dataesr/scientific-paragraphs-categorization huggingface dataset:
Computation times The test calculations, using the final (global) composition of the 13. 4 %FIMA fuel pin, for obtaining the computational time for the different softwares are presented in Table  4. Results from these calculations can also be found in Figure  3 , where the amount of gaseous and liquid phases are shown at different temperatures. Initially OC suffered from a considerable slow-down due to the complexity of the equilibria, but the implementation of an improved solving algorithm improved computation times. Further improvements were obtained by creating a database of solved equilibria, and using these as initial guesses for the calculations (see Sect. 5. 1 for more details).
Release into gap The maximum amount of released FP allowed by the model is the extreme case where all liquid and gaseous phases are transported into the fuel-to-clad gap. This case is illustrated in Figure  4  for the different fuel pins and softwares. Here, the distributions between the volatile fission products are presented, as well as the ratio between OC and ANGE regarding released amount of FP into the gap at the peak power node. 1. The bottom row is the ratio between the total released amount predicted by OC and ANGE. All data is taken from the maximum power node.
This is due to the fact that the simplified model does not give any information regarding the actual chemistry in the gap: everything is assumed to be an imaginary Cs 2 MoO 4 like phase. In all cases, as can be seen, in Figure  4 , Cs is the major contributor to the FP in the gap. In the ANGE calculations, only Cs, Te, and I were released (as well as low amounts of Ba). The OC calculations, using the more advanced database which allows ionic liquids, predicted the release of Mo and Pd in addition to those in the ANGE calculations. The TBASE database utilized by ANGE lacks a thermodynamic description of the liquid metallic Mo and Pd phases. For volatilization to occur, evaporation is required. The varying fraction of released Mo in the OC calculations can be explained by the maximum temperatures during irradiation of the different fuel pins.
Outlook The volatile fission product release fraction has been approximated to correspond to that of stable fission gases, based on the correlation used by GERMINAL V2. A future improvement could be to couple the JOG prediction model with the MARGARET  [65]  fission gas transport code in order to describe more accurately the volatile fission product release rate. Indeed, new functionalities have been added to the current version of the code, designated MARGARET PAF, in order to describe the creation, destruction (by decay or by neutronic reaction), and transport of isotopes in the grain and along the grain boundaries. One factor which has not been accounted for in this work is the JOG porosity. The theoretical density has been used in both of the methods for calculating the JOG thickness, which is expected to underpredict the thickness.
Monte Carlo data processing and event selection The energy depositions generated by geant4 per event undergo a density-based spatial clustering algorithm  [19]  to topologically distinguish signal-like single site (SS) from background-like multi site (MS) events, as discussed in Sect. 3. We assume a separation threshold = 15 mm for the DARWIN TPC. This comparatively coarse clustering inevitably results in a fraction of γ-accompanied β-decays from background events, e. g. , 214 Bi decays which frequently occur with higher multiplicity, being falsely identified as SS and consequentially contributing to the background. To account for the finite energy resolution of the detector, the combined energy deposited inside each cluster is smeared according to a resolution of σ E E = a E[keV] + b, (2) with a = (0.
Acknowledgements This work was supported by the  Swiss National Science Foundation  under grants No  200020-162501  and No  200020-175863 , by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreements No  674896 , No 690575 and No  691164 , by the  European Research Council (ERC)  grant agreements No 742789 (Xenoscope) and No  724320  ( ULTI-MATE ), by the  Max-Planck-Gesellschaft , by the.
Nevertheless, in many proposed solutions, they were usually neglected. As a fact, in the real-time control phase, we can achieve a better algorithm if the changing losses by system operation modes are considered. Most of the researches on energy system required measured data  [15] ,  [16]. That can be completely obtained by setting up an energy management system (EMS). A structure of the EMS  [17]  comprises a sensor network integrated control algorithms and database connection for tasks as monitoring, controlling, optimizing, and reporting. In previous works, the benefits of EMS system in the designs and operations of low-energy building  [18] , performance analysis of the PV system  [19]. The role of sensing and actuation to improve energy efficiency was also discussed in  [20]. However, technical issues (such as accuracy, sampling rate, response-ability of system, and losses) are factors that affect much the quality of monitoring and controlling systems  [13] ,  [14] ,  [19].
