Miracle-dz/UniFAR-FTUs huggingface dataset:
UAV mission planning based on Prognosis & Health Management

Rotorcraft Unmanned Aerial Vehicles (UAV) with take-off and vertical landing capabilities or multirotors have proved to be an efficient and low-cost solution for civil flight applications due to significant advances in the development of robust and more efficient altitude and attitude control strategies, planning and re-planning algorithms capable of detecting and avoiding obstacles and Fault Diagnosis & Fault Tolerant Control methods. In most applications where multirotors are used, they develop different task as exploration, photogrammetry, filming, mapping and more recently all those dedicated to precision agriculture such as irrigation and crop monitoring. During the task development, the multirotor executes a mission which consists to fly through a set of paths connected by n reference points (named way-points) inside a known or unknown area. However, during the mission development, different negative factors decrease the multirotor flight performance such as environmental conditions, occurrence of faults or failures in actuators/sensors and energetic limitations due to the power source constraints. The energetic limitation problem in a multirotor are due to power capabilities that on-board battery can supply. Due to power and energy requirements, multirotors are powered by Lithium Polymer batteries which are rechargeable batteries of Lithium-Ion technology. They possess a polymer electrolyte instead of a liquid electrolyte and provide high power and energy densities. However, according to the use due to the number of charge/discharge cycles and other factors like damage provoked by over-discharges, the battery performances tend to decrease. Such decrease or aging causes a reduction in the efficiency of the UAV multirotor flight by the decrease of the total mission time or flight endurance, and leads to maneuverability problem, which increases the risk of crash and collision. This thesis topic addresses the issues concerning to battery performances and its influence into the mission and path planning tasks. By considering model-based prognosis techniques and path planning methods, a hierarchy mission planning strategy based on energy consumption is proposed and validated at simulation level considering different flight situations. The UAV performances, as well as its capability to execute and fulfill a mission is weighted by the computation of the battery State of Health (SoH) which is an index to measure the degradation level of the battery. The SoH helps to estimate the battery Remaining Useful Life (RUL) and establishes the energy limitation by the computation of the Maximum Flight Endurance (MFE). Such information is necessary to path planning generation which not only consider the constraints related to the power source but also the scopes and limitations of the mission to be executed. In addition, the main concern of this thesis are long time-distance missions e.g exploration or inspection of remote areas where it is fundamental to have a proper use of energy aboard the multirotor.
Annotazione di contenuti concettuali in un corpus italiano: I-CAB

In questo articolo presentiamo I-CAB (Italian Conte nt Annotation Bank), un corpus di articoli in lingu a italiana annotato semanticamente. L’attività di annotazione, realizza ta in modo completamente manuale, prevede tre livel li: le espressioni temporali, le entità (cioè persone, organizzazioni, luoghi ed ent ità geo-politiche) e le relazioni tra entità (per e sempio la relazione di affiliazione che collega una persona a un’organizzazione). I primi d ue livelli di annotazione sono completi, mentre il terzo livello è in fase di realizzazione. Avendo come scopo quello di fare di I-CAB un corpus di riferimento per diversi task di Estrazione automatica di Informazione, abbiamo seguito una politica di riuti lizzo di linguaggi di annotazione già disponibili. In particolare, abbiamo adottato gli schemi di annotazione sviluppati per il task ACE Entity Detection and Recognition e per il task Time Expression Recognition and Normalization. Poiché le linee-guida di questi task sono state s viluppate originariamente per l’inglese, è stato ne cessario adattarle alle caratteristiche morfo-sintattiche dell’italiano; si è deciso inoltre di estenderle in modo tale da inc ludere un insieme più ampio di entità, come ad esempio le congiunzioni.
Interventions for Failed Family Planning

