dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulations of the telescopes' response to showers To train the shower reconstruction algorithm and to evaluate IRFs, the analysis of IACT data requires MC simulations. In the case of LST-1, the development of showers is simulated using CORSIKA  (Heck et al. 1998) , while the response of the telescope is simulated with the sim_telarray program  (Bernlöhr 2008). On the other hand, within MAGIC, MC simulations of showers are generated using a slightly modified version of COR-SIKA, but the response of the telescopes is obtained using MagicSoft programs (reflector and camera;  Majumdar et al. 2005). Common LST-1+MAGIC observations require the analysis chain to be performed within the same framework, and the same should happen for the simulations. We performed simulations of the same showers visible by both MAGIC and LST-1, using the sim_telarray program.
To achieve this, we translated the simulation parameters of the MAGIC telescopes from the reflector and camera simulation programs into the sim_telarray nomenclature. For most of the parameters (e. g. , mirror dish geometry, angular dependence of light guide efficiencies, average quantum efficiency of PMT, jitter of single p. e. times, telescope trigger parameters, and readout pulse shape), the translation was direct and the same values and curves were used in both simulation chains. For some of the parameters, however, minor simplifications or averaging had to be applied due to intrinsic differences between the two softwares. For example, this was the case for the simulation of the mirrors reflectivity and the noise within the pulse integration window. Thanks to the usage of sim_telarray, the LST-1 simulation parameters could be taken directly from the standard configuration, the so-called LSTProd2 (as in  Abe et al.
e. , before and after the culmination). For each branch separately, we performed a one-dimensional (1D) interpolation (over the cosine of zenith distance angle) of the IRFs. Subsequently a global, binned, joint likelihood spectral fit was performed with gammapy 0. 20. 1  (Deil et al. 2017; Donath et al. 2022)  to determine the best parameters of the spectral model for a point-like source at the nominal position of the Crab Nebula. Next, the same software was used to derive individual spectral points by fitting the normalization of the global model in energy bands. In Fig. 5 , we present the resulting spectral energy distribution reconstructed between ∼60 GeV and ∼10 TeV from the total investigated data set. The spectrum is modeled in gammapy with a log parabola spectrum defined as: dN/dE = A(E/E 0 ) -α-β ln(E/E 0 ) , (1) with A = (3.
Moreover, above 600 GeV, where the background is very scarce, and high angular resolution make the optimal angular distance cut from the source small, we increase the background statistics by using a broad cut for the background estimation region (θ < 0. 2 • ) and scale the number of events to the actual θ-cut. The resulting sensitivities are compared with the LST-1 standalone and MAGIC stereoscopic sensitivities in Fig. 7. The corresponding gamma-ray and background rates are presented in Fig. 8. The MAGIC-only sensitivity curve has been derived from the same dataset as used for the joint analysis. As expected, the joint analysis provides significantly better sensitivity. Using the LST-1+MAGIC joint analysis in the medium energy range (i. e. , excluding the first and last two energy bins) allows for the detection of about 30% (40%) weaker fluxes than what can be detected with MAGIC-only (LST-1-only) analysis.
This is related to the addition of LST-1 rather than to the different analysis chain as the MAGIC-only performance is compatible with both chains (see Appendix C). Such a large gain in performance is expected from the stereoscopic technique when using a small number of telescopes, and this is also in line with the previous MC-based study (Di Pierro 2019), which claims a 50% improvement with respect to MAGIC. The gain is twofold: first, the addition of the third telescope improves the shower reconstruction, allowing for a more efficient rejection of the background events. Second, the number of detected gamma-ray events is also increased. Since the observations are performed in software-coincidence mode, the trigger-level collection area cannot be increased with the addition of the LST-1. However, during the regular analysis of data from MAGIC, a fraction of the images do not survive the quality cuts (small showers producing <50 p.
For the MAGIC-only analysis, the angular resolution is slightly higher (∼10%) in the MCP chain, as compared to the dedicated MARS analysis. It should be noted that the MARS A66, page 10 of 21  Comparison of the angular resolution (defined as 68% containment of gamma rays) as a function of the estimated energy of the joint LST-1+MAGIC analysis (orange) from the Crab observations (points) and MC simulations (line) compared to MAGIC-only analysis using MARS (blue) or using MCP (green). Joint analysis in which only all-three telescope events are kept is shown in red (in most of the energy range the curve merges with the orange one). Left panel is for observations at low zenith angle, right panel is for the medium zenith-angle distance. analysis software is optimized for two-telescope observations and it employs slightly different shower reconstruction techniques than ctapipe.
