dataesr/scientific-paragraphs-categorization huggingface dataset:
Defining homogeneous units to optimize the monitoring and management of groundwater is a key challenge for organizations responsible for the protection of water for human consumption. However, the number of groundwater bodies (GWBs) is too large for targeted monitoring and recommendations. This study, carried out in the Provence-Alpes-Côte d'Azur region of France, is based on the intersection of two databases, one grouping together the physicochemical and bacteriological analyses of water and the other delimiting the boundaries of groundwater bodies. The extracted dataset contains 8627 measurements from 1143 observation points distributed over 63 GWB. Data conditioning through logarithmic transformation, dimensional reduction through principal component analysis, and hierarchical classification allows the grouping of GWBs into 11 homogeneous clusters.
Water managers continue to grapple with the question of how to manage this resource, which exhibits great variability in storage over space and time  [11]. Following the Water Framework Directive (WFD) adopted by the European Union in 2000  [12] [13] [14] [15] [16] , an inventory of groundwater bodies (GWB) has been established throughout the EU. In France, the water agencies were in charge of this inventory based on geographical and geological criteria. This inventory has been collected in a French Reference System for Ground Water Bodies  [17] , regularly updated for about twenty years. Independently of this inventory, regular water analyses are carried out by the health agencies as part of the monitoring of the quality of water intended for human consumption. These data, the collection of which was initiated more than 30 years ago, have been compiled in another database called SISE-EAUX, which is managed at the national level, but especially at the level of the administrative regions by the regional health agencies  [18, 19].
This database, continuously updated, includes a wide range of microbiological, physicochemical, and radioactive parameters, making it a valuable source of spatially referenced information on groundwater quality. There are more than 32,000 geo-referenced collection points throughout the country, and each collection point is analyzed several times over time, allowing the measurement of temporal and spatial variability. Despite the large body of information in this database, which allows for a better understanding of key mechanisms shaping water characteristics, the large number of sampling points, even at the scale of the administrative region, does not allow for the development of a monitoring and protection strategy for each catchment point (several thousand) or even for each GWB (typically more than 100).
In addition, the highly dimensional nature (sometimes more than 100 parameters) of the data contained in the database makes it difficult to analyze the information using traditional pairwise parameter crossing methods, not to mention that the information may be redundant, with some parameters being more or less related to each other and to environmental conditions. This work aims to optimize the identification of processes that influence the chemical quality of groundwater bodies, by basing itself on previous research, for a sound management of the resource. Recently, our research team has proposed a classification method to cluster GWBs into homogeneous sets based on multifactorial water quality  [20]. This clustering involves a data conditioning step in order to reduce the weight of outliers in the analysis  [21]  and a dimensional reduction based on principal component analysis  [22].
The shift from the collection point scale to the GWB scale and then to the GWB homogeneous grouping scale is accompanied by a loss of part of the information contained in the dataset, which has not been quantified so far. This is one of the objectives of our study, in order to evaluate the relevance and effectiveness of the method leading to the differentiation of a reduced number of spatial units on the final map. The coefficient of determination (R 2 )  [23]  of the analysis of variance (ANOVA)  [24]  is a relevant tool for this quantification of the rate of information loss during clustering. The higher the R 2 , the less information is lost, and the more relevant is the homogeneous clustering of GWB. This study will also focus on the distinction between temporal and spatial variances for a better understanding of the processes taking place in groundwater.
In this framework, the objective of this study is three-fold. It is to  (1)  analyze the impact of outliers within the dataset without losing the information conveyed by these observations; (2) identify homogeneous subsets of data that allow for better discrimination of processes within each group of GWBs; and (3) quantify the loss of information associated with the reduction of spatial units from the scale of the collection points to the grouping of GWBs.
Data Sources and Processing SISE-EAUX is a groundwater quality database maintained by the French Ministry of Health and its regional and departmental services and is intended for the organized storage of health-related information on water  [18, 19]. It has been progressively computerized since 1994, but its systematic use as a database has been widely adopted since 2000. The database includes 250,000 control points throughout France, covering both raw (untreated) and distributed (potentially treated) water. The analyses were performed by laboratories that have received the necessary approvals and laboratory quality certifications. The data extraction for this study covered the entire PACA region over a period of 14 years, from 2006 to 2020, and focused specifically on raw water before any treatment for potability.
