Miracle-dz/UniFAR-FTUs huggingface dataset:
Improvement of EMI Filter Attenuation Using Shielding

The parasitic couplings between EMI filter components strongly impact the high-frequency attenuation of an EMI filter. One of the most effective ways to reduce the magnetic and capacitive couplings between components and improve the high-frequency attenuation is placing a conductive shield between filter components. Therefore, this paper analyzes and investigates the shielding enclosure's size and volume effect on EMI filter performance in common-mode (CM) and differential-mode (DM) configurations using copper shields positioned over a CM choke.
LibHunter: An Unsupervised Approach for Third-party Library Detection without Prior Knowledge

Third-party libraries (TPLs) are a significant component of mobile apps. They provide various functionalities, and developers employ them to facilitate app development. TPL detection is a fundamental task in security research, as it can impact other security studies. TPL can act as an assistant to malware detection, privacy leakage detection, etc. Because if a TPL carries malicious code, all apps that integrate the TPL can be considered risky. However, in some studies, TPLs can also act as noise, like app traffic fingerprinting. The TPL and app traffic are mixed during app runtime, making it difficult to fingerprint the app traffic accurately. Unfortunately, all existing TPL detection studies are working with prior knowledge of TPLs, as they need a whitelist or a train on known TPLs. However, new TPLs keep emerging, and it is not feasible for existing works to identify them-especially those who have network behaviors, as they may transfer inappropriate contents in the network. To this end, we propose LibHunter - an approach to identify TPLs without prior knowledge. LibHunter inspects the HTTP(S) traffic, logs the corresponding code execution traces, extracts features from the collected data, and performs a clustering algorithm to obtain TPLs. We apply LibHunter to 3000 apps. Results demonstrate that LibHunter can identify 79 TPLs, and about 60% of them are not detected by all existing works. We perform an analysis to show how important these TPLs are; we also present the visiting graph of these TPLs. Our findings bring light to the research community that existing tools are not accurate when encountering contemporary apps.
A Deep Reinforcement Learning-Based Context-Aware Wireless Mobile Charging Scheme for the Internet of Things

The Internet of Things (IoT) has gained in popularity over the years and is used in numerous applications. IoT networks employ many constrained devices, thus, finding energy is mandatory to maximize device and network lifetime. In this paper, we investigate a scheme based on wireless Mobile Chargers (MCs) to maximize device lifetime. Instead of transmitting energy to devices to only charge them back, we design a charging scheme considering the near future needs of the devices. We provide our ongoing research on a context-aware wireless energy transfer scheme to charge the devices according to the current and probable upcoming events. Our scheme is based on two modules: a context reasoning module predicting the possible future events in the IoT network and an intelligent Wireless Mobile Charger using Deep Reinforcement Learning (DRL). Our solution aims to establish a preventive charging scheme, considering the energy status and probable future events.
SLAP-IoD: Secure and Lightweight Authentication Protocol Using Physical Unclonable Functions for Internet of Drones in Smart City Environments

With the emergence of the concept of smart city and the increasing demands for a range of drones, Internet of Drones (IoD) has achieved a variety of attention by providing multiple benefits in academia and industry. IoD offers numerous services, such as traffic monitoring, environmental monitoring, and disaster management by combining infrastructure, Internet of Things (IoT), and Flying Ad-Hoc Networks (FANET) in smart city environments. However, communication among drones is vulnerable to potential security threats because the sensitive messages in various applications are exchanged via an insecure channel in IoD-based smart city environments. Since IoDs can be operated in unattended environment with minimum human interventions, smart devices (e.g., drones and sensors) deployed in IoD architectures are vulnerable to physical capture attacks. In addition, drones are resource-constrained in terms of computation and communication overheads, and it is not much viable to apply public key cryptography (PKC) that requires high computation and communication overheads. Thus, we design a secure and lightweight authentication protocol using a physical unclonable function (PUF) for IoD to guarantee reliable and useful services in smart city environments, called SLAP-IoD. We prove the security of the SLAP-IoD using informal and formal security analyses using the broadly recognized Real-Or-Random (ROR) random oracle model, and also through the formal security verification using the widely-accepted Automated Validation of Internet Security-sensitive Protocols and Applications (AVISPA) security verification. Furthermore, we compare the performance of the SLAP-IoD with related schemes. Consequently, we show that SLAP-IoD offers better security and efficiency than other related schemes and is suitable for IoD-based smart city environments.
TOWARDS A RATIONAL THEORY FOR RC MEMBERS IN SHEAR

