Keywords: Disruptive innovation, Digital platforms and ecosystems, Teaching cases, Long Tail, Artificial intelligence, Blockchain

This case study focuses on Netflix's technological journey, emphasizing its role as a startup that revolutionized a sector through tech-savvy approaches. Up until 2023, Netflix continues to innovate. This exploration delves into how Netflix has evolved as a platform organization, leveraging advanced technologies to enhance user experience and business models. The case discusses Netflix's use of Cloud Computing and proposes elaboration on Artificial Intelligence and Blockchain technologies. This case will be particularly useful for graduate and post-graduate students or MBA students in courses on digital technologies and business perspectives.

Learning Objectives:

To guide students in thinking about the applications of new technologies in businesses and their integration.

To lead students to reflect on the impact of new technologies (Cloud, platforms) on innovation and value creation, as well as in the positioning and differentiation of an organization.

To evaluate and discuss the business challenges of Artificial Intelligence and its characteristics.

To assess opportunities for blockchain development

Netflix Concept

Netflix, established in 1997 by Reed Hastings and Marc Randolph, initially emerged as an online DVD rental service. This innovative approach shifted from the use of VHS tapes to DVDs, a more advanced and long-lasting technology, facilitating DVD delivery by mail and obviating the need to physically visit rental stores. Early on, Netflix distinguished itself by employing recommendation algorithms, using customers' past preferences, ratings, and interactions to suggest movies and shows, a practice that began with its DVD rental service and still persists. The company's foresight in recognizing the potential of the internet for content distribution led to a significant pivot in 2007, with the launch of its streaming service. This move revolutionized how subscribers accessed movies and series, transitioning from physical DVDs to online streaming.

Competitive landscape

Contending with both traditional media companies and emerging streaming services, Netflix operates in a highly competitive streaming market. Conversely to what may seem, entering the streaming market is a long-standing venture. It implies high costs of content creation and/or acquisition, the need for robust technological infrastructure, the challenge of building brand recognition against established giants like Amazon Prime, and Disney+, navigating regulatory hurdles, and overcoming market saturation. These factors make it difficult for new companies to enter and compete effectively. Its key competitors include Amazon Prime Video, renowned for its extensive content range and perks for Prime members, and Disney+, distinguished by its classic and exclusive content from Disney, Pixar, Marvel, and Star Wars. HBO Max also stands out with high-quality original content and a strong brand in premium television. Traditional media players like NBCUniversal's Peacock and CBS's Paramount+ are also significant competitors, offering a blend of new and legacy content and leveraging their existing libraries to create original productions. Currently, the market is shifting towards fragmentation, with consumers often subscribing to multiple services.

Technology adoption: Data Centers and AWS

In 2008, a database issue that halted DVD shipments for three days prompted Netflix to accelerate its transition to cloud services, culminating in a full migration to Amazon Web Services (AWS) by 2015, with the closure of its last data center 12 . This included transferring its recommendation system and billing services to AWS. Despite being a competitor in the streaming market, Netflix embraced AWS for its server hosting, highlighting a strategic shift in its operational infrastructure.

At the same time, Netflix has chosen to continue operating its own content delivery network (CDN), which is essential to its business, especially considering that its video streams can account for up to 30% of peak-time Internet traffic in the USA. These critical infrastructures remain outside AWS, and are strategically positioned close to Internet service providers and major international data exchange nodes.

The two services, streaming and DVD, have coexisted for many years. Netflix had announced that it will end its DVD-by-mail rental service on September 29, 2023. This decision marks the end of an era, as the DVD-by-mail rental service had been one of Netflix's historical activities since its founding in 1997. Netflix CEO Ted Sarandos made the announcement in a blog post in April 2023, bringing to an end 25 years and 52 billion DVD-by-mail deliveries.

Throughout these years of migration, Netflix has developed its own adaptive streaming solutions, which automatically adjust video quality according to the speed of the user's Internet connection. This ensures a smooth viewing experience, even under variable network conditions.

Recommending how?

As part of developing its recommendation algorithm, the company offered a $1 million prize in 2009 as part of the "Netflix Prize", a machine learning competition to improve its recommendation algorithms. The aim was to increase the accuracy of their recommendation systems by 10%.

The "Netflix Prize" attracted teams of researchers from all over the world, and was won in 2009 by BellKor's Pragmatic Chaos team. The competition has helped stimulate research into machine learning and recommender systems. To this day, Netflix invests heavily in machine aiming to consistently enhance the overall 1 https://netflixtechblog.com/netflix-at-aws-re-invent-2015-2bc50551dead 2 https://www.silicon.fr/netflix-dit-adieu-au-datacenter-et-vive-aws-138875.html experience for its members and to refine every aspect of its service. This involves a broad scope of activities, including the development of prototypes, design and implementation of systems, as well as the rigorous evaluation of models and algorithms. These processes are thoroughly tested and validated using both offline experimental methods and online A/B testing strategies 3 .

Machine Learning

The figures to prove it

Netflix's recommendation system is integral to its success, influencing around 80% of what its users watch. With millions of subscribers worldwide, Netflix harnesses a vast amount of data for analysis and to gain valuable insights. The company's AIenhanced content tagging system has boosted metadata tagging accuracy by 20%, leading to more precise content recommendations. As reported by Ampere Analysis, in 2020, Netflix invested $17.3 billion in content, a significant expenditure informed by data-driven insights and predictive analytics. Demonstrating the power of AI in content strategy, in 2021, Netflix released over 440 original titles, reflecting its adept use of AI to make informed content creation decisions [START_REF]What type of cloud does Netflix use? Explain the different types of cloud and their advantages, and why Netflix chose AWS[END_REF] .

3 https://research.netflix.com/research-area/machine-learning [START_REF]What type of cloud does Netflix use? Explain the different types of cloud and their advantages, and why Netflix chose AWS[END_REF] https://medium.com/@EclipseAI/netflix-and-the-power-of-ai-unleashing-customer-insights-forunparalleled-personalisation-dabe43bcdea0

Facilitating global expansion

AWS' migration also fostered global expansion: Netflix rapidly extended its presence worldwide, becoming a major player in content streaming in many countries by 2010. And, at the same time, Netflix invested heavily in producing original series, films and documentaries, creating a vast catalog of exclusive content.

The platform experienced rapid growth in its subscriber base between 2010 and 2020, becoming the global leader in subscription video-on-demand (SVOD).

Netflix's evolution continued with adopting sophisticated machine learning and AI algorithms, enhancing its recommendation system to provide more tailored content suggestions. This has been integral in fostering subscriber loyalty and expanding content discovery.

Diversification and expansion : analyzing customers behaviors to offer them what they like

Starting in the 2020s, Netflix expanded its offering to include different genres of content, including children's series, documentaries, reality shows and more. Netflix uses cutting-edge technologies to produce original series and films. This includes using high-resolution cameras, digital special effects, and sometimes even artificial intelligence to optimize scripting and casting.

Netflix constantly collects and analyzes data on its users' behavior. This data is used to make strategic decisions, such as which original content to produce, where to invest in international expansion, and how to improve subscriber retention.

Netflix's business strategy diversified into movie and series production. Netflix's relationship with content suppliers evolved from heavy reliance on external content to a greater focus on producing its own original content. This strategic shift, driven by increasing licensing costs and the desire for unique offerings, has reduced its dependency on external content providers, although it still maintains significant relationships for international content and specific genres. Netflix's venture into original content has given it considerable bargaining power in attracting top talent, and these high-profile individuals also possess significant bargaining power due to their influence on a project's success. This transition allowed Netflix to not only control the narrative and quality of its content but also reduce reliance on external production studios. This strategic shift has also enabled Netflix to be a major player in the entertainment industry, competing with traditional film studios. Netflix's original productions have gained critical acclaim and popular success, further solidifying its position as a cultural and creative powerhouse This move into content creation is not just about expanding the range of offerings but also about shaping the future landscape of digital entertainment.

Fighting account sharing

Netflix has recently taken steps to combat abusive account sharing, including limiting the number of devices that can be simultaneously connected to an account and introducing verification methods to ensure that users share their accounts with people in their household.

Netflix has introduced limits on the number of devices that can be simultaneously connected to an account. For example, a standard subscription allows up to two devices to be connected at the same time, while the premium subscription offers up to four simultaneous connections. This is intended to discourage excessive account sharing.

Netflix has introduced verification mechanisms to ensure that users share their account with people in their household. For example, when a new device attempts to connect to an account, Netflix can send a verification code to the e-mail address associated with the account.

Netflix monitors account activity for abusive sharing patterns. If suspicious activity is detected, this may result in account suspension or closure.

In July 2023, Netflix recorded an increase of 2.6 million subscribers in the United States. A large proportion of these subscribers had opted for an ad-supported offer.

New services and diversification : Cloud gaming

Mobile and Cloud gaming Netflix began to expand its cloud gaming service trials after first launching pilot tests in Canada and the UK [START_REF]Evaluate and discuss Netflix's use of AI[END_REF] . The company had initially begun its mobile gaming efforts in 2021, where it offered 35 games on its mobile app including Oxenfree and Love is blind. the platform had quietly acquired several small to medium-sized studios, including Boss Fight Entertainment, Next Games, and Night School, a Californian studio whose one of its creations, Oxenfree, gradually proved to be a multiplatform success [START_REF]Based on your knowledge of the technology, recommend uses of blockchain for Netflix[END_REF] .

But it has since expanded its range of services to include this new cloud gaming offering. Already last autumn, Netflix had expressed its intention to enter the cloud gaming market. Netflix's entry into the cloud gaming sector is seen as a major development in the gaming industry, underlining the importance of the US market, which holds a substantial share of the global gaming market. With its broad subscriber base, Netflix could become a strong competitor to established cloud gaming players as it develops its platform.

