dataesr/scientific-paragraphs-categorization huggingface dataset:
E. Cache management and re-usability (routing engine) Different interests issued by different consumers may re-use the same streams, at least partially. Thus, our solution identifies this overlap, to avoid forwarding the same data twice. If partial computations can be re-used as they are cached, we can rely on the NDN cache without any further actions. A border router has just to cache additional transformations (if required). If an interest requires a chain of transformations which is a superset of the chain of an interest already being answered, the cached content is directly re-used. For instance, an interest may specify a set of transformations {T1 T2 T3} that is more specific than an existing cache entry (e. g. , {T1 T2}). Formally, the routing engine can re-use data in cache if the associated set of transformations matches exactly the first transformations of the interest: ∀i ∈ [0, k c ], F cached (i) = F int (i) (2) With {F cached (i)} i∈[0,kc] the sequence of transformations for the cached data, and {F int (i)} i∈[0,kq],kq≥kc the sequence of transformations specified in the interest (k c and k q respectively denoting the number of transformations, applied on the same prefix name, for cached data and the interest).
Basically, the routing engine relies on longest prefix lookup for IoT interests such as as enforced in Eq. 2 but only if Eq. 1 is verified. Then, it applies the transformations that are not present in the cached data (i. e. , {F int (i)} i∈]kc,kq] if k q > k c ). To improve caching efficiency, we force each border router to cache systematically all the chunks after the minimal set of transformations as specified in the exporting policy. This sub-chain corresponds to the smallest sequence in common for all the interests that match the dataset. Optionally, the border router may also cache the data which has undergone a subset of the transformations defined for popular interests. This extension may help to save computational resources in the border router if many interests overlap homogeneously.
Let us consider the example illustrated in Fig. 2. Domain3 is in charge of transforming the data between Domain4 and Domain1/Domain2. It collects the data, which has already been initially transformed with operation T1, and stores it automatically in its cache. That is, Domain3 stores the raw data received from Domain1/Domain2, T1(data x). Since its exporting policy specifies that the transformation T3 has also to be applied before the data exits the domain, Domain3 can also put this additional data in content store to save computational resources (if another interest asks later for the same data).
A. Simulation setup We implement and compare the following approaches: Conventional NDN subscriptions: consumers directly subscribe to multiple data-streams to reach the desired sample size. Thus, all computations are executed by the consumer. This is the best comparison we can muster since non NDN approaches do not directly handle pieces of data, and transformations would not be so straightforward to implement; Transformation overlay: our solution that exploits an overlay of border routers. These border routers implement the transformations, so that only aggregated data is forwarded across the network (cf. section III). We extended the ndnSIM simulator (https://ndnsim. net/) to support all border router features and stream-based subscriptions. Our implementation is freely available  1. consider the following metrics: Size of content stores measures the amount of data in the content store of each node.
We rely on unlimited content stores to analyze the total amount of data required for ideal re-usability conditions; Network load measures the sum of data transmitted by all devices, to quantify both bandwidth requirements and battery consumption utilized by streams; Normalized setup delay measures the time between the transmission of the consumer interest and the arrival of the first data packet for the corresponding stream The value is normalized by the sampling interval of each consumer; Hop count is the average number of hops in the physical network topology between a consumer and the producers which have provided the data for its interest; Data spread counts the number of NDN routers that store each chunk of data. A large data spread means that a private chunk may be largely disseminated.
B. Simulation results We first measure the size of the content store for both solutions (Figure  3 ). The re-usability provided by the conventional NDN cache engine is remarkable, many consumers re-use the same data, and the size of the content store does not increase drastically with the number of consumers. However, retrieving all the raw data is expensive. In other words, although the same chunk of data may be re-used, requesting a large volume of raw data increases the need for large content stores. On the contrary, our transformation overlay based solution allows each NDN router to store the transformed chunks of data. Thus, the volume of data to store in caches is at least 4 times lower in our simulations. Then, we also measure the network load for all the routers (Fig. 4 ). Again, while each chunk of data may be efficiently disseminated in conventional NDN, it is not enough to limit the network overhead.
