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INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES

Volume 6, No 3, 2016

© Copyright 2010 All rights reserved Integrated Publishing services

Research article ISSN 0976 – 4380

Submitted on January 2016 published on February 2016 1708

“Hows” of quality for remote sensing data user- A house of quality

approach Yogdeep Desai1, Kartikeyan B2, Nilam Panchal3

1- Research Scholar, Rai University, Ahmedabad

and Scientist, Space Applications Centre (ISRO), Ahmedabad

2- Scientist, Space Applications Centre (ISRO), Ahmedabad

3- Research Supervisor, Rai University, Ahmedabad

Associate Professor, BK School of Business Management, Gujarat University, Ahmedabad

[email protected]

ABSTRACT

House of Quality (HOQ) is the first phase of Quality Function Deployment (QFD)- A Total

Quality Management (TQM) technique of translating customer expectations-Voice of

Customer (VOC)- into product specifications-Voice of Technician (VOT). The QFD has been

successfully used by manufacturing sector in new product development. This paper

demonstrates how the requirement of user of scientific product like Remote Sensing Data

Product (RSDP) can be translated to technical specification through HOQ, the most crucial

phase of QFD. The paper lists out the organs of HOQ, their significance and also lists out the

quality parameters of RSDP. The paper explains the challenges of defining quality of RSDP

as perceived by the RSDP user owing to its 4-dimensional dependency- its intended use,

satellite, sensor and data product generation. However, first-ever attempt to use HOQ to

understand “Hows” of Quality for RSDP users concludes with optimism about the usability

of this technique which will help in customizing the product to suit user requirement and

increase its utilization in decision-making process related to management of natural resources.

Because the fulfilling of user requirement extend beyond the scope of just the data product

generation process, a broader study encompassing other dimensions need to be undertaken.

Keywords: Quality function deployment, house of quality, TQM, remote sensing

1. Introduction

One of the orthodox definitions of Quality Product is “conformance to specification” which

has been utilized for long time by Quality Departments. This was based on the assumption

that if a product meets the laid down specifications, it is bound to satisfy the customers (Juran

& Godfrey, 1999). The roots of this assumption can be attributed to two aspects prevailing in

those days. The first of which is that the Quality of a product was considered to be the

responsibility of Quality Department and secondly this department rarely had direct contact

with the customer. It was a closed loop interaction between the production team and the

Quality Department team.

The evolution from Quality Control to the Total Quality Management (TQM) philosophy

changed the definition of product quality and the centre of its responsibility. Definitions like

“fitness for use” (Juran & Godfrey, 1999) shifted the focus of quality definition from product

specification to the suitability for its intended use by the customer. In terms of TQM

adoption, the companies which follow the “conformance to specification” quality-standard

have been categorized as “Uncommitted” (Dale, 2003). Thus the advent of TQM has changed

the manner in which the organization pursue Quality. Organizations which are on the path of

“Hows” of quality for remote sensing data user- A house of quality approach

Yogdeep Desai et al.,

International Journal of Geomatics and Geosciences

Volume 6 Issue 3, 2016 1709

TQM have recognized the importance of imbibing Quality into the product right from its

design stage, based on the requirement of customer.

This ‘system of assuring that the customer-needs drive the product design and production

process’ is termed as Quality Function Deployment (QFD) (Chan & Wu, 2005). The first of

the four-phased QFD system, called the House Of Quality (HOQ), identifies the customer

needs of the product quality (Voice Of Customer-VOC) and translates it to technical

requirements (Voice Of Technician-VOT). This paper has developed HOQ for Remote

Sensing Data Products (RSDP) which is generated from optical Earth Observation (EO)

sensors by taking inputs from Agricultural application scientists as well as from literature

survey.

2. Quality of Remote Sensing data product

The RSDP is a digital information generated from the reflected optical signal acquired by the

optical earth observation sensors onboard a satellite orbiting in space. The quality of RSDP is

tied to its end-use (Shen et al., 2010) and hence “one-size fits all” principle is not valid as far

as quality definition of RSDP is concerned. In addition to the application-dependency of

quality parameters of RSDP, the RSDP quality requirement is also controlled by the sensor

which acquires the data, the satellite which carries the sensor and the processes through

which the data is converted to a Product. The 4-dimensional premise about the quality of

RSDP is pictorially depicted in .

