Miracle-dz/UniFAR-FTUs huggingface dataset:
Cryptography Based Security for the ATM Surveillance Chain

Legacy systems are quite common in critical infrastructures. The Air Traffic Management (ATM) domain is no exception. The combination of their legacy technology and use in critical infrastructures make them attractive and sometimes easy targets for cyber-attacks. Like many critical infrastructures, ATM is heavily constrained and strongly regulated. This limits the possibilities of short term changes. The development and operational life-cycles of these systems are usually quite long. This increases the need for transitional security mechanisms that can be retrofitted in a minimal invasive way without compromising safety and performance requirements while taking into account the ATM constraints. In this paper a Proof of Concept study is described that investigates a flexible and lean implementation for the ATM surveillance chain, based on standardized protocols and open source software.
UAV-based system for real-time wildfire perimeter propagation tracking

Real-time wildfire perimeter tracking provides situational awareness and enhances decision-making during firefighting. This paper proposes a UAV-based system that integrates real-time data collection (using onboard sensors) into a fire propagation model to provide accurate state information on the wildfire perimeter and improve fire prediction. Firstly, a data fusion scheme is devised to employ available historical data in combination with real-time measurements to provide updated inputs to the fire propagation model. Then the model is used to predict the future fire perimeter and uses these predictions to guide the UAV to track the fire perimeter better. The proposed system is evaluated in extensive simulation experiments, demonstrating its effectiveness for real-time wildfire perimeter propagation tracking.
A 28nm CMOS Dual-Band Concurrent WLAN and Narrow Band Transmitter with On-chip Feedforward TX-to-TX Interference Cancellation Path for Low Antenna-to-Antenna Isolation in IoT Devices

A dual-band, concurrent 2.4G WLAN and 2.4G narrow band (NB) transmitter (TX) with on-chip feedforward TX-to-TX interference cancellation path for low antenna-to-antenna isolation in IoT devices is proposed. An on-chip cancellation path generates a replica signal of the same magnitude but 180° out-of-phase with respect to the “aggressor” TX signal appeared at the “victim” TX output. With cancellation path properly calibrated, the measured IMD3 product is reduced by 25 dB. Additionally, the maximum output power during concurrent transmission, while meeting the FCC out-of-band emission specification, improves from 10 to 17 dBm across all WLAN channels. With this proposed architecture, the issue of TX-to-TX interference in multi-radio coexistence is finally addressed, opening the door to future high power concurrent multi-band transmitters in reconfigurable IoT devices.
Preliminary Design of Air and Thermal Management of a Nacelle-Integrated Fuel Cell System for an Electric Regional Aircraft

The next generation of civil aircraft aims for low to zero emissions by 2050. Fuel cell-powered electric propulsion system can offer the required performance and low emission levels for regional flights. The challenge is to reduce weight and complexity, while achieving high reliability. This paper focuses on the preliminary design of the air and thermal management of a nacelle-integrated fuel cell system. These subsystems are necessary for the efficient and reliable operation of the fuel cell stacks and contain critical components like air intake, air duct and heat exchanger, which need to be designed and optimized to achieve compact and lightweight solutions. This paper shows design and assessment work on various preliminary concepts of large air ducts and heat exchangers, which are recognized as performance critical and large volume components. These components need to be tailored to the design space inside engine's nacelle. Using parametric 3D modeling, several variations for compact heat exchangers are created and the sensitivity of key dimensions for aero-thermal performance properties is studied. Subsequently, CFD simulations are conducted and different design options are evaluated concerning pressure loss, drag, heat transfer and mass flow rate. Additional suitable components such as filter, compressor and pump are discussed and selected. Finally, two suitable design configurations for the air and thermal management of a nacelle-integrated fuel cell system are selected and evaluated.
Influence of robot motion and human factors on users' perceived safety in HRI

Besides the human physical safety also their perceived safety need to be taken into account in human-robot interaction (HRI). For this reason, the Expectable Motion Unit (EMU) concept was developed, a velocity scaling scheme relating the robot speed and instantaneous human-robot distance to the occurrence of human startle or surprise reactions. Since the perception of safety is highly subjective, the warranty of the EMU concept is expected to be dependent on human factors. Thus, human factors may be incorporated in the concept. Building up on the previous robot-focused analyses of the EMU, in this paper, we analyze the influence of human factors on perceived safety in robot approaching scenarios including 29 participants without the application of the EMU concept as well as in the same scenario with 15 participants under the application of the EMU concept. We report that the influence of sociodemographic factors is not significant which highlights the generalizability of the EMU concept. On the other hand, the experiments show that a strong habituation effect exists. This implies that the robot motion should be highly defensive during the first interaction with lay users. In general, the EMU concept proved its suitability by successfully reducing the influence of robot motion parameters on human perceived safety.
Top-r keyword-based community search in attributed graphs

