dataesr/scientific-paragraphs-categorization huggingface dataset:
Flight altitude distributions SIRTA is equipped with an Automatic Dependent Surveillance-Broadcast (ADS-B) receiver. These receivers record ADS-B signals that are periodically broadcast by the majority of commercial aircraft. The signals contain the aircraft latitude and longitude, altitude, and call sign. Depending on the aircraft altitude and atmospheric conditions, these ADS-B signals can be received from a distance of approximately 50 to 300 km around SIRTA. We derive the vertical distribution of flight altitudes using the recorded ADS-B signals. Flight altitudes in ADS-B data are given in terms of flight levels (FL) expressed in feet but correspond in fact to pressure levels at cruising altitudes. The FL are converted to a "true" geometric altitude above the ground using daily surface pressure and temperature profile from the RS measurement.
The flight altitude distribution (FAD) includes all flights between 9 and 15 km which we take as representative of cruising altitudes. The data are binned into vertical intervals of 250 m. ADS-B data from 2019 are used to compute monthly distributions. However no distinct seasonal cycle in the FAD was detected so we only consider an annual mean distribution in the rest of this study. Air traffic regulations force aircraft to follow specific flight levels, which leads to a FAD that exhibit discrete layers. Given that contrails get mixed and diluted in the atmosphere, the annual mean FAD is smoothed with a boxcar filter and a window of two layers. The FAD is normalized so as to obtain a probability density function (PDF), p FA (z), of the flight altitude over the SIRTA ( z p FA (z) dz = 1). 3 Methods.
Flagging of contrails and ISSR in the RS data For contrail to form, the ambient air must be sufficiently cold and moist. Appleman (1953)  estimated critical threshold temperatures and RH based on thermodynamic principles. This neglects the complicated dynamics that occur in the jet and vortex phases of a contrail but has proved to be a valid first-order approximation. Within this study, RS measurements of temperature and RH are used to determine the potential occurrence of non-persistent contrails (NPC) and persistent contrails (PC). The detection is based on the revised SAc by  Schumann (1996) , which was slightly reformulated by  Rap et al. (2010). Borrowing the notations of  Rap et al. (2010) , the threshold temperature T crit (in K), above which no contrail can form, is approximated by: T crit = 226.
Summary Condensation trails (or contrails) that form behind aircraft are estimated to have a similar radiative forcing (RF) as the CO 2 emitted by aviation  (Lee et al. , 2021). Therefore, the prospect of mitigating contrail formation and persistence is of high interest. A tentative solution that is getting some traction consists in actively rerouting a fraction of the flights to avoid atmospheric regions which are prone to persistent contrail formation. Flying around such regions requires the accurate forecast of their occurrence in time and space. Until today, numerical weather prediction and climate models suffer from large uncertainties in their representation of relative humidity and ice supersaturation. We analyzed an 8 year dataset of radiosonde (RS) observations launched from Trappes, France.
Acknowledgements. The authors acknowledge support from the  Direction Générale de l'Aviation Civile  through the  Convention N • 2021-39 relative  to " Aviation & Climate ". This study benefited from the ESPRI computing and data centre (https://mesocentre. ipsl. fr) which is supported by  CNRS ,  Sorbonne Université ,  Ecole Polytechnique  and  CNES  as well as through national and international grants.
https://doi. org/10. 5194/acp-2022-584 Preprint. Discussion started: 6 September 2022 c Author(s) 2022. CC BY 4. 0 License. Code and data availability. The primary python code with related routines, the pre-processed flight altitude distributions, and the calculated vertical profiles of contrail occurrence are available in the supplementary data set. Further data is available on individual request.
Electrochemical measurements Type 2032 coin cells were used to measure the electrochemical performance of the cells. The coin cells were assembled in an argon-lled glovebox (MBraun), in which both the oxygen and moisture contents were less than 1 ppm. The ZnO-coated or pure Ni foam (1. 27 cm Ø) was used as a working electrode, while the reference and counter electrode was a piece of Li metal foil (1. 2 cm Ø). The electrolyte was 100 mL of 1 M LiTFSI in DOL : DME (1 : 1 volume ratio) with 2 wt% LiNO 3 and we have used a piece of Celgard 2325 as a separator. The data was collected using the potentiostat capabilities of the Biologic MPG-2 system at 25 AE 1 C.