Figure 1: 4 D RSDP quality

The space-borne sensor is characterized primarily by four characteristics, the list and its

significance is as follows:

1) Spatial Resolution: It is the smallest size of an object which the remote sensing sensor can

detect from its orbit. The Linear Imaging Self Scanning-3 (LiSS-3) sensor onboard

ResourceSat-2 (RS2) satellite has a spatial resolution of 23.5m. It means that LiSS-3 can

image an object of size 23.5m on ground from its orbit.

2) Spectral Resolution: It is the capability of a sensor to record the various hue-component

from the feature on the ground. The spectral band specification of RS2 LiSS-3 sensor, which

carries four coloured filters (Bands), is as given in Error! Reference source not found..

3) Temporal Resolution: The satellite orbits around the earth in order to image the earth

surface. The motion of satellite is controlled in such a way that it passes over the same

geographical location after a fixed time interval. This fixed time interval after which the

satellite passes over the same geographical location of the earth is called its revisit capability





or Temporal resolution. The LiSS-3 has a temporal resolution of 24 days (Resourcesat-2

Project Team, ISRO, 2011).

Table 1: Spectral Bandwidth of RS2 LiSS3 sensor

Band

Number

Spectral band width (in

micrometer) Hue

B2 0.52 - 0.59 Green

B3 0.62 - 0.68 Red

B4 0.77 - 0.86 InfraRed (IR)

B5 1.55 - 1.70 Short Wave InfraRed

(SWIR)

Source: (Resourcesat-2 Project Team, ISRO, 2011)

4) Radiometric Resolution: The remote sensing sensor is an electro-optical device which

records the intensity of a continuous (analogue) optical signal, reflected from the earth, in

discrete digital numbers (DN). This ‘sampling’ of Continuous signal into discrete numbers

require the sensor to be designed for pre-specified sampling-interval of the signal. This

sampling interval is termed as Radiometric Resolution or the Quantization (Q) of the remote

sensing sensor. A sensor with quantization Q-bits can sample the signal in 2Q levels and is

recorded as a positive digital numbers in the range from 0 to (one less than) “Q” power of 2.

LiSS-3 sensor has a 10-bit quantization and hence the number of levels in which it records

the signal is given by :

210=1024--------------------------------------(1)

Covering the digital numbers from 0 to 1023 (Levin, 1999).

The design specification of each of the above-listed characteristics of a sensor is determined

based on its intended use. A sensor which is launched to cater to crop monitoring or the one

which is launched for monitoring the movement of clouds or the one intended for Urban

planning can have very different specifications.

The acquisition of signal and its conversion into data product involves a hardware and a

software component. The optical remote sensing sensor is the camera-hardware carried by the

satellite which detects and records the sunlight reflected off the ground as it orbits the earth.

These signals are then processed digitally on ground systems to “pack” them into what is

termed as Remote Sensing Data Product. This is the software component which converts the

raw digital numbers, recorded by the sensor, into physical units which the application

scientists can utilize and interpret meaningfully. Thus the four dimensions on which final

quality of remote sensing data product (RSDP) depend are:

1. The features of the sensor;

2. The satellite on which it resides; which in-turn depend on the intended...

3. Application; the accuracy of which depends on the quality of...

4. Product generation process;

3. House of quality

Quality Function Deployment (QFD) is one of the Total Quality Management (TQM)

quantitative tools and techniques used to translate customer requirements and specifications

into appropriate technical or service requirements (Durga Prasad et al., 2010). Of the four

phases (Error! Reference source not found.) which the QFD approach comprise, the last three





phases take place within the organization which relates a) technical/engineering requirement

to part characteristics b) part characteristics to Process operations and c) Key process

operations to production control requirements. The first phase begins with translating the

Customer demands into technical/engineering requirements. This fundamental phase which

projects outside of the organizational boundaries to reach out to the customer is called the

House of Quality (HOQ) and is the major advancement in Quality perspective.