Community search on attributed graphs has been widely studied recently. Most earlier works aim to retrieve communities relevant to the query nodes QU and query keywords QW , and some recent works begin to focus on keyword-based attributed community search (KACS) with only query keywords QW , aiming to return a structural cohesive community with the highest score relevant to QW . However, these scores only consider the semantic similarity between user attributes and QW and neglect the semantic similarity between users in the community. Thus, we propose a new community model which considers both semantic similarities and uses triangle-connected k-truss to ensure structural cohesiveness, and study the top-r keyword-based attributed community search (rKACS) problem for a given QW to provide more candidates for users to choose the preferred communities. To find the top-r communities, we first propose the Basic algorithm, which gradually finds the communities with large scores through maximal clique enumerations. Then, we further propose an improved algorithm Incremental based on two novel optimization techniques, which can significantly reduce the search space and find the maximal cliques incrementally. Extensive experimental studies on four real-world datasets validated the effectiveness and efficiency of our methods.
PyLatheDB - A Library for Relational Keyword Search with Support to Schema References

Relational Keyword Search (R-KwS) systems enable naive/informal users to explore and retrieve information from relational databases without knowing schema details or query languages. These systems take the keywords from the input query, locate the elements of the target database that correspond to these keywords, and look for ways to "connect" these elements using the information on key/foreign key pairs. Although several such systems have been proposed, most of them only support queries whose keywords refer to the contents of the target database and only a few support queries in which keywords may also refer to elements of the database schema. We showcase PyLatheDB, a Python library for Relational Keyword Search with Support to Schema References. PyLatheDB is based on Lathe, an R-KwS framework that generalizes the well-known concepts of Query Matches (QMs) and Candidate Joining Networks (CJNs) to handle keywords referring to schema elements and introduces new algorithms to generate them. Lathe also introduced a novel approach to automatically select the CJNs that are more likely to represent the user intent when issuing a keyword query. This approach includes two major innovations: a ranking algorithm for selecting better QMs, yielding the generation of fewer but better CJNs, and an eager evaluation strategy for pruning useless CJNs. We demonstrate through a Jupyter 1 notebook the functioning of PyLatheDB for two representative application scenarios, showing each step of the keyword query processing. The users can interact with the notebook by running keyword queries and experimenting with configuration parameters to see how they affect the results. The notebook, a video, and the code of PyLatheDB are available at https://github.com/pr3martins/PyLatheDB.
The “Eve effect bias”: Epistemic Vigilance and Human Belief in Concealed Capacities of Social Robots

Artificial social agents (ASAs) are gaining popularity, but reports suggest that humans don't always coexist harmoniously with them. This exploratory study examined whether humans pay attention to cues of falsehood or deceit when interacting with ASAs. To infer such epistemic vigilance, participants' N400 brain signals were analyzed in response to discrepancies between a robot's physical appearance and its speech, and ratings were collected for statements about the robot's cognitive ability. First results suggest that humans do exhibit epistemic vigilance, as evidenced 1) by a more pronounced N400 component when participants heard sentences contradicting the robot's physical abilities and 2) by overall lower rating scores for the robot's cognitive abilities. However, approximately two-thirds of participants showed a “concealed capacity bias,” whereby they reported believing that the robot could have concealed arms or legs, despite physical evidence to the contrary. This bias, referred to as the “Eve effect bias” reduced the N400 effect and amplified the perception of the robot, suggesting that individuals influenced by this bias may be less critical of the accuracy and plausibility of information provided by artificial agents. Consequently, humans may accept information from ASAs even when it contradicts common sense. These findings emphasize the need for transparency, unbiased information processing, and user education about the limitations and capabilities of ASAs.
VEND: Vertex Encoding for Edge Nonexistence Determination

We propose to design vertex encoding for determinations of no-result edge queries that should not be executed. Edge query is one of the core operations in mainstream graph databases, which is to retrieve the corresponding edges connecting two given vertices. Real-world graphs may be too large to be stored in memory and frequently accessing edge data on disk usually incurs much overhead. Average degree of real-world graph tends to be much less than the vertex number, and edges may not exist in most pairs of vertices. Efficiently avoiding no-result edge query executions will certainly improve performance of graph database. In this paper, we propose a new and important problem for determining no-result edge queries: vertex encoding for edge nonexistence determination (VEND, for short). We build a low dimensional vertex encoding for all vertices, and we can efficiently determine most vertex pairs that are connected by no edges just with their corresponding codes. With VEND, we can utilize in-memory efficient operations to filter no-result disk accesses for edge query. We also design maintenance algorithms for the proposed solution when data updates happen. Extensive experiments on many real-world datasets confirm the ability of our solution on determining a quite high proportion of non-edge vertex pairs, as well as the acceleration for edge queries.
Rockfall Monitoring Using Multi-Technological Wireless Sensor Network