Energy-saving strategies: An autonomous energy system (islanded or connected to the grid) with a system sizing approach was studied. We exploited monitoring data as load consumption, PV production, and losses to analyze energy balance. Then we evaluated the best control strategy to reach autonomy for typical weather conditions days. Those analyses will help us develop smart control algorithms to manage loads operation depending on PV power in a short-term period of several days in further work. IOP Conf. Series: Earth and Environmental Science 505 (2020) 012045 IOP Publishing doi:10. 1088/1755-1315/505/1/012045 3.
Parameters of each part are the following:  Solar panels (x6) by Francewatts: V mpp (V)=8. 03; I mpp (A)=7. 97; P max (Wp)=64; title=27 0 ; azimute with 2 orients are equal 18 0 and -162 0 respectively;  Battery (x2) : 22Ah-12 V  Inverter/charger : 3000VA/2400W; AC Output=13A; AC Input =17. 7A  Pump DCS-4000 by Jacod : DC 24V -30W The energy monitoring and control system needs five sensors (PV Panels, Battery, 2 for AC input/output of the inverter, Pump) for measuring Voltage/Current/Power/Energy and a speed driver for the pump. The measured data from sensors will be stored in time series on the local Raspberry Pi server as in the database and displayed on a web-based dashboard. Therefore, users will be able to access and see data. Our schematic of monitoring and control system and electrical board were shown in Figure  2 , 3.
8A/52A (to have over 50% of 100A). Correction of conversion errors (for instance INA219 sensor) by a calibrated function could reduce voltage/current error from 1. 07% / 1. 7% to 1%, 1. 2% respectively. Uncertainties of measurement can be reduced by averaging measurement results  [27]. Our data were collected every second and then were averaged every 1 minute. The sampling rate shown in  [25]  is seconds to minutes per sample, time uploading data is 1s to 5 min. Other authors in  [19]  suggested timestamp of recorded data is around 5 to 15 min that is enough for tracking and can avoid overload database system. In our application, we collected 20-30 samples/minute (depending on sensors), and then store average data every 1 min. Some importance evaluated factors are often used as RMSE (root mean square error), MAPE (mean absolute percentage error), MRE (mean relative error)  [25] , or STD (standard deviation)  [27].
Energy balance evaluation The characteristics associated with photovoltaic production simulation obtained by PVSyst software are to estimate the energy autonomy in our system. The relationship between demand and self-supply is represented by load matching indicators, including supply cover factor (γ supply in Equation  1 ) and load cover factor (γ load in Equation  2 ) on the different time scale (year, month,. ), which are defined in  [28] : γ , (1) γ , (2) (3) Where: t 1 /t 2 are start/end of the evaluation period; ζ(t) is energy losses; l(t) is the power of loads; S(t) is the storage energy balance defined by Equation  3. In which, s c /s dc are charging/discharging storage energy. On Greenhouse, the nominal power of loads is equal to 42 (W), and the losses system was approximated 10% nominal power of loads (4.
Analysis of the energy autonomy level Therefore, load control strategies in a minute are necessary to avoid oversized storage capacity and increase auto-consumption (γ load =1). A comparison between our PV production prediction model with measured data figured out a small gap, but total energy prediction obtained a better accuracy. Therefore, our model with a load profile can use to predict SOC of battery by Equation15. Then, we can develop energy-saving strategies to avoid power consumption exceeding the system supplying capacity. The evaluation of SOC and PV power will be updated according to the load control and weather conditions, every 3 hours. Then, a controller will reduce the power of the pump when the forecast shows that there is a deficiency of energy in the system and will increase the power of the pump when the system is predicted a surplus of energy.
1 https://www. ebay. com/itm/PEACEFAIR-PZEM-003-DC-Voltage-Current-Power-Consumption-Meter-Communication-/173504669451 2 https://peacefair. en. made-in-china. com/product/FCsEdQqvwkVl/China-Pzem-017-DC-0-300V-Electric-Solar-Power-Meter-with-RS485-Modbus-with-300A-Shunt. html 3 https://innovatorsguru. com/ac-digital-multi-function-smart-meter-using-arduino-and-pzem-004t/ 4 https://pypi. org/project/pi-ina219/ 5 https://www. amazon. com/SainSmart-101-70-100-2-Channel-Relay-Module/dp/B0057OC6D86 https://docs. influxdata. com/influxdb/v1. 7/ 7 https://grafana. com/docs/ 8 https://docs. python. org/3/.