The database includes a total of 1143 georeferenced sampling points, the distribution of which is presented in Figure  1. The data extraction provided 9117 water samples, on which between 4 and 24 parameters were analyzed, resulting in a hollow matrix of 9117 samples on 24 parameters. Afterwards, a data cleansing stage took place, which included removing observations with missing values and parameters that had a limited number of samples, such as thermotolerant coliforms and revivable bacteria at 22 and 37 degrees Celsius, along with total coliforms, which exhibited a data deficiency exceeding 95%. In the end, all parameters with more than 2. 5% missing values were eliminated, resulting in a data loss of 5. 4% compared to the initial extraction. After this cleaning phase, a full matrix of 8627 observations and 18 parameters was retained, which corresponds to an average of 7.
Principal Component Analysis (PCA) A principal component analysis (PCA) based on the correlation matrix was performed on all parameters  [31]  in order to reduce the dimensionality of the data space while minimizing the loss of information contained in the dataset. This procedure, based on standardized data centered on the mean and divided by the standard deviation, ensured that each variable had the same weight in the PCA, regardless of the unit used. This method is based on the principle that the resulting factorial axes (principal components) are orthogonal to each other, and therefore carry information related to interdependent processes  [32, 33].
VI. OPEN AND FUTURE RESEARCH DIRECTION The literature review highlights significant advancements in implementing SDN for vehicular communications. These developments must consider performance requirement standards and vehicular communications use cases. Key enabling technologies like NFV, MEC, network slicing, AI/ML, fog computing, and cloud computing contribute to highperformance, reliable, and scalable vehicular communications beyond RAT-only implementation. SDN is suggested to be applied alongside various enabling technologies to tackle vehicular communications challenges. SDN and other enabling technologies address performance standards, such as latency, mobility, reliability, throughput, security, and maintaining low communication overhead. By improving network architecture, routing, SDN controllers, and data planes, vehicular communications performance can be supported.
Challenges in SDVN architecture, such as scalability, network dependability, and effective communication, have been addressed by proposed hierarchical SDVN architectures like E-HSDV and others. Researchers can focus on creating resilient SDVN designs that adapt to changing network conditions. Technologies like NFV, VNF, MEC, and network slicing can improve vehicular communications, but challenges related to reliability, latency, QoS awareness, and resource efficiency remain. AI, ML, and different algorithms in vehicular communications can address security, mobility management, congestion control, and optimization issues. However, challenges in obtaining accurate data for ML models and algorithms and the scalability of proposed methods to large-scale vehicular networks persist. Fog computing can improve vehicular communications, but issues such as resource availability and capabilities compared to MEC must be resolved.
Based on the papers discussed in this section, we can conclude that platooning optimizes vehicle grouping for fuel efficiency and driving benefits. Various technologies, including 5G RAT, MEC, and blockchain, enhance platooning. SDN tackles challenges like authentication and security, while NFV aids network support. Close-range communication in platoons is used for low-latency parallel MEC  [47] -  [51]. The research suggests potential solutions to enhance vehicle platooning use cases. Leveraging low communication latency within platoons to create a parallel MEC can further improve the efficiency of data processing and decisionmaking within the convoy  [51]. Integration of SDN with authentication methods and aggregated message authentication codes (AMACs) can reduce handover delays and improve the security of platooning systems  [48].
EAWAG AEROBIC GRANULAR SLUDGE MODEL The Eawag AGS reactor model is an integrated model consisting of (1) a biokinetic model, (2) a granule model and (3) a reactor model. The AGS reactor model was implemented in Sumo ® version 20 (Dynamita, Nyons, France) (http://www. dynamita. com). The model is available for download at: https://opendata. eawag. ch/dataset/eawag-ags-model-package. A full description of the model is provided in the package.
