Miracle-dz/UniFAR-FTUs huggingface dataset:
A Model Predictive Control Algorithm for large-scale Integration of Electromobility

Due to the increasing penetration rate of electric vehicles and the associated higher power requirement in low-voltage grids, voltage limit violations and thermal overloads can increasingly occur. In order to be able to recognize and cure limit violations, besides the information and communication infrastructure, algorithms are required to calculate grid states and derive control commands for remote-controllable actuators. The aim of this research is to develop a model predictive control algorithm which integrates the current grid state and load forecasts to avoid congestions in smart grids. This algorithm consists of a state space model and quadratic optimization and is able to maintain operational limits. In the case of existing limit violations, the algorithm is intended to use an existing charging infrastructure in a grid-friendly manner and calculate control commands for present charging processes under consideration of future grid states.
Study on classification of maize disease image based on fast k-nearest neighbor support

Maize disease is the main factor affecting maize yield. Using artificial intelligence method to identify maize diseases is an effective method to prevent maize disease. At present, the method for identifying corn disease images is mainly to classify disease images by means of support vector machines. However, the traditional support vector machine algorithm can not meet the consistency requirements of the algorithm, and can not effectively solve the classification problem of non-convex data sets. Based on the problems existing in SVM mentioned above, we propose a new algorithm of corn disease Image intelligent recognition, which can effectively solve the classification problem of non-convex data sets and satisfy the consistency principle of the algorithm. Experiments on real-world data show that our method has certain advantages in classification accuracy and classification time, and can effectively identify and prevent Maize diseases.
DroidXP: A Benchmark for Supporting the Research on Mining Android Sandboxes

Due to the popularization of Android and the full range of applications (apps) targeting this platform, many security issues have emerged, attracting researchers and practitioners’ attention. As such, many techniques for addressing security Android issues have emerged, including approaches for mining sandboxes using dynamic analysis tools (i.e., automated testing tools). Undoubtedly, the resulting sandboxes’ efficiency depends on the test case generation tools used in the mining procedures. Previous research studies have compared Android test case generation tools for this specific goal. However, it is difficult to increment the research in this field because reproducing these previous empirical studies is a challenging and time-consuming task. This difficulty occurs because it is necessary to integrate test generation tools that often require different and conflicting versions of the Android platform, programming languages (e.g., Python 2 and Python 3), and software libraries. To mitigate this issue, in this paper we present DroidXP, a software infrastructure that allows researchers (and tools developers) to integrate and compare test case generation tools for mining sandboxes. We evaluated DroidXP through a reproduction study of previous research work, though considering additional test case generation tools. Our experiment suggests that DroidXP simplifies the comparison of existing tools for mining sandboxes, and revealed that Sapienz outperforms the other test case generation tools—regardless of the Monkey tool had presented the highest code coverage in our study.
Proactive Dynamic Channel Selection Based on Multi-Armed Bandit Learning for 5G NR-U

With an increasing demand of mobile data traffic in fifth-generation (5G) wireless communication systems, new radio-unlicensed (NR-U) technology has been regarded as a promising technology to address the exponential growth of data traffic by offloading the traffic to unlicensed bands. Nevertheless, how to efficiently share the unlicensed spectrum resource among the NR and Wi-Fi systems is a key challenge to be addressed, especially in a dynamic network environment. In this article, we investigate a distributed channel access mechanism and focus on the channel selection for NR-U users to decide the optimal unlicensed channel for uplink traffic offloading. We formulate the selection problem as a non-cooperative game, which is proven to be an exact potential game. However, the Nash equilibrium (NE) point is hard to achieve, due to the unknown dynamic environment. Based on multi-armed bandit learning techniques, an online learning distributed channel selection algorithm (OLDCSA) is proposed and proven to have similar performance to the NE point. Finally, simulation results reveal that our proposed algorithm outperforms the existing random selection by 16.45% on average and is close to the exhaustive search in the dynamic unknown environment.
Facial Expression Recognition using Deep Learning for Children with Autism Spectrum Disorder

