Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Free-Space Optical (FSO)/Radio Frequency (RF) Antenna for Satellite Applications

Satellite units use reliable, an industrial de facto standard, and scientifically approved method of radio frequency (RF) data transfer. However, RF links are slow and require authorized permission for satellite and in-orbit applications. Recently, free-space optical (FSO) communication links are trending for satellite applications due to their huge potential for data transmission and low-cost implementation. Ideally, satellites would benefit from both RF and FSO technologies to ensure robustness and reliability. However, such a communication system requires the use of dedicated antenna and aperture for RF and FSO, respectively. The key challenges in the satellite transceiver systems are the size and mass. Therefore, having two separate units for each technology is not desirable. In this paper, we present a unique, compact, robust, and flexible design, which integrates optical wireless communication front-end aperture and radio antenna in a single module. We will present the design concept as well as RF and optics simulation results.
Visual spatial attention shifts decoded from the electroencephalogram enable sending of binary messages

Electroencephalographic signals reflect a superposition of activity from neural populations both related and unrelated to the brain processes under investigation. Here we show how these complex signals, containing a considerable amount of noise, can be decoded to send binary messages only by covertly directing attention to a visual target stimulus. We used canonical correlation analysis to decompose the characteristic series of event-related potentials that correlated with a given sequence of stimuli comprising the target. Across 13 participants, we were able to correctly decode the responses to more than 90% of dichotomous questions by using our brain–computer interface (BCI). Although the system relies on visual stimuli, it neither depends on eye movements nor on high visual acuity. Therefore, this BCI might be suitable for patients who are unable to communicate due to motoneuron disorders resulting in complete paralysis of all voluntarily-controlled musculature.
Cyberattacks against Direct Load Control of Residential Electric Water Heaters in Smart Grids

The invasive deployment of digital technologies in power grids increases their exposure to cyber threats. In this paper, we study a type of load-altering cyberattack that targets peak shaving programs wherein a utility directly controls the loads of domestic electric water heaters. We consider a cyberattack scenario in which an adversary gains access to the advanced metering infrastructure and sends malicious activation commands to a large number of water heaters forcing them to simultaneously switch on in the midst of a peak shaving period, thus causing a sudden, large power demand that could lead to a system-wide blackout. We develop a simulation environment to simulate the loads of a population of electric water heaters under different operating conditions as well as under this type of cyberattack. Then, we propose a cybersecurity-by-design approach to mitigate the risk of such cyberattack. The efficacy of the proposed solution is analyzed using software experiments.
Investment Biases in Reinforcement Learning-based Financial Portfolio Management

As one of the most applicable problems in reinforcement learning (RL), a number of RL-based system designs for financial portfolio management (PM) have been proposed by researchers, which are proven to achieve outstanding performance in terms of capital returns. However, these systems' degrees of bias proxies in investment are often ignored and rarely inspected in the existing research. A high-yield RL-based system does not sufficiently indicate it is unbiased. The system might as well display the biases which humans may have during financial trading owing to the resemblant decision-making and rewarding mechanism between human investors and RL agents. In this study, we investigate the proxies of two biases in financial investment, disposition effect (DE) and narrow framing (NF), in a cutting-edge RL-based system for PM: MSPM. The experimental results on 135 different portfolios during the year 2021 indicate that MSPM has significantly lower degrees of DE (-0.1477) and NF (0.0904) compared to human investors. We confirm the RL-based system's capacity to overcome the biases which human investors often have in financial investment.
Quantum error correcting codes based on privacy amplification

Calderbank-Shor-Steane (CSS) quantum error-correcting codes are based on pairs of classical codes which are mutually dual containing. Explicit constructions of such codes for large blocklengths and with good error correcting properties are not easy to find. In this paper we propose a construction of CSS codes which combines a classical code with a two-universal hash function. We show, using the results of Renner and Koenig, that the communication rates of such codes approach the hashing bound on tensor powers of Pauli channels in the limit of large block-length. While the bit-flip errors can be decoded as efficiently as the classical code used, the problem of efficiently decoding the phase-flip errors remains open.
DISCOVERY OF AN EXTENDED X-RAY JET IN AP LIBRAE

