dataesr/scientific-paragraphs-categorization huggingface dataset:
Recently, cooperative simulations were proposed in order to allow submodels of different systems to communicate by exchanging data in response to a certain defined time step. For this, multiple frameworks were developed and are in use (e. g. , BCVTB). In this paper, we main focusing on the exploitation of geothermal energy by integrating an Earth-to-air heat exchanger (EAHE) prototype into a small prefabricated building. This technology is known to be promising for preheating the air in winter and for precooling it in summer to ensure inside-zone thermal comfort. Fresh air circulates through the buried pipes and exchanges air with the ground/soil temperature. During summer, the air is cooled while passing inside the pipes (following the first law of thermodynamics), minimizing the cooling demand.
In winter, the process is reversed as the ground maintains its temperature throughout the year  [1]. The main objective is to evaluate its performance as well as its effectiveness in reducing energy consumption and maintaining occupant thermal comfort in response to real dynamics of the considered building, while deploying the BCVTB platform The work presented in this paper is an extension of the work communicated in  [2]  and is part of an ongoing project named HOLSYS, funded by the Moroccan Research Institute for Solar Energy and New Energies (IRESEN). The HOLSYS project's main objective is to study the impact of the use of information and communication technologies (ICT) and their solutions on occupants' comfort, bills and energy savings in smart buildings, including extracting relevant information from streaming data (e.
Schloegl et al. suggested in  [14]  a classification scheme for co-simulation approaches in energy systems. The main objective is to facilitate communication between the users and developers and to help define the most suitable tool for any simulation purposes. Bleicher et al. [15]  presented in their work a co-simulation environment for optimizing energy efficiency in production processes in order to assure economic competitiveness in the early planning phases. Welsch et al. [16]  developed a TCP/IP interface allowing for the coupling of the MATLAB-SIMULINK software to the FEFLOW finite element program. It was shown that small communication time steps are able to reduce data exchange errors between simulators and make the coupling more robust and reliable. Cellura et al. [17]  proposed a co-simulation approach integrating the building simulation and life cycle assessment.
63 °C in December to 16. 11 °C in January. In this section, we aim to evaluate the thermal comfort inside the zone without any ventilation or air conditioning system activated. For this, the simulation was run using the EnergyPlus simulation tool. This tool is a commonly used building simulation program In this section, we aim to evaluate the thermal comfort inside the zone without any ventilation or air conditioning system activated. For this, the simulation was run using the EnergyPlus simulation tool. This tool is a commonly used building simulation program among researchers, engineers and designers. It is considered to be one of the most robust available simulation tools at both the commercial and academic levels. It combines all the entities of a building, starting with the envelope modeling, HVAC systems, passive systems and integrated renewable energy sources.
Energies 2022, 15, 5407 10 of 22 The modeling and sizing of the system was calculated and simulated using MATLAB. Based on the above equations, we were able to compute the expected outlet air temperature using different pipe lengths and diameters. In what follows, we have conducted a parametric study for different pipe lengths and diameters to determine the best fit to our use, where the length and depth should not exceed 16 and 1. 5 m, respectively. It can be seen in Figure  7  that the total pressure drops, also referred as the charge losses, are greater for long lengths and small diameters. This can be explained according to Bernoulli's equations, which were modified to include head losses and pump work  [27]. P 1 ρg + v 2 1 2g + h 1 + H pump = P 2 ρg + v 2 2 2g + h 2 + H f r ( 21 ) where P is the pressure (Pa), ρ is the density (kg/m 3 ), g is the constant of gravity (m/s 2 ), h is the height above the reference (m), H pump loss is added by the pump (m), and H f r is the loss added by friction (m).
