Keovathana Run
email: keovathana.run@umontpellier.fr

Franck Cévaër

Jean-Franc ¸ois Dubé

Does energy-efficient renovation positively impact thermal comfort and air quality in university buildings?

Keywords: School buildings, Mediterranean weather, IEQ evaluation, Retrofitting, Energy conserving

The University Institute of Technology (IUT) buildings in France were primarily built in the late 1960s without considering the energy issues that were not relevant at the time. At the IUT of Nîmes, during winter and summer, rooms within the same building can be either very hot or very cold, causing discomfort for the users. One of the solutions is the renovation of the building and its systems. This research aims to evaluate the energy-efficient renovation performance associated with thermal comfort and indoor air quality. Modifications to the building envelope and systems were recommended by thermal engineers based on simulations that did not consider the occupants' behavior and operational uncertainties. Therefore, this analysis relies on physical measurements of CO 2 concentrations, humidity, and temperature during the heating period, along with a thermal comfort survey. The analytical result showed no significant change in CO 2 level and humidity, but the temperature reduced up to 3°C as a consequence of adjusting the heating temperature threshold. However, in certain post-renovation classrooms, the CO 2 concentrations increase by 11.7% with an incorrect airflow rate. Although there is no change in measured values, according to the French thermal building code for renovated buildings, these values are on the border of their comfort zone. Overall, thermal renovation saves 85 MWh while maintaining satisfactory thermal comfort and air quality. However, caution should be exercised regarding the air exchange rate.

Introduction

European policy currently aims to improve energy efficiency by 27% by 2030, which can be achieved through using energy-efficient materials when constructing new buildings and improving existing buildings [START_REF] Artola | Boosting Building Renovation: What Potential and Value for Europe? European Parliamentary Research Service[END_REF][START_REF] Trotta | Energy efficiency in the residential sector: identification of promising policy instruments and private initiatives among selected European countries[END_REF]. Renovation strategy could decrease EU energy use by 5-6%, but unfortunately, only 1% of the building stock is renovated annually [START_REF] Energy | New rules for greener and smarter buildings will increase quality of life for all Europeans[END_REF][START_REF] Jens Laustsen | Europe's Buildings under the Microscope: A country-by-country review of the energy performance of buildings[END_REF]. For educational facilities, such as the University Institute of Technology (IUT) buildings, ensuring energy efficiency is vital not only for cost savings but also for the well-being and performance of students and other occupants.

Over the past two decades, extensive research has shown a correlation between Indoor Environmental Quality (IEQ) and occupants' performance, health, and attitudes [START_REF] Giuli | Indoor environmental quality and pupil perception in Italian primary schools[END_REF][START_REF] Pereira | Assessment of indoor air quality and thermal comfort in Portuguese secondary classrooms: Methodology and results[END_REF][START_REF] Geng | The impact of thermal environment on occupant IEQ perception and productivity[END_REF][START_REF] Al Horr | Occupant productivity and office indoor environment quality: A review of the literature[END_REF][START_REF] Mujan | Influence of indoor environmental quality on human health and productivity -A review[END_REF][START_REF] Frontczak | Literature survey on how different factors influence human comfort in indoor environments[END_REF][START_REF] Turunen | Indoor environmental quality in school buildings, and the health and wellbeing of students[END_REF].

The IEQ factors are broad, but most literature focuses on four factors that can be measured and controlled actively: indoor air quality and ventilation, thermal comfort, visual comfort, and acoustic comfort [START_REF] Mujan | Influence of indoor environmental quality on human health and productivity -A review[END_REF]. Previous studies have individually examined the impact of these factors on human perception and performance, emphasizing the importance of maintaining optimal conditions in educational settings. For instance, a study by Clements-Croome [START_REF] Clements-Croome | Ventilation rates in schools[END_REF] found that indoor air quality in school buildings, as assessed by short-term computer-based tests, is associated with poor student performance, mainly due to inadequate ventilation rates and high occupancy densities (1.8-2.4 m 2 /person). Haverinen-Shaughnessy showed that the ventilation rate also plays an essential role in student academic performance [START_REF] Haverinen-Shaughnessy | Association between substandard classroom ventilation rates and students' academic achievement[END_REF] and reducing sick absences [START_REF] Mendell | Association of classroom ventilation with reduced illness absence: a prospective study in California elementary schools[END_REF][START_REF] Haverinen-Shaughnessy | An assessment of indoor environmental quality in schools and its association with health and performance[END_REF]. Additional research [START_REF] Mendell | Association of classroom ventilation with reduced illness absence: a prospective study in California elementary schools[END_REF][START_REF] Langer | Indoor air quality in the Swedish housing stock and its dependence on building characteristics[END_REF][START_REF] Ezzeldin | The potential for office buildings with mixed-mode ventilation and low energy cooling systems in arid climates[END_REF][START_REF] Toftum | Association between classroom ventilation mode and learning outcome in Danish schools[END_REF][START_REF] Tham | Indoor air quality and its effects on humans-A review of challenges and developments in the last 30 years[END_REF]] also supports the previous findings emphasizing the importance of indoor air quality and ventilation factors. Other aspects were also extensively investigated: thermal comfort [START_REF] Bueno | Evaluating the Connection between Thermal Comfort and Productivity in Buildings: A Systematic Literature Review[END_REF][START_REF] Jiang | A study on pupils' learning performance and thermal comfort of primary schools in China[END_REF][START_REF] Kaushik | Effect of thermal comfort on occupant productivity in office buildings: Response surface analysis[END_REF][START_REF] Lipczynska | Thermal comfort and self-reported productivity in an office with ceiling fans in the tropics[END_REF][START_REF] Wang | Experimental investigation of the effect of indoor air temperature on students' learning performance under the summer conditions in China[END_REF][START_REF] Cui | Influence of indoor air temperature on human thermal comfort, motivation and performance[END_REF][START_REF] Sarbu | Experimental and numerical research to assess indoor environment quality and schoolwork performance in university classrooms[END_REF][START_REF] Ahrentzen | Thermal and health outcomes of energy efficiency retrofits of homes of older adults[END_REF], visual comfort [START_REF] Van Den Wymelenberg | Evaluating a New Suite of Luminance-Based Design Metrics for Predicting Human Visual Comfort in Offices with Daylight[END_REF][START_REF] Shen | Energy and visual comfort analysis of lighting and daylight control strategies[END_REF][START_REF] Konstantzos | The effect of lighting environment on task performance in buildings -A review[END_REF][START_REF] ¸akir | Human Factors in Lighting[END_REF][START_REF] Smolders | Bright light and mental fatigue: Effects on alertness, vitality, performance and physiological arousal[END_REF], and acoustic comfort [START_REF] Kang | The impact of indoor environmental quality on work productivity in university open-plan research offices[END_REF][START_REF] Dehghan | Evaluating combined effect of noise and heat on blood pressure changes among males in climatic chamber[END_REF][START_REF] Yang | Cross-modal effects of noise and thermal conditions on indoor environmental perception and speech recognition[END_REF], which demonstrate that each of them could have a significant impact on the overall comfort conditions. According to ASHRAE [START_REF] Standard | Standard 55-2004[END_REF] and a previous review paper [START_REF] Frontczak | Literature survey on how different factors influence human comfort in indoor environments[END_REF], among other components, thermal comfort and indoor air quality are the most significant in defining comfort.

Nonetheless, more effort was put into investigating the combined effects of IEQ indicators [START_REF] Wu | Combined effects of acoustic, thermal, and illumination on human perception and performance: A review[END_REF][START_REF] Yang | Combined effects of short-term noise exposure and hygrothermal conditions on indoor environmental perceptions[END_REF][START_REF] Buratti | A new index combining thermal, acoustic, and visual comfort of moderate environments in temperate climates[END_REF][START_REF] Yang | Combined effects of acoustic, thermal, and illumination conditions on the comfort of discrete senses and overall indoor environment[END_REF][START_REF] Torresin | Combined effects of environmental factors on human perception and objective performance: A review of experimental laboratory works[END_REF] and suggested that the interaction effect could evaluate overall human comfort better when the single factor is still lacking.

