dataesr/scientific-paragraphs-categorization huggingface dataset:
One potential cause of the stream track variations is interaction with perturbers. These would likely be the same interactions that caused the density fluctuations discussed in Section 5. 1. If they are, it could explain why we observe track and density deviations Figure  6. A comparison of four different streams modeled using the same parametric method as described in this work for four streams: Palomar 5  (Erkal et al. 2017) , ATLAS  (Li et al. 2020) , Jet  (Ferguson et al. 2021), and Phoenix (this work). We take the linear intensity model results directly from these three papers to make this comparison between the four streams. We note that none have been studied with data as deep as the DES Y6 data used in this analysis. The shaded areas around each stream are the minimum intervals containing 68% of the posterior probability, similar to those described and shown in Figure  3.
Based in part on observations at  Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory , which is operated by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation. The DES data management system is supported by the  National Science Foundation  under grant Nos. AST-1138766  and  AST-1536171. The DES participants from Spanish institutions are partially supported by  MINECO  under grants  AYA2015-71825 ,  ESP2015-66861 ,  FPA2015-68048 ,  SEV-2016-0588 ,  SEV-2016-0597 , and  MDM-2015-0509 , some of which include ERDF funds from the  European Union. IFAE is partially funded by the  CERCA program  of the  Generalitat de Catalunya. Research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Program  (FP7/2007-2013) including  ERC  grant agreements  240672 ,  291329 , and  306478.
The Johnson-Cook model is described by the expression of average flow stress given by Equation  (1). σ = [A + B ε n ] × 1 + C ln. ε. ε 0 × 1 - T -T room T melt -T room ( 1 ) where ε is the equivalent plastic strain,. ε and. ε 0 are the equivalent and reference plastic strain rates, T, T melt and T room are the local, melting and room temperatures, respectively, n is the strain hardening index and m is the thermal softening index. Johnson-Cook Parameters A, B and C represent the yield strength, strain and strain rate sensitivity of the material. For reasons of data availability, simulations are carried out with a 41NiCrMo7 steel, hardened to 41 HRC. Table  1  shows physical and mechanical properties of the steel hardened to 41 HRC  [9]. Table  2  shows the Johnson-Cook's law parameters related to steel hardened at 41 HRC  [10].
Micromachines 2022, 13, x 3 of 15 is the strain hardening index and m is the thermal softening index. Johnson-Cook Parameters A, B and C represent the yield strength, strain and strain rate sensitivity of the material. For reasons of data availability, simulations are carried out with a 41NiCrMo7 steel, hardened to 41 HRC. Table  1  shows physical and mechanical properties of the steel hardened to 41 HRC  [9]. A method of penalization is used to take into account the contact. When a slave element (particle of the work-piece) penetrates a master element by a certain distance along the normal to the contact, a contact force equivalent to a spring working only in tension is defined and tends to eject the penetrating element. Friction is represented by a Coulomb model  [11]. The work-piece is subjected to boundary conditions in displacement on the lower free edge (fixed support) and on the left edge (zero displacement in X direction).
FE Model Using Lagrangian Formulation A 2D orthogonal micro-cutting FE model based on the Lagrangian formulation is developed using ABAQUS software with an explicit dynamic integration scheme to analyze the cutting mechanism. The use of a Lagrangian formulation requires the use of a separation criterion between the machined part and the chip in finite element modeling. The cutting tool used for the modeling purpose is considered a rigid body and located by a reference point (RP) to acquire the cutting force value. For realistic modeling, a cutting angle of.
FE Model Using Lagrangian Formulation A 2D orthogonal micro-cutting FE model based on the Lagrangian formulation is developed using ABAQUS software with an explicit dynamic integration scheme to analyze the cutting mechanism. The use of a Lagrangian formulation requires the use of a separation criterion between the machined part and the chip in finite element modeling. The cutting tool used for the modeling purpose is considered a rigid body and located by a reference point (RP) to acquire the cutting force value. For realistic modeling, a cutting angle of ɣ = 8°, a clearance angle of α = 6° and a cutting edge radius of 1. 5; 2; 5; 8; 10 (µm) are applied to the cutting tool. The cutting velocity Vc applied to the cutting tool corresponds to the experimental setup (Vc = 40 m/min) and a depth of cut hc = 4 µm is used in the simulations.
