Miracle-dz/UniFAR-FTUs huggingface dataset:
Asynchronous Traffic on the Sidelink of 5G V2X

The Cellular Vehicle-to-Everything (C-V2X) technology has been specified to support V2X communications in Long Term Evolution (LTE) and beyond cellular networks. Whereas Release 14 specifications mainly target the sidelink support of periodic cooperative awareness messages (CAMs) exchanged among vehicles, 3GPP is currently discussing C-V2X enhancements to enable new features in 5G Releases 16 and 17, among which the support of asynchronous messages like Decentralized Environmental Messages (DENMs). Such type of messages can be generated upon an accident or other hazardous events on the road, but also to allow a vehicle to join/leave a platoon. They need to be promptly and reliably disseminated to guarantee safe driving. In this paper, we analyse the DENM delivery performance over the sidelink, when considering the C-V2X autonomous resource allocation scheme, also known as Mode 4 in Release 14 and as Mode 2 in the next Releases. The suitability to DENMs of the resource selection algorithm specified for CAMs in Release 14 is first investigated, before analyzing some suggested parameter adjustments for Release 16, and elaborating on their most suitable tuning under different simulation settings.
Network Traffic Load Balancing Protocol*

Nowadays complex network structures arise in numerous practical areas such as logistics, telecommunication, production chains, power grids, transportation, economics and many others. For efficient operation of a network system it is useful to balance the load on its components. In modern wireless networks it is crucial to avoid congestion that leads to packet losses, time and energy wasting and general effectiveness reduction. In the paper a multihop communication network is considered. The proposed approach to avoid congestion consists in balancing the traffic among the shortest paths in communication graph of the network. A local voting protocol is utilized to balance the load in multihop communication network. Centralized and decentralized solutions are compared. The proposed approach is illustrated by numerical examples.
The Electrolyte Distribution in Insect Olfactory Sensilla as Revealed by X‐ray Microanalysis

X-ray microanalysis allows the quantitative analysis of elemental concentrations in situ with the high lateral resolution of the electron microscope. 'Thus, it is now possible to measure the electrolyte distribution directly in the various cellular and extracellular compartments of insect sensilla. Eventually, this information will provide a link between our understanding of the complex ultrastructure of these sensory organs and their functional interpretation as derived mainly from electrophysiological experiments. Cryofixation and cryoultramicrotomy are essential prerequisites for X-ray microanalysis of electrolyte elements, because any solvent treatment might cause redistribution or even extraction of soluble elements. With antennae of the silkmoth, Bombyx mori, this has been achieved by quick immersion into liquid propane (at 190°C) and cryoembedding into heptane (at 80"C). ' Ultrathin cryosections were cut in a Reichert FC4 cryoultramicrotome at 1 10"C, freeze dried and studied in a Siemens ST lOOF scanning transmission electron microscope. X-ray spectra were processed in an energy-dispersive spectrometer with multichannel analyzer (Link Systems). Dry weight concentrations of the analyzed elements were calculated according to the continuum method of Hall' after calibration with cryosections of salt solutions of known concentration (for a more detailed account of the procedure see Zierold4). The ultrastructure of frozen, dried cryosections allows the unequivocal identification of all important sensillar compartments, such as dendrites and receptor lymph in cross sections of olfactory hairs or, in the antenna1 branches, the receptor-cell somata, dendrites, axons, the different auxiliary cells, as well as the extracellular compartments of the receptor-lymph and hemolymph spaces. The plasma membranes around the cellular compartments and the organelles within them are clearly discernible (FIG. 1). X-ray microanalysis (TABLE 1 ) revealed a high concentration of potassium throughout the cellular compartments, but also in the extracellular receptor lymph, confirming earlier measurements on isolated receptor-lymph droplets? It is assumed
Lean User Research for Agile Organizations

In the context of Digital Transformation, organizations need to react in a flexible manner to rapidly changing markets and disruptive technologies. Customer and user satisfaction are important in order to succeed. Agile User Experience (UX) Design and Lean User Research enable the organization to focus on the needs of its customers and users. The objective of this paper is to investigate the best practices that exist for user research in agile product development. In particular, we want to answer the following question: What impact does Lean User Research have and how is it conducted in industry? We have carried out several studies to answer our questions. First, we created the pattern Lean User Research by means of a systematic pattern mining process. Next, we gathered quantitative data by means of a questionnaire study. This was followed by the collection of qualitative data in a panel discussion with experts from academia and industry. The results reveal that Agile has an impact on how user research is conducted because of the speed of agile product development. In addition, we provide best practices of Lean User Research and show how they are applied in industry by giving practical examples. Moreover, we found that the UX community values the aspects of Lean User Research as important, but do not constantly apply it in their practical work. We can conclude that people have to experiment and adapt the best practices of Lean User Research to their special contexts as well as develop an agile mindset.
A Software Architecture to enable Self-Organizing, Collaborative IoT Ressource Networks