IUT of Nîmes has undergone a significant renovation to enhance its energy efficiency, particularly targeting heating and ventilation energy. It is worth noting that acoustic comfort has not been a subject of concern or complaints from the users at the institution. Consequently, the aspects of visual and acoustic comfort have been excluded from the scope of this investigation, while the primary focus remains on evaluating thermal comfort and indoor air quality. By outlining the key priorities, the study seeks to provide valuable insights into the influence of energy-efficient measures on the indoor environment's thermal conditions and air quality. These two factors primarily depend on the outdoor meteorological condition [START_REF] Asif | Indoor temperature, relative humidity and CO2 levels assessment in academic buildings with different heating, ventilation and air-conditioning systems[END_REF], air exchange rate [START_REF] Bekö | Modeling ventilation rates in bedrooms based on building characteristics and occupant behavior[END_REF], and occupancy patterns [START_REF] Delzendeh | The impact of occupants' behaviours on building energy analysis: A research review[END_REF][START_REF] Andersen | Window opening behaviour modelled from measurements in Danish dwellings[END_REF] for the same building envelope.

When the energy efficiency of school buildings is discussed, it is often relatively difficult to maintain excellent indoor climate conditions and cost-effectively, as indoor climate conditions and energy efficiency are conflicting objectives. Therefore it creates a problem in designing new and renovated buildings [START_REF] Jain | Building performance evaluation: Balancing energy and indoor environ-mental quality in a UK school building[END_REF].

Recent cross-sectional investigations in classrooms at Danish and foreign schools have shown that CO 2 concentrations frequently exceed the maximum of 1000 ppm, which is usually advised by existing recommendations and building regulations in terms of classroom air quality [START_REF]SBi Guidelines[END_REF][START_REF] Comite'europe'en | Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics[END_REF]. It has been reported to occur particularly often in classrooms where ventilation relies on the manual opening of windows, specifically during the heating season in regions with a temperate climate [START_REF] Toftum | Association between classroom ventilation mode and learning outcome in Danish schools[END_REF][START_REF] Gao | Ventilation system type, classroom environmental quality and pupils' perceptions and symptoms[END_REF][START_REF] Heebøll | Window and door opening behavior, carbon dioxide concentration, temperature, and energy use during the heating season in classrooms with different ventilation retrofits-ASHRAE RP1624[END_REF][START_REF] Rosbach | Classroom ventilation and indoor air quality-results from the FRESH intervention study[END_REF][START_REF] Stabile | Effect of natural ventilation and manual airing on indoor air quality in naturally ventilated Italian classrooms[END_REF]. Zhang and Bluyssen [START_REF] Zhang | Energy consumption, self-reported teachers' actions and children's perceived indoor environmental quality of nine primary school buildings in the Netherlands[END_REF] conducted an observational study of existing schools to determine potential correlations between energy consumption and building characteristics. During the in-vestigation, they discovered that when the buildings consumed 71.9 MJ/m 2 of electricity and 358.8 MJ/m 2 of gas, the average indoor climate of classrooms is as follows, 23°C, 41%, and 1260 ppm. Almedia and Freitas [START_REF] Almeida | Indoor environmental quality of classrooms in Southern European climate[END_REF] compared two non-retrofitted and seven retrofitted schools. Throughout the winter, the average temperature was 14.9°C and CO 2 of 1905 ppm in non-retrofitted buildings, while those of certain retrofitted buildings were 20°C and 1052 ppm. They inferred that non-retrofitted schools need to improve their indoor environmental conditions, and in renovated buildings, mechanical ventilation systems are not efficiently utilized, with significant consequences on indoor air quality.

Zinzi et al. [START_REF] Zinzi | Deep energy retrofit of the T. M. Plauto School in Italy-A five years experience[END_REF] did a case study on a school building in Italy. The deep renovation includes an insulated building envelope, installed mechanical ventilation, energy-efficient windows, and new sun shades. The results show that the IEQ indicators of the classroom can increase to 20°C from 18.5°C at 8 am in the early morning and remain below 1600 ppm with mechanical ventilation. However, it exceeds the recommended values, which are far lower than those without mechanical ventilation. A recent study by Mocová and Mohelníková [START_REF] Mocová | Indoor Climate Performance in a Renovated School Building[END_REF], examined the renovated schools from the 1970s including the upgrading building fabrics, installing regulated heating systems, and renovating the water supply. The energy consumption reduced from 271 GJ/year to 105.8 GJ/year. However, an unexpected increase in CO 2 levels within the renovated classroom was observed. Zaeh et al. [START_REF] Zaeh | Indoor Air Quality Prior to and Following School Building Renovation in a Mid-Atlantic School District[END_REF] have investigated seven school buildings in preand post-renovation states. The retrofitting strategy includes replacing windows and renewing the HVAC system. The study has revealed that, prior to renovation, a noteworthy proportion of indoor CO 2 measurements (more than 30%) and indoor temperatures (over 50%) exceeded the recommended guidelines. After the refurbishment, it was observed that 10% of CO 2 levels and 28% of temperatures deviated from the recommended ranges.

Despite solid theological and calculation evidence supporting the need for renovation to meet the required Indoor Environmental Quality values [START_REF]SBi Guidelines[END_REF][START_REF] Comite'europe'en | Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics[END_REF][START_REF] Almeida | Indoor environmental quality of classrooms in Southern European climate[END_REF][START_REF] Zaeh | Indoor Air Quality Prior to and Following School Building Renovation in a Mid-Atlantic School District[END_REF], school renovation projects remain challenging due to multiple stakeholders and limited funding from local authorities. The existing literature on renovation's impact on IEQ remains inadequate, emphasizing the necessity for more case studies involving different school building types and climates.

Moreover, previous research has predominantly focused on evaluating the renovation performance solely in classrooms, while higher education buildings accommodate various activities and functions, including offices, laboratories, workshops, and e-learning spaces [START_REF] Pérez-Lombard | A review on buildings energy consumption information[END_REF]. Ensuring a healthy and comfortable environment in all the rooms within the building is of utmost importance.

To fill the gap in previous papers, the objectives of this study are to: 1) assess the renovation's impact on the thermal comfort and indoor air quality of the entire school building by incorporating classrooms, offices, and workshops in the analysis;

2) investigate the relationship between energy-efficient refurbishment measures and the tackled IEQ components;

3) analyze the level of user comfort during the heating period following the renovation.

Material and methods

This work employs a comprehensive approach encompassing physical measurements and subjective assessment to analyze indoor environment comfort. This approach is a well-established method used in prior literature to evaluate thermal comfort [START_REF] Jiang | A study on pupils' learning performance and thermal comfort of primary schools in China[END_REF][START_REF] Kaushik | Effect of thermal comfort on occupant productivity in office buildings: Response surface analysis[END_REF][START_REF] Lipczynska | Thermal comfort and self-reported productivity in an office with ceiling fans in the tropics[END_REF]. Student t-Test statistics were adopted and performed through "anova test" in RStudio to compare the physical measurements, while questionnaire responses were compared directly by the percentage of votes.

Simultaneously, heat demand was also calculated using a quasi-stationary monthly technique. This mathematical model is based on a monthly energy balance in steady-state conditions between heat gains and thermal losses according to EN ISO 52016-1 standard [59].

The research methodology for this study involved long-term data collection from two renovated buildings, as illustrated in Figure 1. Various data analysis techniques were employed to thoroughly assess the collected data and draw conclusive findings. This campus comprises five buildings, as shown in Figure 2, representing a total SHON surface of 27 666 m².

However, this study was conducted only on two renovated buildings, GC (BAT. 4) and GEII (BAT. 5), with the same floor plan layout consisting of two sections, later referred to as "North" and "Workshop". Figure 3 is an elevation drawing of the GEII building demonstrating a two-level at the North (front) section and a one-level Workshop (back) section. The North dedicates to classrooms and offices while laboratories and workshop halls mostly occupy the Workshop. The details of the building's usage are presented in Table 1. A weather station (Météo France) of Nîmes Courbessac monitored outdoor conditions and was used to calculate DJU (unified degree day) as detailed in Section 2.4.