The tool/part interaction is modeled with a surface contact ("surface to surface contact" in ABAQUS) with Coulomb friction law as defined in the bibliography  [12]  and the coefficient of friction attributed in this case is f = 0. 63  [11]. A fine mesh is applied in the zone of strong deformations with quadratic first-order elements with a reduced integration, see Figure  2. The use of this type of element can lead to zero energy strain modes, called "Hourglass" modes, which must be eliminated. It is commonly admitted in the literature that the artificial energy imposed on the elements to prevent Hourglass modes should not exceed 10% of the internal energy of the model  [13]. In this context, a comparative study between the different management methods of "Hourglass" available in the software led to the choice of the "Combined" one because it imposes the most significant artificial energy on the elements and it gives a greater radius of curvature of the chip compared to the other modes.
They concluded that using the CEL approach in metal cutting modeling guaranteed the accuracy of results and improved simulation parameters. From our side, a 3D FE model of orthogonal micro-cutting of hardened steel with a carbide tool using the CEL approach is developed under Abaqus/Explicit software in order to check its capability to combine advantages inherent to each method by avoiding their drawbacks. The tool was initially motionless in all directions and then animated by the cutting velocity (see Figure  3 ). The cutting velocity V c applied to the cutting tool corresponds to the experimental setup (V c = 40 m/min) and a depth of cut h c = 4 µm is used in the simulations. A cutting angle of 4 of 15 𝐾 = ( ) (3).
The tool/part interaction is modeled with a surface contact ("surface to surface contact" in ABAQUS) with Coulomb friction law as defined in the bibliography  [12]  and the coefficient of friction attributed in this case is f = 0. 63  [11]. A fine mesh is applied in the zone of strong deformations with quadratic first-order elements with a reduced integration, see Figure  2. The use of this type of element can lead to zero energy strain modes, called "Hourglass" modes, which must be eliminated. It is commonly admitted in the literature that the artificial energy imposed on the elements to prevent Hourglass modes should not exceed 10% of the internal energy of the model  [13]. In this context, a comparative study between the different management methods of "Hourglass" available in the software led to the choice of the "Combined" one because it imposes the most significant artificial energy on the elements and it gives a greater radius = 8 • , a clearance angle of α = 6 • and a cutting edge radius of 2 µm are applied to the cutting tool.
At the end of the chip elements, the movement is not enough, which does not help with mesh distortion. We notice that calculations end immediately because of high mesh distortion. In the Eulerian approach, a chip shape needs to be predefined which is a major disadvantage, but on the other hand, it allows for a longer time of cutting process modeling. To overcome these disadvantages, Ducobu et al. [16]  used the coupled Eulerian-Lagrangian approach (CEL) in the modeling of orthogonal cutting of titanium alloy and compared the results to those of ALE and experimental results. They concluded that using the CEL approach in metal cutting modeling guaranteed the accuracy of results and improved simulation parameters. From our side, a 3D FE model of orthogonal micro-cutting of hardened steel with a carbide tool using the CEL approach is developed under Abaqus/Explicit software in order to check its capability to combine advantages inherent to each method by avoiding their drawbacks.
To overcome these disadvantages, Ducobu et al. [16]  used the coupled Eulerian-Lagrangian approach (CEL) in the modeling of orthogonal cutting of titanium alloy and compared the results to those of ALE and experimental results. They concluded that using the CEL approach in metal cutting modeling guaranteed the accuracy of results and improved simulation parameters. From our side, a 3D FE model of orthogonal micro-cutting of hardened steel with a carbide tool using the CEL approach is developed under Abaqus/Explicit software in order to check its capability to combine advantages inherent to each method by avoiding their drawbacks.
