Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Broadband Target Orientation Method Using a Vector Hydrophone Vertical Array

An improved broadband target orientation method using a vector hydrophone vertical array (VHVA) is proposed. This method addresses the issue of low target orientation accuracy in deep-sea environments when using a single vector hydrophone. The pressure sub-array and particle velocity sub-arrays are beamformed first. Then, the output beams of each sub-array are compensated based on the target elevation angles, and an “equivalent vector element” is constructed. Finally, the target azimuth is determined by applying the MUSIC algorithm to a single vector hydrophone. This method utilizes the array gain of sub-arrays to improve the received signal-to-noise ratio (SNR) and employs the high-resolution MUSIC algorithm to minimize orientation errors. Simulation and experimental results demonstrate that this method effectively improves the orientation accuracy in comparison to the single-vector hydrophone orientation method and the multi-vector hydrophone data fusion method.
Experimental and Numerical Analysis on the Inductances of Multi-Layer Superconducting Helical Windings

Equivalent circuits are a versatile approach for electromagnetic modelling of superconductors applications, as they allow to easily include full-scale devices in complex electric grid circuits and to investigate their mutual interaction. However, they rely on the correct definition and estimation of the equivalent circuit parameters. In particular, the inductance can be a critical parameter, especially for AC applications or when fast-transients are required. Nowadays, the estimation of self- and mutual-inductances between helically-wound components (as in superconducting coils or cables) is performed with different analytic formulae or numerical methods. Verifying the reliability of a given approach is not a simple task. This work proposes and validates a numerical model for estimating the self- and mutual-inductances of helical windings realized with High Temperature Superconducting (HTS) tapes. The model is based on the combined use of numerical and analytical methods, consisting of numerically integrating over the whole tape volume the analytical partial mutual-inductances between straight tape segments. The tool allows to get equivalent parameters for both single and multiple tapes’ windings connected in series and in parallel. The model is adopted for an HTS coil, composed by three windings, and supplied with AC currents. A novel sample holder allows the coil to be designed and realized to supply each winding individually. Then, multiple tests are carried out by putting in series the single windings realizing inductive or anti-inductive combinations. The tool is validated by comparing both the numerical voltage profiles with those acquired during tests and the inductances estimated from the measurements with those resulting from the model.
Threat Hunting in Internet of Things Networks with Bio-Inspired Models

Cyber threat hunting is time-consuming as large data quantities are analysed to hunt down attacks that have evaded existing security measures. With an increasing number of Internet of Things (IoT) devices, the subsequently increasing data quantities make finding attacks through traditional approaches impossible. Existing research has highlighted the use of signature-based, visualisation, analytical, and Artificial Intelligence (AI) approaches to threat detection and anomaly detection. This paper contributes to the research on threat hunting using AI and presents novel models for threat hunting in IoT networks. The proposed models effectively demonstrate the importance of preprocessing approaches and model adaptability while challenging the need for overly complicated models.
A MultiLayer BlockChain Based Framework for Internet of Things

With the increasing use of smart devices in IoT networks, ensuring security in communications between these devices has become a major challenge. Blockchain technology, known for its decentralized and secure nature, holds promise in addressing security concerns within 5 G-enabled IoT networks. This research introduces a Multi-layer Blockchain Security model to safeguard IoT networks while simplifying its practical application. They use a technique called clustering to create a multi-layer structure within the IoT network. These clusters are formed using a combination of advanced algorithms, including Evolutionary Computation Algorithm, Simulated Annealing, and Genetic Algorithms. Headers within these clusters handle local authentication and authorization, and they use a private blockchain system to communicate with relevant base stations, adding credibility and security while providing a network authentication mechanism. The widely accepted open-source Hyperledger Fabric Blockchain platform is employed for building this model. In parallel, base stations utilize a global blockchain approach to securely communicate with each other. Simulation results show that this clustering approach outperforms previous methods, and the proposed lightweight blockchain model effectively balances network latency and throughput, offering advantages over traditional global blockchain systems.
Wearable Impedance-Matched Noise Canceling Sensor for Voice Pickup

: Communicating under extreme noise conditions remains challenging in spite of higher-order noise-canceling microphones, throat microphones, and signal processing. Both natural and human-made background ambient noise can disturb the conveyance of information because of high noise levels. Noise cancellation, which is used frequently in audio technology, has limits in noise reduction and does not guarantee clear vocal pickup in these severe situations. A contact microphone that is attached directly to the medium of interest has the potential to pick up vocal signals with reduced noise. In this study, an electrostatic transducer with an elastomer layer that is impedance-matched to the human body is used to pick up speech sounds through constant contact on the chin and cheek. By attaching the wearable device directly to the skin, the medium of air is bypassed, and airborne noise is passively canceled. Because of the acoustic impedance-matched layer, the sensor is more sensitive to low frequencies under 500 Hz, so frequency equalization was implemented to ﬂatten the frequency response throughout the vocal range. The perceptual evaluation of speech quality (PESQ) scores of the wearable device with equalization averaged around 2.6 on a scale from –0.5 to 4.5. Speech recordings were also collected in a noise ﬁeld of 85 dB, and the performance was compared to a cardioid lapel mic, a cardioid dynamic mic, and an omnidirectional condenser mic. The recordings revealed a signiﬁcantly reduced presence of white noise in the contact sensor. This study provides preliminary results that show potential vocal applications for a wearable impedance-matched sensor.
Mushroom Image Classification and Recognition Based on Improved Swin Transformer

