Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Efficient Data Gathering Schema for Wireless Sensor Network: A Matrix Completion Based Approach

In this paper, we seek to address the data gathering in the continually growing Wireless Sensor Networks (WSNs) with the intention to save the nodes' energy. In order to address usual WSN problems, such as data losses, collisions and retransmissions, a twofold data compression pattern is proposed. We consider that a restricted number of sensor nodes are selected to be active and represent the whole network, while the rest of nodes remain idle and do not participate at all in the data sensing and transmission. Furthermore, the set of active nodes' readings is efficiently reduced, in each time slot, according to the cluster scheduling. Relying on the existing Matrix Completion techniques, the sink node is unable to recover the entire data matrix due to the existence of completely empty rows that correspond to the inactive nodes, which can be considered as absent nodes for a very long period, or nodes that do not exist at all. Thereby, we propose a complementary interpolation technique, based on a minimization problem that benefits from nodes inter-correlation, to guarantee the reconstruction of all the empty rows, despite their large number. The simulations confirm the efficiency of the proposed approach and show that it outperforms the existing one by up to 70.101% of Normalized Mean Absolute Error on all missing elements, when the number of active nodes is of about 10% of the total number of nodes.
An Effective Sparse Representation Approach for Wireless Channels Based on the Modified Takenaka-Malmquist Basis

The sparse representation of wireless channels is of great significance for studying channel estimation. However, the existing sparse representation of wireless channel mostly adopts a fixed sparse basis function. The sparse representation will not be performed well when the channel environment is complex. In this paper, a novel sparse representation method for wireless channels based on the modified Takenaka-Malmquist Basis (MTMB) functions is proposed. The representation of the wireless channels is firstly made using a limited number of Takenaka-Malmquist Basis (TMB) function combinations. Further, we modify the TMB function to match the channel model of the desired sparse representation. In addition, in order to make the MTMB functions fit the sparsity model better, the obtained MTMB dictionary is trained by the K-SVD algorithm. Therefore, the sparse representation of the wireless channel can be obtained by these public modified Takenaka-Malmquist Basis (PMTMB) functions. Simulation results show that PMTMB are able to match the wireless channels structure well and can achieve high accurate performance of sparse representation.
L0-Norm Adaptive Volterra Filters

The paper addresses adaptive algorithms for Volterra filter identification capable of exploiting the sparsity of nonlinear systems. While the l1-norm of the coefficient vector is often employed to promote sparsity, it has been shown in the literature that superior results can be achieved using an approximation of the l0-norm.Thus, in this paper, the Geman-McClure function is adopted to approximate the l0-norm and to derive l0-norm adaptiveVolterra filters. It is shown through experimental results, also involving a real-world system, that the proposed adaptive filters can obtain improved performance in comparison with classical approaches and l1-norm solutions.
A Fully Distributed Fault-Tolerant Cluster Head Selection Algorithm for Unit Disk Graphs

Wireless ad hoc and sensor networks, one of the important components of the Internet of Things, consist of hundreds or even thousands of tiny sensor nodes without a fixed infrastructure. Due to lack of transmission power, the presence of obstacles, and other environmental conditions, ordinary sensor nodes that are powered from their batteries may have to send data in a multi-hop manner to the sink node. Therefore, it is very important to conduct an energy-efficient and fault-tolerant routing algorithm. Clustering is one of the most popular methods for routing in wireless sensor networks. With the help of clustering, nodes are classified as cluster leaders and ordinary nodes. In this study, a distributed, fault-tolerant cluster leader selection algorithm is proposed for a wireless sensor and ad hoc networks that can be modeled as a unit disk graph. The proposed algorithm is self-stabilized and it is based on a maximum independent set. The theoretical analysis of the proposed algorithm is performed, it is implemented in SimPy environment and compared with other algorithms. According to the measurements taken, the proposed algorithm performs significantly better than its counterparts.
Dependability of Smart Distribution Grid Protection Using 5G

The future trends for smart distribution grids will be increasingly characterized by more use of advanced communication technologies, like the function virtualization. The smart grid can benefit from virtualization and emerging standard ICT technologies such as 5G, to become more cost effective. However, stringent performance requirements must be met and higher reliability, robustness, flexibility and scalability must be provided. This paper discusses the usage of virtualzation concepts in 5G technology for protection function/application in distribution grid. A 5G based architecture using an edge computing infrastructure to hosting the control and protection applications is proposed. A stochastic activity network based model is used to analyze and compare the reliability of the proposed 5G based architecture with a functionally identical Ethernet based IEC 61850 architecture. The 5G architecture seems to result in a significant gain in availability provided that redundancy on the radio links is used to compensate the loss due to random fading processes.
Defect Detection From Compressed 3-D Ultrasonic Frequency Measurements