ACKNOWLEDGEMENTS The authors would like to express their strong gratitude to the following persons who supported this study: (1)  Andrea Giesen  and  Sjoerd Kerstens  from  RoyalHaskoningDHV  for providing access to AGS samples and for their expert advice, (2)  Andreas Proesl  ( Wabag Water Technology Ltd ) for providing access to Sarneraatal WWTP and for his expert advices, (3)  Stefan Kälin  ( Sarneraatal WWTP ) for providing access to the plant and support during field testing.
This article contains a description of protocol to measure air velocity field (by Particle Image Velocimetry -PIV) and temperature field (by T-type thermocouples) in an insulated box equipped with Phase Change Material (PCM) of melting point 0 °C. The influence of various conditions was studied: i) PCM position (at sidewall and at top), ii) aspect ratio of the box (height/width ∼ 1 and 1. 7), iii) ambient temperature (10 °C, 20 °C and 30 °C), iv) test product initial temperature (4 °C and 10 °C) and vi) spacing beneath the load (0 mm and 20 mm). This article is related to a published research paper, it provides the dataset of all experiments which can be useful for experimenter to understand the phenomena and for expert in numerical model to validate the developed model e. g. , by Computational Fluid Dynamic.
Data Description Data presented in this article include raw data of air velocity and temperature measurement, figures of air velocity field and temperature contour field in an insulated box equipped with a Phase Change Material (PCM) under various PCM position, aspect ratio of the box, ambient temperature, initial load temperature (test product, Tylose) and space beneath the load. Table  1  summarizes 10 experimental conditions presented in this article and their corresponding figures. The air velocity component, its magnitude and uncertainty of measurement under these conditions are shown in Dataset 1: Air velocity in an insulated box ( https://data. mendeley. com/ datasets/ggmkzk634h/1 ) The average temperature in the insulated box under these conditions are shown in Dataset 2: Average temperature in an insulated box ( https://data.
mendeley. com/datasets/sz5dgkz7k8/1 ) The temperature contour field was drawn via MATLAB by interpolating the measured temperature at 30 positions during stable conditions. The codes of this drawing are shown in Tanathepl/Temperature-contour ( https://github. com/Tanathepl/Temperature-contour. git ) The air velocity field at X = 15 mm in a loaded horizontal box (aspect ratio ∼ 1) with PCM on a sidewall under 20 °C ambient, product initial temperature = 4 °C with 20 mm gap below (condition 1) are shown in Fig. 1. Air velocity and temperature fields at X = 250 mm are not shown here, they can be found in Leungtongkum et al. [1]  ( Figs. 2a  and 2a ' ). The air velocity field at X = 15 mm in a loaded horizontal box with PCM at the top under 20 °C ambient, product initial temperature = 4 °C with 20 mm gap below (condition 2) are  shown in Fig.
Thermal study The box was loaded with 16 test product packs initially at 4 °C or 10 °C and a PCM slab initially at -2 °C. The temperature measurement of product, air, internal wall and PCM was carried out with 34 thermocouples (T-type thermocouples) at the middle plane ( x = 250 mm). The diagram showing thermocouples positions is in Fig. 1  of Leungtongkum et al. [1]. The measurement started after the box closing until complete PCM melted without the box opening during the experiment during which the temperatures were recorded continuously (every 30 s) using Agilent 34972A data acquisition unit (Agilent Technologies, CA, USA). The results of the stabilization period ranging from 400 min. to 600 min. were analyzed and compared. This period was chosen based on temperature variations were less than 0.
Acknowledgments King Mongkut's Institute of Technology Ladkrabang, Thailand  (contract no. KREF156402  ),  French Embassy in Thailand , and the  National Research Institute for Agriculture, Food and Environment, France  are gratefully acknowledged for their financial support. The first author,  T. Leungtongkum , would also like to thank the  Office of the Civil Service Commission of Thailand and Chulalongkorn University, Thailand  for the award of his PhD scholarship. Thanks to  LaVision  for PIV technical support.
Data accessibility With the article Repository name: Mendeley Data Data identification number: 10. 17632/ggmkzk634h. 1 10. 17632/sz5dgkz7k8. 1 Direct URL to data: https://data. mendeley. com/datasets/ggmkzk634h/1 https://data. mendeley. com/datasets/sz5dgkz7k8/1 Repository name: GitHub Data identification number: 10. 5281/zenodo. 6900688 Direct URL to data: https://github. com/Tanathepl/Temperature-contour. git Related research article Leungtongkum, T. , Flick, D. , Chaomuang, N. , Denis, A. , & Laguerre, O. (2023). Influence of use conditions on heat transfer in an insulated box equipped with a phase change material. Journal of Food Engineering, 357, 111644. https://doi. org/10. 1016/j. jfoodeng. 2023. 111644.