DFT calculations Periodic calculations were performed within the density functional theory (DFT) framework, using the Vienna ab initio simulation package (VASP). 40 The electronic wave functions have been expanded in a plane-wave basis set with a kinetic energy cutoff up to 450 eV. Projector augmented wave (PAW) pseudopotentials were used as implemented in VASP. Exchange-correlation effects have been accounted for by the generalized gradient approximation (GGA) using the functional of Perdew, Burke, and Ernzerhof (PBE). 41 The convergence criterion has been set to 1. 0 Â 10 À5 eV for the electronic selfconsistent iterations. The structural relaxation was performed until the maximum force on any atom was below 2. 0 Â 10 À2 eV ÅÀ1. Surface calculations were performed on LiZn (100), (110), and (111) slabs with 9, 7, and 13 atomic layers, respectively, on Ni (100) slab with 7 atomic layers and on Li 2 O (100) slab with 9 atomic layers (Fig.
Acknowledgements The authors would like to thank  Anastasiia Mikheenkova  and  Wessel van Ekeren  for recording the SEM images. The  Alistore-European Research Institute (ALISTORE-ERI ) network is warmly thanked for funding the Ph. D. grant of A. M. The authors gratefully acknowledge nancial support from the  French National Research Agency  (project  Labex STORE-EX ,  ANR-10-LABX-76-01 ) and from the  Swedish Energy Agency via StandUp for Energy  for nancial support. Computational work was performed using HPC resources from  GENCI-CINES  (Grant  2021-A0100910369 ).
Automatic segmentation of erythrocytes in microscopic blood smear phone images is a critical step to visualize and identify malaria using machine learning technologies. However, it still remains a challenging problem due to the scarcity of experts, low image qualities, slow manual and inefficient quality of diagnosis. To handle these issues to some extent, we proposed an effective multi-magnification deep residual neural network (MM-ResNet), where we fully automatically classify the microscopic blood smear images as either infected/ non-infected at multiple magnifications. We have experimentally evaluated our approach by using it to train more efficient variants of different compact deep convolutional neural networks (CNN), evaluated on phone datasets. The MM-ResNet end-to-end framework shows similar or superior accuracy than the baseline architectures, as measured by GPU timings on the publicly available microscopic blood smear phone images.
, 2009). The more direct the image and higher its resolution, higher is the chances for better result and accurate analysis. The comprehensive analysis of image datasets promises to reveal many aspects of the complexity and density of cellular functions. The portable smartphones datasets are ubiquitous and transformative in nature. Smartphones provide easy and cost-effectiveness for collecting image datasets with less time. But the image quality of scanning the blood smear slides is quite low compared with the digital camera. The volume of cases and low-resolution images degrade the performance of manual interpretation and are difficult to identify the disease through normal machine learning techniques  (Tek et al. , 2009; LeCun et al. , 2015). These issues can be handled by many fast preprocessing deep learning algorithms by automatic estimation of useful features for malaria identification and grading  (LeCun et al.
, 2015; Garud et al. , 2017). Considering the above challenges and the multi magnification approach of analysis, in this study, we develop a novel approach for fully automated classification of microscopic blood smear images using deep residual neural networks  (Liang et al. , 2016; Carneiro et al. , 2015; He et al. , 2016). Araújo et al. (2017)  introduced a method combining Convolutional Neural Networks (CNNs) and Support Vector Machine (SVM) classifier for classifying hemotoxylin and eosin stained brest images. This introduced method helps to retrieve infromation at different scales by achieving accuracies of 77. 8% for four class and 83. 3% for carcinoma/ noncarcinoma classes. The success stories of CNNs increase the available computing power and dataset size helps to solve image classification problems allowing to reduce the field knowledge to design classification systems.
