dataesr/scientific-paragraphs-categorization huggingface dataset:
According to tradition, before milling, the chestnuts are dried in small stone buildings named "metati", where a hearth on the ground floor heats and dries the chestnuts placed on the upper floor  [42]. Regarding the quality of flours used for flat breads, a limited technical knowledge was observed in all the surveyed countries, especially (but not exclusively) for the most artisanal productions. Consequently, this information often remained undefined in the database (where "not specified" is indicated), reaching 71. 6% of missing data, which was the highest percentage among all collected data. Where information on the quality features was available, protein and gluten content and gluten quality were the most frequently reported. For refined wheat flour the quality parameters generally were: protein content ≥ 9%, wet gluten content ≥ 25%, alveograph W ≥ 180 × 10 -4 J, and P/L ≤ 1.
Twelve flat breads were strongly associated to religious celebrations, and 15 flat breads were rare, prepared only by the household cooks in a rural environment, for family consumption. The collected information, gathered in a publicly available database, will be fundamental for allowing further valorization and dissemination activities, and will be useful for the selection, within each one of the examined country, of the most suitable flat breads for nutritional fortification and technical innovation within the FlatBreadMine or any other research project. Criteria for selecting breads can be proposed, as follows: (1) The type of bread chosen must be native to the country and widely consumed throughout the national territory. To maximize the nutritional impact on the general population, it would be of little usefulness to fortify a flat bread produced on a small-scale, consumed only in a restricted geographical area.
Future Perspectives Traditional and rare flat breads, prepared in a genuine and not globalized way using local raw materials, in addition to not risking the shortage of imported flours for geopolitical or climatic reasons, are well adapted to the territory and sustainable. However, these breads may progressively disappear due to changes in lifestyle and increasing urbanization, and their loss would lead to genetic erosion. Actions are therefore needed to prevent the reduction of cultural and biological diversity related to their disappearance. The strategy for such preventing actions, and directions for future research, should involve: (1) Periodically surveying the existing flat bread diversity, with the same approach used in this article. It is worth noting that the FlatBreadMine database can be easily updated, as well as extended to include many other countries; (2) applying quality schemes to the most genuine and.
Acknowledgments: The authors are thankful to  Dorra Zairi  for the picture of the dough exposed to the sun for fermenting during the preparation of  Shamsi  bread, and to  Ali Abdallah  for the picture of the same bread after baking. The authors are also thankful to  Noel Buttigieg  (Malta),  Salim Makhoul  (Lebanon),  Pasquale Catzeddu  ( Porto Conte Ricerche, Alghero, Italy ),  Abdullah S. Awwad  ( Qabalan Bakery, Amman, Jordan ),  Sheila Mateo Fortea  ( Horno Talayuelas, Talayuelas in Cuenca, Spain ),  Domagoj Radić  (Pekar d. o. o. , Vinkovci, Croatia),  Diamantis Papapanagis ,  Mills of Crete S. A. (Chania, Greece) ,  Tsivakas Sideris ,  Loulis Mills S. A. (Almyros, Greece) , and  Pierre-Tristan Fleury  (Director of the  LEMPA Institute on Baking development with industry, Rouen, France ) for providing valuable information.
Our approach solves the problem of deteriorated segmentation and quantification accuracy when reusing trained classifiers which is due to significant color variability prevalent and often unavoidable in biological and medical images. This increase in efficiency improves the suitability of interactive segmentation for larger image sets, enabling efficient quantification or the rapid generation of training data for deep learning with minimal effort. The presented methods are applicable for almost any image type and represent a useful tool for image analysis tasks in general. The provided free software TiQuant makes the presented methods easily and readily usable and can be downloaded at tiquant. hoehme. com.