Figure 2: Four phases of QFD

Figure 3: Components of HOQ matrix

The HOQ is a matrix (Error! Reference source not found.) which consists of various

components relating the Customer Requirement or ‘Whats’ of Quality expectation to the

Technical Requirements or ‘Hows’ of satisfying this Quality expectation. The Roof of the

House shows the interaction between various technical requirements in terms of Positive or

Negative co-relationship. The central matrix shows the strength of inter-relationship between

‘Whats’ and ‘Hows’ of quality as Strong, Moderate or Weak. The Planning matrix on the

right hand side weighs the competition against the company’s performance for each ‘Whats’





from Customer’s perspective. The analysis of this can help in gaining competitive edge or

identify own inadequacy against the competition. The bottom most matrix specifies the target

values based on industry standards or the competitors. Hence it helps in setting an objective

target values in the form of ‘How Much’. There are various ways the House of Quality is

expressed (Hochman & O'Connell, 1993). However, there is no equivocalness in the purpose

served by HOQ.

3.1 Understanding the requirement of RSDP Customer

A high level committee formed by the Govt. Of India (Public Accounts Committee (2012-

13), 2012) to look into the matters of ‘Activities of National Remote Sensing Centre' based

on the C&AG Report No. 21 of 2010-11 (Performance Audit), relating to the Department of

Space (DoS) observed, among other things, that the NRSC failed to put sufficient efforts in

customization of data according to private users requirement. The first step towards

customization of any product is understanding the requirement of users (“Whats”) and then

incorporating these requirements into the product quality (“Hows”).

Customers of RSDP, unlike the customers of any other product, covers a very broad spectrum

of applications of remote sensing technology. Relating the ‘Whats’ and ‘Hows’ of Quality for

RSDP users/customers necessitate distinguishing the Customers of RSDP based on their end-

use of the product. This is because the ‘Whats’ and ‘Hows’ of RSDP strongly depend on the

use which the RSDP is put to by the customer (Shen et al., 2010).

The importance of understanding the user requirement as accentuated by TQM has been

realized by RS agencies even without making reference to TQM (Nieke & Reusen, 2007);

(Felbermeier et al., 2010). India being largely agricultural country (Oza et al., 2008), the

space program also focuses on the use of space technology for managing natural resources.

Also, the applications are developed by the scientists who are closely involved with the

Indian space program and subsequently these applications are transferred to various

departments (Ray et al., 2014) who are directly responsible for managing the natural

resources. Hence the requirements of ‘external’ customers are rendered by the ‘internal’

customers of RSDP.

This study uses House of Quality to understand the inter-relationship between user’s

expectation from the RSDP and its conversion to technical requirement. Simply put, this

study demonstrates House Of Quality approach of mapping the “Whats” of quality

requirement to “Hows” of Quality for RSDP users. The RS is a specialized field when

compared to any other industrial product and also it is difficult to specify the user

requirement for RSDP (Nieke & Reusen, 2007). As observed by (Deros et al., 2009), there is

no right or wrong ways to choose samples for measuring customers’ satisfaction surveys.

However, it is important that the samples are consistent with the evaluation objectives. With

this philosophy, the inputs on user requirements for Agricultural application was taken from

Agro-scientists with an experience of more than two decades. The inputs to HOQ were also

taken through literature survey.

4. House of quality for Agricultural application of RSDP

Two sources, input from application scientists involved in using RSDP for agricultural

applications and survey of literature related to utilization of remote sensing techniques for

various applications (Shen et al., 2010) (Oza et al., 2008) (Nigam et al., 2011) (Fox et al.,





2011) (Ke & Dang, 2010) (FERENCZ et al., 2004) , were employed for understanding

customer requirement.

The inputs from these sources pointed to unthought-of aspects of the user expectations. In

addition to the Sensor specification and RSDP itself, the mode of dissemination of RSDP

also featured in the customer requirement, which is not covered in this study. The review of

various studies related to the use of RSDP in agricultural application shows that the users are

required to carry out pre-processing related to geometric, radiometric and atmospheric

correction on the supplied data (Ke & Dang, 2010), (FERENCZ et al., 2004). This implicitly

points to the user’s expectation of a ground Reflectance, geo-referenced product which can be

used for their application forthwith.

Following six components were included in building HOQ:

1. Customer requirements

2. Technical Requirements

3. Inter-relationships

4. Technical Interactions

5. Planning Matrix

6. Target matrix (How Much)

The requirement as received from customer/user is shown in Table 2

Table 2: Voice of Customer

One of the published study, concerning evaluation of RS technology for agricultural

management (Moran, 2000) , noted a gap between what user wanted and what products

provided, thus emphasizing the need to understand the user requirement. In the study, the

user information requirements (Error! Reference source not found.) is categorized under

various heads like Measurement accuracy, Product delivery, Location accuracy, Revisit

period, Spatial resolution.