  Machine learning at Netflix serves various critical functions. It plays a key role in shaping the catalog of movies and TV shows by identifying characteristics that contribute to the success of content. Additionally, it optimizes the production processes in Netflix's own studio for both movies and TV shows. Machine learning further enhances the technical quality of streaming, including optimizing video and audio encoding, as well as adaptive bitrate selection. This optimization extends to Netflix's in-house Content Delivery Network, which is a significant component of internet traffic in North America. Moreover, machine learning is instrumental in strategizing advertising expenditures, determining the mix of channels, and crafting advertising content to effectively attract new members who are likely to enjoy what Netflix offers.

https://techcrunch.com/2023/10/16/netflixs-cloud-gaming-service-begins-tests-in-u-s/

https://www.courrierinternational.com/article/analyse-pour-se-faire-un-nom-dans-le-jeu-video-netflixmise-sur-le-mobile


Solène Juteau

PREFER SLOW MOBILITY AND GET REWARDED! CREAT-ING A SUSTAINABLE DIGITAL ECOSYSTEM

Keywords: Sustainability, Digital business ecosystem, Case Study

While experts converge on the necessity of ecological sobriety to fight climate change, our case study aims at presenting an organization intent on building a new digital ecosystem to curb and reward lowcarbon behaviours. An exploratory case study following the idea of the creation of a startup organization offers the right empirical illustration of a digital ecosystem's emergence and creation. The results indicate that leveraging digital technologies can lead to a digital ecosystem engaging organizational members toward ecological sobriety. The emergent digital ecosystem offers a new outlook on IT for Green, on innovating with disruptive technologies and on actions needed to curb carbon emissions.

Introduction

As the only sector doubling the curve of GHG emissions in a decade [START_REF] Pitron | L'enfer numérique : Voyage au bout d'un Like[END_REF], digital technologies also have a paradoxical responsibility toward ecological sobriety. However, digital technologies also are a great source of innovation [START_REF] Baudier | The Impacts of Blockchain on Innovation Management : Sectoral Experiments[END_REF] and may paradoxically be leveraged in order to support and trigger more ecological sobriety [START_REF] Bohas | An empirical exploration of the role of strategic and responsive corporate social responsibility in the adoption of different Green IT strategies[END_REF]. A digital ecosystem puts forth innovative governance practices between organizations and people. As networked entities, digital ecosystems also rely on heterogeneous technologies, further increasing the innovative impact of their activity. We wonder how innovating with digital technologies can bring forth pathways toward more ecological sobriety, face the paradoxical responsibility of engaging people to consume less.

How can the emergence and evolution of a digital ecosystem sustain organizational and institutional carbon footprint reduction leveraging digital technologies?

To answer this question, we rely on a case study of an organization creating and developing a digital ecosystem rewarding low-carbon mobility transportation.

Exploratory research is called for concerning platform development role into the evolution of DBE [START_REF] Senyo | Digital business ecosystem : Literature review and a framework for future research[END_REF][START_REF] Sutherland | The sharing economy and digital platforms : A review and research agenda[END_REF]. Also of interest is the integration of new technologies as blockchain into DBE, especially if it covers governance and regulation into Internet-driven DBEs. Our research is extremely timely in these aspects, as it aims at answering these gaps through our case study.

We extend digital business ecosystem literature studying the creation and emergence of a digital ecosystem [START_REF] Senyo | Digital business ecosystem : Literature review and a framework for future research[END_REF] in phases : first, leveraging IoT and platform architecture and second, leveraging decentralized blockchain-based business ecosystem for more transparency and security. Practitioners will be particularly interested in the particularly unique use of digital business ecosystem leveraging disruptive technologies towards ecological sobriety.

We first present the theoretical background, then expose our methodology. We then move on to the main results and discuss them.

2 Literature review

Digital business ecosystems

Business ecosystems emerged with Moore [START_REF] Moore | Predators and prey : A new ecology of competition[END_REF] from a biological metaphor and were extended to digital ecosystems when digital technology was acknowledged to play a dominant role between members of the ecosystem [START_REF] Senyo | Digital business ecosystem : Literature review and a framework for future research[END_REF]. Digital ecosystems are defined as a community of individual, organizational, and networked entities that co-create value through information and communication technologies, also known as ICTs [START_REF] Jacobides | Towards a theory of ecosystems[END_REF][START_REF] Senyo | Digital business ecosystem : Literature review and a framework for future research[END_REF]. Participating members are linked together by activities that can be developed thanks to the characteristics of involved technologies. Without these technologies, these activities could not have emerged, however ecosystem participating members, and technologies, cannot be seen separately, it is the whole system, the relationships and the members that actively create the innovating collaborative environment [START_REF] Pappas | Big data and business analytics ecosystems : Paving the way towards digital transformation and sustainable societies[END_REF][START_REF] Spagnoletti | Agile practices and organizational agility in software ecosystems[END_REF][START_REF] Subramaniam | The future of competitive strategy : Unleashing the power of data and digital ecosystems[END_REF]. A perspective on digital ecosystem will favoring a lens seeing the ecosystem as whole is missing from the literature [START_REF] Senyo | Digital business ecosystem : Literature review and a framework for future research[END_REF]. Digital ecosystems are particularly relevant when studying the current extension of sharing economy literature [START_REF] Sutherland | The sharing economy and digital platforms : A review and research agenda[END_REF].

Disruptive technologies

Disruptive technologies are known as such because on the more technical aspect, they disrupt the way businesses are formed [START_REF] Gupta | Digital transformation in entrepreneurial firms through information exchange with operating environment[END_REF], create value [START_REF] Henfridsson | Recombination in the open-ended value landscape of digital innovation[END_REF], exchange information and goods along the supply chain [START_REF] De Reuver | The Digital Platform : A Research Agenda[END_REF], promote digital ecosystems formation [START_REF] Senyo | Digital business ecosystem : Literature review and a framework for future research[END_REF] and platformization [START_REF] Gawer | Platforms, Markets and Innovation[END_REF]. On the social side, disruptive technologies also alter how relate to authority, what we consider legitimate [START_REF] Drechsler | At the crossroads between digital innovation and digital transformation[END_REF][START_REF] Hinings | Digital innovation and transformation : An institutional perspective[END_REF], how workers symbolize their organizational identity [START_REF] Eriksson | Speaking things into existence : Ontological foundations of identity representation and management[END_REF], where and how they work [START_REF] Holmström | Recombination in digital innovation : Challenges, opportunities, and the importance of a theoretical framework[END_REF] but also how organizations interact with stakeholders [START_REF] Tapscott | How blockchain will change organizations[END_REF].

Innovating can take several forms, activating business models, processes or management practices. In a digital context, digital innovation needs to be considered as a part of whole, in particular, digital technologies possibilities and constraints need to be explicitly taken into account [START_REF] Nambisan | Digital Innovation Management : Reinventing Innovation Management Research in a Digital World[END_REF]. Disruptive technologies cover a broad array of digital technologies with specificities that go beyond traditional organizational boundaries and alter organizational identities. Their specificities trigger and facilitate what is known as digital transformation [START_REF] Nambisan | Digital Innovation Management : Reinventing Innovation Management Research in a Digital World[END_REF]. However adopting a digital transformation approach needs to be done taking into account the systemic effects between individuals and technologies [START_REF] Bijker | Shaping technology/building society : Studies in sociotechnical change[END_REF], favoring an ecosystem outlook (Subramaniam, 2022). .

Sharing economy

The sharing economy perspective is not new but has similarly been extended by reflections on the effects and possibilities brought by information and communication technologies. While the research remains exploratory, the ensemble perspective delivered by a socio technical approach is the most promising [START_REF] Dillahunt | The sharing economy in computing : A systematic literature review[END_REF] but is still very much lacking while business model and computer science sharing economy applications have taken up [START_REF] Sutherland | The sharing economy and digital platforms : A review and research agenda[END_REF]. Empirical research is needed to understand how technology mediates and is embedded in larger social contexts [START_REF] Nelson | Mobility of knowledge work and affordances of digital technologies[END_REF], or societal challenges like sustainability issues [START_REF] Sutherland | The sharing economy and digital platforms : A review and research agenda[END_REF].

Understanding ecosystems from multiple perspectives

As technical artifacts showcasing modular characteristics, digital technologies can be recombined and arranged according to the needs of their users or of ecosystem designers [START_REF] Holmström | Recombination in digital innovation : Challenges, opportunities, and the importance of a theoretical framework[END_REF][START_REF] Nambisan | Digital Innovation Management : Reinventing Innovation Management Research in a Digital World[END_REF]Spagnoletti, et al., 2015). The modularity enables to give rise to ecosystems mediating data and information.

The dependencies, value co-creation strategies, network externalities or governance practices are shaped and enabled by the relationships and mediations enabled in ecosystem. As networked entities, digital ecosystems rely heavily on the nature of the relationships enabled by the technical architecture.

To better understand how technology mediates and is embedded in larger social contexts, one needs to first explore the specificities of each technology.

Internet of Things, or IoT, " allows information to be exchanged between the product and its operating environment, its maker, its users, and other products and system" (Porter & Heppelmann, 2014, p. 6). Data is exchanged thanks to incorporated captors, labels or 'puce'. Once they can communicate, disruptive technologies appear to be enmeshed: at the core of the system, advanced capabilities of these objects can exist outside the product. These capabilities can become very advanced and leverage other disruptive technologies like AI, blockchain, smart contracts, cloud computing.

IoT enable four activities, made possible by the combination of connectivity and intelligence: monitor, control, optimize and autonomize [START_REF] Porter | How Smart, Connected Products Are Transforming Competition[END_REF].

Blockchain technology provides access to and warrants the authenticity of large amounts of data inside and outside the organization [START_REF] Baudier | The Impacts of Blockchain on Innovation Management : Sectoral Experiments[END_REF]. Blockchain supports AI to provide more insights, manage model sharing and data usage, and create a data economy (Casino et al., 2021).

Two different approaches cover the crossing of technologies and ecology research streams. Green IT focuses on the ecological impacts of digital technology while IT for Green refers to an application of digital technology with an ecological sobriety intention [START_REF] Olawumi | Cloud-based sustainability assessment (CSA) system for automating the sustainability decision-making process of built assets[END_REF].

Methodology

Case study

To study the question of the emergence and creation of a new digital ecosystem to sustain and reward the development of low carbon mobility, we chose to rely on a qualitative methodology [START_REF] Myers | Qualitative research in business and management[END_REF], the exploratory case study [START_REF] Baskarada | Qualitative case study guidelines. Baškarada, S[END_REF]. Indeed, cross referencing both low carbon mobility and digital ecosystem places our research as exploratory, since it has not been researched before.