DNI -Direct Normal Irradiance (W/m 2 ); DHI -Diffuse Horizontal Irradiance (W/m 2 ); GHI -Global horizontal irradiance (W/m 2 );  -Panel Tilt angle (rad); θz -Zenith angle (rad); θ -Theta angle (rad); ρ -Albedo coefficient of Ground. PV power generation forecast is updated every 3 hours from web service. The measured GHI/DNI dataset from Campbell Scientific weather station (including Rotating Shadowband Radiometer) located just beside Greenhouse, and measured PV power data from our building PV's system in 2018, were used to validate our model.
Neural network classifiers (NNCs) are known to be vulnerable to malicious adversarial perturbations of inputs including those modifying a small fraction of the input features named sparse or L 0 attacks. Effective and fast L 0 attacks, such as the widely used Jacobian-based Saliency Map Attack (JSMA) are practical to fool NNCs but also to improve their robustness. In this paper, we show that penalising saliency maps of JSMA by the output probabilities and the input features of the NNC allows to obtain more powerful attack algorithms that better take into account each input's characteristics. This leads us to introduce improved versions of JSMA, named Weighted JSMA (WJSMA) and Taylor JSMA (TJSMA), and demonstrate through a variety of white-box and black-box experiments on three different datasets (MNIST, CIFAR-10 and GTSRB), that they are both significantly faster and more efficient than the original targeted and non-targeted versions of JSMA.
Experiments also demonstrate, in some cases, very competitive results of our attacks in comparison with the Carlini-Wagner (CW) L 0 attack, while remaining, like JSMA, significantly faster (WJSMA and TJSMA are more than 50 times faster than CW L 0 on CIFAR-10). Therefore, our new attacks provide good trade-offs between JSMA and CW for L 0 real-time adversarial testing on datasets such as the ones previously cited.
The first proposed example of L 0 attacks is JSMA, a targeted attack  [19]. In a computer vision application, JSMA achieved 97% adversarial success rate by modifying on average 4. 02% input features per sample. [19]  relates this result to the human capacity of visually detecting changes. An important quality of JSMA is that it is easy to understand, set up and it is relatively fast. For instance, relying on its cleverhans implementation  [20] , JSMA is able to generate 9 adversarial samples on the MNIST handwritten digits dataset  [13]  in only 2 seconds on a laptop with 2 CPU cores. Speed and also the ability to run adversarial attacks with limited resources are important criteria for real-life deployment of neural networks  [4, 6, 14]. JSMA obeys these constraints making its use widespread beyond computer vision applications such as in cybersecurity, anomaly detection and intrusion detection  [23, 2, 24].
Later on,  [1]  proposes the second example of targeted L 0 attacks known as CW L 0. The same paper shows that unlike JSMA, CW L 0 scales well to large datasets by considering the IMAGENET dataset  [3]. CW L 0 is now the state-of-the-art L 0 targeted attack with the lower average L 0 distance. However, it is computationally-expensive, much slower than JSMA on small datasets (a factor of 20 times slower is reported in  [1] ) and therefore, despite efficiency, it is less convenient for real-time applications. Let us now briefly explain how the afore-mentioned attacks concretely work : JSMA. To fool NNCs, this attack relies on the Jacobian matrix of outputs with respect to inputs. By analysing this matrix, one can deduce how the output probabilities behave given a slight modification of an input feature.
-For non-targeted (NT) misclassification, we improve the known NT variants of JSMA called NT-JSMA and Maximal-JSMA (M-JSMA)  [29]. We do this by introducing NT and M versions of WJSMA and TJSMA. Our attacks yield better results than NT-JSMA and M-JSMA. Also, they are as competitive but significantly much faster than NT CW L 0. These claims are illustrated through applications to attack a deep NNC on the GTSRB dataset  [27]  in the white/black-box modes. - We provide a deep comparison between WJSMA and TJSMA which is of independent interest. Our study concludes that in the targeted case, TJSMA is better than WJSMA. However, in the NT case, WJSMA is preferred over TJSMA, mainly due to the simplicity of its implementation. - We provide fast and optimised implementations of the new attacks using TensorFlow and the cleverhans library  [20]  that might help users working in adversarial machine learning (In all our experiments, we use the original implementation of JSMA available in the cleverhans library and the original code of CW publicly available.