Experimental Set-Up Elementary micro-cutting machining tests were carried out on a Röders RP600 CNC, which performed adequately to ensure minimal errors for the cutting speed, feed rate and position (see Figure  7 ). The chip conveyor was switched off to minimize cutting force signal noise resulting from vibrations. As shown in Figure  3 , three tools were used as follows: two to obtain the tube dimensions directly before beginning each test from a larger tube to minimize tube run-out and the last to perform the experiments. Force measurements were carried out using a Kistler Minidyn 9256C2 dynamometer associated with a Kislter amplifier 9017. Data were saved using a National CompactDaq 9174 with NI9215 cards and a dedicated Labview program  [11]. animated by the cutting velocity (see Figure  5 ).
Experimental Set-Up Elementary micro-cutting machining tests were carried out on a Röders RP600 CNC, which performed adequately to ensure minimal errors for the cutting speed, feed rate and position (see Figure  7 ). The chip conveyor was switched off to minimize cutting force signal noise resulting from vibrations. As shown in Figure  3 , three tools were used as follows: two to obtain the tube dimensions directly before beginning each test from a larger tube to minimize tube run-out and the last to perform the experiments. Force measurements were carried out using a Kistler Minidyn 9256C2 dynamometer associated with a Kislter amplifier 9017. Data were saved using a National CompactDaq 9174 with NI9215 cards and a dedicated Labview program  [11]. Reference numerical simulations use the same cutting conditions and tool geometry as the performed elementary micro-cutting machining tests.
Study of the Cutting Edge Radius Influence Using SPH and Lagrangian Models In order to study the influence of the h c /r β ratio on the cutting process, a cut angle 4 of 15 𝐾 = ( ) (3) el Using Lagrangian Formulation orthogonal micro-cutting FE model based on the Lagrangian formulation is using ABAQUS software with an explicit dynamic integration scheme to anatting mechanism. The use of a Lagrangian formulation requires the use of a criterion between the machined part and the chip in finite element modeling. tool used for the modeling purpose is considered a rigid body and located by point (RP) to acquire the cutting force value. alistic modeling, a cutting angle of ɣ = 8°, a clearance angle of α = 6° and a g edge radius of 1. 5; 2; 5; 8; 10 (µm) are applied to the cutting tool. The cutting applied to the cutting tool corresponds to the experimental setup (Vc = 40 a depth of cut hc = 4 µm is used in the simulations.
FE Model Using Lagrangian Formulation A 2D orthogonal micro-cutting FE model based on the La developed using ABAQUS software with an explicit dynamic int lyze the cutting mechanism. The use of a Lagrangian formulati separation criterion between the machined part and the chip in f The cutting tool used for the modeling purpose is considered a ri a reference point (RP) to acquire the cutting force value. For realistic modeling, a cutting angle of ɣ = 8°, a cleara cutting edge radius of 1. 5; 2; 5; 8; 10 (µm) are applied to the velocity Vc applied to the cutting tool corresponds to the expe m/min) and a depth of cut hc = 4 µm is used in the simulations. Th modeled with a surface contact ("surface to surface contact" in A friction law as defined in the bibliography  [12]  and the coefficien this case is f = 0.
1) Medical privacy: training a reliable model requires a huge amount of data, which is often not met by models to be safe and ready to use in real-life clinical situations  [4] ,  [5]. A major factor that contributes to this lack of data is medical privacy: hospitals are not able to share their local datasets without the explicit ongoing consent of their patients  [6]. Furthermore, in a DL project that involves medical imaging, data collection requires approval by institutional review boards to ensure patients' ongoing consent and information, and assess the risks and benefits of the project  [6]. Also, patients can withdraw their consent, which leads to the destruction of their data  [6]. Therefore, alleviating the medical privacy problem would make available a significant amount of data  [7]  and potentially make possible a large public clinical database.
