Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification Weighted Deep Neural Network Based Channel Equalization for Massive MIMO-OFDM Systems

Massive multi-input multi-output (MIMO) has attracted signiﬁcant interest in academia and industry, which can eﬃciently increase the transmission rate. However, the error rate of conventional channel equalizations in massive MIMO systems may be high owing to the dynamic channel states in practical conditions. To solve this problem, in this paper, we propose an improved channel equalization framework based on the deep neural network (DNN). Based on the analyzed relationship between the input and output of the DNN, the data can be recovered without the channel state information. Furthermore, aiming at reducing the convergence time and enhancing the learning ability of the DNN, a classiﬁcation weighted algorithm is proposed to optimize the cost function of the DNN, which is named as classiﬁcation weighted deep neural network (CW-DNN). Simulation results demonstrate that compared to conventional counterparts, the proposed CW-DNN based equalizer can achieve a better normalized mean square error (NMSE). Upon approximating the optimal neural network parameters with the signiﬁcantly improved convergence speed and reduced training time of the network, under the condition of the ﬁxed learning rate.
Cubic–quartic optical soliton perturbation and modulation stability analysis in polarization-controlled fibers for Fokas-Lenells equation

The objective of this study is to investigate miscellaneous wave structures for perturbed Fokas–Lenells equation (FLE) with cubic-quartic dispersion in polarization-preserving fibers. Based on the improved projective Riccati equations method, various types of soliton solutions such as bright soliton, combo dark–bright soliton, singular soliton and combo singular soliton are constructed. Additionally, a set of periodic singular waves are also retrieved. The dynamical behaviors of some obtained solutions are depicted to provide a key to understanding the physics of the model. The modulation instability of the FLE is reported by employing the linear stability analysis which shows that all solutions are stable.
A wideband monopole antenna with diplexer and rectifier for simultaneous energy harvesting and data communication

A wideband coplanar waveguide fed monopole antenna with diplexer and rectifier for microwave wireless power transmission and communication is proposed. The antenna is comprised of an asymmetric ground plane with an open stub, a rectangular monopole, and a square loop with a gap at the bottom. A microstrip line diplexer is implemented by using stepped impedance resonator slots to split the signal without extra matching circuits. A microwave rectifier based on the voltage doubler topology is designed for wireless power transmission. The prototypes are fabricated, measured, and compared with the simulated results. Measured results demonstrate that the −10 dB impedance bandwidth (1.42 GHz) of the antenna reaches 69.6% (1.33–2.75 GHz), the diplexer minimum insertion losses ( S21 , S31 ) are 2.13 dB, 3.27 dB at pass band frequencies, the output isolation ( S23 ) better than 20 dB from 1 to 3 GHz, and the peak RF to DC conversion efficiency of a rectifier is 56%. The integration of the proposed antenna, diplexer, and rectifier novel configuration is suitable for simultaneous wireless power transmission and communication applications.
Laser polarization driven micromanipulation and reorientation dynamics of an asymmetric shaped microscopic biomaterial using optical tweezers

We present measurements and a theoretical model that describes the dynamics of ellipsoidal shaped, chicken red blood cells (cRBCs) reorienting in an optical trap and demonstrates the ability to control their reorientation through changes in the intensity distribution that results from the different states of the polarization of the trapping laser. We have observed that in linearly polarized light, cRBC, a type of avian RBC, undergoes dual reorientation, with the first reorientation about the cell’s major axis and the second, about its short minor axis, with the major axis aligning with the laser propagation direction at equilibrium. We compute the work done for each of these reorientations and attribute the observed dynamics to a minimization of the energy cost for the particular sequence of the reorientations that we observe. Further, we achieve a controlled second orientation of the major axis along the laser propagation direction by varying the ellipticity of the polarization of the laser. We explain these partial second reorientation results by employing a geometrical optics-based model. Characterizing the dynamics and control of these regular-shaped natural soft materials through optical polarization is relevant in the context of current work in the design and development of microscopic artefacts such as lab-on-a-chip platforms.
Spatio-temporal fire and forest loss patterns in the Dominican Republic