This is not the case for M-TJSMA since for example (θ maxx a ) has to be changed to x aθ min when decreasing features. As a consequence of this fact, M-WJSMA is less cumbersome to implement than M-TJSMA. Trying to keep the paper and code as simple as possible, we choose M-WJSMA over M-TJSMA and do not include M-TJSMA in our experiments. M-WJSMA already gives us satisfactory results.
Experiments on targeted attacks In the following, we give attack and defense applications illustrating the interest of WJSMA and TJSMA over JSMA. In doing so, we also compare WJSMA and TJSMA and report overall better results for TJSMA despite the fact that for a large part of samples WJSMA outperforms TJSMA. Finally, we provide a comparison with CW L 0 attack and comment on all the obtained results. The datasets used in this section are: MNIST  [13]. This dataset contains 70,000 28 × 28 greyscale images in 10 classes, divided into 60,000 training images and 10,000 test images. The possible classes are digits from 0 to 9.
CIFAR-10 [9]. This dataset contains 60,000 32 × 32 × 3 RGB images. There are 50,000 training images and 10,000 test images. These images are divided into 10 different classes (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck), with 6,000 images per class. On each dataset, a deep NNC is trained and then confronted to attacks. NNC on MNIST. Similarly to  [19] , we consider LeNet-5 model on this dataset  [12]. Its full architecture is described in Appendix A. We implement and train this model using a cleverhans model that optimises crafting adversarial examples. The number of epochs is fixed to 20, the batch-size to 128, the learning rate to 0. 001 and the Adam optimizer is used. Training results in 99. 98% accuracy on the training dataset and 99. 49% accuracy on the test dataset.
NNC on CIFAR-10. We consider a more complex NNC, trained to reach a good performance on this dataset. Its architecture is inspired by the All Convolutional model proposed in cleverhans and is fully described in Appendix A. Likewise, this model is implemented and trained using cleverhans for 10 epochs, with a batch size of 128, a learning rate of 0. 001 and the Adam optimizer. Training results in a 99. 96% accuracy on the training dataset and 83. 81% accuracy on the test dataset. For both experiments, TJSMA has a notable advantage over WJSMA and JSMA. The benefit of WJSMA over JSMA is also considerable. This shows that, in most cases, WJSMA and TJSMA craft better adversarial examples than JSMA, while being faster. Our results are indeed better when directly comparing WJSMA or TJSMA with JSMA.
5%, are added to the original MNIST training set, crafted by either JSMA, WJSMA or TJSMA. Then, three distinct models are trained on these augmented datasets. The models roughly achieve an accuracy of 99. 9% on the training set and 99. 3% on the test set, showing a slight loss compared to our previous MNIST model. Nevertheless, the obtained neural networks are more robust to the attacks as shown in Table  4. Note that each experiment is made over the well-predicted samples of the test images. For each model and image, nine adversarial examples are generated by the three attacks. Overall, the attacks are less efficient on each of these models, compared to Table  1. The success rates drop by about 8 pp, whereas the number of iterations is increased by approximately 26%. From the defender's point of view, networks trained against WJSMA and TJSMA give the best performance.
Experiments on non-targeted attacks In this part, we test the new NT attacks in the white/black-box modes and compare their performances with NT-JSMA and M-JSMA. In the white-box mode, we also compare with NT CW L 0. For experimentation, we chose the GTSRB dataset  [27]  widely used to challenge neural networks, especially in autonomous driving environments  [21]. We recall that GTSRB contains RGB 32 × 32 × 3 traffic signs images, 86989 in the training set, and 12630 in the test set classified into 43 different possible categories. Figure  5  displays some images from this dataset. We implemented our attacks and those of  [29]  again with TensorFlow using cleverhans. During the first experiment, we run the attacks given in Table  6  on the first 1,000 test images after taking a maximum distortion γ = 3.