Despite the introduction of family planning services in most areas of the world, failures of contraceptive use often lead to unplanned pregnancies. These women often resort to unsafe methods to end their pregnancies, which contributes to the fourth leading cause of maternal mortality worldwide. Demographic variables that may affect the intention to discontinue the pregnancies would be demonstrated. Pros and cons of different types of induced abortions would be presented and discussed. Programs integrating counseling for women after failed family planning for future comprehensive reproductive health care will be shown. The gap between women’s reproductive desire to avoid pregnancy and altitude of contraceptive use may contribute to the prevalence of unintended pregnancy. Age, race/ethnicity, marital status, and socioeconomic characteristics should be considered in counseling women after failed family planning. Expanding the program that offers integrated abortion training would prepare more physicians to provide comprehensive care for family planning.
Functional Outcome of the Wrist according to Degree of Distraction with External Fixation in the Treatment of Distal Radius Fracture

Purpose: This study aimed to evaluate the relation between the degree of wrist joint distraction and postoperative restoration of joint function after performed external fixation with distraction of wrist joint in distal radius fracture. Methods: This study was conducted retrospectively on 55 patients who underwent reduction using external fixation for distal intra-articular radius fracture from January 2007 to December 2014. The degree of preand post-operative distraction was measured radiographically using the carpal height ratio (CHR). For postoperative functional assessment, range of motion of the wrist, Gartland Werley classification, grip strength, and pinch strength were measured. Results: In the functional assessment based on the Gartland Werley classification, excellent was reported by 31 patients, good; 16 patients, fair; 8 patients, and poor; 0 patients. The mean CHR was 0.519 preoperatively and 0.565 immediately after surgery. The mean postoperative CHR of excellent group was 0.581, demonstrating better clinical outcome in distraction than other groups. Eight case of joint stiffness was manifested when CHR value was lower than 0.54 and only 1 case of joint stiffness was manifested when CHR value was higher than 0.54, showing a tendency for lower incidence of joint stiffness when distracted over 0.54 of CHR. Conclusion: External fixation after sufficient distraction of the wrist joint produced an excellent outcome in the functional assessment. The better the prognosis in the Gartland Werley classification, the higher the CHR and the group with a CHR greater than 0.54 immediately after surgery showed less joint stiffness than the lower group.
Authorization Technique for Disclosing the Location of IP Spoofers

: It is known that assailant may use some of the IP addresses to misuse the locations of the original IP address . This paper proposes passive IP trace back that bypasses the deployment difficulties of IP trace back techniques to apprehend the assailant, a number of IP trace back mechanisms have been initiated. Even though due to the provocation of stationing, there has been not a vastly adopted IPT solution. The mist on the locations of spoofers has not been self-indulgent even today. Provisional IP trace back scrutinize Internet Control message Protocol error messages stimulated by spoofing traffic and tracks the spoofers based on public accessible information in this way, PIT can find the spoofers without any apprehend requirement. This abstract epitomizes the collection, statistical results and causes on path backscatter, reveals the processes and effectiveness of PIT and shows the confiscation locations of spoofers by applying PIT on the path backscatter dataset. This message can be helpful in finding the assailants in order to decrease the alignment.
MagLand: Magnetic Landmarks for Road Vehicle Localization

Satellite-based systems are the most widespread solution for outdoor localization. However, they present well-known limitations in multipath environments and non-line-of-sight satellite conditions, e.g. tunnels, underground, urban canyons, and multilevel roads, being frequently combined with dead reckoning techniques. Inertial sensors present cumulative errors, and geomagnetic-field information is often distorted by strong local magnetic fields caused by road infrastructure. We turn this magnetic weakness into strength by proposing MagLand, an approach with detection and matching steps to leverage these anomalies as signatures for localization purposes. For anomaly detection, we adopt a window-based technique and apply supervised binary classification, choosing a random forest. We select one nearest centroid algorithm with dynamic time warping to match input data streams to reference signatures, providing guidelines to define and collect them. Real data experiments with off-the-shelf devices in challenging road scenarios show MagLand's feasibility with anomaly detection accuracy of 91% and matching with lane distinction of up to 93%. Magnetic landmarks can be extremely useful to address limitations of current localization systems and improve their performance, e.g. by providing an alternative in GPS limited areas, anchors for integrity monitoring, or resetting dead reckoning cumulative errors.
Online streaming feature selection with incremental feature grouping