Classification of mushrooms are essential for preventing even life-threatening consequences of accidentally eating wild mushrooms. In this study, a dataset including 114 varieties of mushrooms is collected and built. Next, the Swin Transformer model with robust classification characteristics is improved to classif mushroom images. Also, the classification accuracy of the improved Swin Transformer with different parameters was compared. In addition, ResNet50 is compared with the improved Swin Transformer under the optimal parameters. The model's training speed is further enhanced by integrating the improved Swin Transformer with ResNet50, thereby proposing the Swin_ResNet classification algorithm. Experimental results show that the classification accuracy of the optimal improved Swin Transformer algorithm is 87.66%, which is 14.86% and 7.64% higher than the ResNet50 and Swin Transformer models, respectively. In addition, the classification accuracy of Swin_ResNet is 85.31%, and the training time is 86.57% shorter than that of the improved Swin Transformer. This greatly improves the training efficiency.
PRELIMINARY RESULTS OF COSMO MODEL FORECAST FOR THE ROMANIAN TERRITORY CASE STUDIES

The aim of this paper is to analyze the performance of COSMO (Consortium for small Scale Modeling) model in situation with severe weather cases (strong atmospheric instability, observed heavy precipitation) and made a statistical inter-comparison between COSMO and HRM (High Resolution Model) models. The period of statistical evaluation is February-August 2005, corresponding to first pre-operational COSMO model runs at the National Meteorological Administration, in Bucharest. The statistical scores were made using the surface observations from 160 meteorological stations on the Romanian territory. The experiments for the severe weather situation was realized using two different precipitation schemes for COSMO model at two horizontal resolutions (7 km and 2.8 km). The results show an improvement of quality forecast for COSMO model, most evident for summer months and the ability of the model to realistically simulate small- scale features in experiments at fine resolution. Also, the experiments for cases with heavy precipitations showed the model capability to simulate well the spatial distribution of the precipitation field.
Development of Quench Detection Method Around the Joint Box for HTS Cable Using TFDR and EM Algorithm

A high-temperature superconducting (HTS) cable system requires cooling system to maintain a superconducting state. If a local fault occurs in the HTS cable, a fault current that causes heat can flow. This leads to a quench, where the superconductivity breaks down, inflicting critical damage on the HTS cable system. Conventional reflectometry method can detect the local quench point, but it is difficult to distinguish between a reflected signals from quench around the joint box and that from the joint box. In this paper, we propose two steps to solve this problem. 1) Anomaly detection step: we propose the integrating the autoencoder (AE) with time-frequency domain reflectometry (TFDR) to detect anomaly situation, and localize impedance mismatch point through time-frequency cross correlation (TFCC) function, which calculate the similarity between a reference signal and reflected signal. 2) Source separation step: If TFCC of signals overlap with each other, A expectation-maximization (EM) algorithm is used to separate each overlapped TFCC. The proposed method enables efficient monitoring of the HTS cable system by effectively detecting quench that occur around the joint box. We simulate the quench situation, and verify the proposed method through the simulation.
A Framework for Active DMPs in Photon and Neutron Science Large-Scale Facilities

In this paper, a framework and a system architecture are presented to support researchers in DMP creation and execution, with a focus on the generation of FAIR data. Using the research data lifecycle within Photon and Neutron analytical facilities as a detailed exemplar of this approach in practice, it shows how combining the creation of the DMP with the project management framework PMBOK makes it easier to integrate DMP creation within the researchers’ workflow and reuse pre-existing information within the research infrastructure and related project roles. The paper identifies requirements and introduces a lifecycle for pre-existing information that helps in automatic population of the DMP. This paper also discusses a data model for the reuse of pre-existing information. It shows possible approaches to support scientists through the (semi-)automation of the creation, execution, and use of a DMP and knowledge transfer. The approach is based on work within the PaNData ODI, ExPaNDS, and PaNOSC projects.
Geomorphologic units and sediment transport in a very low relief basin : Rio Quequén Grande, Argentina

