Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermal Management of planar magnetics for Spacecrafts: characterization and modeling

The dissipation of the generated power losses of on-board magnetic components is a major concern regarding the thermal management of the power systems. In this study, two planar transformers designed for common space power converters are chosen as representative samples to analyze the effect of different elements on heat transfer (vias, connectors to a baseplate, gap filler pads, glue, integration in the main PCB). 3D thermal simulations are used to quantify it. It has been found out that the contribution of vias to heat transfer can reach a 8.9%, and the use of metallic connectors, window filling or gap filler pads can reduce the maximum temperature rise between 18.4% and 57.4%. Additionally, a simplified 2D model is proposed as a fast solution to characterize the thermal behavior of planar transformers and determine if a design is valid or if it requires any modification. Finally, some guidelines to address the thermal management of these components are explained.
Optimal Day Ahead Scheduling for Plug-in Electric Vehicles in an Industrial Microgrid Based on V2G System

With the increasing amount of electric vehicles (EVs), Vehicle to Grid (V2G) technology has attracted enormous attention from researchers and industries. The major benefit of using V2G is to enable the interaction between EVs and grid. For example, EV batteries can work as responsive sources to provide auxiliary support to a power grid. This paper presents an optimal day-ahead scheduling strategy for a fleet of EVs plugged-in to the grid using V2G operation. Here, lithium-ion EV batteries are managed to reduce peak load constrains in a microgrid and to reduce energy bill. The proposed scheduling scheme is computed based on a linearized model of the lithium-ion battery with an optimization approach considering battery and grid constraints. A case study with an industrial microgrid application is carried out by simulations, to prove the advantages of the proposed technique.
Postoperative Visual Loss After Spine Surgery: A Case Report

Postoperative visual loss subsequent spine surgery is a rare but devastating complication with an estimated rate varying between 0.05% and 1.0%. However ischemic optic neuropathy, central artery thrombosis, and cortical blindness have been mentioned as recognized causes of postoperative visual loss, the exact etiologies are still unclear. The patient was a 58-year-old diabetic woman referred to our department with sudden visual loss, ophthalmoplegia, periorbital edema, and ptosis eyelid in her left eye 1 day after surgery for spinal spondylolisthesis. She mentioned the past medical history of rhinosinusitis and polyps. She did not smoke or drink alcohol.
Quality Indicators for Social Business Intelligence

The main purpose of Social Business Intelligence is to help companies in making decisions by performing multidimensional analysis of the relevant information disseminated on social networks. Although data quality is a general issue in SBI, few approaches have aimed at assessing it for any data collection, being this a context dependent task. In this paper, we define a quality indicator as a metric that serves to assess the overall quality of a collection, and that integrates the measures obtained by several quality criteria applied to filter the posts relevant for a SBI project. The selection of the best quality criteria to include in each quality indicator is a complex task that requires a deep understanding of both the context and objectives of analysis. In this paper, we propose a new methodology to design quality indicators for SBI projects whose quality criteria consider contents coherence and data provenance. Thus, for the context defined by an objective of analysis, this methodology helps users to find the quality criteria that best suit both the users and the available data, and then integrate them into a valid quality indicator.
Atmospheric parameters influence on mm-wave propagation in 5G communication

This paper intends to give an overview about atmospheric changes effects on millimeter wave propagation, more precisely the collective attenuation caused by: dry air (including oxygen), water vapor, rain rate and haze/fog. Theoretical aspects of each parameter are presented throughout the paper and the attenuation statistics are estimated from meteorological data, for three Moroccan cities (Ifrane, Rabat, Laayoune) known to have an extreme atmospheric conditions, respectively in the three seasons: winter, spring and summer. We emphasize at the end, the role of each atmospheric parameter on deteriorating the mm-wave signal, which will provide further insights about the phenomena discussed.
A Word Representation to Improve Named Entity Recognition in Low-resource Languages

Named Entity Recognition (NER) is a fundamental task in many NLP applications that seek to identify and classify expressions such as people, location, and organization names. Many NER systems have been developed, but the annotated data needed for learning is not available for low-resource languages, such as Cameroonian languages. In this paper we exploit the low frequency of named entities in text to define a new suitable word representation for named entity recognition. We build the first Ewondo (a Bantu language of Cameroon) named entities recognizer by projecting named entity tags from English using our word representation. In terms of Recall, Precision and F-score, the obtained results show the effectiveness of the proposed word representation
Tin-based materials: the future of anode materials for lithium ion battery?