Renovation measures

This case study adopts passive renovation strategies by replacing conventional single-glazed and double-glazed with low-emissivity double-glazed windows and adding exterior insulation. Such renovations are found to elevate the internal temperature by enhancing the air-tightness of buildings [START_REF] Chen | Measured air tightness performance of residential buildings in North China and its influence on district space heating energy use[END_REF] and simultaneously lowering the overall energy consumption [START_REF] Lee | Impact of external insulation and internal thermal density upon energy consumption of buildings in a temperate climate with four distinct seasons[END_REF][START_REF] Reilly | The impact of thermal mass on building energy consumption[END_REF][START_REF] Somasundaram | Energy saving potential of low-e coating based retrofit double glazing for tropical climate[END_REF]. These measures were commonly used in residential [START_REF] Pungercar | A new retrofitting strategy for the improvement of indoor environment quality and energy efficiency in residential buildings in temperate climate using prefabricated elements[END_REF][START_REF] Kuusk | CASE-STUDY ANALYSIS OF CONCRETE LARGE-PANEL APARTMENT BUILDING AT PRE-AND POST LOW-BUDGET ENERGY-RENOVATION[END_REF][START_REF] Földváry | Effect of energy renovation on indoor air quality in multifamily residential buildings in Slovakia[END_REF][START_REF] Bjørneboe | Evaluation of the renovation of a Danish single-family house based on measurements[END_REF] and school buildings [START_REF] Mocová | Indoor Climate Performance in a Renovated School Building[END_REF][START_REF] Gil-Baez | Passive actions in the building envelope to enhance sustainability of schools in a Mediterranean climate[END_REF][START_REF] Liébana-Durán | Identification of Cost-Optimal Measures for Energy Renovation of Thermal Envelopes in Different Types of Public School Buildings in the City of Valencia[END_REF].

Moreover, natural ventilation was replaced by single-flow mechanical ventilation. Some work has also been done on the heating system, such as installing a heat exchanger beneath each building and incorporating thermostats into new radiators.

Table 2 displays the U-values of the building envelope, along with a short description of the ventilation and heating system, for both the pre-building and post-building phases. The installation of mechanical ventilation and thermostats were limited to the North section. Before the refurbishment, the heating system was controlled manually, in contrast to its current state of operation, which is managed and operated through a computer program.

Heat demand calculation

This study utilized a quasi-stationary monthly calculation of heat loss and usable heat gains for the building [START_REF] Wittchen | Calculation of building heating demand in EPIQR[END_REF].

The calculation expressed in equation 1 is made only for this case study's potential heating season from January to March.

Q h = Q 1 -η × Q g (1)
Where:

Q h : total heat demand (kWh)
Q 1 : total heat loss (kWh), defined in equation 2 Q g : total heat gain (kWh), defined in equation 6

η : overall production efficiency monthly (generation, distribution, transmission, regulation), defined in equation 9

Total heat loss Q 1 of a national level that incorporates the use of degree days is:

Q 1 = H × DJU × 0.024 (2)
Where :

H : total heat loss coefficient of the building (W/K)

H = H T + H V
H T is the specific transmission loss through each building fabric (W/K) namely, walls, windows, roofs and floors. It is calculated as:

H T = b i × U i × S i + b i × Ψ i × l i (3)
where b i is the reduction factor of fabric's temperature depending on the condition that the element is exposed to, b = 1 for exterior, and b < 1 for non-heating space, i.e., crawl space. U i is the transmission coefficient (W.m -2 .K -1 ) and S i is the transmission surface area (m 2 ). Ψ is the thermal bridge coefficient (W.m -1 .K -1 ), and l is the transmission length (m).

H V is the specific heat loss through ventilation (W/K), calculated as follows:

H V = V × ρ a × c a ( 4
)
where V is the air flow rate through the building ( m 3 .h -1 ); including air flow through unheated space, and ρ a c a is the heat capacity per volume = 0.34 (Wh.m -3 .K -1 ).

DJU: average unified degree day (°C.day) defined in equation 5.

DJU is the unified degree day on a base of 18 °C of the Nîmes Courbessac region during the heating period from January 1 st to March 31 st .

DJU 18 = mar jan (18 -T e j max (t) + T e j min (t) 2 ).dt [START_REF] Giuli | Indoor environmental quality and pupil perception in Italian primary schools[END_REF] where T e j max is the maximum daily outdoor temperature (°C), T e j min is the minimum daily outdoor temperature (°C) and t is time (day).

Available heat gains Q g are the combination of solar heat gain through windows Q s (kWh) and internal heat gain Q i (kWh) released by human body heat, thermal machines, computers, servers in educational buildings, etc. The Q s and Q i are evaluated monthly during heating.

Q g = Q s + Q i (6)
Where:

Q s : solar gains for each individual window facing different orientations (kWh).

Q s for a window is calculated from the solar incident through a type of window glass, a free horizon, and the same orientation of the corresponding windows as

Q s = F × S i × I s (7)
where F (F = F s F a F g ) is the reduction factor of the insulation through the actual window anticipates the effect of shadows F s , the area the frame F a and the glass type F g . S i is the transmission surface area (m 2 ) and I s is the insulation through the reference pan (kWh.m 2 ).

Q i : internal heat gain (kWh)

Q i for each month over the heating period is calculated as:

Q i = 0.024 × D × S e × Φ i ( 8
)
where D is the number of days of the month, S e is the total heated floor surface area of the building (m 2 ), and Φ i is the average internal heat gain (W.m -2 ).

Not all the free gains that are available can be effectively utilized for heating the building. This is attributed to an excess of temperatures in certain periods, which can often be traced back to the substantial solar gains in periods with elevated external temperatures. The utilization of the heat gained in the course of the heating season depends on the time constant of building τ and the total heat gain as a ratio of the total heat loss, γ. The utilization factor for the heat gain η indicates how big a part of the potential heat gain can be utilized. The residual heat gain is mitigated through implementing solar shades and curtains, accompanied by enhanced ventilation. Typically, the model calculates the utilization factor η for the free gain as:

η 1 = 1 -γ a 1 -γ a+1 η 2 = a 1 + a (9)
where γ is the relative heat gain (γ = Q G Q 1 ). For γ 1 , apply η 1 and for γ = 1 and apply η 2 . τ is the building's time constant in hours (a = 1 + τ 16 ).

Results

Energy consumption

For this study, the usable free heat gains Q g are considered to remain the same between the pre-building and the post-building. Therefore, the changes before and after in heat demand Q h are placed on the difference in total heat loss

Q 1 .
The equation of heat demand saving ∆Q h is expressed in equation 10.

∆Q h = ∆H × DJU × 0.024 ( 10
)
The calculation is applied to ventilation and upgraded building envelopes: walls and windows. Table 3 presents the inputs and outputs of heating saving ∆Q h . The total saving was 85 MWh during the studied period.

Through the implementation of single-flow ventilation and the removal of permanent openings, heating conserved from ventilation contributes 9% of the total savings. In natural ventilation schools, air renewal relies on manually opening of windows and doors in classrooms [START_REF] Stabile | Effect of natural ventilation and manual airing on indoor air quality in naturally ventilated Italian classrooms[END_REF]. In winter, it can be observed that both teachers and students exhibit a lack of motivation to manually open windows, as reported in academic studies [START_REF] Gao | Ventilation system type, classroom environmental quality and pupils' perceptions and symptoms[END_REF][START_REF] Wargocki | Use of visual CO2 feedback as a retrofit solution for improving classroom air quality[END_REF]. Consequently, retrofits incorporating automatically controlled ventilation have been found to present greater dependability than traditional window opening methods when enhancing the quality of air within the classroom [START_REF] Heebøll | Window and door opening behavior, carbon dioxide concentration, temperature, and energy use during the heating season in classrooms with different ventilation retrofits-ASHRAE RP1624[END_REF].

Hence, the ventilation system implemented in the present study serves the dual purpose of diminishing thermal dissipation and regulating air permeation. The mechanism ensures a consistent and ample airflow supply in compliance with the recommended healthy regulation value of 18 m 3 /h.per throughout occupied periods and stops utterly when unoccupied. When considering the building envelope, incorporating insulation within the walls constitutes the most significant means of reducing heat loss, up to 59%.

