Ibrahim Khalil

Soufiane Hamzaoui

Pierre Dahmani
email: s.dahmani@ump.ac.ma

Boulet

Hamzaoui Khalil
email: hamzaoui.khalil@gmail.com

Dahmani Soufiane

Boulet Pierre
email: pierre.boulet@univ-lille.fr

Mohammed Hamzaoui

Mostafa Berrajaa

Giuseppe Azizi

Pierre Lipari

"Tour de France" data for the improvement of energy consumption in devices powered by limited energy sources

Keywords: Energy Consumption, Collection of Data, Internet of Things, Data Processing

teaching and research institutions in France or abroad, or from public or private research centers.

Specifications table

Subject

Computer Science, Computational

Theory and Mathematics

More specific subject area Improvement of energy consumption in devices powered by limited energy sources Type of data Text (csv file)

How data was acquired These experimental data were collected during collection a 33 day long "Tour de France" with an electrical wheelchair: https://www.univ-lille.fr/Tour_de_France

The data were collected on the basis of several scenarios in different places (3006 kilometers(Km)).

These measures were operated out in a real environment.

Parameters for data Tablet HP Pro Slate 8 (Snap Dragon), Trepn Profiler V6.2s [START_REF] Bakker | Comparing energy profilers for android[END_REF], Cpu Frequency, Cronoid 3.5.1 [START_REF] Chen | Method for changing cpu frequency under control of neural network[END_REF] Data format Raw Description of data Scientific data was collected on the basis of several collection scenarios in different places. These measures were operated out in a real environment and confronted with the consumption measures carried out beforehand in a controlled environment.

After each experiment, the obtained data is stored in a csv file with a large number of parameters and data retrieved.

Data source location Cities : Lille, Amiens, Rouen, Le Havre, Caen, Rennes, Nantes, La Rochelles, Bordeaux, Toulouse, Montpellier, Avignon, Grenoble, Lyon, Dijon, Troyes, Paris, Valenciennes.

Country: France.

Value of the data:

• The presented experimental data are useful to measure several metrics related to the energy variation in a mobile device.

• The data are beneficial for all the scientists who are exploring energy consumption in devices powered by limited energy sources as a knowledge resource.

• The data can be used to analyze the load of the Central Processing Unit (CPU) and the Graphics Processing Unit (GPU) as well as to make a more detailed follow-up of the energy variation on other type of mobile environment.

• The provided data can be used for further analysis in order to define models to improve energy handling in mobile devices, to define other types of scenarios given the large size of the data measured during the collection, or to optimize our methodology (based on part of the dataset [START_REF] Hamzaoui | Contribution à la modélisation de la consommation d'énergie dans un dispositif mobile[END_REF][START_REF] Hamzaoui | Measurement-based methodology for modelling the energy consumption of mobile devices[END_REF]).

Data description

The presented research is part of the development of mathematical models, for modeling and evaluating the energy cost in mobile environments.

Our study focused on the Android system (version 4.4.4 (KitKat)). The main objective of the data collection was to model the energy consumption of a particular application running on a mobile device.

The data collection made possible to develop a specific mathematical model of energy consumption for monitoring the energy consumption of playing a local video, remote video streaming as well as in the case of navigating with a Global

Positioning System (GPS) by acting on the following parameters:

• Processor frequency,

• Initial battery level,

• Dissipated energy.

This analyse is focused on the development and measurement of energy costs.

Our work focused on monitoring energy consumption based on several experimental scenarios.

Scientific data was collected on the basis of several scenarios in different locations. These measures were carried out in a real environment and confronted with the consumption measures made beforehand in a controlled environment.

Collecting and analyzing massive data in a real environment and diversifying it in a reasonable time creates a scientific challenge. The objective of the carried out research is to model the energy consumption of a particular application running on a mobile device.

In the rest of our paper, we will base ourselves on two types of case studies:

• The disconnected mode in which all the measurements were carried out in the presence of only Global System for Mobile Communications (GSM).

• Connected mode where all connection techniques have been activated (3G, 4G, Wi-Fi, GPS, etc.) depending on the chosen scenario.

The raw data are stored in text files (in CSV format). They are divided into four main groups depending on the chosen scenarios. Table 1 presents the different characteristics of the group of scenarios used for the evaluation.

