dataesr/scientific-paragraphs-categorization huggingface dataset:
• The second sensor, the ISL29125 from RENESAS [20], uses a matrix of three photodiodes, each sensible to different parts of the visible spectrum: blue, green and red. It provides a set of three digital values R, G, and B. A low-cost low-power ESP32 microcontroller, able to communicate through WiFi, is used. Its purpose is to gather, treat and send the data via wireless communication protocols to a database which is stored in our server. This microcontroller is mounted on the commercially available HUZZAH32 board (Adafruit) that integrates a USB connector used for battery charge and serial communication. An additional PCB has been designed, as a mother board to connect the two sensors to the ESP32 board, resulting in a compact device as small as 35 cm 3 shown in Figure  2 (a-b). This device can acquire what we call here "pseudo-spectra", which can be assimilated as very low-definition spectra.
Classification and training From the low-resolution experimental pseudo-spectra provided by the low-cost device, machine learning algorithms are used to classify the different possible light sources. The method chosen for that purpose, called 'supervised machine learning classification', is a classical Artificial Intelligence (AI) machine learning method. This method was implemented through the Classification Learner App made available by MATLAB in its Statistics and Machine Learning toolbox. In our study, we have divided the light sources into 7 classes: LED 3000 K, LED 4000 K, CFL 2700 K, CFL 6500 K, natural scattered light from a clean sky through a window, natural scattered light from a cloudy sky through a window and finally mere darkness. Varying the light intensity of each class, under controlled lab condition, we have built a dataset of 126 different pseudo spectra on which is based on our classification training.
For each measurement of the different light source, we had to 'manually' inform the machine of the class of the tested light. Several classical classifiers included in the Classification Learner app can be simultaneously trained. Thanks to the dataset of the 126 pseudo-spectra, each associated with one of the 7 defined classes, the training of the 24 classifiers can be done. It allows the building of the trained classifiers seen in the 'Data' part of the Figure  1. One of the challenges is to have classifiers reliable enough to be able to classify properly unknown new pseudo-spectra.
Spectral Reconstruction and Calculation of the Harvestable Energy The spectrum database seen in the 'data' block of Figure  1  has been recorded thanks to a commercial calibrated spectroradiometer, the StellarRAD from StellarNet Inc. , equipped with a CR2 cosine receptor with a wavelength range from 350 nm to 1100 nm. The high resolution 7 spectra of the database, corresponding to the 7 classes used in our study, are necessary for the reconstruction stage one reliable trained classifier have given its classification results of new light environment observations. Then, depending on the class found by the proper classifier and the LUX value to tune the intensity properly, a highresolution spectrum can be reconstructed. Finally, to estimate the harvestable light energy in the surroundings of the low-cost sensor, we use a model, published in a previous article  [15] , that takes the reconstructed spectra as input instead of 'bare' high definition spectra from an expensive spectrometer.
This model is based on a one-diode photovoltaic model using the measured external quantum efficiency (EQE) and the saturation current density-voltage characteristic measured in darkness (J-Vdark), both unchanging intrinsic values, depending only the PV converter technology to be tested. These EQE and J-Vdark characteristics are contained in the database called 'Spectral response' and 'Dark Current Density' of the Data block of Figure  1. The spectral response was measured with a custom-built setup composed of a Xenon lamp, a monochromator equipped with two diffraction gratings, a filter wheel to remove the higher diffraction orders of radiation, and a lock-in amplifier. The measurements were calibrated with Si and Ge photodiodes (Thorlabs FDS100-CAL and FDG03-CAL, respectively) to cover the whole wavelength range of interest.
Instrumented prototype: Energy Harvester To evaluate the accuracy of the method presented below, it is mandatory to compare the energy harvesting calculation results with experimental measurements of the energy harvested by real energy harvesting systems. To perform such measurements an instrumented energy harvesting prototype has been created, based on 2 GaAs solar cells. It is embedded with power measurement units (INA219 from Texas Instrument Inc. ) capable of following the power harvested from solar cells by a power management integrated circuit (PMIC AEM10941 produced by ePEAS) then stored into a battery. The picture shown in the Figure  3  displays this equipment and the data recovery system attached to it. In the same way as the low-cost light analysis tool, this system gathers data with a microcontroller which transfers its measurements to another microcontroller responsible for the data transmission to our server database.