Autism Spectrum Disorder is a neurodevelopment disorder that can affect the ability to communicate, socialize, and behavioral challenges. Facial emotion recognition is a methodology that helps to detect the emotions of humans. But, people generally suffering from ASD lack in expression recognition. In order to improve their ability to detect emotions, it is necessary to teach them at an early stage, which is childhood. This research work showcases the use and implementation of the basic architecture of the Convolutional Neural Network to educate children with ASD to identify human emotions. An existing dataset from the literature has been used to validate the model and an accuracy of 67.50% has been achieved.
Correlated Multiple Sampling Techniques for Sensor Signal Conditioning

Correlated Double Sampling (CDS) is a widely used technique in sensor signal conditioning. It effectively cancels offset and low-frequency (flicker) noise. CDS is a discrete time signal processing technique, implemented with Switched Capacitor (SC) circuits or after ADC with a Digital Signal Processing (DSP) algorithm. This paper describes a simple approach wherein the CDS technique is extended to Correlated Multiple Sampling (CMS) techniques, and with the price of processing of more samples, the new CMS techniques greatly improves the amplifier offset and flicker noise suppression.
Automatization of Antenna Measurements in the Anechoic Chamber

Anechoic chambers are used for obtaining exact results of small-sized antennas parameters measurements. Such measurements in anechoic chambers are a complex and lengthy process with the usage of costly equipment. The accuracy increasing and measures time reducing are reached by automatization of measuring processes. The paper presents the results of hardware and software system development for antennas measurements on the base of the anechoic chamber of National Aerospace University "Kharkiv Aviation Institute", Kharkiv, Ukraine. The anechoic chamber with a volume of 28 m3 via the adjustable diaphragms for reducing the level of wall reflections ensures the anechoic zone with a volume of 1 m3 at the level of -30 dB on the distance of 3 meters from measuring antennas in the frequency range from 8.5 GHz to 12.6 GHz. The object is installed on a remotely controlled rotary support made of an RF transparent material which provides fixation of the object position in space and its uniform rotation in the horizontal plane by 360°. Transceiver, measure equipment, controlling and information processing apparatus are placed outside the camera. The paper considers the structure of measuring complex, features of measurement technique and processing their results, presents an example of the results of measuring the antenna pattern of the small-sized horn antenna of the X-band.
Investigation of Cascading Events in Power Systems with Renewable Generation

The paper introduces a framework for characterisation and investigation of cascading events in power systems with renewable generation using time domain dynamic simulations. The paper aims at identifying the cascading event patterns by including protection device operation in RMS simulations and analyzing them. The cascading events are characterised by the power system components involved, the sequence of trippings and the reason for failure (e.g. voltage/frequency), while considering a wide range of possible operating conditions defined by economic dispatch. Changes in observed cascading failure patterns for different operating conditions are identified and investigated, taking also into consideration the impact of renewable generation. The framework is demonstrated on a modified version of the Anderson-Fouad 9 bus model incorporating renewable generation and protection devices.
Processing of Wide Band Acoustic Signals During Detection of Unmanned Aerial Vehicles

The problem of space-time processing of the received acoustic signal of an unmanned aerial vehicle, which is performed according to the criterion of the maximum of the ratio of useful signal/interference at the output of the adaptive antenna array, is considered. Mathematical relations are given that determine the operation of the space-time filter of the acoustic signal broadband in the space-time sense. The effectiveness of the considered signal processing method is shown using the computer simulation method in the Matlab environment.
An Indoor Positioning Approach Using Smartphone Based on PDR and EKF

