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Copy Detection Patterns (CDPs) are crucial elements in modern security applications, playing a vital role in safeguarding industries such as food, pharmaceuticals, and cosmetics. Current performance evaluations of CDPs predominantly rely on empirical setups using simplistic metrics like Hamming distances or Pearson correlation. These methods are often inadequate due to their sensitivity to distortions, degradation, and their limitations to stationary statistics of printing and imaging. Additionally, machine learning-based approaches suffer from distribution biases and fail to generalize to unseen counterfeit samples. Given the critical importance of CDPs in preventing counterfeiting, including the counterfeit vaccines issue highlighted during the COVID-19 pandemic, there is an urgent need for provable performance guarantees across various criteria. This paper aims to establish a theoretical framework to derive optimal criteria for the analysis, optimization, and future development of CDP authentication technologies, ensuring their reliability and effectiveness in diverse security scenarios.

Despite their highly anisotropic complex-oxidic nature, certain delafossite compounds (e.g., PdCoO2, PtCoO2) are the most conductive oxides known, for reasons that remain poorly understood. Their room-temperature conductivity can exceed that of Au, while their low-temperature electronic mean-free-paths reach an astonishing 20 microns. It is widely accepted that these materials must be ultrapure to achieve this, although the methods for their growth (which produce only small crystals) are not typically capable of such. Here, we first report a new approach to PdCoO2 crystal growth, using chemical vapor transport methods to achieve order-of-magnitude gains in size, the highest structural qualities yet reported, and record residual resistivity ratios (>440). Nevertheless, the first detailed mass spectrometry measurements on these materials reveal that they are not ultrapure, typically harboring 100s-of-parts-per-million impurity levels. Through quantitative crystal-chemical analyses, we resolve this apparent dichotomy, showing that the vast majority of impurities are forced to reside in the Co-O octahedral layers, leaving the conductive Pd sheets highly pure (~1 ppm impurity concentrations). These purities are shown to be in quantitative agreement with measured residual resistivities. We thus conclude that a previously unconsidered "sublattice purification" mechanism is essential to the ultrahigh low-temperature conductivity and mean-free-path of metallic delafossites.

Nowadays, copy detection patterns (CDP) appear as a very promising anti-counterfeiting technology for physical object protection. However, the advent of deep learning as a powerful attacking tool has shown that the general authentication schemes are unable to compete and fail against such attacks. In this paper, we propose a new mathematical model of printing-imaging channel for the authentication of CDP together with a new detection scheme based on it. The results show that even deep learning created copy fakes unknown at the training stage can be reliably authenticated based on the proposed approach and using only digital references of CDP during authentication., Comment: Paper accepted at the IEEE International Workshop on Information Forensics and Security (WIFS) 2022

The Line Emission Mapper (LEM) is an X-ray Probe for the 2030s that will answer the outstanding questions of the Universe's structure formation. It will also provide transformative new observing capabilities for every area of astrophysics, and to heliophysics and planetary physics as well. LEM's main goal is a comprehensive look at the physics of galaxy formation, including stellar and black-hole feedback and flows of baryonic matter into and out of galaxies. These processes are best studied in X-rays, and emission-line mapping is the pressing need in this area. LEM will use a large microcalorimeter array/IFU, covering a 30x30' field with 10" angular resolution, to map the soft X-ray line emission from objects that constitute galactic ecosystems. These include supernova remnants, star-forming regions, superbubbles, galactic outflows (such as the Fermi/eROSITA bubbles in the Milky Way and their analogs in other galaxies), the Circumgalactic Medium in the Milky Way and other galaxies, and the Intergalactic Medium at the outskirts and beyond the confines of galaxies and clusters. LEM's 1-2 eV spectral resolution in the 0.2-2 keV band will make it possible to disentangle the faintest emission lines in those objects from the bright Milky Way foreground, providing groundbreaking measurements of the physics of these plasmas, from temperatures, densities, chemical composition to gas dynamics. While LEM's main focus is on galaxy formation, it will provide transformative capability for all classes of astrophysical objects, from the Earth's magnetosphere, planets and comets to the interstellar medium and X-ray binaries in nearby galaxies, AGN, and cooling gas in galaxy clusters. In addition to pointed observations, LEM will perform a shallow all-sky survey that will dramatically expand the discovery space., Comment: 18 pages. White paper for a mission concept to be submitted for the 2023 NASA Astrophysics Probes opportunity. v2: All-sky survey figure expanded, references fixed. v3: Added energy resolution measurements for prototype detector array. v4: Author list and reference fixes

