Miracle-dz/UniFAR-FTUs huggingface dataset:
Channel-Agnostic Radio Frequency Fingerprint Identification Using Spectral Quotient Constellation Errors

Radio frequency fingerprint identification (RFFI) is a physical layer security methodology to recognize individual devices by leveraging hardware imperfections inevitably induced in the manufacturing process. However, the performance degradation caused by the time-varying channel impacts and interferences has severely restricted the development of RFFI. To this end, we present a channel-agnostic RFFI system, which consists of three modules, i.e., signal preprocessing module, feature extraction module, and classification module. In the signal preprocessing module, we first propose a novel approach, referred to as limiter-based spectral circular shift bidirectional division (LB-SCSBD), to generate two parallel spectral quotient (SQ) sequences. Then, we define the spectral quotient constellation (SQC) symbols according to different modulation formats, and thereby transform the SQ sequences into four magnitude-based sequences in terms of two channel-robust signal representations, i.e., the SQ magnitude (SQM) and SQC error vector magnitude (SQC-EVM). In the feature extraction module, we present a moment-based statistical feature extractor (MB-SFE) to extract the device-specific information from the above four sequences. In the classification module, the extracted statistics are fed into the multi-class support vector machine (SVM) for training and testing. We take WiFi as a case study and evaluate the performance of the proposed RFFI system by classifying eight simulated device models and six universal software radio peripheral (USRP) transmitter radios. Experimental results show that (i) the proposed method achieves the accuracies of 99.84% and 98.26% with eight devices in QPSK and 16QAM cases, as well as the accuracy of 92.42% with six USRP devices (ii) the proposed method exhibits superior classification performance in comparison to some existing RFFI methods, leading to a significant accuracy improvement of at least 38.33%.
Performance Evaluation of Multi-Hop LoRaWAN

Although LoRa (radio) technology offers customization of different physical layer transmission parameters such as bandwidth, spreading factor (SF), transmission power, and coding rate to obtain the desired data rate and coverage, the currently preferred single-hop transmission mode cannot simultaneously achieve both. The LoRaWAN community is therefore focusing on multi-hop networks to increase network longevity while extending coverage. In this paper, a detailed mathematical model for a multi-hop network that modifies the Distance Ring Exponential Stations Generator (DRESG) framework is presented. The relay operation is carried out through intermediate gateways, and a distance-based adaptive transmission configuration approach is used. On the basis of the performance metrics, i.e., packet delivery ratio (PDR) and energy usage of end nodes, we compare and contrast the performance characteristics of several routing schemes, including single-hop (SH), next-ring-hop (NRH), and variable-hop (VH) routing. The research covers some core issues, including interference, environmental conditions, and transceiver power constraints, and sets the stage for evaluating various multi-hop LoRa solutions as well as optimizing several implementation factors.
Axially Symmetric Willmore Surfaces Determined by Quadratures

The work is concerned with a special family of axially symmetric surfaces providing local extrema to the so-called Willmore functional, which assigns to each surface its total squared mean curvature. The components of the position vector of the proﬁle curves of the regarded Willmore surfaces satisfy a system of ﬁrst-order ordinary differential equations. The solutions of this system are expressed by quadratures in terms of the tangent angle and, in this way, the corresponding Willmore surfaces are determined. MSC: 34A05, 53A05,
Phytochemical Compounds Involved in the Bone Regeneration Process and Their Innovative Administration: A Systematic Review