A supervoxel-based approach for segmentation of mitochondria in volumetric electron microscopy images was developed by  [7] , though the published executable software and code encompassed only the supervoxel algorithm SLIC. SuRVos  [14]  is a software for interactive segmentation of 3D images using a supervoxel-based hierarchy, which lays a focus on segmentation of noisy, low-contrast electron microscopy datasets. The extendable software package Microscopy Image Browser  [15]  allows for processing of multidimensional datasets and features a selection of conventional processing algorithms, several region selection methods aiding manual segmentation, and methods for semi-automatic segmentation, including a supervoxel-based classification approach. FastER  [16]  is designed specifically for cell segmentation in grayscale images, using features that are very fast to compute.
Dimensionality reduction through use of supervoxels, precomputation of supervoxel features and a convenient graphical user interface enable rapid, intuitive refinement of training annotations by iterative correction of classification errors or uncertainties. (ii) We introduce a color-based image clustering method that enables automated grouping of image sets into subsets of similarly colored images. A corresponding number of so-called prototype images is identified which serve as eligible candidates for interactive training of classifiers for within-subset reuse. These new methods, that are applicable to two-and three-dimensional images, have been fully incorporated into our freely available image processing software TiQuant. The first version of TiQuant, released in 2014, included various processing methods specifically for liver tissue segmentation of 3D confocal micrographs, as well as the corresponding analysis functionality  [1, 2].
5k x 7. 5k pixel image into 100k superpixels and six minutes to calculate all available features for them. However, this step is usually performed only once per image. Subsequently, the user annotates exemplary fore-and background regions in the original image to generate a training database. A classifier is then fitted to the training data, and probability estimates for class membership are predicted for all supervoxels in the image. In the final step, a segmentation is generated based on these probability estimates, which can be refined through post-processing. If segmentation quality is insufficient, the user can provide further training data, especially for regions that were poorly segmented. Thereby, high-quality segmentations can be generated quickly by iterative annotation refinement.
Supervoxel-Classification Random Forests (RF) and Support Vector Machines (SVM) are supported as classifiers to learn the class membership of supervoxels given their feature vectors. We included both classifiers to choose from as they were found to be the two best-performing classifier families when evaluated on a diverse set of learning problems  [21]. Furthermore, both classifier families are able to handle non-linear data and have few parameters, however, RFs are faster in regard to training and execution and are therefore set as default. The user-supplied training data, which is summarized in the training database, is split into a training set and a test set in a 70:30 ratio to allow evaluation of classifier performance (see Fig. 3 ). Since training data can be relatively sparse, we use this skewed ratio to ensure a sufficiently large database for training, potentially at the expense of accuracy in assessing classifier performance.
Moreover, normalization methods can easily introduce systematic errors in subsequent image analysis if differences in visual appearance originally result from changes in tissue conditions to be analyzed. In this case, normalization could remove properties that actually should be quantified. We propose a different method to overcome the problem of visual heterogeneity in microscopic images by automatically and robustly grouping images of a given dataset into visually homogeneous subsets. Thereby, we can not only guide the selection of training images, but also facilitate classifier reuse leading to minimized manual annotation effort. The underlying assumption is that minor procedural discrepancies in the image acquisition process introduce systematic changes of coloring. Possible causes comprise slight variations between sample preparation sessions (e.
Experiments Validation We validated our interactive segmentation and image set clustering method exemplarily on a set of 22 brightfield micrographs of mouse liver paraffin slices. These images were hand-labeled by a trained expert to automatically extract architectural and process parameters, which were used to parameterize a spatio-temporal tissue model of tissue regeneration after CCl4 intoxication  [3]. Tissue slices were immunostained for BrdU positive nuclei to visualize proliferation and typically show a single liver lobule centered by a central vein. Image data was obtained for a control (t=0) and at 7 time points after administration of CCl4, which causes necrotic tissue damage in the area around the central vein. Over the observed time period, this damage regenerates and the lost cells are replenished by cell proliferation of the surviving liver cells.
