dataesr/scientific-paragraphs-categorization huggingface dataset:
Combining Data Sets and Integrating Experiments-The Interoperability Question An important aspect of research in the PUNCH disciplines is the combination of data from different experiments and observatories. Such combinations facilitate entirely new questions or even research directions. They are, however, technically challenging, and difficult questions of data access and compatibility (data formats etc. ) need to be addressed. In high-energy physics, platforms like HEPData offer easy access to a multitude of experimental data at high abstraction level, with links to the relevant publication. But these high abstraction levels are not conducive to combining data sets (or selections) from experiments. However, the combination of different data sets has recently been advanced a lot. Typically, re-analysis is performed by a team different from the original one, by combining with data sets from other collaborations, or by appending theory interpretations later.
Combining systematic uncertainties from various measurements is also a crucial technical problem and the REANA project  [53]  is an effort to address this. The theory interpretations and the necessary theory input are made available in a unified format in which theorists make their predictions available for the experimentalists. REANA uses a couple of workflow languages that were partly developed by the HEP community, and also includes an effort to look for more common workflow languages. REANA is trying to support multiple languages, e. g. from the life sciences. In astroparticle physics, the multi-messenger approach to correlate gravitational wave events with other observations is an example of data combination. Reconstruction is, however, still done individually by the participating collaborations based on real-time alert systems.
However, KCDC is in its beta-version to provide data from different experiments which can be selected and combined on the same open platform. During the workshop aspects of the interoperability of metadata semantics in lattice QCD (quantum chromo dynamics) were discussed, the usability of lattice QCD workflows and procedures outside of of ILDG (International Lattice Data Grid), the semantic enhancement of the data of high-energy physics, and the necessity of machine-readable/ actionable metadata in general. A question that is of concern for all PUNCH disciplines is how to integrate experiments that have not yet started their own open data initiative or are simply too small to make resources available for this into developments of PUNCH-4NFDI. No simple solution has so far been put forward.
Licensing, DOIs, and Data Management Plans A final block of the discussion centered around the topics of licensing, DOIs (digital object identifiers), and data management plans, and in particular around the question whether a coherent approach in using DOIs and licences for open data will be possible, i. e. whether the way that high-energy physics typically deals with the subject will be accepted also by, e. g. the providers of the Virtual Observatories in astronomy? As an example, KCDC was mentioned again: in KCDC, the user license is provided by a custom-made EULA. If KCDC will be integrated in a larger frame (e. g. by PUNCH-4NFDI), one has to consider the varying community usances (culture) and legal aspects, while continuing to ensure good scientific practice. It was also pointed out that data management tools need to enter the thinking of all activities in the PUNCH disciplines, also for smaller collaborations.
Summary-Open Data in PUNCH Sciences The workshop, its presentations and the corresponding discussions are a good starting point and will serve as input to the activities and measures of the PUNCH4NFDI consortium towards a coherent PUNCH open data policy and the PUNCH Science Data Platform. We have seen very different approaches and now need to discuss and agree on levels of preparation of data we will make accessible as open data in the PUNCH sciences. The data must be prepared, appropriate metadata need to be provided, and a coherent data curation procedure must be established to be scientifically beneficial. To view the current situation in a more general way, we need to discuss the developments also under the angle of Open Science. Here, everyone tries to fit their achievements and ideas, partly developed with a long history, into the new concept.
The other development path, the FAIR data requirements, is in fact even older, and originates in the scientific communities' demand of having data exchangeable and with sufficient metadata descriptions (geophysics in 1957 and NASA in 1970, with shared data policies for satellite data). This scientific demand clearly has contributed to the various approaches to Open Data as presented above. The efforts unfolded within the various disciplines, but only the advent of the global digital network (aka internet) made the idea of cross-disciplinary use of digital data in a new, multidimensional space feasible. At this development stage of the new realm different tasks have to be addressed: Making data FAIR is chiefly a science driven intent to maximise the benefit of digital data for the progress of scientific knowledge.
Its requirements open up and reorganise the scientific processes, align them with the advanced digital methods and possibilities. Going from FAIR data to Open Data is addressing societal processes as well, since the current scientific systems are intertwined with their respective societal structures. As the different examples above show, publicly accessible data are one aspect, which also requires many changes to transform the paradigm of the "single scientist and his data", originating from the end of the 19th century, into a current one, where teamwork, division of scientific labour and public funded research on a digital platform play the most significant role, particularly in PUNCH sciences. For Open Data, we need to develop improved policies and agreements for data sharing across the disciplines.