Black-box experiments Here, we consider that black-box attack means the algorithm can use the targeted model as an oracle : when feeding it an image, the oracle returns a single class label. Moreover, we want to make only a limited amount of queries, which in a realistic setup would mean avoiding any suspicious activity, or at least trying not to depend too much on the oracle. To overcome this restriction, we train a substitute NNC using the Jacobian-based Dataset Augmentation (JBDA)  [21]  method. We then perform white-box attacks on the substitute NNC. If it is good enough, the resulting adversarial images should transfer to the oracle, i. e. effectively fool it even though they were designed to fool the substitute. Details about the substitute NNC architecture can be found in Appendix A.
The JBDA method allows us to make queries to the oracle only during the training of the substitute network. Black-box attacks are thus closer to real-life setups, as one only needs to access for a short period of time the targeted model before being able to durably fool it. A dangerous application of such techniques is against autonomous driving cars and their sign recognition systems. NT black-box attacks are exactly the kind of threat that can be put in practice with relative ease and still disrupt considerably the car's behaviour. We will illustrate this insecurity through the GTSRB dataset. Attacks in this paragraph are NT-JSMA and M-JSMA  [29] , along with our contributions NT-WJSMA, NT-TJSMA and M-WJSMA. We experiment with different distortion rates. Contrary to white-box attacks, in the context of black-box attacks, the distortion rate is not an upper bound on the percentage of pixels that can be modified, but the exact percentage of pixels we want to perturb.
We recall that despite the fact that TJSMA is a more elaborate version of WJSMA, it is hardly compatible with the "Maximal" approach, which in turn proves to be very efficient for non-targeted purposes. For this reason, as we have demonstrated, M-WJSMA is indeed the right choice for this type of attacks. On the other hand, because the "Maximal" approach has not proved to be very efficient on black-box non-targeted attacks, it is the non-targeted version of TJSMA (NT-TJSMA) that is the best in this case. Finally, we should mention that despite improving JSMA in different ways, like JSMA, our approach is still not scalable to large datasets. This is because of the high computational cost of saliency maps when the dimension of inputs becomes large. Our approach is therefore intended for "small" datasets such as those considered in the paper.
Nevertheless, this kind of datasets is very common in real-life applications. See also the recent paper  [8]. Supplementary Material All our codes are publicly available through the link https: //github. com/probabilistic-jsmas/probabilistic-jsmas. Further analysis of the results obtained on MNIST reveals that, even for examples where JSMA is better than WJSMA or TJSMA, on average, less than 10 more components are changed by WJSMA or TJSMA, whereas JSMA changes more than 17 more components on average when it is dominated by WJSMA or TJSMA. A similar gap can be noticed on CIFAR-10.
Acknowledgments: This work was done in the context of an internship by  T. Combey ,  A. Loison  and  M. Faucher  supervised by  H. Hajri. We thank  Gabriel Zeller  for his assistance. We are grateful to  Wassila Ouerdane  and  Jean-Philippe Poli at CentraleSupélec  for their support. We thank the  mesocentre de calcul Fusion, Metz computing center of CentraleSupélec and Stéphane Vialle  for providing us effective computing resources. H. Hajri  is grateful to  Sylvain Lamprier  for useful discussions, the scientific direction and the  EPI project ( Évaluation des Performances de systèmes de décision à base d'Intelligence Artificielle) at IRT SystemX  for their support.
Targeted (Training dataset: Nb of well predicted images=9 995) Success rate 86. 17 95. 91% 97. 40% Mean L 0 distance on successful samples 47 38. 54 36. 86 Targeted (Test dataset: Nb of well predicted images=8 381)) Success rate 84. 91 94. 99% 96. 96% Mean L 0 distance on successful samples 46. 13 38. 82 37. 45.
Even if massive (10 M < ∼ M < ∼ 10 4 M ) primordial black holes (PBHs) can only account for a small fraction of the dark matter (DM) in the universe, they may still be responsible for a sizable fraction of the coalescence events measured by LIGO/Virgo, and/or act as progenitors of the supermassive black holes (SMBHs) observed already at high redshift (z > ∼ 6). In presence of a dominant, non-PBH DM component, the bounds set by CMB via an altered ionization history are modified. We revisit the cosmological accretion of a DM halo around PBHs via toy models and dedicated numerical simulations, deriving updated CMB bounds which also take into account the last Planck data release. We prove that these constraints dominate over other constraints available in the literature at masses M > ∼ 20-50 M (depending on uncertainty in accretion physics), reaching the level fPBH < 3 × 10 -9 around M ∼ 10 4 M.