Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Trafﬁc Flow: Utilizing IR and Load Cell Sensors for Cost-Effective Trafﬁc Congestion Alleviation at Smart City Intersections †

: Cities face a signiﬁcant challenge due to increasing trafﬁc congestion and the rising number of vehicles. Developed countries have introduced smart trafﬁc management systems as a solution to mitigate congestion and improve trafﬁc ﬂow. These systems employ various techniques such as image processing, radar sensing, ultrasonic and microwave detectors, and other sensors. However, each of these methods has its drawbacks, including susceptibility to weather conditions, high costs, and low accuracy. Intelligent trafﬁc control methods like inductive loop detection, wireless sensor networks, and video data analysis have proven to be efﬁcient. However, they suffer from lengthy installation processes and high installation and maintenance expenses. In response to this issue, this article proposes a system that can detect lane density and adjust trafﬁc signal timers accordingly to optimize trafﬁc ﬂow. The proposed system utilizes IR sensors and load sensors to calculate the density of each lane at an intersection, and an RFID system is implemented to accommodate emergency response vehicles. This system is centered around an ATmega 2560 chip. To demonstrate the effectiveness of the proposed approach, real-time experiments were conducted on a scaled-down model of the system. The results showed promising outcomes. The authors argue that this system could serve as a cost-effective and efﬁcient solution for managing trafﬁc in cities, particularly in Pakistan.
Review of Public Administration and Management

The paper examined the consequences of poverty on mortality rate in Nigeria from 1980-2013 using Error-Correction Model as estimation technique. The paper revealed that economic growth, debt and poverty have significant relationships with mortality rate. On the other hand, corruption, inflation, investment, human capital development and unemployment presented negative relationships with mortality rate. Human capital development and unemployment was significant while corruption, inflation and investment proved to be insignificant. The paper further revealed that all the variables proved to be determinants of mortality rate in Nigeria except corruption, inflation and investment. The paper concluded that economic growth, debt and poverty aggravated or increased mortality rate while other variables reduced mortality rate in Nigeria. The paper therefore, recommends that government should put in place quality institutions and sincere poverty alleviation programmes to reduce mortality rate in Nigeria.
Behavioral and Physiological Effects of Hindlimb Unloading in Rats

The "slow" antigravity muscle adductor longus was studied in rats after 14 days of spaceflight (SF). The techniques employed included standard methods for light microscopy, neural cell adhesion molecule (NCAM) immunocytochemistry and electron microscopy. Light and electron microscopy revealed myofiber atrophy, segmental necrosis and regenerative myofibers. Regenerative myofibers were N-CAM immunoreactive (N-CAM-IR). The neuromuscular junctions showed axon terminals with a decrease or absence of synaptic vesicles, degenerative changes, vacant axonal spaces and changes suggestive of axonal sprouting. No alterations of muscle spindles was seen either by light or electron microscopy. These observations suggest that muscle regeneration and denervation and synaptic remodeling at the level of the neuromuscular junction may take place during spaceflight. In a separate study, GABA immunoreactivity (GABA-IR) was evaluated at the level of the hindlimb representation of the rat somatosensory cortex after 14 days of hindlimb unloading by tail suspension ("simulated" microgravity). A reduction in number of GABA-immunoreactive cells with respect to the control animals was observed in layer Va and Vb. GABA-IR terminals were also reduced in the same layers, particularly those terminals surrounding the soma and apical dendrites of pyramidal cells in layer Vb. On the basis of previous morphological and behavioral studies of the neuromuscular system after spaceflight and hindlimb suspension it is suggested that after limb unloading there are alterations of afferent signaling and feedback information from intramuscular receptors to the cerebral cortex due to modifications in the reflex organization of hindlimb muscle groups. We propose that the changes observed in GABA immunoreactivity of cells and terminals is an expression of changes in their modulatory activity to compensate for the alterations in the afferent information. © 1998 COSPAR. Published by Elsevier Science Ltd. All rights reserved INTRODUCTION The first section of this report will place emphasis upon some particular responses to weightlessness observed in the adductor longus muscle of rats flown in the Soviet COSMOS flight 2044, namely, 1) muscle fiber injury, 2) regenerative phenomena, and 3) alterations of the neuromuscular junctions. In previous studies, investigations carried out upon different muscles after both flight and ground-based (mostly hindlimb suspension) experiments have provided information on the effects of microgravity and "simulated" microgravity upon morphology, metabolic properties, histochemistry and electrophysiology (see Edgerton and Roy, for review, 1994). Through these studies we have learned that "slow" muscles, mostly composed of type I fibers (e.g., soleus, adductor longus), carry the burden of the changes while "fast" muscles, mostly composed of type H fibers (e.g., tibialis anterior) are relatively unaffected.
Ultra-Low Power MEMS Inertial Switch Based Wake-Up Wireless Sensing Node for Door Lock Monitoring