Classification Methods Training Performances on Raw Data Thanks to the Statistics and Machine Learning toolbox from Matlab and its Classification Learner app, classifiers can be trained very quickly. Those training can be performed with all six values of the pseudo-spectra but also with a reduced number of them. In our study, we have chosen the 11 configurations that we can see on the bottom of the Figure  4. For example, the configuration A relies on all the values from the sensors, while the configuration I utilizes only the Red (R) and InfraRed (IR) values from the sensors. Combining each of the 24 classification methods with each of these 11 configurations, it results in 264 classifiers to train. Those trainings begin with the 126 pseudo-spectra observed and each classifier uses a 5-fold cross validation.
The cross-validation process consists of separating the dataset randomly into five folds. Four of them are then used to train the classifier. Once the training done, the classification capability of the classifier is tested on the remaining fold. This process is reproduced to obtain five iterations where each fold is used as the test fold. Finally, the overall performance of the classifier is find calculating the mean success rate of the five classification tests. Results of that process is shown in Figure  4  for the classifiers and configuration described previously. A first observation is that none of the 264 classifiers reach a total 100% of classification success. A second look at the results shows that some classifiers seem more adapted to our application: Cubic SVM, Fine KNN or Weighted KNN.
It is to be noted that the configurations with only 2 or 3 features of the dataset can perform almost as well as configurations with 5 or all data features. Finally, what can be concluded out of this example is that, with this dataset, the 'best' classifier is the Cubic SVM for the configuration A, which obtained 96. 8% of correct classification: it was able to correctly classify 122 observations over 126. To understand errors produced by the trained classifiers, a 'decision surface' of each of them can be plotted. Indeed, each classification method uses a different algorithm to establish the boundaries that separate the observations of different light sources. Once trained, each classifier uses its algorithm to determine the parameters of the equations that define these boundaries. Decision surfaces help visualize the limits established by a classification method between classes.
Normalization Impact on Classification Performances Because the light source classes are not concentrated in zones distinct from each other but distributed in intensity over all their features, it is complicated for classifiers to achieve successful light source classification. The poor results obtained during training suggest an underfitting for the dataset used. Although underfitting can be attributed to the small sample size of our dataset, unlike natural light sources, artificial light emissions tend to remain constant once turned on. Consequently, a high number of samples for artificial light should not be necessary. However, for natural light sources, the problem of the number of observations necessary to cover their intensity range remains. One way to get around this problem is to eliminate the intensity variation for the classifiers training.
𝑁𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝐷𝑎𝑡𝑎 = 𝐷𝑎𝑡𝑎 𝑡𝑜 𝑁𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒 -𝑁𝑜𝑟𝑚𝑎𝑡𝑖𝑣𝑒 𝐷𝑎𝑡𝑎 (𝐷𝑎𝑡𝑎 𝑡𝑜 𝑁𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒 + 𝑁𝑜𝑟𝑚𝑎𝑡𝑖𝑣𝑒 𝐷𝑎𝑡𝑎). (1) As an example, Figure  6 (a) displays the 126 data normalized to the Blue (B) value. Applying this method, to each measurement, a clearer map of the light source classes emerges in the Figure  6   Tests have been conducted for all the 264 configurations to establish the impact of the Blue (B) normalization of the differences. As visible in Figure  6 (b) for a Fine KNN classifier with Blue normalization, classifiers trained on normalized datasets are fed with classes distinctively grouped and no longer spread out according to their intensity. More decision surfaces from different classification algorithms trained for different normalization values can be found in the supplementary Figure  S2.