In this paper, we address the problem of modeling a printing-imaging channel built on a machine learning approach a.k.a. digital twin for anti-counterfeiting applications based on copy detection patterns (CDP). The digital twin is formulated on an information-theoretic framework called Turbo that uses variational approximations of mutual information developed for both encoder and decoder in a two-directional information passage. The proposed model generalizes several state-of-the-art architectures such as adversarial autoencoder (AAE), CycleGAN and adversarial latent space autoencoder (ALAE). This model can be applied to any type of printing and imaging and it only requires training data consisting of digital templates or artworks that are sent to a printing device and data acquired by an imaging device. Moreover, these data can be paired, unpaired or hybrid paired-unpaired which makes the proposed architecture very flexible and scalable to many practical setups. We demonstrate the impact of various architectural factors, metrics and discriminators on the overall system performance in the task of generation/prediction of printed CDP from their digital counterparts and vice versa. We also compare the proposed system with several state-of-the-art methods used for image-to-image translation applications., Comment: Paper accepted at the IEEE International Workshop on Information Forensics and Security (WIFS) 2022

Copy detection pattern (CDP) is a novel solution for products' protection against counterfeiting, which gains its popularity in recent years. CDP attracts the anti-counterfeiting industry due to its numerous benefits in comparison to alternative protection techniques. Besides its attractiveness, there is an essential gap in the fundamental analysis of CDP authentication performance in large-scale industrial applications. It concerns variability of CDP parameters under different production conditions that include a type of printer, substrate, printing resolution, etc. Since digital off-set printing represents great flexibility in terms of product personalized in comparison with traditional off-set printing, it looks very interesting to address the above concerns for digital off-set printers that are used by several companies for the CDP protection of physical objects. In this paper, we thoroughly investigate certain factors impacting CDP. The experimental results obtained during our study reveal some previously unknown results and raise new and even more challenging questions. The results prove that it is a matter of great importance to choose carefully the substrate or printer for CDP production. This paper presents a new dataset produced by two industrial HP Indigo printers. The similarity between printed CDP and the digital templates, from which they have been produced, is chosen as a simple measure in our study. We found several particularities that might be of interest for large-scale industrial applications.

Copy detection patterns (CDP) are recent technologies for protecting products from counterfeiting. However, in contrast to traditional copy fakes, deep learning-based fakes have shown to be hardly distinguishable from originals by traditional authentication systems. Systems based on classical supervised learning and digital templates assume knowledge of fake CDP at training time and cannot generalize to unseen types of fakes. Authentication based on printed copies of originals is an alternative that yields better results even for unseen fakes and simple authentication metrics but comes at the impractical cost of acquisition and storage of printed copies. In this work, to overcome these shortcomings, we design a machine learning (ML) based authentication system that only requires digital templates and printed original CDP for training, whereas authentication is based solely on digital templates, which are used to estimate original printed codes. The obtained results show that the proposed system can efficiently authenticate original and detect fake CDP by accurately locating the anomalies in the fake CDP. The empirical evaluation of the authentication system under investigation is performed on the original and ML-based fakes CDP printed on two industrial printers.

Copy detection patterns (CDP) are an attractive technology that allows manufacturers to defend their products against counterfeiting. The main assumption behind the protection mechanism of CDP is that these codes printed with the smallest symbol size (1x1) on an industrial printer cannot be copied or cloned with sufficient accuracy due to data processing inequality. However, previous works have shown that Machine Learning (ML) based attacks can produce high-quality fakes, resulting in decreased accuracy of authentication based on traditional feature-based authentication systems. While Deep Learning (DL) can be used as a part of the authentication system, to the best of our knowledge, none of the previous works has studied the performance of a DL-based authentication system against ML-based attacks on CDP with 1x1 symbol size. In this work, we study such a performance assuming a supervised learning (SL) setting., Comment: Paper accepted at the 29th IEEE International Conference on Image Processing (IEEE ICIP)

"A Guide to Best Practice in Special Education, Health and Social Care" explores and explains the changes in governmental policies across the education, health and social care services, and what they mean for young individuals, parents and professionals. In a period of significant change, many practitioners need to understand the government's plans for bringing about a more efficient, effective and sustainable system to meet the needs of young people and their families. Without trawling through reviews, green papers, white papers and bills, this book not only explains the significance of recent events, but provides practical examples, in the form of conversations and case studies, about how parents and professionals are making change happen. With decades of experience, Rona Tutt and Paul Williams delve deep into the separate origins of the three strands -- the SEND Review, the review of children's social care, and the Health and Care Act 2022. The book explores how pupils in different types of schools can have their needs met more effectively; how to make better use of available resources; and how to create a culture of mutual respect across all the three services. "A Guide to Best Practice in Special Education, Health and Social Care" not only fills in gaps in readers' knowledge about the working of all three services, but provides innovative examples of how change is happening at ground level. People of all age groups working in schools and seeking to enhance their knowledge will find it to be an essential read. It will also be of interest to parents and professionals from across health and social care.