Contribution The main contributions of this study are as follows: 1) This paper is an extension of  Carneiro et al. (2015) , where a multi-magnification erythrocytes classifier was developed using deep convolutional neural networks (CNN), using phone image datasets. This framework takes multiple views of images as inputs and concatenates the outputs of each Resnet at the second to the last layer followed by a final, fully connected, layer. The multiple layers converge to form initial as well as intermediate normalization layers in order to reduce vanishing gradients. 2) We proposed a novel widened MM-ResNet framework classifying the blood smear images into infected and noninfected erythrocytes images with an average accuracy of 98. 08%. The MM-ResNet works well and carries the greatest benefit of preserving information across layers and unlike, sigmoid and tanh activation functions, the linear regime preserves more information  (Abdi and Nahavandi, 2016).
There occurs an increase of functions with the increase of width as per need, resulting in better accuracy. 3) The approach follows the parallelism technique that helps to reduce the computational cost of MM-ResNet. The model was given three inputs and allows the multiple inputs and outputs work simultaneously parallelly using GPU  (Abdi and Nahavandi, 2016). 4) Using the phone image datasets, we have evaluated the different compact baseline deep convolutional neural networks. The MM-ResNet system enables faster convergence rate and less computational load with superior performance than other deep learning baseline results on microscopic blood smear phone images. 5) Using skip connection approach  (Orhan and Pitkow, 2017)  enables training improvements and are quite effective at dealing with the problem of vanishing gradients.
The proposed methodology produces a classic result using benchmark datasets of breast imaging reporting and data system (BI-RADS) score. Kaiming He et al. gives a brief research of different residual networks and states how residual units are easier to optimize and can gain accuracy from considerable depths. This paper provides comprehensive empirical evidence that residual learning (ResNet) is generic and better in performance than other deeper multiple layers machine learning architectures. The ResNet handles the problem of vanishing gradients and accuracy degradation significantly and extracts the useful features from the image. Many variations of residual unit architectures have been proposed to make the learned representations more stable against small variations of the input patterns.
, 2015). The ResNet connections significantly reduce the time for convergence with a relatively low computational cost  (Zhao and Koch, 2012). Abdi and Nahavandi (2016)  proposes a new CNN multi-residual network that is capable of enhancing classification accuracy compared with other existing models on Image Net, CIFAR-10, and CIFAR-100 datasets using the original residual network. This model outperforms yielding up to 15% computational complexity improvement. Orhan and Pitkow (2017)  briefly describes the concept of skip connections and its satisfactory success in eliminating singularities. The paper addresses various issues of singularities caused by the permutation symmetry, linear dependence of the nodes, many more. The deeper networks significantly slow down learning along with the overlap directions due to increase in hidden layers, thus skip connections overcome this difficulty to a certain extent  (Greenspan et al.
, 2016). In summary, the deep neural networks are increasingly finding their way into solving microscopic blood smear image and other medical related analysis challenges on relatively image sets. Although the review outcomes are good, all techniques still need to prove their stability and performance on a huge set of unlabelled/ unregistered images. It is equitable to say that the current systems still leave much space for improvement in the medical field. Hence, we are proposing a novel approach that aims to execute an accurate and robust machine learning model and is compared with some deep learning based neuron crafted features in terms of accuracy of performance on blood smear microscopic phone image datasets.
Proposed multi-magnification deep residual network (MM-ResNet) The proposed multi-magnification residual network architecture is a modified ensemble of individual ResNets as shown in Fig. 1. Blood smear images classification is a very challenging task where MM-ResNet need to learn from a very small number of training examples and each fold can get similar class distribution. The images captured using microscope smartphone adapter visualization of erythrocytes differs too much as opposed to those from the high-end microscope. The MM-ResNet is trained to recognize these erythrocytes and a proper understanding of the shape, color and texture are faded to classify the erythrocytes in the lowquality images. The smartphone datasets are ubiquitous and have great potential to lighten the burden of malaria in remote areas.
Data collection and experiment The datasets obtained from the AI research group at Makerere University are used to validate the effectiveness of the constructed multi-magnification deep residual neural network (MM-ResNet). Our approach uses the publicly available Field-stained malariainfected blood smear microscopic images obtained from Android smartphone to a Brunel SP150 microscope. The dataset consists of 1182 images at three different magnifications of 200Â, 400Â, and 1000Â with a 750 Â 750 pixel resolution and bounding boxes of 7245 parasites. The complete access to the entire malaria blood smears microscopic phone image dataset. 1  We carried out the experiments with a patient level of fivefold cross-validation by randomly dividing the whole dataset into 80% training and rest 20% for testing purpose.
