Miracle-dz/UniFAR-FTUs huggingface dataset:
The global context of the 14 November 2012 storm event

From 2 to 5 UT on 14 November 2012, the Van Allen Probes observed repeated particle flux dropouts during the main phase of a geomagnetic storm as the satellites traversed the post‐midnight to dawnside inner magnetosphere. Each flux dropout corresponded to an abrupt change in the magnetic topology, i.e., from a more dipolar configuration to a configuration with magnetic field lines stretched in the dawn‐dusk direction. Geosynchronous GOES spacecraft located in the dusk and near‐midnight sectors and the LANL constellation with wide local time coverage also observed repeated flux dropouts and stretched field lines with similar occurrence patterns to those of the Van Allen Probe events. THEMIS recorded multiple transient abrupt expansions of the evening‐side magnetopause ∼20–30 min prior to the sequential Van Allen Probes observations. Ground‐based magnetograms and all sky images demonstrate repeatable features in conjunction with the dropouts. We combine the various in situ and ground‐based measurements to define and understand the global spatiotemporal features associated with the dropouts observed by the Van Allen Probes. We discuss various proposed hypotheses for the mechanism that plausibly caused this storm‐time dropout event as well as formulate a new hypothesis that explains the combined in situ and ground‐based observations: the earthward motion of magnetic flux ropes containing lobe plasmas that form along an extended magnetotail reconnection line in the near‐Earth plasma sheet.
Electromagnetic scattering from geophysical targets by means of the T matrix approach

In the present contribution we review some recent work on scattering of electromagnetic waves from various geophysical targets, by means of P. C. Waterman's approach. As an introduction to the specific applications to be described, a general outline is given of the basic features of this approach, as applied to electromagnetic waves. The algebraic structure of the resulting equations for single and multiple scattering situations is discussed with special emphasis on the case of an infinite interface and an adjacent finite inhomogeneity in one of the half spaces. We review a number of applications relevant for electromagnetic prospecting. The bedrock is modeled as a lossy half space, and various configurations of ore bodies in this half space are considered. The measurements are assumed to be made on the surface or along a borehole into the ground, and the radiation source is again either on the ground or in the borehole. The behavior of the field in the vicinity of the inhomogeneity and interference effects are discussed. Actual ore bodies are often rather thin plates, and the influence of a layered structure of the half space may be of importance. Therefore in other applications the half space is taken to be layered, and the inhomogeneity is taken to be a perfectly conducting spheroid or a perfectly conducting disc.
Edible Insects for Humans and Animals: Nutritional Composition and an Option for Mitigating Environmental Damage

Simple Summary A potential substitute to reduce the greenhouse gas emissions caused by the current food business is edible insects. This is possible since eating insects contributes to a healthy dietary pattern and lowers the use of nonrenewable resources. This study describes the primary nutritional contributions of insects, emphasizing their protein content (20–70%), amount of essential amino acids (46–96%), lipid content (10–50%)—which includes saturated and unsaturated fatty acids—fiber content (8.5–27%), and contribution of minerals (primarily calcium, copper, manganese, and zinc) and vitamins (mainly B complex vitamins). The concentrations of nutrients can vary considerably according to the species of insect and its stage of development. It can be concluded that the consumption of insects in humans and animals can reduce the environmental impact on the planet and provide a food source that meets all the nutrients necessary for a daily intake. Abstract Every day, there is an increase in environmental damage on the planet regarding human action. One of the causes is food production. Edible insects are presented as an option to mitigate the environmental damage generated by the production of conventional food for humans and animals. The objective of this study was to investigate the main nutritional aspects of insects and how they can provide a nutritional and sustainable alternative to the planet. As the main results, the nine orders of insects that are most consumed on the planet are presented: Blattodea, Coleoptera, Diptera, Hemiptera, Hymenoptera, Isoptera, Lepidoptera, Odonata, and Orthoptera. Their main macro- and micronutrient aspects as well as their bioavailable and bioaccessibility proteins and essential amino acids, monounsaturated fatty acids, minerals, vitamins, and fiber (chitin) are also explored. Additionally, some of the species that are used for animal food processing and the possible risks that insects can present when used as food are discussed. With this, edible insects are established as a real option to mitigate climate change being an important nutritional source for the development of food for humans and animals.
Practical Asynchronous High-threshold Distributed Key Generation and Distributed Polynomial Sampling