The diagonal compression field theory for symmetrically reinforced concrete beams monotonically loaded in shear at sections where flexure is unimportant, is presented. This theory assumes that after cracking the concrete can resist no tension and that the shear will be carried by a field of diagonal compression. As well as considering the equilibrium conditions that relate the average stresses in a beam to the applied loads the theory considers the compatibility conditions that exist between the average strains in the various directions. Expressions are given which enable the shear deformations (and thus the effective shear modulus) of diagonally cracked reinforced and prestressed beams to be calculated. Based on a suggested failure criterion for diagonally cracked concrete, equations for the shear capacity of reinforced and prestressed beams are derived. A design chart based on these equations is given.
Killing Tensors from Conformal Killing Vectors

Some years ago Koutras presented a method of constructing a conformal Killing tensor from a pair of orthogonal conformal Killing vectors. When the vector associated with the conformal Killing tensor is a gradient, a Killing tensor (in general irreducible) can then be constructed. In this paper it is shown that the severe restriction of orthogonality is unnecessary and thus it is possible that many more Killing tensors can be constructed in this way. We also extend, and in one case correct, some results on Killing tensors constructed from a single conformal Killing vector. Weir's result that, for flat space, there are 84 independent conformal Killing tensors, all of which are reducible, is extended to conformally flat spacetimes. In conformally flat spacetimes it is thus possible to construct all the conformal Killing tensors and in particular all the Killing tensors (which in general will not be reducible) from conformal Killing vectors.
VBI-based Uplink Channel Estimation for Massive MIMO Systems

Uplink channel estimation is a classical and important issue for massive multiple-input multiple-output (MIMO) communication systems. Due to the existence of the pilot coupling effect, it is difficult to directly utilize the sparse characteristics of massive MIMO channels. The existing methods usually adopt a prior least squares (LS) estimator to decouple the pilot matrix, which could introduce significant modeling errors. Another solution is to utilize the expectation propagation approximation (EPA) instead to decouple the pilot matrix, but it always suffers from the performance loss caused the approximate message passing. In this regard, we try to propose a new sparse Bayesian learning (SBL) uplink channel estimation method, which decouples the sparse signals-of-interest automatically using the independent variational Bayesian inference (VB I) factorization, thereby avoiding the performance loss brought by the prior LS estimator and approximations. Numerical simulation results verify the superiority of our method.
Be SMART, Save I/O: A Probabilistic Approach to Avoid Uncorrectable Errors in Storage Systems

Silent data corruption poses a significant risk to the integrity of data in storage systems. Although error correction codes (ECC) can recover the majority of such errors, a non-negligible portion of them escape ECC, referred as uncorrectable errors (UEs). Despite being rare in nature, increasing scale of storage systems and fast-growing I/O rates decreased the mean time between UEs from months to hours. Yet, unlike disk failures, UEs are hard to predict with high precision, making it difficult to adopt proactive measures. In this paper, we introduce a probabilistic approach to deploy UE mitigation strategies that can capture significant portion of UE while keeping the system overhead at a tolerable range. To achieve this, we first estimate the probability of I/O operations to be exposed to UEs and find a minimum subset of disks for which employing UE avoidance strategies can lead to significant decrease in UE exposure. We demonstrate through extensive simulations that when the proposed probabilistic model is used to implement write verification strategy to detect and recover from UEs, more than 50% of all write-triggered UEs can be avoided with 1% read overhead, and more than 70% of UEs can be mitigated with less than 3.5% read overhead. We further measure the impact of incurred read overhead on write performance in production Lustre and GPFS file systems and validate our findings that more than half of UEs can be avoided while degrading write I/O throughout by less than 0.9%.
Missing data recovery using autoencoder for multi-channel acoustic scene classification