Chandra observations of the low-energy-peaked BL Lac object (LBL) AP Librae (AP Lib) revealed the clear discovery of a non-thermal X-ray jet. AP Lib is the first LBL with an extended non-thermal X-ray jet that shows emission into the very high energy range. The X-ray jet has an extension of ∼15″(≈ 14 kpc). The X-ray jet morphology is similar to the radio jet observed with Very Large Array at 1.36 GHz emerging in the southeast direction and bends by 50° at a distance of 12″ toward the northeast. The intensity profiles of the X-ray emission studied are consistent with those found in the radio range. The spectral analysis reveals that the X-ray spectra of the core and jet region are both inverse-Compton-(IC)-dominated. This adds to a still small sample of BL Lac objects whose X-ray jets are IC-dominated and thus more similar to the high-luminosity Fanaroff–Riley II sources than to the low-luminosity Fanaroff–Riley I objects, which are usually considered to be the parent population of BL Lac objects.
Efficient frequency conversion through absorptive bands of the nonlinear crystal

Two simultaneous three wave mixing processes are analyzed, where an input frequency is converted to an output frequency via an intermediate stage. By employing simultaneous phase-matching and an adiabatic modulation of the nonlinear coupling strengths, the intermediate frequency is kept dark throughout the interaction, while obtaining high conversion efficiency. This feat is accomplished in a manner analogous to population transfer in atomic stimulated Raman adiabatic passage (STIRAP). Applications include conversion between remote frequencies, e.g. mid-IR to visible, and study of electronic crystal properties in the UV absorption band.
Local Control is All You Need: Decentralizing and Coordinating Reinforcement Learning for Large-Scale Process Control

Deep reinforcement learning (RL) approaches are an appealing alternative to conventional controllers in process industries as such methods are inherently flexible and have generalization abilities to unseen situations. Namely, they alleviate the need for constant parameter tuning, tedious design of control laws, and re-identification procedures in the event of performance degradation. However, it remains challenging to apply RL to real-world process tasks, which commonly feature large state-action spaces and complex dynamics. Such tasks may be difficult to solve due to computational complexity and sample insufficiency. To tackle these limitations, we present a sample-efficient RL approach for large-scale control that expresses the global policy as a collection of local policies. Every local policy receives local observations and is responsible for controlling a different region of the environment. In order to enable coordination among local policies, we present a mechanism based on action sharing and message passing. The model is evaluated on a set of robotic tasks and a large-scale vinyl acetate monomer (VAM) plant. The experiments demonstrate that the proposed model exhibits significant improvements over baselines in terms of mean scores and sample efficiency.
The Mellin Transform Method: Electromagnetics, Complex Analysis, and Educational Potential [Education Corner]

The Mellin transform (MT) method (MTM) is a method of analytical evaluation of definite integrals that utilizes key concepts of complex analysis (CA). Although sometimes laborious, the algorithmic, stepwise character of the method as well as its vast field of applications render it powerful and versatile. In particular, certain integrals that are presented in the literature without proof (or with proofs that are ingenious and nonintuitive) can be directly attacked in a standard manner. This is true for the three examples presented herein, all taken from standard and advanced electromagnetics textbooks.
Cost, power consumption and performance analysis in SDM ROADM architectures for uncoupled spatial channels