Based on a series of analyses and observations, the average number of blood smear image slides (training instances) per fold is small, as shown in Table  2. Blood smear images classification is a very challenging task where MM-ResNet need to learn from a very small number of training examples and each fold can get similar class distribution. The images captured using microscope smartphone adapter visualization of erythrocytes differs too much as opposed to those from the high-end microscope. The MM-ResNet is trained to recognize these erythrocytes and a proper understanding of the shape, color and texture are faded to classify the erythrocytes in the low-quality images. The smartphone datasets are ubiquitous and have great potential to lighten the burden of malaria in remote areas. The images obtained from the blood smear slides are of low cost and easily transformative in nature.
Similarly, the 4 th , 5 th and 6 th residual units contained 16 filters of size 3 Â 3 and so on, the 7 th , 8 th and 9 th units had 32 filters of 3 Â 3. For each ResNet, we concatenate the 32 output features to form 96 (32 Â 3) features, proceeded by a fully connected layer giving two outputs (normal/non-infected and infected/malaria). Our approach was performed using a server with an Intel Xeon E5 (Core TM i7-2600k) processor, 32 GB RAM with software implementation on Pycaffe and Pytorch, and graphics card NVIDIA GeForce TITAN X. The entire MM-ResNet system was trained over 1000 epochs with a learning rate of 1 Â 10 À3 and decay rate of 0. 1 per epoch.
Result and discussion Next, we evaluate the performance of our approach vs. deep neural baseline architectures on the malaria validation set. Table  1  shows that our results compare favorably with MM-ResNet and gives an average accuracy of classifying each slide using all views better than other architectures. The MM-ResNet framework comprises a convolutional layer, batch normalization, rectilinear unit and an identity mapping and is trained in one single pass, which is more robust than the greedy training process. In addition, the potential to learn more robust higher level features of the data makes MM-ResNet deep architecture as a unique model than others. As per the Table  1 , while all five baseline architectures achieved classification accuracies above 95%, the SVM technique has a lower accuracy than other deep learning networks.
As per the experimental study, more depth the number of layers in a CNN more the convergence hampers and leads to degradation. The use of residual networks are easier to optimize and can gain accuracy of 98. 08% from considerably increased depth with multi magnification inputs. Concurrently, GoogleNet and Faster CNN obtained a first and third place in the malaria dataset classification task with 98. 13% and 98% accuracy respectively. Although our 1 http://air. ug/microscopy/1.
The deep MM-ResNet introduces the concept of skip connections to facilitate the computation faster and results with less training error than other architectures in Table  1. By splitting the computation of the MM-ResNet blocks among processors, the whole network works faster. Moreover, the parallel processing of MM-ResNet helps the data samples among the available GPUs, a different part of the model on the same data. All experiments are done using NVIDIA GeForce TITAN X, the inputs and outputs of blocks must be transferred between the GPUs, which offers up to 18% computational complexity improvement with respect to the other deep learning networks. Finally, given our use of publicly available malaria datasets only, these experimental results can also be used as a baseline for other medical images datasets for future proposals.
Conclusion In this paper, we proposed an advanced deep learning approach -Multi-Magnification Deep Residual Network -for the classification of microscopic blood smear phone image dataset. Specifically, our proposed framework combined batch normalization and individual residual units to solve the problem of vanishing gradients, degradation, low-quality image and provides a reasonable classification into infected or non-infected, with an average accuracy of 98. 08%. The results show the effect of our proposed MM-ResNet compared with other existing methods.
66 MM-ResNet (Proposed System) 98. 08 proposed approach does not have the highest accuracy as compared to GoogleNet, it does repel the shortest training time and test error rate than existing approaches with superior/ comparable performance in complex malaria phone data. The MM-ResNet have lower training error, also lower test error and computes test error rate of 19. 93% whereas GoogleNet by 25. 86% error rate on the public malaria dataset.