Recently, Pedestrian Dead Reckoning (PDR) based methods, which perform mobile continuous indoor positioning, have obtained remarkable performance. However, the precision of the hardware commonly used is pretty low, many existing methods may produce large errors. In this paper, we propose a novel method, which uses low power Bluetooth Beacon as auxiliary sensor, to improve positioning accuracy and reduce Bluetooth deployment costs. To get more accurate steps, we use a filtering window to filter the acceleration feature. Then, we introduce an Extended Kalman Filter (EKF) method to correct PDR navigation. Furthermore, we reduce the number of Bluetooth Beacon by using a Cooperation-Proximity method. The experimental results show that the filtering method proposed in this paper can filter out invalid acceleration feature, so as to accurately measure the number of steps. In terms of the fitting degree between the walking track and the set route, the proposed method is 2.5% - 12% higher than the traditional methods, which reflects the improvement on positioning accuracy.
Unsupervised Detection of Microservice Trace Anomalies through Service-Level Deep Bayesian Networks

The anomalies of microservice invocation traces (traces) often indicate that the quality of the microservice-based large software service is being impaired. However, timely and accurately detecting trace anomalies is very challenging due to: 1) the large number of underlying microservices, 2) the complex call relationships between them, 3) the interdependency between the response times and invocation paths. Our core idea is to use machine learning to automatically learn the overall normal patterns of traces during periodic offline training. In online anomaly detection, a new trace with a small anomaly score (computed based on the learned normal pattern) is considered anomalous. With our novel trace representation and the design of deep Bayesian networks with posterior flow, our unsupervised anomaly detection system, called TraceAnomaly, can accurately and robustly detect trace anomalies in a unified fashion. TraceAnomaly has been deployed on 18 online services in a company S. Detailed evaluations on four large online services which contain hundreds of microservices and a testbed which contains 41 microservices show that the recall and precision of TraceAnomaly are both above 0.97, outperforming the existing approach in S (hard-coded rule) by 19.6% and 7.1%, and seven other baselines by 57.0% and 41.6% on average.
A Thin Film Storage Device to Characterize the CDM Event Distribution of a Process

A Non-Volatile Memory (NVM) technology was incorporated into chips to detect, measure, and record Charge Device Mode (CDM) events experienced during package level production test. Transmission Line Pulse (TLP) and CDM test results show a VT shift within the NVM bitcell that is proportional to the event discharge magnitude. The circuit can be tuned to quantify process CDM events to industry standards.
Access Control Policies for Network Function Virtualization environments in Industrial Control Systems

Virtualization environments offer interesting advantages for Industrial Control Systems (ICS), including being a tool to manage cybersecurity of such systems and a backup tool to control or optimize system behavior. Nevertheless, such virtual environments will likely be shared by different parties that have different access control requirements. In this paper, we discuss architecture and capabilities of virtualized ICS, and provide an analysis of an access control framework for ICS systems. We also present a language to define access control policies for virtualized ICS and developed an open source proof of concept implemented in Open Stack.
Hardware-Limited Cooperative SWIPT-enabled NOMA Networks

In this paper, the outage probability (OP) of a two-hop cooperative non-orthogonal multiple access (NOMA) network over Nakagami-m fading channels with an energy restricted relay is evaluated, while considering the hardware impairments at the network entities. Two energy-harvesting protocols, namely, power splitting relaying (PSR) and time-switching relaying (TSR), are considered. Analytical expressions for the outage probability (OP) are derived and Monte Carlo simulations are provided to validate the correctness of analysis. Obtained results highlight the importance of hardware impairments in the OP evaluation at the high signal-to-noise ratio regime. Moreover, the PSR demonstrates better performance than the TSR one. Finally, results reveal the benefit of the usage of cooperative NOMA reflected by significant improvement in the OP for both protocols.
Food Flavors : Chemical, Sensory and Technological Properties