Currently optical networks are reaching their maximum transport capacity. Different solutions can be used to overcome this capacity limit, but we are going to study, in particular, space division multiplexing (SDM). To use SDM, the reconfigurable optical add/drop multiplexers (ROADMs) need to be adapted to support this multiplexing. In this paper we analyze four switching strategies used in SDM ROADMs and the respective SDM ROADM architectures. These strategies are explained and analyzed, for uncoupled scenarios, in terms of cost and power consumption. The impact of the physical layer impairments (PLIs) amplified spontaneous emission noise, non-linear interference, passband narrowing due to optical filtering and in-band crosstalk is also assessed, considering a cascade of SDM ROADMs with spatial and spatial-wavelength switching granularities. The PLI in-band crosstalk, has insignificant impact, in networks with a single spatial channel or in SDM networks with spatial granularity ROADM architecture, but in a SDM network with spatial wavelength granularity this PLI can lead to an OSNR penalty of around 2 dB, when the number of spatial channels is high. The other PLIs have similar impacts in all networks studied.
Measuring primordial gravitational waves from CMB B−modes in cosmologies with generalized expansion histories

We evaluate our capability to constrain the abundance of primordial tensor perturbations (primordial gravitational waves, PGWs) in cosmologies with generalized expansion histories in the epoch of cosmic acceleration. Forthcoming satellite and sub-orbital experiments probing polarization in the Cosmic Microwave Background (CMB) are expected to measure the B−mode power in CMB polarization, coming from PGWs on the degree scale, as well as gravitational lensing on arcminute scales; the latter is the main competitor for the measurement of PGWs, and is directly affected by the underlying expansion history, determined by the presence of a Dark Energy (DE) component. In particular, we consider early DE possible scenarios, in which the expansion history is substantially modified at the epoch in which the CMB lensing is most relevant. We show that the introduction of a parametrized DE may induce a variation as large as 30% in the ratio of the power of lensing and PGWs on the degree scale. We find that adopting the nominal specifications of upcoming satellite measurements, the constraining power on PGWs is weakened by the inclusion of the extra degrees of freedom, resulting in a reduction of about 10% of the upper limits on r in fiducial models with no GWs, as well as a comparable increase in the error bars in models with non-zero tensor power. Moreover, we find that the inclusion of sub-orbital CMB experiments, capable of mapping the B−mode power up to the angular scales which are affected by lensing, has the effect of restoring the forecasted performances with a fixed cosmological expansion history corresponding to a cosmological constant. Finally, we show how the combination of CMB data with Type Ia SuperNovae (SNe), Baryonic Acoustic Oscillations (BAO) and Hubble constant allows to constrain simultaneously the primordial tensor power and the DE quantities in the parametrization we consider, consisting of present abundance and first redshift derivative of the energy density. We compare this study with results obtained using the forecasted lensing potential measurement precision from CMB satellite observations, finding consistent results.
Centralized Hybrid Equalization for Cell Free mMIMO mmWave based Systems

Cell-free massive multiple input multiple output (CFmMIMO) is one of the promising technologies for beyond 5G/6G wireless communications due to its advantages in overcoming the inter-cell interferences constraints in ultra-dense networks. In this paper, we propose a low complexity analog precoder with a centralized hybrid analog-digital multi-user equalizer for millimeter-wave (mmWave) CF mMIMO systems. In the transmitter, a low complexity analog precoder is applied based on the partial channel state information (CSI) knowledge, i.e., only the average angle of departure (AoD) information. While in the centralized hybrid receiver, the central unit (CU) handles all processing that requires access to the global CSI. Herein, the analog coefficients of the equalizer are obtained by minimizing the mean square error (MSE) between the transmit and receive signals using the weighted Frobenius norm as a metric, whereas an optimum linear MSE equalizer is used to get the digital part. We also compare the considered CF scenario with the small cells (SCs) based one for different hybrid schemes. The results show that the proposed precoder combined with the hybrid equalizer can achieve very close results to a fully digital counterpart. Moreover, the performance of the considered CF scenario is better than the SCs based one for all hybrid schemes.
Iterative Feedback Tuning for Regulatory Control Systems Using Identified Sensitivity Functions via Predictive Error Method