We present the first demonstration of our event-driven ultra-low power MEMS inertial switch for dry cabinet door lock monitoring. The MEMS inertial switch is integrated into a wireless sensing node (WSN) which houses a commercial accelerometer (ADXL355) and Bluetooth Low Energy (BLE) module to detect the door lock status. Upon the acceleration generated by unlocking of the dry cabinet, the MEMS inertial switch is turned on and wakes up the BLE module and the accelerometer sequentially. After wake-up, the BLE module streams the accelerometer data to a remote PC tracking the activities on the dry cabinet door. The inertial switch successfully detects the door unlock events and wakes up the WSN to stream the accelerometer data for 2 hours. The data signature of the accelerometer is able to classify the status of the opening, closure and locking of door.
A 30-nΩ Accuracy Low Power Two-step Ratiometric Shunt Resistance Measurement System Using a Switching Regulator-based Current Generator for Shunt-based Current Sensors

This paper presents a high-precision resistance measurement system for shunt-based current sensors in battery-powered vehicles to calibrate the resistance drift of the shunt resistor due to aging. To measure the resistance of a 25 µΩ shunt resistor using a 10 Ω reference resistor with a ratio of 400,000:1, we proposed a two-step ratiometric resistance measurement technique using a second reference resistor with an intermediate resistance value. In addition to a linear regulator-based current generator (LRCG) that generates a DC excitation current for resistance measurement, a switching regulator-based current generator (SRCG) generates an AC excitation current to improve power efficiency. The implemented system demonstrates less than 0.12 % (30 nn) resistance measurement error with a 25 µΩ shunt resistor.
Unsupervised Community Discovery Algorithm via Reconstructed Graph Neural NEtwork

Community discovery can help discover potential community structures in the network, which is a fundamental and important issue in network science. Graph neural network-based algorithms are receiving increasing attention. However, there is relatively little work on community discovery directly using graph neural networks in reconstruction design. This paper presents an Unsupervised Community Discovery Algorithm based on reconstructed graph neural network (UCDA) to resolve the above issues. To be specific, this paper proposes a framework that learns the representation based on network structure and attribute information. By designing input network features, encoders, decoders, this algorithm uses an unsupervised loss function to train neural networks to extract communities in an integrated way. These designs integrate higher-order modularity information with network features and use variational graph auto-encoders to train data end-to-end under the graph neural network framework. Based on a series of experiments with a wide range of datasets and advanced algorithms, UCDA has achieved superior performance and shows strong competitiveness. Finally, the last section reports the results analysis, t-SNE visualization and hyperparameter analysis, which depicts stable performance and powerful network modularity ability.
Photochemistry and budget of ozone during the Mauna Loa Observatory Photochemistry Experiment (MLOPEX 2)

During the Mauna Loa Observatory Photochemistry Experiment (MLOPEX 2), simultaneous measurements of a large number of photochemical species were measured during different seasons at Mauna Loa Observatory (MLO), Hawaii. In this study, these measurements are used to constrain a detailed photochemical box model and evaluate our understanding of the tropospheric photochemistry in this region of the Pacific. The simulations generally reproduce satisfactorily the NO/NO 2 photostationary state, which controls the ozone production rate. However, the model fails in simulating the concentration of peroxy radicals (PO 2 ) during all seasons and of hydroxyl radical (OH) during summer. Several hypotheses are considered to assess this discrepancy, including the removal of radicals by unidentified mechanisms and the potential impact of biogenic organic compounds. None of the tested hypotheses give satisfactorily results in terms of OH, PO 2 and NO/NO 2 simultaneously. Although experimental uncertainties are large for radicals, this issue constitutes a major inconsistency between measurements and model results during MLOPEX. Another disagreement arises from the simulation of peroxides for free tropospheric conditions. The model tends to overestimate H 2 O 2 and CH 3 OOH by a factor of 1.5-2.5. On the other hand, a fair agreement is achieved in simulating formaldehyde when CH 3 OOH is constrained in the model. Finally, we find that the gross ozone production and destruction rates are nearly in balance in this region of the Pacific troposphere. The net production is slightly negative, ranging from nearly 0 in winter to about -1.4 ppbv/d during summer. In contrast, the NO x budget shows a severe imbalance. Our results indicate that an additional source of NO x ranging from 18 to 48 pptv/d (in winter and summer, respectively) would be required to sustain the 30 pptv of NO x measured on average at the site during free tropospheric conditions. Acetone has little effect on the budget of HO x at the altitude of MLO (3.4 km). However, including this species in the model induces an even larger imbalance in the NO x budget through the production of peroxyacetylnitrate.
Ground Vehicle Generalized Forces and Moment Governor Design Via Noncertainty-Equivalent Adaptive Prescribed Performance Control