Subsequently, to evaluate how well the trained classifiers generalize to unseen images and whether a restriction to images with similar coloration improves performance, we grouped the image set into ki=6 subsets. The number of clusters was determined experimentally using the Elbow method, a heuristic for determining the optimal number of clusters representing the majority of the variability in a dataset. The number of dominant color sets kc=4 was chosen manually to correspond with the different image compartments of relevant size (i. e. , background, nuclei, and two tissue compartments for healthy tissue/necrotic lesion, or glutamine synthetase positive/negative zone, respectively). Per image subset the image with the smallest Euclidean distance to the image cluster center was selected as prototype image.
The previously trained classifiers of these prototype images were applied (i) intra-cluster to all other images in the respective cluster and (ii) inter-cluster to all images not belonging to the respective cluster. Segmentation post-processing was done as described before. We compare our approach with the established image processing software ilastik. For the results to be comparable, we used ilastik's 'Pixel Classification + Object Classification' workflow. All 37 predefined features were selected for training. The training annotations created with our software were imported and an object size filter was used for segmentation post-processing.
Runtime evaluation A detailed discussion of aspects of computational performance is given in the SI Appendix. The interactive processing steps training and prediction are independent of pixel count and linear in the number of supervoxels. For the segmentation step, runtime scales linear with image size and number of supervoxels. Exemplarily, execution times for an image with 100M pixels and 100k supervoxels are ~2min and 9min for supervoxel generation without and with feature generation. Training database generation, prediction and segmentation were completed in 5sec, 12sec and 51sec, respectively. The analysis was done on an Intel Core i9-7900X with 10 logical cores at a 3. 3 GHz clock speed with 64 GB RAM.
Moreover, custom-made solutions require profound expertise in image analysis techniques including knowledge of particular algorithms and the impact of parameter choices. In contrast, interactive image segmentation using machine learning from manual image annotations has a much flatter learning curve and greatly simplifies the process of quantifying the content of complex images enabling users to directly apply their expert knowledge of, for example, biological or medical entities in the analyzed images. The method described in this paper reduces the technical parameters virtually to zero. Only the size of the superpixels has to be set, however, it can be inferred from the size of the structures to be quantified. Furthermore, interactive segmentation is also applicable for larger datasets consisting of many images, when no ground truth or appropriate pre-trained deep learning models are available.
The features implemented in the presented work are designed for application to color images and might be less effective in representing learning problems formulated based on grayscale or binary images, for example electron microscopy or x-ray, for which optimized features could be integrated. Learning from annotations on single images and reusing these interactively trained classifiers for unseen images would further decrease manual effort. However, the downside of learning from such sparse annotations is a decreased generalizability which is especially deteriorating results in case of high color variations between the images to be quantified. We have demonstrated that limiting classifier reuse to similarly colored images significantly increases the average performance on a validation dataset, and thus improves the suitability of interactive image segmentation for processing moderately sized image sets.
AERONET observations The AErosol RObotic NETwork (AEROENT) is a federation of ground-based remote sensing aerosol networks, established and expanded by various institutions from different countries  (Holben et al. 1998 ). It has contributed continuous and long-term aerosol optical, microphysical, and radiative properties for more than 25 years in major ecosystems and human activity areas around the world. The AOD data used for validation were acquired from Level 1. 5 and Level 2. 0 AERONET products, which have been cloud-screened and quality controlled. The uncertainties of AOD are less than 0. 02  (Eck et al. 1999). In order to match the AERONET data to the satellite observations, a common approach is followed to averages satellite data within ±30 min and a circle of 0. 25° (~25 km) radius centered at the selected site  (Sayer et al.
2013). The relationship between multi-wavelength AOD proposed by  Ångstrom (1964)  was applied to calculate the AOD at corresponding wavelength of satellite bands from AERONET data. GRASP is an open-source software package (https://www. grasp-open. com/) for calculating and retrieving various optical and microphysical properties of aerosol and surface from observations of different remote sensing instruments, such as satellite, lidar, radiometer, and radiosonde  (Dubovik et al. 2021). It was originally designed to improve and solve the problem of aerosol retrieval under high surface reflectance conditions from the PARASOL observations  (Dubovik et al. 2014) , while now has become a scientifically rigorous and versatile algorithm based on generalization principles that works with diverse remote sensing applications in the community after continuous development  (Dubovik et al.