Bone metabolism is a complex process which is influenced by the activity of bone cells (e.g., osteocytes, osteoblasts, osteoclasts); the effect of some specific biomarkers (e.g., parathyroid hormone, vitamin D, alkaline phosphatase, osteocalcin, osteopontin, osteoprotegerin, osterix, RANKL, Runx2); and the characteristic signaling pathways (e.g., RANKL/RANK, Wnt/β, Notch, BMP, SMAD). Some phytochemical compounds—such as flavonoids, tannins, polyphenols, anthocyanins, terpenoids, polysaccharides, alkaloids and others—presented a beneficial and stimulating effect in the bone regeneration process due to the pro-estrogenic activity, the antioxidant and the anti-inflammatory effect and modulation of bone signaling pathways. Lately, nanomedicine has emerged as an innovative concept for new treatments in bone-related pathologies envisaged through the incorporation of medicinal substances in nanometric systems for oral or local administration, as well as in nanostructured scaffolds with huge potential in bone tissue engineering.
Little tools, big job: The periglacial conveyor system in cryoplanated uplands

The mechanisms of sediment removal associated with the nivation process suite are underinvestigated, a situation that hinders development of a unified, coherent theory of cryoplanation terrace formation. This study links sorted stripes—a type of periglacial patterned ground frequently encountered on cryoplanation terrace treads—to active hydrologic networks capable of transporting large quantities of fine sediments on periglacial hillslopes. Traditional interpretations hold that the presence of sorted patterned ground indicates geomorphic quiescence, a view that has contributed to the dismissal of these features as a factor in the formation of erosional periglacial topography. We address the geomorphic role of sorted stripes as fluvial features by investigating their hydrologic potential for transporting weathered material across and off developing cryoplanation terraces. Flow modeling and watershed geomorphometric analyses were conducted using a high‐resolution digital elevation model of a large cryoplanation terrace in a geomorphically active periglacial upland near Atlin, British Columbia, Canada. Results demonstrate the landscape‐scale spatial organization and geomorphic effectiveness of sorted‐stripe networks—“little tools”—for transporting water and suspended sediment across large cryoplanated surfaces. We present a qualitative model of sediment production and transportation, “the periglacial conveyor system,” that outlines erosional processes responsible for cryoplanation terrace formation and defines the distinctive hydrologic–geomorphic imprint imparted by sorted stripes on periglacial hillslopes.
A DL-Based High-Precision Positioning Method in Challenging Urban Scenarios for B5G CCUAVs

Unmanned aerial vehicles (UAVs) facilitate services in civilian and industrial fields but suffer from a limited direct link operating range and unreliable satellite positioning in urban canyons. Fortunately, cellular-connected UAVs (CCUAVs) overcome these shortcomings, benefitting from the beyond 5th generation (B5G) network’s city-level coverage and high-precision positioning capabilities, and are considered a paradigm of 5G-advanced and beyond. However, in a challenging airspace (e.g., urban canyon), the CCUAV localization accuracy deteriorates due to low signal-to-interference-plus-noise (SINR) air-ground channels and strong multipath effects. To solve these problems, we first construct channel amplitude-phase response (CAPR) images to characterize the cellular channel in a challenging airspace for CCUAV positioning. In particular, the effect of down-tilted antennas and high-dimensional channel features are embedded into CAPR images, to meet the relevant cellular communication criteria. Subsequently, a deep learning (DL) model, the scale-shared quarter network (SSQ-Net), is devised for CAPR image-based positioning, along with a robustness enhancement method. With this method, the multipath effects and interference in challenging environments are exploited to improve positioning accuracy and robustness, instead of being treated as detriments. Finally, the experimental results in a typical urban canyon show that our method outperforms state-of-the-art methods in terms of accuracy and robustness.
Seasonal Variation and Factors Affecting Trypanosoma theileri Infection in Wild Sika Deer (Ezo Sika Deer Cervus nippon yesoensis) in Eastern Hokkaido