Today, the dimensionality of data is increasing in a massive way. Thus, traditional feature selection techniques are not directly applicable. Consequently, recent research has led to the development of a more efficient approach to the selection of features from a feature stream, known as streaming feature selection. Another active research area, related to feature selection, is feature grouping. Feature grouping selects relevant features by evaluating the hidden information of selected features. However, although feature grouping is a promising technique, it is not directly applicable to feature streams. In this paper, we propose a novel and efficient algorithm that uses online feature grouping, embedded within a new incremental technique, to select features from a feature stream. This technique groups similar features together; it assigns new incoming features to an existing group or creates a new group. To the best of our knowledge, this is the first approach that proposes the use of incremental feature grouping to perform feature selection from features. We have implemented this algorithm and evaluated it, using benchmark datasets, against state‐of‐the‐art streaming feature selection algorithms that use feature grouping or incremental selection techniques. The results show superior performance by the proposed technique through combining the online selection and grouping, in terms of prediction accuracy and running time.
A8321 Delayed post-stroke administration of human amnion epithelial cell-derived exosomes improve outcomes

Objectives: We have previously found that intravenously injected human amnion epithelial cells (hAECs) provide neuroprotection following stroke by reducing inflammation and apoptosis. However, little is known about the factors these cells release to elicit neuroprotection. Interestingly, exosomes secreted by hAECs have been shown to have anti-inflammatory and regenerative properties as well as having an improved safety profile compared with their parent cell. Thus, the aim was to test whether delayed administration of amniotic exosomes reduce brain injury and enhance functional recovery following ischemic stroke. Methods: Male mice (8–12 weeks old) were subjected to photothrombotic stroke (n = 17) or sham surgery (n = 8). At 1 day post-stroke, mice were injected intravenously with vehicle (saline; n = 10) or 10 &mgr;g of amniotic exosomes (n = 7). Functional outcomes were assessed at 3 and 7 days post-stroke, after which the mice were euthanized and the brains removed. Infarct volume was assessed by thionin staining and immune cell infiltration was determined via immunohistochemistry. Results: 3 days post-stroke, mice treated with amniotic exosomes were able to grip a wire for 40% longer compared to mice treated with vehicle (p < 0.05), although significance was lost by 7 days. Infarct volume was strikingly reduced by 71 % (p < 0.05) following exosome administration compared to vehicle. Consistent with these data, amniotic exosomes reduced the number of infiltrating neutrophils and macrophages/microglia within the ischemic hemisphere. Finally, exosomes attenuated stroke-induced glial scar formation as well as neuronal apoptosis. Conclusion: These data indicate that delayed post-stroke administration of amniotic exosomes are neuroprotective and are thus a potentially safer and more viable stroke therapy than hAECs.
Dynamic Resource Allocation and Computation Offloading for IoT Fog Computing System

Fog computing system is able to facilitate computation-intensive applications and emerges as one of the promising technology for realizing the Internet of Things (IoT). By offloading the computational tasks to the fog node (FN) at the network edge, both the service latency and energy consumption can be improved, which is significant for industrial IoT applications. However, the dynamics of computational resource usages in the FN, the radio environment and the energy in the battery of IoT devices make the offloading mechanism design become challenging. Therefore, in this article, we propose a dynamic optimization scheme for the IoT fog computing system with multiple mobile devices (MDs), where the radio and computational resources, and offloading decisions, can be dynamically coordinated and allocated with the variation of radio resources and computation demands. Specifically, with the objective to minimize the system cost related to latency, energy consumption, and weights of MDs, we propose a joint computation offloading and radio resource allocation algorithm based on Lyapunov optimization. Through minimizing the derived upper bound of the Lyapunov drift-plus-penalty function, we divide the main problem into several subproblems at each time slot and address them accordingly. Through performance evaluation, the effectiveness of the proposed scheme can be verified.
ADAPTIVE NEURAL NETWORK FOR REAL-TIME TRACKING CONTROL OF A DRIVE SYSTEM