Results of monitoring of indoor climate

Continuous measurements of CO 2 concentrations, relative humidity (HR in ), and indoor temperature (T in ) were carried out in two classrooms, one office, and two workshops of each studied building before and after renovation, from January to March. The cumulative percentage distribution of the measured values of CO 2 , HR in , and T in is shown in Figure 4.

In both buildings and states, the CO 2 trends concave up nearly vertically at 500 ppm and then remain at the same level horizontally from 750 ppm to 1500 ppm or more. This means that approximately 75% of CO 2 data are lower than 500 ppm, another 20% is between 500 ppm and 750 ppm, and the rest of the data less than 5%, are higher than 750 ppm. On the other hand, the level of HR in differs remarkably in the GEII building after renovation. The data initially concave upwards simultaneously at 20% HR in for all cases. But while the others continue to increase to 50% gradually, the HR in of GEII in the after state increases more rapidly from 40% to 65%, surpassing that of the others. As the rise is rather loose (diagonally), the interpretation of that variation is that after renovation, the HR in level of GEII appears to be higher than in another state casually from time to time. On the other hand, the temperature's frequency evolves differently in all cases. In post-GEII building, the data quickly moves from 10°C, horizontally, to 18°C then proceeds to concave upward vertically between 18°C and 20°C. Whereas it is of pre-GEII building, concaves up slower and further from 18°C to 24°C. This demonstrates that the majority of temperature in the post-building ranges from 18°C to 20°c and is cooler than the temperature in the pre-building, explaining the rise of HR in level as previously stated.

GC buildings have a more diagonally rise in temperatures, especially in the post-state showing that the temperature A study in 2015 by Almeida [START_REF] Almeida | School Buildings Rehabilitation: Indoor Environmental Quality and Enclosure Optimization[END_REF] determined that the occupancy levels of educational institutions may significantly surpass those found in conventional office buildings, with up to four times more individuals occupying a given area per square meter. Therefore, a prominent focus of previous research has been the examination of classroom indoor climates as they are particularly susceptible to the impact of CO 2 levels. However, universities possess various functions and activities, including administrative units, lecture halls, classrooms, e-learning, laboratories, and workshops [START_REF] Pérez-Lombard | A review on buildings energy consumption information[END_REF]. They are differentiated regarding spatial requirements, building equipment, and occupancy rate. The study of classrooms alone is insufficient to evaluate the overall efficacy of a building's renovation; all spaces must adhere to the appropriate health-related standards for each IEQ indicator.

This paper integrates room types into the analysis in order to undertake discrete evaluations and attain a greater comprehension of statistical accuracy. Figure 5 presents a boxplot of classrooms, offices, and workshops featured by indoor climate during school hours from 8h00 to 18h00 over the weekdays. In France, according to regulations, EN 15251 [START_REF] Franc | Norme française: NF EN 15251[END_REF], the acceptable indoor environment of school buildings for category II (Renovated buildings) and category III (Existing buildings) are the following, respectively, indoor temperature ranges for heating: 20°C -24°C and 19°C to 25°C, CO 2 concentrations: 500 ppm and 800 ppm, relative humidity range: 25% to 60% and 20% to 70%. ASHRAE's journal just published a summary article by Stumm et al. [START_REF] Stumm | Revisiting the 1,000 ppm CO 2 Limit[END_REF], which takes a fresh look at the guideline of 1000 ppm of CO 2 as an upper limit in indoor environments. Based on that, the average values of CO 2 and HR in in both buildings and and 25% in the existing state and 53% and 90% in the renovated state. While the temperatures were generally warmer than wanted in the old buildings, the retrofitted buildings, particularly GEII, got cooler than the comfort temperature.

These are due to the lack of energy control in the pre-state and the rise of energy prices in the post-state. In the previous state, indoor temperature accumulated per day takes into account the heat by heaters and available internal heat gain.

Although some internal heat gains compensate for heat loss, solar radiation in the Mediterranean region is relatively robust even in winter making rooms facing south sensitive to the solar gains. Without indoor regulator valves, these internal heat gains are ignored and heaters usually waste energy on unnecessary heating making the indoor temperature warmer than the threshold. In 2023, corresponding to the after-state, the energy price surged enormously. To reduce energy bills, the new temperature threshold during occupied hours is lowered to 18°C. When coupled with the available thermostats, the indoor temperature is maintained at 19°C after including the internal heat gain.

T-Test comparison of before and after renovation

The ANOVA test (one-way analysis of variance) is employed to compare data points before and after renovation.

This statistical test is an extension of the independent two-sample t-Test and is specifically designed to compare the means of multiple groups. As such, ANOVA is the appropriate technique for this case study, where the means of three groups of room types are compared. The ANOVA of "rstatix" package of RStudio [START_REF] Kassambara | rstatix: Pipe-Friendly Framework for Basic Statistical Tests[END_REF] takes into account two types of variations -the SSn Sum of Squares in the numerator (i.e., SS effect), the SSd Sum of Squares in the denominator (i.e., SS error). The ANOVA test also calculates effect sizes ges by dividing S S e f f ect by S S total as follows:

ges = S S e f f ect S S e f f ect + S S error [START_REF] Turunen | Indoor environmental quality in school buildings, and the health and wellbeing of students[END_REF] Table 4 summarizes the t-Test results of the pre-buildings and post-buildings of each room type. The p-value is the significant value if it is less than 0.05 and is denoted with a star "*". The ges value (generalized eta squared) is the level of significance with three ranges, namely, "0.2" for small and not noticeable by experts, "0.13 +" for medium and noticeable to the expert eye, and "0.26 +" is large and noticeable even to the untrained eye. This t-Test is vital to determine the degree of change in indoor climate, whether remarkable or subtle. As a result of both buildings combined, a statistically relevant relationship between renovation and indoor environmental quality is found for a p-value lower than 0.05 except for CO 2 of the workshop with p-value = 2.86 × 10 -01 in consequence of unchanged ventilation in the workshop section. Drawing on ges values, this degree of significance is relatively minimal, for they are inferior to 0.2. Globally, this denotes that the pre-building and post-building progress in similar patterns, if not the same, for each indicator. The most prominent changes, according to ges values, are the temperature of the workshop (0.094) and of the classroom (0.084), attributed to a change in temperature threshold.

Indicators

Investigation of indoor air quality indicators

Ventilation inside schools is vital for at least two reasons. One is the question of air quality and its impact on well-being and performance. And second, it is a heat loss path. There is varying evidence about the effect of low ventilation rates on performance and health [START_REF] Fisk | The ventilation problem in schools: literature review[END_REF][START_REF] Maddalena | Effects of ventilation rate per person and per floor area on perceived air quality, sick building syndrome symptoms, and decision-making[END_REF]. The role of ventilation as a heat loss path in school classrooms is sometimes misconstrued. The occupation rate in a classroom is commonly exceptionally high, and thus, the metabolic gains are also high. Modern designs incorporate typical gains to compensate for fabric losses, even in winter. This means modest ventilation levels are unlikely to imply a load on the heating system [START_REF] Coley | Carbon Dioxide Levels and Ventilation Rates in Schools[END_REF].

Figure 6 illustrates the evolution of the average hourly CO 2 levels on weekdays during the pre and post-renovation period. It incorporates an unoccupied period at nighttime, an occupied period from 8h00 to 18h00, and a lunch break from 12h30 to 13h30. Surprisingly, the concentration of CO 2 in the GC building is higher after retrofitting than before, particularly in the classroom and office, by 11.7% and 3.8%, respectively. When non-ventilated, enclosed spaces are subjected to elevated concentrations of metabolically produced carbon dioxide emissions from the occupants' breathing [START_REF] Liu | Contribution of human-related sources to indoor volatile organic compounds in a university classroom[END_REF]. A lower CO 2 level indicates the indoor air quality's freshness. This depends on the implementation of an appropriate ventilation strategy. Insufficient ventilation in buildings is a prevalent cause leading to a potential risk of sick building syndrome [START_REF] Wargocki | Ten questions concerning thermal and indoor air quality effects on the performance of office work and schoolwork[END_REF]. A test was conducted to assess the mechanical ventilation system's efficacy through airflow rate measurements. The results indicate that the extraction rate of each ventilation outlet is 50% lower than the proposed design value of 90m 3 /h. This design value conforms to the regulatory requirement for ventilation rates of 18m 3 /h.per. When it is not respected, excessive CO 2 levels happen, and more seriously for renovated buildings when they are more air-tightness following the renovation of windows and walls.

