConfidenceMatching = 1;

	/// TempConfidenceMatching is the confidence how bad a sample matches the
	/// state. However, the value of this variable is only needed temporarly.
	CONFDATATYPE TempConfidenceMismatching = 0;

	public:
	/// Creates an instance by setting all parameters
	/// \param SignalStateID The Id of the SignalStateinfo \c
	/// SignalStateInformation.
	///
	/// \param FuzzyFunctionSampleMatches The FuzzyFunctionSampleMatches is the
	/// fuzzy function that gives the confidence how good the new sample matches
	/// another sample in the sample history.
	///
	/// \param FuzzyFunctionSampleMismatches The FuzzyFunctionSampleMismatches is
	/// the fuzzy function that gives the confidence how bad the new sample
	/// matches another sample in the sample history.
	///
	/// \param FuzzyFunctionNumOfSamplesMatches The
	/// FuzzyFunctionNumOfSamplesMatches is the fuzzy function that gives the
	/// confidence how many samples from the sampe history match the new sample.
	///
	/// \param FuzzyFunctionNumOfSamplesMismatches The
	/// FuzzyFunctionNumOfSamplesMismatches is the fuzzy function that gives the
	/// confidence how many samples from the sampe history mismatch the new
	/// sample.
	///
	/// \param FuzzyFunctionSignalIsDrifting The FuzzyFunctionSignalIsDrifting is
	/// the fuzzy function that gives the confidence how likely it is that the
	/// signal (resp. the state of a signal) is drifting.
	///
	/// \param FuzzyFunctionSignalIsStable The FuzzyFunctionSignalIsStable is the
	/// fuzzy function that gives the confidence how likely it is that the signal
	/// (resp. the state of a signal) is stable (not drifting).
	///
	/// \param SampleHistorySize Size of the Sample History \c
	/// DynamicLengthHistory . SampleHistory is a history in that the last sample
	/// values are stored.
	///
	/// \param DABSize Size of DAB \c DynamicLengthHistory . DAB is a (usually)
	/// small history of the last sample values of which a average is calculated
	/// if the DAB is full.
	///
	/// \param DABHistorySize Size of the DABHistory \c DynamicLengthHistory .
	/// DABHistory is a history in that the last DABs (to be exact, the averages
	/// of the last DABs) are stored.
	///
	SignalState(uint32_t SignalStateID, SignalProperties SignalProperty,
	uint32_t SampleHistorySize, uint32_t DABSize,
	uint32_t DABHistorySize,
	PartFuncReference FuzzyFunctionSampleMatches,
	PartFuncReference FuzzyFunctionSampleMismatches,
	StepFuncReference FuzzyFunctionNumOfSamplesMatches,
	StepFuncReference FuzzyFunctionNumOfSamplesMismatches,
	PartFuncReference FuzzyFunctionSampleValid,
	PartFuncReference FuzzyFunctionSampleInvalid,
	StepFuncReference FuzzyFunctionNumOfSamplesValid,
	StepFuncReference FuzzyFunctionNumOfSamplesInvalid,
	PartFuncReference FuzzyFunctionSignalIsDrifting,
	PartFuncReference FuzzyFunctionSignalIsStable) noexcept
	: SignalStateInfo{SignalStateID, SignalProperty},
	FuzzyFunctionSampleMatches(FuzzyFunctionSampleMatches),
	FuzzyFunctionSampleMismatches(FuzzyFunctionSampleMismatches),
	FuzzyFunctionNumOfSamplesMatches(FuzzyFunctionNumOfSamplesMatches),
	FuzzyFunctionNumOfSamplesMismatches(
	FuzzyFunctionNumOfSamplesMismatches),
	FuzzyFunctionSampleValid(FuzzyFunctionSampleValid),
	FuzzyFunctionSampleInvalid(FuzzyFunctionSampleInvalid),
	FuzzyFunctionNumOfSamplesValid(FuzzyFunctionNumOfSamplesValid),
	FuzzyFunctionNumOfSamplesInvalid(FuzzyFunctionNumOfSamplesInvalid),
	FuzzyFunctionSignalIsDrifting(FuzzyFunctionSignalIsDrifting),
	FuzzyFunctionSignalIsStable(FuzzyFunctionSignalIsStable),
	SampleHistory(SampleHistorySize), DAB(DABSize),
	DABHistory(DABHistorySize),
	LowestConfidenceMatchingHistory(SampleHistorySize),
	HighestConfidenceMismatchingHistory(SampleHistorySize) {}