The aim of this work was to identify the geomorphological units within the drainage network of the Rio Quequen Grande low relief basin, situated in the southeast section of the province of Buenos Aires, and to quantify the present sediment transport rate. For this purpose field surveys and satellite images were analysed. The former included measurement of bed material grain size and channel hydraulic geometry, in order to set up a bed and a suspended load measurement programme. Six main geomorphic units were identified: Ranges, Perirange Aeolian Hillocks, Hillocks with Lagoons, Relic Hills, a Poor Drainage Alluvial Plain and an Alluvial Plain. Bed load discharge and suspended sediment concentration measured during a bankfull flood are very low, given the relatively high stream power of 287 W m -1 . The measured bed load discharge was three to five orders of magnitude lower than six applied bed load equations, presumably due to low sediment availability.
Circular Arc Length-Based Kernel Matrix For Protein Sequence Classification

Biological sequence analysis is crucial in understanding the sequence structure, function, and evolutionary relationships. In traditional methods, using Euclidean distance metrics is common in measuring the similarity between sequence embeddings. However, they fail to capture sequence space’s inherent curvature and spherical nature. Therefore, we explore the application of spherical geometry and distance metric, circular arc length (CAL) based distance for comparing biological sequence embeddings. Spherical geometry is a non-Euclidean geometry that models the surface of a sphere, accounting for its curvature. By leveraging CAL, we can more accurately measure the pairwise distances between bio-sequence embeddings. In this study, we propose the utilization of CAL for comparing bio-sequence embeddings. We develop a function to compute the CAL distance between two sequence embeddings, enabling researchers to accurately measure the similarity between sequences while considering the underlying spherical geometry. Incorporating spherical geometry enables a more comprehensive understanding of the relationships and similarities between biological sequences, improving various downstream tasks such as classification, clustering, and evolutionary analysis. Our experimental evaluation demonstrates the advantages of using a spherical distance metric over Euclidean metrics for bio-sequence analysis. Our proposed CAL-based approach outperforms the Euclidean geometry-based baselines by depicting a huge performance improvement for the protein subcellular location classification task. In our experiments, accuracy is improved by 52.5% and 60. 3% compared to the PWM2Vec and Autoencoder methods, respectively, corresponding to the DT classifier.
Evaluating Representativeness in PDF Malware Datasets: A Comparative Study and a New Dataset

With the widespread use of the Portable Document Format (PDF), it’s increasingly becoming a target for malware, highlighting the need for effective detection solutions. In recent years, machine learning-based methods for PDF malware detection have grown in popularity. However, the effectiveness of ML models is closely related to the quality of the training datasets. In this research, we investigated two widely used PDF malware datasets: Contagio and CIC. We found biases and representativeness issues that could affect the reliability and applicability of models built on them. Our statistical analysis revealed marked difference between these datasets and PDF malware samples from VirusTotal, as well as benign PDFs from Govdocs, pointing to the necessity for more representative datasets in PDF malware research. To address this gap, we introduce a novel dataset: PdfRep. Our findings demonstrate that PdfRep outperforms both CIC and Contagio across various evaluation metrics. The main contribution of this paper is the introduction of PdfRep, a new PDF malware dataset that overcomes the limitations of representativeness in existing datasets. This enhancement substantially increases the accuracy of PDF malware detection models and holds promise for advancing the field of PDF malware detection research.
Research and Implementation of 77GHz Automotive Radar Target Detection Technology

Today, with the rapid development of autonomous driving technology, ensuring the accuracy of information when entering subsequent tracking algorithms is crucial. To address potential issues of target loss and information ambiguity in radar signal processing, a fast signal processing system suitable for high-resolution radar has been researched and proposed. This system relies on the MIMO radar framework. After signal preprocessing, the MIMO radar's equivalent aperture provides ADC data from multiple channels, thereby improving angular resolution. After obtaining discrete valid points in the R-D-A domain, dimensionality compression is performed to compress the R-D-A domain into the R-A domain. The valid points in the R-A domain are then transformed into the real Cartesian coordinate system and subjected to weighted DBSCAN clustering. Finally, computing the centroids of the valid cluster points yields the exact relative position of the targets for subsequent algorithm processing. This framework aims to minimize false alarms while utilizing the subsequent weighted clustering process to remove noise points and invalid targets. Overall, the algorithm has low complexity and achieves good target detection performance.
Vehicle Trajectory Tracking for Intelligent Transportation System Using Distributed Acoustic Sensing

This paper proposes a vehicle trajectory tracking algorithm based on distributed acoustic sensing(DAS). The DAS system can detect and locate along the optical fiber, but because the traditional DAS data processing method is only suitable for counting the traffic flow, it does not have a good effect on obtaining the vehicle's driving track. To track the vehicle trajectory in real time, this paper first uses the double threshold endpoint detection algorithm to process the vehicle vibration signal collected by DAS and then uses the Kalman filter algorithm to optimize the estimation of the result to obtain the vehicle trajectory. The experimental results show that the above method can not only track the vehicle trajectory, but also have a good effect on the estimation of the vehicle speed.
Sound Source Localization of Cars at Intersections Based on Deep Learning