Since the power of digital science is very unevenly distributed across the world, there are many further tasks to solve. This is only pointed out here, since many more actors have to be included in this discussion in the future. For the purpose and scope of starting a thorough assessment of the situation for building up FAIR data infrastructures for the physics disciplines and with NFDI, the workshop gave a first overview and material for further discussion and research. Some topics need immediate follow-up: a more detailed review of data publication policies, a fitting metadata scheme, a collection and refinement of data curation procedures from our disciplines, and also the implementation of data provenance. These are, therefore, the main tasks and direct items for the activities within the PUNCH4NFDI consortium.
Acknowledgements The authors would like to thank  Hannah Elfner  (GSI and Frankfurt) and  Jan Mayer  ( Cologne ) for their input to section 3. 7 and  Carsten Urbach  ( Bonn ) for his input to section 3. 9. The authors also thank all the participants of the workshop for their contributions to the discussion. This work was performed in the framework of the PUNCH4NFDI consortium which will be supported by  DFG  fund " NFDI 39/1 ", Germany.
The algorithm's performance has been evacuated on a real cloud dataset, and the obtained results reveal that migration cost has a noticeable impact on the achieved overall profit within the cloud. To minimize the communication cost induced by fault tolerance and deal with dynamic distributed platforms, the authors, in  [23] , propose a new collaborative checkpoint-rollback recovery scheme which takes partial snapshot of the system by using appropriate coordination among the network's nodes. They demonstrate throw simulations, that the proposed approach is able to decrease the number of exchanged messages of the coordination process. A new Fault-tolerant and real time algorithm which is called ReadyFS is proposed in  [24]  for scheduling scientific workflows in a cloud. The proposed approach seeks to improve resource utilization while guaranteeing fault tolerance and deadline requirements.
This recent data-driven wave finally touched the CFD community, who made substantial efforts in the past 5 years to apply ML to flow problems. ML techniques are optimization algorithms that extract patterns and structures from data, "generalizing" the valuable information found on its training data. When applied to Fluid dynamics, this usually translates into the creation of Reduced-order Models (ROM), a simple representation of the information contained in the dataset. These simpler representations can be used in two different ways in CFD, as reviewed by Brunton et al. [7] : either discovering unknown models, or improving existing models. Discovering unknown models from data has been a long-standing problem in the scientific community. Throughout the history of humanity, people built models to describe the behavior of observed phenomena, in order to make future predictions.
Recently, Raissi et al. [28]  introduced physics informed neural networks (PINN), where a-priori physical knowledge is introduced into the training process of neural networks. By using the residual of the physical equation to optimize the network's trainable parameters, the network ensures physical laws and may even lead to training processes which do not need a database  [29]. Such approach was tested on different PDEs, including the incompressible Navier Stokes-equations  [30, 31] , which have also been tackled by several works which employ networks to predict the future state of fluid flows, i. e. as end-to-end solvers. Some recent works include recurrent network architectures, where a LSTM (Long Short-Term Memory  [32] ) network was used to encode fluid structures into a latent space  [33] , or more classical CNN, where an unsupervised network training is evaluated to test the performance gain of CNNs  [34].
* * * * * * R R R R R R ∇ • u Object Pressure 2C 32C 32C 128C 128C 128C 32C 32C 8C 1C 2 × n 2 1 × n 2 The MonoScale and MultiScale networks are then compared with the well-known Unet architecture (Fig. 4 ), which was first developed by Ronneberger et al. [74]  for the segmentation of biomedical images. Apart from the initial biology-related applications, this network has also been used in many other fields, including for regression tasks in fluid mechanics. For instance, Lapeyre et al. [15]  employed this architecture on turbulent sub-grid scale modeling for reacting flows. The structure of the Unet is similar to the MultiScale, as it also combines information extracted at different spatial scales. However, the Unet differs from MultiScale on the scale treatment: while several scales are managed in parallel in the MultiScale network and the input image is fed directly to each branch, the Unet acts as a simple feed-forward network with a decreasing-increasing resolution to encode information in a low-dimensional manifold (at the smaller scale).
5 ), and added to the initial divergence (short-term loss). The network weights are then updated using a gradient descent algorithm. Note that in the backpropagation algorithm, the chain rule  [81]  is used, yet here the advection part is not differentiated, and therefore does not appear explicitly in the gradients. The number  of time steps to compute the long-term divergence term is either 4 (90% of the times) or 16 (10% of the times). As a summary, the total loss function is: L = 𝛼 𝑁 𝑁 ∑︁ |∇ • u t+1 | + 𝛽 𝑁 𝑁 ∑︁ |∇ • u t+n | (4) where  and  are two hyperparameters controlling the relative importance of the short-term and long-term losses. As a semi-supervised training procedure is considered, a complete training dataset is not necessary to train the network since no 'ground truth' target is needed.