	/// Destroys \p this object.
	~SignalState(void) = default;

	void leaveSignalState(void) noexcept {
	DAB.clear();
	SignalStateInfo.NumberOfInsertedSamplesAfterEntrance = 0;
	SignalStateInfo.StateIsValidAfterReentrance = false;
	}

	SignalStateInformation<CONFDATATYPE>
	insertSample(INDATATYPE Sample) noexcept {

	+
	+
	SignalStateInfo.NumberOfInsertedSamplesAfterEntrance++;

	validateSignalState(Sample);

	SampleHistory.addEntry(Sample);

	- if (SignalStateInfo.FirstCnt<10){
	+ if (SignalStateInfo.FirstCnt<MOORING_SIZE){
	SignalStateInfo.FirstCnt += 1;
	SignalStateInfo.StateFirstMean += Sample;
	}
	- else if(SignalStateInfo.FirstCnt==10)
	+ else if(SignalStateInfo.FirstCnt==MOORING_SIZE)
	{
	- SignalStateInfo.StateFirstMean = SignalStateInfo.StateFirstMean/10;
	+ SignalStateInfo.StateFirstMean = SignalStateInfo.StateFirstMean/MOORING_SIZE;
	SignalStateInfo.FirstCnt += 1;

	}
	//std::cout << SignalStateInfo.StateID <<": Cnt=" << SignalStateInfo.FirstCnt << ", Mean=" << SignalStateInfo.StateFirstMean << "\n";

	DAB.addEntry(Sample);
	if (DAB.full()) {
	PROCDATATYPE AvgOfDAB = DAB.template average<PROCDATATYPE>();
	DABHistory.addEntry(AvgOfDAB);
	DAB.clear();
	}

	FuzzyFunctionNumOfSamplesMatches.setRightLimit(
	static_cast<INDATATYPE>(SampleHistory.numberOfEntries()));
	FuzzyFunctionNumOfSamplesMismatches.setRightLimit(
	static_cast<INDATATYPE>(SampleHistory.numberOfEntries()));

	checkSignalStability();

	SignalStateInfo.ConfidenceOfMatchingState = TempConfidenceMatching;
	SignalStateInfo.ConfidenceOfMismatchingState = TempConfidenceMismatching;

	return SignalStateInfo;
	}

	/// Gives the confidence how likely the new sample matches the signal state.
	///
	/// \param Sample is the actual sample of the observed signal.
	///
	/// \return the confidence of the new sample is matching the signal state.
	CONFDATATYPE
	confidenceSampleMatchesSignalState(INDATATYPE Sample) noexcept {

	CONFDATATYPE ConfidenceOfBestCase = 0;

	DynamicLengthHistory<PROCDATATYPE, HistoryPolicy::FIFO>
	RelativeDistanceHistory(SampleHistory.maxLength());

	// Calculate distances to all history samples.
	for (auto &HistorySample : SampleHistory) {
	PROCDATATYPE RelativeDistance =
	relativeDistance<INDATATYPE, PROCDATATYPE>(Sample, HistorySample);
	RelativeDistanceHistory.addEntry(RelativeDistance);
	}

	// Sort all calculated distances so that the lowest distance (will get the
	// highest confidence) is at the beginning.
	RelativeDistanceHistory.sortAscending();

	CONFDATATYPE ConfidenceOfWorstFittingSample = 1;

	// Case 1 means that one (the best fitting) sample of the history is
	// compared with the new sample. Case 2 means the two best history samples
	// are compared with the new sample. And so on.
	// TODO (future): to accelerate . don't start with 1 start with some higher
	// number because a low number (i guess lower than 5) will definetely lead
	// to a low confidence. except the history is not full.