Tin and tin oxide materials have been considered as the promising new materials to replace the current graphite anode of lithium ion battery because of their exceptionally high theoretical capacity at least 3 times higher than those of graphite, low cost and environmental benignity. However, tin-based anode performances and durability are still under debate and linked on the texture, structure and morphology of those materials. In this work, we provide systematic and reproducible results to compare electrochemical performances of tin-based anode materials. Nanostructured Sn, SnO and SnO2 have been investigated in half-cells. Capacities, fading upon cycling, energy density and power density have been studied and related to the oxygen content. Post-mortem characterizations have been done by scanning electron microscopy (SEM), X-ray diffraction (XRD) in order to determine the failure modes of the materials. The results showed that SnO appears as a promising candidate to be inserted in negative electrode.
Towards a DRS Parsing Framework for French

Combinatory Categorial Grammars provide a transparent interface between surface syntax and underlying semantic representation. Discourse Representation Theory allows the handling of meaning across sentence boundaries. Based on the foundations of these two theories along with the work of Johan Bos on the Boxer framework for English language, we propose an approach to the task of semantic parsing with Discourse Representation Structure for the French language. By giving an example of discourse analysis on French sentences and experimenting on 4,525 sentences taken from the French Treebank corpus, we demonstrate and evaluate the outcomes of our framework.
III-V on Si solar cells behavior at NIRT and LILT conditions for space applications

In this study, we review the potential of III-V/Si solar cell technology for space applications. We present here the experimental results of wafer bonded 2-terminal III-V/Si solar cells electron irradiation. The Begin-Of-Life (BOL) and End-Of-Life (EOL) electrical performances, after 1-MeV electron irradiations, are characterized under AM0 spectrum, and the radiation hardness is compared in Normal Irradiance Room Temperature (NIRT) and Low Irradiance Low Temperature (LILT) conditions.
On the optimal plasmonic resonances in gold nanospheres embedded in dispersive media

New fundamental upper bounds have recently been given regarding the optimal plasmonic multipole resonances of a rotationally invariant sphere embedded in a lossy surrounding medium. The new theory is based on a generalized optical theorem for the absorption of a sphere in a lossy medium and employs straightforward analysis to explicitly maximize a concave function. The new bounds are briefly summarized in this paper and then employed in a study concerning the effectiveness of using gold nanospheres as absorbers of the sizes typically used in biomedical applications and plasmonic photothermal therapy.
A Bayesian Approach for Degradation Analysis with Individual Differences

Reliability assessments of long-life and high-reliability products often face the difficulty of lack of failure time data. Meanwhile, there existing individual differences in produces due to the uncertainties during their whole lifetime, such as manufacturing, assembly, work environment and work load. This paper develops a reliability modeling and assessment method considering individual differences based on performance degradation data. Firstly, the model selection method is employed into the analysis of degradation process. And according to the model selection results, the appropriate degradation model for the degradation process can be chosen. Then, the Bayesian method is applied to establish the parameter estimation method and reliability assessment framework based on the appropriate model. At last, a real engineer example is presented to illustrate the effectiveness of the proposed degradation analysis method with individual differences.
A Comparison of Hough Transform and Deep Neural Network Methods on Road Segmentation

Thanks to developments in the computer hardware systems, deep learning has been an attractive field for many researchers in different disciplines. Aim of deep learning is to extract the desired features of raw data as a learning method by operating many hidden layers. Accomplished results of learning methods on complex issues as face recognition, object detection, motion recognition etc. led researchers to think about applying deep learning methods to road lane detection-segmentation which is one of the very important issues of Advanced Driver Assistance Systems (ADAS). Considering main limitations of conventional methods for lane detection, deep learning approach can provide more robustness than existing approaches. The objective of work is to compare the effectiveness of conventional and deep learning applications to improve accuracy of the road segmentation
Plane Segmentation of Point Cloud Data Using Split and Merge Based Method

In indoor environments, segmentation of planar surfaces such as wall, floor, and door can contribute the efficiency of robots in performing tasks. In this study, a split and merge based method is developed to segment planar surfaces via point cloud data in indoor environments. Apart from the previous split and merge studies, fixed-size regions are used instead of octree data structure. In this way, the segmentation time can be decreased as low as possible. In the split phase, the fixed-size regions are assigned to one of the three categories, the outer edge, the inner edge, and the non-edge. In the merge phase, each of these categories is processed separately. Thus, the segmentation success can be increased. The proposed method is tested with point cloud data captured in ESOGU Electrical Engineering Laboratory building modelled in Gazebo simulation environment. In addition, RANSAC and region growing methods are implemented for comparison. Experiments are conducted to analyze performance of the proposed method in terms of segmentation time and segmentation success.
Torque Ripple Reduction for Interior Permanent Magnet Synchronous Machines under Load Excitation by Optimizing Rotor Skew Angles