However, physical initial conditions are still possible, in particular to enforce the spatial coherence and structures relevant from a physical point of view, and to avoid overfitting. Thus, even for this PDNN, a training dataset was computed with the open-source code Mantaflow  [57]. This dataset consists of closed boundary domains, with randomly initialized velocity fields and no density variations (i. e. the Richardson number is   = 0 for all training examples). Random geometries are placed in arbitrary locations, and velocity divergence sources are introduced in the domain to initialize the flow motion. As a result, the dataset consists of 320 simulations on a 128 × 128 uniform cartesian grid. Each simulation is run for 64-time steps.
Hybrid Methodology The main difficulty when building a Machine Learning-based CFD solver is to guarantee its precision and robustness, in particular for cases far from the learning database. To do so, Ajuria et al. [39]  introduced a hybrid strategy, where the network error is tracked over time. Depending on this error level, the network prediction is used as an initial guess for the Jacobi method to further improve its quality. Note that a similar procedure was also proposed by Hsieh et al. [82] , where an iterative solver is coupled to a deep linear network to guarantee the accuracy level of the network prediction. An overview of the hybrid method is shown in Fig. 6 : after the advection step, the pressure correction term is predicted by the deep neural network architectures proposed in Section 3.
Preliminary results without hybrid approach First, the four networks described in Section 3. 1 are tested without the hybrid approach on a plume test case with a Richardson number   = 14. 8, in order to evaluate the network accuracy and its generalization capabilities (since the training dataset was obtained for   = 0). As the Boussinesq approximation is used, the density variation should remain small and is set here to Δ/ 0 = 0. 01. The lighter fluid is injected at the inlet boundary with the velocity   = 0. 01 -1. The CFD domain size is   =  = 512, discretized on a uniform cartesian grid of 512 × 512 cells. The gravity is set to  = 0. 002 -2 , and the inlet radius is  = 74, 25, taken as the characteristic length to define the Richardson number. The divergence is normalized with the inlet velocity and the characteristic length, and time is normalized as well by the characteristic time needed for the plume to reach the top boundary at   = ∞, i.
Performance evaluation on various grid sizes The previous performance assessment was made at a fixed resolution of 2. 6 10 5 cells. Recent work  [85]  revealed that networks can encode spatial location and resolution on the training dataset, which can lead to poor performance when applied to a different resolution than the one used during the training phase (2. 6 10 5 cells in this work). This drawback is here tackled by the hybrid approach, which ensures the desired accuracy level whatever the grid size. It is also found in this work that the present CNN architectures are capable of generalizing to different grid sizes, even if no clear explanation can be given for this specificity. As a reminder, Fig. 14  showed that the number of operations per network evolves linearly with the grid size.
Acknowledgement This work was founded by the  CERFACS institute and ISAE-SUPAERO in Toulouse, France. The calculations were performed using HPC resources from  CALMIP on Olympe  (Grant  2020-p20035 ). The presented fluid solver was initially developed by  Antonio Alguacil  in collaboration with the Jolibrain company, who also offered their technical support throughout this project. Finally, the  Physics-based Simulation group (Thuerey group ) of the  TUM (Technical University of Munich ) should be acknowledged for their support.
Despite many conveniences of using IoT devices, they have suffered from various attacks due to their weak security. Besides well-known botnet attacks, IoT devices are vulnerable to recent covert-channel attacks. However, no study to date has considered these IoT covertchannel attacks. Among these attacks, researchers have demonstrated exfiltrating users' private data by exploiting the smart bulb's capability of infrared emission. In this paper, we propose a power-auditing-based system that defends the data exfiltration attack on the smart bulb as a case study. We first implement this infrared-based attack in a lab environment. With a newly-collected power consumption dataset, we pre-process the data and transform them into two-dimensional images through Continous Wavelet Transformation (CWT).
Thus, it is nearly impossible to deploy a universal solution to resourceconstrained IoT devices. Besides the well-known IoT botnets, the IoT security literature has also examined recent side-channel attacks that exploit covert channels using IoT devices. In this attack scenario, instead of being exploited as malicious bots, IoT devices were also used for covertchannel attacks in order to exfiltrate users' private data. For example, Maiti and Jadliwala established a potential IoT attack where adversaries exfiltrate users' private data through infrared-enabled smart bulbs  [41]. This exfiltrated data such as banking accounts, authentication information, or private photos that can be observed by nearby receivers with infrared ability. Another example of IoTrelated covert-channel attacks is the use of ultrasound transmission.
For example, Jung et al. [29, 30]  designed a deep learning classifier to identify IoT botnets via power consumption modeling. However, those studies were not designed to detect or protect against information leakage attacks through covert channels. In this paper, we monitor the power consumption data generated by the smart bulb's infrared emission in order to defend against data exfiltration attacks. To answer the second question, we design a two-dimensional convolutional neural network (2D-CNN) classifier that can identify the data exfiltration events via infrared emissions. We first convert raw power consumption data to Continuous Wavelet Transform (CWT)  [53]  images as our input instances. We made this design choice because CWT images include time-and frequency-domain information, whereas raw power consumption data only contains time-domain data.
