Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of Extremely Low-Frequency Magnetic Field from Multiple High-Voltage Overhead Power Lines in Parallel Configuration

The study investigates the distribution of the extremely low-frequency magnetic field around multiple highvoltage overhead power lines in parallel configuration. It is conducted using a computation approach based on the finite element method for an arrangement of five 110-kV doublecircuit lines located in a suburban area of the Iasi city, Romania. Results obtained by numerical simulation are then compared to appropriate regulations regarding human exposure to magnetic fields. The cumulative effect of the magnetic fields from multiple overhead power lines is also discussed.
Cyber Edge Intelligent Intrusion Detection Framework For UAV Network Based on Random Forest Algorithm

The synchronization of swarms of drones (also known as unmanned aerial vehicles (UAV)) in a network can be attributed to their high mobility and maneuverability capabilities, making them deployable for time-critical operations such as security surveillance, disaster management, and search and rescue operations. However, the resource constraints of these flying robots are limitations to their functionalities. Likewise, the neglect of the security status of this network significantly promotes attacks by invaders, thus, thwarting the mission of this network. In this study, mobile edge computing (MEC) technology and anomaly-based intrusion detection scheme are leveraged to curb these challenges using an optimized Random Forest (RCSV) model embedded in dedicated UAV-MEC servers. The selection of prominent features and hyperparameters for modeling an optimized attack predictor is enabled by Pearson correlation coefficient (PCC) and randomized search cross-validation techniques. Also, the training and evaluation of the proposed model were achieved using intrusion detection data set (CICIDS2017 data set) comprised of complex network attack types. The simulation results obtained by the model in the detection and classification of the different attacks in the network (accuracy = 99.87%, precision = 99.32%, recall = 98.81 % and F1-score = 99.06%) shows its superiority over other optimized machine learning models and some existing models utilized in previous research.
Variation in the emission rates of striped dolphin's vocalizations recorded in the Gulf of Taranto (Northern Ionian Sea, Central Mediterranean Sea)

Despite significant research effort in the past decade was done to better understand the effect of noise on cetaceans, remarkable gaps still remain, especially in understanding the patterns of the vocalizations emitted in different behavioural contexts. This study focused on the bioacoustics of the striped dolphin in the Gulf of Taranto (Northern Ionian Sea, Central Mediterranean Sea) in order to understand possible differences in emission rate of whistles, clicks, burst pulses and buzzes depending on the behavioural and size of groups observed. Results demonstrated that both the behavioural context and the group size affect the vocalizations emission rate although with effects that are not always generalizable. However, studies on this issue should be strongly encouraged to disentangle the effect of anthropogenic noise on cetaceans.
Orthogonal waves and currents interacting with a fixed rippled bed

Wave and currents often are simultaneously present in coastal areas. In fact, currents originated by tides, by temperature or salinity gradients, by waves approaching the coast obliquely and so on, coexist with waves, giving rise to complex non-linear interactions. At the seabed the combined flow modifies the boundary layer structure depending on the bed roughness type. In particular, sandy bottoms are often modelled to form small scale sedimentary structures called ripples. This work presents some results of an experimental campaign which was carried out within the Hydralab+ WINGS project, aimed at investigating the effects of orthogonal waves and currents over a rippled bed. In this framework, velocity profiles along the water column and surface wave data were acquired. Close to the bed a couple of recirculating cells were identified along the ripple flanks, which are weakened by the current superposition with respect to the wave only case.
Detecting Depression from Social Media Data as a Multiple-Instance Learning Task

Major Depression Disorder (MDD) is a mental condition that causes severe impairments in a person's life. Early detection of such a condition is imperative, especially to detect cases where the person does not even know that they are affected by it. Social media provide a ubiquitous platform with self-generated data that Machine Learning models enhanced with Natural Language Processing mechanisms can extensively explore. Nonetheless, we advocate that previous Machine Learning approaches to early detection of MDD do not adequately model the problem at hand. Most assume that the isolated publications on social media are enough for early detection. This paper proposes a new problem formulation for this task using the multiple-instance learning paradigm. Furthermore, we include a theoretical and experimental analysis of the method on a dataset of Brazilian university students using the transformer and LSTM architectures. The results indicate that the proposed approach yields better accuracy and explanation capabilities than previous studies that do not rely on multiple-instance learning.
Real-time moving target detection in infrared maritime scenarios