In this paper, we propose a compressed sensing scheme for volumetric synthetic aperture measurements in ultrasonic nondestructive testing. The compression is achieved by limiting the measurement to a subset of the Fourier coefficients of the full measurement data, where we also address the issue of a suitable hardware architecture for the task. We present a theoretic analysis for one of the proposed schemes in terms of the Restricted Isometry Property and derive a scaling law for the lower bound of the number of necessary measurements for stable and efficient recovery. We verify our approach with reconstructions from measurement data of a steel specimen that was compressed synthetically in software. As a side result, our approach yields a variant of the 3-D Synthetic Aperture Focusing Technique which can deal with compressed data.
On the Usage of DSCP and ECN Codepoints in Internet Backbone Traffic Traces for IPv4 and IPv6

Differentiated Services Code Points are values that can be used on IP packets to mark traffic and allow for different Quality of Service treatment during the traversal through a network. Explicit Congestion Notifications are used to indicate that congestion on a network occurred. Both values are encoded in the previously called Type of Service field in the IP header. In this paper, we look at codepoint values for DSCP and ECN in relation to the ports used on the transport layer, which lets us infer the application that is generating the traffic. We provide new measurement data by analyzing traffic from Internet backbone links collected by CAIDA from the months March, April, and May 2018 in New York City. Our results show that DSCP codepoints are rarely used in IPv4 but even less in IPv6. Moreover, most traffic using DSCP codepoints is only using default values and not values designed for prioritization of packets. ECN-enabled traffic is scarce in IPv4, while in IPv6 it appears to be neglectable. However, we could observe differences for certain application traffic in the usage of DSCP and ECN codepoints and elaborate on their distribution.
Enhanced Localization Systems with Multipath Fingerprints and Machine Learning

We propose a new method to enhance the performance of radio frequency localization in strong multipath and non-line-of-sight (NLOS) situations. The knowledge about the geometrical structure of multipath propagation environment is exploited by using a ray-tracing tool. We further apply the Random Forest (RF) algorithm embedded in a machine learning framework to extract a reference data-set of Time Differences of Arrival (TDOA) fingerprints in multipath outdoor scenarios. Site-specific fingerprints are processed with a multidimensional cross-correlation, called Volume Cross-Correlation function (VCC), to extract the multipath features from measurements. The performance and feasibility of our method was evaluated by simulations and measurements.
Developmental health services leadership: Integrating hierarchical and shared leadership for health services organizational learning

The purpose of this article is to articulate a model of, as well as a call to action for, health services leadership, with a particular emphasis on the role of health services leadership in organizational learning. More specifically, I articulate a model that poses two antecedents of health services hierarchical leadership—leader responsibility disposition and environmental cues—as important predictors of empowering leadership. In turn, I assert that empowering leadership from above will engender an inclination toward shared leadership among followers. Subsequently, I venture that both empowering hierarchical leadership and shared leadership are precursors of health services organizational learning. I specify several research directions for future scholarship. More importantly, I identify multiple measures that policy makers and practitioners can take to enhance health services leadership and organizational learning.
Shape Recognition of Material Heaps in Outdoor Environments and Optimal Excavation Planning

For the realization of an automated material heap excavation system in a recycling plant, an algorithm for its planning process is proposed. The approach uses depth and color data from a 3D stereo camera. Based on the obtained information, the material heap is recognized and segmented. The resulting segments are evaluated according to a reward function for optimality in terms of efficiency, safety and practicability for excavation. While this approach is developed for recycling plants in particular, other applications can be expected.
A tightly coupled LIDAR-IMU SLAM in off-road environment

A tightly coupled LIDAR-IMU SLAM is proposed in this paper to make precise and robust estimation on position, posture, velocity as well as bias of accelerometers and gyros in off-road environment where features are not sufficient. This method is based on optimization of residuals both produced by LIDAR point clouds and IMU integration. The first part of residuals comes from the sum of distance between current sweep point clouds and voxel centroid of relative maps which are built simultaneously. The second part of residuals comes from a pre-integration procedure which takes LIDAR and IMU calibration error into account. A series of experiments based on data collected from an intelligent vehicle platform is carried out to evaluate the SLAM system. The experimental results have proven the ability of the system for precise pose estimation. Compared with the LIDAR-only method, LIDAR-IMU SLAM shows better performance on the estimation accuracy and robustness on position and posture as well as obtaining convergent result of pitch and roll angle.
Automatic Electron Density Determination by Using a Convolutional Neural Network