	// Case 1 means that one (the best fitting) sample of the history is
	// compared with the new sample. Case 2 means the two best history samples
	// are compared with the new sample. And so on.
	for (uint32_t Case = 0; Case < RelativeDistanceHistory.numberOfEntries();
	Case++) {

	CONFDATATYPE ConfidenceFromRelativeDistance;

	if (std::isinf(RelativeDistanceHistory[Case])) {
	// TODO (future): if fuzzy is defined in a way that infinity is not 0 it
	// would be a problem.
	ConfidenceFromRelativeDistance = 0;
	} else {
	ConfidenceFromRelativeDistance =
	FuzzyFunctionSampleMatches(RelativeDistanceHistory[Case]);
	}
	ConfidenceOfWorstFittingSample = fuzzyAND(ConfidenceOfWorstFittingSample,
	ConfidenceFromRelativeDistance);

	ConfidenceOfBestCase =
	fuzzyOR(ConfidenceOfBestCase,
	fuzzyAND(ConfidenceOfWorstFittingSample,
	FuzzyFunctionNumOfSamplesMatches(
	static_cast<CONFDATATYPE>(Case) + 1)));
	}
	- if(SignalStateInfo.FirstCnt==11){
	+ if(SignalStateInfo.FirstCnt==(MOORING_SIZE+1)){

	- if(std::abs(Sample-SignalStateInfo.StateFirstMean)>std::abs(0.1*SignalStateInfo.StateFirstMean)){
	+ if(std::abs(Sample-SignalStateInfo.StateFirstMean)>std::abs(GLOBAL_OUTERBOUND*SignalStateInfo.StateFirstMean)){
	ConfidenceOfBestCase=0;
	- //std::cout << "Value " << Sample << "is too far away from " << SignalStateInfo.StateFirstMean << " so this state does not match mean \n";
	+ //std::cout << "Value " << Sample << "is too far away (>" << GLOBAL_OUTERBOUND*100<< "%) from " << SignalStateInfo.StateFirstMean << " so this state does not match mean \n";
	}
	}
	TempConfidenceMatching = ConfidenceOfBestCase;

	return ConfidenceOfBestCase;
	}

	/// Gives the confidence how likely the new sample mismatches the signal
	/// state.
	///
	/// \param Sample is the actual sample of the observed signal.
	///
	/// \return the confidence of the new sample is mismatching the signal state.
	CONFDATATYPE
	confidenceSampleMismatchesSignalState(INDATATYPE Sample) noexcept {

	float ConfidenceOfWorstCase = 1;

	DynamicLengthHistory<PROCDATATYPE, HistoryPolicy::FIFO>
	RelativeDistanceHistory(SampleHistory.maxLength());

	// Calculate distances to all history samples.
	for (auto &HistorySample : SampleHistory) {
	RelativeDistanceHistory.addEntry(
	relativeDistance<INDATATYPE, PROCDATATYPE>(Sample, HistorySample));
	}

	// Sort all calculated distances so that the highest distance (will get the
	// lowest confidence) is at the beginning.
	RelativeDistanceHistory.sortDescending();

	CONFDATATYPE ConfidenceOfBestFittingSample = 0;

	// TODO (future): to accelerate -> don't go until end. Confidences will only
	// get higher. See comment in "CONFDATATYPE
	// confidenceSampleMatchesSignalState(INDATATYPE Sample)".