In maritime surveillance, real-time moving target detection is a crucial task. The purpose of our work was to test some moving target detection techniques inspired by the state-of-the-art on a specific dataset of interest. In this paper, we analyze the performance obtained by Frame Difference and Gaussian Mixture Model-based methods in static InfraRed video sequences. The dataset was collected under real operational conditions during a recent experimental activity. The frames in the dataset are characterized by heterogeneous backgrounds and different targets covering a wide range of sizes and speeds. To evaluate the performance, the ground truth has been manually labeled through direct observation in 4706 frames. All the examined techniques are used to estimate the background. They are preceded by a frame-based z-normalization. After the background estimation, the absolute difference with the normalized frame is computed to highlight both hot and cold targets. Once highlighted, the targets can be separated from the background with a threshold. Then, morphological operations both in time and space are executed to delete small and brief false alarms. Finally, blob analysis is computed to extract the Regions of Interest. The algorithms have been evaluated based on their Precision-Recall curves, and Mean Execution Times.
Joint Optimization of Multiple UAV-mounted RISs Deployment and RIS Elements Allocation

This paper considers deploying multiple UAV- mounted reconfigurable intelligent surfaces (RISs) to reflect signals from the base station (BS) to users to provide line- of-sight (LoS) communications. We aim to jointly optimize the deployment of UAV-mounted RISs, the assignment of RIS elements to users, and the BS power allocation to minimize the number of required UAVs while satisfying the user signal-to- noise ratio (SNR). To achieve the above goal, we divide the area of interest (AOI) into grid cells and propose an integer linear programming (ILP) model. Numerical results show that the proposed ILP model outperforms the baseline scheme under the constraints of user SNR thresholds and successfully serving all users.
Goal-oriented a posteriori error control for nonstationary convection-dominated transport problems

The numerical approximation of convection-dominated problems continues to remain subject of strong interest. Families of stabilization techniques for finite element methods were developed in the past. Adaptive techniques based on a posteriori error estimates offer potential for further improvements. However, there is still a lack in robust a posteriori error estimates in natural norms of the discretizations. Here we combine the dual weighted residual method for goal-oriented error control with stabilized finite element approximations. By a duality argument an error representation is derived on that a space-time adaptive approach is built. It differs from former works on the dual weighted residual method. Numerical experiments illustrate that our schemes are capable to resolve layers and sharp fronts with high accuracy and to further reduce spurious oscillations of approximations.
Connected Intelligence for Smart Water Quality Monitoring System in IIoT

Unclean water for drinking is unavailable to many people. By enabling automatic water quality measurement, this problem can be mitigated. In recent years, edge computing has gained popularity for remote monitoring. In order to track the water quality in rivers and household tanks in response to this trend, we developed an edge computing support system. For the application, the wireless module and sensor module are both required. The sensor connects wirelessly to a wireless module in order to collect and send data there. Data is transferred to the cloud using the wireless protocol once it reaches the wireless node. The conductivity, turbidity, oxygen, pH, and temperature are among the sensors included in the sensor module. Databases are used to store the data that the sensors have collected. A mobile AI-powered interactive app is developed to evaluate the water quality instantaneously based on sensor measurements. Retrieving sensor data from a distance and monitoring sensor performance are made simpler by the app's architecture. The simulation demonstrates how a straightforward AI model set up in an edge device helps with accurate water quality prediction.
New identities for linearized gravity on the Kerr spacetime

In this paper we derive a differential identity for linearized gravity on the Kerr spacetime and more generally on vacuum spacetimes of Petrov type D. We show that a linear combination of second derivatives of the linearized Weyl tensor can be formed into a complex symmetric 2-tensor Mab which solves the linearized Einstein equations. The identity makes this manifest by relating Mab to two terms solving the linearized Einstein equations by construction. The self-dual Weyl curvature of Mab gives a covariant version of the Teukolsky-Starobinsky identities for linearized gravity which, in addition to the two classical identities for linearized Weyl scalars with extreme spin weights, includes three additional equations. In particular, they are not consequences of the classical Teukolsky-Starobinsky identities, but are additional integrability conditions for linearized gravity. The result has direct application in the construction of symmetry operators and also yields a set of nontrivial gauge invariants for linearized gravity.
Fingerprint-aided mmWave Beam Selection for Enhanced Beamspace Massive MIMO Communication

