esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Adhesion and Hysteresis Friction of Rubber-Pavement System
## 7 Test Results and Discussion
### Adhesion Friction

Table 5: Measured surface energy of rubber materials using the Sessile drop device. Figure 8: Adhesive bond energy between rubbers and aggregates materials. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multicolumn{3}{c}{**Surface Energy Components (ergs/cm\({}^{2}\))**} \\ \cline{2-5}  & \(\gamma^{\text{LW}}\) & \(\gamma^{+}\) & \(\gamma^{-}\) & \(\gamma^{\text{Total}}\) \\ \hline
**Limestone** & 69.35 & 0.28 & 1075.40 & 104.18 \\
**Gabbro** & 57.37 & 3.34 & 6277.96 & 346.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Measured surface energy of aggregates using universal sorption device (USD) device, (adapted from [20]). The coefficient of friction between rubber sliders and HMA substrates was measured using the DFT at low speed (20 km/h) in dry conditions. The adhesion component of the friction is dominant at low speed. Figure 9a shows the relationship between the adhesive bond energy and the coefficient of friction for gabbro and limestone separately, while Figure 9b shows the relationship between the coefficient of friction versus the adhesive bond energy between test rubber materials and all the test HMA substrates (both gabbro and limestone). It was found that there is a fair correlation between the adhesive bond energy and measured coefficient of friction. This relationship demonstrates that higher adhesion between rubber and pavement surface increases the coefficient of friction at low speed as one expects; however, this study provided an experimental verification to such relationship. In the meantime, we believe that this relationship is affected by two experimental limitations. First, the asphalt binder film was not fully removed from the aggregates on the surface. We attempted to minimize this effect be removing most of the film thickness by polishing the slab using the three-wheel polisher for 5000 cycles until the aggregate particles surface is exposed. The application of more cycles was found to polish the aggregates which should be avoided since the surface energy components of aggregate will change with change in the texture of the surface. Second, the surface energy components of rubber were calculated based on the contact angle between smooth rubber samples and probe liquids. Figure 9: Measured coefficient of friction vs. the adhesive bond energy for (**a**) limestone and gabbro slabs separately (**b**) both gabbro and limestone slabs. Meanwhile, we observed that the surface of rubber sliders, used in DFT, was not completely smooth due to cutting the rubber sheet to prepare the sliders. These two limitations may contribute to this fair correlation between adhesive bond energy and measured coefficient of friction at low speed.
# Recent Advances of Exciplex-Based White Organic Light-Emitting Diodes
Nowadays, many approaches have been reported to develop high-performance WOLEDs, including managing singlet and triplet excitons for hybrid structures [72; 73; 74; 75], harvesting triplets from blue fluorophores with high T\({}_{1}\)[76; 77; 78; 79; 80], having charge- and exciton-confining structures to reduce the efficiency roll-off [81; 82; 83], combining blue/green/red phosphors with light-outcoupling enhancement techniques [84], adopting color down-conversion media [85], comprising ultrathin emitting layers (EMLs) [86; 87; 88; 89; 90], using tandem architectures [91; 92; 93; 94; 95], employing excimer systems [96; 97; 98], eliminating interlayer in hybrid WOLEDs [99; 100], and exploiting double multifunctional blue EMLs [101]. Among them, the use of exciplexes in WOLEDs has been actively investigated, since exciplexes can be effectively functioned as (i) fluorescent or TADF emitters; (ii) the hosts of fluorescent, phosphorescent and TADF dopants [102; 103; 104; 105; 106; 107; 108; 109; 110]. At the early stage, the efficiency of exciplex-based WOLEDs is poor. With the understanding of TADF exciplexes [111], the performance of exciplex-based WOLEDs has been remarkably enhanced. As an excellent result, the PE of exciplex-based WOLEDs can be \(>\)100 lm W\({}^{-1}\)[112], which is comparable to that of state-of-the-art WOLEDs. On one hand, when the exciplex is acted as an emitter, the molecular emitter is unnecessary. If the donor and acceptor of the exciplex are also used as the hole transport layer (HTL) and electron transport layer (ETL), respectively, the number of evaporation sources can be reduced. On the other hand, when the exciplex is functioned as the host, the charge balance in the EML can be well regulated by changing the ratio of the donor and acceptor. Besides, by selecting appropriate donor and acceptor materials, the energy difference between the exciplex host and guest can be small enough, and the energy level of the donor and acceptor can well match with the HTL and ETL, respectively. Therefore, the unique merits of exciplexes render that them have been aggressively investigated in WOLEDs. Herein, the fundamental concepts of exciplexes and their use in OLEDs will be first introduced. Then, the primary strategies to develop exciplex-based WOLEDs will be concluded. In particular, representative WOLEDs using exciplex emitters or hosts will be emphasized. More specifically, the design strategy, device architecture, emission mechanism as well as electroluminescence (EL)procedure for exciplex-based WOLEDs will be presented. In the end, an outlook for the future development of exciplex-based WOLEDs will be proposed.
## 3 Incident Detector Using Big Data Analytics and Neural Networks
### Other Approaches for Road Accident Detectors
#### 3.1.3 Detecting Road Accidents Using Traffic Management  
Most traffic accidents take place in metropolitan areas, where there is a larger concentration of road users who are more prone to injury and mortality in the case of an accident, as well as a more complex traffic environment. Reducing speed restrictions is a reasonably simple and economical traffic management strategy. In the literature on traffic safety, the association between vehicle speed, accident risk, and accident result seriousness is well established [51].  
Figure 5: Real-time GPS tracker system.  