The files are named as follows:

scenario_name_freq_GHz_[Option]_[departure_city]_[arrival_city]
Table 2 shows some examples of measurement files for all scenarios in disconnected mode. The list of all files relating to the disconnected mode is presented in Tables 3 and4.

For convenience, each measurement file is divided into two sub-files: The first one contains the list of measurements made during the experiment. The second one has the same name with the suffix "_info" which gives information about active applications during the experiment.

The empty files "_info files" (Zero line), means that the option which gives information was deactivated at the time of collection for these experiments. The connected mode can be described as follows in Table 5 Tables 6 and7 show the storage structure of the connected mode files.

File name Number of lines

Meaning of the columns of Table 9:

• Time [ms]: The interval of measurements in milliseconds (ms),

• Memory Usage [Kb]: Memory used per measurement interval in Kilobyte [Kb],

• Screen Brightness: Screen brightness state,

• Battery Power* [µW]: Power consumption per measurement interval in microwatt(µW),

• Battery Remaining [%]: Remaining battery capacity,

• GPU Load [%]: Total Graphics Processing Unit load,

• CPU Load [%]: Total CPU load.

• CPU Temp [1/10 C]: Processor Temperature per measurement interval (by 1/10°C).

The average number of accesses per application and statistics on the sensors (Table 10), the "_info file" contains also information about the percentage of use of CPU resources, the average use of virtual memory per application, the maximum use of virtual memory per application, average real memory usage per application and maximum real memory usage per application, all these information are detailed in the Table 11. The meaning of the applications not recognized are:

• GsmaService: The GSMA provides a range of services to assist the broader mobile industry.

• Launcher3: Application launcher, it is the equivalent of the desktop on Android.

• Google Partner Configuration: Application that helps for run and configure applications in conjunction with Google products.

• Play-Fi: Application for set up to streaming music.

• Cron Tasker Free: Application for Scheduling tasks simply by configuring the time.

• SmartcardService: The smart card service is a included system in the operating system in a root directory. it appears to the smart card provider [START_REF] Mandelbaum | Smartcard adapted for a plurality of service providers and for remote installation of same[END_REF].

• SVI Settings: A Switch Virtual Interfaces Represents a logical Layer 3 interface on a switch.

Experimental design, materials, and methods

Methodology

This dataset was used to model the energy consumption of one or more specific applications in a mobile device [START_REF] Pathak | Where is the energy spent inside my app?: fine grained energy accounting on smartphones with eprof[END_REF][START_REF] Mercati | Blast: Battery lifetime-constrained adaptation with selected target in mobile devices[END_REF]. On the one hand, it allows the monitoring of energy consumption according to the frequency and the total number of operations, on the other hand, to put into practice our proposed methodologies through several concrete case studies.

To identify the parameters of the model, our methodology must determine the type of correlation between the number of total operations and the elementary energy dissipated at a previously defined frequency. The total number of operations is calculated by summing the active clock cycles of each processor per measurement interval.

This active clock cycle number is the load of the CPU multiplied by the measurement interval duration and by the CPU frequency. The dissipated energy during a measurement interval is the product between the battery power and the measurement interval duration.

The average consumption per clock cycle can be expressed as follows [START_REF] Hamzaoui | Measurement-based methodology for modelling the energy consumption of mobile devices[END_REF]:

E clock cycle = Regression( 4 i=1
Loadi, E dissipated ) E clock cycle shows the total energy per clock cycle, Loadi the load of CPU i and E dissipated corresponds to the dissipated energy measured experimentally.

Our methodology consists in establishing a regression of energy per measurement interval as a function of the total number of operations. The research presented proposes a methodology to evaluate and modeling the energy costs of mobile devices.

The proposed methodology is developed in three stages:

• data collection,

• data preparation,

• energy consumption modelling.

The aim is to monitor energy consumption by acting on the following parameters:

• frequency of the processors,

• initial level of the battery,

• energy dissipated by the clock cycle.

Tools data collection

The tools used to develop our methodology are [START_REF] Hamzaoui | Contribution à la modélisation de la consommation d'énergie dans un dispositif mobile[END_REF]:

1. Trepn Profiler, a diagnostic tool for profiling performance and power consumption of Android applications. All tests of this experimentation were processed by version V6.2s. Trepn profiler provides information on system status, network status, graph performance, speed, processor frequency etc.