Harvestable Energy Calculation A proper calculation of the harvestable energy in specific locations requires pseudospectra to be classified correctly. Then from the class recognized, a full spectrum can be 'reconstructed' to meet the resolution of a spectrum acquired by a spectrometer. This 'reconstruction' process relies on different pieces of data: reference spectra acquired by a compact spectrometer and the light intensity (LUX) data acquired by our low-cost sensor device. First, the intensity of the spectrum must be tuned such as its illuminance (irradiance weighted according to the photopic luminous efficiency function) corresponds to the LUX value. Then, the harvestable energy is then estimated as described in Politi 2021. The light source classification method used in the following is one of the most efficient we've seen in the previous chapter, the Fine KNN with normalization by Blue and associated with configuration R.
Implementing more precise/complex calculation model a larger number of different PV technology can also be added in our database to give the user the choice of the solar cell. For further improvements, the development of a digital twin of the sensor could help with correction of its readings. Finally, for the moment, our proof-of-concept works using Matlab pretreatment. By minimizing the energy required to use a classifier, it would be feasible to use it directly on our measurement system, as demonstrated in the work of Micheals et al. [19] , and even to train a classifier in an embedded way. As said, many improvements could be done, but we believe our work opens an opportunity for engineers and researchers to deploy many autonomous devices in indoor conditions by using this method.
At the beginning of the chemical analysis, nepheline was dissolved in weak HCl. The insoluble residue was removed and the composition of the solution was analyzed. This made it possible to completely exclude the presence of aegirine in the analyzed sample. Single-crystal X-ray diffraction data were collected at room temperature using an Rigaku XtaLAB Synergy-S diffraction diffractometer with monochromatized radiation MoKα (λ = 0. 71073 Å) and a Hybrid Pixel Array detector in the ω-scanning mode. A semi-empirical absorption correction based on the intensities of equivalent reflections was applied, and the data were adjusted for Lorentz effects, polarization, and background using CrysAlis software  [45]. Refinement of the unit cell parameters was also performed using the CrysAlis software  [45].
The definition and refinement of the structure were carried out using the Jana 2006 software package  [46]. The illustrations were created using the JANA2006 software package in combination with the DIAMOND program  [47]. The atomic scattering coefficients for neutral atoms, together with corrections for anomalous dispersion, were taken from International Tables of Crystallography  [48]. Fourier-transform infrared spectroscopy (FTIR) spectra were obtained at the Laboratoire Magmas et Volcans (LMV; Université Clermont Auvergne, France). Nepheline FTIR spectra were acquired on double-polished 200 µm-thick sections on a Bruker Vertex 70 FTIR (Fourier-transform infra-red spectroscope) coupled with a Hyperion microscope equipped with ×15 objective and condenser at LMV. Beam size in the analyses varied from 30 to 50 µm.
Chemical Composition of Nepheline and Aegirine (Samples LV-00-16 and LV-335E) The chemical composition of pure nepheline (sample LV-00-16) and nepheline saturated with inclusions of aegirine, as well as aegirine located inside nepheline and aegirine in the rock (sample LV-335E) was obtained by microprobe analysis. Additionally, the composition of pure nepheline (sample LV-00-16) was also determined by wet chemistry. The results are presented in Figure  3. Nepheline without aegirine inclusions contains an order of magnitude more iron than nepheline saturated with aegirine inclusions (Figure  3e ,f), with a third of this iron being ferrous. According to microprobe analysis, the formula for pure nepheline (based on  16   ) and matches the formula calculated from the data of microprobe analysis.
0211, wR 2 = 0. 0334, GOF = 1. 27 for 1510 I > 2σ(I) for sample LV-335E. The deepest minimum and highest peak in the final residual electron density map were: 0. 27 eÅ -3 and -0. 27 eÅ -3 for sample LV-00-16; 0. 24 eÅ -3 and -0. 30 eÅ -3 for sample LV-335E. CCDC 2, 194, 355 and 2, 194, 356  contain the supplementary crystallographic data for these compounds. The data can be obtained free of charge from The Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/structures. Bond valence sums (BVS) calculations were performed using the bond length parameters from  [51, 52]. The crystal structure of nepheline is characterized by a tetrahedral framework of a tridymite-type topology with the presence of wide channels of two types filled by extra-framework A and B type cations. The general formula of nepheline can be written as (Z = 2): |AB 3 |[T(1)T(  2 )T(3) 3 T(4) 3 O 16 ], where A cations occupy the channels with the regular hexagonal ring in cross-section, while B cations occupy the channels with the oval hexagonal rings in cross-section.