Photon counting detector systems on sounding rocket payloads often require interfacing asynchronous outputs with a synchronously clocked telemetry (TM) stream. Though this can be handled with an on-board computer, there are several low cost alternatives including custom hardware, microcontrollers and field-programmable gate arrays (FPGAs). This paper outlines how a TM interface (TMIF) for detectors on a sounding rocket with asynchronous parallel digital output can be implemented using low cost FPGAs and minimal custom hardware. Low power consumption and high speed FPGAs are available as commercial off-the-shelf (COTS) products and can be used to develop the main component of the TMIF. Then, only a small amount of additional hardware is required for signal buffering and level translating. This paper also discusses how this system can be tested with a simulated TM chain in the small laboratory setting using FPGAs and COTS specialized data acquisition products., Comment: 16 pages, 11 pages (also published as a 2015 SPIE conference proceeding)

In the recent years, the copy detection patterns (CDP) attracted a lot of attention as a link between the physical and digital worlds, which is of great interest for the internet of things and brand protection applications. However, the security of CDP in terms of their reproducibility by unauthorized parties or clonability remains largely unexplored. In this respect this paper addresses a problem of anti-counterfeiting of physical objects and aims at investigating the authentication aspects and the resistances to illegal copying of the modern CDP from machine learning perspectives. A special attention is paid to a reliable authentication under the real life verification conditions when the codes are printed on an industrial printer and enrolled via modern mobile phones under regular light conditions. The theoretical and empirical investigation of authentication aspects of CDP is performed with respect to four types of copy fakes from the point of view of (i) multi-class supervised classification as a baseline approach and (ii) one-class classification as a real-life application case. The obtained results show that the modern machine-learning approaches and the technical capacities of modern mobile phones allow to reliably authenticate CDP on end-user mobile phones under the considered classes of fakes.

Scintillators with pulse-shape discrimination (PSD) capability are of great interest to many fields in the scientific community. The ability to discern a gamma ray from a neutron using PSD varies between different types of scintillator materials and dopants. A new generation of organic scintillator materials with PSD capability were studied to determine their radiation hardness to ionizing and non-ionizing radiation. The PSD capability, average pulse shapes, and light output of four types of organic scintillator were characterized before and after neutron and gamma-ray irradiation. The main goal of this investigation is to study the effects of long-term irradiation that may be experienced in space applications on the light output and particle discriminating capabilities of each material. EJ-270, EJ-276, organic glass, and Stilbene were tested. Damage due to non-ionizing (neutron) radiation was not observed in any of the scintillators up to $2.56\times10^{11}$ n/cm$^2$, except for Stilbene which showed a small (12$\%$) decrease in light output. All scintillators presented some light output reduction after ionizing (gamma-ray) irradiation, with reductions of 17$\%$ (EJ-276 and OGS), 32$\%$ (EJ-270), and 42$\%$ (Stilbene) observed immediately after 100 kRad.

Nowadays, the modern economy critically requires reliable yet cheap protection solutions against product counterfeiting for the mass market. Copy detection patterns (CDP) are considered as such solution in several applications. It is assumed that being printed at the maximum achievable limit of a printing resolution of an industrial printer with the smallest symbol size 1x1 elements, the CDP cannot be copied with sufficient accuracy and thus are unclonable. In this paper, we challenge this hypothesis and consider a copy attack against the CDP based on machine learning. The experimental based on samples produced on two industrial printers demonstrate that simple detection metrics used in the CDP authentication cannot reliably distinguish the original CDP from their fakes. Thus, the paper calls for a need of careful reconsideration of CDP cloneability and search for new authentication techniques and CDP optimization because of the current attack.

Protection of physical objects against counterfeiting is an important task for the modern economies. In recent years, the high-quality counterfeits appear to be closer to originals thanks to the rapid advancement of digital technologies. To combat these counterfeits, an anti-counterfeiting technology based on hand-crafted randomness implemented in a form of copy detection patterns (CDP) is proposed enabling a link between the physical and digital worlds and being used in various brand protection applications. The modern mobile phone technologies make the verification process of CDP easier and available to the end customers. Besides a big interest and attractiveness, the CDP authentication based on the mobile phone imaging remains insufficiently studied. In this respect, in this paper we aim at investigating the CDP authentication under the real-life conditions with the codes printed on an industrial printer and enrolled via a modern mobile phone under the regular light conditions. The authentication aspects of the obtained CDP are investigated with respect to the four types of copy fakes. The impact of fakes' type used for training of authentication classifier is studied in two scenarios: (i) supervised binary classification under various assumptions about the fakes and (ii) one-class classification under unknown fakes. The obtained results show that the modern machine-learning approaches and the technical capacity of modern mobile phones allow to make the CDP authentication under unknown fakes feasible with respect to the considered types of fakes and code design.