2) Models' lack of validation: this issue is directly linked to the previous one, as lack of validation can be mitigated by access to more data. Despite the plethora of papers presenting novel DL models as state-of-the-art exceeding their predecessors in performance and accuracy, there should be a certain degree of skepticism regarding researchers' optimism. The reason being DL studies tend to be retrospective and locally validated  [8]. It is undeniable that impressive progress has been made; however, the lack of validation of DL models remains a major problem. Employment of such models in real-life clinical scenarios would not be desirable considering the high risks involved. Most models are single-purpose and were tested and validated using limited and single-institution datasets  [5] ,  [8].
3) High cost of data annotation: The tasks of clinical data collection and labeling can prove to be heavy on radiologists  [8]. These tasks require human workforce and are not automated. Training reliable DL models requires thousands of images, hence the heavy nature of data annotation. Additionally, it is safe to assume that annotation by expert radiologists requires a significant amount of financial resources and time  [5]. Reducing the time and cost of this process would help accelerate the DL solutions development workflow. In this context, the present work contributes to the line of research aiming to use GAN to alleviate the above-mentioned problems. GAN has the potential of overcoming privacy issues by generating synthetic samples containing features from real datasets that otherwise would be publicly inaccessible.
Thus making more data accessible and improving existing and future models' validation process. Additionally, GAN can also function in a semi-supervised way  [9] , which makes it usable even with small annotated datasets. Our objective is to use GAN in an X-ray imaging scenario: we build a model for generating realistic chest X-ray images. Additionally, we provide an empirical analysis of three aspects of GAN: (1) different implementation configurations, (2) memory requirements, and (3) GAN assessment metrics. The paper is organized as follows: we start with a general overview of the GAN framework, providing some background and limitations of GAN. Afterward, the model design is described in terms of architectural constraints. We then present a memory requirements analysis and explore the possibility of compressing the conventional DCGAN convolution blocks.
C. Model Design The models presented in this work are all built using fully convolutional layers, similarly to DCGAN (Radford et al. [13] ), which is also used as a baseline model. While DCGAN uses LeakyReLU and ReLU nonlinearities, we implement our models using Exponential Linear Units (ELU) nonlinearity, proposed by Clevert et al. [14]. It is defined by 𝑓(𝑥) = { 𝑥 𝑖𝑓 𝑥 > 0 𝛼(𝑒𝑥𝑝(𝑥) -1) 𝑖𝑓 𝑥 ≤ 0, (3) with α > 0. The choice of ELU is justified by its superior performance reported in  [14]  and its mitigating effect on vanishing gradients, which happens to be a major issue in GANs. It exhibited superior precision and learning speed in fewer epochs of training on the ImageNet dataset  [14]. Additionally, ELU fulfills the first convergence assumption for the next implementation choice: the Two Time-Scale Update Rule (TTUR) proposed by Heusel et al.
In the author's repository  [15] , it was reported that, on some datasets, AdaS outperformed AdaM (Kingma & Ba  [16] ), one of the widely used optimization algorithms. On the MNIST dataset  [17] , training a shallow network using AdaS led to impressive results compared to AdaM; training was more stable and faster. Although adaptive optimizers like AdaM are widely used in the DL community, they still suffer from a major problem. As compelling as the training outcomes may be, these adaptive algorithms suffer from poor generalization compared to the standard Stochastic Gradient Descent  [18]. On this front, AdaS has an advantage over AdaM when it comes to generalization. Although experiments with this optimizer were not extensive, the official repository provides a comparison between both optimizers on the CIFAR-100 dataset  [19] : the results showed that AdaS surpasses AdaM in generalization  [15].
III. EXPERIMENTS AND RESULTS In this work, unless stated otherwise, the models are built to generate 64 × 64 images. The dataset used is a portion of the VinBigData Chest X-ray Abnormalities Detection dataset  [21]. For model assessment, both qualitative and quantitative metrics were used. The latter consist of mere human eye observation of texture details, blur, and apparent similarity to real X-ray chest images. Quantitative measures are more emphasized and include FID scores and the mean loss value of D, which we explore and analyze to conclude whether it can be used as a GAN performance metric.