As for the GEII building, it was observed that the single-flow ventilation rate is correct, prompt to similar patterns of CO 2 evolution trends, except for the office. Nonetheless, this is not a problem caused by inefficient ventilation but rather by the change in room function. After renovation, this office was altered into a classroom, increasing the average CO 2 abruptly. This effect emphasizes previous findings that the CO 2 levels of the school are more important than the office's for it has higher occupant density.

Although the average CO 2 concentrations have worsened due to a defective ventilation system and a change in room utilization, they remain below 1000 ppm, with the highest peak at 900 ppm in the GC classroom at 17h00. The rest of the renovated buildings, which differ from the existing ones, reach a maximum of 700 ppm for the GC classroom at 10h00, 600 ppm for the GC office at 10h00 and 15h00, and 650 ppm for the GEII office at 9h00.

Investigation of thermal comfort indicator

In both states, the heating control system consists of two operational modes. The first mode referred to as " Com f ort " operates from 7h00 to 18h00. The second mode, known as " Eco " is intended for night-time use and on weekends.

Following the renovation, it was decided to reduce the established temperature threshold in the Com f ort mode from 20°C to 18°C for financial reasons. Nonetheless, it is essential to note that the actual temperature development may diverge from the pre-established setting as a consequence of the inefficient controlling system. An occurrence is observable occasionally wherein radiators maintain a consistent temperature at the designated comfort mode across the entire day, which may persist for several weeks.

Figure 7 presents the average hourly indoor temperature on weekdays of the two buildings in each state. The temperature in the pre-GC building appears to be higher than in pre-GEII. This is associated with the heat exchanger that is now installed beneath the individual building. Prior to that, the heat energy came from the GC sub-station, which benefits one of the two. This means that if the optimal temperature requested in GC is between 19°C and 25°C, depending on the set temperature, the temperature supplied in GEII tends to be lower due to distribution energy loss.

Since the installation of separate heat exchangers, the transmitted energy is consistent with the energy needed in both buildings. The historical distribution of heat has been found to significantly influence the fluctuation of temperature experienced by the GEII and GC buildings. Given the new interior temperature setpoint at 18°C, the temperature variation of the GEII building is less severe than that of GC because its final temperature was close to 19°C. Meanwhile, in the GC building, it is apparent that the temperature differences between before and after renovation are more prominent, with a maximum decrease of 3°C.

The temperature trends in GC are steady all day, unlike the progressions of GEII. Upon verification with GC's heating control, the supplied energy was consistently at the same level across all modes of operation. Another issue is related to continuous nighttime heating requested by an energy company in 2021 to avoid the energy spike that could cause temporary shutdowns. Since the GC's office space area is relatively small, this results in consistently elevated temperatures at 24.5°C and 23.5°C in the pre-state and post-state.

As the number of individuals within the classroom increases, there is a direct proportional rise in temperature. In contrast, the temperature decreases when students are absent, end-of-activity, or depending on the type of ventilation employed [START_REF] Satish | Is CO2 an Indoor Pollutant? Direct Effects of Low-to-Moderate CO2 Concentrations on Human Decision-Making Performance[END_REF]. Based on the aforementioned studies, the discernible surge observed in GC's classroom before the renovation, between 10h00 and 13h00, may be attributed to the occupants' heat release.

In the GEII, the typical temperature peaks can be observed at 11h00 in both states when provided with appropriate heat supply and scheduling as per demand. Besides occupancy density, numerous other factors significantly contribute to the fluctuations in average indoor temperature following retrofitting. These may encompass outdoor conditions, room characteristics such as size, orientation, and the surface of external walls, and the type of ventilation system employed, such as natural or mechanical ventilation with extraction. The enhancement of thermal insulation and implementation of new airtight windows serve as measures for reducing external influence on measurements, thereby decreasing the observed differences following retrofitting. Following the refurbishment, the disparities in the temperature range of GEII have been mitigated, indicating the efficacy of the restoration in preserving thermal energy.

Maintaining consistent temperatures during both nighttime and daytime periods results in effective energy conservation for subsequent heating purposes.

Thermal comfort survey

Students of the GC building were asked to fill out a questionnaire in two periods, before and after renovation. The main question is, "How do you perceive the indoor temperature at this instant?". The answer is on a scale of five, from "Cold" to "Hot". In the pre-state, the survey was conducted in two classrooms repeatedly, at least twice a day, for five consecutive days giving 159 responses in total. However, using this longitudinal survey, students tend to lose interest in answering, making certain votes unreliable. Therefore, the survey conducted in the post-state was limited to a single day and a single time per classroom, yielding a total response of 50. Figure 8 A) illustrates the distribution of thermal sensation vote (TSV) in percentages. Before the implementation of retrofitting measures, a small fraction of the survey participants, 2.6%, reported experiencing indoor temperatures that were deemed extremely cold and hot. In the after stage, the response rate for the "Neutral" category demonstrated a statistically significant increase, with a recorded rate of 76% compared to 57.2% in the before stage. When comparing the TSV to the actual indoor temperature, as shown in Figure 8 B), it is interesting to see that in pre-building, the distribution of votes correlates to the temperature distribution. For instance, users in pre-renovation buildings evaluate the rooms as cool at an average temperature below 20°C, neutral at 20.4°C, and warm at 21°C or higher. On the contrary, after renovation, as revealed in Section 3.2.3, the air temperature is constantly lower than 20°C due to heating regulations. As a result, for the same temperature range, 76% of responses are neutral, and another 20% find it cool. Herewith suggests that reducing the temperature for energy-efficient purposes is still statistically comfortable for occupants.

Discussion

After investigating two renovated buildings, there are several factors to take into account for a better assessment.

The heating system in a school building differs from those in residential and office buildings since individuals, such as students and teachers, lack the capacity to regulate it. Unless their impact pertains to the opening and closing of windows or doors. But it has been studied and proved by Gao et al. [START_REF] Gao | Ventilation system type, classroom environmental quality and pupils' perceptions and symptoms[END_REF] that it is rare for users to change them manually during the winter. Therefore, the change in the heating system relies heavily on the management strategy and its efficiency. In this case study, results have found that the supplied energy in one of the buildings, GC, does not conform to the energy demand by overheating predominantly. This effect not only jeopardizes energy consumption but also the occupant's comfort.

Furthermore, the new heating temperature threshold was reduced to 18°C to conserve energy. This effect leads to 90% of the temperature data falling into the uncomfortable zone with respect to the recommended temperature range of the thermal guidelines for renovated buildings. Nevertheless, concerning the thermal comfort vote, occupants tend to be less affected by the renovation. According to the survey results, at approximately 19.5°C, 20% of participants rated the post-classrooms as cool, and the other 76% voted for the neutral temperature. The evidence suggests that individuals have the capacity to acclimate and exhibit an increased level of acceptance toward unfamiliar temperature settings. Similar situations were already reported in other studies [START_REF] Ezzeldin | The potential for office buildings with mixed-mode ventilation and low energy cooling systems in arid climates[END_REF][START_REF] Stabile | Effect of natural ventilation and manual airing on indoor air quality in naturally ventilated Italian classrooms[END_REF] and reinforce the idea that satisfactory comfort levels can be attained through internal heat gains and a thermally efficient building envelope.

Nonetheless, the designated temperature threshold may not be optimal for the workshops and the offices, where the number of occupants is minimal. After renovation, the average temperature of these two room types is as low as 18°C is equal to the threshold temperature since the internal heat gains are unavailable. Hence, it is crucial to maintain a delicate equilibrium between the user's comfort in different room types and overall energy saving. For instance, supplementary heating devices such as portable heaters or convertible air-conditioners may be installed within office spaces. In the workshops, students are advised to put on additional clothing to decrease personal heat loss.

