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The synthesis of aluminum substituted strontium hexaferrite nanoparticles (SrFe12-xAlxO19 with x = 0-3), via three different preparation methods, is investigated. The synthesis methods are hydrothermal autoclave (AC) synthesis, a citrate sol-gel (SG) synthesis and a solid-salt matrix (SSM) sol-gel synthesis. Evaluation of macroscopic magnetic properties and of lattice parameters obtained by Rietveld analysis of powder X-ray diffraction (PXRD) data indicate that successful substitution of Al into the crystal structure is only achieved for the SG method. For the SG sample with x = 3, the coercivity was found to increase by 73% to 830 kA/m, while the saturation magnetization was reduced by 68% to 22.6 Am2/kg compared to the non-substituted x = 0 SG sample. The SSM and AC samples did not show any significant changes in their magnetic properties. To examine the nature of the Al insertion in detail, neutron powder diffraction (NPD) data were collected on the SSM and SG samples. Combined Rietveld refinements of the PXRD and NPD data confirm that effective substitution of the Al ions is only achieved for the SG sample and reveal that Al occupies mainly the (2a)Oh and (12k)Oh sites and to a lesser extent the (4e)BP, (4f)Oh and (4f)Td sites. Moreover, the relative degree of site occupation varies with increasing Al substitution. The intrinsic magnetization according to the refined atomic magnetic moments and Al site occupation fractions was extracted from the NPD data and compared with the measured macroscopic magnetization. A remarkable agreement exists between the two, confirming the robustness and accuracy of the Rietveld analysis.

This paper explores the integration of ring attractors, a mathematical model inspired by neural circuit dynamics, into the reinforcement learning (RL) action selection process. Ring attractors, as specialized brain-inspired structures that encode spatial information and uncertainty, offer a biologically plausible mechanism to improve learning speed and predictive performance. They do so by explicitly encoding the action space, facilitating the organization of neural activity, and enabling the distribution of spatial representations across the neural network in the context of deep RL. The application of ring attractors in the RL action selection process involves mapping actions to specific locations on the ring and decoding the selected action based on neural activity. We investigate the application of ring attractors by both building them as exogenous models and integrating them as part of a Deep Learning policy algorithm. Our results show a significant improvement in state-of-the-art models for the Atari 100k benchmark. Notably, our integrated approach improves the performance of state-of-the-art models by half, representing a 53\% increase over selected baselines.

The nucleation, crystallization, and growth mechanisms of MnFe2O4, CoFe2O4, NiFe2O4, and ZnFe2O4 nanocrystallites prepared from coprecipitated transition metal (TM) hydroxide precursors treated at sub-, near-, and supercritical hydrothermal conditions have been studied by in situ X-ray total scattering (TS) with pair distribution function (PDF) analysis, and in situ synchrotron powder X-ray diffraction (PXRD) with Rietveld analysis. The in situ TS experiments were carried out on 0.6 M TM hydroxide precursors prepared from aqueous metal chloride solutions using 24.5% NH4OH as the precipitating base. The PDF analysis reveals equivalent nucleation processes for the four spinel ferrite compounds under the studied hydrothermal conditions, where the TMs form edge-sharing octahedrally coordinated hydroxide units (monomers/dimers and in some cases trimers) in the aqueous precursor, which upon hydrothermal treatment nucleate through linking by tetrahedrally coordinated TMs. The in situ PXRD experiments were carried out on 1.2 M TM hydroxide precursors prepared from aqueous metal nitrate solutions using 16 M NaOH as the precipitating base. The crystallization and growth of the nanocrystallites were found to progress via different processes depending on the specific TMs and synthesis temperatures. The PXRD data show that MnFe2O4 and CoFe2O4 nanocrystallites rapidly grow (typically <1 min) to equilibrium sizes of 20-25 nm and 10-12 nm, respectively, regardless of applied temperature in the 170-420 C range, indicating limited possibility of targeted size control. However, varying the reaction time (0-30 min) and temperature (150-400 C) allows different sizes to be obtained for NiFe2O4 (3-30 nm) and ZnFe2O4 (3-12 nm) nanocrystallites.