On the contrary, the transformation overlay, which is first designed for a privacy purpose, is also able to reduce significantly the network load. Our proposal can greatly improve scalability. Note that both approaches reach a plateau: with the number of consumers increasing, the interests start to necessarily present a strong overlap, and the cached data becomes sufficient for any novel interest (i. e. , the content stores already have all the data). Figure  5  focuses on the privacy characteristics. The data spread measures, for each dataset, the number of nodes whose content store has at least one raw sample of the given dataset. Basically, a chunk of data is produced and forwarded inside the domain, but the spread is limited by our transformation based overlay that transforms it before sending it to the consumer or the next level of the tree.
The conventional NDN approach spreads the raw data in all the networks, the spreading being significant even if shortest routes are used. While data may be ciphered, it results in the need for complex access control schemes, which can become very challenging in complex multi-domain situations. Besides, the consumer still knows the identity of the producers since they get directly their data. We argue that it leads to a possible privacy leak if no further mechanism is here implemented to anonymize the data. Finally, figure  6  illustrates the average physical hop count from the producers to the consumers. Indeed, our transformation overlay has a cost: domains have to forward data through the overlay, leading to sub-optimal, longer paths. We can see that our transformation based overlay constructs physical paths twice as long as with the conventional NDN solution.
V. CONCLUSION AND FUTURE WORKS In this paper, we have presented a novel privacy-aware solution to safely exchange streams of private data in multidomain IoT networks. We proposed an overlay of NDN border routers which forward the anonymized data that producers accept to export. More precisely, the control plane we propose leads to a tree structure that exploits exporting policies describing which data can be exported, at which extent, for which usage, and after which transformations. Data is transformed and cached before exiting the domain so that privacy concerns are respected while enabling re-usability using smart content stores looking at overlaps between interests among transformed datasets. Our simulations highlight the scalability of our solution. It is both able to enable finegrained privacy rules along with efficient large scale multidomain data exchanges.
In particular, applications interested in aggregated data acquisition can strongly benefit from our proposal. In the future, we plan to extend our routing scheme to take into account monetized data exchanges, with dynamic pricing strategies. Typically, a broker may advertise a lower price when several subscribers use the same dataset. Also, since the routing engine update existing subscriptions when novel interests arrive, many complex strategies become possible if overlaps with already handled interest are not sufficient. Such dynamic re-optimization and pricing models are challenging as subscriptions have to be constantly updated on-the-fly while still guaranteeing self-stabilization.
Experimental Results To demonstrate the above algorithm, this study uses the GPS, Galileo and GLONASS data from ABMF station (RINEX version 2. 11 (GPS and GLONASS) and version 3. 02 (Galileo) format). This reference station is located in Guadeloupe. RINEX data and broadcast ephemeris data used for calculation were downloaded from RGP network and CDDIS for day of 1 January 2019. In order to assess the algorithm performance, five scenarios are investigated under MATLAB environment: Figure  2  illustrates the number visible satellites of GPS (red curve), Galileo (black curve), GLONASS (cyan curve), and combined GPS/Galileo/GLONASS (blue curve) systems over an elevation mask of 10 •. It demonstrates that the number Galileo and GLONASS satellites is zero during some epochs. The average number of visible satellites is about 20 satellites when GPS, Galileo, and GLONASS are combined for positioning, which presents an advantage since they bring more signals to the receiver.
However, it also presents a disadvantage since they may bring fault signals which can penalize the position estimation accuracy. The results are given Figure  3  which represents the position errors of the five scenarios with respect to time. dE, dN, dU are the errors in the East, North and Vertical components of the user's position estimates. In Figure  3 , the red curve pertains to scenario #1 with GPS data only. The black curve represents scenario #2 with Galileo data only. The cyan curve is related to scenario #3 with GLONASS data only. It can be seen that the position errors of GPS and Galileo data are small and stable whereas those from GLONASS data are large and unstable. The blue curve represents for scenario #4 with combination of GPS, Galileo, and GLONASS data. The position accuracy from the combination improves compared to the use of GLONASS data only.