Millimeter-wave communications are suitable for application to massive multiple-input multiple-output systems in order to satisfy the ever-growing data traffic demands of the next-generation wireless communication. However, their practical deployment is hindered by the high cost of complex hardware, such as radio frequency (RF) chains. To this end, operation in the beamspace domain, through beam selection, is a viable solution. Generally, the conventional beam selection schemes focus on the feedback and exhaustive search techniques. In addition, since the same beam in the beamspace may be assigned to a different user, conventional beam selection schemes suffer serious multi-user interference. In addition, some RF chains may be wasted, since they do not contribute to the sum-rate performance. Thus, a fingerprint-based beam selection scheme is proposed to solve these problems. The proposed scheme conducts offline group-based fingerprint database construction and online beam selection to mitigate multi-user interference. In the offline phase, the contributing users with the same best beam are grouped. After grouping, a fingerprint database is created for each group. In the online phase, beam selection is performed for purposes of interference mitigation using the information contained in the group-based fingerprint database. The simulation results confirm that the proposed beam selection scheme can achieve a signal-to-interference-plus-noise ratio and sum-rate performance which is close to those of a fully digital system, and having much higher energy efficiency.
Simple stably projectionless C*-algebras with generalized tracial rank one

We study a class of stably projectionless simple C*-algebras which may be viewed as having generalized tracial rank one in analogy with the unital case. Some structural question concerning these simple C*-algebras are studied. The paper also serves as a technical support for the classification of separable stably projectionless simple amenable Jiang-Su stable C*-algebras.
Output feedback stabilization of SISO nonlinear systems via dynamic sliding modes

Previous work on asymptotic stabilization of SISO nonlinear systems using dynamic sliding mode control to produce dynamic state feedback has been developed further to produce dynamic output feedback. All the states in the feedback controller are replaced with estimated states which come from a semi-high gain observer. An upper bound of the semi-high gain parameter is explicitly given. It is proved that if the given differential I-O system is minimum phase and proper, local uniform asymptotic output feedback stabilization can be achieved. Pertinent examples are given to show how the method works for both differential inputoutput models and state space models.
Exploring Multimodal Fusion for Continuous Protective Behavior Detection

Chronic pain is a prevalent condition that affects everyday life of people around the world. Protective behaviors (strategies that are naturally but unhelpfully adopted by people with chronic pain to cope with fear of pain in executing harmless everyday movements) can lead to further disability over time if not recognized and addressed appropriately. In this paper, we build on previous work on unimodal, activity-independent, time-continuous protective behavior detection (PBD) by focusing on the fusion of muscle activity and body movement modalities for characterizing both protective behavior and its physical activity context. We explore different fusion strategies based on consideration of the manner in which protective behavior influences muscle activity and overt body movement as well as the relationship between the two modalities. We evaluate the various strategies on the multimodal EmoPain dataset containing data from people with and without chronic pain engaged in physical activities that reflect everyday challenges for those with chronic pain. Our results show that a central (model-level) fusion approach leads to better PBD performance than input- and decision-level fusions, or unimodal approaches. We also show that additional use of attention mechanism, typifying shifts in attention characteristic of protective behavior, further improves the sensitivity of the model, i.e. detection of the positive class (which is the minority class). We analyze these results and suggest that fusion in modelling a motor condition should consider how emotional responses (fear of movement and pain in this case) triggered by a condition affect each of the given modalities and hence their contributions to the modelling task.
Learning PLC-based Automation by Using an Educational Elevator

Laboratory experiments provide substantial understanding and quantitative information about a series of actions or steps involved in that direction of engineering. The laboratories of engineering are key spaces, students thus learning to integrate theory and practice, to solve problems and apply experimental methods. One of the most popular training systems in engineering laboratories of electrical and control systems is the educational elevator, designed for the formation of basic skills and related specialist knowledge to electric drives and automation systems. The paper presents and discuss one upgraded educational elevator system in order to offer the possibility apply various control methods by means of the hardware controllers and to implement various control strategies.
On the symplectic curvature flow for locally homogeneous manifolds

Recently, J. Streets and G. Tian introduced a natural way to evolve an almost-K\"ahler manifold called the symplectic curvature flow, in which the metric, the symplectic structure and the almost-complex structure are all evolving. We study in this paper different aspects of the flow on locally homogeneous manifolds, including long-time existence, solitons, regularity and convergence. We develop in detail two classes of Lie groups, which are relatively simple from a structural point of view but yet geometrically rich and exotic: solvable Lie groups with a codimension one abelian normal subgroup and a construction attached to each left symmetric algebra. As an application, we exhibit a soliton structure on most of symplectic surfaces which are Lie groups. A family of ancient solutions which develop a finite time singularity was found; neither their Chern scalar nor their scalar curvature are monotone along the flow and they converge in the pointed sense to a (non-K\"ahler) shrinking soliton solution on the same Lie group.
Efficient DC Algorithm for Constrained Sparse Optimization