Co-Simulation Platform: Problem Formulation In this work, the Buildings Controls Virtual Test Bed (BCVTB) was used as the main platform for co-simulation. The BCVTB framework links a diversity of simulation tools to serve our holistic approach. It is based on the Ptolemy II software environment and couples various simulation programs for distributed simulations. In our case, the main purpose is to simulate a small-scale building named EEBLab and an Earth-to-air heat exchanger in both the EnergyPlus and Matlab simulation tools. The main idea behind integrating the EAHE system into the EEBLab is to come up with an effective solution to cool and heat the building while reducing energy consumption. Hence, the BCVTB platform was used to evaluate the building performances with their different linked equipment and systems as a holistic complex system for more accurate simulations and results.
The choice of BCVTB can be justified by many reasons. For instance, it has BACnet modules that directly read from and write to BACnet compatible controllers. Furthermore, BCVTB provides built linkages to connect simulation software tools, such as EnergyPlus, TRNSYS, Dymola and MATLAB. Ultimately, if we consider creating a hardware-in-the-loop platform, the co-simulation capability of the BCVTB makes it straightforward to realize time synchronization between the control hardware and simulation programs. Figure  15  presents the communication flow between EnergyPlus and MATLAB through BCVTB. First, EnergyPlus communicates the weather data, extracted from the TMY/EPW weather file, to MATLAB which uses the external ambient air temperature as an input to run the simulation, resulting in a simulated outlet temperature of the EAHE system.
The EMS-defined sensor reuses the normal output variables of EnergyPlus to provide a general way of obtaining a variety of input data with minimal complications. Once the sensor was defined and activated, the EMS program calling manager was activated to call a defined program developed by the user. Finally, EMS output variables were defined to obtain the required output data of the simulation. The EMS program was set to calculate indoor temperature, using mainly the same formula of the analytical solution. The main purpose behind investigating the BCVTB platform is to allow the evaluation of energy systems linked to the considered building. This takes into account the real dynamics of the building in response to weather conditions as well as other indoor stimuli (occupants, internal loads, infiltrations, lighting, etc.
Once the sensor was defined and activated, the EMS program calling manager was activated to call a defined program developed by the user. Finally, EMS output variables were defined to obtain the required output data of the simulation. The EMS program was set to calculate indoor temperature, using mainly the same formula of the analytical solution. The main purpose behind investigating the BCVTB platform is to allow the evaluation of energy systems linked to the considered building. This takes into account the real dynamics of the building in response to weather conditions as well as other indoor stimuli (occupants, internal loads, infiltrations, lighting, etc. ). A system evaluated in a standalone manner may not show its real performances and drawbacks. However, co-simulating the exchange of data occurs in real time and mimics the real responses of the system as if it was installed in a real case.
19  presents the temperature evolution inside the EEBLab in response to external temperature variation. Both curves evolve, qualitatively, the same way. The temperature inside the zone reaches a maximum of 29. 54 °C, while it reaches 31. 32 °C outside. There is not much of a difference between both ambiances, which is mainly due to the fact that: (i) no heating or cooling devices are active; (ii) a window is placed on the south facade of the EEBLab; (iii) the envelope's thermal inertia is weak since the walls are thick, although they are insulated. In order to evaluate the accuracy of the obtained simulation results, we have conducted experiments by means of our deployed IoT and big data platform  [31] [32] [33]. We used digital temperature sensors (DS18B20 and DHT22) connected to microcontrollers as sensing nodes.
The internal and external temperatures almost had the same evolution tendency. The indoor temperature reached a maximum value of 31. 10 °C, and the outdoor temperature reached a maximum of 31. 32 °C. Figure  21  is a presentation of the temperature deviation between the simulation and experimental results of the indoor temperature evolution. Concerning the indoor temperature, the difference between simulation and the experiment reached a maximum of 0. 35 °C. This negligeable offset can be justified due to the sensor's precision range as well as the input weather data average. In order to evaluate the accuracy of the obtained simulation results, we have conducted experiments by means of our deployed IoT and big data platform  [31] [32] [33]. We used digital temperature sensors (DS18B20 and DHT22) connected to microcontrollers as sensing nodes.