Specificity of Food Odorants Henryk H. Jelen Important Tastants and New Developments Jakob Ley, Katharina Reichelt, Katja Obst, Gerhard Krammer, and Karl-Heinz Engel Smell, Taste, and Flavor Han-Seok Seo and Thomas Hummel Lipid-Derived Flavor Compounds Henryk Jelen and Envin Wasowicz Saccharides-Derived Flavor Compounds Matgorzata Majcher Flavors from Amino Acids H. -E. Spinnler Binding and Release of Flavor Compounds Elisabeth Guichard Flavor Suppression and Enhancement Jakob Ley, Katharina Reichelt, and Gerhard Krammer Legislation, Safety Assessment, and Labeling of Food Flavors and Flavorings Manfred Lutzow Essential Oils and Spices Danuta Kalemba and Anna Wajs Functional (Non-Flavor) Properties of Flavor Compounds Alfreda Wei and Takayuki Shibamoto Flavors from Cheeses H. -E. Spinnler Red Meat Flavor Jennie M. Hodgen and Chris R. Calkins Flavor of Wine Vicente Ferreira and Felipe San Juan Flavor of Bread and Bakery Products Salim-ur-Rehman and Javaid Aziz Awan Food Taints and Off-Flavors Kathy Ridgeway Volatile Compounds in Food Authenticity and Traceability Testing Tomas Cajka and Jana Hajslova Mapping the Combinatorial Code of Food Flavors by Means of Molecular Sensory Science Approach Peter Schieberle and Thomas Hofmann Methods for Sensory Analysis Renata Zawirska-Wojtasiak Machine Olfaction: A Devices Approach to Measurement of Food Aroma Corrado Di Natale Index
Comparison of mortality and medical complications between older and younger malignant melanoma inpatients

Previous studies have identified older age as a negative prognostic factor in malignant melanoma patients. To compare comorbidities, medical complications, and inpatient mortality between younger and older malignant melanoma inpatients. All adult patient encounters with a diagnosis of cutaneous malignant melanoma were identified using the 2003 to 2012 National Inpatient Sample database. Concurrent comorbidities and inpatient medical complications were queried. Comparisons were made between older and younger adults, defined as ≥65 years and 18 to 64 years, respectively. In total, 8153 patient encounters were identified in the database (51.6% older and 48.4% younger). Older adults had a higher prevalence of numerous comorbidities, including chronic pulmonary disease, chronic renal failure, congestive heart failure, diabetes mellitus, hypertension, hypothyroidism, peripheral vascular disease, and pulmonary circulation disorder (P < .001). In contrast, younger adults were more likely to have obesity (P < .001) and coagulopathy (P = .005). On multivariable analysis of medical complications, older adult inpatients were more likely to experience urinary tract infection (OR = 1.54, P = .021), but less likely to experience acute respiratory failure (OR = 0.46, P = .012) and venous thromboembolism (OR = 0.44, P = .026). Notably, inpatient mortality did not significantly differ. Older adult inpatients with malignant melanoma have different comorbidities than younger patients and have a larger overall comorbidity burden. Surprisingly, however, the odds of most inpatient medical complications and mortality were found to be similar in younger vs older patients.
On the Node Energy Efficiency of Full Duplex Two-way Ultra-Reliable Short Packet Communications

Full-duplex (FD) is a promising technique to improve the spectrum efficiency in wireless communications. However, it is noteworthy that there is a game for both node performances in a two-way channel, since self-interference can not be eliminated completely in FD communications. In this paper, we investigate the node energy efficiency (EE) for FD two-way ultra-reliable short packet communications in an additive white Gaussian noise (AWGN) channel. We focus on two cases. One is that the two nodes are noncooperative, each node only intends to maximize its own EE at any moment. The other one is that two nodes are cooperative, i.e., either of them is willing to improve their optimal EE simultaneously. For the first case, we establish a Game in terms of node EE and design an iterative algorithm to search for a Nash Equilibrium. In the second case, we establish a convex optimization problem of node EE and find the optimal solution by a standard convex optimization algorithm. It is shown by numerical results that the iterative algorithm converges and each node can achieve a higher optimal EE with a lower transmit power if they cooperate with each other.
Morning Sympathetic Activity after Evening Binge Alcohol Consumption.