Fires in the Anthropocene, whether natural or human-induced, are among the main factors of deforestation, threatening forest resilience and biodiversity (Kelly et al. 2020). Fire events have also increased in occurrence and severity worldwide in the past decade (e.g. Whitman et al. 2022, Ribeiro et al. 2020). In this context, we need to better understand the links between fire occurrence and their impact on forest loss, especially in countries where such knowledge is lacking.
A Mathematical Theory of the Gauged Linear Sigma Model

We construct a mathematical theory of Witten's Gauged Linear Sigma Model (GLSM). Our theory applies to a wide range of examples, including many cases with non-Abelian gauge group. Both the Gromov-Witten theory of a Calabi-Yau complete intersection X and the Landau-Ginzburg dual (FJRW-theory) of X can be expressed as gauged linear sigma models. Furthermore, the Landau-Ginzburg/Calabi-Yau correspondence can be interpreted as a variation of the moment map or a deformation of GIT in the GLSM. This paper focuses primarily on the algebraic theory, while a companion article will treat the analytic theory.
Wearable Biosensor Standardization: How to Make Them Smarter

The availability of low-cost plug-and-play devices may contribute to the diffusion of methods and technologies for the personalized monitoring of physiological parameters by wearable devices. This paper is focused on biosensors, which represent an interesting enabling technology for the real-time continuous acquisition of biological or chemical analytes of physio-pathological interest, e.g., metabolites, protein biomarkers, and electrolytes in biofluids. Currently available commercial biosensors are usually referred to as customized and proprietary solutions. However, the efficient and robust development of e-health applications based on wearable biosensors can be eased from device interoperability. In this way, even if the different modules belong to different manufacturers, they can be added, upgraded, changed or removed without affecting the whole data acquisition system. A great effort in this direction has already been made by the ISO/IEC/IEEE 21451 standard that introduces the concept of smart sensors by defining the main and essential characteristics that these devices should have. Following the guidelines provided by this standard, here we propose a set of characteristics that should be considered in the development of a smart biosensor and how they could be integrated into the existing standard.
N-trans-feruloyltyramine Protects Human Neuroblastoma SK-N-SH Cell Line Against H2O2-Induced Cytotoxicity

Excessive accumulation of reactive oxygen species (ROS) has been regarded as a major contributor of pathogenesis in neurodegenerative diseases. N-trans-feruloyltyramine (NTF), an alkaloid isolated from several plants, has demonstrated an ability to be a potent antioxidant. In this study, the antioxidative and anti-apoptotic properties of NTF extracted from the stems of Polyalthia suberosa were investigated in the human neuroblastoma cell line SK-N-SH. NTF at concentrations ranging from 10 µM to 500 µM were not toxic to cells and reduced intracellular ROS levels significantly. Furthermore, pre-treatment of NTF significantly decreased H2O2-induced ROS generation and attenuated H2O2-mediated cytotoxicity. An increase in the expression of Bax and activated caspase-3 and reduction of Bcl-2 mediated by H2O2 was reversed by pre-treating the cells with 100 µM NTF. Likewise, NTF suppressed the increase of caspase-3 activity induced by H2O2. In conclusion, the findings reveal that NTF improves H2O2-induced intracellular ROS generation and decreases apoptosis. These protective effects of NTF could be useful for oxidative stress-related neurodegenerative conditions.
Gamma-ray bursts : second workshop, Huntsville, AL, October 1993

This book contains the papers from the AIP conference on gamma-ray bursts, with the principle focus on presentation and interpretation of observational results. Two possibly significant breakthroughs in solving the mystery of burst origins are discussed, viz., that the distribution of burst duration is double peaked; and evidence of a correlation of burst duration with burst intensity in the manner expected for a cosmological time dilation. Also discussed in the book is the claim that some burst sources produce more than one gamma-ray burst. Improved methods of burst localization and experiments that determine a burster's distance scale by measuring the photoelectric absorption of soft gamma-rays by the galactic interstellar medium are viewed as well. This book should be of interest to astronomers and astrophysicists involved with studying the mystery of gamma-ray bursts.
Water Quality Prediction Based on SSA-MIC-SMBO-ESN