Neural Networks are attractive alternative to the classical techniques for identification and control of complex physical systems, because of their ability to learn and approximate functions. This paper presents the development and implementation of adaptive Multilayer Neural Network (MNN) controller in real-time for a drive system. A MNN is first trained off-line to learn (identify) the inverse dynamics of the system, after the training is successfully completed, the MNN is used as a feedforward controller in the control scheme. The advantage of the proposed controller is that the MNN is permanently training. On-line learning is applied while the system is under control to capture any system parameter variations or disturbances. Simulation results are presented to show the advantages of adaptive MNN controller compared to non-adaptive MNN controller. Also, experimental results show that the adaptive MNN controller is able to control the speed trajectory of the chive system with a high degree of accuracy, even in the presence of disturbances.
Observer Design for Interconnected Systems and Implementation via Differential-Algebraic Equations

A new approach to the design of observers of nonlinear dynamical systems is presented. Generally, linear or nonlinear control systems are expressed as explicit systems of differential equations and solved either analytically or numerically. If numerically, they are implemented using standard ordinary differential equation (ODE) solvers. In this thesis, a system is decomposed and modeled as an interconnection between two observer subsystems, particularly, as canonical DAE observers. In general, control design engineers may be faced with a formidable problem of solving this system analytically or in obtaining closed-form solutions. To attest to the complexity and complications in treating a system of interconnected DAE observer systems, a scaled-down version of a publication on “Small-Gain Theorem” is included in the appendix for the reader’s perusal. (A brief introduction to “Small-Gain Theorem” can be found in Chapter 4). The premise of this thesis is to demonstrate that, where the design of an observer plays a major role involving output feedback, there may be advantages in formulating a control system as a differential-algebraic equation (DAE), especially in the case of interconnected subsystems. An implicit system of interconnected DAE observers is considered and shown implementable using an existing DAE solver, whose resolution allows one the capability of computing input and output bounds. This is based on fixed or variable timesteps within the operating interval of each subsystem to ensure input-output stability (IOS) and the observability property of the interconnected observer system. The observer design method is based on the extended linearization approach. The basic background is provided for the design process of an interconnected observer system using DAE. Note, the application of the new approach has not been considered previously for the case of an interconnected DAE observer system.
APPLYING DEEP LEARNING WITH EXTENDED KALMAN FILTER AND GENETIC ALGORITHM OPTIMIZATION FOR WATER DISTRIBUTION DATA-DRIVEN MODELING

Data-driven analysis has recently emerged as an important task for smart water management as large amount of various data collected via smart meters, sensors and data loggers. Among the methods developed for data-driven modeling, deep neural network (DNN) is proved as the competitive and generic approach to solve many challenging problems, including but not limited to voice recognition, natural language processing, image classification etc. Deep belief network (DBN) is one of the DNNs and widely used for data analysis. This paper extended authors ’ previous research in applying DBN model with the genetic algorithm to integrate with the Extended Kalman Filter (EKF). It results in a comprehensive and generic approach, by which the genetic algorithm is employed to optimize the configuration of the DBN and the EKF is applied to assimilate the newly available data with the trained DBN model so that the model can be updated whenever new data becomes available. The proposed method has been tested in the case studies of different domains, including but not limited to water distribution systems. The results show that the deep learning method integrated with EKF has resulted in good performance.
Implementation and comparison of hybrid encryption model for secure network using AES and Elgamal