In this work, we propose an information theoretic order parameter able to characterize the presence and breaking of categorical symmetries in (1 + 1)-d rational conformal field theories (RCFT). Specifically, we compute the quantum relative entropy between the ground states of RCFTs representing the critical point of phase transitions between different symmetry-broken phases of theories with categorical symmetries, and their symmetrized versions. We find that, at leading order in the high temperature limit, this relative entropy only depends on the expectation values of the quantum dimensions of the topological operators implementing the categorical symmetry. This dependence suggests that our proposal can be used to characterize the different broken phases of (1 + 1)-d theories with categorical symmetries., Comment: 14 pages

A comprehensive symmetry resolution of the entanglement entropy (EE) in $(1+1)$-d rational conformal field theories (RCFT) with categorical non-invertible symmetries is presented. This amounts to symmetry resolving the entanglement with respect to the generalized twisted and anyonic charge sectors of the theory. The anyonic sectors label the irreducible representations of a modular fusion category defining the symmetry and can be understood through the $(2+1)$-d symmetry topological field theory (SymTFT) that encodes the symmetry features of the CFT. Using this, we define the corresponding generalized boundary dependent charged moments necessary for the symmetry resolution of the entanglement entropy, which is the main result of this work. Furthermore, contrary to the case of invertible symmetries, we observe the breakdown of entanglement equipartition between different charged sectors at the next-to-leading order in the ultraviolet cutoff., Comment: 6 pages + 5 pages appendices, 1 figure. v2: added references and minor clarifications, main results unchanged

Recent advances have improved autonomous navigation and mapping under payload constraints, but current multi-robot inspection algorithms are unsuitable for nano-drones due to their need for heavy sensors and high computational resources. To address these challenges, we introduce ExploreBug, a novel hybrid frontier range bug algorithm designed to handle limited sensing capabilities for a swarm of nano-drones. This system includes three primary components: a mapping subsystem, an exploration subsystem, and a navigation subsystem. Additionally, an intra-swarm collision avoidance system is integrated to prevent collisions between drones. We validate the efficacy of our approach through extensive simulations and real-world exploration experiments involving up to seven drones in simulations and three in real-world settings, across various obstacle configurations and with a maximum navigation speed of 0.75 m/s. Our tests demonstrate that the algorithm efficiently completes exploration tasks, even with minimal sensing, across different swarm sizes and obstacle densities. Furthermore, our frontier allocation heuristic ensures an equal distribution of explored areas and paths traveled by each drone in the swarm. We publicly release the source code of the proposed system to foster further developments in mapping and exploration using autonomous nano drones., Comment: Submitted to IEEE Robotics and Automation Letters

We initiate the study of stability and pseudospectra of complex momentum modes of asymptotically anti-de Sitter black holes. Similar to quasinormal modes, these can be defined as the poles of the holographic Green's function, albeit for real frequency and complex momentum. Their pseudospectra are in stark contrast to the pseudospectra of quasinormal modes of AdS black holes. Contrary to the case of quasinormal mode pseudospectra, the resolvent is well-defined, and the numerical approximation shows fast convergence. At zero frequency, complex momentum modes are stable normal modes of a Hermitian operator. Even for large frequencies, they show only comparatively mild spectral instability. We also find that local potential perturbations cannot destabilize the lowest complex momentum mode., Comment: 36 pages, 11 figures

This work investigates the behavior of impurities in edge plasma of tokamaks using high-resolution numerical simulations based on Hasegawa--Wakatani equations. Specifically, it focuses on the behavior of inertial particles, which has not been extensively studied in the field of plasma physics. Our simulations utilize one-way coupling of a large number of inertial point particles, which model plasma impurities. We observe that with Stokes number ($St$) which characterizes the inertia of particles being much less than one, such light impurities closely track the fluid flow without pronounced clustering. For intermediate $St$ values, distinct clustering appears, with larger Stokes values, {\it i.e.} heavy impurities even generating more substantial clusters. When $St$ is significantly large, very heavy impurities tend to detach from the flow and maintain their trajectory, resulting in fewer observable clusters and corresponding to random motion. A core component of this work involves machine learning techniques. Applying three different neural networks - Autoencoder, U-Net, and Generative Adversarial Network (GAN) - to synthesize preferential concentration fields of impurities, we use vorticity as input and predict impurity number density fields. GAN outperforms the two others by aligning closely with direct numerical simulation data in terms of probability density functions of the particle distribution and energy spectra. This machine learning technique holds the potential to reduce computational costs by eliminating the need to track millions of particles modeling impurities in simulations., Comment: 12 pages, 19 figures