The next step in HOQ analysis (Error! Reference source not found.) is translating the Voice of

Customer (VOC) to Voice of Technician (VOT) and attribute the inter-relationship between

the two voices as 1,3 or 9; 9 depicting strongest relationship between the demanded quality

and the Functional Requirement identified by the technical team. This is the most crucial step





because from here onwards the product specifications and related processes become

internalized to the organization and separated from user’s lexicon of product quality. This

translation of Demanded Quality or “Whats” of the user to the Quality Characteristics or

“Hows” of the quality in the language of technician needs to be most faithfully carried out.

Table 3: Summary of PCM user information requirements

User Information Requirements

Parameter Specifications

Measurement Accuracy 70-75%

Product Delivery <24 hours

Location Accuracy 2m

Revisit Period 1 week

Management Unit 10-20m

Source: Adapted from (Moran, 2000)

This process is facilitated by the interrelationship-matrix between the two “languages of

quality”.

Table 2: House Of Quality showing translation from VOC to VOT

The Direction-row indicates whether to Maximize (▲) the target parameter, Minimize(▼)

the target parameter or Achieve (x) the targeted value. Broadly speaking, wherever the

specifications of the parameter are defined, the Direction aims at Achieving the targeted

value The qualitative parameters like Experience or Ambiguity requires to be either

Maximized or Minimized respectively.

In the Error! Reference source not found., the User Demanded Quality of Single Sensor

Usability has been shown to have strong relationship with the Functional requirement of

absolute Calibration. Additionally, a moderate relationship also exist with sensor’s spectral

characterization and band-to-band mis-registration. This means that if the sensor is

radiometrically and spectrally calibrated accurately in the laboratory prior to launch and





subsequently remains calibrated in absolute terms throughout the project life of the user, a

single Sensor data can be used for various Agricultural activities and hence bears a strong

inter-relationship between the “Whats” as demanded by the users and “Hows” as offered by

the technicians. However, the absolute radiometric calibration requirement has been assigned

relatively higher Difficulty index of 8 as it is difficult to maintain a accuracy target of 2% and

maintain it throughout the life time. On the other hand, the relative weightage given by the

user is also low. This situation can be comfortably used through the second demand-

Usability in conjunction with contemporary sensors- through another Quality Characteristics

- Relative Radiometric Calibration. If two sensors are cross calibrated, they can be used in

mutually exclusive manner for Agriculture application (Nigam et al., 2011).

The strong relationship with absolute calibration implies that the cross calibration will be

aided if the sensors are independently calibrated in terms of absolute radiometry. The Precise

Location can be achieved through Location Accuracy target of 1-pixel. Aesthetics and

Spectral Purity have been given maximum importance with values 7 and 6 respectively.

These two Customer Requirements identified as the most important rely on Photo Response

Non-Uniformity (PRNU) correction and Spectral characterization by the technical team

respectively. The aesthetics as well as spectral purity is also strongly related to band-to-band-

registration (BBR) quality. The Voice of Technician requires to control BBR in order to

satisfy the two most coveted demand of RSDP user. Also, ‘Aesthetics’ being higher on

importance scale, the detector-to-detector photo-response-non-uniformity, which is the major

cause of striping (Shen et al., 2010) requires to be characterized and corrected with utmost

accuracy. The spectral purity is the function of spectral filter and plays a crucial role in Cross

calibration of sensors with bands having identical bandwidth and coinciding central

wavelength but having different Relative Spectral Response (RSR). Instruments with

difference in spectral characteristics has its bearing on the results obtained through cross-

comparison of these sensors (Nigam et al., 2011).