The initiative of rewarding low carbon mobility is disruptive and underexplored. Relying on the creation of a new digital ecosystem is made possible through disruptive digital technologies. Because of the importance of the evolution across time, our research also needs to be longitudinal to reflect the different phases of the development (Van de Ven, 2007).

Data collection

We collected data from the beginning of the idea in 2019 throughout the creation and development of the case organization until 2022. We collected data mainly through bi-monthly interviews with the case organization founder, also relying on internal confidential documents like pitch decks and slide presentations, external press releases, email exchanges, market studies or organization presentations to prospective partners. We also observed during 40 hours pitch interviews with prospective partners and customers, temp workers management meetings for external consulting missions or internal meetings. Data analysis consisted in an events-based analysis (Hussenot et Missonier).

Case setting

The case organization, C02mmon is a startup aiming to incentive low carbon mobility through rewards that can be redeemed within an ecosystem of partners. C02mmon rewards individuals who favor low carbon mobility that can be tracked and measured through their application in a first stage. Every time a user avoids high carboned mobility transportation and prefers a low carboned mobility, he will earn greencents. The reward, greencents, can be redeemed through an ecosystem of partners. In exchange, partners such as restaurants, shops, cities or companies use the membership to the ecosystem as Marketing tool. Indeed they benefit from their public participation into a promoting sustainability ecosystem. Also, they will be charged much less when receiving greencents than when emitting them, further encouraging them to commit to the digital ecosystem. In addition, C02mmon will charge partners for using the system for official, financial, tax, HR or marketing purposes through the rewards and certificates it emits, by accepting and being visible in the application to attract new or existing customers interested by sustainable initiatives. Another phase is planned to develop a decentralized functioning of the ecosystem.

Case results

A process for an emergent digital business ecosystem involving disruptive emergent technologies

Phase zero : incubating the idea 2018: Initial founder's first book on digital currencies and registers is published in France, in which Initial founder mentions the concept of dedicated currencies and tax rates correlated to the environmental impact of products. October 2019 -January 2020: Initial founder takes part in the Wirecard Innovation Challenge 2019 first as an evaluator, then as a competitor with Tailored Contribution Systems, a proposition for a new generation VAT based solely on the carbon footprint of individual components and ingredients of products, as can be seen in this article. TCS relies on governmental stable digital currencies which are dedicated to a specific ingredient/component and to levying corresponding taxes when the product is purchased. Initial founder won the contest against 60 competitors but could never enjoy his prize because of the pandemic! The event (SXSW) and the flight to the event were cancelled. See here for more information. January -June 2020: During the finale of the Wirecard Innovation Challenge in Munich in January 2020 and at the beginning of his collaboration with Wirecard toward the implementation of TCS, several people advise Initial founder to turn his idea of a tax into a reward, which he does in February, after having contacted and received a negative answer from the EU about his idea of a VAT based exclusively on the carbon footprint of ingredients and components. Wirecard pays for a graphic designer and a consultant to accompany Initial founder. He also starts discussing with iExec, a figurehead of the French blockchain scene, about the adaptation of TCS into a congestion charge called Ignite. June 2020: Initial founder participates in Startup online, an event for founders organised by an Austrian incubator (Zero21) and meets Cofounder. Since they have a very similar concept, they decide to unite their forces. Initial founder accepts to use the name "Common" and has been working with Cofounder ever since. November 2020: Initial founder creates the first business plan for Common, which describes the B2C business model Common was then developing. Initial founder and Cofounder talk with an Italian public incubator emanating from the Lazio region (Rome) about a possible investment of €100k, but they choose to invest in other projects. June 2022, talks with the city of Leipzig (600,000 inhabitants) to implement Common into their public transportation system fail. Leipzig chooses another startup: Ryde up.

But in August 2022 Common is chosen by the city of Berlin for an intensive incubation program with financial support for the three associates.

Figure 2 Centralized multi-sided platform Common sell partners access to the greencent system they created and manage, as well as the services provided by the Common app, most notably measurement services, which enable users to be rewarded from this system through the activities they perform.

Common charge partners for using the ecosystem for official, financial, tax, HR or marketing purposes through the rewards and certificates it emits, by accepting greencents as a means of payment and being visible in the app to attract new or existing customers. Common charge them a monthly fee or a fee per greencent depending on their decision (see business model part). As partners give away something in exchange for greencents, they are charged much less when receiving greencents than when emitting them.

The validator system :

Common and the greencent system are two different entities: -In the greencent system, greencents are allocated to users according to the "1 greencent for 1 kg of non-emitted CO2" principle. The system receives raw, anonymised data from so called validators and converts this data into greencents. If it receives for example "user xyz performed 23 km by bus", then it would allocate 0.7659 GC to this user. The greencent system will be decentralised and conceived to be used by validators measuring various activities in various countries. -Validators are centralised, identifiable entities recruiting, onboarding and managing users and partners, and measuring user activities in specific areas and on specific territories, for example energy consumption in Belgium. They are all connected to the greencent system, to which they constantly send raw, anonymised data on user activity which the system converts into greencents as described in the previous point. Common is a validator of the greencent system in France, Germany and Hungary for mobility and food (hospitality).

Phase 2 API implementation

Common plans to implement an API to better track movement services. Doing so, they plan to become a true data provider. The objective is to develop an API protocol to let Common connect easily with other apps. For example, in mobility, connecting directly with the public transportation app or the app of an e-scooter provider such as Bolt or Lime will enable them to get the data first hand. For the hospitality sector, shops and supermarkets, connecting Common directly with their cashing system, implementing Common as a loyalty system based entirely on the CO2 footprint of goods and meals bought by users. The price of goods and services isn't taken into consideration at all. There are a great deal of private and public organisations who are already capable of measuring activities and managing customers: let's enable them to connect easily to the greencent system in order to expand rapidly and establish the greencent as the currency representing the environment in digital payment systems of the near future. Validators will measure specific activities on specific territories and report their measures in a standardised and anonymised way to the greencent system Measure at least one specific activity on one specific territory in terms of CO2 savings Apply the greencent allocation rules of the system, for example currently 0.1303 GC per km of cycling Get reference figures specific to their territory and their partners, for example the CO2 footprint of tramways of a city which belongs to their territory Acquire and manage users, which they could have had before joining the greencent system Provide them with measurement services as well as offers to spend their greencents through an app which must be free to download and use for users Deliver the greencent system with raw and anonymised data on activities performed by their users Common intends to establish the greencent system as a decentralised system. They plan to base on an existing cryptocurrency ecosystem such as the iExec ecosystem or the Flow service. They will use the Proof of Stake algorithm to continue to be in line with their sustainable goal. Indeed, contrary to Proof of Work algorithm, popularized by Bitcoin, Proof of Stake algorithm warrants authenticity with lesser energy consumption. At that stage, users will be equipped with a wallet and Common will develop a protocol to enable organisations to become validators As soon as every vehicle is equipped with a wallet and every good and meal has a digital identity on a digital register, Common plans to be connected with these and truly evaluate the carbon footprint needed for measure. Common will connect through the user wallet and the vehicle wallet with buses, subways, trains, e-scooters etc. in a couple of years, so we will know for sure 1) in which vehicle our user is currently traveling, 2) how much CO2 this vehicle emits per km and possibly 3) how many people are currently in the same vehicle, enabling them to calculate the reward in a very accurate and transparent way.

Discussion

We wondered how the emergence and evolution of a digital ecosystem could sustain organizational and institutional carbon footprint reduction leveraging digital technologies. To answer this question, we relied on a case study of an organization creating and developing a digital ecosystem rewarding low-carbon mobility transportation. Exploratory research had been called for concerning platform development role into the evolution of DBE [START_REF] Senyo | Digital business ecosystem : Literature review and a framework for future research[END_REF][START_REF] Sutherland | The sharing economy and digital platforms : A review and research agenda[END_REF].

Our case study and findings illustrate how the process of creating a Digital Business Ecosystem integrates the phase of a multi-sided platform to establish legitamcy and build a Business to business ecosystem that brings value to all partners and users. The multi-sided platform phase is important to build the legitimacy, bring together the ecosystem and bring value to all partners and users before going to a more complex phase. Acting as an anchoring organization, validating the data, implementing dual accounting, training partner organisations and educating to a CO2 accounting represents a transitionary phase. To fully build up awareness, practices and expertise, the anchoring organization, acting as a platform, first centralize the legitimacy, the expertise, knowledge and strategic vision. The platform also carries the responsibility for reference figures, movement tracking and true CO2 weight and gains.

Platform development and management [START_REF] Sutherland | The sharing economy and digital platforms : A review and research agenda[END_REF]. Benefits of platfom development come from network externalities, the capacity to scale and reach, which are echoed in our findings. In the creation of DBE, the need for legitimacy will go through scaling and reaching. Visibility is an accelerating factor. Getting known, and committing partners and users is a difficult first phase in the creation. While usually, when creating a business, an organization will look for a customer base, will share content and use marketing tools, among which some are going to be digital. In the case of the creation of an ecosystem, there is an added need to convince the value brought by the new ecosystem, on top of creating a sound business model. While transactions have also been outlined as central in platform research (S&J 2018), our findings show how the evaluation, confrontation to a reference figure and truthfulness of data is central to the reflection underlying the digital technologies evolution process. Indeed, the integration of blockchain technology puts forward the will to decentralize the responsibility scale and reach rapidly to better automatize transactions and validate them through APIs with other Internet-based applications.

The control and governance of the service is central to understanding the integration process of digital technologies.

The integration of new technologies as blockchain into the DBE comes later. Governance and regulation have been outlined as central in the first transitionary phase.

The transition from a native external application and service, centralized by an external organisation through a platform organisation to a fully decentralized digital ecosystem suggests sharing economy platforms could be a transitionary phase towards a digital decentralized ecosystem, where certificates are emitted by partners aiming to reward individuals who prefer low carbon emission mobility over other types of mobility.

As our case shows, to fully muster a digital ecosystem, an anchoring organisation is necessary before evolving towards more decentralized, waiting for practices and expertise to be transferred to participating members.

Conclusion

This paper discussed the launch of a digital ecosystem, committed to support and reward low carbon footprint mobility of individuals and grant higher visibility to organisations who commit to this emergent ecosystem. This digital ecosystem is birthed out of sheer will of a start-up organisation that transitions from leveraging IoT and mobile phones to fully activate a digital ecosystem relying on a digital currency and blockchain certificates.