Rockfall phenomena are caused by the exposure of rock masses to weather and erosion. Over time, a rock or boulder can become unstable and fall along a slope. To monitor these gravitative elements, several methodologies based on sensor networks have been developed in recent times; among these we find geotechnical monitoring Wireless Sensor Network (WSN). The flexibility of these structures makes them a well-suited solution. In this paper, a multi-technological rockfall-oriented WSN is presented; this system is composed of several Long Range (LoRa) based sensors that retrieve various geotechnical parameters, along with other cellular based nodes for rock impact monitoring. Sensors of different technologies are integrated in the network to obtain a wider spectrum of monitored events in order to enable a complete observation possibility for the status of the rock formations. The entire system follows an Internet of Things (IoT) scheme, where data is accessible anytime from an online platform. The experimental system is installed in the locality of San Demetrio Ne’ Vestini, Italy, in particular at the Grotte di Stiffe site.
An Adaptive and Multifunctional DC-DC Converter for Onshore Ship Charging

Maritime electrification is a critical upgrade to enable sustainable and environmentally friendly shipping. Therefore, in recent years, there has been a significant interest in the development of battery-based boats and ferries. A key element for the reliable operation of these vessels is the onshore charging interface. Unlike in electric cars, the onboard power system and the operating voltage in different vessels can vary over a significantly large range. Therefore, the development of a multi-functional charging unit can be challenging. This paper proposes a new power electronics converter that comprises an adaptable charging interface that can operate efficiently over a very wide range of output voltage, and deliver power to different vessels. Simulation results are presented to illustrate the operation of the proposed circuit.
Analyzing the Feasibility of Privacy-Respecting Automated Tracking of Devices Fleeing a Burglary

Criminals bringing their mobile devices to a burglary and subsequently fleeing the crime scene may be tracked and re-identified by digital traces left behind by their devices, e.g., by utilizing WiFi routers listening to their communication. Currently, it is unclear how a sensor network for automated tracking of these fleeing devices should be designed, especially considering a privacy-respecting operation. While various WiFi device tracking techniques are well researched for indoor applications, tracking non-cooperative devices using a sensor network through a town or city is a unique use-case that is yet to be examined. In this paper a tracking simulator is implemented which allows for multiple parameters of both the fleeing device as well as the sensors to be thoroughly tested and its tracking performance based on these parameters to be evaluated. For this, different metrics such as device activity, detection events, and path reconstruction are gathered by running the simulation using different sensor activation strategies with the goal to have as many sensors as needed but as few as possible active. Through this approach, the device tracking success is evaluated while having minimal infringement on the privacy of devices not being involved with the criminal activity.
Object Activity Tracking for Webcam Using Hog Algorithm Comparing Background Subtraction Algorithm

Object activity tracking for webcam using HOG Algorithm Comparing Background Subtraction Algorithm. To accurately recognise the item, Noise Removal and also the HOG method with a random sample of 32 were repeated ten times. The recognition of entities using history historical information is a component of motor action recognition. The Noise Removal and HOG algorithms are used to monitor objects motion for webcams. Background Subtraction and HOG algorithms output is between the optimized values. Object activity tracking for webcam using HOG algorithm is observed and compared for the findings.
Deep Learning Assisted Channel Estimation for Cell-Free Distributed MIMO Networks

Pilot contamination poses a critical challenge for channel estimation in dense cell-free (CF) distributed multiple-input multiple-output (CF-DMIMO) wireless networks. State-of-the-art channel estimation schemes require inversion of a high-dimensional channel covariance matrix, which is practically infeasible for dense CF-DMIMO networks owing to the requirement of large storage and high dimensional computational complexity. In this work, we investigate channel estimation problem for a CF-DMIMO network, where both terrestrial and aerial users are jointly supported by distributed access points. We formulate the problem of estimating channel coefficients from the received in-phase/quadrature (I/Q) samples as a non-linear regression problem and propose two deep-learning aided channel estimation schemes for the considered network, namely, deep model-agnostic neural network (DMANN) and deep successive contamination cancellation (DSCC) schemes. Compared to the state-of-the-art channel estimation schemes for CF-DMIMO networks, the proposed schemes (i) tackle the unavoidable pilot contamination issue in dense CF-DMIMO networks while estimating the channel gains for both terrestrial and aerial users; (2) does not require prior knowledge of signal-to-noise ratios; and (3) works well in the presence of non-Gaussian correlated noise. Simulation results demonstrate the effectiveness of the proposed schemes over state-of-the-art channel estimation schemes in various use cases of the CF-DMIMO networks.
Performance Evaluation of Cruise-Controlled Vehicles on a Macroscopic Scale