In this paper, we propose a method of missing data recovery using an autoencoder for multi-channel signals. Recently, many deep neural network-based classification methods using multi-channel signals have been proposed. The advantage of using multi-channel signals is that both frequency and spatial information can be used. However, systems using such signals are vulnerable to missing data because of a mismatch between the training and testing data. To minimize the mismatch, some techniques that include simulated missing data to the training data have been proposed. However, it is difficult to prepare missing data covering all possible mismatch situations. Therefore, we focus on using an autoencoder to recover the missing data without any assumptions of missing situations. In the case of multi-channel data inputted into an autoencoder, channel relationships are compressed into a low-dimensional hidden layer. Then, the autoencoder outputs data to reconstruct the input data from the layer. Therefore, when multi-channel input into the autoencoder has some missing channels, the output of the autoencoder is expected to recover some missing channel information by using the hidden layer. Since the autoencoder is regarded as a preprocessing of acoustic tasks, we evaluated the proposed method using an acoustic classification task. From the experimental results, we confirmed that the proposed method can recover missing data and improve the classification performance.
A Comparative Study of Vegetation Attenuation at Millimeter Waves Bandwidth

The development of radio communication systems makes it necessary to look for higher frequency bands to meet the growing needs of users. An increased interest in the socalled millimeter waves band raises questions about the adequacy of the use of propagation models for higher frequency ranges. An interesting aspect is vegetation attenuation which is analyzed in detail in the paper. In addition to the analysis of the components of the attenuation of the foliage, the article focuses on the implementation of the measurement campaign in the vicinity of the buildings of Poznan University of Technology. The results obtained were compared with external measurement works and the calibration of the foliage attenuation coefficients appropriate for native species of vegetation was performed.
Effects of transspinal electrical stimulation estimated by transcranial magnetic stimulation

The current study aimed to demonstrate the specifics of the effect of anodal transspinal direct current stimulation (tsDCS) applied at the level of the cervical spinal cord thickening on the corticospinal system of a healthy person. The study involved 54 healthy adults aged 22+/-4 years. Our results showed that application of a 1l-minute anodal tsDCS at the level of the cervical spinal cord C7-Th1 with a current value of 1.5 mA has an effect on the muscles of the upper limbs. In particular, the effect of tsDCS is a decrease in motor evoked potentials (MEP) amplitude immediately after stimulation, and an increase in amplitude 15 minutes after stimulation. These results can be explained by the fact that tsDCS induces changes in the excitability of the corticospinal tract, thereby causing suprasegmental effects in the brain and brainstem. The statistical analysis demonstrated that the dynamics of MEP amplitudes differs between groups receiving anodal tsDCS and sham stimulation. Altogether, these findings point to the need for further research into the effects of tsDCS on the upper limbs, as the effects of tsDCS are often limited to studies of the lower limbs. The current study showed that the noninvasive cervical tsDCS can modulate the excitability of the corticospinal tract, and thus can probably have a positive effect on the production of motor skills.
Synthesis of Band-Pass Filters Made Using SIW Technology

SIW technologies are becoming more and more popular and relevant. It allows you to significantly reduce the weight and size parameters of the devices, and the accuracy of the devices is practically no different from full-size analogues. So In this paper, we will consider studies of filters of the 3rd, 5th and 7th orders, made using SIW technology. Computer models, theoretical calculations and their comparison with filters made according to these models are presented. The paper also presents a band-pass filter made in a multilayer design
Weighted Edit Distance for Country Code Recognition in License Plates