Distributed Key Generation (DKG) is a technique to boot-strap threshold cryptosystems without a trusted party. DKG is an essential building block to many decentralized protocols such as randomness beacons, threshold signatures, Byzantine consensus, and multiparty computation. While significant progress has been made recently, existing asynchronous DKG constructions are inefficient when the reconstruction threshold is larger than one-third of the total nodes. In this paper, we present a simple and concretely efficient asynchronous DKG (ADKG) protocol among n = 3 t + 1 nodes that can tolerate up to t malicious nodes and support any reconstruction threshold ℓ ≥ t . Our protocol has an expected O ( κ n 3 ) communication cost, where κ is a security parameter, and only assumes the hardness of Discrete Logarithm. The core ingredient of our ADKG protocol is an asynchronous protocol to secret share a random polynomial of degree ℓ ≥ t , which has other applications such as asynchronous proactive secret sharing and asynchronous multiparty computation. We implement our high-threshold ADKG protocol and evaluate it using a network of up to 128 geographically distributed nodes. Our evaluation shows that our high-threshold ADKG protocol reduces the running time by 90% and reduces the bandwidth usage by 80% over state-of-the-art.
Reasoning about the antecedents of emotions: {{Bayesian}} causal inference over an intuitive theory of mind

It is commonly believed that expressions visually signal rich diagnostic information to human observers. We studied how observers interpret the dynamic expressions that people spontaneously produced during a real-life high-stakes televised game. We find that human observers are remarkably poor at recovering what events elicited others’ facial and bodily expressions. Beyond simple inaccuracy, people’s causal reasoning exhibits systematic model-based patterns of errors. We show that latent emotion representations can explain people’s reasoning about the unseen causes of expressions. A hierarchical Bayesian model simulates which events people infer to be the cause of others’ expressions by comparing the emotions inferred from the expressions against the emotions people were predicted to experience in various situations. This causal model provides a close, parameter-free fit to human causal judgments, suggesting that humans interpret expressions in the context of emotion predictions generated by a causally-structured mental model of other minds.
Obstructive Sleep Apnea in Pregnancy: A Narrative Review

Obstructive sleep apnea (OSA) is a sleep-related breathing disorder that remains underdiagnosed in pregnancy and can cause severe maternal and fetal complications. The prevalence of OSA in pregnancy had a wide variation and increases with increasing gestation. At-risk women are those with increased body mass index (BMI), increasing age, and chronic hypertension. Screening can be done by polysomnography but as it is expensive and necessitate overnight hospital stay, makes it even harder to diagnose and leads to delay in treatment. Once diagnosed, continuous positive airway pressure (CPAP) is the gold standard treatment. Early diagnosis and effective treatment can considerably improve the outcome. This review aims to discuss the diagnosis, treatment, and prognosis of OSA in pregnancy.
Tunable and Switchable Multi-Wavelength Erbium-Doped Fiber Laser Based on Composite Structure Filter

A multi-wavelength erbium-doped fiber laser (MW-EDFL) with wide tuning range, switching and adjustable wavelength interval is designed and tested, which is based on a composite filter. The filter consists of a tapered microfiber coupler loop (TMCL) with a nested single mode fiber (SMF)-two mode fiber (TMF)-SMF (STS) structure, which has a comb spectrum with obvious envelope and uniform fluctuation. Our experimental and theoretical results show that the laser can output thirteen wavelengths, when the angles of two polarization controllers (PCs) in the TMCL are accurately set. Moreover, by adjusting the PCs, the tuning range of single- to sextuple-wavelength can reach about 40 nm. Six non-adjacent multi-wavelength outputs can be observed in some specific polarization states. The maximum side-mode suppression ratio (SMSR) of the output laser is 40.6 dB. Compared with other multi-wavelength EDFL, the output characteristics of the laser, such as the adjustability and flexibility of wavelength spacing and the switch-ability of wavelength number, have been improved.
Developments in the computation of turbulent boundary layers

Computational techniques applicable to turbulent boundary layers are classified into solutions of Reynolds-averaged equations, in which all the effects of the turbulence are modelled, and solutions of three-dimensional, time dependent Navier-Stokes equations, in which the large eddies are calculated and only the turbulence at scales smaller than the computational mesh spacings has to be modelled. Current computation costs place engineering computations in the first of these categories; large eddy simulations are appropriate currently for special studies of the dynamical processes of turbulence in idealized flow fields. It is shown that the two methods are interrelated and that each can gain from advances in the other. The degree of success of a pair of increasingly complex Reynolds stress models to broaden their range of applicability is examined through comparisons with experimental data for a variety of flow conditions. An example of a large-eddy simulation is presented, compared with experimental results, and used to evaluate the models for pressure rate-of-strain correlations and dissipation in the Reynolds-averaged equations.
Modeling Effective Lifespan of Payment Channels