Cryptography is a concept to protect network and data transmission over network. Data Security is the main aspect of securing data transmission over unreliable network. Cryptography is a method of storing and transmitting data in a secret form so that only those for whom it is intended can read and process it. There are two types of cryptography algorithms such as symmetric key cryptography and asymmetric key cryptography. At present there are various types of cryptographic algorithms provide high security to information on networks, but they also have some drawbacks. To overcome the drawbacks of these algorithms, we propose a new hybrid cryptographic algorithm in this paper. The algorithm is designed using combination of two cryptographic algorithms AES and Elgamal. Analyse and compare the performance of existing and proposed algorithm on parameters security, encryption time, decryption time and throughput. This new hybrid cryptographic technique has been designed to decrease encryption and decryption time and increase throughput. In this work core java + swing in NetBeans IDE 7.0. 1 jdk 8.0. is used. Implementation and performance analysis of proposed model done by JAVA. Results shows the Hybrid model perform better than AES and Elgamal individually. Keywords: Cryptography, AES, Elgamal and Hybrid AES and Elgamal
Falsification of Signal-Based Specifications for Cyber-Physical Systems

In the development of software for modern Cyber-Physical Systems, testing is an integral part that is rightfully given a lot of attention. Testing is done on many different abstraction levels, and especially for large-scale industrial systems, it can be difficult to know when the testing should conclude and the software can be considered correct enough for making its way into production. This thesis proposes new methods for analyzing and generating test cases as a means of being more certain that proper testing has been performed for the system under test. For analysis, the proposed approach includes automatically finding how much a given test suite has executed the physical properties of the simulated system. For test case generation, an up-and-coming approach to find errors in Cyber-Physical Systems is simulation-based falsification. While falsification is suitable also for some large-scale industrial systems, sometimes there is a gap between what has been researched and what problems need to be solved to make the approach tractable in the industry. This thesis attempts to close this gap by applying falsification techniques to real-world models from Volvo Car Corporation, and adapting the falsification procedure where it has shortcomings for certain classes of systems. Specifically, the thesis includes a method for automatically transforming a signal-based specification into a formal specification in temporal logic, as well as a modification to the underlying optimization problem that makes falsification more viable in an industrial setting. The proposed methods have been evaluated for both academic benchmark examples and real-world industrial models. One of the main conclusions is that the proposed additions and changes to analysis and generation of tests can be useful, given that one has enough information about the system under test. It is difficult to provide a general solution that will always work best -- instead, the challenge lies in identifying which properties of the given system should be taken into account when trying to find potential errors in the system.
Teores minerais em pastagens do Rio Grande do Sul. I. Cálcio, fósforo, magnésio e potássio

Foram coletadas amostras de solo e de pastagem natural, em onze Unidades de Mapeamento de Solo (U.M.) do RS, objetivando conhecer a concentracao de alguns macrominerais comparando-a com as exigencias dos bovinos. As amostras de solo foram obtidas na primavera de 1989, e as de pastagens, em cinco estacoes, da primavera de 1989 ate a primavera de 1990. Os teores de Ca nos solos foram superiores a 4,0 me/100 ml de solo, e nas pastagens a variacao foi de 0,32 a 1,20%; a media de P nos solos variou entre 3,0 e 4,6 ppm, e nas pastagens, entre 0,06 e 0, 19%; o teor medio de Mg nos solos oscilou entre 3,5 e 17,2 me/100 ml, e nas pastagens, entre 0,06 e 0,15%; os teores medios de K nos solos ficaram compreendidos entre 46,7 e 240 ppiri, e nas pastagens, entre 0,44 e 1,8 1 %. Do total das amostras de pastagem, 98,2% nao atingiram o teor minimo de 0, 18% de P necessario para atender ao requerimento minimo de bovinos. Quanto ao K e Mg, apenas 16,4 e 14,5%, respectivamente, nao apresentaram o teor minimo necessario para bovinos. Os teores de Ca foram suficientes para atender as necessidades de bovinos de corte.