2021). The GRASP algorithm contains two pivotal and independent modules. One is used to calculate the scattering, absorbing, and extinction of light between different media from the physical level, simulating theoretical observational radiation signal, called "Forward Model". It allows define various complex aerosol (size distribution, refractive index, and sphere fraction, etc. ) and surface properties (Bidirectional Reflectance/ Polarization Distribution Function, BR/PDF, etc. ) in the construction of model. Therefore, this makes it possible to transform from optical observations to aerosol microphysical properties and estimate the surface parameters in the meantime  (Dubovik et al. 2011 ). The other module can be thought of as general mathematical operations without any particularly physical nature, called "Numerical Inversion".
Construction of Multi-pixel Retrieval Unit Next, we will explain the necessary operations and settings of parameters to apply the GRASP algorithm to DPC data in detail. The GRASP algorithm can use the temporal and spatial continuity of pixels, and allow a group of pixels to be inverted at the same time. The multi-pixel retrieval unit for DPC in the study is shown as Figure  1. Each small cube represents a pixel in geographic grids with a spatial resolution of 0. 1°×0. 1° (3×3 DPC pixel averaged). This is in accordance with the MODIS 04_L2 product (~10 km). The projection is determined by the DPC data. Each pixel is guaranteed to have at least 3 different observation angles. Size of the retrieval unit can be arbitrarily selected, but limited by the hardware memory. Different colors show the percentage of land or sea, and usually do not change with time.
To summarized, the DPC showed spatial ability of AOD retrieval based on GRASP algorithm in China region and the similar results have also been reported recently by using the GRASP/component module  (Li et al. 2022). Another case was selected in Western Europe where the air is clean and aerosol loading is low was applied globally in the GRASP/Model. Therefore, the difference in aerosol optical properties and spatial-temporal heterogeneity in different regions may be not considered appropriately. The optimization of the region is expected to improve the inversion effect. (< 0. 2) in Figure  8  showed three cases at different underlying surface to display the time series of AOD retrieved from DPC GRASP/Model on the basis of AERONET observations. The DT AOD was also compared as a reference, due to its stable performance.
Conclusion and Summary The MODIS observations. The main purpose is to evaluate the performance of the DPC to monitor global aerosols. On a global basis, a uniform parameterization scheme, which defined the variation ranges and start values of the optical and microphysical properties (realized by aerosol type) of the aerosol, was applied in the "Model" module of GRASP. Validations against AERONET showed that the R and EE% of DPC GRASP/Model were 0. 8590 and 79. 68%, respectively, in the first attempt. The SCA, number of averaged pixels in retrieval units, and fitting residual showed an impact on the results of AOD. A larger number of pixels in retrieval units and a smaller fitting residual can help improve the quality of retrieval. By quality control (SCA > 150, number of averaged pixels < 4, non-polarized fitting residual < 8%, and 0.
Acknowledgement This study was funded by the  National Key R&D Program of China  (Grant No. 2018YFB0504500 ),  National Natural Science Foundation of China  (Grant No. 41875038 , No. 42071348 , and No. 42001291 ), the  Key R&D projects in Hubei Province  (Grant No. 2021BCA220 ) and supported by the  LIESMARS Special Research Funding. We are grateful to the  Moderate Resolution Imaging Spectroradiometer (MODIS) Team , the  Aerosol Robotic Network (AERONET) Organization  and the  GaoFen-5 Directional Polarimetric Camera (DPC) Developed Team  for freely distributed their aerosol products and measurements. The numerical calculations in this paper have been done on the supercomputing system in the  Supercomputing Center of Wuhan University. Finally, we would also like to thank all reviewers for their constructive and valuable comments.