The internal and external temperatures almost had the same evolution tendency. The indoor temperature reached a maximum value of 31. 10 • C, and the outdoor temperature reached a maximum of 31. 32 • C. Figure  21  is a presentation of the temperature deviation between the simulation and experimental results of the indoor temperature evolution. Concerning the indoor temperature, the difference between simulation and the experiment reached a maximum of 0. 35 • C. This negligeable offset can be justified due to the sensor's precision range as well as the input weather data average. In this subsection, we present the results of the cooperative simulation by combining EnergyPlus and MATLAB to model the building and the EAHE system, using the BCVTB framework. The main objective is to be able to simulate the building as a complex system while taking into consideration its linked equipment.
Furthermore, these results will allow for visualizing the influence of the EAHE system on the thermal comfort inside the EEBLab. In in order to study the correlation between the simulation and the experiments, real experimental outdoor temperatures were collected and used as input in the simulation. Using the collected outdoor environmental data of our test site, we updated an original EPW file and created a customized one with the collected temperatures using the Elements software. However, other ambient parameters (e. g. , pressure, relative humidity, solar radiation) were not changed, since the variation was not influential. The co-simulation was run for a period of 37 days from 27 December 2021 to 2 February 2022. Figure  22  presents the temperature evolution inside the building without the EAHE system activated (blue x-marked curve), with the EAHE activated (black triangle-   In this subsection, we present the results of the cooperative simulation by combining EnergyPlus and MATLAB to model the building and the EAHE system, using the BCVTB framework.
The main objective is to be able to simulate the building as a complex system while taking into consideration its linked equipment. Furthermore, these results will allow for visualizing the influence of the EAHE system on the thermal comfort inside the EEBLab. In in order to study the correlation between the simulation and the experiments, real experimental outdoor temperatures were collected and used as input in the simulation. Using the collected outdoor environmental data of our test site, we updated an original EPW file and created a customized one with the collected temperatures using the Elements software. However, other ambient parameters (e. g. , pressure, relative humidity, solar radiation) were not changed, since the variation was not influential. The co-simulation was run for a period of 37 days from 27 December 2021 to 2 February 2022.
Conclusions The main contributions of this paper are two-fold: The first is the evaluation of the performances of an Earth-to-Air heat exchanger in maintaining indoor thermal comfort in buildings. For this, a parametric study was run, and the most adequate parameter values were chosen to help us size the system and locally construct it. The second concerns shedding light on the importance of co-simulation approaches in evaluating system performances linked to buildings, in response to the actual real dynamic outdoor and indoor contexts. For this, the BCVTB framework was investigated to evaluate the EAHE performances while integrating it into the EEBLab. This platform allows for real-time data exchange between subsystems, namely, the EnergyPlus tool, which was used to simulate the indoor air temperature variations within the EEBLab and MATLAB to model the EAHE system and to calculate its performance based on various parameters.
For validation purposes, a locally developed internet of things platform named HOLSYS was deployed to collect and monitor data from the sensors. The results proved to be a good agreement between both the experimentation and the simulation, in terms of the ability of the EAHE to maintain a comfortable indoor temperature range inside the EEBLab. Moreover, an economical modeling was created to evaluate the ability of such integrated systems in decreasing energy bills. It was shown that the power gained by using the EAHE system is about 1. 4 kWh daily, resulting in 511 kWh per year, which mainly leads to a billing savings of MAD 827/year (USD 82). Our future work will focus on the integration of the developed EAHE system with the heating ventilation and air conditioning (HVAC) system. The main aim will be to optimize the use of the HVAC system when the EAHE system is capable of satisfying the cooling and heating needs.
