Miracle-dz/UniFAR-FTUs huggingface dataset:
Capacity reserve assessment using system well-being analysis

Deterministic methods are widely used in operating reserve assessment and in small isolated power system capacity planning. These approaches do not normally include any explicit recognition of system risk and do not provide comparable risks for systems of different size or composition. Many large power systems, therefore, use probabilistic methods for generation adequacy evaluation. The reluctance to utilize the existing probabilistic techniques in both large and small systems dictates a need to create a bridge between the two different approaches. This paper presents a new probabilistic method designated as 'system well-being analysis', which incorporates the accepted deterministic criteria in the definition of 'healthy' and 'marginal' power system states. This paper illustrates methodologies to evaluate the system well-being indices of practical systems and describes how they can be used in the assessment of power system capacity reserves.
Call Admission and Assignment in Cellular Networks with Vehicular Relay Nodes

We investigate cellular networks supported by on-street parked vehicles, termed as Vehicular Relay Nodes (VeRN), which have been proposed to boost performance and scale the network to varying demands. We study the problems of Call Admission and Call Assignment, which are key to efficient operation. To achieve an efficient and practical solution, we propose to decouple this problem and solve it via two weakly-coupled algorithms. For Admission, we formulate a non-trivial Markov Decision Process, introducing a dynamic operator that stochastically accounts for the possible assignments. For the Assignment problem, we derive a local Deep Reinforcement Learning algorithm using Imitation Learning, while introducing several novel improvements. Performance evaluation shows that these strategies offer a significant improvement over baselines.
Cardinality and Short-Term Memory Concepts based Novel Feature Extraction for Myoelectric Pattern Recognition

The quality of the extracted traditional hand-crafted Electromyogram (EMG) features has been recently identified in the literature as a limiting factor prohibiting the translation from laboratory to clinical settings. To address this limitation, a shift of focus from traditional feature extraction methods to deep learning models was witnessed, as the latter can learn the best feature representation for the task at hand. However, while deep learning models achieve promising results based on raw EMG data, their clinical implementation is often challenged due to their significantly high computational costs (significantly large number of generated models’ parameters and a huge amount of data needed for training). This paper is focused on combining the simplicity and low computational characteristics of traditional feature extraction with the memory concepts from Long Short-Term Memory (LSTM) models to efficiently extract the spatial-temporal dynamics of the EMG signals. The novelty of the proposed method can be summarized in a) the memory concept leveraged from deep learning structures, capturing short-term temporal dependencies of the EMG signals, b) the use of cardinality to generate logical combinations of spatially distinct EMG signals and as a feature extraction method and 3) low computational costs and the enhanced classification performance. The performance of the proposed method is validated using three EMG databases collected with 1) laboratory hardware (9 transradial amputees and 17 intact-limbed), and 2) wearables (22 intact-limed using two wearable consumer armbands). In comparison to several other well-known methods from the literature, the proposed method shows significantly enhanced myoelectric pattern recognition performance, with accuracies reaching up to 99%.
SEQUENTIAL STRUCTURE OF BEHAVIOR OF LAYING HENS IN AN ENRICHED ENVIRONMENT

restriction to the general freedom of movement (Baxter, 1994). It seems that a systematic approach by the translation of behavioral needs of laying hens into welfare-based design parameters for laying hen-housing systems has received modest attention (Schwabenbauer, 1999). The welfare concept has to be translated via behavioral needs into quantifiable behavioral parameters that can be used as inputs to model a housing system and to assess minimal space and facility requirements. Therefore, definition, prioritization, and measurement of behavioral needs and sequence are crucial for the design. Quantification of sequences of events of various behaviors may yield insights as to how hens distribute their time over various resources and what their behavioral priorities are. These properties of hen behavior will be used to design hen-housing systems, based on behavioral needs, behavioral priorities, needed facilities, and space allocations. The aim of this paper was to estimate quantify sequential organization of a behavior. To assess the behavioral sequential organization the first-order transition matrix were analyzed, and percent-ages of behavioral occurrences at different places in the pen were calculated. The results of these analyses will provide the input parameters for a simulation model, which can then be used as a tool to optimize fulfillment of hen needs in interaction with environmental conditions for laying hen houses in the egg industry.
Secure Intermediate-Variable-Based Estimation for Multi-Area Power Systems under FDI Attacks