Torque-vectoring technology demonstrates great potential in improving the safety and performance of ground vehicles. In this paper, a novel generalized forces and moment governor for torque vectoring is suggested. The proposed solution strategically combines prescribed performance control, noncertainty equivalent adaptive control design, and a smooth projection operator. The main advantage of the proposed control strategy lies in its capability to guarantee both the transient performance and prompt recovery of the desired deterministic behavior of the closed-loop adaptive system, even in the presence of parametric uncertainties. ASM simulation results are presented to validate the efficacy of the proposed generalized forces and moment governor and to demonstrate its superiority over a baseline solution.
Integration and Assessment of Photovoltaic Systems in Puerto Rican Communities

This paper presents an study of the integration of photovoltaic systems in Puerto Rican communities focused on the analysis of the hosting capacity of the power system and the limits regulation around the voltage profiles, thermal limits for transformers, and community benefits. For the study, two communities in Puerto Rico was considered, and a brief description of load consumption and power system characteristics are presented. For development of this research work, an open distributed system simulator was used with Matlab as Component Object Model interface. The obtained results allow to analyze the effects of photovoltaic integration in the power grid, as well as the benefits of the renewable resources installation.
Unveiling the Road Ahead: An MPC-Based Approach for Autonomous Intersection Navigation with Occlusion

Occluded intersections pose challenges and potential danger for autonomous vehicles (AVs) operating in partly unknown environments. The limited visibility caused by occlusions introduces uncertainties and risks in decision-making processes. This paper proposes a Model Predictive Control (MPC)-based motion planning approach for AVs at occluded intersections, aiming to ensure safety while maximizing travel efficiency. The proposed approach guarantees safety by considering the worstcase scenario, reducing computational complexity and avoiding subjective tolerance of collision risk. It integrates risk assessment into the MPC-based motion planning process, allowing the AV to actively learn the environment and optimize its path. By consistently updating environmental information, the AV gradually eliminates risks. Simulation results demonstrate the effectiveness of the proposed method in achieving safe and efficient navigation at occluded intersections.
Response of the Saladette Tomato of Growth Determinate to Different Sources and Doses of Calcium in Greenhouse

To determine the efficacy of formulations Ca(NO3)2 liq, Ca(NO3)2 sol-1, Ca(NO3)2 sol-2 and Ca(NO3)2 sol-3, as well as the most appropriate dose of each formulation to induce the growth and yield of tomato (Lycopersicon esculentum Mill.), the research was c.
Settling Issues in IEEE 802.1AS Networks in PI Based Clock Servos

Time-sensitive networking (TSN) is a set of standards for establishing real-time, deterministic communication in industrial applications. One of the core tasks of TSN is providing accurate and precise time synchronization between nodes by using a profile for the Precision Time Protocol (PTP). This profile defined by IEEE 802.1AS standard - often called gPTP - is restrictive regarding the synchronization method, only allowing peer-to-peer (P2P) synchronization protocol between the grand-master (GM) and slaves. Most use cases apply PI controller as a servo, which affects the overall accuracy and precision. Here we show that the increasing number of intermediate hops between the slave and the GM significantly deteriorates the synchronization accuracy. Based on a closed-loop model, we simulated the behavior of the slave clock during multi-hop P2P synchronization by investigating the step response of the system. These results were validated by measurements in real network setups using embedded devices as endpoints. Our results demonstrate that such errors cannot be completely eliminated using PI controller even if optimal tuning is used; therefore, researching and implementing other methods are recommended.
Automatically-Tuned Model Predictive Control for an Underwater Soft Robot

Soft robots have desirable qualities for use in underwater environments thanks to their inherent compliance and lack of need for exposed hardware. Nevertheless, these advantages come at the cost of considerable control challenges. Data-driven model predictive control (MPC) is an approach that has shown promise in controlling soft robots. However, manually tuning the many hyperparameters in the learned dynamics model and the optimizer can be extremely tedious. In this work, we explore using data-driven MPC to control an underwater soft robot, and employ the AutoMPC method to automatically tune the hyperparameters and generate the controller. In the process, we extend AutoMPC's capabilities to handle multi-task tuning and we add a barrier cost function to enforce actuator constraints. Our experiments show that the AutoMPC controller reaches targets with significantly higher accuracy and reliability than state-of-the-art baselines both in- and out-of-distribution of the training data.
RotICP: A Spherical-Based Distance Algorithm for Rotation Search and Point Cloud Registration