Languages differ markedly in the number of colour terms in their lexicons. The Himba, for example, a remote culture in Namibia, were reported in 2005 to have only a 5-colour term language. We re-examined their colour naming using a novel computer-based method drawing colours from across the gamut rather than only from the saturated shell of colour space that is the norm in cross-cultural colour research. Measuring confidence in communication, the Himba now have seven terms, or more properly categories, that are independent of other colour terms. Thus, we report the first augmentation of major terms, namely green and brown, to a colour lexicon in any language. A critical examination of supervised and unsupervised machine-learning approaches across the two datasets collected at different periods shows that perceptual mechanisms can, at most, only to some extent explain colour category formation and that cultural factors, such as linguistic similarity are the critical driving force for augmenting colour terms and effective colour communication.
An advantage of them is that they do not assume commensurate feature dimensions, or normally distributed feature values. To generalise our observations to any colour and identify the indispensable colour terms in the Himba language, we will use a Rotated Split Trees (RST) approach in regression mode that predicts probabilities and provides state-of-the-art performance in computational colour naming models  (Mylonas, 2020). Given our training set of colour points X = x 1 ,. , x n with their Himba naming responses Y = y 1 ,. , y T , and a free parameter B = 100 trees, RST ensembles B random split trees, f b = {T 1,…, T B } by using for each tree T the full training dataset  (Geurts et al. , 2006). Prior to any splitting, a proper rotation matrix R is generated using Householder QR decomposition  (Andrews et al.
, 2017; Blaser and Fryzlewicz, 2016; Householder, 1958). The rotated trees have different orientation and vastly dissimilar data partition and are capable of producing smoother, non-axis, parallel decision boundaries than un-rotated trees. The regular, un-rotated, trees, would make splits parallel to the axis in the feature space of the dataset by selecting at each node the split on the attribute that produces the maximum gain. In contrast, rotated trees, combine attribute values and produce a rotated hyperplane with a smaller number of splits that tend to outperform un-rotated trees by separating better instances of the dataset that pertain to distinguishable categories. For growing a tree, RST splits the training data at each node independently of the target variable fully at random, unlike the optimum criterion of Random Forests  (Breiman, 2001).
The measured CIE 1931 chromaticity coordinates of the white point of the monitors were x = 0. 3067, y = 0. 3318, and x = 0. 3055, y = 0. 3296 with a correlated temperature of 6816 K and 6907 K, respectively. Repeating the spectro-radiometric measurements of the monitors a month later after the fieldwork showed only a minimal drift of their white point over time (<0. 003). The stimulus presentation was controlled by PsychoPy-version 1. 84. 2 software  (Peirce, 2007). Procedure. Observers were seated inside a tent during daytime approximately 80 cm away from a monitor. The task of the observers was to name out loud the colour of the stimulus so that others would know to which colour they were referring. Observers were free to use as broad or narrow names as they liked. They were not instructed to answer "don't know" if unsure but such answers were always accepted without question.
Results The new Himba colour naming dataset included in total 33,000 raw naming responses for 600 colour samples from 55 observers (mean age = 27. 38, SD Age = 9. 79, age range = 16-60; Female = 41. 82%, Male = 58. 18%). For the data analysis, we considered colour names given by two or more observers. Unique responses (0. 8%) from single observers were excluded because we could not be confident that other observers would understand the colour name and, therefore, these responses were considered idiosyncratic. Contrary to  Lindsey et al. (2015)  where the Hadza observers were explicitly instructed to use a specific don't know response to cluster together difficult to name regions, Himba observers did not offer a name to a colour in 665 responses. These were sparsely distributed to 434 samples of colour space (maximum 4 don't know responses to any sample) and were excluded from further analysis (see Fig.
S1 ). This filtering resulted in a dataset of 32,087 responses. Before considering outcomes from machine learning, we first analyse our data in terms of frequency and modal naming as is usual in previous studies. Frequency of colour terms. In line with the analysis in  Roberson et al. (2005) , we report the frequency of each colour term in our new Himba data and the number of observers producing each term. The meaning of frequency for our data needs clarification. It is, as usual, the number of times that each colour name is used to describe our colour stimuli but, as we sample from the whole of colour space, frequency of any colour term is likely to increase with the extent of a category in colour space. Figure  1  shows the centroids of the more frequent Himba colour names given by two observers or more scaled by their frequency of occurrence.