Regarding the modification in temperature consequent to the transition from the Com f ort to Eco heating mode, it has been observed that the temperature experiences a more rapid and abrupt decline prior to the retrofitting measures.

The findings indicate that the upgrade of the envelope has led to a decrease in heat dissipation through both the fabrics and the ventilation system, resulting in prolonged conservation of heat and a reduction in the variance between nocturnal and diurnal temperatures.

Relevant to single-flow ventilation systems, the exchange air rate has become more vital in the post-building than it is in the pre-building. In extremely air-tight rooms, indoor air quality is at risk if the air-flow rate does not meet the design value of 18 m 3 /h.per, primarily for classrooms. The analyses in Section 3.2.2 have proved that when the exchange air rate is incorrect, the CO 2 levels on average can increase up to 11.7% more than the typical CO 2 values of the pre-state. Consequently, regular checks and maintenance of the ventilation system are mandatory.

Overall, the t-Test statistics have shown a slight significance between indoor climate before and after renovation for ges values less than 0.2. Suggesting that this retrofitting strategy maintains the IEQ at roughly the same range, without any noticeable improvement or deterioration. Although the CO 2 concentrations, HR in , and T in values are acceptable by remaining the same, they are sitting on the edge of the recommended zone according to the thermal benchmark of renovated buildings.

The results also show that during the occupied period of the heating season, the CO 2 levels exceeding 1000 ppm is only 1.3% of measured data in both states. However, occasional spikes can reach up to 3000 ppm within a brief 15-minute. The analysis was carried out during a period of use, but the rooms are not always occupied. Thus, the analysis presented in Figure 6 smoothes out the average hourly rate of CO 2 . Previous research has found similar patterns. For instance, [START_REF] Ramalho | Air Stuffiness and Air Exchange Rate in French Schools and Day-Care Centres[END_REF] [START_REF] Ramalho | Air Stuffiness and Air Exchange Rate in French Schools and Day-Care Centres[END_REF], investigated 108 campus buildings in France and revealed that in the occupation period, 33% of the schools revealed CO 2 concentrations above 1700 ppm in more than 66% of the records. [START_REF] Santamouris | Experimental investigation of the air flow and indoor carbon dioxide concentration in classrooms with intermittent natural ventilation[END_REF] [START_REF] Santamouris | Experimental investigation of the air flow and indoor carbon dioxide concentration in classrooms with intermittent natural ventilation[END_REF], monitored the indoor air quality in 27 naturally ventilated schools in Athens.

The measurements were taken in the spring and fall seasons when window opening is the main ventilation procedure.

The finding indicates that 52% of the classrooms presented CO 2 concentrations greater than 1000 ppm with a median of 1070 ppm. At the end of the class period, there was a maximum concentration of 3000 ppm with a median of 1650 ppm. Similarly, a study by Almeida found a marginal enhancement in indoor environmental conditions following the retrofitting process compared to the pre-existing state, albeit still falling short of the recommended criteria [START_REF] Almeida | Indoor environmental quality of classrooms in Southern European climate[END_REF][START_REF] Mocová | Indoor Climate Performance in a Renovated School Building[END_REF][START_REF] Zaeh | Indoor Air Quality Prior to and Following School Building Renovation in a Mid-Atlantic School District[END_REF].

These similarities with previous research highlight the importance of ongoing efforts to improve indoor air quality, especially during peak occupancy periods, in order to enhance the occupants' health and comfort within the building premises.

Conclusions

Students spend most of their time in school. Therefore good indoor environmental quality must be provided. With the current trend of low-energy consumption buildings, university buildings, like many others, have undergone energyefficient renovation. This renovation, however, does not guarantee better indoor environmental quality. This study is designed to comprehensively analyze the impact of refurbishment on thermal comfort and indoor air quality.

The key outcomes of the research revealed substantial reductions in heating consumption, amounting to 85 MWh over the studied period, as a result of the retrofitting efforts. Through t-Test statistics, the study identified subtle but significant changes in indoor climate parameters, including CO 2 levels, temperature, and humidity. However, these changes were not discernible to the occupants or experts.

Specifically, the detailed analyses yielded several noteworthy findings:

-In Mediterranean weather conditions, considering usable internal heat gain is crucial for retrofitted buildings. Accounting for heat release from occupancy and solar radiation in heating design and control strategies, such as thermostats, can prevent unnecessary energy wastage on heating;

-The adjustment of heating set points to 18°C post-renovation did not impact the thermal comfort perception of occupants in classrooms. Surprisingly, occupants displayed an increased tolerance towards cooler environments compared to their pre-existing attitudes. However, this threshold was suboptimal for offices and workshops, given their relatively low occupancy rates, limiting available internal heat gain. As a result, supplementary heating devices should be provided in these room types;

-Exchange air rate is more vital in the renovated building with extreme air-tight than in the existing building. This case study proves that defective ventilation systems led to a significant 11.7% increase in CO 2 concentrations, emphasizing the need for regular verification and maintenance to meet regulatory standards and prevent building sick syndrome.

The research confirms that the retrofitting of buildings using energy-efficient methods offers substantial benefits, as it significantly reduces heating demand and overall energy consumption without compromising indoor climate condi-

Figure 1 :

 1 Figure 1: Research methodology

Figure 2 :

 2 Figure 2: IUT of Nîmes campus showcases studied buildings, BAT 4 (GC) and BAT 5 (GEII)

Figure 3 :

 3 Figure 3: West elevation plan of GEII building

Figure 4 :

 4 Figure 4: Overall cumulative frequency of the indoor climate in each building

Figure 5 :

 5 Figure 5: Boxplot of the indoor climate in different room type on weekdays during the occupied hours from 8h00 to 18h00

Figure 6 :

 6 Figure 6: Mean CO 2 concentrations over the studied period on weekdays from January to March of Before (2021) and After (2023) renovation. The filled color represents the global lunch break that usually takes place between 12h30 and 13h30, a typically occupied period of school hours and typical non-occupied periods

Figure 7 :

 7 Figure 7: Mean indoor temperature over the studied period on weekdays from January to March of Before (2021) and After (2023) renovation. The filled color represents two heating operation modes, namely 'Com f ort' that maintains a comfortable room temperature and 'Eco' that economizes energy during nighttime and on weekends

Figure 8 :

 8 Figure 8: Distribution of thermal sensation vote in GC building before (from February 17 th to 23 rd , 2022) and after (March 7 th ,2023) renovation: A) frequency distribution; B) correlation between perception vote and measured indoor temperature

Table 1 :

 1 Building characteristics and usage detail

	Element	GC	GEII
	Construction year		1969
	Renovation	May to October 2022 May to October 2021
	Number of floors	North Workshop	2 (2.5m height each floor) 1 (3.5m height)
	Floor area (m 2 )		5467	4168
	Net floor area (m 2 )		4762	3627
		Classroom	19	14
	Number of rooms	Workshop Office	26 7	20 7
		Others	7	4
	database. Grafana is a tool used to visualize and export data, which is then used in RStudio for analysis. In this
	paper, Elsys and Class'Air sensors that measure indoor air quality indicators (CO 2 levels) and hygrometer comfort
	indicators (relative humidity and air temperature) [61] were utilized. The data were collected from two buildings, GC
	and GEII, that underwent renovation. Rooms with available data both before and after refurbishment were selected
	for the analysis: 4 classrooms, 2 offices, and 4 workshops. To acquire the most accurate readings, the sensors were
	mounted at a height of 0.6 meters on the wall and a fair distance from the heat and CO 2 emission sources, following
	ISO standards [62].
	Class'Air (± 0.1 °C, 1% and ± 50 ppm + 3% precision) is set to 60 minutes, whereas Elsys (± 0.2 °C, 2% and ±
	50 ppm + 3% precision) is programmed to a 15-minute interval. The continuous measurements from January to March
	in each state result in data gaps. Approximately 20% of the collected data was missing due to data acquisition failure

and power outage. Since this data is the primary source for this analysis, it is essential to interpolate the values every fifteen minutes to get the same time base and find missing values.