We recall that the intraday functions are not computed for all possible days in the time horizon, but only for a day representing each periodicity class. Here we split the year of the industrial site data into the four traditional trimesters, each trimester corresponding to one periodicity class. For each trimester, we model the net demand at a given half hour of the day by a discrete random variable with a support of size 10. The probability distribution of each discrete random variable is obtained by clustering, using k-means algorithm, the net demand realizations in the dataset associated with the half hour under consideration. In the case of resource (resp. price) decomposition, we compute the intraday functions L R d (resp. L P d ) for every possible capacity c d and every possible exchangeable energy h d -h d+1 (resp.
Numerical model This section details the numerical model used to verify the proposed analytical model. A Finite Element Model (FEM) is implemented using the commercial software COMSOL Multiphysics® v. 5. 6. The material is modeled within a cylindrical impedance tube to conduct virtual measurements and the absorption coefficient under normal incidence is obtained using the two-microphone method  [25]. To minimize the computational time, a two-dimensional axisymmetric model is employed, taking advantage of the system's radial symmetry (see Fig. 3a ) and b)). It is assumed here that the solid walls have infinite rigidity, thereby excluding any consideration of frame vibrations. The simulations are done with a Thermo-Visco-Acoustic (TVA) module which solves the full system of equations of fluid dynamics in the built geometry: the continuity equation, the momentum conservation equation, the energy conservation equation and the linearized equation of state, all recalled in  [14].
Hamdaoui et al. [20]  evaluated the thermal performance of an office building with intermittent occupancy according to three different construction scenarios. TRNSYS software was used to simulate the six Moroccan cities which represent the country's typical climate zones. The authors have investigated the effects of envelope construction type, air infiltration, and glazing surface. Still, this study did not include phase change materials, thus, we can say that there is a need for studies that include phase change materials and assess their impact on different constructions, glazing, and air infiltration scenarios. Generally, in Morocco, the window-to-wall ratio (WWR) used is around 25%  [21]  , whereas other studies have estimated for buildings located in Marrakech city a WWR range of 16 to 25%  [22].
Moreover, after studying the effect of building volume on PCM performance, we will add a double-glazed window with a 12 mm air gap between two 6 mm clear glasses that covers a window-to-wall ratio of 25%. This double-glazed window configuration was chosen as it is one of the most available window constructions in Morocco and it has been studied in the literature for light-weight buildings  [26]. The thermal transmittance (U-value) of this window is 2. 8 W/m 2. K with a solar heat gain coefficient of 0. 75 and visible transmittance of 0. 8. These values were extracted from the Moroccan construction database  [27]. This window will be placed in four different orientations: South, North, West, and East. This is done to evaluate the PCM effect on indoor temperature fluctuation reduction, passive thermal regulation, and energy savings.
Moreover, this weather station is inside the Green Energy Park research platform. Nevertheless, in this paper, we will conduct an evaluation study on the final selected PCM building with the optimal window orientation and the standard air infiltration in the 12 regions of Morocco shown in Fig. 3. The simulation was conducted for 13 different cities shown as red dots in Fig. 3  , the weather data for all regions except 'Marrakech-Safi', were extracted from the software Meteonorm  [30]. Fig. 3  demonstrates that only a single city in each region was simulated. Whereas, only in the 'Fes-Meknes' region, two cities were simulated. This is because we initially selected cities with the densest population. Yet, according to the Moroccan thermal regulation  [31]  , there are six climatic zones shown in Table  4.