The Off-plane Grating Rocket Experiment (OGRE) is a soft X-ray grating spectrometer to be flown on a suborbital rocket. The payload is designed to obtain the highest-resolution soft X-ray spectrum of Capella to date with a resolution goal of $R(\lambda/\Delta\lambda)>2000$ at select wavelengths in its 10--55 Angstrom bandpass of interest. The optical design of the spectrometer realizes a theoretical maximum resolution of $R\approx5000$, but this performance does not consider the finite performance of the individual spectrometer components, misalignments between components, and in-flight pointing errors. These errors all degrade the performance of the spectrometer from its theoretical maximum. A comprehensive line-spread function (LSF) error budget has been constructed for the OGRE spectrometer to identify contributions to the LSF, to determine how each of these affects the LSF, and to inform performance requirements and alignment tolerances for the spectrometer. In this document, the comprehensive LSF error budget for the OGRE spectrometer is presented, the resulting errors are validated via raytrace simulations, and the implications of these results are discussed., Comment: 26 pages, 9 figures, preprint of a manuscript accepted for publication in the Journal of Astronomical Telescopes, Instruments, and Systems (JATIS)

This paper presents Rocket Experiment (REX) that was part of a dual payload rocket campaign for NASA's sounding rocket Black Brant IX with water recovery technology. This mission was a suborbital sounding rocket flight that was launched and recovered on April 4, 2018 and targeted the Vela supernova remnant. The purpose of REX was to classify the Technology Readiness Level of onboard devices designed for space applications. The devices were two wide-field X-ray telescopes consisting of a combination of Lobster-Eye (LE) optics with an uncooled Timepix detector (256 x 256 px @ 55 um), and additional sensors. The first telescope uses a two-dimensional combination of LE modules with a focal length of 1 m and a Field of View (FOV) of 1.0 x 1.2 deg and operates in the energy range of 3 - 60 keV. The second telescope was a one-dimensional LE with a focal length of 250 mm and a FOV of 2.7 x 8.0 deg for the energy range 3 - 40 keV. The X-ray telescopes were supplemented by a camera in the visible spectrum with 1,280 x 1,024 px resolution, which was used to obtain images of the observed sources and to verify the resulting pointing of the rocket carrier. Other devices also include infrared array sensors and inertial measurement units tested for future small satellite missions. The data handler and communication system were built using the Robot Operating System, and both the system and the electronics were deployed and operated in flight. The hardware was successfully recovered after the launch and the data were extracted., Comment: Submitted to Acta Astronautica

The soft X-ray grating spectrometer on board the Off-plane Grating Rocket Experiment (OGRE) hopes to achieve the highest resolution soft X-ray spectrum of an astrophysical object when it is launched via suborbital rocket. Paramount to the success of the spectrometer are the performance of the $>250$ reflection gratings populating its reflection grating assembly. To test current grating fabrication capabilities, a grating prototype for the payload was fabricated via electron-beam lithography at The Pennsylvania State University's Materials Research Institute and was subsequently tested for performance at Max Planck Institute for Extraterrestrial Physics' PANTER X-ray Test Facility. Bayesian modeling of the resulting data via Markov chain Monte Carlo (MCMC) sampling indicated that the grating achieved the OGRE single-grating resolution requirement of $R_{g}(\lambda/\Delta\lambda)>4500$ at the 94% confidence level. The resulting $R_g$ posterior probability distribution suggests that this confidence level is likely a conservative estimate though, since only a finite $R_g$ parameter space was sampled and the model could not constrain the upper bound of $R_g$ to less than infinity. Raytrace simulations of the system found that the observed data can be reproduced with a grating performing at $R_g=\infty$. It is therefore postulated that the behavior of the obtained $R_g$ posterior probability distribution can be explained by a finite measurement limit of the system and not a finite limit on $R_g$. Implications of these results and improvements to the test setup are discussed., Comment: 25 pages, 16 figures, preprint of an article accepted for publication in the Journal of Astronomical Instrumentation \copyright 2020 [copyright World Scientific Publishing Company] [https://www.worldscientific.com/worldscinet/jai]