Experimental Results In this section we aim at comparing the position estimation performance of the two methods : the robust-EKF and the rEKF-LSTM. A total of 20 hidden layers were set up for the LSTM in this paper. The LSTM set up to H = 20 hidden layers, N = 3 inputs features (positions), S = 1440 number of timesteps and M = 3 outputs features (errors positions). The data used are still from ABMF station recorded on the 1 January 2019 (as in Section 4. 2). Figure  13  represents the position errors with respect to time for the two methods: robust-EKF and rEKF-LSTM. The period of data is from 12 h to 24 h, since data from 0 h to 12 h is used to train the model. In Figure  13 , the green curve represents the position errors for the robust-EKF method, whereas the purple curve represents the errors produced by the rEKF-LSTM method.
Conclusions and Perspectives In this paper, the SPP technology is applied to determine the user position calculated by GPS, Galileo and GLONASS data fusion. Data fusion may be containing outliers which negatively impact the final results. To address this issue, the MM-Estimation method is used to eliminate the outliers and IRWLS method is used to optimize the estimated results. Although the MM-Estimation and the IRWLS are effective, a method to detect the positioning errors is needed. The RAIM algorithm was used for this purpose to detect positioning errors exceeding the alert limit. As a result, the proposed robust Extended Kalman Filter is built by combining the Extended Kalman Filter, the MM-Estimation, the IRWLS and the RAIM method to improve the position accuracy. Robust GPS, Galileo and GLONASS data fusion using the robust-EKF increases the position precision by about 84.
0% compared to non-robust estimation using data of ABMF base station. Furthermore, the position precision is significantly enhanced using the rEKF-LSTM method by about 95% compared to non-robust data combination. To prove the power of rEKF-LSTM, the approach is used for data from three other base stations: AJAC, GRAC and LMMF. The position accuracy is significantly improved by about 87%, 77% and 93% compared to the non-robust data combination, respectively. In this work, the emphasis is laid on the improvement of the positioning accuracy for a slowly moving receiver. Only data from base stations are used, for which velocities are almost equal to zero and helps enhancing the performance of the LSTM model. A perspective of our work could be assessing the performance and limitations of the method on data from a user in motion and adapting or potentially developing a new method taking into account additional velocity information.
The target outputs are the errors of the estimated positions by robust-EKF (δ ENU ). The errors in the estimated positions are the difference between the true and the estimated (robust-EFK) positions. Similarly, we standardize the target outputs to have zero mean and unit variance and the standardized target outputs are now the training target outputs of the LSTM model. The model is trained on the training dataset using supervised learning with optimization methods such as: stochastic gradient descent, Adam or RMSProp. Each time, the LSTM model is run with the training inputs and returns the training outputs, which are then compared with the targeted ones. Based on the results of the comparison, the parameters of the model (weights and biases) are adjusted. The training process works until the training errors reach the error threshold and the inferred LSTM model is referred to as the "fitting LSTM model".
X-ray diffraction All X-ray diffraction datasets were collected using a Rigaku Oxford Diffraction SuperNova single-crystal diffractometer equipped with a CCD-type area detector and copper microfocus X-ray source. Additionally, our homemade lightdelivery device  (Kamin ´ski et al. , 2016) , specifically redesigned to fit the SuperNova setup to facilitate in situ photocrystallographic experiments, was installed. The data-collection strategy employed the mounted light-delivery device and was prepared using the native diffractometer software  (Rigaku Oxford Diffraction, 2020). All data collections were performed in complete darkness. The overall experimental procedure was as follows: crystal mounting at room temperature and cooling to the desired temperature, measurement of a ground-state structure and/or irradiation of the crystal for $3 h (during this time the crystal was continuously rotated to ensure uniform exposure), measurements of the crystal at various temperatures ranging from 100 to 240 K (see text) in 20 K steps.