Exploratory empirical research is needed to provide an illustration of the platform and digital ecosystem creations [START_REF] Holmström | Recombination in digital innovation : Challenges, opportunities, and the importance of a theoretical framework[END_REF][START_REF] Subramaniam | The future of competitive strategy : Unleashing the power of data and digital ecosystems[END_REF][START_REF] Tan | Leveraging Digital Business Ecosystems for Enterprise Agility : The Tri-Logic Development Strategy of Alibaba[END_REF]. Our research suggests platform could be a transitionary phase towards a digital ecosystem leveraging innovative disruptive technologies. We also extend previous research on digital ecosystems [START_REF] Senyo | Digital business ecosystem : Literature review and a framework for future research[END_REF]

Figure 1

 1 Figure 1 : chrnonology Common 2018 Idea : : Initial founder's first book on digital currencies and registers is published in France 2019 the Wirecard Innovation Challenge 2019 first as an evaluator, then as a com-

  petitor with Tailored Contribution Systems, a proposition for a new generation VAT based solely on the carbon footprint of individual components and ingredients of products, as can be seen in this article. TCS relies on governmental stable digital currencies which are dedicated to a specific ingredient/component and to levying corresponding taxes when the product is purchased.

	2020	Wirecard finale : advice to turn the tax into an ecosystem
		Meeting with cofounder via Startup online, an event for founders organised by
		an Austrian incubator (Zero21)
		1st business plan ( B2C business model)
		Talks with an Italian public incubator from Rom about a possible investment of
		€100k, but they choose to invest in other projects.
		1st website published based on WordPress.
	2021	B2B business model adoption
		Programming for first application
		Reflection around IoT (movement measurement algorithm)
		Android application publication
	2022	first partner acquisition campaign in Paris
		Other partner acquisition campaigns in Munich
		4 th business plan
		Apple app publication
		Leipzig refusal
		Berlin success

  Our first website is published; it is developed by Initial founder and based on WordPress. January -March 2021: Initial founder and Cofounder participate in eBridge, an acceleration program led by the Munich University of Applied Sciences (Hochschule München), get a dozen of coaching sessions and a prize at the final pitch session, see attachment. They also recruit two additional team members, Youjin and Michael: Initial founder and Cofounder are not alone anymore. All work with the French start-up Rob on the second version of the Common website. April -June 2021: Common adopts a B2B business model. A new business plan is published in June. They participate in MGIS, an acceleration program led by the three main universities from Munich. Social media manager joins Common; she takes over our social media communication.

	They	part	ways	with	Michael		and	Youjin	in	June.
	In March 2022, a first customer acquisition campaign is led with students in Paris.
	In	April	2022,	a	fourth	version	of	the	business	plan	is	published.
	May 2022: A

Phase 1 creating the greencent ecosystem : a centralized multi-sided platform based on IoT and an application After a period of incubation, Common adopts a business to business model. They develop an app and recruit programmers to do so.

August 2021Common starts testing the first version of the app developed by 1st programmer; two interns work with 1st programmer on the movement measurement algorithm. September 2021: 1st programmer publishes the app on Android; theyrecruit 3rd programmer to work on the Apple version. 3 rd programmer is a student in computer science at the RWTH (Aachen). October 2021: the third version of the business plan is published. Common participates in a contest called M-Sec with a Japanese city (Fujisawa) to use Common for garbage collection as well as a Horizon tender with partners from several European countries (France, Slovenia, Italy, Austria). November 2021: Common is a finalist of the Circular Urban Challenge in Spain. Initial founder presents Common physically in Logroño, La Rioja.

Table 1

 1

	Criteria for validators


Adil Baigadilov
email: a.baigadilov@brgm.fr

Stéfan Colombano

Sagyn Omirbekov

Maxime Cochennec

Dorian Davarzani

Fabien Lion

Laurent Oxarango

Hugues Bodiguel

Stability assessment of foam enhanced by surfactant, polymer and nanoparticles in the presence of petroleum hydrocarbons

Light Non-Aqueous Phase Liquids (LNAPLs) pose significant environmental and health risks and can be challenging to remediate. Pump-and-treat method's efficiency rarely exceed 60% due to the heterogeneity of the subsurface [START_REF] Colombano | Free Product Recovery of Non-aqueous Phase Liquids in Contaminated Sites: Theory and Case Studies[END_REF].

The use of aqueous foam in environmental remediation has already proven to be a promising technique for in situ remediation of NAPLs [START_REF] Maire | Surfactant foam flushing for in situ removal of DNAPLs in shallow soils[END_REF].

However, contact with petroleum compounds tends to significantly deteriorate the stability of foam, making it's application challenging. [START_REF] Forey | Saponin foam for soil remediation: On the use of polymer or solid particles foam resistance against oil[END_REF].

This study employs a range of additives, including co-surfactant, polymer and nanoparticles, with the primary focus on the use of foam for environmental remediation purposes in order to address this challenge.

Main objectives of the study:

Highlight the challenges associated with foam stability in presence of LNAPL, particularly refined petroleum hydrocarbons;

Investigate the synergetic effects of two environmentally friendly surfactants, sodium dodecyl sulfate (SDS) and cocamidopropyl hydroxysultaine (CAHS) on foam performance; Assess the stability of foam generated with two or more bio-additives in the presence of LNAPL, both in bulk and in highly permeable porous media. SC mixture is able to strengthen the foam with increased pressure drop more than 2 times, and increased recovery by 12.7 % NPs shows better performances in porous media than anticipated from bulk foam exp. ΔP increased more than 2.5 times, RF increased by 36% XG polymer has the best ability to enhance the foam. ΔP is almost 3 times higher than SDS alone. 91.8 % of Diesel is recovered by SC + XG mixture

Main conclusions:

Diesel has detrimental effect on foam stability both in bulk and porous media Synergy in SC surfactant mixture improves stability of foam with and without diesel. The ratio of SDS and CAHS was identified as crucial, with a 50:50 ratio demonstrating the highest tolerance to diesel and improved foam stability XG polymer at lower concentrations up to 0.2 g/L drasticaly improves bulk foam stability and noticeably enhances recovery of diesel in highly permeable porous media Nanoparticles require additional study since their behaviour is not trivial

  mixture SC (SDS + CAHS) Effect of additives on foam performance in porous mediaImpact of diesel on foam stabilityEffect of additives (polymer & NPs) on foam stability in presence of dieselDrainage half-life time (s)Polymer concentration (g/L) a 0 a 0.1 a 0.2 a 0.5 SC has no effect on foam liquid drainage time after foaming (s) Liquid height (cm) Diffusion of gas Liquid drainage SDS alone has poor efficiency in porous media (similar to bulk foam exp.). ΔP reduced 2.4 times in presence of diesel. Maximum RF is 49.6 %.

Fig. 3 :

 3 Fig.3: Drainage of bulk foam (amount of liquid drained from foam).

Fig. 1 :Fig. 2 :FOAMFig. 4 :

 124 Fig. 1: Working principle of the Dynamic Foam Analyzer -DFA100.

Fig. 8 :Fig. 5 :Fig. 6 :Fig. 7 :

 8567 Fig.8: Pressure drop of as a function of injected PV for different foaming formulations.

Fig. 9 :

 9 Fig.9: Recovery Factors of diesel for each employed foaming formulations.


Hung Quoc

Jean-Michel Vu

Anh Minh Pereira

Tang Navier

Dr Anh Minh Tang

Effect of clay content on the thermal conductivity of unfrozen and frozen sandy soils

Keywords: clay content, thermal conductivity, prediction, unsaturated soils, frozen soils

Thermal conductivities of five sandy soils subjected to freezing-thawing and drying-wetting cycles were determined. Specimens were prepared at five different clay contents of 0, 5, 10, 15, 20% prior to compaction at the Proctor maximum dry density. Soil specimens were saturated prior to freezing-thawing and drying-wetting experiments. During a freezing-thawing cycle, soil's thermal conductivity and unfrozen water content were measured at different temperatures ranging from -3 °C to 0 °C. During a drying-wetting cycle, soil's thermal conductivity was measured at different water contents. The experimental results were then compared with predictions by two of the most appropriate existing models, namely De Vries's and Johansen's models, in order to provide insights into the effects of clay content on soil thermal conductivity at (saturated and unsaturated) unfrozen and frozen states.

Introduction

Soil thermal conductivity is one of the key parameters for designing the balance and transfer of heat in many areas of engineering. In cold regions, for instance, soils exist in two states:

unfrozen and frozen, depending on many factors such as their composition, pressure and particularly environmental condition [START_REF] Andersland | Frozen Ground Engineering[END_REF]Cui et al., 2020a;[START_REF] Johansson | Artificial ground freezing in clayey soils[END_REF][START_REF] Tian | A simplified de Vries-based model to estimate thermal conductivity of unfrozen and frozen soil[END_REF][START_REF] Zhao | Thermal properties of sandy and peat soils under unfrozen and frozen conditions[END_REF]. The state change strongly alters soil thermal conductivity (Cui et al., 2020b;[START_REF] Farouki | Thermal Properties of Soils[END_REF][START_REF] Ji | Semiempirical Correlation between P-Wave Velocity and Thermal Conductivity of Frozen Silty Clay Soil[END_REF] leading to complexity in the prediction of soil thermal conductivity.

In general, thermal conductivity of soils can be measured using two approaches, based either on steady-state or transient-state regimes [START_REF] He | A review and evaluation of 39 thermal conductivity models for frozen soils[END_REF]Zhao et al., 2019). Each approach includes various methods, such as steady-state heat flow meter, steady-state flat plate, transient spherical probe, transient plate source and transient heat pulse (Cui et al., 2020b;Zhao et al., 2019). Within the steady-state approach, thermal conductivity is determined from the proportionality between heat flow and the applied temperature gradient when heat transfer in soil has already reached the steady state. Reversely, within the transient-state approach, a heat input is applied to the tested soil during a certain time and thermal conductivity is determined from the measurement of increasing rate of soil's temperature and the calibration of Fourier's heat transfer law. These methods have been commonly used for measuring thermal conductivity of unfrozen soils. However, their efficiency when applied to frozen soils is questionable. For steady-state methods, a temperature gradient across a soil specimen can result in water redistribution and ice melting. These problems were also found in application of transient-state methods but with smaller impact because small heat input can be considered [START_REF] He | A review and evaluation of 39 thermal conductivity models for frozen soils[END_REF][START_REF] Yan | A generalized model for estimating effective soil thermal conductivity based on the Kasubuchi algorithm[END_REF].