In this study, we developed a technique for automatically determining upper hybrid resonance (UHR) frequencies using a convolutional neural network (CNN) to derive the electron density along the orbit of the Arase satellite. We used three CNN models (AlexNet, VGG16 and ResNet) to determine the UHR frequencies without additional features based on an expert’s knowledge. We also reproduced the multi-layer perceptron (MLP) model that had been used for the Van Allen probes mission, which requires observed electric field spectra and additional five features (i.e., decimal logarithm of electron cyclotron frequency (<inline-formula> <tex-math notation="LaTeX">$\textrm {log}_{10} f_{\textrm {ce}}$ </tex-math></inline-formula>), <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula>-value, geomagnetic index (<inline-formula> <tex-math notation="LaTeX">$K_{p}$ </tex-math></inline-formula>), magnetic local time, and frequency bin with the highest power spectral density from the electric field spectra (<inline-formula> <tex-math notation="LaTeX">${f\textrm {bin}}_{\textrm {max}}$ </tex-math></inline-formula>)). We confirmed that the proposed method using CNN more accurately determined the UHR frequencies than did the conventional method. The mean absolute error (MAE) of the VGG16 model was 3.478 bins when the input vector comprised both the observed electric field spectrum and the additional five features. In contrast, the MAE of the conventional method was 5.986 bins (72.1% worse). Moreover, we confirmed that the proposed method achieves a high accuracy regardless of the use of the additional five features (the MAE of the ResNet model was 3.664 bins when excluding the additional five features). This suggests that the feature map of the ResNet model acquired a representation ability beyond the five features.
Modeling and Simulation of Civil Aviation Disaster Relief Logistics within Multiairport Network

The civil aviation is an important part in emergency relief logistic system. Considering the characteristics of civil aviation disaster relief logistics, this paper models and simulates civil aviation disaster relief logistics within multiairport network. The mathematic model has constraints by airplane performance and emergency resource reserves. It aims to cost minimum transport time and obtain optimal relief effect. In order to better compute this mathematic model, a modified cellular genetic algorithm is proposed. It designs a modified cellular evolutionary rule which simulates closer to real eco-system. This algorithm consists of five parts, including chromosome encoding, cellular space initialization, cellular genetic operation, cellular evolutionary rule and termination condition. Specifically, the chromosome is generated via concatenating the transportation task sequences of each airplane. Then an arithmetic case is given to test the algorithm. The test results prove the feasibility and excellence of the mathematic model. Further comparative analysis demonstrates the advantages of this modified algorithm in terms of global searching ability and convergence performance.
Evaluación ultrasonográfica de la tiroides en crías de vacas lecheras tratadas con yodo en el preparto Ultrasonographic evaluation of the thyroid gland in offspring of dairy cows supplemented with iodine during the prepartum

SUMMARY The aim of this work was to determine the size of the thyroid gland and the weight gain of calves born from dairy cows supplemented with iodine (I) during prepartum. Fourteen Holstein-Friesian calves were used. They were born from 7 cows supplemented with a single dose of I (I-S, 1.6 g/cow, Lipiodol®) at seven month of pregnancy and 7 non supplemented cows (I-C). Cows grazed a low I content pasture (0.19ppm) until calving. The thyroid gland of the calves was examined by ultrasonography at days 36 and 128 (± 7.5) of age. Calves were weighted at 26 and 110 (± 7.5) days of age and the daily weight gain (DWG) was determined. The thickness of the isthmus of the thyroid at 36 days of age were similar between groups (P > 0.05). A reduction in thickness was observed in both groups at 128 days, more pronounced in I-C group (P 0.05) in both periods. Calves of both groups had a similar weight and DWG at 36 and 128 days of age. It was concluded that a single supplementation with I for pregnant dairy cows grazing a low iodine content pasture induced a reduction in the growth of the left lobule of the thyroid in their offspring without affecting the growth of the calves.
MWF-based speech dereverberation with a local microphone array and an external microphone

A method for estimating the relevant quantities in a multi-channel Wiener filter (MWF) for speech dereverberation is proposed for a microphone system consisting of a local microphone array (LMA) and a single external microphone (XM). Typically these MWF quantities can be estimated by considering pre-whitened correlation matrices with a dimension equal to the number of microphones in the system. By following another procedure involving a pre-whitening-transformation operation, it will be demonstrated that when a priori knowledge of the relative transfer function (RTF) vector pertaining to only the LMA is available and when the reverberant component of the signals received by the LMA is uncorrelated with that of the XM, the MWF quantities may be alternatively estimated from a 2 × 2 matrix. Simulations confirm that using such an estimate results in a similar performance to that obtained by using the higher-dimensional correlation matrix.
Compilation of Data Link Protocols: Bluetooth Low Energy (BLE), ZigBee and Z-Wave

The network called the Internet of Things (IoT) is used to communicate of many devices with each other that make our lives easier during the day. The functioning of this communication system is possible thanks to various IoT protocols. In this study, Bluetooth Low Energy (BLE), ZigBee and Z-Wave that wireless network protocols are described. Explaining the structure of these protocols, the features such as coverage area, data transmission speed, power consumption and usage areas are compared. At the same time, it is aimed that the study will be a general information source for researchers who want to learn about wireless network protocols.
Getting in touch with paramedic student career intentions