We performed comparison checks, both against the MAGIC standard simulation software as well as comparisons of the simulation results to the data and showed a rather good agreement. The Crab Nebula spectrum obtained from the joint analysis can be described as dN/dE = (3. 48 ± 0. 09 stat ) × 10 -11 (E/1TeV) -2. 49±0. 03 stat -(0. 117±0. 017 stat ) ln(E/1TeV) cm -2 s -1 TeV -1. It is within about 10% of the earlier measurements. Also, the derived flux stability is comparable to the one of MAGIC, pointing to similar systematic uncertainty of the joint analysis. using the data obtained by observations and MC simulations, the differences for MAGIC-only analysis are typically ∼ 20 -30%, except at the highest energies for low-zenith case, where the MC sensitivity uncertainty is very large. Similar differences at mid energies between the data and MC are also reported in the integral sensitivity of  Aleksić et al.
Acknowledgements. We gratefully acknowledge financial support from the following agencies and organisations:  Ministry of Education, Youth and Sports ,  MEYS LM2015046 ,  LM2018105 ,  LTT17006 ,  EU/MEYS CZ. 02. 1. 01/0. 0/0. 0/16_013/0001403 ,  CZ. 02. 1. 01/0. 0/0. 0/18_046/0016007  and  CZ. 02. 1. 01/0. 0/0. 0/16_019/0000754 ,  Czech Republic ;  Max Planck Society ,  German Bundesministerium für Bildung und Forschung (Verbundforschung/ ErUM) ,  Deutsche Forschungsgemeinschaft  ( SFBs 876  and 1491), Germany;  A66 , page 13 of 21 Abe, H. , et al. : A&A, 680,  A66  (2023)  Istituto Nazionale di Astrofisica (INAF) ,  Istituto Nazionale di Fisica Nucleare (INFN) ,  Italian Ministry for University and Research (MUR) ;  ICRR ,  University of Tokyo ,  JSPS ,  MEXT, Japan ;  JST SPRING -JPMJSP2108 ;  Narodowe Centrum Nauki , grant number  2019/34/E/ST9/00224 , Poland; The  Spanish groups  acknowledge the  Spanish Ministry of Science and Innovation  and the  Spanish Research State Agency (AEI)  through the government budget lines  PGE2021/28.
ESFRI-2017-IAC-12 ),  Ministerio de Ciencia e Innovación , 15% co-financed by  Consejería de Economía, Industria, Comercio y Conocimiento del Gobierno de Canarias ; the " CERCA" program  of the  Generalitat de Catalunya ; and the  European Union 's " Horizon 2020 " GA: 824064  and  NextGenerationEU ; We acknowledge the  Ramon y Cajal program  through grant  RYC-2020-028639-I  and  RYC-2017-22665 ;  State Secretariat for Education, Research and Innovation (SERI)  and  Swiss National Science Foundation (SNSF), Switzerland ; The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) under grant agreements No  262053  and No  317446. This project is receiving funding from the  European Union 's  Horizon 2020 research and innovation programs  under agreement No  676134.
ESCAPE -The European Science Cluster of Astronomy & Particle Physics ESFRI Research Infrastructures  has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement no. 824064. We would like to thank the  Instituto de Astrofísica de Canarias  for the excellent working conditions at the  Observatorio del Roque de los Muchachos in La Palma. The financial support of the  German BMBF , MPG and HGF; the  Italian INFN and INAF ; the  Swiss National Fund SNF ; the grants  PID2019-104114RB-C31 ,  PID2019-104114RB-C32 ,  PID2019-104114RB-C33 ,  PID2019-105510GB-C31 ,  PID2019-107847RB-C41 ,  PID2019-107847RB-C42 ,  PID2019-107847RB-C44 ,  PID2019-107988GB-C22  funded by the  Spanish MCIN /AEI/ 10. 13039/501100011033 ; the  Indian Department of Atomic Energy; the  Japanese ICRR , the  University of Tokyo ,  JSPS , and  MEXT ; the  Bulgarian Ministry of Education and Science, National RI Roadmap  Project  DO1-400/18.
Di Pierro : development of the simultaneous observations' database and of the first versions of ctapipe_io_magic, cross-checks with the MARS analysis, coordination of the analysis cross-checks;  E. Jobst : implementation of tests for low-level comparison of shower images and parameters for MAGIC-only analysis, upgrade of ctapipe_io_magic to new releases of ctapipe, test of magic-cta-pipe for MAGIC-only analysis;  Y. Ohtani : responsible for most of magic-cta-pipe analysis code, many tests and optimization of the pipeline, calculation of performance parameters;  J. Sitarek : reoptimization and processing of Monte Carlo simulations, calculation of performance parameters, paper drafting;  Y. Suda : contribution to the first iteration of implementation of MAGIC to sim_telarray and coordinating the group from the MAGIC side together with  F.