Various studies investigated the effects of several parameters on thermal conductivity of unfrozen soils such as temperature [START_REF] Sepaskhah | Thermal Conductivity of Soils as a Function of Temperature and Water Content[END_REF][START_REF] Tarnawski | Thermal conductivity of soils at very low moisture content and moderate temperatures[END_REF], water content [START_REF] Abu-Hamdeh | Thermal properties of soils as affected by density and water content[END_REF][START_REF] Nguyen | Water retention and thermal conductivity of a natural unsaturated loess[END_REF][START_REF] Smits | Thermal Conductivity of Sands under Varying Moisture and Porosity in Drainage-Wetting Cycles[END_REF], dry density [START_REF] Abu-Hamdeh | Thermal properties of soils as affected by density and water content[END_REF][START_REF] Tang | A study on the thermal conductivity of compacted bentonites[END_REF], salinity [START_REF] Abu-Hamdeh | Soil thermal conductivity effects of density, moisture, salt concentration, and organic matter[END_REF], mineralogy [START_REF] Bristow | Measurement of thermal properties and water content of unsaturated sandy soil using dual-probe heat-pulse probes[END_REF]Côté and Konrad, 2005b) and fines content [START_REF] Zhang | A new generalized soil thermal conductivity model for sand-kaolin clay mixtures using thermo-time domain reflectometry probe test[END_REF][START_REF] Zhang | Effects of particle size and fines content on thermal conductivity of Quartz sands[END_REF]. These effects are directly related not only to thermal properties of each component but also to the arrangement of soil particles.

A significant effort in measurement of thermal conductivity of frozen soil has been made, particularly, over recent years [START_REF] Kersten | Thermal properties of soils[END_REF][START_REF] Lu | Experimental study on the thermal conductivity of aeolian sand from the Tibetan Plateau[END_REF][START_REF] Penner | Thermal conductivity of frozen soils[END_REF][START_REF] Tian | An improved thermotime domain reflectometry method for determination of ice contents in partially frozen soils[END_REF][START_REF] Zhang | Variation of the thermal conductivity of a silty clay during a freezing-thawing process[END_REF]. These studies also demonstrated the effects of various parameters on thermal conductivity of frozen soils. The presence of ice (replacing water) changes thermal properties of components (2.2 W/(m.K) for ice and 0.57 W/(m.K) for liquid water) and also in arrangement of grain particles (ice segregation). Consequently, the ratio between ice and unfrozen water strongly affects the heat conduction in soils. In addition to temperature, the amount of unfrozen water or ice in frozen soils also depends considerably on the nature of fine particles and their fraction [START_REF] Li | Pulsed NMR Measurements of Unfrozen Water Content in Partially Frozen Soil[END_REF][START_REF] Tian | Freezing and thawing characteristics of frozen soils: Bound water content and hysteresis phenomenon[END_REF][START_REF] Tice | The prediction of unfrozen water contents in frozen soils from liquid limit determinations 76[END_REF]Zhang et al., 2020).

Since several parameters can affect the thermal conductivity of soils, the prediction of the thermal conductivity requires adequate models, able to represent this complexity. For unfrozen soils, many models, developed over the past decades, have shown their efficiency [START_REF] De Vries | Thermal properties of soils[END_REF][START_REF] Farouki | Thermal Properties of Soils[END_REF][START_REF] Johansen | Thermal conductivity of soils[END_REF][START_REF] Kersten | Thermal properties of soils[END_REF][START_REF] Mickley | The Thermal Conductivity of Moist Soil[END_REF]. In particular, by reviewing and evaluating 14 models applied to 62 sands from 20 studies, recently Wang et al. (2020) found that the models of [START_REF] Chen | Thermal conductivity of sands[END_REF] and [START_REF] Zhang | A new generalized soil thermal conductivity model for sand-kaolin clay mixtures using thermo-time domain reflectometry probe test[END_REF] are the most accurate for thermal conductivity of unfrozen sands over the full range of water content. Unfrozen soils models were also applied for predicting thermal conductivity of frozen soils (Côté and Konrad, 2005a;[START_REF] Johansen | Thermal conductivity of soils[END_REF][START_REF] Tian | A simplified de Vries-based model to estimate thermal conductivity of unfrozen and frozen soil[END_REF]. By reviewing and evaluating 39 thermal conductivity models for frozen soils, recently [START_REF] He | A review and evaluation of 39 thermal conductivity models for frozen soils[END_REF] identified four best models corresponding to four categories consisting of linear/non-linear regression model, physical model, mixing model and normalized thermal conductivity model. However, none of these models could prove a high prediction accuracy. The occurrence of ice at decreasing temperature indeed complicates the role played by soil components (content and distribution) on soil thermal conductivity.

Among various parameters, fine particles play an important role in filling the pores between coarse grains and also in increasing surface contact between coarse grains. Depending on the nature of each type of fine particles, their impact on thermal conductivity of soil varies. In the study of [START_REF] Zhang | Effects of particle size and fines content on thermal conductivity of Quartz sands[END_REF] with pure quartz sand, thermal conductivity of sands was increased to a peak value by increasing the fines content to a critical value. However, the critical fines content depends on the mean grain size ratio of fines to coarse grains, the grain shape and also moisture state. Additionally, in the case of clay-sand mixture, [START_REF] Zhang | A new generalized soil thermal conductivity model for sand-kaolin clay mixtures using thermo-time domain reflectometry probe test[END_REF] showed lower values of critical clay content.

Measurements of thermal conductivity were also performed on different types of frozen soils [START_REF] He | A review and evaluation of 39 thermal conductivity models for frozen soils[END_REF][START_REF] Tian | A simplified de Vries-based model to estimate thermal conductivity of unfrozen and frozen soil[END_REF]. [START_REF] Kersten | Thermal properties of soils[END_REF] conducted thermal conductivity measurements on 19 soils at various densities, water contents and negative temperatures (-28 °C, -18 °C and -4 °C). These 19 studied soils are very diverse in terms of grain size, from clay to gravel. The study showed a higher range and a lower range of thermal conductivity corresponding to sandy soils and clayey soils, respectively. [START_REF] Penner | Thermal conductivity of frozen soils[END_REF] studied thermal conductivity of two clays at temperatures between 0 °C and -22 °C. Afterwards, [START_REF] Penner | Thermal conductivity laboratory studies of some Mackenzie Highway Soils[END_REF] also studied 10 other natural soils of different grain sizes at -5 °C and -15 °C. These two studies showed that, in the high-water content range, thermal conductivity of soils increases with increasing grain size at frozen state. Recently, [START_REF] Tian | A simplified de Vries-based model to estimate thermal conductivity of unfrozen and frozen soil[END_REF] showed measurements on five soils consisting in loams, clays and sands at -10 °C. The measurements were combined with data of previous studies to elaborate a simplified de Vries-based model for better prediction. In addition, [START_REF] Lu | Experimental study on the thermal conductivity of aeolian sand from the Tibetan Plateau[END_REF] specifically studied thermal conductivity of one aeolian sand at -15 °C. In general, these previous studies mainly focused on the effects of dry density or water content on thermal conductivity of frozen soils. Consequently, no experimental study has been conducted on the effects of clay content on frozen soils. However, clay content would modify the microstructure of sandy soils and its hydro-thermal behaviour such as soil freezing characteristic curve [START_REF] Vu | Effect of fines content on soil freezing characteristic curve of sandy soils[END_REF].

The present study aims at systematically investigating the effect of clay content on thermal conductivity of unfrozen and frozen sandy soils in two series of experiments corresponding to freezing-thawing and drying-wetting tests. Clean sand was mixed with clay to obtain sandy soils with clay content of 0, 5, 10, 15, and 20% by dry weight prior to compaction at the Proctor maximum dry density followed by a saturation stage. For freezing-thawing process, soil's thermal conductivity and unfrozen water content were measured at different soil's temperatures (ranging from -3 °C to 0 °C) during a freezing-thawing cycle. In parallel, measurement of soil thermal conductivity during a drying-wetting cycle was performed on the same materials. Finally, the performance of two of the most appropriate models (after a recent review) in predicting the obtained experimental results was assessed in order to provide indepth understanding of the heat conduction process in unfrozen and frozen soils within various clay contents.

Materials and experimental method

Material

Fontainebleau sand (97-99% quartz) and Speswhite Kaolin clay, whose physical properties determined by [START_REF] Boussaid | Sols intermédiaires pour la modélisation physique : application aux fondations superficielles[END_REF] are shown in Table 1, were mixed with distilled water to obtain optimum water content determined from Standard Proctor compaction (ASTM, 2021) curves obtained on the same soils. Prior to compaction in a rigid metallic cylindrical cell (150 mm in height and 150 mm in diameter) to reach the predetermined density, the wet soils were packed in a plastic bag for at least 24 h for moisture distribution homogenisation. Fig. 1 presents the grain size distribution of the investigated soils (the name of each soil corresponds to its clay content: for instance, S10 corresponds to a soil having 10% of clay and 90% of sand in dry mass). Table 2 shows physical properties of specimens in two experiments: freezing-thawing (noted by T1) and drying-wetting (noted by D1). The dry density of the samples shown in Table 2 is close to the maximum dry density of the corresponding sand-clay mixture. Water content at compaction was taken equal to the optimum water content determined by [START_REF] Boussaid | Sols intermédiaires pour la modélisation physique : application aux fondations superficielles[END_REF]. It is worth noting that optimum water contents for intermediate fines content (5% and 15%) have been interpolated from the available data.

Setup and procedure for freezing-thawing experiments

The experimental setup used for freezing-thawing experiments is shown in Fig. 2. The sensors used in the freezing-thawing experiments are shown in Table 3. Soil temperature was measured with a PT100 sensor, soil thermal conductivity was measured with a KD2-Pro (RK-1) probe and soil volumetric water content was measured with a ML2x Thetaprobe sensor.