In this paper, we study the performance of a class of cruise-controllers for automated vehicles on a macroscopic scale. We first show that the solution of the corresponding second-order macroscopic model can be approximated by the solution of a nonlinear heat-type equation. To study the behavior induced on the macroscopic model by the first-order approximation, we derive a conservative finite-difference scheme that respects the corresponding entropy conditions. Finally, a traffic simulation scenario and a comparison with the Lighthill-Witham-Richards (LWR) model are given, illustrating the benefits of the use of cruise-controlled vehicles.
Mesoscopic approach to fluid-solid interaction: Apparent liquid slippage and its effect on permeability estimation

The liquid slippage behavior due to molecular interactions at fluid-solid (F-S) interfaces is of great importance to the transport of shale oil and clay water. A mesoscopic single-phase lattice Boltzmann method (LBM), based on a continuous and exponentially decaying F-S interaction force and a midgrid bounce-back boundary condition, is proposed to be responsible for the apparent liquid slippage. The F-S interaction force is established at the particle level and thus can be readily extended to porous media. When it is repulsive (attractive), the phenomena of positive (negative) slip lengths and fluid slip (damping) are successfully recovered. This model is validated by the velocity profiles on hydrophobic and hydrophilic surfaces in a benchmark microchannel flow experiment. The slip length is found to be independent of shear rate (its constituents including body force, pore diameter, and kinematic viscosity), but dependent on pore geometry (smaller in porous media than in capillary tubes). Both slip length and permeability ratio follow a power law relationship with interaction parameters (strength and decay length) in capillary tubes and porous media. The permeability ratio estimated analytically with the slip length considered agrees well with that calculated from the LBM simulations, except for the fluid slip in porous media with a significant overestimation. The estimated permeability ratio indicates that it increases (decreases) nonlinearly as the pore diameter decreases, suggesting the great importance of the F-S interaction particularly for thin capillary tubes and microporous media.
Nested Loops Revisited Again

Hash joins and sort-merge joins have been considered the algorithms of choice for analytical relational queries in most parallel database systems because of their performance robustness and ease of parallelization. On the other hand, nested loop joins have been considered less attractive and are conservatively used. In this paper, we revisit the potential of nested loop joins in a cluster environment. We focus on exploring the parallelism aspect of nested loop joins because there could still be space for improvement by fully exploiting the parallelism of current commodity hardware, which could handle more than thousands of concurrent IOs. We also introduce scalable massively-parallel execution as one of the approaches for achieving massive parallelism in nested loop joins to explore how it widens the potential benefit of nested loop joins. Finally, we discuss future research directions based on our exploration.
A Comparative Analysis for the Detection of COVID-19 from Chest X-ray Dataset

Corona Virus Disease (COVID-19) is probably one of the most dreadful disease globally and the root cause of people's demise. Advance forms of COVID-19 are being discovered every time, and it has become hard to find solutions to get rid of this severe trouble. Scientists, Researchers, and others who work in medical fields are trying to implement a system that will find symptoms of COVID-19 within a short time so that necessary treatment can be taken before the disease becomes acute. Deep learning methods in that scenario are giving some promising results in detecting COVID-19 efficiently. Deep CNN (Convolutional Neural Network) architectures like ResNet, VGG-16, EfficientNet and other models are accomplishing excellent results in identifying COVID-19. In this paper, some works related to deep learning and COVID-19 by several researchers are discussed. Furthermore, four models were proposed, which are four versions of EfficientNet, and the models were trained on the largest dataset named COVIDx CXR-3, containing approximately 30,882 images, where 16690 are COVID positive and 14190 are COVID negative. Among the models, EfficientNet B3 provided a good enough accuracy of 97.89 % with a sensitivity of 98.02 % and a specificity of 97.67 %. In the end, EfficientNet B3 was compared with other CNN algorithms published by different researchers and found that the proposed model provided a more acceptable accuracy than the other models compared. Moreover, the methodology of the proposed models and their descriptions have been given in this paper.