This paper studies the secure state estimation problem for multi-area power systems with unknown load deviation when the sensor measurements of power systems encounter false data injection (FDI) attacks. By modeling a new state-space model including the variables of load deviation and FDI attack signals, an intermediate observer-based method is developed to simultaneously estimate the load deviation and FDI attacks. Then, the secure state estimator is designed by compensating FDI attacks, while the unknown load deviation in power systems is compensated by its estimated values to eliminate system oscillations as much as possible. Simulations show the effectiveness of the proposed methods.
Electric Vehicle Traction Control for Optimal Energy Consumption

An electric vehicle (EV) with four in–wheel motors (IWMs) offers several advantages over other types of EVs. Due to a limited electrical energy source and a long battery charging time, any way to minimise energy consumption in an EV has to be fully utilised. Undoubtedly one of the systems in an EV that drains the most amount of energy is the propulsion system. In this work the existence of an optimal slip ratio that enables energy saving in an EV propulsion system is investigated. A controller is designed to ensure the slip ratio of each wheel is limited by the optimal value. Simulation results demonstrate the effectiveness of the proposed traction control scheme in energy optimisation in an EV.
SNR improvement, filtering and spectral equalisation in cochlear implants using wavelet techniques

This paper contains a brief outline of the engineering requirements for a cochlear implant speech processor based on wavelet techniques. Signal processing in cochlear implants typically involves filtering and noise removal, spectral estimation (to the frequency response of the hearing-impaired individual) and equalisation across desired frequencies. These various signal processing functions are typically implemented using separate hardware and microprocessor stages. In contrast, wavelet analysis inherently supports feature extraction, ambient noise removal and spectral estimation through the use of selected frequency-localised functions. Appropriate basis selection can incorporate noise, spectrum and equalisation (scaling) considerations. Thus it is possible to integrate the various signal processing requirements into a single DSP-based (digital signal processor) solution.
Induced stresses on silicon die by the molding process: experimental

The induced stresses by two molding compounds on silicon chips are experimentally investigated. Determination of induced stresses is a very important step towards the research and development of new packages, and for layout optimization. The analysis has been carried out through a straightforward methodology based on a suitable model of the piezoresistive effect, which is shown by the semiconductor materials (silicon in the present case). The used method has been found effective to obtain a good evaluation of the stresses distribution due to the plastic packages. The investigation is carried out onto a TO220 package using two last generation compounds, and statistical measurement tools are applied to the experimental results. Finally, the induced stresses after reliability tests are determined and discussed.
On Quantum Query Complexities of Collision-Finding in Non-Uniform Random Functions

Collision resistance and collision ﬁnding are now extensively exploited in Cryptography, especially in the case of quantum computing. For any function f : [ M ] → [ N ] with f ( x ) uniformly distributed over [ N ], Zhandry has shown that the number Θ ( N 1 / 3 ) of queries is both necessary and suﬃcient for ﬁnding a collision in f with constant probability. However, there is still a gap between the upper and the lower bounds of query complexity in general non-uniform distributions. In this paper, we investigate the quantum query complexity of collision-ﬁnding problem with respect to general non-uniform distributions. Inspired by previous work, we pose the concept of collision domain and a new parameter γ that heavily depends on the underlying non-uniform distribution. We then present a quantum algorithm that uses O ( γ 1 / 6 ) quantum queries to ﬁnd a collision for any non-uniform random function. By making a transformation of a problem in non-uniform setting into a problem in uniform setting, we are also able to show that Ω ( γ 1 / 6 log − 1 / 2 γ ) quantum queries are necessary in collision-ﬁnding in any non-uniform random function. The upper bound and the lower bound in this work indicates that the proposed algorithm is nearly optimal with query complexity in general non-uniform case.
Losses influenced by third-harmonic flux in induction motors

The trend toward increasing flux density in modern induction motors results in the increase of third-harmonic flux in the magnetic paths. The effects of third-harmonic flux on energy losses in induction motors are discussed. The shapes of flux distributions in various sections of an induction motor are interrelated. The most saturated section normally has a flattened shape of flux density distribution. The effects of the shapes of flux density distribution on losses are compared from the third-harmonic flux standpoint. The losses influenced by the third-harmonic flux may be controlled through lamination punching designs and winding connections. >
Pattern detection in cloud computing: Bibliometric mapping of publications in the field from past to present

The use of cloud computing has become widespread with the rapid development of technology. In this context, it is important to make a bibliometric analysis to identify developments in cloud computing research and to guide future research. For bibliometric analysis of cloud computing research, this study examines 48692 studies scanned in the Web of Science database as of July 2021, without year and index limits. VOSviewer software was used for bibliometric analysis. Bibliometric analysis showed that the research in the field of cloud computing increased exponentially until 2016, but after 2016, the research decreased and turned to more specific areas. China, USA, and India, respectively, are the countries that contributed the most to the cloud computing literature. According to the keyword co-occurrence analysis, the prominent keywords are fog computing, mobile cloud computing, data security, and trust. In addition, edge computing and blockchain stand out as trending topics in recent years. Future Generation Computer Systems and IEEE Access are the journals that contribute the most to the field. Taken as a whole, the findings of the study describe the field in general and provide important, enlightening, and stimulating information for researchers who are or will be interested in cloud computing.
Training in Production Output Management