2. Cronoid, an automation tool that allows performing tasks on a regular basis (like KornShell). It also enables automatic task running when the status of the terminal changes. The version used is Cronoid-3.5.1.

3. CPU Frequency (V1.0.2), a tool that allows the user to change the CPU frequency setting to save energy or to achieve better performance.

The data collection stage of our methodology is based on the following steps:

-Preparation of the test platform (CPU frequency management based on the governor) in order to have the rights to fix the frequencies of the CPU with the CPU Frequency tool.

-The role of the Cronoid tool is to automate tasks in order to minimize interaction with the user.

In the case of variable frequencies, the same methodology can be applied by filtering the rows of the measurement table recorded by group of identical frequencies. To collect the data, a "Tour de France" was carried out using an electrical wheelchair to perform measurements in a diversified environment based on several scenarios. details related to the location of the measurements are detailed in Table 12 The tool used for all the experiments is a tablet (HP ProSlate 8) which was previously rooted. This choice was linked to the capacity of the battery which allows a large number of tests for each charge cycle, as well as the architecture of the processor (Snapdragon 800) for its performance, minimal power use and temperature. The full characteristics of this tablet are :

•

Data exploitation

The purpose of our study is to provide detailed monitoring of the main sources of The second case aims at evaluating and measuring the energy consumed in connected use cases. Sources related to the data communications are activated completely (3G/4G or Wi-Fi) or partially (GPS) depending on the scenarios of the experiment.

Another mobile device was present to serve as an access point for sharing the Wi-Fi connection.

We have developed a methodology based on the processor frequency parameters, the dissipated energy and the initial level of the battery [START_REF] Hamzaoui | Measurement-based methodology for modelling the energy consumption of mobile devices[END_REF]. To this end, part of the collection of the produced experimental data was analyzed by statistical tools. To validate our model, we compared our results with other existing models.

We have presented a methodology to build a model of energy consumption of applications on mobile devices. This methodology begins with recording precise measurements of energy consumption when using the applications. The strong point of our methodology is in the technique of processing recorded data which leads to a precise model. The proposed solution can be used to define an optimal frequency for one or more applications in order to provide a better experience for users with reduced energy consumption.

For the implementation of our methodology, we selected the following parameters, obtained by Trepn Profiler:

• Total load per CPU,

• Memory usage,

• CPU frequency,

• Battery level,

• Battery power.

Disconnected state

The measurements of the dissipated energy were carried out according to the number of total operations at a fixed frequency. Figure 1 show example of experimentation by setting the processor frequencies at 1.5 GHz.

Connected state

The aim of this section is to follow behaviour the energy consumption variation by activating Wi-Fi. To stay in the same context, the remote video was identical to that used in disconnected mode. We determine the type of correlation between energy consumption and the number of total operations, then we compare the obtained result with those in offline mode. Figure 2 shows an example of experimentation for a remote video in navigation mode with variable frequency (NFV).

Raw data sources of this figure is stored in:

NVF_default_freq_youtube_with_WiFi_Carcassonne_Narbonne.csv at Zenodo platform: https://zenodo.org/record/3739472 In this study, we have treated the different types of dependencies between energy dissipation and the frequency of several cases in disconnected mode (LVFF, LVVF) and connected mode (NFF, NVF).

The difference between the types of regressions can be explained in part by the variability of the signal strength received for GPS and Wi-Fi (for connected mode). On the other hand, the scenarios concerned in connected mode require the activation of other parameters, which increases the rate of inputs / outputs which are partly responsible for the additional energy cost.

4 •• 1 •••

 41 Operating system: Android 4.4.4 (KitKat) • Processor: QUALCOMM Snapdragon 800 • Max. CPU frequency: 2.3 GHz • Number of cores: Sensors: Accelerometer, ambient light, proximity, compass, barometer,gyroscope, Hall effect Number of batteries: Technology: Lithium polymer • Autonomy: Up to 13.75 h • Operating temp mini: 0°C • Operating temp maxi: 40°C Storage temp mini: -20°C Storage temp maxi: 60°C

  energy consumption. In the first case, only the Global Mobile Communications system (GSM) is active. The other communication networks (third generation of mobile wireless telecommunications (3G), fourth generation of mobile wireless telecommunications (4G), Wireless Fidelity (Wi-Fi) and Global Positioning System (GPS) remain inactive throughout the different experiences.