Binge alcohol consumption elicits acute and robust increases of muscle sympathetic nerve activity (MSNA), yet the impact of evening binge drinking on morning-after MSNA is unknown. The present study examined the effects of evening binge alcohol consumption on polysomnographic sleep and morning-after MSNA. We hypothesized that evening binge drinking (i.e. 4-5 drink equivalent in <2hrs) would reduce sleep quality and increase morning-after blood pressure (BP) and MSNA. Following a familiarization night within the sleep laboratory, twenty-two participants (12 men, 10 women; 25±1 years) were examined after simulated binge drinking or fluid control (randomized, crossover design). Morning MSNA was successfully recorded across both conditions in 16 participants (8 men, 8 women) during a 10-minute baseline and three Valsalva's maneuvers (VM). Binge drinking reduced rapid eye movement (REM) sleep (15±1 vs. 20±1%; p=0.003), increased stage II sleep (54±1 vs. 51±1%; p=0.002), increased total urine output (2.9±0.2 vs. 2.1±0.1 liters; p<0.001), but did not alter morning-after urine specific gravity. Binge drinking increased morning-after heart rate (65 (54-72) vs. 58 (51-67) beats/min; p=0.013), but not resting BP or MSNA. Binge drinking elicited greater sympathoexcitation during VM (38±3 vs. 43±3 bursts/min, p=0.036). Binge drinking augmented heart rate (p=0.002), systolic BP (p=0.022) and diastolic (p=0.037) BP reactivity to VM phase IV, and blunted cardiovagal baroreflex sensitivity during VM phases II (p=0.028) and IV (p=0.043). In conclusion, evening binge alcohol consumption disrupted REM sleep and morning-after autonomic function. These findings provide new mechanistic insight into the potential role of binge drinking on cardiovascular risk.
Proteome Analysis of Human Natural Killer Cell Derived Extracellular Vesicles for Identification of Anticancer Effectors

Cancer immunotherapy is a clinically validated therapeutic modality for cancer and has been rapidly advancing in recent years. Adoptive transfer of immune cells such as T cells and natural killer (NK) cells has emerged as a viable method of controlling the immune system against cancer. Recent evidence indicates that even immune-cell-released vesicles such as NK-cell-derived exosomes also exert anticancer effect. Nevertheless, the underlying mechanisms remain elusive. In the present study, the anticancer potential of isolated extracellular vesicles (EVs) from expanded and activated NK-cell-enriched lymphocytes (NKLs) prepared by house-developed protocol was evaluated both in vitro and in vivo. Moreover, isolated EVs were characterized by using two-dimensional electrophoresis (2-DE)-based proteome and network analysis, and functional study using identified factors was performed. Our data indicated that the EVs from expanded and active NKLs had anticancer properties, and a number of molecules, such as Fas ligand, TRAIL, NKG2D, β-actin, and fibrinogen, were identified as effector candidates based on the proteome analysis and functional study. The results of the present study suggest the possibility of NK-cell-derived EVs as a viable immunotherapeutic strategy for cancer.
Characterization of electromagnetic fields of radiating systems by thermo-fluorescence

Characterization of the electromagnetic field emitted by various sources (antenna, radar) is an important issue, either for civil or defense applications. The measurement of the electromagnetic field may be performed by a local probe, the infrared thermography imaging being an alternative way. The latter method, called EMIR [1] (ElectroMagnetic InfraRed) has been used for years at ONERA where it had been developed. We have recently successfully implemented this technique in the domain of visible light, named as EMVI [2] (ElectroMagnetic Visible Imaging). As in EMIR, a thin film (sensitive to either electric field or magnetic field) is heated by the emitted field. But here the film is coated with a polymer doped with fluorescent molecules. As the fluorescent emission depends on the temperature of the film, we achieve a novel thermofluorescent sensor. The results presented here illustrate that new method: both magnetic and electric field imaging of two different radiating systems