The present work proposes the Iterative Feedback Tuning (IFT) for regulatory control systems for single-input, single-output, linear time-invariant (LTI) systems in order to optimize a cost criterion that evaluates the sum of the variance of input and output regulatory control data. The proposed approach estimates the gradient of a cost criterion using identified sensitivity functions at every iteration via the predictive error method (PEM). In the proposed approach, the identified sensitivity function is corrected through the optimization of the sum of squares of one-step predictive error in another regulatory control data with different controller parameters. The correction makes it possible to derive the unbiased estimation of sensitivity function even when the identifiability condition does not hold. Also, the proposed one provides a method for optimization of the step-size of the gradient vector using the identified sensitivity function. A numerical example shows that the proposed one can improve the accuracy of the estimate of sensitivity functions, and achieves faster convergence to the optimal value in the IFT for regulatory control systems.
Fractal Modulation Scheme for Optical Camera Communication

This paper proposes a novel spatial modulation scheme for optical camera communication based on the use of a fractal structure on the transmission. This system is able to send different amounts of data depending on the distance between the transmitter (Tx) and the receiver (Rx). Two simulation experiments were carried out to validate the proposed system. In the first experiment, the number of received bits was obtained as a function of the distance between the Tx and the Rx and compared to a theoretical expression that defines a lower bound. In the second experiment, the communication channel was tested by analyzing the behaviour of the bit error rate against the quantity of noise in the image using the peak signal-to-noise-ratio. It was found that for a PSNR value of 40 dB or more, the system is able to achieve a bit error rate below the forward error correction limit of $3. 8\cdot 10^{-3}$. This architecture has potential applications in: hierarchization of data in vehicle-to-vehicle communications, distance multiplexing of data streams in cultural spaces, and security applications.
Performance Analysis of M-ary DPSK with SIMO in Ground-to-HAP FSO System in the Presence of Beam Wander and Scintillation

Studying the performance of communication systems is essential to establish a reliable and stable link with an acceptable quality of service and minimum amount of resources. This paper evaluates the ground to HAP FSO link that uses the M-ary differential phase-shift keying (MDPSK) modulation technique in the presence of turbulence and beams wandering. The performance is analyzed by comparing the Average Symbol Error Rate (ASER) with an applicable threshold of 10-4. The results confirmed that, when limiting the transmitted power to 33 dBm, which is consistent with a practical perspective, the ASER is below the threshold only at a constellation size of 4. Therefore, a single input multiple output (SIMO) technique is introduced to improve the performance. The channel capacity is also shown to demonstrate the benefit of increasing the constellation size.
LoCuSS: Subaru Weak Lensing Study of 30 Galaxy Clusters

(Abridged) We use Subaru data to conduct a detailed weak-lensing study of the dark matter distribution in a sample of 30 X-ray luminous galaxy clusters at 0.15<z<0.3. A weak-lensing signal is detected at high statistical significance in each cluster, the total detection S/N ranging from 5 to 13. In this paper we concentrate on fitting spherical models to the tangential distortion profiles of the clusters. When the models are fitted to the clusters individually, we are unable to discriminate statistically between SIS and NFW models. However when the tangential distortion profiles of the individual clusters are combined, and models fitted to the stacked profile, the SIS model is rejected at 6- and 11-sigma, respectively, for low- and high-mass bins. We also use the individual cluster NFW model fits to investigate the relationship between cluster mass (M_vir) and concentration (c_vir), finding an anti-correlation of c_vir and M_vir. The best-fit c_vir-M_vir relation is: c_vir(M_vir) propto M_vir^{-alpha} with alpha=0.41+/-0.19 -- i.e. a non-zero slope is detected at 2sigma significance. We then investigate the optimal radius within which to measure cluster mass, finding that the typical fractional errors are improved to sigma(M_Delta)/M_Delta ~ 0.1-0.2 for cluster masses at higher over-densities Delta=500-2000, from 0.2-0.3 for the virial over-density (~110). Further comparisons between mass measurements based on spherical model fitting and the model-independent aperture mass method reveal that the 2D aperture mass enclosed within a cylinder of a given aperture radius is systematically greater than the 3D spherical mass obtained from NFW model fitting: M_2D/M_3D= 1.34 and 1.40 for Delta=500 and 110, respectively. The amplitude of this effect agrees well with that predicted by integrating the NFW model along the line-of-sight.
A Model-Driven Methodology to Accelerate Software Engineering in the Internet of Things