Water pollution threatens the safety of human production and life. To quickly respond to water pollution, it is important for water management staff to predict water quality changes in advance. Drawing on the temporality of water quality data, the leaky integrator echo state network (ESN) was introduced to construct the water quality prediction models for dissolved oxygen (DO), permanganate index (CODMn), and total phosphorus (TP), respectively. First, the missing values were filled by the linear trend method of adjacent points, and the outliers were detected and corrected by the Z-score method and the linear trend method. Second, the singular spectrum analysis (SSA) was performed to denoise the original monitoring data, such that the predicted data catch up with the real data, and the model accuracy is not affected by the hidden noise in the data. Third, the correlation between water quality indices was measured by the maximum information coefficient (MIC), and the strongly correlated indices were imported to the prediction model. Finally, according to these strong correlation indicators, the water quality prediction models based on multiple features were constructed, respectively, using the offline and online learning algorithms of the ESN. The hyperparameters of the models were optimized through the sequential model-based optimization (SMBO). Experimental results show that the proposed water quality prediction models, namely, SSA-MIC-SMBO-Offline ESN and SSA-MIC-SMBO-Online ESN, predicted DO, CODMn, and TP accurately, providing suitable tools for practical applications.
An FPGA-Based Transformer Accelerator Using Output Block Stationary Dataflow for Object Recognition Applications

The transformer-based model has great potential to deliver higher accuracy for object recognition applications when comparing it with the convolution neural network (CNN). Yet, the amount of weight sharing of a transformer-based model is significantly lower than that of the CNN, which should apply different dataflow to reduce the memory access. This brief proposes a transformer accelerator with an output block stationary (OBS) dataflow to minimize the repeated memory access by block-level and vector-level broadcasting while preserving a high digital signal processor (DSP) utilization rate, leading to higher energy efficiency. It also lowers the memory access bandwidth to the input and output. Verified through an FPGA, the proposed accelerator evaluates a transformer-in-transformer (TNT) model with a throughput of 728.3 GOPs, corresponding to energy efficiency of 58.31 GOPs/W.
Comparative Analysis of Radar-Cross-Section- Based UAV Recognition Techniques

This work investigates the problem of unmanned aerial vehicle (UAV) recognition using their radar cross section (RCS) signature. The RCS of six commercial UAVs is measured at 15 and 25 GHz in an anechoic chamber for both vertical–vertical (VV) polarization and horizontal–horizontal (HH) polarization. The RCS signatures are used to train 15 different recognition algorithms, each belonging to one of three different categories: statistical learning (SL), machine learning (ML), and deep learning (DL). The study shows that, while the average accuracy of all the algorithms increases with the signal-to-noise ratio (SNR), the ML algorithm achieved better accuracy than the SL and DL algorithms. For example, the classification tree ML achieves an accuracy of 98.66% at 3-dB SNR using the 15-GHz VV-polarized RCS test data from the UAVs. We investigate the recognition accuracy using the Monte Carlo analysis with the aid of boxplots, confusion matrices, and classification plots. On average, the accuracy of the classification tree ML model performed better than the other algorithms, followed by Peter Swerling’s statistical models and the discriminant analysis ML model. In general, the accuracy of the ML and SL algorithms outperformed the DL algorithms (Squeezenet, Googlenet, Nasnet, and Resnet 101) considered in the study. Furthermore, the computational time of each algorithm is analyzed. The study concludes that, while the SL algorithms achieved good recognition accuracy, the computational time was relatively long when compared to the ML and DL algorithms. Also, the study shows that the classification tree achieved the fastest average recognition time of about 0.46 ms.
Pronounced Seismic Anisotropy in Kanto Sedimentary Basin: A Case Study of Using Dense Arrays, Ambient Noise Seismology, and Multi‐Modal Surface‐Wave Imaging