Figure 1 :

 1 Figure 1: Energy dissipation for LVFF model, Frequency=1.5GHz [3]

Figure 2 :

 2 Figure2: Energy dissipation for NFV scenario (default frequency)[START_REF] Hamzaoui | Measurement-based methodology for modelling the energy consumption of mobile devices[END_REF]

Table 1 :

 1 Characteristics of the reference scenarios

	Model Meaning	Mode	Description
	name
	LVFF	Local Video	Disconnected Monitoring the energy
		with fixed		behavior of a local video
		frequency		under a fixed frequency
	LVVF Local Video	Disconnected Monitoring of energy
		with variable		behavior by of a local
		frequency		video at a variable
				frequency
	NFF	Navigation	Connected	Monitoring of the energy
		with fixed		behavior by launching
		frequency		navigation via GPS under
				a fixed frequency
	NVF	Navigation	Connected	Monitoring of the energy
		with variable		behavior by launching
		frequency		navigation via GPS
				at a variable frequency

Table 2 :

 2 Description of the disconnected scenario file names

	File name	Meaning
	Scenario
	LVFF_freq0.3GHz	Measurement with the LVFF scenario by setting the
	_Caen_Domfront.csv	frequency at 0.3GHz between Cean (departure city)
		and Domfront (arrival city).
	LVFF_freq0.3GHz	Information about present applications during the
	_Caen_Domfront_info.csv experiment, Percentage of use of CPU resources,
		Average of use of virtual memory per application,
		Average use of real memory per application,
		Maximum use of real memory per application,
		statistics of the active sensors between
		Caen and Domfront.
	LVVF_default_freq	Mesurement with LVVF scenario with variable
	_Lille_Amiens.csv	frequency (the default frequency) between
		Lille and Amiens.
	LVVF_default_freq	Information about present applications during the
	_Lille_Amiens_info.csv	experiment, Percentage of use of CPU resources,

Average of use of virtual memory per application, Average use of real memory per application, Maximum use of real memory per application, statistics of the active sensors between Lille and Amiens.

Table 3 :

 3 List of the LVFF files in the dataset

	File name	Number
		of lines
	LVFF_freq0.3GHz_Caen_Domfront.csv
	LVFF_freq0.3GHz_Caen_Domfront_info.csv
	LVFF_freq0.3GHz_Castelsarrasin_Toulouse.csv
	LVFF_freq0.3GHz_Castelsarrasin_Toulouse_info.csv
	LVFF_freq0.3GHz_Domfront_Rennes.csv
	LVFF_freq0.3GHz_Domfront_Rennes_info.csv
	LVFF_freq0.3GHz_Rouen_Le_Havre2.csv
	LVFF_freq0.3GHz_Rouen_Le_Havre2_info.csv
	LVFF_freq1.0GHz_Le_Havre_Domfront.csv
	LVFF_freq1.0GHz_Le_Havre_Domfront_info.csv
	LVFF_freq1.5GHz_Beziers_Montpellier.csv
	LVFF_freq1.5GHz_Beziers_Montpellier_info.csv
	LVFF_freq1.5GHz_Carcan_Bordeaux.csv
	LVFF_freq1.5GHz_Carcan_Bordeaux_info.csv
	LVFF_freq1.5GHz_Le_Havre_Domfront.csv
	LVFF_freq1.5GHz_Le_Havre_Domfront_info.csv
	LVFF_freq1.7GHz_Carcans_Bordeaux.csv
	LVFF_freq1.7GHz_Carcans_Bordeaux_info.csv
	LVFF_freq2.2GHz_Bordeaux_Marmande1.csv
	LVFF_freq2.2GHz_Bordeaux_Marmande1_info.csv
	LVFF_freq2.2GHz_Bordeaux_Marmande2.csv
	LVFF_freq2.2GHz_Bordeaux_Marmande2_info.csv
	LVFF_freq2.2GHz_Rouen_Le_Havre1.csv
	LVFF_freq2.2GHz_Rouen_Le_Havre1_info.csv
	LVFF_freq2.2GHz_Valence_Grenoble.csv
	LVFF_freq2.2GHz_Valence_Grenoble_info.csv

Table 4 :