	// Case 1 means that one (the worst fitting) sample of the history is
	// compared with the new sample. Case 2 means the two worst history samples
	// are compared with the new sample. And so on.
	for (uint32_t Case = 0; Case < RelativeDistanceHistory.numberOfEntries();
	Case++) {

	CONFDATATYPE ConfidenceFromRelativeDistance;

	if (std::isinf(RelativeDistanceHistory[Case])) {
	ConfidenceFromRelativeDistance = 1;
	} else {
	ConfidenceFromRelativeDistance =
	FuzzyFunctionSampleMismatches(RelativeDistanceHistory[Case]);
	}

	ConfidenceOfBestFittingSample = fuzzyOR(ConfidenceOfBestFittingSample,
	ConfidenceFromRelativeDistance);

	ConfidenceOfWorstCase =
	fuzzyAND(ConfidenceOfWorstCase,
	fuzzyOR(ConfidenceOfBestFittingSample,
	FuzzyFunctionNumOfSamplesMismatches(
	static_cast<CONFDATATYPE>(Case) + 1)));
	}


	TempConfidenceMismatching = ConfidenceOfWorstCase;


	return ConfidenceOfWorstCase;
	}

	/// Gives information about the current signal state.
	///
	/// \return a struct SignalStateInformation that contains information about
	/// the current signal state.
	SignalStateInformation<CONFDATATYPE> signalStateInformation(void) noexcept {
	return SignalStateInfo;
	}

	private:
	void validateSignalState(INDATATYPE Sample) {
	// TODO (future): WorstConfidenceDistance and BestConfidenceDistance could
	// be set already in "CONFDATATYPE
	// confidenceSampleMatchesSignalState(INDATATYPE Sample)" and "CONFDATATYPE
	// confidenceSampleMismatchesSignalState(INDATATYPE Sample)" when the new
	// sample is compared to all history samples. This would save a lot time
	// because the comparisons are done only once. However, it has to be asured
	// that the these two functions are called before the insertation, and the
	// FuzzyFunctions for validation and matching have to be the same!
	CONFDATATYPE LowestConfidenceMatching = 1;
	CONFDATATYPE HighestConfidenceMismatching = 0;

	for (auto &HistorySample : SampleHistory) {
	// TODO (future): think about using different fuzzy functions for
	// validation and matching.
	LowestConfidenceMatching = fuzzyAND(
	LowestConfidenceMatching,
	FuzzyFunctionSampleMatches(relativeDistance<INDATATYPE, PROCDATATYPE>(
	Sample, HistorySample)));

	HighestConfidenceMismatching =
	fuzzyOR(HighestConfidenceMismatching,
	FuzzyFunctionSampleMismatches(
	relativeDistance<INDATATYPE, PROCDATATYPE>(
	Sample, HistorySample)));
	}
	LowestConfidenceMatchingHistory.addEntry(LowestConfidenceMatching);
	HighestConfidenceMismatchingHistory.addEntry(HighestConfidenceMismatching);

	LowestConfidenceMatching = LowestConfidenceMatchingHistory.lowestEntry();
	HighestConfidenceMismatching =
	HighestConfidenceMismatchingHistory.highestEntry();

	SignalStateInfo.ConfidenceStateIsValid =
	fuzzyAND(LowestConfidenceMatching,
	FuzzyFunctionNumOfSamplesValid(static_cast<INDATATYPE>(
	SignalStateInfo.NumberOfInsertedSamplesAfterEntrance)));

	SignalStateInfo.ConfidenceStateIsInvalid =
	fuzzyOR(HighestConfidenceMismatching,
	FuzzyFunctionNumOfSamplesInvalid(static_cast<INDATATYPE>(
	SignalStateInfo.NumberOfInsertedSamplesAfterEntrance)));

	if (SignalStateInfo.StateIsValid)
	SignalStateInfo.StateJustGotValid = false;

	if (SignalStateInfo.ConfidenceStateIsValid >
	SignalStateInfo.ConfidenceStateIsInvalid) {

	if (!SignalStateInfo.StateIsValid)
	SignalStateInfo.StateJustGotValid = true;