Metropolitan Tokyo is subject to significant seismic hazards because it is located near a triple junction of tectonic plates and because it sits atop the soft and thick sediments of the Kanto sedimentary Basin. Numerical simulations of earthquake ground motions rely on accurate velocity models of the basin elastic and anelastic structure, which remains to be improved. Here, we leverage the density of permanent stations of the Metropolitan Seismic Observation network seismic network to construct a high‐resolution radially anisotropic (VSV ≠ VSH) shear wave velocity model of the Kanto basin. We construct surface waves using ambient seismic noise cross correlations. In sedimentary structures, it is common for several surface‐wave modes to arise and to couple in time, so we use local sub‐arrays to extract the phase velocity of the fundamental mode and first overtone of Rayleigh and Love waves at short periods (1–7 s). We find that a strong and negative shear‐wave anisotropy (−5%) is confined to the upper 300 m depth range, and a strong and positive anisotropy (+5%) at greater depths. We interpret them as a result of the vertical cracks from repeated earthquake damage and sediment stratigraphy in the Kanto Basin. Because anisotropy models are not routinely used in physics‐based ground motion prediction, our study motivates the consideration of seismic anisotropy in sedimentary basins for the ground motion of future earthquakes.
Sigma‐point particle filter for parameter estimation in a multiplicative noise environment

A pre‐requisite for the “optimal estimate” by the ensemble‐based Kalman filter (EnKF) is the Gaussian assumption for background and observation errors, which is often violated when the errors are multiplicative, even for a linear system. This study first explores the challenge of the multiplicative noise to the current EnKF schemes. Then, a Sigma Point Kalman Filter based Particle Filter (SPPF) is presented as an alternative to solve the issues associated with multiplicative noise. The classic Lorenz '63 model and a higher dimensional Lorenz '96 model are used as test beds for the data assimilation experiments. Performance of the SPPF algorithm is compared against a standard EnKF as well as an advanced square‐root Sigma‐Point Kalman Filters (SPKF). The results show that the SPPF outperforms the EnKF and the square‐root SPKF in the presence of multiplicative noise. The super ensemble structure of the SPPF makes it computationally attractive compared to the standard Particle Filter (PF).
Adaptive neural control of nonlinear fractional order multi- agent systems in the presence of error constraintion

: In this paper, the problem of fractional order multi-agent tracking control problem is considered. Uncertainties, error constraints, transient response suitability and desirable response tracking problems are the challenges in this study. Because of these problems and challenges, the adaptive control, and Neural Networks approximators approaches are used in this study. In the first part of this article, the fractional order multi-agent systems are investigated in the presence of uncertainties and error constraints. Then, a controller is designed on the basis of adaptive control and dynamic surface control so that the control objective of pursuing the desired output is achieved in the presence of the required constraints. The effective performance of the proposed controller is demonstrated by simulation by MATLAB software.
INTRACELL CHANGES IN EPITAXIALLY STRAINED YBA2CU3O7-X ULTRATHIN LAYERS IN YBA2CU3O7-X/PRBA2CU3O7 SUPERLATTICES

M. Varela,1 Z. Sefrioui,2 D. Arias,2,* M. A. Navacerrada,3 M. Lucia,3 M. A. Lopez de la Torre,4 C. Leon,2 G. D. Loos,2 F. Sanchez-Quesada,3 and J. Santamaria2, 1Departamento de Fisica, Universidad Carlos III de Madrid, Leganes, 28911 Madrid, Spain 2GFMC Departamento de Fisica Aplicada III, Universidad Complutense de Madrid, 28040 Madrid, Spain 3Departamento de Fisica Aplicada III, Universidad Complutense de Madrid, 28040 Madrid, Spain 4Departamento de Fisica Aplicada, Universidad de Castilla-La Mancha, 13071 Ciudad Real, Spain (Received 8 March 1999)
On the Investigation of Frequency-Related Fingerprints of Meminductor/Capacitor and Their Duals Realized by Circuit Emulators