This paper presents the problem of country code recognition from li-cense plate images. We propose an approach based on character de-tection and subsequent clustering for country code localization. We further propose three weighted Edit Distance metrics for country of origin prediction from imperfect detections, namely based on char-acter similarity, detection confidence, and relative operation impor-tance. Experimental results show the benefit of proposed approaches on real-world data. The proposed method is lightweight and inde-pendent of the underlying object detector, facilitating its application on edge devices.
A comparative study of autoencoder architectures for mental health analysis using wearable sensors data

In this study, the application of deep learning models for the detection of relapses in patients with psychotic disorders (i.e., bipolar disorder and schizophrenia) is examined, using physiological signals, collected by smartwatches. In order to tackle the problem of relapse detection, which in our case is handled as an anomaly detection task, four different autoencoder architectures, based on Transformers, Fully connected Neural Networks (FNN), Convolution Neural Networks (CNN) and Gated Recurrent Unit (GRU), are implemented as personalized and global models. In this work, time-scaled data of total duration of 1569 days, segmented into five minutes intervals, from ten patients suffering from psychotic disorders have been examined yielding encouraging results. Furthermore, since the patients' relapses were appropriately annotated by clinicians as low, moderate or severe, we conducted a post hoc analysis using the models that performed best, to examine the importance of the severity level among three participants who relapsed multiple times with different severity level, providing important evidence.
Single-Pulse Estimation of Target Velocity on Planar Arrays

Doppler velocity estimation in pulse-Doppler radar is done by evaluating the target returns of bursts of pulses. While this provides convenience and accuracy, it requires multiple pulses. In adaptive and cognitive radar systems, the ability to adapt on consecutive pulses, instead of bursts, brings potential performance benefits. Hence, with radar transceiver arrays growing increasingly larger in their number of elements over the years, it may be time to re-evaluate how Doppler velocity can be estimated when using large planar arrays. In this work, we present variance bounds on the estimation of velocity using the Doppler shift as it appears in the array model. We also propose an efficient method of performing the velocity estimation and we verify its performance using Monte Carlo simulations.
Inter-channel Covariance Matrices Based Analyses of EEG Baselines

Electroencephalographic (EEG) signals are used to assess neurological disorders as well as different states of the brain. The choice of EEG baseline in any analysis is crucial as the two EEG baselines (Eyes Open and Eyes Closed) have differences in connectivity and power levels. The work proposes the use of inter-channel covariance matrices of multi-channel EEG to differentiate the two baselines. Two avenues of approach are explored using: (1) Tangent Vectors of Riemannian Manifold and (2) Covariance matrix properties such as Eigen Values, Eigen Vectors, Spectral Radius and the coefficients of Characteristic Polynomial. K-nearest neighbors, Ensemble of Decision Tree classifier with Bagging and Support Vector Machines are used in both scenarios with 10-fold cross-validation repeated 10 times. Tangent Vectors, Eigen Values, Spectral Radius, coefficients of Characteristic Polynomial and Eigen Vectors result in a mean performance of 80.78%, 95.56%,95.05%, 95.15% and 94.5% respectively. Changes in the inter-dependencies of the considered brain regions are captured more effectively by the covariance matrix properties than by the direct use of covariance matrices. These analyses clearly show that the two baselines have different inter-dependencies of the considered brain regions. The prop-erties of covariance matrices prove to be effective in exploiting these differences to distinguish the baselines.
Analysis of Electromagnetic Properties from Time-Varying Moving Plasma with Lorentz-FDTD