Problems of management of production output in a 4-tier corporation in the face of uncertainty are investigated. There is an activity of the elements of the lower tiers of this corporation’s hierarchy associated with the presence of their own objectives that do not coincide with the objectives of higher managers. These active elements may select indicators of their production outputs to maximize own objective functions, rather than the objective functions of senior executives. In case of insufficient awareness of these executives, such activity of the corporation’s elements can lead to a decrease in the volume of its production. To avoid this, managers of the top two levels are advised to use training procedures based on machine learning algorithms as well as supervisor’s instructions. In addition to this, the corporate production management mechanism includes rating and promotion procedures at the two lower tiers of the corporation. Depending on this mechanism, decisions of games of active elements with higher managers are determined. Sufficient conditions are found for the synthesis of such a mechanism in which the active elements of the corporation fully use their aptitude to increase the volume of production. The results obtained are illustrated by the example of a car repair management mechanism at Russian Railways.
Beyond mie theory

Light scattering in participating media and translucent materials is typically modeled using the radiative transfer theory. Under the assumption of independent scattering between particles, it utilizes several bulk scattering parameters to statistically characterize light-matter interactions at the macroscale. To calculate these parameters based on microscale material properties, the Lorenz-Mie theory has been considered the gold standard. In this paper, we present a generalized framework capable of systematically and rigorously computing bulk scattering parameters beyond the far-field assumption of Lorenz-Mie theory. Our technique accounts for microscale wave-optics effects such as diffraction and interference as well as interactions between nearby particles. Our framework is general, can be plugged in any renderer supporting Lorenz-Mie scattering, and allows arbitrary packing rates and particles correlation; we demonstrate this generality by computing bulk scattering parameters for a wide range of materials, including anisotropic and correlated media.
Performance of a OTDOA-IPDL positioning receiver for 3GPP-FDD mode

Location services (LCS) are gaining increasing commercial interest in the mobile communications industry, with exponential growth predicted for the next decade. The capability of finding the accurate position of a mobile phone is one of the features that will help third generation (3G) systems in reaching a wide success triggering a high number of innovative interactive applications. This paper gives an overview of algorithms for positioning a mobile phone using 3GPP-FDD mode signals. The OTDOA-IPDL mechanism is detailed, and implemented in a typical simulation scenario. The performance of the system is evaluated in different environments, showing the severe effects of multipath over the radio link.
Predicting high-resolution turbulence details in space and time

Predicting the fine and intricate details of a turbulent flow field in both space and time from a coarse input remains a major challenge despite the availability of modern machine learning tools. In this paper, we present a simple and effective dictionary-based approach to spatio-temporal upsampling of fluid simulation. We demonstrate that our neural network approach can reproduce the visual complexity of turbulent flows from spatially and temporally coarse velocity fields even when using a generic training set. Moreover, since our method generates finer spatial and/or temporal details through embarrassingly-parallel upsampling of small local patches, it can efficiently predict high-resolution turbulence details across a variety of grid resolutions. As a consequence, our method offers a whole range of applications varying from fluid flow upsampling to fluid data compression. We demonstrate the efficiency and generalizability of our method for synthesizing turbulent flows on a series of complex examples, highlighting dramatically better results in spatio-temporal upsampling and flow data compression than existing methods as assessed by both qualitative and quantitative comparisons.
What Does “Asymmetric VR” Mean? A Directed Content Analysis of Co-Located Use of VR by Users on Reddit

Technology is often framed in terms of space and time of use, such that a mobile phone is used to either send asynchronous messages or host synchronous conversations with remote others, while a classroom smartboard supports co-located, synchronous learning. As the technology becomes more immersive, the applicability of frameworks such as the time/place matrix becomes less clear. This study attempts to provide clarity by applying the Composite framework for Asymmetric VR (CAVR) to online forum descriptions of the use of immersive virtual reality (VR) in co-located groups. A related framework, Roles of Technology, is also explored; however, the authors argue the framework must be expanded before application of it beyond mobile technology. To better understand one possible solution to co-located VR’s isolation problem, a directed content analysis was conducted, exploring the discussion of co-located and asymmetric VR use on various subreddits. As a result, 11 patterns of co-located use of VR, including 8 which specify asymmetric VR designs, are identified. The researchers update the dimensions of CAVR according to these results, compare CAVR to another nascent framework, and offer suggestions for future work and applicability to practice. This work is intended to help guide future creation and research of asymmetric VR experiences through the deconstruction of existing asymmetric VR experiences to their key parts via the application of CAVR.
Solifuge (camel spider) reproductive biology: an untapped taxon for exploring sexual selection