This paper presents a skew angle optimization method to reduce torque ripples for an interior permanent magnet synchronous machine (IPMSM) under load excitation. Single- and multi-objective optimizations are employed to maximize the output torque and/or minimize the torque ripple for an IPMSM with four-step linear rotor skew. The motor model is based on look-up tables. The rotor skew angle vector is optimized for priority operating points in both constant-torque and constant-power regions of the torque-speed map. It is found that the output torque and torque ripple can be balanced to meet the targets of the output torque quality and the voltage constraint. Both two- and three-dimensional finite element analyses are used to verify the optimized results.
Hybrid Possibilistic-Probabilistic Energy Flow Assessment for Multi-Energy Carrier Systems

The uncertainty is a pivotal problem in Multi-Energy Carrier (MEC) systems, which leads to the strong demand of reasonable tools to evaluate uncertainties. When both possibilistic and probabilistic uncertainties exist in the real MEC systems, traditional possibilistic or probabilistic methods are no more suitable to be applied. Therefore, this paper proposes a hybrid possibilistic-probabilistic energy flow assessment method to evaluate these uncertainties. Firstly, to build a more precise uncertain model, the probabilistic and possibilistic uncertainties are respectively modeled by considering different uncertainties of sources, networks and loads of MEC systems, and the correlations among wind generation and energy loads. Then, the product t-norms of the extension principle plus <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-cut method is firstly implemented in processing fuzzy energy flow, which can reduce overestimation compared with the sole <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-cut method. Next, on the basis of Dempster-Shafer evidence theory, the hybrid possibilistic-probabilistic energy flow assessment approach is presented. Finally, two cases are carried out to verify the effectiveness and practicability of the proposed method.
High Efficiency Rectifier Circuit For Ambient RF Energy Harvesting Applications At 5.8 Hz

In the last several decades, the development of efficient and compact rectifying circuits has become a vital research topic. In this paper, two microstrip RF-DC rectifier circuits have been proposed at 5.8 GHz for low RF energy harvesting applications. They combined series and shunt topologies and included schottky diodes HSMS2860 and HSMS 2S6C. The proposed circuits are designed, simulated and analyzed using LSSP simulation and Harmonic Balance method provided by ADS software. They have achieved respectively output voltages of 5.13 V and 4.54 V, and RF-DC conversion efficiencies of 68.06 % and 70 % with optimum load resistances of 4 kΩ and 3 kΩ at input power level of 10 dBm.
Trustworthiness in IoT – A Standards Gap Analysis on Security, Data Protection and Privacy

With the emergence of new digital trends like Internet of Things (IoT), more industry actors and technical committees pursue research in utilising such technologies as they promise a better and optimised management, improved energy efficiency and a better quality living through a wide array of value-added services. However, as sensing, actuation, communication and control become increasingly more sophisticated, such promising data-driven systems generate, process, and exchange larger amounts of security-critical and privacy-sensitive data, which makes them attractive targets of attacks. In turn this affirms the importance of trustworthiness in IoT and emphasises the need of a solid technical and regulatory foundation. The goal of this paper is to first introduce the concept of trustworthiness in IoT, its main pillars namely, security, privacy and data protection, and then analyse the state-of-the-art in research and standardisation for each of these subareas. Throughout the paper, we develop and refer to Unmanned Aerial Vehicles (UAVs) as a promising value-added service example of mobile IoT devices. The paper then presents a thorough gap analysis and concludes with recommendations for future work.
Image based guidance for near rendezvous and docking of spacecraft

Autonomous, energy saving and survivable systems are crucial for space related systems in order to achieve sustainable goals. In such systems, instead of a single method, alternative and ancillary methods are required to be used to accomplish space missions successfully, and increase independency of operating systems. In this paper, an image based alternative guidance method is investigated. Hence, independent from radio signaling, spacecraft is able to track the target station as soon as the station is detected by the camera which is mounted on spacecraft. Proposed method can be applied purely at near rendezvous phase and docking since the range of camera is constrained. A camera projection model is derived to estimate relative motion of spacecraft by camera parameters. Smooth approach, collision avoidance and energy saving would be achieved by means of the approaching strategy improved by using this image based method.
Effects of Preoperative Honey Drink on Gastric Content, Perioperative Discomfort and Insulin Resistance in Patients Undergoing Open Colorectal Surgery a Randomized, Controlled Trial