	SignalStateInfo.StateIsValid = true;
	SignalStateInfo.StateIsValidAfterReentrance = true;
	}
	}

	void checkSignalStability(void) {

	if (DABHistory.numberOfEntries() >= 2) {
	SignalStateInfo.ConfidenceStateIsStable = FuzzyFunctionSignalIsStable(
	relativeDistance<INDATATYPE, PROCDATATYPE>(
	DABHistory[DABHistory.numberOfEntries() - 1], DABHistory[0]));
	SignalStateInfo.ConfidenceStateIsDrifting = FuzzyFunctionSignalIsDrifting(
	relativeDistance<INDATATYPE, PROCDATATYPE>(
	DABHistory[DABHistory.numberOfEntries() - 1], DABHistory[0]));
	} else {
	// Initializing the following variables because (at this moment) we do not
	// know if the signal is stable or drifting.
	SignalStateInfo.ConfidenceStateIsStable = 0;
	SignalStateInfo.ConfidenceStateIsDrifting = 0;
	}

	if (SignalStateInfo.ConfidenceStateIsStable >
	SignalStateInfo.ConfidenceStateIsDrifting) {
	SignalStateInfo.StateCondition = StateConditions::STABLE;
	} else if (SignalStateInfo.ConfidenceStateIsStable <
	SignalStateInfo.ConfidenceStateIsDrifting) {
	SignalStateInfo.StateCondition = StateConditions::DRIFTING;
	std::cout << "DRIFTING";
	} else {
	SignalStateInfo.StateCondition = StateConditions::UNKNOWN;
	}
	}
	};

	} // End namespace agent
	} // End namespace rosa

	#endif // ROSA_AGENT_SIGNALSTATE_HPP
	diff --git a/include/rosa/support/math.hpp b/include/rosa/support/math.hpp
	index 1df8d7e..ae375f1 100644
	--- a/include/rosa/support/math.hpp
	+++ b/include/rosa/support/math.hpp
	@@ -1,156 +1,162 @@
	//===-- rosa/support/math.hpp ------------------------------------ C++ --===//
	//
	// The RoSA Framework
	//
	// Distributed under the terms and conditions of the Boost Software License 1.0.
	// See accompanying file LICENSE.
	//
	// If you did not receive a copy of the license file, see
	// http://www.boost.org/LICENSE_1_0.txt.
	//
	//===----------------------------------------------------------------------===//
	///
	/// \file rosa/support/math.hpp
	///
	/// \author David Juhasz (david.juhasz@tuwien.ac.at)
	///
	/// \date 2017
	///
	/// \brief Math helpers.
	///
	//===----------------------------------------------------------------------===//

	// !!!!!! Please check lines 60 - 180 forward !!!!!!!!!!!!!!

	#ifndef ROSA_SUPPORT_MATH_HPP
	#define ROSA_SUPPORT_MATH_HPP

	+
	+#define EPSILON 0.0001
	+
	#include "debug.hpp"
	#include <algorithm>
	#include <array>
	#include <cmath>
	#include <cstdarg>
	#include <cstdlib>
	#include <limits>
	#include <type_traits>

	namespace rosa {

	/// Computes log base 2 of a number.
	///
	/// \param N the number to compute log base 2 for
	///
	/// \return log base 2 of \p N
	constexpr size_t log2(const size_t N) {
	return ((N < 2) ? 1 : 1 + log2(N / 2));
	}

	/// Tells the next representable floating point value.
	///
	/// \tparam T type to operate on
	///
	/// \note The second type argument enforces \p T being a floating point type,
	/// always use the default value!
	///
	/// \param V value to which find the next representable one
	///
	/// \return the next representable value of type \p T after value \p V
	///
	/// \pre Type \p T must be a floating point type, which is enforced by
	/// `std::enable_if` in the second type argument.
	template <typename T,
	typename = std::enable_if_t<std::is_floating_point<T>::value>>
	T nextRepresentableFloatingPoint(const T V) {
	return std::nextafter(V, std::numeric_limits<T>::infinity());
	}