Further data processing was consistent for all data collections. All crystal structures were solved using an intrinsic phasing method as implemented in the SHELXT program  (Sheldrick, 2015) , and refined with the JANA package (Petr ˇı ´c ˇek et al. , 2014) within the independent atom model approximation. For more details see the supporting information  (Allen, 2002; Groom et al. , 2016; Fournier & Coppens, 2014; Schmøkel et al. , 2010). The CIFs are provided in the supporting information, but can also be retrieved from the Cambridge Structural Database  (CCDC deposition numbers: 1975196-202, 1975732-736).
Theoretical computations All computations were performed with the GAUSSIAN16 package  (Frisch et al. , 2016). In the case of quantummechanics/molecular-mechanics (QM/MM) computations, the model of a ground-state or metastable-state molecule in a crystal environment  (Kamin ´ski et al. , 2010)  is composed of a central molecule (QM part) and a shell (MM part) cut-out from the studied crystal structure with a radius of 12 A ˚[all C-H distances were set to the neutron-normalized values  (Allen & Bruno, 2010; Allen et al. , 1987) ]. The density functional level of theory (DFT(B3LYP)/6-311++G**  (Becke, 1988; Perdew, 1986; Lee et al. , 1988; Krishnan et al. , 1980; Clark et al. , 1983; McLean & Chandler, 1980)  was applied for the optimization of the central molecule and calculation of Hirshfeld atomic charges  (Hirshfeld, 1977)  used later for the purpose of molecular shell approximation with the Universal Force Field (UFF)  (Rappeet al.
, 1992). Dimer interaction energies, isolated-molecule geometry optimizations and normal-mode frequencies were also calculated at the DFT(B3LYP)/6-311++G** level of theory. In the case of interaction energy computations, the Grimme empirical dispersion correction  (Grimme, 2006 (Grimme, , 2004) )  modified by the Becke-Johnson damping function  (Grimme et al. , 2010 (Grimme et al. , , 2011)) , and correction for BSSE  (Boys & Bernardi, 1970; Simon et al. , 1996)  were applied. The input files were generated using the CLUSTERGEN program  (Kamin ´ski et al. , 2013).
Funding information SEK and KNJ would like to acknowledge the grant PRELUDIUM (grant No. 2017/25/N/ST4/02440 ) from the  National Science Centre in Poland  for financial support. SEK is grateful for the  ERASMUS+ travel grant  (agreement between  University of Warsaw  and  University of Lorraine ). This work was also partly supported by the  French PIA  project ' Lorraine Universited'Excellence ' (grant No. ANR-15-IDEX-04-LUE ) and the  CPER (SusChemProc). The in-house X-ray diffraction experiments were carried out at the  Department of Physics, University of Warsaw , on a  Rigaku Oxford Diffraction SuperNova diffractometer , which was cofinanced by the  European Union  within the  European Regional Development Fund  (grant No. POIG. 02. 01. 00-14-122/09 ). We are additionally grateful for X-ray diffraction measurement time on the PMD 2 X platform of the  Jean Barriol Institute.
However, to the author's knowledge, no similar study has yet been published on an aerospace application. Increased geometric complexity, higher thermal gradients and even wider range of velocities make the solution even more difficult but also valuable. This study aims to develop an alternative way to the tests to predict heat up in the engine bay after cutoff using the Computational Fluid Dynamics (CFD) commercial Software suite SIMULIA PowerFLOW edited by Dassault Systèmes. The work presented here is part of the Soakback Assessment using LAttice Boltzmann Method and Aerothermal Nodal-network for the Design of the Engine-bay Region (SALAMANDER) project conducted by Dassault Systèmes, Altran Technologies and Safran Helicopter Engines, itself part of the CleanSky 2 framework. The project objective is to use experimental data to develop and validate a methodology to predict natural cooling with coupled CFD simulations and deploy it on a demonstrator once ready.