We address the minimization of a smooth objective function under an $\ell_0$-constraint and simple convex constraints. When the problem has no constraints except the $\ell_0$-constraint, some efficient algorithms are available; for example, Proximal DC (Difference of Convex functions) Algorithm (PDCA) repeatedly evaluates closed-form solutions of convex subproblems, leading to a stationary point of the $\ell_0$-constrained problem. However, when the problem has additional convex constraints, they become inefficient because it is difficult to obtain closed-form solutions of the associated subproblems. In this paper, we reformulate the problem by employing a new DC representation of the $\ell_0$-constraint, so that PDCA can retain the efficiency by reducing its subproblems to the projection operation onto a convex set. Moreover, inspired by the Nesterov's acceleration technique for proximal methods, we propose the Accelerated PDCA (APDCA), which attains the optimal convergence rate if applied to convex programs, and performs well in numerical experiments.
Contrast-enhancement, image noise, and dual-energy simulations for quantum-counting clinical CT

The spectral sensitivity of quantum-counting detectors promises increased contrast-to-noise ratios and dualenergy capabilities for Computed Tomography (CT). In this article we quantify the benefits as well as the conceptual limitations of this technology under realistic clinical conditions. We present detailed simulations of a CT system with CdZnTe-based quantum-counting detector and compare to a conventional energy-integrating detector with Gd2O2S scintillator. Detector geometries and pixel layouts are adapted to specific requirements of clinical CT and its high-flux environment. The counting detector is realized as a two-threshold counter. An image-based method is used to adapt thresholds and data weights optimizing contrasts and image noise with respect to the typical spectra provided by modern high-power tungsten anode X-ray tubes. We consider the case of moderate X-ray fluxes and compare contrasts and image noise at same patient dose and image sharpness. We find that the spectral sensitivity of such a CT system offers dose reduction potentials of 31.5% (9.2%) maintaining Iodine-water contrast-to-noise ratios at 120kVp (80kVp). The improved contrast-to-noise ratios result mainly from improved contrasts and not from reduced image noise. The presence of fluorescence effects in the sensor material is the reason why image noise levels are not significantly reduced in comparison to energy-integrating systems. Dual-energy performance of quantum-counting single-source CT in terms of bone-Iodine separation is found to be somewhat below the level of today's dual-source CT devices with optimized pre-filtration of the X-ray beams.
Controlling Dependency: Selectively Resetting Channels for Pre-trained CNN Backbone Network on Hand Pose Estimation

Transfer Learning is the de facto standard on numerous downstream tasks in computer vision. Previous works have shown diverse architectural approaches to enhance trans-ferability to transfer to downstream tasks. However, due to the complexity of the downstream tasks, the importance of a pre-trained feature extractor is sincerely overlooked. In this paper, we propose a simple method coined selective channel resetting(SCR), to enhance transfer learning performance for hand-pose estimation with the backbone network with the convolutional neural network. In this scheme, we apply a channel resetting module on the feature extractor to reset a portion of features to adjust the features to solely depend on the downstream task, and prevent the overfitting of the upstream task. The reset channels are then prepared to be trained freshly to fit the target task. Despite its simplicity, we can achieve +2.6% of AUC on the EgoDexter dataset. Empirical evidence demonstrates that the proposed method outperforms the conventional pretrain-finetune-based transfer learning on the hand pose estimation task. We also define the training stability to identify the benefits of pretraining.
FPGA-Based Tensor Compressive Sensing Reconstruction Processor for Terahertz Single-Pixel Imaging Systems

Terahertz (THz) imaging system has great potentials for material identification, security screening, circuit inspection, bioinformatics and bio-imaging because it can penetrate various non-metallic materials and inhibits unique spectral fingerprints of a great variety of optically opaque materials in our daily lives. However, THz emitters and detectors are still extremely expensive. Therefore, the single-pixel compressive sensing imaging technique becomes a potential solution for the implementation of a THz imaging system. This paper presents a tensor-based single-pixel compressive sensing model and a reconstruction algorithm for THz single-pixel imaging systems based on the generalized tensor compressive sensing framework. To accelerate the THz image reconstruction, a low-complexity 2-D compressive sensing processor based on power singular value decomposition method (2DCS-PSVD) was designed and implemented in this paper. In the $32\times32$ single-pixel THz imaging system, the 2DCS-PSVD algorithm requires 78.9% complexity of the modified generalized tensor compressive sensing parallel algorithm (GTCS-P) with little image quality degradation. The 2DCS-PSVD processor was further designed and implemented in the Xilinx ZCU102 SOC FPGA plate-form. The proposed FPGA-based tensor-based compressive sensing processor achieved a throughput of 1127 frames/sec and had the highest normalized hardware efficiency compared to other state-of-the-art works in the literature.