Since recent years, the industrial world is facing a significant change from components and systems that are designed and configured for one well-analyzed scenario using well-established technologies to a demand for highly dynamic systems that continuously introduce the latest technologies and are capable of handling ever-changing use case environments. Thus, there is a need for new approaches on how to design and setup such systems. One of these approaches is Collaborative IoT, which focuses on researching the necessary designs, infrastructure, and technology to build ecosystems of loosely coupled, autonomous systems that interact in an ad-hoc fashion and establish service compositions and new distributed applications on demand. This paper presents a software architecture design for Industrial IoT systems to approach these targets as well as a reference implementation showing the feasibility of the architectural design in a Collaborative IoT smart factory scenario. The developed AiB architecture enables heterogeneous IoT systems to form task execution ecosystems that dynamically address new incoming tasks by creating service compositions across participating devices, which were not defined during design time.
Virtual Capacity of Hybrid Energy Storage Systems Using Adaptive State of Charge Range Control for Smoothing Renewable Intermittency

This paper presents a method for improving capability of a Hybrid Energy Storage System (HESS) comprised of a battery and supercapacitor (SC), for smoothing power fluctuations of renewable energy sources by adaptively controlling the state of charge (SOC) allocation range using automatic SOC management. The proposed method secures the preset SOC range of both battery and SC for power smoothing applications of renewables and also has the ability to significantly smooth power fluctuations using the virtual capacity concept of an energy storage system (ESS) by extending the SOC usage range. The bode plots of the proposed method are analyzed to observe the coverage of the power spectrum in the frequency domain in each storage based on the case studies. A criterion of HESS sizing, both battery and SC, is also developed based on the SOC range, smoothing time constant, and the power rating of renewables. The proposed method for the integration of HESS on renewable energy sources is verified with simulation results and characteristic analysis using PSCAD/EMTDC and MATLAB software.
Predicting Link Sign in Online Social Networks based on Social Psychology Theory and Machine Learning Techniques

Online social networks provide a great platform for internet users to share their views and ideas. Social media provides a dynamic platform that includes the formation and deformation of connections. Two types of connections, i.e., Positive and Negative, can exist in a social network. Positive connections are a sign of friendship or trust, while negative connections show enmity or distrust. Various applications in several fields have networks containing both positive and negative edges. Reliable prediction of edge sign can greatly influence in recommending friendly relationships while preventing enemy relationships across the network. Prediction of edge signs has been explored previously also. However, we intend to predict the sign of edges based on extracted features of nodes constructed upon theories of social psychology that includes classical balance theory and the status theory. Moreover, we employ emotional information theory and use the combined extracted features from all the theories to analyze networks for better prediction. Our results show that the proposed methodology has obtained significant accuracy when implemented on two real-life datasets, namely Slashdot and Epinions.
Improving Farmers’ Participation in Agri Supply Chains with Blockchain and Smart Contracts

Farmers are inhibited from Agri supply chains due to upfront transaction costs. There is ample evidence that farmers’ participation in supply chains produce benefits for farmers. This paper analyses how blockchain might support farmers to reduce transaction costs and improve their involvement in supply chains by reviewing the literature. The study contributes some theoretical insights to our knowledge on how transaction costs can be reduced by implementing supply chain collaboration with blockchain technology. It further presents some design implications for a blockchain-enabled system that would also support collective marketing. Practitioners in the Agri domain can use this analysis as a support for developing a system that improves farmers’ participation in Agri supply chains.
Using Distance Landscape Strategy for RGV Dynamic Scheduling Problem