Context. In dwarf novae (DNe) and low-mass X-ray binaries, the tidal potential excites spiral waves in the accretion disc. Spiral-wavedriven accretion may be important in quiescent discs, where the angular momentum transport mechanism has yet to be identified. Previous studies were limited to unrealistically high temperatures for numerical studies or to specific regimes for analytical studies. Aims. We perform the first numerical simulation of spiral-wave-driven accretion in the cold temperature regime appropriate to quiescent discs, which have Mach numbers 100. Methods. We used the new GPU-accelerated finite-volume code Idefix to produce global hydrodynamics 2D simulations of the accretion discs of DN systems with a sufficiently fine spatial resolution to capture the short scale-height of cold, quiescent discs with Mach numbers ranging from 80 to 370.
Algorithm The simulations presented in this paper all use the Idefix code, which is a finite-volume conservative Godunov grid-based code with a structure similar to Pluto  (Mignone et al. 2007 ). The code is written in C++17 and uses the Kokkos portability library  (Edwards et al. 2014 ) for many-core shared memory parallelisation and MPI for distributed memory parallelisation, allowing for high performance on most available architectures. We use the HLLC Riemann problem solver  (Harten et al. 1983; Toro et al. 1994 ). We also use the Fargo algorithm   Masset 2000)  with a Keplerian advection velocity to speed up the integration, as implemented in  Mignone et al. (2012). We verified that we are able to reproduce the work of  Ju et al. (2016)  with this code (see details in Appendix B).
Acknowledgements. The authors would like to thank the anonymous referee for their comments that helped them improve this work. GL acknowledges support from the  European Research Council (ERC)  under the  European Union Horizon 2020 research and innovation program  (Grant agreement No. 815559  (MHDiscs)). The simulations and data reduction used in this article were performed using the GRICAD infrastructure (https://gricad. univ-grenoble-alpes. fr), which is supported by the Grenoble research communities, as well as the Jean-Zay supercomputer at IDRIS http://www. idris. fr/jean-zay/. This work was granted access to the HPC resources of IDRIS under the allocation 2021- A0120402231  made by GENCI. Data reduction for this paper was performed by making extensive use of the SciPy (Virtanen et al.
We propose a novel privacy-preserving, two-party computation of various distance metrics (e. g. , Hamming distance, Scalar Product) followed by a comparison with a fixed threshold, which is known as one of the most useful and popular building blocks for many different applications including machine learning, biometric matching, etc. Our solution builds upon recent advances in function secret sharing and makes use of an optimized version of arithmetic secret sharing. Thanks to this combination, our new solution named Funshade is the first to require only one round of communication and two ring elements of communication in the online phase, outperforming all prior state-of-the-art schemes while relying on lightweight cryptographic primitives. Lastly, we implement our solution from scratch in portable C and expose it in Python, testifying its high performance by running secure biometric identification against a database of 1 million records in ∼10 seconds with full correctness and 32-bit precision, without parallelization.
We draw inspiration from the family of distance metrics covered in GSHADE. Integrating FSS-based threshold comparison primitives from  [11]  with an optimized version of Secret Sharing  [53]  in a twoparty computation (2PC) protocol to perform privacy-preserving distance metric computations with a subsequent comparison to 𝜃. To summarize our contributions, our solution: • requires just one round of communication in the online phase, lowering the communication costs with respect to the two-round state-of-the-art solutions from AriaNN  [58]  and Boyle et. al. [11]  by merging the communication required for the scalar product with that of the comparison to 𝜃 , • sends two ring elements only in the online phase, reducing the communication size of previous solutions by a factor of 2𝑙 (for input vectors with 𝑙 elements), • features 100% correctness in the comparison result, as opposed to  [58] , • is implemented and open-sourced in a portable C module with lightweight wrapping to high-level languages such as Python, Rust or Golang, • is extensively tested, showcasing its high efficiency and correctness in a variety of scenarios (LAN/WAN, Authentication/ Identification), requiring less than 300ms for realistic biometric identification against 5000 identities with full 32bit precision, and ∼10s against 1 million identities.