Simple Summary The prevalence of parasitic infection exhibits seasonal variation and is affected by several factors, including environmental and host conditions. In this study, we investigated the seasonal variation in and factors affecting Trypanosoma theileri Laveran, 1902, infection in wild sika deer (Ezo sika deer) Cervus nippon yesoensis (Heude, 1884) in Eastern Hokkaido, Japan. The seasonal prevalence of trypanosome infection ranged from 0 to 41% (mean: 13%) based on hematocrit concentrations and from 17 to 89% (mean: 46%) based on PCR results. The factors identified as significantly affecting trypanosome infection included host age and sampling season, as per multiple logistic regression analysis. This is the first study to investigate the seasonal variation of and risk factors affecting trypanosome infection in wild deer. Abstract Trypanosoma (Megatrypanum) spp. are isolated from domestic and wild ruminants, including deer, worldwide. The prevalence of trypanosomes in mammals is influenced by a number of factors such as host age and vector abundance. However, the seasonal variation of and factors affecting trypanosome infection in the wild deer population remain elusive. In this study, we analyzed the seasonal variation in trypanosome prevalence and the factors that affect Trypanosoma theileri Laveran, 1902, infection in wild sika deer (Ezo sika deer) Cervus nippon yesoensis (Heude, 1884) in Eastern Hokkaido through a two-year survey. Seasonal variation in the prevalence of trypanosome infection in the deer population ranged from 0 to 41% as per hematocrit concentration and 17 to 89% as per PCR results. In general, the prevalence of T. theileri by PCR in 2020 was higher than that in 2019. Moreover, the prevalence was significantly higher in the aged population than among the younger population. These findings may explain why individual conditions and sampling season were associated with trypanosome prevalence. This is the first study to investigate the seasonal variation in and risk factors affecting trypanosome infection in wild deer.
A Robust Local Linear Data Decomposition

Principal Component Analysis (PCA) is one of the most fundamental tools for general data analysis, but suffers from severe sensitivity to outliers such as impulse noise or (in the context of image and video analysis) small occlusions. A recent development in matrix completion (a branch of compressed sensing) is Principal Component Pursuit [1], a robust form of PCA that decomposes a data matrix into low rank and sparse components, the former representing a low-dimensional linear model of the data, and the latter representing sparse deviations from the low-dimensional subspace. This decomposition has been applied, with promising results, to a variety of data analysis problems, including in image and video analysis. A significant limitation, however, is that the underlying model assumes the data lie in a single global low-dimensional subspace. We have generalized this model to a union of low-dimensional subspaces, which can describe data lying within a nonlinear manifold. Our approach is motivated by the observation that it should be possible to represent a point in a low-dimensional subspace as a sparse linear combination of its neighbors in the same subspace. The decomposition represents the data matrix as the sum of a sparse additive component and a structured sparse representation using itself as a dictionary. An efficient Alternating Direction Method of Multipliers [2] based algorithm has been developed for this decomposition. We demonstrate the performance of this new decomposition on a video background modeling problem. Principal Component Pursuit achieves very good results for video from a stationary camera, but the single low dimensional subspace model of the background is violated when the camera is slowly panning. In this case, our more general model shows significantly better performance.
A Monte Carlo Assessment of the Effect of Different Ventilation Strategies to Mitigate the COVID-19 Contagion Risk in Educational Buildings

The COVID-19 pandemic outbreak has increased the general awareness of the importance of proper ventilation in the indoor environment to reduce the contagion risk. In particular, attention has been paid to specific categories of buildings, such as schools, due to two factors: (1) high occupancy density and (2) the presence of young and sometimes more susceptible people. Despite the high level of alertness towards the ventilation of classrooms, robust analyses of the effectiveness of the different strategies to mitigate the contagion risk have been difficult to perform. Indeed, the COVID-19 pandemic is still ongoing, and many factors, such as the presence of multiple viral strains, use of facial masks, progression in vaccination, and installation of air purifiers and other sanitization devices, make it difficult to fully quantify the impact of room ventilation by simply analysing available monitoring data. Moreover, mitigation strategies related to ventilation are often dynamic, increasing the complexity of the problem to assess. In this framework, this work proposes a new Monte Carlo method integrated with building performance simulation to evaluate the number of infected occupants under different scenarios, considering also the dynamic boundary conditions. The described approach has been applied to a case study classroom at the Free University of Bozen-Bolzano, Italy, analysing almost 100 different scenarios and discussing the effectiveness of different ventilation strategies traditionally adopted to ensure suitable IAQ according to CO2 concentration limits. Results highlight the importance of combining different solutions (e.g., mixed-mode ventilation and facial masks) to limit the risk for both students and lecturers.
Finite Weibull Mixture Distribution Model of VOT Distribution Forecasting