	/// Conjuncts two or more values with each other.
	///
	/// \param Data an array of the data
	///
	/// \return the conjunction of the values given as parameter.
	template <typename CONFDATATYPE, std::size_t size>
	CONFDATATYPE fuzzyAND(const std::array<CONFDATATYPE, size> &Data) noexcept {
	STATIC_ASSERT(std::is_arithmetic<CONFDATATYPE>::value,
	"Type of FuzzyAnd is not arithmetic");
	STATIC_ASSERT(size > 1, "Number of Arguments is to little");
	- for (auto tmp : Data)
	- ASSERT(tmp <= 1 && tmp >= 0);
	+
	+ for (auto tmp : Data){
	+ ASSERT(tmp <= (1+EPSILON) && tmp >= (0-EPSILON));
	+ }
	+
	return *std::min_element(Data.begin(), Data.end());
	}

	/// Conjuncts two or more values with each other. It's a wrapper for \c
	/// fuzzyAND() [array]
	///
	/// \param Data first data to get the type explicitly
	///
	/// \param Datan a package of data
	///
	/// \note the types of Datan must be the same type as Data
	///
	/// \return the conjunction of the values given as parameter.
	template <typename CONFDATATYPE, typename... _CONFDATATYPE>
	std::enable_if_t<
	std::conjunction_v<std::is_same<CONFDATATYPE, _CONFDATATYPE>...>,
	CONFDATATYPE>
	fuzzyAND(const CONFDATATYPE Data, const _CONFDATATYPE... Datan) noexcept {
	return fuzzyAND(
	std::array<CONFDATATYPE, sizeof...(Datan) + 1>{{Data, Datan...}});
	}

	/// Disjuncts two or more values with each other.
	///
	/// \param Data an array with the data.
	///
	/// \return the disjunction of the values given as parameter.
	template <typename CONFDATATYPE, std::size_t size>
	CONFDATATYPE fuzzyOR(const std::array<CONFDATATYPE, size> &Data) noexcept {
	STATIC_ASSERT(std::is_arithmetic<CONFDATATYPE>::value,
	"Type of FuzzyAnd is not arithmetic");
	STATIC_ASSERT(size > 1, "Number of Arguments is to little");
	ASSERT(std::all_of(Data.begin(), Data.end(),
	- [](const auto &v) { return v <= 1 && v >= 0; }));
	+ [](const auto &v) { return v <= (1+EPSILON) && v >= (0-EPSILON); }));
	return *std::max_element(Data.begin(), Data.end());
	}

	/// Disjuncts two or more values with each other. It's a wrapper for \c
	/// fuzzyOR() [array]
	///
	/// \param Data first data to get the type explicitly
	///
	/// \param Datan a package of data
	///
	/// \note the types of Datan must be the same type as Data
	///
	/// \return the disjunction of the values given as parameter.
	template <typename CONFDATATYPE, typename... _CONFDATATYPE>
	std::enable_if_t<
	std::conjunction_v<std::is_same<CONFDATATYPE, _CONFDATATYPE>...>,
	CONFDATATYPE>
	fuzzyOR(const CONFDATATYPE Data, const _CONFDATATYPE... Datan) noexcept {
	return fuzzyOR(
	std::array<CONFDATATYPE, sizeof...(Datan) + 1>{{Data, Datan...}});
	}

	template <typename INDATATYPE, typename PROCDATATYPE>
	PROCDATATYPE relativeDistance(INDATATYPE NewValue,
	INDATATYPE HistoryValue) noexcept {
	PROCDATATYPE Dist = HistoryValue - NewValue;

	if (Dist == 0) {
	return 0;
	} else {
	Dist = Dist / NewValue;
	if (Dist < 0) {
	// TODO: I guess this multiplication here should not be done because
	// it could be that the distance fuzzy functions are not symetrical
	//(negative and positive side)
	Dist = Dist * (-1);
	}
	return (Dist);
	}
	}

	} // End namespace rosa

	#endif // ROSA_SUPPORT_MATH_HPP

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