Two empirical equations due to (1) [START_REF] Smith | Measurement of the unfrozen water content of soils: comparison of NMR and TDR methods[END_REF] and ( 2) [START_REF] Topp | Electromagnetic determination of soil water content: Measurements in coaxial transmission lines[END_REF] were used to infer soil volumetric water content from apparent dielectric constant for frozen and unfrozen soil, respectively:

where is the apparent dielectric constant and is the unfrozen volumetric water content.

After compacting the soil specimen in the cylindrical cell at optimum water content to the desired dry density (Table 2) using Standard Proctor compaction procedure, the sensors were inserted into the soil as shown in Fig 2 . Afterwards, an expanded polystyrene layer was placed to cover the soil specimen for heat insulation purposes. The soil specimen was then saturated by injecting water from its bottom (for 0.5 to 2 days depending on clay content) until a 10 mm layer of water is visible on the top of the specimen. Afterwards, the cell containing the soil specimen was immersed in a temperature-controlled bath (F38-EH JULABO with ±0.03 °C accuracy), as shown in Fig. 2, to start the freezing-thawing experiment. The bath temperature was first set at a temperature between 0 °C and -1 °C (slightly higher than the expected temperature of spontaneous ice nucleation). After the temperature stabilised at this initial state, it was then decreased in steps of 0.1 °C to freeze the soil pore water. Once freezing was triggered (most of the liquid water was transformed into ice), the temperature was decreased in steps of 0.2 °C until -2 °C or -3 °C to determine the change in thermal conductivity and liquid water content upon further cooling. Afterwards, during the heating path, the bath temperature was increased in steps of 0.2 °C to 0 °C in order to thaw the frozen soil. During both cooling and heating paths, after the equilibrium was reached at each step, the thermal conductivity was measured at least three times with a 1-hour interval between two consecutive measurements to verify the equilibration of soil's temperature and unfrozen water content. The equilibrium state was considered reached when changes (during 2 h) in the soil temperature and volumetric unfrozen water content (measured by the sensors) were negligible (< 0.05 °C for temperature and < 1% for water content). Data from the tensiometer, providing cryo-suction developing when the freezing process is triggered, were not analysed because they are out of the scope of this paper.

Setup and procedure for drying-wetting experiments

The drying-wetting experiments conducted in this study followed a similar methodology to that described by [START_REF] Nguyen | Water retention and thermal conductivity of a natural unsaturated loess[END_REF]. The sensors and experimental setup are shown in Table 3 and Fig. 3. A thermal conductivity sensor KD2-Pro (TR-1) was installed in the centre of the specimen while two other sensors including water content sensor and tensiometer were installed close to the cell wall. This configuration minimizes the effect of these two sensors on the thermal conductivity sensor. The empirical equation (2) due to [START_REF] Topp | Electromagnetic determination of soil water content: Measurements in coaxial transmission lines[END_REF] was also used to infer volumetric water content from apparent dielectric constant for soils during drying-wetting processes. Data from the tensiometer, providing suction developed during the initial drying, were not analysed because they are out of the scope of this paper. After compaction and saturation, the soil specimen was first subjected to a drying path in steps. For this purpose, two holes (50 mm in diameter) were created on the top cover allowing soil water to evaporate. For each drying step, the two holes were opened to allow evaporation from the top surface. A fan was put next to the specimen to increase the evaporation rate.

After a certain duration, the two holes were covered by two lids to avoid moisture exchange with the atmosphere. The equilibrium moisture was reached after several hours. Moisture equilibrium was tracked by volumetric water content and suction sensors. When water evaporation took place, the measured suction increased quickly and the measured water content decreased quickly. During the equilibrium phase, suction decreased and water content increased slowly to reach equilibrium. Similar phenomena have been observed in [START_REF] Nguyen | Water retention and thermal conductivity of a natural unsaturated loess[END_REF]. Thermal conductivity measurements were performed at equilibrium prior to the subsequent drying step.

After the drying path, the soil specimen was also subjected to a wetting path in steps. For each step, a small quantity of water was added through the two holes on the top cover, which were then closed for several hours to ensure that water was redistributed homogeneously within the specimen.

The homogeneity of water distribution (equilibrium state), during both drying and wetting paths, was assumed to be reached when changes in soil suction and soil volumetric water content during a 1-hour period were negligible (< 1 kPa for suction and < 1% for water content). At the end of each step, soil thermal conductivity was measured three times with a 1-hour interval between two consecutive measurements.

Experimental results

Thermal conductivity of frozen soil

As an example of a freezing-thawing test, the results of test S10-T1 are shown in Fig. 4, where soil temperature ( ), volumetric unfrozen water content ( ) and thermal conductivity ( ) are plotted versus elapsed time. Initially, was imposed at -1.2 °C without inducing freezing (formation of ice). A cooling path was first applied by decreasing in steps of 0.1 °C. During the period where remained higher than -1.6 °C, was controlled through the bath's temperature, and remained constant. At 77 h (when was imposed at -1.6 °C), freezing process occurred showing an abrupt increase of to -0.1 °C prior to a progressive decrease to reach the imposed temperature (-1.6 °C) again. At this time, decreased to 3%

(because of the formation of ice) while increased from 4 W/(m.K) to 5 W/(m.K). After this first formation of ice, was further decreased in steps of -0.2 °C from -1.7 °C to -2.9 °C.

increased while slightly decreased during this further cooling path. When reached -2.9 °C, a heating path was imposed in steps of 0.2 °C. During this path, increased progressively while decreased. It is noted that the two particularly high values of at 400 h and 560 h were observed but they were not considered as reliable values due to the effect of latent heat on measurement. Actually, during the measurement by the linear heat source method, a constant heat power was supplied and slightly heated the probe. Thermal conductivity was estimated by the rate of probe's temperature increase with the logarithm of time. When measurements were performed close to the freezing temperature, this heating would induce water thawing in the vicinity of the probe. This thawing required heat (latent heat) and reduced the probe's temperature (inducing an overestimation of thermal conductivity). These measurements are thus not presented in the following figures. Fig. 5 shows, for test S10-T1, the relationships between , , and obtained at the end of each step included in a freezing path and a subsequent thawing path (the considered equilibrium state corresponds to the vertical dotted lines shown in Fig. 4). During the cooling path (dark line), as shown in Fig. 5a, freezing occurred when reached -1.6 °C, inducing a sudden decrease of from 28% to 3%. When continued to decrease to -2.9 °C, decreased slightly to 2%. During the heating path (red line), thawing occurred progressively inducing an increase of . The relationship between and is similar between the cooling and the heating paths, except for the period before the occurrence of freezing. Similar conclusions can be observed on the relationship between and (Fig. 5b). However, a unique relationship can be observed between and (Fig. 5c). Detailed analyses of the relationship between volumetric unfrozen water content and temperature (soil freezing characteristic curve) obtained for all soils were presented in [START_REF] Vu | Effect of fines content on soil freezing characteristic curve of sandy soils[END_REF]. In the present work, results on thermal conductivity are further analysed.

Fig. 6 presents the results of all the freezing-thawing experiments where is plotted versus .

It can be observed that all these results show similar trends. During the initial cooling, did not change when decreased. increased abruptly when freezing occurred. Afterwards, the relationship between these two quantities were similar on the freezing and thawing paths.

Generally, was higher at a lower . It can be observed that all these results show a reversible behaviour of at frozen state, particularly with . increased significantly with decreasing in the range of low values of unfrozen water content. In addition, to provide the same thermal conductivity, volumetric unfrozen water content has to be higher for a higher clay content. Otherwise, as shown in Fig. 7b, at the same temperature in frozen state, adding fines (from 0% to 15%) decreases significantly.

Thermal conductivity of unfrozen soil

Fig. 9 shows the evolution of and versus time for test S10-D1. From the beginning to 650 h, the specimen was subjected to various drying steps prior to be subjected to further wetting steps until 1000 h. From the initial state with = 25.3%, water evaporation takes place when removing the two lids and decreased slowly. After several hours, covering the holes with the lids again prevented water evaporation. continued to decrease with lower rate prior to stabilizing in further hours at 21.6% and measurement of was then conducted.

Such drying step was repeated 8 times until = 14.2% prior to wetting path. From 650 h, first addition of water in two holes induced a slow increase of , which stabilized at 15%.

The measurement of was then also performed prior to the second water addition. In the subsequent wetting steps, adding water induced an abrupt and immediate increase of , which decreased afterward with slow rate prior to stabilizing. The wetting path was stopped at = 22.3%. Fig. 10 shows the relationship between and water degree of saturation ( ) for all dryingwetting experiments. was calculated from and soil porosity ( ). In this figure, was plotted instead of in order to facilitate the comparison with data taken from existing studies. Measurements of thermal conductivity of pure sand in drying-wetting experiment (S0-D1) were not conducted and available data found from the literature [START_REF] Vieira | Some aspects of measurement of sand thermal conductivity from laboratory tests[END_REF] were used. The results show that adding fines (from 0% to 15%) increased significantly (pure sand with more than 99% quartz content). These results are also plotted in Fig. 11.

Results obtained by models' prediction are described in Section 4. All the results showed a strong correlation between and for a given porosity (a higher was generally obtained at a higher ). It is noted that, above = 60%, thermal conductivity remained constant for all soils. Results on pure sand (Fig. 11a) showed that the relationship between and was independent of the porosity. For sand-clay mixture, at a given , a higher was generally obtained at a lower porosity (Fig. 11b,c,e). [START_REF] Zhang | A new generalized soil thermal conductivity model for sand-kaolin clay mixtures using thermo-time domain reflectometry probe test[END_REF] did not perform tests on mixture with clay content of 15% (Fig. 11d). The results obtained in the present work correspond to the range of high degree of saturation (from 50 % to 100%), this range was not investigated in the work of [START_REF] Zhang | A new generalized soil thermal conductivity model for sand-kaolin clay mixtures using thermo-time domain reflectometry probe test[END_REF].