Optical Inter-Satellite Links (OISLs) are being considered for future Global Navigation Satellite System (GNSS) constellations. Thanks to OISLs, the constellation incorporates improved clock synchronization and precise ranging among the satellites, which are essential features to achieve accurate time and orbit determination. High data rate communications within the space segment also reduce ground segment dependency, by means of decentralized access to information. However, the dual optimization of data and navigation performance metrics requires a careful assignment of OISLs to the available laser communication terminals on-board. To this end, we present a Contact Plan Design (CPD) scheme based on a Degree Constrained Minimum Spanning Tree heuristic applied to such OISL-enabled GNSS (O-GNSS) constellations.
Unfortunately, combined orbital mechanics, gravitational perturbations, station-keeping maneuvers, mission parameter changes, and the dynamic relative orientation of the satellites forbid the consideration of fixed-configuration mechanisms based on periodicity. Instead, a persistent planning process is required so that the utilization and pointing information of available LCTs is continuously decided and timely provisioned to the satellite constellation in advance. The contact planning must consider the overall system-level implications of the chosen links, as they affect the aforementioned navigation precision and data connectivity metrics. Thus, the efficient Contact Plan Design (CPD)  [14]  is crucial to ensure the benefits that gave rise to future GNSS constellations enhanced by OISLs.
B. Contact Plan Design To profit from the aforementioned navigation and data transfer benefits, a careful assignation of LCT pairs among satellites is required for GNSS constellations supported by OISLs  [12]. The general link selection under constrained resources in intermittent and partitioned networks is a well-known problem called CPD. The objective of the CPD is to select the pair of nodes between which a contact shall be established by means of limited resources (e. g. , amount of available transponders or antennas) to meet an overall system-level goal. The topic has enjoyed an increasing interest of the academic community, including a series of publications focused on modern GNSS constellations supported by ISLs. CPD was first applied to GNSS in 2011 considering both ranging and communication needs to support autonomous navigation  [33].
A. Preliminary Steps 1) Data Collection: The constellation under test is simulated through the AGI Systems Tool Kit (STK) software. For the current case study, the Kepler system has been simulated according to parameters in Fig. 1. Information from visibility windows (line-of-sight visibility) among satellites for potential communication and ranging is retrieved. Also, the relative positions (in azimuth and elevation  [52] ) during each visibility window is then exported to MATLAB in order to compute H i,n and the PDOP values. 2) Fixed Links: Firstly, FLs are analyzed to build the fixed portion of H i,n. MEO-to-MEO intra-plane links are therefore used to this end in the Kepler case. Thanks to the stability of such links and the symmetry of satellite distribution within orbital planes, we can consider the elements of the first two lines of H i,n invariant both with respect to time and to the chosen MEO satellite.
Furthermore the model is sampled on the satellite orbital path, the fully overcast cases are masked out to take account of the impossibility for a space lidar to observe aerosols below optically thick clouds, and only the signal above the instrumental noise is retained. We incorporate modules included in previously developed simulators  (Ma et al. 2018 , Vuolo et al. 2009 , Hodzic et al. 2004 ) into the community tool CFMIP Observation Simulator Package (COSP) to create a simple base on which each group can build up its own analysis. The goal is to facilitate the comparison between GCMs and space lidar aerosols data. Besides CALIPSO operating at 532 nm and 1064 nm, the ATmospheric LIDar (ATLID) instrument of the EarthCARE mission is expected to become operational in 2023. In synergy with other instruments, it will provide vertical profiles of aerosols and thin clouds, operating at 355 nm with a high-spectral resolution (HSR) receiver and depolarization channel.
Later on another HSR Lidar operating at 532 nm and 1064 nm is expected to be launched. The COSPv2 lidar simulator will thus be a useful tool for the exploitation of these new datasets and the comparison with General Circulation Models (GCMs) of several modeling groups. We choose to implement the lidar aerosol simulator within the COSPv2 infrastructure because COSPv2 infrastructure already contains several capabilities (from the cloud lidar simulator but not only) that can be re-used for the lidar aerosol simulator. Moreover, COSPv2 is already implemented in several GCMs  (Webb et al. 2019 ) so the addition of the lidar aerosol simulator module requires only minimum effort for the modeling groups.