Rail guide vehicle (RGV) dynamic scheduling problems have attracted increasing attention in recent years, which determines a great impact on the working efficiency of the entire scheduling system. However, the relative intelligent optimization study of RGV dynamic scheduling problems are insufficient scheduling of different working components in the previous works, it is easy to appear idle waiting, resulting in reduced operating efficiency during operation. Analysis of the fitness landscape is essential to understand the behavior of evolutionary algorithms for solving dynamic optimization problems in the evolutionary dynamics of biological evolution. With the continuous advancement of evolutionary algorithm optimization, the fitness landscape can present more abundant feature information around the fitness value, including autocorrelation, fitness distance correlation, landscape walks, local optima, and landscape roughness. This paper proposes a new distance landscape strategy for the RGV dynamic scheduling problems. The combination of the fitness landscape and dynamic search strategy are established according to the operating principle of the RGV system. In order to solve the RGV dynamic scheduling problem more effectively, experiments are conducted based on the type of computer numerical controller (CNC) with one procedure programming model in solving the RGV dynamic scheduling problems. The experiment results reveal that this new distance landscape strategy can provide promising results and solve the considered RGV dynamic scheduling problems effectively.
Jay: Adaptive Computation Offloading for Hybrid Cloud Environments

Edge computing is a hot research topic given the ever-increasing requirements of mobile applications in terms of computation and communication and the emerging Internet-of-Things with billions of devices. While ubiquitous and with considerable computational resources, devices at the edge may not be able to handle processing tasks on their own and thus resort to offloading to cloudlets, when available, or traditional cloud infrastructures. In this paper, we present Jay, a modular and extensible platform for mobile devices, cloudlets, and clouds that can manage computational tasks spawned by devices and make informed decisions about offloading to neighboring devices, cloudlets, or traditional clouds. Jay is parametric on the scheduling strategy and metrics used to make offloading decisions, providing a useful tool to study the impact of distinct offloading strategies. We illustrate the use of Jay with an evaluation of several offloading strategies in distinct cloud configurations using a real-world machine learning application, firing tasks can be dynamically executed on or offloaded to Android devices, cloudlet servers, or Google Cloud servers. The results obtained show that edge-clouds form competent computing platforms on their own and that they can effectively be meshed with cloudlets and traditional clouds when more demanding processing tasks are considered. In particular, edge computing is competitive with infrastructure clouds in scenarios where data is generated at the edge, high bandwidth is required, and a pool of computationally competent devices or an edge-server is available. The results also highlight JAY's ability of exposing the performance compromises in applications when they are deployed over distinct hybrid cloud configurations using distinct offloading strategies.
Interspecific Interactions among High Marsh Perennials in a New England Salt Marsh

High marsh habitats in New England exhibit conspicuous zonation of vas- cular plants. Spartina patens and Juncus gerardi dominate the seaward and terrestrial borders of the high marsh, respectively, whereas Distichlis spicata is common in disturbed habitats. In this paper I examine the role of interspecific interactions among these marsh perennials in maintaining marsh plant zonation. Removal and transplant experiments were performed to examine species interactions in dense stands. In both types of experiments J. gerardi competitively dominated S. patens and D. spicata, while S. patens competitively dominated D. spicata. Each of these perennials showed maximum growth when transplanted into the J. gerardi zone without neighbors. J. gerardi appears to dominate the terrestrial border of the marsh by competitively excluding S. patens to lower marsh levels, while D. spicata is competitively restricted to disturbed habitats by both J. gerardi and S. patens. Species interactions during the colonization of bare patches were strikingly different than those found in dense vegetation. Colonization of bare patches by both S. patens and D. spicata facilitated the colonization of J. gerardi, the competitive dominant in dense vegetation. Bare patches in the high marsh are typically hypersaline due to increased surface evaporation in the absence of plant cover. Patch colonization by competitive subordinates, which are relatively salt tolerant, appears to reduce substrate salinity by passively shading the substrate and, as a by-product, facilitate the invasion of a superior competitor. While interspecific competition in dense vegetation dictates the zonation of New En- gland perennials in the high marsh, secondary succession in this assemblage may commonly be driven by facilitations. This scenario may not be uncommon among assemblages in harsh physical environments where plants or sessile animals ameliorate their physical environment.
Collaboration and Interventions on Urban Environment Mapping with Graph-based SLAM Algorithm

The article describes the implementation of a collaborative approach for localization and mapping of large urban environments. This approach foresees the presence of multiple human operators, endowed with wearable sensors, whose outputs are processed in real-time in order to estimate human odometry. A Graph-based CLAM approach, generating a global map, is then applied: in order to improve the optimization phase of the algorithm, deterministic and probabilistic intervention approaches are proposed. More in detail, the loop closure procedure has been improved according to human sensing and the observations of external sensors. Experimental tests have been carried out in the historical center of Genova, proving that the proposed method overcomes classical multi-operator SLAM approaches.
Random Disturbance Attenuation in Discrete-time Polytopic Systems: Performance Analysis and State-Feedback Control*