Aimed at problem that data of complicated value of time(VOT) can not be fitted by simple distribution mixture functions frequently,the finite Weibull mixture distribution model for modelling VOT distribution was proposed.Expectation maximum(EM) algorithm was used to deduce the interative formulae of the model and the formulae were applied to fitting of VOT.In order to explore fitting characteristics of the model on complicated data distribution,simulated comparisons were carried out and the model based on EM algorithm was applied to analysis and calculation of urban passenger traffic mode sharing ratio.Results show that the model based on the EM algorithm is the optimal choice.The model can be effectively applied to VOT distribution modelling and forecasting of traffic mode sharing ratio.
Spectral Efficiency Analysis Using Underlay Cognitive Radio Transmission over Rician Fading Channels

The unlicensed users utilize a frequency band with the licensed users with no adverse interference on the licensee, this can be achieved by the cognitive radio (CR) technique. This study examines how effectively underlay CR transmission improves spectrum efficiency. We specifically calculated generalized area spectral efficiency (GASE) parameter assessment and design of underlay CR transmission. The ergodic capacity of primary and secondary users in underlay CR transmission over a Rician fading channel was initially described in closed-form expressions. Further, the GASE performance of the underlay CR transmission is examined mathematically in relation to the interference restriction on the primary receiver. Finally, all the MATLAB simulations are exactly intact with the theoretical analysis.
Feature preserving point cloud simplification

3Dscanning devices generally produce a large amount of dense points.This paper presents a feature preserving point cloud simplification method to reduce redundant points while preserving original geometric features well.Firstly,K-mean clustering algorithm was employed to globally gather similar points in a spatial domain.By constructing aK-dtree structure for the point cloud,some nodes of the K-d tree were used as initial clustering centroids.Then,normal vector of point cloud and candidate feature points were estimated with principal component analysis method.Traversing every cluster,if feature points were contained in the cluster,the cluster was subdivided into a series of sub-clusters and the cluster was mapped to a Gaussian sphere.Finally,adaptive mean shift algorithm was employed to classify the data in Gaussian sphere and the clusters in Gaussian sphere were corresponded to the sub-clusters in the spatial domain.The cluster centroids present the simplification data.Several real object models were used to verify the effectiveness of the proposed method.The experiment results demonstrate that the proposed method generates sparse sampling points in flat areas and high density points in high curvature regions.As comparing with the nonuniform grid,hierarchical agglomerative,and K-means methods,the proposed method obtains thesmallest simplification error and preserves original geometric features.
Exact solutions and transformation properties of nonlinear partial differential equations from general relativity

Various families of exact solutions to the Einstein and Einstein-Maxwell field equations of General Relativity are treated for situations of sufficient symmetry that only two independent variables arise. The mathematical problem then reduces to consideration of sets of two coupled nonlinear differential equations. The physical situations in which such equations arise include: a) the external gravitational field of an axisymmetric, uncharged steadily rotating body, b) cylindrical gravitational waves with two degrees of freedom, c) colliding plane gravitational waves, d) the external gravitational and electromagnetic fields of a static, charged axisymmetric body, and e) colliding plane electromagnetic and gravitational waves. Through the introduction of suitable potentials and coordinate transformations, a formalism is presented which treats all these problems simultaneously. These transformations and potentials may be used to generate new solutions to the Einstein-Maxwell equations from solutions to the vacuum Einstein equations, and vice-versa. The calculus of differential forms is used as a tool for generation of similarity solutions and generalized similarity solutions. It is further used to find the invariance group of the equations; this in turn leads to various finite transformations that give new, physically distinct solutions from old. Some of the above results are then generalized to the case of three independent variables.
Contracting Out and the Fiscal Sustainability of Public Services