In later sections we will examine the indispensability of these terms using computational models but first we will compare naming differences from 2005 to the present and then consider the naming that occurs when using the full colour gamut. Comparison of Himba naming in 2005 and 2019. To illustrate the change in naming behaviour between the earlier and new studies, in Fig. 3  we show the estimated categories on the traditional Mercator projection of the surface of the Munsell Book of Color (n = 320 samples) by RST using the Himba data of our earlier study  (Roberson et al. , 2005)  and the Himba data of this study. The classification of the Munsell array into the five Himba colour terms (serandu, dumbu, zoozu, burou, and vapa) by RST when trained by the earlier Himba data was overall consistent with the distributions of the five modal terms reported in Fig.
1  of  Roberson et al. (2005)  with a classification accuracy of 88% for the n = 160 samples. We note that using our probabilistic approach for identifying modal terms (peak of P(n|c)) in the earlier data, we found an additional minor modal term (vinde) for a few inconsistently brownish and purplish samples but RST classified them to the major modal terms. RST with our new data show six colour terms on the Munsell array (the earlier five terms plus grine). There was a large area (19. 4%) on the surface of the colour solid named consistently across observers as grine. RST found no minor terms on the surface on the Munsell system. We considered if the grine term could have been latent in our 2005 data but there is no evidence of separate green and blue foci within the GRUE category. In fact, the confidence for the boundary chips (7.
5BG) between GREEN and BLUE in the new data (M conf = 0. 6, SD = 0. 1) is significantly lower, t(14) = 3. 5519, p = 0. 003, than in the old data (M conf = 0. 8, SD = 0. 1). In other words, the chips with the highest confidence of the GRUE category have become boundary colours between GREEN and BLUE categories. Similarly, the location of the colour chips with the highest confidence for grine (P conf = 0. 8, h = 7. 5GY, V = 6) and for burou (P conf = 0. 9, h = 7. 5PB, V = 5) in our new data were boundary colours in our earlier data. We also note that our highest confidence stimuli correspond roughly with the locations of the green and blue foci in English  (Sturges and Whitfield, 1995). Colour naming across the full colour gamut. To explore the confidence of Himba colour naming across the full colour gamut we classified a grid in CIELAB of 4-unit bins at 8 lightness levels constrained in the sRGB gamut  (Heer and Stone, 2012)  using RST and the recent Himba data.
Interestingly, there is no neutral category at mid lightness levels nor a category between red and blue as the Himba are missing both consistent grey and purple terms. Comparing supervised and unsupervised learning. To gain some leverage on whether cultural (i. e. , linguistic) or perceptual similarity is the critical driver for effective colour communication with the Himba, we evaluated the classification accuracy of supervised (RST) learning in a leave-one-out cross validation mode and unsupervised (k-means) learning on predicting the observed modal terms for each colour stimulus in both Himba datasets. First, we consider the 160 colour samples on the surface of the Munsell system in the approximately perceptually uniform colour space of CIELAB used by  Roberson et al. (2005) , second the 600 colour stimuli used in this study in CIELAB and finally we compare the performance of both methods, again for the new dataset, but in a different colour space, namely sRGB, a nonuniform but the most widely used colour space of the Internet.
For each Himba dataset that we assessed, we built j separate RST classifiers where j is equal to the number of colour examples in each dataset (c = 160 and c = 600). Each RST classifier was trained on c -1 labelled colour samples, with a different colour sample left out. The communication accuracy for each colour sample was then computed by the classifier, which was trained with it left out. Then we aggregate the results of all these classifiers for all left out test colour samples. For  Roberson et al. (2005) , on the surface of the Munsell solid, the classification accuracy of k-means with k = 5 (equal to the reported modal terms) was 64% while RST produced a better accuracy by classifying 89% of the samples correctly. RST identified five colour categories (serandu, dumbu, zoozu, burou, and vapa).