Objective: This study of paramedic and nursing/paramedic students aimed to determine the initial career intentions of full-time paramedic students and to identify the key factors that influence their career choices. Design: A longitudinal study design, utilizing surveys and a focus group, was employed. Setting: The study was conducted in an Australian regional university with a large, on-campus paramedic programme. Method: Students were surveyed to ascertain the factors that shape their preferences for employment and a focus group was conducted with final year students to further investigate the responses. Respondents were asked to nominate their preferences for employment and identify key factors that influence their decision-making. Data were analysed using inferential statistics and textual analysis. Results: There were 508 questionnaires returned from 704 paramedic students (72 per cent response rate). Of the respondents 82 per cent were originally from New South Wales and the Australian Capital Territory, the balance from other States/Territories and overseas. Students’ career intentions did not necessarily follow their place of origin with 58 per cent preferring New South Wales or the Australian Capital Territory, 17 per cent Victoria, 3.5 per cent other States, and 7 per cent indicating other employers. The identified influences on student career intentions were personal (53 per cent), professional (20 per cent), organizational (8 per cent), and community factors (3 per cent). Conclusion: Potential employers need to address graduate needs if they expect to attract the most talented graduates. Universities educating paramedics should focus on the needs of students and the profession.
Impact of Transmitter Phase Noise on NFDM Transmission With Discrete Spectral Modulation

Nonlinear frequency division multiplexing (NFDM) systems have been showing remarkable progress in the past few years. However, the majority of the demonstrations have neglected the impact of laser phase noise by employing narrow-linewidth lasers and self-homodyne receivers. The impact of transmitter laser linewidth on NFDM transmission is here numerically and experimentally investigated for dual-polarization discrete NFDM systems. The scaling of linewidth tolerance is analyzed for different signal symbol rates and the results are validated experimentally at 250 MBd. Numerical and experimental analysis show a limited degradation due to laser phase noise with relevant penalty appearing only for linewidth-symbol duration products (<inline-formula> <tex-math notation="LaTeX">$\Delta \nu \times T_{s}$ </tex-math></inline-formula>) above <inline-formula> <tex-math notation="LaTeX">$10^{-3}$ </tex-math></inline-formula>. The 250-MBd experimental results indeed show limited penalty for laser linewidths up to 750-kHz and 100-kHz, for back-to-back and 2000-km transmission, respectively.
Mode Division Multiplexing as a Technique Counteracting Intrusion to Multimode Fiber Systems

The presented material shows that a fruitful way to counteract illegal data reading from the system trunk fiber could be application of mode division multiplexing technique, particularly under special manner of data transmission via the few- or multimode trunk fiber. The principle of namely data distributive technique is described. The content of the technique include division of the data sequence to time periods each containing several data blocks and simultaneous transmission of these blocks over different modes of the trunk fiber. If eavesdropping occurs, the illegally registered signal is a mix of different data blocks. Transmission of random signals via the extra fiber mode could provide additional impeding of decoding the illegally read data. At the end of the line, reversal processing of the received data restores the original data sequence. Advantageous feature of the technique is full compatibility with all developed methods of data security. Cooperative application of these techniques could enhance the protection of transmitted data.
Comparison of Topic Modeling Methods for Type Detection of Turkish News

Today, with the increase of Internet-based documents, we are presented with many data that need to be processed and evaluated. Media, news and advertising are some of the areas where these data are evaluated. For the news, the classification of people in the media sector is an important problem in terms of time. In this paper, it is aimed to determine which types of news titles belong to. The dataset consists of 4200 Turkish new titles belonging to 7 class labels. In order to determine the types, classical Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA) and Non-Negative Matrix Factorization (NMF) algorithms were used in topic modeling. In addition, the LDA-based n-LDA method was also used. The accuracy of all methods used was measured and compared. NMF was the most successful method for three classes, while for five and seven classes LSA was the most successful method.
A Traffic Anomaly Detection Method based on Multi-scale Decomposition and Multi-Channel Detector

This paper proposes a new multi-channel network traffic anomaly detection method combined with the idea of multi-scale decomposition and multi-channel detection theory. It can be learned that anomalies could change the characteristics of traffic data at different scales. Traditional anomaly detection methods usually work on each scale independently thus mainly focused on temporally correlated traffic. With the fully exploration on internal frequency-time correlations within multiple scales, this method first obtained the multi-scale decomposition of original traffic data using Ensemble Empirical Mode Decomposition (EEMD), then it is combined with a multi-channel Generalized Likelihood Ratio Test (GLRT) detector, for anomaly detection and decision-making. It can be verified with experiments that this method performs better than other traditional methods, thus gives a new sight on the anomaly detection with different types of traffic data.