Numerical procedure The phase change model with hysteresis was applied using the conduction finite difference mode of the simulation program Energyplus v22. 1. The following equations serve as a representation of PCM modeling in EnergyPlus: 𝑐 𝑝 𝜌Δ𝑥 ( 𝑇 𝑗+1 𝑖 -𝑇 𝑗 𝑖 ) Δ𝑡 = 𝑘 𝑤 ( 𝑇 𝑗+1 𝑖 +1 -𝑇 𝑗+1 𝑖 ) Δ𝑥 + 𝑘 𝐸 ( 𝑇 𝑗+1 𝑖 -1 -𝑇 𝑗+1 𝑖 ) Δ𝑥 (1) 𝑘 𝑤 = ( 𝑘 𝑗+1 𝑖 +1 -𝑘 𝑗+1 𝑖 ) 2 (2) 𝑘 𝐸 = ( 𝑘 𝑗+1 𝑖 -1 -𝑘 𝑗+1 𝑖 ) 2 (3) Δ𝑥 = √ 𝑐𝛼Δ𝑡 (4) Eq. (  1 ) illustrates the EnergyPlus fully implicit scheme, which determines the thermal conductivity between nodes through linear interpolation. Eqs. (  2 ) and (3) are used to compute the interpolation of heat conductivity between nodes in the interior and the exterior, respectively. Eq. (  4 ) determines the space discretization in each construction layer, which is determined by the space discretization coefficient (c) and the time step ( Δt).
The authors use four low-level graph coloring methods (largest, saturation, largest colored, and largest enrollment degrees) that have been hierarchically hybridized. The suggested method was evaluated using the 2007 International Timetabling Competition datasets. Results show that constructive hyper-heuristic graph coloring gives better solutions than other procedures. Routing/scheduling problems is another field where heuristic-based hyper-heuristics are applied frequently. For resolving challenging cases of the dynamic vehicle routing issue,  Garrido and Riff (2010)  devised and examined an evolution-based hyper-heuristic strategy. Perturbative, constructive, and noise were low-level heuristics used in their study. The heuristics are used complexly to create a new hyper-heuristic algorithm.
The best hyper-heuristic for selection was examined for the timetabling problem. The findings showed that the proposed method could yield better solutions than other proposed solvers and CP Solver. In another study, Vela, Cruz-Duarte, Ortiz-Bayliss, and Amaya (2022) developed an extra layer of generalization to select an appropriate solver. The new model outperformed the base hyper-heuristics. In a recent study, Doerner and Schmid (2010) examined the evolution of hybrid solution strategies for several vehicle routing challenges. Only hybrid heuristics and exact solution strategies are taken into account. Most extant hybrid methods rely on set-covering formulations, local branching, or other decomposition strategies. Amaya, Cruz-Duarte, and Ortiz-Bayliss (2022) presented MatHH algorithm, a Matlab-based application that allows rapid hyper-heuristics prototyping.
Gonçalves, Kuk, Almeida, and Venske (2015)  enhanced the MOEA/D framework for developing a multi-objective selection hyperheuristic. They evaluated MOEA/D-HH on scenarios from the competition problem set. The comparison of MOEA/D-HH with various significant multi-objective optimization techniques yields encouraging results. Walker and Keedwell (2016) combined different comparison operators and examined them in conjunction with a hyper-heuristic to optimize many-objective problems. They found that the favor relation or hypervolume yields the best results. Kumari and Srinivas (2016)  proposed a hyper-heuristic algorithm to cluster the software modules into clusters according to their cohesion and coupling values. They reported significant improvements in their study. Qian, Tang, and Zhou (2016)  deployed MOEA components (selection, mutation, and acceptance procedures) as low-level heuristics, showing that heuristic selection might be quicker than a single low-level heuristic.
Significant performance improvements are obtained. Segura, Miranda, and León (2011) developed a parallel hyper-heuristic for the frequency assignment problem. For each instance to be solved, a memetic algorithm is used in the study and adapted to the solutions. The algorithm configured the settings that are executed on each island processor. The configurations were decided during the execution of the algorithms. Van Onsem and Demoen (2013) presented a framework called ParHyFlex. The implementation of different hyper-heuristics was included in the software. It is tested on the Maximum Satisfiability Problem. Chana et al. (2013)  proposed a hyper-heuristic based on bacterial foraging to schedule resources. The algorithm was compared to other recent heuristics. The results showed that the algorithm outperforms the others by minimizing the total cost.