Applications and Practical Considerations We can employ our solution in a variety of use-cases, from biometric verification to CSAM detection. In this section we apply Funshade to a scenario of biometric authentication for access control. There is a Gate allowing access to a flight/train/concert with a small computer and a camera, and a Biometric Provider (BP) operating a server that holds the database of registered/enrolled users. 𝒚 corresponds to a biometric template belonging to an enrolled user, and 𝒙 to a freshly captured biometric template. This setting naturally includes two computing parties {𝐺𝑎𝑡𝑒, 𝐵𝑃 } ≡ {P 0 , P 1 }, each of them playing specific roles, with 𝐺𝑎𝑡𝑒 ⊇ R 𝑖𝑛 𝒙 , R 𝑟𝑒𝑠 and 𝐵𝑃 ⊇ R 𝑖𝑛 𝒚. The solution is split into two phases: Enrollment (offline): 1 R 𝑠𝑒𝑡𝑢𝑝 carries out the generation of preprocessing material and distributes it to BP and Gate.
2 BP (R 𝑖𝑛 𝒚 ) enrolls an user by collecting its biometric template 𝒚, and then secret shares it with Gate (P 0 ). Π-sharing based scalar product: 1: ẑ𝜃 𝑗 ← 𝑟 𝜃 𝑗 +𝑑 𝒙 𝑗 +𝑑 𝒚 𝑗 + 𝑓 𝑐𝑝 𝑓 • 𝑙 [ 𝑗•Δ 𝒙 •Δ 𝒚 -Δ 𝒙 •𝛿 𝒚 𝑗 -Δ 𝒚 •𝛿 𝒙 𝑗 +𝛿 𝒙𝒚 𝑗 ] Reconstruction of masked input to FSS gate:  4 Gate (P 0 ) and BP (P 1 ) jointly perform Protocol 5, incurring in one round of communication where each party sends its share of the distance evaluation to the other. 5 The final result is sent to Gate (R 𝑟𝑒𝑠 ), who accepts or rejects the user accordingly. This instantiation can be parallelized for different reference inputs 𝒚 (𝑘) in cases where the reference database contains more than one record, e. g. , biometric identification against a database of multiple subjects, CSAM detection against a large database of image hashes.
In these cases, the individual secret shared outputs 𝒐 (𝑘) 𝑗 can be locally summed up to yield a single value as output. Additionally, these use-cases normally gather their reference databases ahead of time, allowing for early deployment of the Π-shares of 𝒚 (𝑘). Realizing the setup R 𝑠𝑒𝑡𝑢𝑝. In line with previous work on semihonest MPC  [8, 26, 45, 56, 64] , fresh randomness is generated for each 1:1 verification in the offline phase and used only once in the online phase, as the security of our protocols would weaken if we were to reuse randomness/masks. Protocols in this work are presented in the preprocessing model, where P 0 , P 1 receive correlated randomness from a trusted dealer taking the role of R 𝑠𝑒𝑡𝑢𝑝. Protocols within this model can be converted to protocols in the standard model by: (a) Resorting to trusted hardware  [52].
• Computation: F 𝑡ℎ-𝑑𝑖𝑠𝑡 reconstructs 𝒙 = Δ 𝒙 -(𝛿 𝒙 0 + 𝛿 𝒙 1 ) and 𝒚 = Δ 𝒚 -(𝛿 𝒚 0 +𝛿 𝒚 1 ), computes 𝑧 = 𝑓 𝑑𝑖𝑠𝑡 (𝒙, 𝒚) and 𝑜 = 1 𝑧⩾𝜃. • Output: Sends 𝑜 𝑗 to P 𝑟𝑒𝑠. Theorem 1. In the F Funshade. 𝑠𝑒𝑡𝑢𝑝 -hybrid model, protocols 4-5-6 (online phase) securely realize the functionality F 𝑡ℎ-𝑑𝑖𝑠𝑡. Proof. The semi-honest adversary corrupts P 𝑗 during the sequential execution of protocols 4-5-6. For this case, S executes the setup phase honestly on behalf of P 1-𝑗 (in case of interactive setup), and will simulate the entire circuit evaluation, assuming the circuit-inputs of P 1-𝑗 to be 0. In the Funshade. Result protocol, S adjusts the shares of ⟨𝑜⟩ on behalf of P 1-𝑗 so that A sees the same transcript as in the real-world protocol. • Funshade. Setup: For the offline phase, we consider it as an ideal functionality F Funshade.
EXPERIMENTS 4. 1 Implementation and Environment We implement Funshade in a compact 5 , portable 6 and standalone 7  C module, and we open source it 8. We also provide a lightweight wrapper to Python by virtue of Cython. We instantiate the PRG function 𝐺 (employed in the DCF gate) with a Miyaguchi-Preneel oneway compression function over an AES block cipher, an extended variant of Matyas-Meyer-Oseas function used in previous works  [58]. We concatenate several fixed key block ciphers to achieve the desired output length.