Table 2 :

 2 Thermal

	Element		Before	Materials	After	U-value (W/m 2 .K) Before After
	Floor		Concrete	Unchanged		3.08	3.08
			Concrete +
	Roof	North	40 mm polyurethane foam	Unchanged		0.51	0.51
			Concrete +
		Workshop	mineral wool	Unchanged		0.25	0.25
			120 mm
	Wall		Hallow brick	Hallow brick + rock wool insulation 120 mm	3.52	0.27
	Window		Single-glazed / Double-glazed	Double-glazed argon 16mm	3.6	1.5
			Building information		Program
	Ventilation	Natural (0.5 vol/h)	Mechanical extract ventilation (0.23 vol/h)	Permanent	6h00-20h00
	Central heating	North	District heating + Convection heater	District heating + Radiator + Thermostat control	7h00 -18h00 7h00 -18h00
	system	Workshop	District heating + Unit heater	Unchanged

properties of building envelope and a short description of ventilation and heating systems before and after renovation

Table 3 :

 3 Type of heat loss Element ∆H(W/K) DJU (°C.day) Heat saving (MWh) Saving shares (%) Calculated heating requirements saving before and after renovation of the GEII building during studied period from January to March

	Envelope	Wall Window	2640 1440	784.67	50 27	59 32
	Ventilation		413	-	8	9
	Total		4822	-	85	-

Table 4 :

 4 Relationship between pre-and post-renovation states of indoor climate in different room types of studied buildings

		Type	Effect p-value	p <0.05 ges
		Classroom		3.05e-05	*	0.021
	CO 2	Office		1.75e-13	*	0.072
		Workshop		2.86e-01		0.002
		Classroom		1.00e-17	*	0.084
	Temperature	Office	State	1.20e-02	*	0.009
		Workshop		2.06e-17	*	0.094
		Classroom		2.06e-16	*	0.078
	Humidity	Office		1.91e-04	*	0.019
		Workshop		7.31e-06	*	0.027

Acknowledgement

The authors gratefully acknowledge the support from the OEHM project supported by Occitanie region and Europe.

tions. While values of certain indoor environmental quality indicators may sit on the edge of the comfort zone defined by building thermal regulations for renovated buildings, they remain within acceptable limits. Future studies should confirm heat demand assessments through measurements of heating energy supplied by the building's substation, which will be accessible for the next heating campaign. Additionally, further investigations should explore combined effects, incorporating user comfort surveys in similar or mid-season periods, and consider visual comfort and indoor air quality, including odor comfort. These endeavors will contribute to a more comprehensive understanding of the impact of energy-efficient renovations on indoor climate and occupant comfort.

Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Keovathana Run reports financial support was provided by regional scholarship and the University of Montpellier.


A Kacem
email: afef.kacem@isg.rnu.tn

A Belaïd

M Ben

EXTRAFOR : automatic EXTRAction of mathematical FORmulas

We present a method for automatic extraction of mathematical formulas from images of documents without character recognition. Formula extraction is first done by location of its most significant symbols, then extension to adjoining symbols using contextual rules until delimitation of the whole formula space. Mathematical symbols labelling is realised from models created at the learning stage using fuzzy logic. From the experiments, we found that the average rate of primary labelling of mathematical symbols is about 95.3%.The obtained results have demonstrated the applicability of our system since 90% of mathematical formulas are well extracted from documents printed with high quality.

Introduction

A document may consist of various kinds of components, such as text, images, graphics, and mathematical formulas. Most systems proposed to analyse images of printed documents cannot handle all kinds of components. In fact, these components, having different structures and typographies, need to be separated in order to be analysed more efficiently by dedicated systems. This paper intends to present a system which will separate formulas from other components of document.

Mathematical formulas are involved in scientific documents, either as isolated expressions, or embedded directly into a text-line. Thus, the first step in mathematics recognition is to identify where expressions are located on the page. Recently several researchers have proposed algorithms for recognition of mathematical expressions [START_REF] Anderson R | Two-Dimensional Mathematical Notation[END_REF][START_REF] Belaid Abdelwaheb | A syntactic ApProc.h for Handwritten Mathematical Formula Recognition[END_REF][START_REF] Grbavec Ann | Recognition of mathematical notation[END_REF][START_REF] Twaakyondo | Structure Analysis and Recognition of Mathematical Expressions[END_REF][START_REF] Hsi-Jian | Understanding Mathematical Expression in a Printed Document[END_REF][START_REF] Ha | Understanding mathematical expressions from document images[END_REF][START_REF] Lavriolle Stéphane | Optical formula recognition[END_REF][START_REF] Okamoto | An experimental Implementation of Document Recognition System for Papers Containing Mathematical Expressions[END_REF][START_REF] Zhao | On line recognition handwritten mathematical symbols[END_REF][START_REF] Wang Zi-Xiong | Structural analysis of mathematical expressions[END_REF][START_REF] Chang | A Method for the Structural Analysis of 2-D Mathematical Expressions[END_REF]. They showed some sound recognition results. But most of the work we survey assume that recognition system begins with an isolated mathematical expression. An exception is Lee and Wang [14], who present a method for extracting both embedded and isolated mathematical expressions in a text document. Text lines are labelled as isolated expressions based both on internal properties and on having increased white space above and below them.

The remaining text lines consist of a mixture of pure text and text with embedded expressions. No details of this process are given, except that it is done "according to some basic expressions forms". So several problems still exist in these recognition systems, which include how to extract automatically mathematical expressions from a document, how to improve the recognition rate of multifont characters, and how to correct the recognition errors in a mathematical expression. In this paper, we simplify the problem by only extracting mathematical formulas from documents to be able to read, parse and re-use them in other applications.

In a mathematical formula, characters and symbols can be arranged as a complex two-dimensional structure, possibly of different characters and symbol sizes. They differ greatly from text since a line of text is onedimensional and discrete : characters are placed one after another on the same line when symbols in formulas may be under, above, on the right and far, included in another, etc, with continuous distances. This makes its extraction process more complicated even when all the individual characters and symbols can be recognised correctly.

To restore planar structure to formulas, two solutions are often proposed : recognition of characters, then restructuring or else labelling then recognition. The first solution assumes that optical reader has succeeded to segment formulas and is able to provide location of each character. The second solution simplifies the work, since it segments formula in characters before presenting them individually to an optical reader system. This method avoids OCR segmentation procedures which are too general. Noticing the lack of success of the first method, we have experimented the second using adaptive segmentation of text. The idea is to do labelling at several steps : extraction of lines, then location of isolated and embedded formulas.

This paper is organised as follows : in the next section, we present the overview of our system. We then describe our current approach to identifying mathematical symbols, analysing and extending their context in a way to extract formulas from text. Afterwards, we present and discuss some experimental results which are then followed by some concluding remarks, We finally, give some conclusions and prospects.

EXTRAFOR system

Fig. 1. shows the overview of EXTRAFOR, a system that we developed to extract mathematical formulas automatically from images of documents.

Fig.1. Overview of EXTRAFOR system

Extraction of CCXs

At this stage, the document is scanned, its image is straightened and its connected components (CCXs) are extracted. These CCXs constitute the basic datum from which our system starts its analysis. Each CCX is described by co-ordinates of upper left (X min , Y min ) and lower right (X max , Y max ) corners of its bounding box and the number of its black pixels (NBP). Let W and H be respectively the width and the height of CCX, then the following parameters are determined : aspect ratio, R=W/H, area A=W*H and density D=NBP/A.

After extraction of CCXs, it is convenient to restrict ulterior processing to those susceptible to be symbols of formulas which could improve precision and speed of their extraction. The filtering of CCXs is based on their area and aspect ratio to avoid noise, diacritical, punctuation signs, graphs, horizontal and vertical separators.

Extraction of lines

This step consists in grouping, into lines, horizontally adjacent CCXs. First, CCXs are sorted by ascending Y min .

Then, lines co-ordinates (X minl , Y minl , X maxl , Y maxl ) are updated from CCXs having a common intersection of their heights. Once a set of CCXs is associated to a particular line, we can sort them by ascending X min .