Abstract. The exploration of new and diverse animal groups in the study of sexual selection is both necessary and important to help better understand broad patterns and test sexual selection hypotheses regarding the evolutionary origins and maintenance of reproductive tactics and associated traits. Solifuges are, in this matter, an exceptional group and very little explored from the sexual selection point of view. At first glance, mating is apparently quite simple and conserved within this arachnid order, but solifuge reproductive behavior is unique among arachnids and more diverse than previously thought. In particular, these voracious animals appear to exhibit high sexual conflict, as males need to avoid being eaten by their aggressive female partners and mating encounters in some species involve periods of apparently male-induced female inactivity during sperm transfer. The extent to which reproductive encounters are coercive versus collaborative, however, remains largely unknown. In this review, we begin with a historical perspective of sexual behavior research in solifuges. We then discuss precopulatory mating patterns, the role of the female and male during mating, sexual dimorphism, and the influence of sexual selection during different stages of mating. In addition, we explore cases of sexual cannibalism and provide an updated analysis of how postcopulatory sexual selection may be acting on these amazing arachnids. This review shows that there is much to be done in this extraordinary group of animals.
Efficient Analysis of a Catenary Riser

The paper deals with the challenge of predicting the extreme response of catenary risers, a topic of both industry and academic interest. Large heave motions introduced at the upper end of a catenary riser can lead to compression and large bending moments in the region immediately above the touch down area. In the worst case, dynamic beam buckling may occur. The focus of the paper will be on understanding the riser behaviour in extreme, low-tension response and in establishing suitable analysis strategies to predict the extreme response. Results from long nonlinear stochastic simulations of many sea states with varying environmental and operating conditions may be combined to describe the long-term response of a nonlinear structure such as a catenary riser. However, this theoretically straight-forward approach is very demanding computationally and ways to limit the extent of nonlinear stochastic simulations are therefore sought. The usefulness of simpler methods such as regular wave analysis to improve understanding of the physical behaviour and to aid in concentrating the nonlinear simulations to where they are most useful, will be demonstrated.Copyright © 2006 by ASME
A 1.68-23.2 Gb/s Reference-Less Half-Rate Receiver with an ISI-Tolerant Unlimited Range Frequency Detector

The data rates of high speed serial link keep increasing in the past decades. To accommodate multi-standard and legacy mode operation, there is a need for a high speed receiver providing wide range and continuous rate operation, which is difficult to achieve. Limited by the frequency capture range of the CDR, an auxiliary frequency locked loop (FLL) is commonly used for a broad band operation. An FLL with a reference/crystal input is a common approach for a reliable operation. To provide the merits of low cost and small form factor for system integration, reference-less CDRs have drawn the attention of many researchers recently [1]–[5]. There are two conventional approaches to accomplish reference-less CDRs. One is based on a modified quadri-correlator architecture, which tries to detect the fast/slow frequency offset based on the sampling phase rotation [2][3], and blocks the inefficacious indicators when the sampling phases cross two quadrants. Under ISI channels where the data edge is smeared out, frequency detection based on sampling phase rotation becomes unreliable if the data edge is not properly equalized. Another approach to reference-less frequency detectors uses stochastic divider or fast/slow histograms to detect frequency offset[1][5]. A posteriori knowledge of the data pattern, transition density, and channel characteristics are required to properly setup the threshold and weighting coefficients of the frequency detectors. To overcome the aforementioned shortcomings and design challenges, this paper presents a pulse-width based frequency detector, which provides unlimited range frequency detection. To the author’s best knowledge, the proposed high speed receiver, which consists of CTLE, DFE, and CDR, demonstrates the broadest operation range (13.8X) as are reported to date.
An End-to-End Bitstream Tamper Attack Against Flip-Chip FPGAs

—FPGA bitstream encryption and authentication can be defeated by various techniques and it is critical to understand how these vulnerabilities enable extraction and tampering of commercial FPGA bitstreams. We exploit the physical vulnerability of bitstream encryption keys to readout using failure analysis equipment and conduct an end-to-end bitstream tamper attack. Our work underscores the feasibility of supply chain bitstream tampering and the necessity of guarding against such attacks in critical systems.