This article investigates the frequency-related fingerprints of the meminductor/capacitors and their duals realized by the circuit emulators. The direct dependency of the hysteresis loop area on the inverse of operating frequency is an important property of the memristor confirm-ing its resistive memory nature. This work shows that not all such elements (which exhibit hysteresis characteristics) seem to follow this fingerprint on subjected to the sinusoidal current/voltage excitation signal when they are realized by the emulator circuits. It is found that in some cases PHL (Pinched Hysteresis Loop) characteristics of the mem-capacitor/inductor and their elements, may seem to create a fallacy in their appearance. Although this behavior is natural (but distinct from the memristor), it does produce some challenges during the measurements of these memelements and non-memelements. The behavior has been demonstrated in the MATLAB generated plots and also verified in the experimental and simulation results obtained for the designed emulators for the memcapaci-tor/meminductor and their duals. The paper also attempts to propose potential solutions to avoid this delusion per-ceived in the PHL characteristics of memcapacitor/memin-ductor and their duals, due to conventional measuring methods.
Five Lectures on Projective Invariants

We introduce invariant rings for forms (homogeneous polynomials) and for d points on the projective space, from the point of view of representation theory. We discuss several examples, addressing some computational issues. We introduce the graphical algebra for the invariants of d points on the line. This is an expanded version of the notes for the School on Invariant Theory and Projective Geometry, Trento, September 17-22, 2012.
The Longest Baseline Record of Vegetation Dynamics in Antarctica Reveals Acute Sensitivity to Water Availability

Against a changing climate, the development of evidence‐based and progressive conservation policies depends on robust and quantitative baseline studies to resolve habitat natural variability and rate of change. Despite Antarctica's significant role in global climate regulation, climate trend estimates for continental Antarctica are ambiguous due to sparse long‐term in situ records. Here, we present the longest, spatially explicit survey of Antarctic vegetation by harmonizing historic vegetation mapping with modern remote sensing techniques. In 1961, E. D. Rudolph established a permanent survey plot at Cape Hallett, one of the most botanically diverse areas along the Ross Sea coastline, harboring all known types of non‐vascular Antarctic vegetation. Following a survey in 2004 using ground‐based photography, we conducted the third survey of Rudolph's Plot in 2018 using near‐ground remote sensing and methodologies closely mirroring the two historic surveys to identify long‐term changes and trends. Our results revealed that the vegetation at Cape Hallett remained stable over the past six decades with no evidence of transformation related to a changing climate. Instead, the local vegetation shows strong seasonal phenology, distribution patterns that are driven by water availability, and steady perennial growth of moss. Given that East Antarctica is at the tipping point of drastic change in the near future, with biological change having been reported at certain locations, this record represents a unique and potentially the last opportunity to establish a meaningful biological sentinel that will allow us to track subtle yet impactful environmental change in terrestrial Antarctica in the 21st century.
Pade-Jacobi Approximants

We discuss projection based Pad e-Jacobi approximants in general and present in particular an exact rational approximation to the Sign function. This serves as vehicle to analyze the behavior of Pad e-Jacobi approximants for discontinuous functions. The analysis shows that the Pad e-Jacobi approximant is superior in several ways to classic polynomial approximations of discontinuous functions, provided the parameters in the approximations are chosen carefully. Guidelines for this is obtained through the analysis.
Molekulare Diagnostik und Therapie des Differenzierten Schilddrüsenkarzinoms

Zusammenfassung Zusammenfassend ergeben sich somit eine Vielzahl an Therapiemöglichkeiten für radiojodrefraktäre differenzierte Schilddrüsenkarzinome ( Abb. 1 ). Primär kann durch eine gezielte Redifferenzierungstherapie eine erneute Radiojodtherapie ermöglicht werden. Des Weiteren stehen mit Sorafenib und Lenvatinib in der Erstlinie der medikamentösen Systemtherapien 2 effektive VEGFR-Inhibitoren zur Verfügung. Lenvatinib wird hierbei als wirksamer angesehen. Bei Versagen von Sorafenib ist der Wechsel auf den noch breiter wirksamen VEGFR/FGFR-Inhibitor Lenvatinib sinnvoll. Bei Progress auf Lenvatinib ist eine molekulare Diagnostik mittels frischer Biopsie notwendig, um spezifische, medikamentös angehbare Läsionen wie BRAF-V600E-, RET- und NTRK-Fusionen zu identifizieren und mittels spezifischer Inhibitoren zu blockieren. Bei Fehlen spezifischer Treibermutationen stehen als Salvage-Therapien Cabozantinib oder die Hinzunahme eines Immuncheckpoint-Inhibitors zur Verfügung.