Abstract This study explores how contracting out can contribute to the fiscal sustainability of public services. Together with the economic advantage that contracting out can lower production costs over government production in house, contracting theory informs that contracting out can offer an insulation of efficient and effective service production from the political incentive problem of the principal. Hypotheses from the perspective are tested over panel data on subnational public service facilities operated via user fees in South Korea. As hypothesized, the analysis results confirm a positive relationship of contracting out with net revenue over government production. While both regionally/municipally owned corporations (R/MOCs) and nonprofits demonstrate their capabilities as competent partners of a government, nonprofits are more effective at the generation of net revenue. The study discusses the implications of contracting out for publicness and a style of governance that actively utilizes the flexibility and expertise of organizations outside a government.
Implementing a Public Policy to Extend Social Security to Informal Economy Workers in Zambia

Abstract This article analyses the strengths and bottlenecks of institutional capacity between social security institutions implementing the reform in Zambia, which focuses to provide social security to small-scale dairy farmers, a group of informal economy workers. Zambia’s informal economy workers absorb over 80 per cent of the labour force. This is a qualitative study of institutional capacity in the extension of social security. Twenty-one interviews were conducted with participants from Farmers’ Cooperatives (MCC), National Pension Scheme Authority (NAPSA), and Dairy Association of Zambia (DAZ). We selected participants through a purposive sampling technique. We reflected on data using a Consolidated Framework for Implementation Research (CFIR) built on thematic analysis. Analysis suggests that the institutions of the partnership are committed towards extending social security to informal economy workers. There was low involvement of local NAPSA officers in the project design and their role during implementation of the pension extension was unclear. This contributed to a lack of trust by some non-NAPSA members towards social security institutions. Knowledge and beliefs about the capabilities of implementers were essential in the activities for implementing the public policy on the extension of social security. However, there are several implementation lacunas concerning the process, its overarching infrastructure, and adequate human resources. There is a critical need to address gaps in process and procedures, equipment and materials, infrastructure, human resource, trust, and knowledge of context for the extension of social security to informal economy workers in Zambia. This could make the new public policy scheme more attainable.
Fixed and Recursive Right-Tailed Dickey–Fuller Tests in the Presence of a Break under the Null

Abstract Right-tailed Dickey–Fuller-type unit root tests against the explosive alternative have become popular in economics and finance for detecting asset price bubbles. This paper studies the size properties of fixed sample and recursive right-tailed Dickey–Fuller tests if the relevant series contains a unit root, but a structural break in the drift parameter occurs. It is shown that positive size distortion and therefore spurious rejections of the unit root null hypothesis in favour of the explosive alternative can be a problem for both types of test. Some possible solutions to this problem are briefly discussed.
Research on transition state control strategy of propfan engine based on SAC algorithm

The engine performance search control plays a key role in the engine operation. How to find a suitable algorithm to make the control process fast and stable without exceeding various engine constraints is very important. With the continuous development of deep reinforcement learning in recent years, not only many deep reinforcement learning algorithms have emerged, but also deep reinforcement learning has made rapid development in the field of control. Considering the strong perception and decision-making ability of the deep reinforcement learning algorithm, and in order to adapt to the strong nonlinearity of the engine model and avoid falling into the local optimal value, this paper uses the SAC (Soft Actor Critical, SAC) algorithm to optimize the transition state performance. By introducing the strategy information entropy into the objective function, the search stability of the algorithm and the final optimization effect are significantly improved. Finally, the SAC algorithm is compared with the DDPG algorithm to optimize the transition state performance, and the results show that the SAC algorithm has a better optimization effect on the transition state performance.