 4 list of the LVVF files in the dataset

	File name	Number
		of lines
	LVVF_default_freq_Chateau_Thierry_Reims.csv
	LVVF_default_freq_Chateau_Thierry_Reims_info.csv
	LVVF_default_freq_Lille_Amiens.csv
	LVVF_default_freq_Lille_Amiens_info.csv
	LVVF_default_freq_Narbonne_Beziers.csv
	LVVF_default_freq_Narbonne_Beziers_info.csv

Table 5 :

 5 Description of the connected scenario file names

	File name	Meaning
	Scenario
	NFF_freq1.0GHz_GPS	Mesurement with NVF scenario with
	_facebook_Provins_Paris.csv	variable frequency (the default frequency)
		between Provins and Paris.
	NFF_freq1.0GHz_GPS	Information about present applications during
	_facebook_Provins_Paris_info.csv	the experiment, Percentage of use of CPU
		resources, Average of use of virtual memory
		per application, Average use of real memory
		per application, Maximum use of real memory
		per application, statistics of the active sensors
		between Provins and Paris.
	NVF_default_freq_GPS	Mesurement with NVF scenario with variable
	_facebook _Provins_Paris.csv	frequency (the default frequency) between
		Provins and Paris.
	NVF_default_freq_GPS	Information about present applications during the
	_facebook_Provins_Paris_info.csv	experiment, Percentage of use of CPU resources,
		Average of use of virtual memory per application,
		Average use of real memory per application,
		Maximum use of real memory per application,
		statistics of the active sensors between
		Provins and Paris.
	NVF_Optimal_Freq_GPS	Mesurement with NVF scenario with variable
	_facebook_Lyon_Tournus.csv	frequency, choosed by tool of mesurement
		(Optimal frequency) between Lyon and Tornus.
	NVF_Optimal_Freq_GPS	Information about present applications during
	_facebook_Lyon_Tournus_info.csv the experiment, Percentage of use of CPU resources,
		Average of use of virtual memory per application,
		Average use of real memory per application,
		Maximum use of real memory per application,
		statistics of the active sensors between
		Lyon and Tournus.

Table 6 :

 6 composition of NFF files in the dataset

Table 7 :

 7 list of the NVF files in the dataset

	File name

Table 8 :

 8 Example of raw measured data (part 1)

	Time	Cpu1	Cpu1 Cpu2 Cpu2 Cpu3 Cpu3	Cpu4	Cpu4
	[ms]	Freq	Load	Freq	Load	Freq	Load	Freq	Load
		[kHz]	[%]	[kHz]	[%]	[kHz]	[%]	[kHz]	[%]
	1208013 300000	42	0	0	0	0	2265600	57
	1208113 1728000	50	0	0	0	0	2265600	41
	1208218 300000	90	0	0	0	0	2265600	65
	1208272 1728000	100	0	0	0	0	2265600	66
	1208373 300000	66	0	0	0	0	2265600	41
	1208475 1728000	20	0	0	0	0	2265600	11
	. . .	. . .	. . .	. . .	. . .	. . .	. . .	. . .	. . .
	3778779 1728000	33	0	0	0	0	2265600	18
	Meaning of the columns of Table 8:
	• Cpu1 Freq [kHz]; Cpu2 Freq [kHz]; Cpu3 Freq [kHz]; Cpu4Freq [kHz]: The re-
	spective frequencies of the 4 Central Processing Units (CPUs) in kiloHertz [kHz].
	• Cpu1 Load, Cpu2 Load, Cpu3 Load, Cpu4 Load: Percentages of respective loads
	of the 4 CPUs [%].

• Time [ms]: The interval of measurements in milliseconds (ms),

Table 9 :

 9 Example of raw measured data (part 2)

	Time	Memory Screen Battery	Battery	GPU CPU	CPU
	[ms]	Usage	Brigh-	Power	Remaining Load Load	Temp
		[kb]	tness	[µW]	[%]	[%]	[%]	[1/10 C]
	1208013	1797872	15	935452	19	0	42	460
	1208113	1798120	15	1120303	19	0	50	460
	1208218	1798860	15	1061060	19	0	42	460
	1208272	1798860	15	1955387	19	0	78	460
	1208373	1797636	15	680417	19	0	80	460
	1208475	1797704	15	380873	19	0	45	460
	. . .	. . .	. . .	. . .	. . .	. . .	. . .	. . .
	3778779	1748624	15	315252	19	0	38	530