Then, we tuned the hyper-parameters of the proposed CNN in order to classify behaviors of the smart bulbs, including malicious attacks. To answer the third question, we validate whether the proposed classifier can distinguish covert-channel attacks from typical behaviors on smart bulbs. In five-fold cross-validation tests, the proposed CNN classifier achieves an accuracy of approximately 88%, which is close to other transfer-learning classifiers with a higher number of parameters. Moreover, our CNN model achieves almost the same results in identifying unseen exfiltration attacks, including different encoding schemes and different bitrates. Lastly, our CNN classifies input data 7 times faster than AlexNet and 10 times faster than GoogLeNet. Overall, we propose the first detection approach against the infrared-based attack that leaks private data.
Using the HTTP APIs provided, the adversary was able to set the amplitude of the infrared light between 0 and 65, 535. We wrote a python script that encodes a target image file to Amplitude-shift Keying (ASK)-encoded data. For example, a sample picture of 128 × 128 pixels can be converted into a 128 × 128 two-dimensional array where each pixel has a value between 0 and 255. Thus, pixel value 100 would be mapped to infrared amplitude value 25, 700 (100 ÷ 255 × 65, 535). By doing so, attackers can exfiltrate private data values sequentially through the covert-light medium. The python source codes of this attack will be available for research use only. It should also be noted that through this covert-light attack, any type of data can be transmitted, such as text or voice, whereas voice or image data tolerate bit errors better than text data.
LIGHT AUDITOR SYSTEM OVERVIEW In this section, we briefly introduce a system overview. Figure  5  illustrates an overview of our system, LightAuditor. In our application scenario, an IoT smart bulb is being used in the user's network. Then, we aim to detect whether the smart bulb is being exploited for data exfiltration attacks via power consumption monitoring on the infrared channel. As an input source for detection, we measure continuous raw power consumption data of the IoT device. This requires a one-toone connection between the IoT device and the power auditing device. Thus, we develop a power auditing device that can measure the AC data of the connected IoT device. Our power auditing device and its testbed environment will be presented in Section 5. 1. With the measured raw power traces, we conduct several data pre-processing jobs for the system to predict inference results best.
In the sensing literature, several research have also utilized the frequency-domain conversion for their learning problems, such as EEG-to-CWT  [44] , ECG-to-CWT  [5] , Vibration-to-FFT  [38] , and Accelerometer-to-Images  [57]. The above pre-processing procedures will be explained in Section 5. 2. The impact of the CWT conversion will be discussed in Section 7. Furthermore, we increase the number of input data instances before feeding them into our classifier. This data augmentation method is known to help avoid overfitting concerns, caused by small sample datasets  [31]. Since the number of collected instances in our experiments may not be enough, we doubled the size of our dataset by applying an augmentation method during training, but not testing. Our augmented dataset will be presented in Section 5.
3. The benefit of this data augmentation will also be described in Section 7. Finally, once securing the power-based CWT dataset, we design a 2D-CNN model and validate its performance in our lab environment. We chose to use CNN for our system because we utilized CWT images as input data, and CNNs have shown its effectiveness for image classification. The design choices and the impact of the design factors are shown in Section 6, accordingly. We then compare our model with a 1D-CNN approach and other well-known transfer learning CNNs, such as AlexNet and GoogLeNet, in Section 7.
Overall, power consumption data of IoT devices can easily be measured by our proposed power-auditing hardware. This powermonitoring approach is a reasonable and practical solution for abnormal behavior detection, as validated in our experiments (Section 7). Moreover, measuring power consumption data is more practical than deploying detection algorithms into IoT devices even though our approach requires additional hardware for each IoT device. Our reasoning is that the proposed device can be considered a prototype of smart plugs  [56] , whose market also grows substantially  [27]. In addition, smart plugs are already capable of providing and measuring power consumption data. Therefore, we leverage this market situation; in the future, most IoT devices may be connected individually by various forms of power-auditing devices  [36].
Collected Power Trace Dataset In our lab setting, we collected the power consumption data generated by a smart bulb. In total, we collected six types of different behaviors to detect abnormal behaviors. The smart bulb provides several functionalities that can be considered normal behaviors: Normal visible light, Infrared light, and Visualizer. The normal visible light is working by either a dimmable controller or an instant switch. The visualizer feature responds to background sound. As music varies, the color and its amplitude randomly change. Overall, we define five normal behaviors as follows: 1) Idle, 2) Visible Light (Dimmable), 3) Visible Light (Instant Switch), 4) Infrared Light, and 5) Visualizer. We also define a malicious power consumption pattern of the covert-channel attack. As shown in Figure  11 , the ASK-based data exfiltration attacks generate specific power consumption patterns.