Consequently, the project is divided in several Works Packages and the outcome of the first two is highlighted here. The first Work Package targeted the development of a methodology capable to simulate both the stabilized regime and the natural convection phase after the cut off with simplified conditions (no core flow nor bench geometry, simplified boundary conditions and nozzle geometry). The second Work Package improves this methodology by introducing more complexity and fidelity to the tests. The exact geometry of the test campaign is used (still no core flow) and the key-off scenario is set to match the one used in the tests. The experimental data are extracted from a 2008 joint campaign on a RTM322 engine between Safran Helicopter Engines and Airbus Helicopters. After a short introduction to the technology implemented in the software tools used, we shall focus on the methodology development work conducted on a simplified case first.
Lattice-Boltzmann Numerical Method The study has been completed here with the 6-2019 release of the commercial softwares PowerFLOW and PowerTHERM edited by Dassault Sytèmes. PowerFLOW is a Lattice-Bolzmann Method Very Large Eddy Simulation (LBM-VLES) based fluid solver  [6] [7] [8] [9]. While most CFD codes are based upon the Navier-Stokes equations describing the flow at a macroscopic level, the LBM scheme uses Boltzmann's description at a mesoscopic level. Distribution functions are introduced to follow the fluid particles depending of both their positions and velocities. The distribution functions exchange between them particles through elastic collisions, leading to the Boltzmann equation. The collision operator is a challenging part of the modelization for which we use the Bhatnagar-Gross-Krook (BGK) form  [10, 11].
Experimental Setup The tests occurred in the framework of the "DTP avionnage" project. It aimed to improve the methodology and tools used to evaluate both the temperature in the engine area (carter, accessories, underhood) and induced flows driven by the exhaust. The experimental campaign was conducted to build an empirical database for future validation works of numerical aerodynamic and thermal codes. The engine used for the tests is a RTM322 from Safran Helicopter Engines. A specific compartment was designed for the tests, allowing to deploy a large set of thermal probes on the engine and the bench installation. K-type thermal probes were used to record temperatures inside the carter thickness, on its walls and in the fluid in the compartment. PT100 thermal probes were used for the fluid temperatures close to the devices around the carter.
The cut-off phase looks therefore much more elaborated than what has been modeled here. The PowerFLOW data have been positioned timewise at the moment of the cut-off. The starting point of the PowerFLOW data corresponds to the stabilized values. Compared to the tests data before 1980 seconds, they seem in line at first glance. However, the tests show a large azimuthal spread on the carter annulus, up to 50 degrees which is completely missed in the simulations in which the differences do not go beyond 6 degrees. This is expected as the input data do not account for such a variation in the core flow and correction of this shift is part of the global project. The tests slow down to the intermediate regime proves to be responsible of a first cooling by more than 150 degrees. This is obviously missed in the simulation since this phase is discarded in the modelization.
In addition, the temperature drop rates seem to be too slow. On figure  11  are plotted the temperature comparisons on the 8 thermal probes located on the High Pressure Turbine carter walls. PowerFLOW data are plotted with dotted lines at start at the cutoff, around 2070 seconds. Their starting points are very close, less than 15 degrees in dispersion. In comparison, the tests show for the stabilized point an azimuthal spread up to 150 degrees. The absolute starting point of the PowerFLOW data is within the range of variation observed in the tests though seven out of the eight thermal probes have significantly higher values. The cooling rates predicted by PowerFLOW are about 10% higher.
Results discussion The stabilized simulation shows a global behavior similar to the one observed on the simplified model. Cold air enters through the air inlets for 69% by the upper entry, 31% through the lower one. The massflow however is now 3. 5 times what it was with the straight simplified nozzle. The measured massflow in the chimney is about twice what exits from the compartment exhaust. These results show a bias with respect to the test data within 5%. However, since no change has been introduced in the thermal boundary conditions, the wall temperature azimuthal dispersion remains mostly missed. The cut off process however now behaves differently from the simplified configuration as the air enters through both the upper and lower entries and exits through the exhaust down to the chimney.