Discussion Consistently with previous works  [39] , our microprobe and wet chemistry dataset shows that iron is present in the chemical composition of nepheline from the rocks of the Lovozero massif. In addition, we evidence the presence of molecular water in nepheline from the Lovozero massif based on spectroscopy data. The incorporation of ferric iron into the tetrahedral sites of natural and synthetic aluminosilicates and zeolites frameworks of have been studied in detail using different spectroscopic methods  [59] [60] [61] [62] [63] [64]. Within the felspar family  [65] , the incorporation of Fe 3+ into the framework sites has been observed for natural ferrisanidine, K[Fe 3+ Si 3 O 8 ] [66] as well as different synthetic analogs  [67]. The maximal content of Fe 3+ has been observed in synthetic Cs[Fe 3+ SiO 4 ]  [68]  and Cs[Fe 3+ TiO 4 ]  [69]  with the ABW type framework.
Conclusions RB coupled to NPs is a good approach for PDT applications (Figure  47 ). RB is a xanthene dye that has interesting photo-and sono-sensitive properties. RB is already used for clinical applications, and an RB formulation for cancer known as PV-10 is currently undergoing clinical trials for use in different types of cancer (melanoma, breast cancer) or infection (clinicaltrials. gov). RB can be used alone, but can also be used as a PS in PDT applications. RB presents several advantages, including the ability to produce 1 O 2 upon light illumination and solubility in water. One disadvantage is its absorption spectra, which have an absorption maximum wavelength in water of 550 nm, which does not allow great penetration of light into the tissue. Moreover, RB is not selective for cancer cells.
Conclusions RB coupled to NPs is a good approach for PDT applications (Figure  47 ). RB is a xanthene dye that has interesting photo-and sono-sensitive properties. RB is already used for clinical applications, and an RB formulation for cancer known as PV-10 is currently undergoing clinical trials for use in different types of cancer (melanoma, breast cancer) or infection (clinicaltrials. gov). RB can be used alone, but can also be used as a PS in PDT applications. RB presents several advantages, including the ability to produce 1 O2 upon light illumination and solubility in water. One disadvantage is its absorption spectra, which have an absorption maximum wavelength in water of 550 nm, which does not allow great penetration of light into the tissue. Moreover, RB is not selective for cancer cells.
Data We study the monthly OPEC and non-OPEC production of oil together with oil price for the sample period from January 1993 to March 2020. 3 The data are collected from the US Energy Information Administration (https://www. eia. gov). OPEC and non-OPEC production is expressed in 1000 barrel per day while oil price is expressed in USD. 4 ig. 1  plots the OPEC and non-OPEC production. The study period includes several economic and financial crises together with several episodes of geopolitical turmoil. OPEC production dynamics appear to reflect such global events as many decreases of OPEC production are recorded. In particular, Fig. 1  indicates that OPEC production decreased in  1998-2000, 2001, 2006, 2009 (global financial crisis) , 2011 and 2013. In contrast, non-OPEC production had been growing from 1993 to 2020, and hence seems less reflective of economic turbulences occurred during the study period.
S1 ), it will be summed for every sample and could be revealed by the cumulative sum; a more detailed description of this approach can be found in  Scholler (2019). We used the cumsum function of Matlab which, when a small bias is present, is more adapted to reveal anomalous Brownian motion and, in addi-240 tion, increase the signal-to-noise ratio of the DCI. The normalization used the shot noise normal distribution in the background. All experiments were repeated three times.
The figures display 320 the cells' metabolic activity  (Apelian et al. , 2016) ; the colour map code (ImageJ, 16 colours) is such that red corresponds to the highest activity and dark blue to the lowest activity that is linked to the speed of the scatterers' movements. In the 325 vicinity of the chloroplast, vesicles or lipid droplets of different sizes appeared green, yellow and red with the most dynamic regions located at edges of the droplet (Fig. 1. , red arrow). The other highly dynamic location corresponded to 330 the chloroplast membranes and often looked punctuated (Fig. 1. , white arrows). Interestingly, these results were observed independently of the three morphotypes that were present in the cultures (fusiform, triradiate or oval)  (De Martino 335 et al. , 2007). P. tricornutum cells were also illuminated with a green LED (505 nm) or a deep red LED (735 nm, a spectral region where no P.