The Internet of Things (IoT) aims for connecting. This assumption brings about several software engineering challenges that constitute a serious obstacle to its wider adoption. The main feature of the IoT is genericity w.r. t the variability of software and hardware technologies. Model-driven engineering (MDE) is a paradigm that advocates using models to address software engineering problems. It can help to meet the genericity of the IoT from a software engineering perspective. Existing MDE approaches for the IoT focus only on modeling the internal behavior of things but lack a comprehensive approach dedicated to network modeling. In the present article, we introduce a network-oriented methodology based on MDE to unify the IoT’s heterogeneous concepts. Fundamentally, we avoid the intrinsic heterogeneity of the IoT by separating the network’s specification (i.e., the things, the communication scheme, and its constraints) from its concrete implementation (i.e., the low-level artifacts, such as source code and documentation). Technically, the methodology relies on a model-based domain-specific language (DSL) and a code generator. The former enables the modeling of the network’s specification, and the latter provides a procedure to generate the low-level artifacts from this specification. Our results show that this methodology makes iot’s software engineering more rigorous, helps prevent bugs earlier, and saves time.
Bioinspired controller for a rqobotic knee orthosis

Robotic wearable orthosis faces many challenges. A list, but not exhaustive, can include biomechanical coupling, wearability, intention detecting, functional coupling, energy autonomy, trajectory, and torque anticipation. Notably, from a rehabilitative perspective emerges the necessity to define the more proficient rule of interaction to induce learning processes or to support the function when the recovery limit is reached. This study aims to select the optimal solution among the available technologies. We developed an orthosis with a controller that follows the equilibrium-point hypothesis. Three experimental protocols test the transparency and the gait pattern implicit modification during the orthosis use.
Bayesian Kernel Methods for Non-Gaussian Distributions: Binary and Multi-class Classification Problems

Abstract : Recent advances in data mining have integrated kernel functions with Bayesian probabilistic analysis of Gaussian distributions. These machine learning approaches can incorporate prior information with new data to calculate probabilistic rather than deterministic values for unknown parameters. This paper analyzes extensively a specific Bayesian kernel model that uses a kernel function to calculate a posterior beta distribution that is conjugate to the prior beta distribution. Numerical testing of the beta kernel model on several benchmark data sets reveal that this model's accuracy is comparable with those of the support vector machine and relevance vector machine, and the model runs more quickly than the other algorithms. When one class occurs much more frequently than the other class, the beta kernel model often outperforms other strategies to handle imbalanced data sets. If data arrive sequentially over time, the beta kernel model easily and quickly updates the probability distribution, and this model is more accurate than an incremental support vector machine algorithm for online learning when fewer than 50 data points are available.
Development of Wireless Multi-Sensor Module for Monitoring Environment of Smart Manufacturing Workplace

This paper originally aggregates the all light, temperature, humidity, PM2.5, oxygen, and air quality sensors as well as WeMos D1 mini WiFi module into a wireless multi-sensor module for monitoring the environment of smart manufacturing workplace. Firstly, these sensors are integrated in a multi-sensor shield with the WiFi device based on the UART/I2C interface standard of series communication. The observations of sensors are then transmitted by a WiFi network to ThingSpeak IoT platform. The results illustrate the proposed module for has been validated with sufficient stability and durability for in-situ monitoring the monitoring the environment of smart manufacturing workplace in Da-Yeh University and will be set up in Texture Maker Enterprise CO., Ltd., Taiwan. The accuracy, reliable confidence, and tolerance will be conducted in the future.