Highly automated and autonomous vehicles currently rely on three complementary main sensors to identify visible objects, namely cameras, lidar and radar. However, the function of these traditional sensors may be limited in urban environments when the sight is blocked by narrow streets, trees, parked vehicles and other traffic. Acoustic perception does not depend on the line of sight, Multiple, cheap microphones are used to capture sound as an auxiliary sensing mode for the early detection of vehicles approaching behind blind spots in an urban environment. We use a CNN to learn on data converted to Mel spectrum features and achieve 86% accuracy, our program can determine the direction of approaching cars at any traffic intersection.
Rooting of blueberry hardwood cuttings as affected by wood type

Difficulty in propagation using cuttings is a factor that limits blueberry expansion. The success of propagation by cuttings depends on the capacity of adventitious root formation of the cultivar and new root system quality. This work aimed to evaluate the effect of branch type on the rooting of hardwood cuttings of the blueberry cultivars ‘Delite’, ‘Bluebelle’ and ‘Briteblue’. The plant material used was derived from whip segments originating from the crown and lateral shoots from old canes, with about 7.0 cm length and 5.5 mm diameter, and 6.0 cm and 3.5 mm, respectively. The cultivar ‘Briteblue’ showed the highest percentage of rooting (60%), while the cultivar ‘Bluebelle’ had the lowest (35%). The source of cutting wood affected the percentage of cuttings that survived with 81 and 65% of the cuttings from lateral and whip canes rooting, respectively. Therefore, it was concluded that the cultivar ‘Briteblue’ was easy to propagate with hardwood cuttings and lateral branches provided a high percentage of rooting, independent of the cultivar.
Stem Cell Transplant in Immune-deficiency–associated Vaccine-derived Poliovirus

Abstract Patients with severe primary immunodeficiency are at risk for complications from live-attenuated vaccines. Here, we report a case of a vaccine-associated paralytic polio and Bacille Calmette-Guérin disease in a 6-month-old girl with severe combined immunodeficiency resulting from homozygous recombinant activating gene 1 deficiency. The patient was successfully treated with intravenous immunoglobulins and oral pocapavir for poliovirus, and antimycobacterial therapy for regional Bacille Calmette-Guérin disease, allowing stem cell transplant. Following transplantation, poliovirus type 3 with 13 mutations was detected from cerebrospinal fluid but not from stool, indicating ongoing viral evolution in the central nervous system despite pocapavir treatment. Clinical improvement and immune reconstitution allowed the patient to be successfully discharged with no further detection of poliovirus.
Étude théorique de la structure et de la dynamique de l’hémoglobine tronquée N de Mycobacterium tuberculosis

The truncated hemoglobin N from Mycobacterium tuberculosis (TrHbN) protects aerobic respiration of Mycobacterium bovis BCG cells from the inhibitory effect of •NO. In addition, TrHbN catalyses the very rapid dioxygenation of •NO into the innocuous NO3 ions (NOD reaction: TrHbN-Fe 2+ –O2 + •NO  TrHbN-Fe 3+ + NO3 ) with a bimolecular rate constant of 745 μM -1 s -1 at 20°C. This high efficiency was largely associated to the presence of two hydrophobic tunnels visible in the 3D-structure of TrHbN. In this context, the main goal of this thesis is to study TrHbN structure and dynamics with bioinformatics tools, especially molecular dynamics simulations. Several molecular dynamics simulations of TrHbN under its deoxy, oxygenated and cyanomet forms were conducted. These simulations allowed to study dynamics of TrHbN backbone, that of the active site and especially, that of the tunnels. As a main result, our simulations revealed that tunnels are highly dynamics, more complex than anticipated from the 3D-structure and that they are hosted in a very rigid two-on-two fold. Other simulations, this time including free •NO molecules, highlighted the use of these tunnels to reach the buried active site. These simulations allowed to propose many hypotheses regarding the preferred routes and to propose diffusions mechanisms from the solvent to the active site. In order to validate our hypotheses and to push further our knowledge on TrHbN, other simulations coupled with some experimental approaches were performed. First, simulations on TrHbN under its cyanomet form coupled with a detailed NMR confirmed that the backbone of the protein is ridig. In addition, this work revealed i) the presence of μs-ms motions localized along B and G helices and ii) that the pre-A region is not structured in contrast to the alpha helice seen in the crystal structure. Finally, other simulations along with kinetics characterizations of obstructed tunnel mutants were conducted. As a main result, the latter work revealed that TrHbN core is quite plastic, allowing substrate diffusion despite the presence blocking mutations. Our comprehension on TrHbN is still incomplete, however the work presented in this thesis constitutes a considerable progress. Moreover, the work presented herein contributes to other fields of