Prediction of soil thermal conductivity

(c) (d) (e)
Based on the recent review of [START_REF] He | A review and evaluation of 39 thermal conductivity models for frozen soils[END_REF] on 39 models to predict thermal conductivity of frozen soils, two appropriate models were chosen in this study, namely De Vries's model [START_REF] De Vries | Thermal properties of soils[END_REF]) and Johansen's model [START_REF] Johansen | Thermal conductivity of soils[END_REF]. Actually, these models can be used for both frozen and unfrozen soils. They can consider the change in unfrozen water content in frozen soil with temperature change and the effect of clay content.

For the prediction of thermal conductivity of unfrozen and frozen soil in this study, frozen soil was considered as a medium made of four components: unfrozen water, ice, sand and clay, whose volumes are noted as , , respectively while unfrozen soil was also considered as a medium made of four components: water, air, sand and clay, whose volumes are noted as , , respectively.. The thermal conductivity of water ( ) was taken equal to 0.57 W/(m.K) while those of ice ( and air were taken equal to 2.28 W/(m.K) and 0.025 W/(m.K), respectively [START_REF] Farouki | Thermal Properties of Soils[END_REF]. For the soil particles, the thermal conductivity of kaolin ( ) was taken equal to 2.44 W/(m.K) while that of sand ( ) equal to 7.70 W/(m.K) [START_REF] Tarnawski | Canadian Field Soils III. Thermal-Conductivity Data and Modeling[END_REF].

De Vries's model

The De Vries's model was extended for predicting thermal conductivity of frozen soil [START_REF] Farouki | Thermal Properties of Soils[END_REF][START_REF] Tian | A simplified de Vries-based model to estimate thermal conductivity of unfrozen and frozen soil[END_REF]. In the review of [START_REF] He | A review and evaluation of 39 thermal conductivity models for frozen soils[END_REF], the simplified De Vriesbased model with the considered shape factors was proved as the best among physical models [START_REF] Tian | A simplified de Vries-based model to estimate thermal conductivity of unfrozen and frozen soil[END_REF]. In this study, for better fitting experimental data, De Vries's model is applied considering that ice and sand particles are components immersed in the continuous medium (fluid) that contains water and clay particles, as shown in Fig. 12. The thermal conductivity of frozen soil is then expressed as:

29

Where , are the weighting factors depending on the shape and the orientation of sand and ice particles, respectively. Considering ellipsoidal shape for ice and sand particles, the weighting factor (with ) is given from shape factor as follows:

with [START_REF] Tarnawski | A new computerized approach to estimating the thermal properties of unfrozen soils[END_REF]) and where, is the porosity of soil, is the ice content. The fluid mixture (water and clay particles) properties are given by: and the total volume is:

The De Vries's model was also used to predict thermal conductivity of unfrozen soils in drying-wetting process. In this case, unfrozen soil is composed of sand, air immersed in liquid (mixture of clay and water). The thermal conductivity of unfrozen soil is expressed as:

The weighting factors , are similar to the previous expression with the shape factor below:

(a) (b)
In this study, for unfrozen soils subjected to drying-wetting process, the thermal conductivity introduced by Johansen was expressed as:

where , are respectively the thermal conductivity in saturated and dry states at the same dry density, is a function representing the influence of water degree of saturation on the thermal conductivity. According to Côté and Konrad (2005a), is proposed as follows:

where is an empirical parameter accounting for the soil type effect. [START_REF] Zhang | A new generalized soil thermal conductivity model for sand-kaolin clay mixtures using thermo-time domain reflectometry probe test[END_REF] proposed an equation to illustrate the relationship between and quartz (sand) content. In this study, was chosen because it provided the best prediction.

where the dry unit weight, , and the unit weight of the solids, , are expressed in kg/m 3 .

For frozen soils, compared to De Vries's model, Johansen's model predicted higher values for soils with low clay content (S0-T1, S5-T1, S10-T1) but the improvement was not significant (Fig. 8). For soils with high clay contents, the two models provided similar results.

For unfrozen soils, the modified Johansen's model proposed by [START_REF] Zhang | A new generalized soil thermal conductivity model for sand-kaolin clay mixtures using thermo-time domain reflectometry probe test[END_REF] gave a better prediction compared to the De Vries's model (Fig. 11). It gave better prediction for soils with low clay contents (S0-D1, S5-D1, S10-D1). However, for soils with high clay contents, the improvement compared to De Vries's model was not significant.

Discussion

The measurement of unfrozen water content in the present study was converted from the measurement of the apparent dielectric constant. The latter was calculated from the dielectric constant of each component in soils, particularly water and ice, which are influenced by temperature variation [START_REF] Haynes | CRC Handbook of Chemistry and Physics[END_REF][START_REF] Wraith | Temperature effects on soil bulk dielectric permittivity measured by time domain refiectometry: Experimental evidence and hypothesis development[END_REF]. Several existing models can estimate moisture content from unfrozen soil apparent dielectric constant [START_REF] Birchak | High dielectric constant microwave probes for sensing soil moisture[END_REF][START_REF] He | Application of multiphase dielectric mixing models for understanding the effective dielectric permittivity of frozen soils[END_REF][START_REF] Nagare | Laboratory calibration of time domain reflectometry to determine moisture content in undisturbed peat samples[END_REF][START_REF] Roth | Calibration of time domain reflectometry for water content measurement using a composite dielectric approach[END_REF][START_REF] Schafer | Estimating soil-water characteristic curve from soil-freezing characteristic curve for mine waste tailings using time domain reflectometry[END_REF][START_REF] Smith | Measurement of the unfrozen water content of soils: comparison of NMR and TDR methods[END_REF][START_REF] Stähli | Measurement of water and solute dynamics in freezing soil columns with time domain reflectometry[END_REF][START_REF] Topp | Electromagnetic determination of soil water content: Measurements in coaxial transmission lines[END_REF][START_REF] Watanabe | Measurement of unfrozen water content and relative permittivity of frozen unsaturated soil using NMR and TDR[END_REF]. The most used is Topp's empirical model [START_REF] Topp | Electromagnetic determination of soil water content: Measurements in coaxial transmission lines[END_REF] but it is not compatible with frozen soils [START_REF] Smith | Measurement of the unfrozen water content of soils: comparison of NMR and TDR methods[END_REF][START_REF] Spaans | Examining the use of time domain refiectometry for measuring liquid water content in frozen soil[END_REF][START_REF] Zhou | Simultaneous measurement of unfrozen water content and ice content in frozen soil using gamma ray attenuation and TDR[END_REF].

Otherwise, [START_REF] Smith | Measurement of the unfrozen water content of soils: comparison of NMR and TDR methods[END_REF] proposed a model based on the comparison of unfrozen water content measured from NMR and TDR methods for 25 soils covering a wide range of specific surface areas. For this reason, in the present work, the model of [START_REF] Smith | Measurement of the unfrozen water content of soils: comparison of NMR and TDR methods[END_REF], which provides an accuracy of ±3% compared to measurements from NMR method, considered as the reference, was used for frozen soils.

During the freezing process, the volume of specimen would increase, thus modifying the contact between soil and the probes. In the present work, the potential lateral expansion of the specimen is prevented by the rigid metallic cell. The soil/probe contact was thus supposed to be not influenced by the freezing process.

The obtained results show that the thermal conductivity of soil ( ) at frozen state is higher than that at unfrozen state. Even if this trend was observed in various existing studies, most of the existing works provide data corresponding to one or few values of negative temperature.

Few works provided an almost continuous relationship between and soil temperature. In the present work, during the thawing path, the rate of decrease of with temperature change for temperature in the range of -3 °C to -1 °C was lower than in the range of -1 °C to 0 °C (Fig. 6). Similar trend was observed by Overduin et al. (2006) and [START_REF] Zhang | Variation of the thermal conductivity of a silty clay during a freezing-thawing process[END_REF]. Such a difference induced by the phase change of water can be explained by the conductivity of ice being approximately four times higher than that of liquid water . These values depend significantly on temperature and pressure. However, [START_REF] Bonales | Thermal conductivity of ice prepared under different conditions[END_REF][START_REF] Slack | Thermal conductivity of ice[END_REF] and [START_REF] Lawson | Thermal Conductivity of Water at High Pressures[END_REF] can be considered as constants in the relatively small temperature range investigated in the present study. It is believed that the increase of after freezing occurrence relates mainly to the small amount of water transforming into ice because there was almost no hysteresis observed in the relationship between and in this range. As illustrated in Fig. 12, ice is mainly formed from water in the centre of the pores. In the case where ice could not contain clay, the decrease of unfrozen water content caused the increase of clay concentration in unfrozen water. It is noted that this latter plays a role as a contact between sand grains. The phenomenon of increasing clay concentration in water contributes considerably to the efficiency of heat conduction because it not only increases the thermal conductivity of the fluid [START_REF] Kim | Thermal Conductivity of Saturated Unfrozen Kaolinite during Consolidation[END_REF], but also strengthens heat transfer between sand grains [START_REF] Johansen | Thermal conductivity of soils[END_REF].

The two chosen models were not able to predict well the thermal conductivity of frozen sandy soils in this study. For De Vries's model, considering the continuous medium (fluid) as a mixture of water and clay particles with ellipsoidal shape for ice and sand particles could contribute to the low accuracy of the model. It is noted that in this model, considering and as constants, the application of geometric mean for is consistent with the measurements on saturated kaolinite in the study of [START_REF] Kim | Thermal Conductivity of Saturated Unfrozen Kaolinite during Consolidation[END_REF]. For Johansen's model, geometric mean formula could only consider the thermal conductivity of each component and their fraction. So that small change in and could not induce significant changes in the thermal conductivity of frozen soils. In addition, both models could not properly capture the components arrangement that affects how heat transfers through the clayey sands.

The results obtained on frozen soil show that at the same temperature, thermal conductivity measured on the thawing path was slightly higher than that on the freezing path (Fig. 6 and Fig. 7b). In the work of [START_REF] Zhang | Variation of the thermal conductivity of a silty clay during a freezing-thawing process[END_REF], similar trend was observed on unsaturated silty clay compacted at a dry density of 1.50 Mg/m 3 while inverse trend was observed at a dry density of 1.75 Mg/m 3 for the same range of temperature (between 0 and -5 °C). Zhang et al.