Background and Aims: Preoperative fasting and surgery cause uncomfortable condition, metabolic stress and insulin resistance for the patient. A recent study in colorectal patients indicates that even small elevations of insulin resistance increase the risk of complications. Preoperative carbohydrate can reduce perioperative discomfort and insulin resistance. We investigated the effects of honey as preoperative carbohydrate-rich drink on the residual gastric contents, Original Research Article Sudiyatmo et al.; BJMMR, 14(7): 1-8, 2016; Article no.BJMMR. 24374 2 perioperative discomfort, and insulin resistance. Methods: Randomized, prospective, controlled study. Thirty colorectal cancer patients who underwent elective open colorectal surgery were randomized and divided into the treatment or control group. Patients in treatment group (n = 15) received 250 ml water containing 50 ml honey on the evening and 2-3 hours preoperatively. Control patients (n = 15) underwent overnight fasting. Three different discomfort variables (thirst, hunger and tiredness), residual gastric content, plasma glucose, insulin concentrations and insulin resistance index were recorded during the perioperative period. Results: Patient and operative characteristics did not differ between groups. There was no statistical difference between the two groups with respect to gastric residue content. Subjective wellbeing was significantly better in the treatment group. Plasma glucose, serum insulin levels and insulin resistance index were found to remain significantly lower in the treatment group. Conclusion: The preoperative honey drink does not appear to alter the volume of residual gastric contents, suggesting that this is safe procedure. Preoperative honey intake is advantageous due to reducing perioperative discomfort and insulin resistance.
Brain Computer Interface (BCI) For Controlling Path Planning Mobile Robots: A Review

There are many methods to control a mobile robotic system depending on the users of these systems and their preferences. Using Electroencephalography (EEG) of brain activity to command the mobile robotic system is one of the unique ways. Specially for users suffering from disabilities or having spinal cord injury (SCI). in this work the robots with shared control of BCI and robot intelligence are reviewed. Path planning is one of the features that improves the quality of the systems since it allows robot to be more independent from the user instantaneous diversion by BCI. According to the performance these systems have the advantages over other systems in assisting the user. These systems are easier to be used and with safer and smother movements.
Hand Gesture Recognition Using Convolutional Neural Network for People Who Have Experienced A Stroke

A human gesture is a non-verbal form of communication and is critical in human-robot interactions. Vision-based gesture recognition methods play a key role to detect hand motion and support such interactions. Hand gesture recognition allows a appropriate, and usable interface between users and devices. Hand gestures can be used for various fields which makes it be able to be implemented for communication and further. Hand gesture recognition is not only useful for people who are hearing-impaired or disabled but also for the people who have experienced a stroke, as they need to communicate with other people using different common essential gestures such as the sign of eating, drink, family and, more. In this paper, a system for recognizing hand gesture based on Convolutional Neural Network (CNN) is proposed. The developed method is evaluated and compared between training and testing modes based on several metrics such as execution time, accuracy, sensitivity, specificity, positive and negative predictive value, likelihood and root mean square. Results show that testing accuracy is 99% using CNN and is an effective technique in extracting distinct features and classifying data.
Programmable Design of Soft Actuators and Robots*

Soft pneumatic actuators are widely used for achieving sophisticated motions. However, to achieve extending, bending, bending-twisting and winding in a class of actuator is challenging. Here, we propose a programmable design to enable soft actuators to execute such complex motions. These motions are mainly owing to adjusting structure parameters, the position and angle of the elastic layer and the strain limiting layer. In addition, various deformation modes of actuators are demonstrated by our prototypes. By tuning the structure parameters, the deformation of actuators is explored and becomes more sophisticated and dexterous. Moreover, winding actuator shows a more stable grasping performance, which can grasp objects of different shapes and weights. We further demonstrate that by combining different forms of soft actuators, crawling soft robots with different functions can be developed. This programmable design method guides the fabrication of actuators and robots, making them promising candidates for more complex motions and new and innovative applications.
Applying 3D Vision Sensing to an Industrial Robot for Vehicle Assembly under Noisy Environment

This paper presents a method for automated wheel hub assembly which is important work in the industrial applications. A key requirements for this work are the positioning accuracy of manipulator in working space and the 6D object's pose determined by the 3D vision algorithms. A noncontact method using 3D scanner mounted on the end-effector robot is utilized for providing measurements of object pose in workspace. By controlling the end-effector of robot moving to the working space, the errors of the end-effector are compensated directly. Some experiments to pick up the wheel hub using an industrial robot were implemented to validate the assembly performance of the proposed method.