We propose a symmetry resolution of entanglement for categorical non-invertible symmetries (CaT-SREE) in (1 + 1)-dimensional CFTs. The definition parallels that of group-like invertible symmetries, employing the concept of symmetric boundary states with respect to a categorical symmetry. Our examination extends to rational CFTs, where the behavior of CaT-SREE mirrors that of group-like invertible symmetries. We find that CaT-SREE can be defined if there is no obstruction to gauging the categorical symmetry, as happens in the case of group-like symmetries. We also provide instances of the breakdown of entanglement equipartition at the next-to-leading order in the cutoff expansion. Our findings shed light on how the interplay between conformal boundary conditions and categorical symmetries lead to specific patterns in the entanglement entropy., Comment: 6 pages + 3 pages appendix, 1 figure. v2: matches published version

Collaborative Simultaneous Localization and Mapping (CSLAM) is critical to enable multiple robots to operate in complex environments. Most CSLAM techniques rely on raw sensor measurement or low-level features such as keyframe descriptors, which can lead to wrong loop closures due to the lack of deep understanding of the environment. Moreover, the exchange of these measurements and low-level features among the robots requires the transmission of a significant amount of data, which limits the scalability of the system. To overcome these limitations, we present Multi S-Graphs, a decentralized CSLAM system that utilizes high-level semantic-relational information embedded in the four-layered hierarchical and optimizable situational graphs for cooperative map generation and localization in structured environments while minimizing the information exchanged between the robots. To support this, we present a novel room-based descriptor which, along with its connected walls, is used to perform inter-robot loop closures, addressing the challenges of multi-robot kidnapped problem initialization. Multiple experiments in simulated and real environments validate the improvement in accuracy and robustness of the proposed approach while reducing the amount of data exchanged between robots compared to other state-of-the-art approaches. Software available within a docker image: https://github.com/snt-arg/multi_s_graphs_docker, Comment: 8 pages paper presented to IEEE RA-L

Permanent magnets are integral components in many of the modern technologies that are critical for the transition to a sustainable society. However, most of the high-performance (BHmax > 100 kJ/m3) permanent magnets that are currently employed contain rare earth elements (REE), which have long been classified as critical materials with a high supply risk and concerns regarding pollution in their mining. Therefore, suitable REE-lean/free magnets must be developed in order to ensure the sustainability of clean energy generation and electric mobility. The REE-free hexagonal ferrites (or hexaferrites) are the most used permanent magnets across all applications, with an 85 wt.% pie of the permanent magnet market. They are the dominant lower-grade option (BHmax < 25 kJ/m3) due to their relatively good hard magnetic properties, high Curie temperature (>700 K), low cost and good chemical stability. In recent years, the hexaferrites have also emerged as candidates for substituting REE-based permanent magnets in applications requiring intermediate magnetic performance (25-100 kJ/m3), due to considerable performance improvements achieved through chemical tuning, nanostructuring and compaction/sintering optimization. This chapter reviews the state-of-the-art sintering strategies being investigated with the aim of manufacturing hexaferrite magnets with optimized magnetic properties, identifying key challenges and highlighting the natural future steps to be followed.

Powder X-ray diffraction (PXRD) and neutron powder diffraction (NPD) have been used to investigate the crystal structure of CoFe2O4 nanoparticles prepared via different hydrothermal synthesis routes, with particular attention given to accurately determining the spinel inversion degrees. The study is divided into four parts. In the first part, the investigations focus on the influence of using different diffraction pattern combinations (NPD, Cu-source PXRD and Co-source PXRD) for the structural modelling. It is found that combining PXRD data from a Co source with NPD data offers a robust structural model. The second part of the study evaluates the reproducibility of the employed multipattern Rietveld refinement procedure using different data sets collected on the same sample, as well as on equivalently prepared samples. The refinement procedure gives reproducible results and reveals that the synthesis method is likewise reproducible since only minor differences are noted between the samples. The third part focuses on the structural consequences of (i) the employed heating rate (achieved using three different hydrothermal reactor types) and (ii) changing the cobalt salt in the precursors [aqueous salt solutions of Co(CH3COOH)2, Co(NO3)2 and CoCl2] in the synthesis. It is found that increasing the heating rate causes a change in the crystal structure (unit cell and crystallite sizes) while the Co/Fe occupancy and magnetic parameters remain similar in all cases. Also, changing the type of cobalt salt does not alter the final crystal/magnetic structure of the CoFe2O4 nanoparticles. The last part of this study is a consideration of the chemicals and parameters used in the synthesis of the different samples.