In this paper linear discrete-time systems with polytopic uncertainties affected by random external disturbances are under consideration. The input disturbance is supposed to be a random sequence with known mean anisotropy, which stands for a spectral color of the signal. The anisotropic norm of the system indicates its stochastic gain from the input disturbance with the same mean anisotropy level to the output of the system. The aim of the paper is to derive numerical procedures to analysis and state-feedback control of linear discrete systems with polytopic uncertainties using anisotropy-based performance criterion. The analysis problem is to check robust stability and performance index of the uncertain system with respect to random input disturbances with known mean anisotropy level. The control problem is to find state-feedback gain that robustly stabilizes the uncertain system and guarantees desired anisotropy-based performance index for all possible uncertainties. In order to solve this problem matrix inequalities approach is used to obtain the numerically effective procedure. To illustrate the efficiency of the proposed conditions, a numerical example is considered.
Training Channel Selection for Learning-Based 1-Bit Precoding in Massive MU-MIMO

Learning-based algorithms have gained great popularity in communications since they often outperform even carefully engineered solutions by learning from training samples. In this paper, we show that the selection of appropriate training examples can be important for the performance of such learning-based algorithms. In particular, we consider non-linear 1-bit precoding for massive multi-user MIMO systems using the C2PO algorithm. While previous works have already shown the advantages of learning critical coefficients of this algorithm, we demonstrate that straightforward selection of training samples that follow the channel model distribution does not necessarily lead to the best result. Instead, we provide a strategy to generate training data based on the specific properties of the algorithm, which significantly improves its error floor performance.
Preliminary Experiments Conducted on Prototypes of Sensitivity-changeable Soft Tactile Sensors Utilizing Phase Change*

This paper presents the fabrication and preliminary experiments conducted on two prototypes of sensitivity-changeable soft tactile sensors, which utilize the phase change of gallium between the solid and liquid states. Ordinal materials change their stiffness by the process of melting or coagulation, which leads to stiffness and sensitivity changes in the entire tactile sensor. We fabricated two types of prototypes, namely flat and dome-shaped sensors, created from silicone rubber, gallium, and strain gauges. The sensitivity of the former in the liquid phase is higher than in the solid phase. Conversely, the sensitivity of the latter is higher in the solid state than in the liquid phase. We performed preliminary experiments and confirmed that these tactile sensors are responsive to sensitivity changes.
Interactions between insect herbivores and a plant architectural dimorphism

1 Plants possess many traits that influence their resistance to insect herbivores and thus can shape the structure of herbivore assemblages. While plant chemistry and structural defences have received the most attention, plant architecture may also be important. We examined patterns and consequences of insect herbivory for two distinct architectural morphs (prostrate or erect) of the perennial shrub, Baccharis pilularis (Asteraceae, coyote bush) in coastal California, USA. 2 For B. pilularis, differences in plant height, branch architecture and leaf size persisted in a common garden, consistent with previous evidence suggesting a genetic basis for dimorphism in plant architecture. 3 In both naturally occurring plants and in a common garden experiment, the composition of the herbivore assemblage varied strongly with plant architectural morph. Prostrate plants attracted higher densities of a gall‐forming midge (Rhopalomyia californica, Diptera), whereas erect plants supported more moth galls (Gnorimoschema baccharisella, Lepidoptera) and experienced greater folivory. Furthermore, architectural traits were correlated with herbivory levels both across and within architectural morphs. 4 Prostrate plants had greater reproductive output (flowers or seeds) than erect plants in the presence of herbivores. However, under experimental reductions of herbivory spanning 40 months, the morphs had similar reproductive output, demonstrating that herbivores have the potential to act as agents of selection on the dimorphism in plant architecture. 5 These results confirm that intraspecific variation in plant traits can shape the composition of herbivore assemblages. Furthermore, in this system, herbivores alter the performance of the architectural morphs, suggesting a dynamic system of feedbacks between the population genetic and community levels.
Web Insights for National Security: Analysing Participative Online Activity to Interpret Crises

Social Media has become the primary communication channel to share feelings and opinions to big audiences. This characteristic is especially important when dealing with a crisis because people use it to send a variety of messages; however, to emphasise or complement what they post, individuals include links to web pages or other web services. Contributing a component to a novel framework for National Security crises analysis and management, this paper focuses on Web Insights as a mechanism that can help to comprehend the nature and state of a crisis. The mechanism analyses web resources and provides insights along three different dimensions: Firstly, the escalation of the crisis over time. Secondly, the interpretation of the information that the main actors have published (Social Networking Services, Information Outlets, Non-Governmental Organisations and Independent Websites). Thirdly, an estimate whether the crisis has turned violent.