Subsequently, we assess the communication costs for each step of our solution using the Scalar Product (SP) as metric 9 , and we list them in Table  4. The size of the offline phase's output can be interpreted as the total preprocessing. Overall, we highlight the extremely low communication size in the online phase, of just 2𝐾𝑛 bits (2 ring elements for each of the 𝐾 distance evaluations). 5 Around 1000 Lines of Code for the core implementation. 6 We employ only generic integer types, C arrays and plain C89 instructions (supported by every C compiler) to integrate smoothly with higher-level languages such as Python, Rust or Golang. 7 The only optional dependency is libsodium for secure randomness generation. 8 Our code is available at https://github. com/ibarrond/funshade. 9 Note that the evaluation of other distance metrics would require adding just 𝑛 bits (one ring element, corresponding to a share of 𝑑 𝒗 ) to the sharing of 𝒙 and 𝒚.
10 Depends on the instantiation of Funshade. Setup (Section 3. 5). Trusted hardware or a third party would yield the results from Table  2  while requiring only one round of communication; a 2PC instantiation would require more rounds and more computation. Focusing on the biometrics use-case, we resort to the Labelled Faces in the Wild (LFW) dataset  [40]  (13233 faces from more than 5000 identities) and extract biometric templates of length 𝑙 = 128 with an ArcFace-based  [30]  feature extractor  11. We set 𝑛 = 32 to ensure that the protocols perfectly mimic the plaintext operations and thus obtain the exact same results, without any drop in accuracy. We report in Table  2  the results for two scenarios: • Authentication, a 1:1 verification against a single reference. • Identification, a 1:K verification of the live template with a set of 𝐾 references, employing a subset (𝐾 = 5000) of the identities in the LFW dataset, enough to be of use for the applications mentioned beforehand (e.
We record the total online latency of all these experiments in the identification scenario for increasing references 𝐾. Figure  3  displays these results and shows how the vector size 𝑙 has a much lower impact in the latency than 𝑛, hinting that applications that require lower numerical precision (e. g. , 𝑛 = 16) will be noticeably faster. Figure  4  shows the ratio of the communication latency to the total online latency. We observe that communication is the main bottleneck up until 𝐾 ≈ 1000 in the LAN setting, and 𝐾 ≈ 10000 in WAN. While this suggests that applications with higher requirements of 𝐾 (e. g. , nation-wide identification) should consider additional optimizations for the local computation (e. g. , using multiple cores for parallelization or even resorting to a GPU), our non-parallelized solution requires around 10𝑠 to identify an individual against a database of 1 million records for 𝑛 = 32.
CONCLUSIONS In this work we presented Funshade, a novel 2PC privacy preserving solution of various distance metrics (e. g. , Hamming distance, Scalar Product) followed by threshold comparison. We build our protocols upon ΠSS, a version of arithmetic secret sharing optimized for the secure evaluation of scalar products, and function secret sharing with 100% correctness for comparison. Thanks to this, Funshade proposes the first solution in the 2PC literature requiring one single round of communication in the online phase while outperforming all previous works in online communication size (two ring elements), all while relying on lightweight cryptographic primitives. We implement our solution from scratch in a portable C module, and showcase its extreme efficiency by achieving secure biometric identification against 5000 records in less than 300ms with 32-bit precision, and against 1 million records in ∼10s.
Model Geometry The broadband horn antenna that was utilized had initially been designed using computer-aided design (CAD) software, specifically AutoCAD 2022. The dimensions of the antenna's aperture were 590 mm × 560 mm, while its height measured 660 mm. The antenna was connected to a rectangular waveguide that had dimensions of 36 mm × 62 mm and a length of 150 mm. Figure  1  illustrates the dimensions and design of the antenna under consideration. The aforementioned geometry was imported into the computational modeling software COMSOL. The geometry of the antenna enabled the stimulation of the specimen with a larger surface area, which is useful for the structures found in the civil engineering field. The dimensions of the aperture allow for heating samples with an almost 1 m width (with the inclination direction of the antenna).
The structure of the sample was described previously. Table  1  presents the selected properties of the materials used in the simulation. The number of elements in the simulated model was dependent on the analyzed case. The utilized mesh consisted of 76,093 tetrahedral elements for the case with no defect, 79,677 elements for the case with a defect located in the center of the sample, and 80,049 for the case with a defect located outside the center. The generated mesh is shown in Figure  6. As can be noticed, the sizes of the elements changed between the domains. The mesh refinement process was performed manually by utilizing the scale option provided in the COMSOL software. This approach was specifically applied to the domains that encompassed the sample, as these regions were associated with calculations related to both the utilized physics: EMW and HT.