During the past decade, CoFe2O4 (hard)/Co-Fe alloy (soft) magnetic nanocomposites have been routinely prepared by partial reduction of CoFe2O4 nanoparticles. Monoxide (i.e., FeO or CoO) has often been detected as a byproduct of the reduction, although it remains unclear whether the formation of this phase occurs during the reduction itself or at a later stage. Here, a novel reaction cell was designed to monitor the reduction in situ using synchrotron powder X-ray diffraction (PXRD). Sequential Rietveld refinements of the in situ data yielded time-resolved information on the sample composition and confirmed that the monoxide is generated as an intermediate phase. The macroscopic magnetic properties of samples at different reduction stages were measured by means of vibrating sample magnetometry (VSM), revealing a magnetic softening with increasing soft phase content, which was too pronounced to be exclusively explained by the introduction of soft material in the system. The elemental compositions of the constituent phases were obtained from joint Rietveld refinements of ex situ high-resolution PXRD and neutron powder diffraction (NPD) data. It was found that the alloy has a tendency to emerge in a Co-rich form, inducing a Co deficiency on the remaining spinel phase, which can explain the early softening of the magnetic material.

CoFe2O4/Co-Fe magnetic composites are usually prepared through partial reduction of CoFe2O4, which often yields monoxides (i.e., FeO, CoO) as secondary phases. Since these compounds are paramagnetic at ambient conditions, the presence of a small amount of monoxide is generally downplayed in the literature, and the possible effects on the magnetic properties are simply ignored. However, the present study shows that even a low concentration of monoxide results in decoupling of the soft and hard magnetic phases, which inevitably leads to a deterioration of the magnetic properties. Additionally, it is confirmed that a partial reduction of CoFe2O4 is a suitable method to produce CoFe2O4/Co-Fe nanocomposites, provided that the treatment is well controlled with respect to duration, temperature and flow of reductant. A monoxide-free nanocomposite was produced and its magnetic properties evaluated both at room and low temperature. Our model system exemplifies the potential of exchange-coupling (and decoupling) as a tool to tune the magnetic properties of a material within a relatively wide range of values, thus widening its spectrum of potential applications.

Agile autonomous drones are becoming increasingly popular in research due to the challenges they represent in fields like control, state estimation, or perception at high speeds. When all algorithms are computed onboard the uav, the computational limitations make the task of agile and robust flight even more difficult. One of the most computationally expensive tasks in agile flight is the generation of optimal trajectories that tackles the problem of planning a minimum time trajectory for a quadrotor over a sequence of specified waypoints. When these trajectories must be updated online due to changes in the environment or uncertainties, this high computational cost can leverage to not reach the desired waypoints or even crash in cluttered environments. In this paper, a fast lightweight dynamic trajectory modification approach is presented to allow modifying computational heavy trajectories using Local Gaussian Modifiers (LGMs), when recalculating a trajectory is not possible due to the time of computation. Our approach was validated in simulation, being able to pass through a race circuit with dynamic gates with top speeds up to 16.0 m/s, and was also validated in real flight reaching speeds up to 4.0 m/s in a fully autonomous onboard computing condition.

The development of autonomous aerial systems, particularly for multi-robot configurations, is a complex challenge requiring multidisciplinary expertise. Unlike ground robotics, aerial robotics has seen limited standardization, leading to fragmented development efforts. To address this gap, we introduce Aerostack2, a comprehensive, open-source ROS 2 based framework designed for creating versatile and robust multi-robot aerial systems. Aerostack2 features platform independence, a modular plugin architecture, and behavior-based mission control, enabling easy customization and integration across various platforms. In this paper, we detail the full architecture of Aerostack2, which has been tested with several platforms in both simulation and real flights. We demonstrate its effectiveness through multiple validation scenarios, highlighting its potential to accelerate innovation and enhance collaboration in the aerial robotics community., Comment: Submitted to IEEE Robotics and Automation Letters

A muon collider would enable the big jump ahead in energy reach that is needed for a fruitful exploration of fundamental interactions. The challenges of producing muon collisions at high luminosity and 10~TeV centre of mass energy are being investigated by the recently-formed International Muon Collider Collaboration. This Review summarises the status and the recent advances on muon colliders design, physics and detector studies. The aim is to provide a global perspective of the field and to outline directions for future work., Comment: 118 pages, 103 figures

We revisit the relation between black hole geometries and chiral transport. Integrating the anomaly equation in a black hole geometry allows to derive the chiral transport coefficients for the thermal gas far from the horizon. The key ingredient is to impose vanishing of the covariant current on the horizon. We extend the method to include the usual gauge anomaly for charged black holes and to weak magnetic fields. This allows to derive the full set of transport coefficients describing the chiral magnetic and chiral vortical effects., Comment: 16 pages