Table 10 :

 10 Statistics and Information about active applications

	Identifier of the	Type of	Average Number of
	Application Sensor Application	Accesses per Application.
		Mobile Data State	0.00
		Battery Remaining % 47.634
		Battery Status	0.00
		Memory Usage	1772465.436
		Screen Brightness	247.383
		Battery Power	989798.485
		GPU Frequency	226874.220
		GPU Load	32.800
		CPU Load	75.213
		CPU1 Frequency	856941.408
		CPU2 Frequency	664897.739
		CPU3 Frequency	812255.094
		CPU4 Frequency	860962.047
		CPU1 Load	76.488
		CPU2 Load	68.417
		CPU3 Load	73.458
		CPU4 Load	74.583

Table 11 :

 11 Example of System resource allocation

	Applications	CPU	Average	Max	Average	Max
		[%]	Virtual	Virtual	Resident Resident
			Memory	Memory	Set Size	Set Size
			Size [MB] Size [MB] [MB]	[MB]
	Facebook	0.18	1784.41	1794.03	26.99	34.0
	GsmaService	0.0018	1483.33	1485.60	5.79	6.09
	Clock	0.006	257.32	1493.86	1.42	8.37
	CPU Frequency	5.51E-5 606.2	1489.42	2.79	7.64
	Google Play Store	0.004	15.80	1545.80	8.33	9.59
	Launcher3	0.004	1589.49	1589.49	12.08	13.789
	Services Google Play 0.34	4096.29	6540.66	34.89	60.18
	Google Partner	5.04E-4 24.55	1486.86	0.09	5.97
	Configuration
	Play-Fi	0.0017	1483.88	1483.88	5.05	5.69
	Personal Dictionary	0.007	99.57	1491.23	0.52	7.94
	YouTube	20.51	1966.10	1985.42	42.51	48.83
	Cron Tasker Free	0.06	1330.37	1492.48	7.004	8.63
	Gmail	0.050	893.47	1834.24	12.28	33.15
	Google Application	0.032	3084.76	3091.49	16.81	19.653
	Cronoid	0.62	1507.74	1520.84	7.97	8.94
	Power Battery	0.47	1682.2	3114.73	10.07	29.05
	Hangouts	0.05	19.82	1665.26	0.18	21.22
	Google+	0.031	64.793	1545.664	0.487	12.356
	Google keyboard	0.078	1548.13	1607.44	11.58	14.71
	Mobile Network	0.28	3034.45	3035.91	14.26	16.59
	Configuration
	SmartcardService	0.006	2987.34	2991.18	13.007	14.622
	System Interface	0.25	1727.72	1805.57	41.59	51.07
	Messenger	0.14	1871.18	1910.15	46.07	56.89
	SVI Settings	3.46	9172.51	9217.14	55.79	64.84
	Multimedia Storage	0.002	277.25	1495.03	1.28	7.613

Table 12 :

 12 Measurement locations in the Scientific "Tour de France"

	Name of the city Visit date
	Lille	02 Mai 2016 (Departure)
	Amiens	02 Mai 2016
	Rouen	03 Mai 2016
	Le Havre	04 Mai 2016
	Caen	05 Mai 2016
	Rennes	07 Mai 2016
	Nantes	08 Mai 2016
	La Rochelle	09 Mai 2016
	Bordeaux	12 Mai 2016
	Toulouse	15 & 16 Mai 2016
	Montpellier	19 Mai 2016
	Avignon	20 Mai 2016
	Grenoble	23 Mai 2016
	Lyon	24 Mai 2016
	Dijon	26 Mai 2016
	Troyes	28 Mai 2016
	Paris	30 Mai 2016
	Valenciennes	02 Juin 2016
	Lille	03 Juin 2016(Arrival)

Acknowledgements

The authors wish to acknowledge the following institutions for their help in collecting this data and their financial support:

• University of Lille France, • CNRS : National Center for Scientific Research (CNRS France), • The Research Institute for Software and Hardware Components for Information and Advanced Communication (IRCICA Lille, France), • Association of the Paralyzed in France (APF), • City of Villeneuve d'Ascq, France.

Data accessibility

The raw data are available at:

https://zenodo.org/record/3739472, with

Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethics statement

Our work does not involve any use of human subjects nor animal experiments.

The data are purely technological.