The quality of RSDP as perceived by users is influenced by a) satellite- its position and

stability; b) sensors- in terms of available bands, spatial resolution, spectral characteristics,

calibration accuracy and ;c) Data product generation system: the use of geometric and

radiometric references, image processing techniques. Thus the Demanded Quality from a

RSDP User for a specific application is an amalgamation of satellite, sensor and product

generation processes. The parameter like Frequency of Data Availability is controlled by the

satellite orbit and frequency of launching the mission. It also partially depends on the agility

(the capability of a sensor camera system to be tilted in a specified angle) of the sensor. The

LiSS-4 camera onboard Resourcesat-2 can be tilted up to ± 26° in the across track direction

thereby providing a revisit period of 5 days (Resourcesat-2 Project Team, ISRO, 2011). The

mechanism is pictorially depicted in Error! Reference source not found.5.





Figure 4: LiSS-4 repeativity obtained through tilting mechanism

The Geo-synchronous orbit has been found to give constant viewing geometry with respect to

earth targets. This, it has been reported (Nigam et al., 2011), is because the orbital drift in

polar orbiting sensors are absent in case of geostationary sensors. However the decision of

launching a satellite in Geo-synchronous orbit is very difficult as it affects the sensor-design,

the launching mechanism, and the product generation process. Similarly the inclusion of

Thermal Band is also a difficult decision to take which increases the satellite configuration

and data processing regimen.The translation of PCM-user requirement (

User Information Requirements

Parameter Specifications

Measurement Accuracy 70-75%

Product Delivery <24 hours

Location Accuracy 2m

Revisit Period 1 week

Management Unit 10-20m


) to specifications for RS system has been shown in

Table 5: Specifications for a RS system

5.

Table 5: Specifications for a RS system

Parameters Values

Algorithm Accuracy 70-75%

Turnaround Time <24 hours

Geo-registration Accuracy 1 pixel

Repeat Cycle 3 days

Pixel Size 2-5m


As against the researcher’s objective of 90-95% accuracy in measurement, the user

requirement is limited only to 70-75 %. Similarly the repeat cycle, rather than repeat period,

was found to be more appropriate parameter from user’s perspective due to the fact that the

later is contained by selection of orbit and repeat cycle often resulted in less than half of

repeat period due to cloud coverage and adverse atmospheric conditions.

In terms of product, the users looked for information on anomaly and its cause so as to spend

resources on decision making rather than identification of errors. A 100% reliability in

product dissemination was a preferred requirement with turn-around-time of less than 24

hours. In spite of having image and related information, a personal interaction with

researchers for its interpretation was also a preferred requirement. A validated image and

“deliver what is promised” were the logical expectations from users.

The “Planning matrix” of HOQ begins with analyzing the strength of competition with that of

organization’s product. The users input on Demanded Quality and the competition’s position

on each with respect to that of organization’s product is shown in Error! Reference source not

found..





Table 5: Competitive Analysis

The pictorial depiction of competitive analysis for four sensors is shown in CA-plot Error!

Reference source not found.6.

Of the eleven parameters listed in Error! Reference source not found., the

Biophysical+Geophysical and Advanced Application products have been put in Level-3 and

Level-4 product category respectively by the user. This study aims at covering basic products

(Level-1 and Level-2) the accuracy of which will have its impact on subsequent levels of

product.

Figure 5: Competitive Analysis Plot





The comparative pictorial representation (Error! Reference source not found.5 indicate relative

strengths and weaknesses among competing sensors and allows the technical team to focus

the efforts to improve upon the weak parameters, especially those with higher Importance

Index from User’s viewpoint. Restricting the analysis to Level-1 and Level-2 products, the

Landsat/MODIS seems to be excelling on all expectations followed by SPOT/Sentinel1A-1B.

However referring to the Error! Reference source not found., the GEO mission INSAT tops the

satisfaction index of the user. INSAT acquires data every half an hour, although with 1km

resolution. An important parameter on which the organization’s product has been rated

“Worst” is the non-availability of Thermal Band. Combined with the Importance index of 10,

it might be an effort worth putting for the technical team to ensure that Thermal band is

included in future missions. The next step in HOQ is analyzing the correlation between

various technical requirements to understand their inter-dependencies.

Termed as the Roof of HOQ (Error! Reference source not found.7), also called correlation

matrix, express the conflict or agreement between multiple quality characteristics which gives

another criterion to the technical team to decide on the allocation of resources. This

correlation matrix between characteristics along with the difficulty level and Importance

index of the user, aids the Technical team to select the quality characteristics and allot

optimum resource to meet or compromise the customer satisfaction.