Modelling The first goal is to assess the key parameters involved in the mathematical model used to fit the measured data. Indeed, the light scattering is monitored by the size particles, the concentration and the refractive index of the plate components. The light scattering is simulated by a model  [35]  using a Monte-Carlo ray tracing process combined with the Mie scattering theory. The goal is to compare the experimental data with the theoretical data, as a function of particles concentrations, plate thickness and phosphor/TiO 2 ratio. The weight percentage w t of the particles is defined by w t (%) ≡ w p w p +w s × 100%, which w p. is the weight of the particles, and w s is the weight of the silicone. The total volume of the phosphor particles is equal to V ph = V t 1 + D p D s (︂ 1 w[%] -1 )︂ (1) with D p is the density of the phosphor particles, D s is the density of silicone, and V t is the total volume of the phosphor particles and the silicone.
Model identification The reference for the model parameter identification is the PSD of one of the 28 wind speed datasets recorded next to the city of Montpellier, France. The studied signals have a period of 3 hours and a quasi-constant mean speed. The nonlinear simplex algorithm  (Subrahmanyam (1989) ) is used to estimate the parameters of the von Kármán model. It will estimate the parameters K and τ of the von Kármán PSD expression (  6 ) by minimizing the cost function corresponding to the normalized sum of the square errors between the measured PSD, S, and the estimated model, S, as: J = 1 N N i=1 S dB (f i ) -S dB (f i , K, τ ) 2 ( 8 ) where N is the number of samples and f i are the frequencies defined in the chosen frequency range. The optimization is carried out in dB in order to maintain a better accuracy in the considered frequency range.
Indeed, the PSD modulus is tending towards zero, thus leading to suboptimal optimization. The estimated von Kármán model obtained on Matlab® is presented in Fig. 1. Fig. 1  shows that the von Kármán model provides a good fit in low frequencies. However, for f > 0. 01Hz, the slope of the model seems to be different from the one of the measurements. The frequency range of wind turbulence is between 0. 01Hz and 0. 1Hz (van der Hoven (1957)) which corresponds to the range where the model is less accurate. This can be explained by the fact that the von Kármán model was originally designed for aircraft, and consequently for high altitudes and fast moving systems.
Model parameters In this section, the von Kármán model estimated parameters K and τ (Fig. 2 ) are analyzed for all 28 datasets recorded next to the city of Montpellier, France. Fig. 2. The estimated parameters K and τ of the von Kármán model for different datasets Both model parameters K and τ depend on the mean speed V , the reference standard deviation σ and on the length scale L, and can be expressed as: K = 4σ 2 L V (9) τ = √ 70. 8 L V = 8. 4 L V. ( 10 ) V and σ are assumed known. As for L, it is very difficult to know its exact value because it depends on several factors, and at best, it can be approximated by mathematical models. The approximated value of the length scale from the ESDU model  (ESDU (1985) ) is: L ESDU = 25z 0. 35 z -0. 063 0 = 109. 8m, (11) where z = 40m is the measuring altitude and z 0 = 0.
The CC×2 model has two time constants τ 1 and τ 2. The length scale L 12 is defined from τ 1 and τ 2 , inspired by the von Kármán model: L 12 = V √ τ 1 τ 2 4. 7. ( 22 ) Fig. 7  shows the values of L K and L 12 for different datasets. The obtained values of L K and L 12 are close to the approximated L ESDU (11), therefore, the estimated model parameters do not provide aberrant values of length scale. L K and L 12 have the same shape, which confirms that K and time constants are related as in the von Kármán model. Concerning the time constants, τ 1 can be calculated using the mean speed and the length scale and τ 2 can then be deduced from τ 1. Indeed, the ratio between τ 1 and τ 2 varies between 2. 6 and 4. 6 with an average of 3. 6 (Fig. 8 ). Therefore, τ 2 can be approximated by: τ 2 = τ 1 3.
6. ( 23 ) This is an optimistic approximation but it allows to have consistent values for the model in the absence of a reference dataset. The equation to calculate τ 1 from the inputs becomes: Therefore, the CC×2 model parameters can be tuned using equations (  21 ), (  23 ) and (  24 ) and the constants: the mean speed V , the standard deviation σ and the length scale L. τ 1 = 8. 9 L V. ( 24 For the order ν, it does not vary much, so it can be fixed at the estimations mean value: ν = 0. 516.