Recent advances in wireless communication technologies are having a great impact on people's life, especially in the field of healthcare industry that provides a remote monitoring enabling medical staff to follow up the patient and diagnose his disease. LoRa wireless technology is becoming one of the most energetic effective solutions for Wireless Body Area Networks (WBANs) communications. In this work, we propose a LoRa-based low power healthcare WBAN platform called HeaLoRa for an adaptive patient monitoring process. Physicians can remotely monitor patient's temperature, oxygen saturation level, blood pressure and heart rate to prevent any abnormality. Moreover, data acquisition and transmission are controlled based on a power consumption optimization strategy aiming to reduce redundant data.
Moreover, LoRaWAN, the networking protocol of LoRa, is an Open Standard unlike Sigfox and NB-IoT which facilitates the widespread deployment of this technology. In addition, LoRaWAN has the less equipment and installation cost  [9]. Furthermore, LoRa is highly immune to interference and its data rate is adapted dynamically according to the employed Spreading factor (SF)  [7]. In fact, Sigfox technology was excluded from this work due to lack of acknowledgment signals for all uplink messages  [10]. This drawback is highly risky for medical applications, especially in critical cases. Compared to NB-IoT, LoRa is selected because LoRa devices consume less power providing longer battery life  [11]. Thanks to all these reasons, LoRa is a good network technology choice for the communications between the WBANs and the medical center (see figure  3 ) in our IoT based healthcare system.
The healthcare systems has taken a great attention aiming to monitor the physiological human body parameters, especially with the spread of Covid-19 pandemic. In fact, the need of WBAN employment has increased in order to use the body sensors to collect data and then send them to the medical center. However, the sent data rate should be reduced to ensure a long life for the battery without losing the necessary information that indicates the patient's medical situation, especially in urgent cases  [12]. A list of the acronyms frequently used in this work is given in table  II.
A. Related work for health monitoring using LoRaWAN Recently, the integration of new emerging wireless technologies in the medical systems has received a great attention from the industry and the research community  [13] ,  [14] ,  [15]  and  [16]. Due to its wide range, power efficiency, and many other advantages, LoRa is one of the wireless technologies that could be used in healthcare systems to monitor patients. Hereinafter, we present some LoRaWAN based healthcare systems. The researchers in  [17]  present a monitoring system using LoRaWAN network where an oxygen saturation, electrocardiogram, body temperature and pulse rate sensors are explored, followed by a statistical analysis to prove the efficiency of the system. In  [15] , the authors present a system performed by biomedical sensors that transmit health data to a cloud using a LoRaWAN network.
It has been shown that during the transmission, more the distance is longer, more the power consumption increases. In order to minimize the number of healthcare visits and to reduce the communication cost, the work of  [18]  focuses on the monitoring of the body temperature, the blood pressure and the glucose level, where the collected data are sent to an analysis module via LoRaWAN network. The experiments have shown that a wide coverage area is achieved with a power consumption 10 times less than other long range cellular transmission. In  [8] , many sensors are used to monitor some environmental parameters that can affect the human health. The collected data are displayed on a graphical user interface and transmitted to a remote server through LoRaWAN network in order to send early warnings for unsafe people.
The results have been shown that the system works reliably while consuming less energy. The sensors wake up each minute in order to monitor environmental data. These sensors can also be programmed to monitor continuously according to the users' requirements. In  [19] , a system that is distributed on three levels (sensors, IoT gateway and Internet cloud) was presented to achieve a safe working environment by reducing health risks of workers in the construction industry. The collected environmental and physiological data is transmitted via Bluetooth Low Energy (BLE), then transmitted using LoRa technology to a gateway. In  [20] , the paper proposes an energy efficient health monitoring system using LoRa for data communication. A remote health monitoring system is proposed for patients living in places where no Internet or health services are available.