The Absolute Radiometric accuracy is positively related to relative radiometric accuracy,

PSF/MTF, PRNU correction, and Spectral characterization. This means that improvement in

Absolute radiometric accuracy will result in improved Relative Radiometry. Nevertheless, the

user’s requirement on absoluteness of radiometry is lower (Table 2, (Moran, 2000)).

Similarly an accurate spectral characterization will result in better Absolute radiometry.

Table 7: HOQ Roof indicating correlation between Quality Characteristics





The Band-to-Band registration (BBR) positively impacts the relative radiometry. This is

because, the spectral purity resulting from accurate BBR assists in cross-calibration of

sensors. The improved Location Accuracy, which has been rated highest (Error! Reference

source not found.6 and (Moran, 2000)) by users also has a positive impact on Relative

Radiometric accuracy. However, an agile mission satisfy increased frequency of data

availability can adversely affect the Location accuracy which can however be improved by

using more Ground Control Points and improved PSF/MTF of the sensor. The frequency of

data availability can be increased either by selecting GEO orbit or by launching constellation

of satellites with good relative radiometric accuracy. The BBR is negatively correlated with

number of bands because the presence of more number of bands will increase the probability

of mis-registration among bands. The PSF/MTF- a measure of spatial characteristics- is

positively correlated to radiometric as well as geometric accuracy and hence this parameter

needs to be precisely controlled. PSF/MTF quality itself is controlled by the accuracy with

which PRNU correction is carried out and are positively correlated. An precise PRNU

correction will reduce striping helping accurate spatial characterization and correction of the

sensor.

The first phase of QFD ends with a summarized analysis of HOQ. The summary lists out the

quality characteristics, the action to be taken, the target value and most importantly the

requirement weightage





(

8).

The two most important parameters from the view point of requirement weightage are

Absolute Radiometric Accuracy and Location accuracy. The absolute radiometric accuracy

results in accurate reflectance product, which has a high importance index. The availability of

Ground Control Points impacts positively the Location accuracy and thus results in having

high Requirement Weightage. The inclusion of Thermal Band has also found high

Requirement Weightage followed by Relative radiometric accuracy.

Table 8: Summary Analysis of HOQ

Once the HOQ has been developed, the subsequent phases are contrived for implementation

by the organization. Second phase of QFD translates the characteristics into part

characteristics followed by identifying key operations. Lastly, these key manufacturing





operations are assigned specific control requirements to insure reliability and consistent

quality.

5. Conclusion

Quality Function Deployment (QFD) has been successfully used in manufacturing sector in

optimizing the product development process through understanding the customer

requirement. The QFD begins with House of Quality (HOQ), arguably the most crucial phase

of QFD, which translates the Voice Of Customer (VOC) to Voice of Technician (VOT). This

study takes the HOQ approach to understand the requirement of RSDP users for the first

time. Of the many applications, the study focuses on agricultural application because in

country like India with large variety of geographical features and vast population to feed,

agricultural applications like crop forecasting and production monitoring through RS

technique is a fruitful strategy. During this endeavour, the peculiarity of defining RSDP

quality as against any other product and various agents of RSDP quality have been discussed.

The cyclical relationship-Application driving Specification driving RSDP driving Accuracy

of application- has also been highlighted.

The users of RSDP in agricultural application requires to carry out pre-processing of supplied

data related to geometric, radiometric and atmospheric correction. The users requirement of

accurate geo-referenced product translates to stable platform or availability of ground control

points. The VOT of accurate Absolute Radiometric Accuracy stems from the user’s demand

of a ground Reflectance product. The users in this study expect data on daily basis which is

evident from highest rating given to GEO sensor. These findings are in line with published

literature. Lastly, it is worth the effort to include Thermal Band to fully utilize the RSDP

capability.

5.1 Future scope

The application of RSDP for climate monitoring requires stringent quality requirements. A

similar study on translating Climate Quality data to corresponding technical requirement will

go a long way in monitoring the global warming phenomena. Additionally, a HOQ with a

holistic view of user requirements including method of data dissemination needs to be

developed.

Acknowledgement

The authors acknowledge the support from Director-Space Applications Centre (SAC) and

DD-Signal and Image Processing Area (SIPA) for carrying out research. The study would not

have been possible without unambiguous inputs from application scientists. Their

contribution to this study is thankfully acknowledged.

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