V. CONCLUSION AHA is a meta-heuristic optimization algorithm, proposed in this work using MATLAB script on the PV parameter extraction problem. The only required information is the measured I-V characteristic curves of the PV modules under outdoor operational conditions. Limiting the search space of the five parameters and assuming that all cells are identical ensure the robustness of the algorithm by overcoming the complexity of the numerical solution. This work was validated using three cases from different sites at different ranges of solar irradiance conditions and temperatures: RTC France cell based SDM model, Poly-solar panel module type 320W-72P, and three polycrystalline PV modules (CLS-220P) connected in series. Results based on real data demonstrated that AHA algorithm gives best performance in point of view accuracy, reliability and convergence speed compared with others algorithms.
Embodiment accounting In this study, an embodiment accounting model combined with global NMVOCs emission data and a global multi-regional input-output (MRIO) table is raised to track NMVOCs flows across the global supply chain. The concept of embodiment accounting first appeared in the work of Herendeen et al. [31] [32] [33]  , an embodiment energy accounting model was proposed to analyze the direct and indirect energy consumption associated with the United States manufacturing industry. Recently, based on the embodiment energy accounting model, incorporating the embodiment concept of system ecology raised by Odum  [34] [35] [36]  , Chen et al. put forward the embodiment accounting model for general ecological elements. A series of ecological elements flows within the world economy, e. g. , energy  [ 37 , 38 ] , arable land  [39] [40] [41]  , greenhouse gas  [ 42 , 43 ]  and air contaminants such as mercury emission  [44]  , have been studied and analyzed by Chen and his colleagues.
Data sources We choose the MRIO table from the EXIOBASE V3. 4 database considering its detailed global economic sectors classification and abundant emission categories present in its extensional environmental accounts. EXIOBASE V3. 4 provides global multi-regional input-output tables from 1995 to 2011, the latest data of 2011 was used in this study. It contains the NMVOCs emission data for more than 200 countries worldwide (28 EU members, other 16 major economies, and 5 integrations containing the rest of world regions in each continent respectively)  [49]. Thus covering 90% of global gross domestic product (GDP)  [50]. And emission data from its environmental extension account comprises 18 NMVOCs types: 2-propanol, acetone, acetylene, benzene, butane, ethane, ethanol, ethylene, hexane, isobutane, isopentane, m-xylene, o-xylene, pentane, propane, propylene, p-xylene, and toluene.
Apart from these independent regions, RoW Middle East (1. 93 million tonnes), RoW Africa (0. 48 million tonnes), and RoW America (0. 26 million tonnes) contribute a large part of net NMVOCs export. Fig. 9  illustrates sectoral contributions to NMVOCs emission embodied in final trade of top five net final importers and exporters. For the convenience of discussion, 163 sectors classified in EXIOBASE 3 database's MRIO table are further aggregated into nine categories according to United Nations International Standard Industrial Classification of All Economic Activities  [52]. As seen in Fig. 9  , the United States, The United Kingdom, Germany, France, and Australia are the top five    net NMVOCs emission importers embodied in final trade (import and export). For these five independent areas, the manufacturing industry shares the most significant proportion, accounting for about 80% of their NMVOCs emission embodied in final trade.
Contact with interview partners was established through two NGOs. In Porto, the partner NGO provided food to low-income households and shared the individual datasheets for the 246 households they supported, comprising income and energy bills for January and February 2015. In Barcelona, the partner NGO was created to implement a pilot scheme designed to expand social housing into the private rental sector. 7 For 100 of these households, individual datasheets comprising income and energy bills covering the years 2015-2016 were collected, along with the energy certificates for each apartment, and a questionnaire on thermal discomfort was applied. Further data were collected through qualitative interviews with ten Spanish households and eight households in Porto, using an open interview guide supplemented by the individual NGO protocols for eliciting socio-demographic data.