The Iterative Closest Point (ICP) algorithm is one of the most widely used point cloud registration methods with a well known problem of requiring a good initial guess to reach a satisfactory solution. In particular, the ICP may encounter issues with large initial differences between point clouds, especially regarding the rotation. We contribute a new iterative algorithm, named RotICP, which solves the challenging simultaneous rotation and correspondence search registration problem in a vastly more robust way than compared methods. The RotICP handles rotations in a natural manner using a proposed spherical-based distance measure which captures an intuitive notion of ‘rotational closeness’. We additionally prove a theorem that RotICP converges monotonically to a local minima and compare it with the classical ICP and one state-of-the-art registration algorithm on numerous simultaneous rotation and correspondence search registration problems including both synthetic and real-world data. The results show that RotICP not only handles large rotations significantly better than the other methods, but also converges in significantly less iterations than ICP while achieving state-of-the-art accuracy.
Low Creep 3D-Printed Piezoresistive Force Sensor for Structural Integration

Attempts in research to equip robotic end-effectors with tactile sensing facilitate an advanced environment perception and provide the means for dexterous interaction. Sensing at the fingertips can be realized using force sensors. In this work, we present an additively manufactured universal force-sensor offering structural integration to accomplish fast adaptation to application-specific needs. The piezoresistive sensor consists of commercially available conductive polylactic acid (PLA). Its geometry is based on rigid PLA spring elements to overcome the inherent limitations of elastomers. A curved shape increases the length of the deformation element, thus, the sensitivity, while retaining the flexibility necessary to allow for a displacement-induced change of the electrical resistance. The sensor features an additional integrated spring, which enables the adaptation of the mechanical stiffness and therefore of the measurement range. We use thread-forming screws to achieve a robust and enduring electrical connection between wires and the conductive polymer. The characterization of the sensor takes place in a universal testing machine with an applied load up to 5 N. The resistance measured gives a nearly linear characteristic and is proportional to the displacement. We obtain a sensitivity of 6.5 Ohm/N and a relative change of resistance of 6%. Low creep (0.12%) during phases with constant load reveals an advanced geometry-induced mechanical behavior. Thus, our printed piezoresistive PLA sensor demonstrates the suitability of conductive rigid materials for their tailored application as force sensors in robotics.
Joint Probabilistic Data Fusion for Pedestrian Detection in Multimodal Images

Pedestrian detection systems are one of the emerging technologies due to their wide range of applications. However, due to the dynamic environments, the data obtained from a single visual sensor for these operations is not sufficient to cover all the environmental conditions, such as varying natural light, weather conditions, etc. Therefore, the use of multiple heterogeneous visual sensors for such systems is indispensable. However, this introduces another challenge of fusing diverse data from multiple visual sensors needing precise geometric alignment. This paper focuses on late fusion based on colour and thermal images using a probabilistic approach conditioned on the environmental variables affecting the sensor data, using off-the-shelf pedestrian detectors. This eliminates the need to train detectors on large annotated dataset and provides the way to deal with the situation when one of the sensor starts degrading or is unavailable. The results in this paper outperform state-of-the-art fusion methods by improving the overall accuracy of the system and providing adaptability when one sensor is unavailable.
DEFT: Distributed, Elastic, and Fault-Tolerant State Management of Network Functions

Network function virtualization is the key to developing elastically scalable and fault-tolerant network functions (e.g. load balancer, firewall etc.). By integrating NFV and SDN technologies, it is feasible to dynamically reroute traffic to new network function (NF) instances in the event of an NF failure or overload scenario. The fact that the majority of network functions are stateful makes the task more challenging. State migration and state replication are common approaches in achieving elasticity and fault tolerance. The majority of the studies in the literature either emphasize fault tolerance or elastic scalability while designing a state management system for network functions. In this paper, we have designed a complete state management system, called DEFT, that meets both elasticity and fault-tolerance goals. Our system also supports strong consistency on global state updates. While existing designs rely on a central controller or remote central storage to achieve strong consistency on state updates, DEFT utilizes distributed consensus mechanism to achieve the same. We have done a proof of concept implementation of DEFT and extensively tested DEFT under several model conditions to evaluate its scalability and performance. Our experimental results show that DEFT is scalable and maintains a considerably high throughput throughout. It incurs minimal performance overhead while achieving strong consistency on state updates.
Making A Microwave Sandwich from Tunable Impedance Sheets for Complex Permittivity Extraction

This study presents a method for extracting the complex permittivity of an unknown material positioned between two tunable impedance sheets. By illuminating this configuration with a transceiver antenna and monitoring the changes in the resonant profile of the reflected signal over the tunable space, the complex permittivity of the unknown material can be determined. This method was investigated using pairs of inductive and capacitive impedance sheets that were used to sandwich dielectric materials with permittivities up to 10 and loss tangents up to 0.4. Here we show that inductive sandwiching sheets provide higher sensitivity with respect to loss tangent than capacitive sandwiching sheets; however, the range of detectable values is more limited. This method requires only scalar reflection measurements and therefore provides a low-cost method of wireless complex permittivity extraction.