While being decentralized, secure, and reliable, Bitcoin and many other blockchain-based cryptocurrencies suffer from scalability issues. One of the promising proposals to address this problem is off-chain payment channels. Since, not all nodes are connected directly to each other, they can use a payment network to route their payments. Each node allocates a balance that is frozen during the channel's lifespan. Spending and receiving transactions will shift the balance to one side of the channel. A channel becomes unbalanced when there is not sufficient balance in one direction. In this case, we say the effective lifespan of the channel has ended. In this paper, we develop a mathematical model to predict the expected effective lifespan of a channel based on the network's topology. We investigate the impact of channel unbalancing on the payment network and individual channels. We also discuss the effect of certain characteristics of payment channels on their lifespan. Our case study on a snapshot of the Lightning Network shows how the effective lifespan is distributed, and how it is correlated with other network characteristics. Our results show that central unbalanced channels have a drastic effect on the network performance.
Open Research Online The role of claw color in species recognition and mate choice in a ﬁddler crab

Many animal signals are brightly colored and convey information about species identity as well as information about individual conspecifics. Colorful bird and lizard signals have received much attention, and many studies have related specific spectral properties of these signals to variation in mating success and territory defense. Far less attention has been given to invertebrates even though there are spectacularly colorful species. The enlarged claw of the male banana fiddler crab Austruca mjoebergi , for example, is bright yellow and contrasts vividly against the mudflat substrate. It is used in waving displays to attract females and in male territory defense and combat. Claw color varies among males in the degree of “ yellowness, ” ranging very pale yellow to orange. In this study, we examined female responses to claw color variation in two-choice tests using robotic crabs. We found that although females strongly discriminate against colors that fall outside the natural range of intensity, hue, and chroma, they show no consistent preferences for different claw colors within the natural range, and no single component of claw color (hue, chroma, or intensity) independently affected female choices. Using three-choice tests, we also showed that female preferences induce stabilizing selection on male claw color. We conclude that, although claw color is sufficient to facilitate species recognition, it is unlikely to be used in intraspecific mate choice to provide information about male quality. mate choice decisions.
A compliant thorax design for robustness and elastic energy exchange in flapping-wing robots

Flapping wing insects benefit from a compliant thorax that provides elastic energy exchange and resiliency to wing collisions. In this paper, we present a flapping wing robot that uses an underactuated compliant transmission inspired by the insect thorax. We developed a novel fabrication method that combines carbon fiber (CF) laminate and soft robotics fabrication techniques for transmission construction. The transmission design is optimized to achieve desired wingstroke requirements and to allow for independent motion of each wing. We validate these design choices in bench-top tests measuring transmission compliance and kinematics. We integrate the transmission with laminate wings and two types of actuation, demonstrating elastic energy exchange and limited lift-off capabilities Lastly, we tested collision mitigation through flapping wing experiments that obstructed the motion of a wing. These experiments demonstrate that an underactuated compliant, transmission can provide resilience and robustness to flapping wing robots.
Social Considerations: Health, Economics, and Risk Communication

At a fundamental level, smoke from wildland ﬁre is of scientiﬁc concern because of its potential adverse effects on human health and social well-being. Although many impacts (e.g., evacuations, property loss) occur primarily in proximity to the actual ﬁre, smoke can end up having a signiﬁcant social impact far from the source. This dynamic, combined with lengthening ﬁre seasons, suggests that understanding how wildland ﬁre smoke affects diverse social values will be increasingly critical. This chapter reviews the existing scientiﬁc knowledge related to wildland ﬁre smoke with respect to four topic areas: human health, economics, social acceptability, and risk communication. The broadest existing knowledge base, regarding the health effects attributed to wildland ﬁre smoke exposure, stems from decades of research on the health effects of exposures to ambient ﬁne particulate matter (PM 2.5 ). Despite the potential consequences, scientiﬁc knowledge about chronic health effects, economic impacts, and effectiveness of protective actions in response to wildﬁre smoke risk communication is fairly limited. The chapter concludes with identiﬁcation of (1) key areas where the need for more empirical information is most critical, and (2) challenges that inhibit an improved scientiﬁc understanding.
Primary production export flux in Marguerite Bay (Antarctic Peninsula): linking upper water-column production to sediment trap flux