After that, it is convenient to assemble CCXs of not linear formulas as numerators and denominator lines of fractions as well as limit expressions of summations or products. In fact, their CCXs may be separated after line extraction step as seen in Fig. 2. Line fusion phase needs results of CCXs primary labelling and their proximity study.

Fig.2. Line extraction and fusion

First labelling of CCXs

This is about locating CCXs representing mathematical symbols. A label is assigned to each component according to the role it could play in formula composition. We will distinguish between Functional Symbols (FS: summations or product signs), Integral (IS) and Radical Signs (RS), Horizontal Fraction Bars (HFB), Small and Vertical Great Delimiters (SD, VGD), Binary Operators (BO: signs of subtraction), Subscripts and Superscripts (SUB, SUP). To classify these different types of mathematical symbols, we have thought of topographical (position according the central band), morphological (aspect ratio) and typographical (area and density) classification of their CCXs. (See

Mathematical symbols training

For an effective classification, the system must analyse the greatest possible number of symbols deduced from different mathematical documents to extract ranges of aspect ratio, density and area from their CCXs. These intervals being representatives of symbol type.

To have an assessed idea of similarity that can exist between different typographies and morphologies of the same symbol, we have taken a sample of mathematical symbols with different fonts, areas and styles. For each instance of symbol, values of aspect ratios, areas and densities are calculated, observed and only lower and upper bounds (LB, UB) are considered. We have studied 175 FS, 57 RS, 69 IS, 92 HFB, 116 VGD, 205 SD and 140 BO. The following results were obtained (See Table .2

.).

Symbols LB(R) UB(R) LB(A) UB(A) LB(D) UB(D)

Fuzzy identification of CCXs

To identify mathematical symbols, we have introduced degrees of membership to different symbol classes and constitute corresponding histograms. The abscissa of histograms represents all classes : the whole measured values shared in regular width intervals. The ordinate is the relative frequency: the number of measurements belonging to a class, divided by the total number of measurements. The ordinate can be considered as the membership degree to a class. It varies between 0 and 1. The generated histograms have to be as representative as possible, so the closest to a continuous function.

To identify a mathematical symbol given its CCX, values of each parameters P={R,D,A}are calculated. By referring to histograms of each type of symbol, we each time keep the membership degree of that CCX to a type of symbol: MS according to one parameter noted µ MS,P (CCX). We then keep, for each type of symbol, the minimal membership degree of that CCX according to its aspect ratio, density and area. We finally take their maximal value. Thus, the membership degree of the CCX to the type of symbol MS is defined by: µ MS (CCX)=Max(Min(µ MS,R (CCX),µ MS,A (CCX),µ MS,D (CC X))=Max(µ FS (CCX), µ IS (CCX), µ RS (CCX), µ HFB (CCX), µ VGD (CCX), µ SD (CCX), µ BO (CCX)).

MS SD

Table.3.: Fuzzy identification of a small delimiter

The obtained label corresponds to the true type of symbol although there could be confusion with class of functional symbols. But, it is clear that µ SD (CCX) is superior than µ FS (CCX).

Lines classification

Once lines are extracted and their CCXs are labelled, isolated formulas could be located. Generally, the height of lines containing isolated formulas is superior than the average height of lines. Besides, they are often centred that is the distances that separate them from the right and left margins are almost equal. Based on theses characteristics, isolated formulas could be extracted from images of documents, which restrict next stages to text-lines which could contain embedded formulas. Elsewhere, we decide to abandon processing of formulas which are very linear since they can be recognised by OCR systems.

Second labelling of CCXs

This concerns CCXs of text-lines. It is a finer labelling of CCXs, belonging to the same line, in which we have considered their position from central band to solve certain ambiguities observed at their primary labelling. In fact, the topographical classification of CCXs could distinguish between functional symbols and characters or digits similarly between integral symbols and oblique fraction bar, since integral and functional symbols are overflowing while characters, digits and oblique fractions bars are not. In addition, with this classification, subscripts and superscripts of formulas could be detected since their CCXs are generally deepen or higher. For those having descending or ascending components, we have considered two other features which are : the relative size : X=RS/LS (RS: Right component Size, LS: Left component Size) and the relative position : Y=D/LH (D: Distance between the top of the right component and the bottom of the left component).

Contextual analysis

At this stage, we tried to delimit embedded formulas. We have considered mathematical formula as a set of subexpressions that could be spread to the left or the right. Initially, these sub-expressions are symbols of formula. Then, by ascending successive fusion, these symbols will include their operands and other neighbouring expressions in a way to separate formulas from pure text. We present here some heuristics rules we used to extend context of mathematical symbols.

-R1: If a subscript or superscript is found, then it is grouped with its closest neighbour.

-R2: If a radical symbol encloses others components, then their fusion is a formula. -R3: If an horizontal fraction bar is found, then its numerators and denominators are joined to it.

-R4: If a functional or an integral symbol is found, then its lower and upper bounds in addition of the first component of its sub expression are joined to it. -R5: Each CCX enclosed inside a pair of vertical great delimiters should form a formula. -R6: A sign of subtraction, a subscript or a superscript, a functional, a radical, an integral symbol or a horizontal fraction bar or else a reduced number of characters enclosed inside a pair of small delimiters should form a formula.

-R7: If a binary operator is found, then its left and right operands are joined to it. -R8: Two horizontal adjacent formulas constitute one formula -R9: Two formulas, separated by a reduced number of CCXs compose one formula.

Experimental results

We carried our experimental formula extraction for a variety of mathematical documents. Images of documents are scanned at a resolution of 300dpi. In the experiments, we trained our system using 854 mathematical symbols and tested 460 symbols and about 50 formulas. Fig. 4 shows the results obtained after extraction of some embedded formulas. About 90% of formulas are well extracted form mathematical documents. The main errors are due to confusion of subscripts and superscripts with diacritic signs.

Fig.4. : Examples of formulas extraction

The main difficulties concern functional and integral formulas inserted in a text where we must detect the presence of a specific character like the 'd' of an integral formula or one character of the lower limit of a functional formula.

Conclusion and prospects

In this paper, we have developed a method to extract formulas automatically from images of mathematical documents without using OCR system. We have introduced fuzzy logic at CCXs labelling which has been useful to identify symbols and consequently to delimit formulas by a contextual analysis of their CCXs. Thus, we have been able to separate them from other components of the document.

Our prospects will include: dealing with more complex alignment symbols of formulas and confirming the efficiency and the performance of our method for a large data base of mathematical formulas.

  Table.1. and Fig.3.)

		Image of document
			Mathematical
		Extraction of CCXs	symbols	Fractional	ó CCXi extraction
		Line extraction	Models Base
		First labelling of CCXs		ì Line extraction	ö Line fusion
		Line classification
	Text-lines		Isolated formulas
	Second labelling of CCXs
	Contextual analysis
	Pure text	Embedded formulas
				Topography	Morphology	Typography	Symbols
				Overflowing	Squared, Great	Enlarged, Normal	FS
				Overflowing,	Large	Enlarged	RS
				Ascending
				Overflowing	Very extensive	Enlarged	IS
				Centred	Very lengthened Dense, Very dense,	HFB
					Enlarged, Normal
				Overflowing	Very extensive	Enlarged	VGD
				Ascending	Extensive	Normal	SD
				Centred	Lengthned	Very dense,	BO
					reduced
				Descending, Deep	SUB
				Ascending, High	SUP
				Centred, Very lengthened,	Ascending, Extensive, Dense,
				Very dense, Enlarged	Normal
				Centred, Lengthened,
				Very dense, Reduced	Overflowing, Squared,
					Few dense, Enlarged

Table.1.Symbols classification

Fig.3. Symbols classification

Table .

 .

					IS	0.40	0	0	0
					HFB	0	0.12	0.76	0
					VGD	0	0.36	0.20	0
					SD	0.35	0.44	0.49	0.35
					B O	0000
						µMS (CCX)	0.35
					3. presents an
	example of fuzzy identification of one small delimiter
	where R= 0.27, D= 0.32 and A= 418.
	MS	µMS,R(CCX) µMS,D(CCX) µMS,A(CCX)	µMS(CCX)
	FS	0.04	0.16	0.22	0.04
	R S	0000