(2018) explained these trends by mentioning two factors: the change of soil density after a freezing-thawing cycle and hysteresis of the soil freezing characteristics curve. In the present work, as the experiments were conducted in a rigid metallic cell, the change of soil density could be ignored. Thus, at a given temperature, volumetric water content was lower on the thawing path (ice content was higher, see more details on the soil freezing characteristic curves of these soils in [START_REF] Vu | Effect of fines content on soil freezing characteristic curve of sandy soils[END_REF], which induced a higher thermal conductivity.

Thermal conductivity of unfrozen soils is mostly controlled by water content. The measurements of this study are supplemented by the data from Zhang et al. ( 2017) not only in the range of high degree of saturation, but also at various porosities. Regarding the plateau of in the high range of (higher than 60%), considering that the soils in the present study are dense, it can be explained by a low weighted factor of heat transfer in the pores filled with water and air. In this range of high degree of saturation, it is believed that heat conduction between sand grains through their contact (water) is dominant while the other heat transfer modes, such as conduction in air, radiation in air, convection in air, or vapour diffusion are not significant. could decrease only when the amount of water improving the connection between sand grains decreases due to drying process. The later mechanism is observed in Fig. 11 where significant decrease of in the range of low water degree of saturation follows a nonlinear trend. In addition, for each clay content, the porosity also considerably influences .

The effect of porosity is also clearly demonstrated by many studies [START_REF] Chen | Thermal conductivity of sands[END_REF][START_REF] Smits | Thermal Conductivity of Sands under Varying Moisture and Porosity in Drainage-Wetting Cycles[END_REF][START_REF] Tarnawski | Thermal Conductivity of Standard Sands. Part I. Dry-State Conditions[END_REF]. The increase of volume of sand grains characterized by the highest among the soil components along with decreasing volume of water with low contributes to a significant increase of of soils.

Johansen's model gave a good prediction for unfrozen soils except S15-D1 (fairly good)

while De Vries's model gave a fairly good prediction only for S20-D1. Johansen's model, within = 8 for all different soils, showed more accurate prediction of measurements than proposed formula of based on quartz content in the study of [START_REF] Zhang | A new generalized soil thermal conductivity model for sand-kaolin clay mixtures using thermo-time domain reflectometry probe test[END_REF]. The formula could consider the influence of porosity of soils for better prediction.

In this study, increasing clay contents increases in unfrozen state but an opposite trend is almost observed in frozen state. Considering that the sand skeleton is approximately formed by its optimum structure in terms of limiting pore volume by means of Proctor procedure, adding clay would fill the pores between sand grains. Available space for water is thus reduced by the presence of clay. Due to the replacement of water with = 0.57 W/(m.K) by clay with higher thermal conductivity = 2.44 W/(m.K), increasing clay content increases the thermal conductivity at unfrozen state. In frozen state, the opposite trend is almost observed because of the transformation of liquid water into ice. Actually, while S0-T1 contains pure sand and almost 37% of total soil volume (Table 2) (corresponding to = 0.5% at frozen state as shown in Fig. 8) is filled with ice at frozen state, S20-T1 with 20% clay in solid volume (solids are sand and clay) contains only 21% (corresponding to = 20% and 26% of porosity) of total soil volume filled by ice at frozen state. It is noted that at higher clay contents, more water is kept unfrozen due to the presence of capillary and bound water which could not be frozen at temperatures close to 0 °C. By comparing two extreme cases in this study, it is clear that adding clay in soils not only decreases the available volume for ice in the soils' porosity, but also increases unfrozen water content in frozen state. Consequently, soils with lower clay content have lower at unfrozen state but higher at frozen state.

Conclusions

The variation of clay content (from 0 to 20 %) in sandy soils significantly influences thermal conductivity of soils in both freezing-thawing and drying-wetting processes. Based on the obtained results, some conclusions can be summarized as follows:

-In freezing-thawing process of saturated soils, thermal conductivity in frozen state is considerably higher than that in unfrozen state. The thermal conductivity of frozen soils is higher at a lower unfrozen water content.

-For frozen soil, to provide the same thermal conductivity, volumetric unfrozen water content has to be higher for a higher clay content.

-For unfrozen soil, at a given volumetric water content, thermal conductivity of soils is higher at a higher clay content.

Table captions

Table 1. Physical properties of Speswhite Kaolin clay and Fontainebleau sand [START_REF] Boussaid | Sols intermédiaires pour la modélisation physique : application aux fondations superficielles[END_REF].

Table 2. Physical properties of soils in freezing-thawing (T1) and drying-wetting (D1) experiments.

Table 3. Properties of sensors used in freezing-thawing and drying-wetting experiments.

Figure captions

Fig. 1 .

 1 Fig. 1. Grain size distribution curves.

Fig. 2 .

 2 Fig. 2. Setup for freezing-thawing experiments: (1) Temperature-controlled bath; (2) Soil

Fig. 3 .

 3 Fig. 3. Setup for drying-wetting experiments: (1) Soil; (2) Metallic cylindrical cell; (3)

Fig. 4 .

 4 Fig. 4. Time evolution of temperature, unfrozen volumetric water content and thermal

Fig. 5 .

 5 Fig. 5. Relationships between (a) volumetric unfrozen water content and temperature, (b)

Fig. 6 .

 6 Fig. 6. Thermal conductivity versus temperature for all freezing-thawing experiments.

Fig. 7

 7 Fig. 7 presents the results of all specimens at frozen state where is plotted versus and .

Fig. 7 .Fig. 8 .

 78 Fig. 7. Thermal conductivity versus (a) volumetric unfrozen water content and (b)

Fig. 9 .

 9 Fig. 9. Volumetric water content and thermal conductivity of unfrozen soils in wetting-drying

Fig. 10 .

 10 Fig. 10. Thermal conductivity versus water degree of saturation for all drying-wetting

Fig. 11 .

 11 Fig. 11. Experimental results (obtained in this study and that of Zhang et al. 2017) and models

Fig. 1

 1 Fig. 1 Grain size distribution curves.

Fig. 2 .

 2 Fig. 2. Setup for freezing-thawing experiment: (1) Temperature-controlled bath; (2) Soil

Fig. 4 .

 4 Fig. 4. Temporal variation of temperature, unfrozen volumetric water content and thermal

Fig. 5 .

 5 Fig. 5. Relationships between thermal conductivity, temperature and volumetric unfrozen

Fig. 6 .

 6 Fig. 6. Thermal conductivity versus temperature for all freezing-thawing experiments.

Fig. 7 .

 7 Fig. 7. Thermal conductivity versus (a) volumetric unfrozen water content and (b)temperature

Fig. 8 .

 8 Fig. 8. Experimental results and models' prediction for thermal conductivity versus water

Fig. 9 .Fig. 11 .

 911 Fig. 9. Volumetric water content and thermal conductivity of unfrozen soils in wetting drying

Table 1 . Physical properties of Speswhite Kaolin clay and Fontainebleau sand (Boussaid, 2005). Properties Speswhite Kaolin clay Fontainebleau sand

 1

	Median grain size, D 50 (mm)	-	0.21
	Uniformity coefficient, C U	-	1.52
	Minimum void ratio, e min	-	0.54
	Maximum void ratio, e max	-	0.94
	Particle density, ρ s (Mg/m 3 )	2.65	2.65
	Minimum dry density, ρ d,min (Mg/m 3 )	-	1.37
	Maximum dry density, ρ d,max (Mg/m 3 )	1.45	1.72
	Liquid limit, LL (%)	55	-

Test No. Clay content (%) Water content at compaction (%) Dry density (Mg/m 3 ) Porosity (-)

	S20-T1	20	11.2	1.98	0.25
	S15-T1	15	9.1	1.99	0.25
	S10-T1	10	8.0	1.91	0.28
	S5-T1	5	7.0	1.78	0.33
	S0-T1	0	5.6	1.67	0.37
	S20-D1	20	11.2	1.96	0.26
	S15-D1	15	9.1	1.99	0.25
	S10-D1	10	8.0	1.93	0.27
	S5-D1	5	7.0	1.77	0.33
	S0-D1	0	-	1.61	0.39

Table 3 .

 3 Properties of sensors used in freezing-thawing and drying-wetting experiments.

	Measured	Principle	Type	Supplier	Accuracy	Range
	parameters
	Temperature	Resistance temperature	PT100	Mersurex	±0.03 °C	-200 to
	(Freezing-Thawing	detector				400 °C
	test)
	Volumetric water	Time domain	ThetaProbe	AT Delta-T	0.01 m 3 /m 3	0.01 to 1
	content (both tests)	reflectometry (dielectric	ML2x (4 rods)	service		m 3 /m 3
		permittivity)
	Suction (Freezing-	Tensiometer	T5x	Meter	±0.5 kPa	-160 to 100
	Thawing test)			Group		kPa
	Thermal	Transient line heat	KD2-Pro (RK-	Meter	10% of	0.1 to 4
	conductivity (both	source	1)	Group	measured	W/(m.K)
	tests)				value
	Suction (Drying-	Tensiometer	T8	Sol Mesures	±0.5 kPa	-100 to 100
	wetting test)					kPa

Funding

The authors declare no specific funding for this work.

Data availability

Data generated or analysed during this study are available from the corresponding author upon reasonable request.

The comparison (Fig. 8) showed that the De Vries's model underestimated measured values of of all frozen soils. The model also showed a higher at a lower but could not capture the strong effect of when this later is small. For unfrozen soils (Fig. 11), the De Vries' model also showed a higher at a higher but could not capture accurately the relationship between these two parameters for the whole range of (varying from 0 to 100%).

Johansen's model

Geometric mean was proposed to calculate the thermal conductivity ( ) of saturated frozen soil by Johansen [START_REF] Johansen | Thermal conductivity of soils[END_REF]:

where , , , are volume fraction of water, ice, clay and sand, respectively.

-Johansen's model and De Vries's model cannot predict well thermal conductivity of soil in freezing-thawing process in the studied range of temperature. The models need further improvement for better prediction.

-Johansen's model can predict satisfactorily the thermal conductivity in drying-wetting process but not De Vries's model.

The findings of the present study could be helpful for various engineering applications such as artificial ground freezing or cold regions geo-technologies. In these applications, soils can exist in both unfrozen and frozen states and the effects of clay content on thermal conductivity of sandy soils should be considered when analysing thermal transfers in the ground.

Competing interests

The authors declare there are no competing interests.

Author contribution

Q.H.