A study was carried out to assess primary production and associated export flux in the coastal waters of the western Antarctic Peninsula at an oceanographic time-series site. New, i.e. exportable, primary production in the upper water-column was estimated in two ways; by nutrient deficit measurements, and by primary production rate measurements using separate C-labelled radioisotope and N-labelled stable isotope uptake incubations. The resulting average annual exportable primary production estimates at the time-series site from nutrient deficit and primary production rates were 13 and 16 mol C m respectively. Regenerated primary production was measured using N-labelled ammonium and urea uptake, and was low throughout the sampling period. The exportable primary production measurements were compared with sediment trap flux measurements from 2 locations; the time-series site and at a site 40 km away in deeper water. Results showed ~1% of the upper mixed layer exportable primary production was exported to traps at 200m depth at the time-series site (total water column depth 520m). The maximum particle flux rate to sediment traps at the deeper offshore site (total water column depth 820m) was lower than the flux at the coastal time-series site. Flux of particulate organic carbon was similar throughout the spring-summer high flux period for both sites. Remineralisation of particulate organic matter predominantly
Anonymizing Trajectory Data: Limitations and Opportunities

A variety of conditions and limiting properties complicate the anonymization of trajectory data, since they are sequential, high-dimensional, bound to geophysical restrictions and easily mapped to semantic points of interest and regions with known properties like suburban neighborhoods, industrial areas or city-centers. Learning the places where one has been is extremely privacy-invasive. However, analyzing real trajectories holds numerous promises, ranging from better informed traffic management, to location recommendations or computational social science, infrastructure and even urban development planning. The aim of this paper is to establish various challenges, stemming from ideas and also limitations of existing proposals for the anonymization of trajectories, and subsequently identify research opportunities. Keeping both utility and privacy challenges prominent, we sketch the way towards establishing a useful research framework and propose possible research venues towards privacy-preserving trajectory publication.
Disentangling Rent Index Differences: Data, Methods, and Scope

Prominent rent growth indices often give strikingly different measurements of rent inflation. We create new indices from Bureau of Labor Statistics (BLS) rent microdata using a repeat-rent index methodology and show that this discrepancy is almost entirely explained by differences in rent growth for new tenants relative to the average rent growth for all tenants. Rent inflation for new tenants leads the official BLS rent inflation by four quarters. As rent is the largest component of the consumer price index, this has implications for our understanding of aggregate inflation dynamics and guiding monetary policy. File is available with NTRR and ATRR indices through 2022q3.
On the existence of a positive definite solution of the matrix equation X+AXA=I

In this paper the question is raised under which conditions on the real T -1 (square) matrix A, the matrix equation X f A X A- I has a real symmetric positive definite solution X. Both necessary and sufficient solvability conditions on A are derived. Moreover, we give an algorithm to calculate the solution. For a number of special cases we also present an analytic solution.
Trip Planning for Autonomous Vehicles with Wireless Data Transfer Needs Using Reinforcement Learning

With recent advancements in the field of commu-nications and the Internet of Things, vehicles are becoming more aware of their environment and are evolving towards full autonomy. Vehicular communication opens up the possibility for vehicle-to-infrastructure interaction, where vehicles could share information with components such as cameras, traffic lights, and signage that support a country's road system. As a result, vehicles are becoming more than just a means of transportation; they are collecting, processing, and transmitting massive amounts of data used to make driving safer and more convenient. With 5G cellular networks and beyond, there is going to be more data bandwidth available on our roads, but it may be heterogeneous because of limitations like line of sight, infrastructure, and heterogeneous traffic on the road. This paper addresses the problem of route planning for autonomous vehicles in urban areas accounting for both driving time and data transfer needs. We propose a novel reinforcement learning solution that prioritizes high bandwidth roads to meet a vehicle's data transfer requirement, while also minimizing driving time. We compare this approach to traffic-unaware and bandwidth-unaware baselines to show how much better it performs under heterogeneous traffic. This solution could be used as a starting point to understand what good policies look like, which could potentially yield faster, more efficient heuristics in the future.
Multiclass classification for Hawkes processes

We investigate the multiclass classiﬁcation problem where the features are event sequences. More precisely, the data are assumed to be generated by a mixture of simple linear Hawkes processes. In this new setting, the classes are discriminated by various triggering kernels. A challenge is then to build an efﬁcient classiﬁcation procedure. We derive the optimal Bayes rule and provide a two-step estimation procedure of the Bayes classiﬁer. In the ﬁrst step, the weights of the mixture are estimated; in the second step, an empirical risk minimization procedure is performed to estimate the parameters of the Hawkes processes. We establish the consistency of the resulting procedure and derive rates of convergence. Finally, the numerical properties of the data-driven algorithm are illustrated through a simulation study where the triggering kernels are assumed to belong to the popular parametric exponential family. It highlights the accuracy and the robustness of the proposed algorithm. In particular, even if the underlying kernels are misspeciﬁed, the procedure exhibits good performance.