During flight of hypersonic vehicle, a layer of plasma sheath will be formed around the aircraft due to the high temperature. As the results of turbulence, the plasma sheath is time-varying. In addition, the hypersonic vehicle and plasma sheath keeps a high-speed relative motion with the radar on the ground. The plasma sheath will influence the original electromagnetic scattering properties of the aircraft, which will affect the normal observation of the aircraft from the radar. Therefore, it is significant to study the interaction between plasma sheaths and electromagnetic wave. In this paper, Lorentz-FDTD (Finite-Difference Time-Domain) method is used to analyze the electromagnetic properties of time-varying plasma sheaths in a 2-D case, and the result show that the time-varying and moving plasma will modulate the electromagnetic waves.
Characterization of Full-Duplex Constant-Envelope Transceiver for Emerging Multifunction Systems

We assess the operation of a special multifunction full-duplex transceiver that uses its frequency-shifting constant-envelope transmitted signal as the downconversion carrier (in contrast to a tone for conventional direct conversion). While self-interference suppression is greatly simplified by using this architecture, the spectra of other downconverted signals turn up sweeping through the frequency domain as the cost of that. Adequate characterization and compensation of these consequences is the key to guarantee the required performance of emerging multifunction systems. We develop solutions to these effects and evaluate the behavior of the transceiver concept by varying transmission- and reception-related parameters.
Standard big bang nucleosynthesis and primordial CNO abundances after Planck

Primordial or big bang nucleosynthesis (BBN) is one of the three historical strong evidences for the big bang model. The recent results by the Planck satellite mission have slightly changed the estimate of the baryonic density compared to the previous WMAP analysis. This article updates the BBN predictions for the light elements using the cosmological parameters determined by Planck, as well as an improvement of the nuclear network and new spectroscopic observations. There is a slight lowering of the primordial Li/H abundance, however, this lithium value still remains typically 3 times larger than its observed spectroscopic abundance in halo stars of the Galaxy. According to the importance of this ``lithium problem", we trace the small changes in its BBN calculated abundance following updates of the baryonic density, neutron lifetime and networks. In addition, for the first time, we provide confidence limits for the production of 6Li, 9Be, 11B and CNO, resulting from our extensive Monte Carlo calculation with our extended network. A specific focus is cast on CNO primordial production. Considering uncertainties on the nuclear rates around the CNO formation, we obtain CNO/H ≈ (5-30)×10-15. We further improve this estimate by analyzing correlations between yields and reaction rates and identified new influential reaction rates. These uncertain rates, if simultaneously varied could lead to a significant increase of CNO production: CNO/H∼10-13. This result is important for the study of population III star formation during the dark ages.
The role of penetrating gas streams in setting the dynamical state of galaxy clusters

We utilize cosmological simulations of 16 galaxy clusters at redshifts \zeq{0} and \zeq{0.6} to study the effect of inflowing streams on the properties of the X-ray emitting intracluster medium. We find that the mass accretion occurs predominantly along streams that originate from the cosmic web and consist of heated gas. Clusters that are unrelaxed in terms of their X-ray morphology are characterized by higher mass inflow rates and deeper penetration of the streams, typically into the inner third of the virial radius. The penetrating streams generate elevated random motions, bulk flows and cold fronts. The degree of penetration of the streams may change over time such that clusters can switch from being unrelaxed to relaxed over a time-scale of several giga years.
Secure and Lightweight Data Sharing Mechanism for Medical IoT

The healthcare sector has engaged in substantial research in terms of technological advancement to provide patients with efficient and secure services. With the adaption of the internet of things (IoT) in the healthcare domain, remote patients are now able to share their health records with medical experts at distant locations, leading to more efficient and less expensive services. Given that data sharing over the internet entails the patients’ privacy, therefore, it is necessary to ensure that data is transmitted securely so that an adversary cannot tamper with it. Again, as IoT devices are resource-constrained, therefore, it is very important to transmit the data in a lighter format. This paper presents a mechanism for the communication of data or images over the network in a secure and lighter format. The proposed mechanism is implemented on Modified Chebyshev Polynomial and suitable data and image compression techniques with minimum distortion.