The goal of traffic management is to affect supply and demand in traffic in a way that improves the temporal and spatial alignment between traffic demands and the network's capacity supply. The issues with the road network are primarily related to certain bottlenecks and times (i.e., peak hours, incidents, and events). The intelligent transportation system (ITS) can provide safety, effectiveness, and sustainability for critical complications with vehicle traffic on a broad scale. ITS uses machine learning with the current traffic control system to ensure traffic flows smoothly, and it offers a real-time strategy [24].  
Additional applications include real-time transportation management, ridesharing design, implementation, and management, and the scheduling of logistical systems and freight transportation planning and predicting, as well as the administering of transportation policy [122]. Others include environmental, toll-road, dependability, and safety-related questions about operating transport systems. Moreover, the utilization and the management of emerging technologies analysis, forecasting, and planning of travel require travel information systems and services technology for the transportation and environmental intelligence analysis and simulations of pedestrian and herd behavior. The planning for sustainable mobility in cities requires vehicles with service-oriented design and those in a communication infrastructure, the review and assessment of transportation technologies, and simulated and artificial transportation systems [123].
# Model-Based Slippage Estimation to Enhance Planetary Rover Localization with Wheel Odometry
## 5 Summary  
In this paper, we presented a method to determine the wheels' slippage through an accurate modeling of the rover's dynamical equations. This technique enables a model-based correction of WO measurements that are significantly affected by non-systematic effect associated with the terrain properties. An accurate model of the soil is fundamental to enhance the navigation of rovers across off-road terrains. The interaction forces between the rover's wheels and the soil are used to predict the slippage condition that affect the accuracies of WO data. We modeled these forces by assuming compliant wheel on compliant ground.  
Figure 16: Reconstructed speed and yaw rate through the calibration vs simulated vehicle’s speed and yaw rate.  
Figure 15: Slope angle used in the slip prediction method.  
Numerical simulations of different mission scenarios were carried out by considering a 3D model of the rover that operates on a Martian-like surface. We analyzed the rover's trajectory evolution by accounting for different terrain types and conditions. Homogeneous and constant soil characteristics are assumed across the rover's path. Our results are consistent with ExoMars expectations in terms of traversability of steep terrains, showing 21\({}^{\circ}\) and 26\({}^{\circ}\) slope limits for very fine sand terrain and coarse sand, respectively.  
The dynamical equations are integrated in a localization system based on the processing of WO and IMU data only. We presented a method to correct the WO measurements by predicting the wheels' slippage. This technique enables significant improvements in the estimates of the WO-based localization system. By perturbing the terrain properties in the dynamical model, we demonstrated that our method is robust to a partial lack of knowledge regarding soil physical parameters and slope angle. This method only requires a rough a priori characterization of the environment that could also be based on measurements collected by orbiters.  
Conceptualization, A.M.G., Id.S. and A.G.; methodology, A.M.G., Id.S. and A.G.; software, A.M.G.; validation, A.M.G., Id.S. and A.G.; formal analysis, A.M.G., Id.S. and A.G.; investigation, A.M.G., Id.S. and A.G.; data curation, A.M.G., Id.S. and A.G.; writing--original draft preparation, A.M.G., Id.S. and A.G.; writing--review and editing, Id.S. and A.G.; visualization, A.M.G., Id.S. and A.G.; supervision, A.G.; project administration, A.G. All authors have read and agreed to the published version of the manuscript. This research was supported by the _Rita Levi Montalcini Programme_ of the Italian Ministry of Education (MUR).  
Not applicable.  
The method presented in this work is based on algorithms implemented in C++ and MATLAB as plugin-based interface to Gazebo simulator [38]. Available online: [[http://gazeboim.org](http://gazeboim.org)]([http://gazeboim.org](http://gazeboim.org)) (accessed on 1 June 2020).  
The authors declare no conflict of interest.
## 2 Challenges and State of the Art  
This section explores the key limitations and challenges faced by current certification schemes, as well as recent advances aimed at addressing these gaps.
## 5 Simulation Results  
We built up a C-based simulator to evaluate the SNR and the power consumption of the SOA-enabled mesh-based HONoC in comparison with an HONoC without SOAs. The typical values of insertion loss and crosstalk coefficient for system analysis are presented in Table 3. The chip size of 16 \(\times\) 16 HONoC is assumed as 1 cm\({}^{2}\) and in other cases, the chip sizes are assumed to be proportional to the number of cores. It is reasonable to suppose that the chip size of the SOA-enabled HONoC is the same as that of the conventional one, as the SOA is small enough to be embedded in a waveguide without widening the space between photonic devices. The traffic patterns are generated using modified BookSim [26] and Crux router is deployed because it is optimized for insertion loss and SNR in the mesh topology.
## 2 Related Work
In [46], a hybrid technique having adaptive DE (ADE) and BPNN, termed ADE-BPNN, is developed to enhance the accuracy of BPNN. To validate the effectiveness of the proposed technique, two real-world time-series data sets are used. The results show that the proposed approach gives higher efficiency. The authors in [47] proposed an improved differential evolution BA algorithm by combining BA algorithm with DE for optimizing NN for optimizing fuzzy NN to predict the traffic of the network. Due to the problems of DE such as the movement of premature convergence and inactive convergence rate, Gaussian disturbance crossover operator and adaptive mutation operator are used in an introduced variant of DE. The improved operators are utilized to design the crossover parameter and enhance the mutation of traditional DE. To test the performance of the proposed modification, four traditional functions and network traffic used. The results describe that the proposed variant of DE improves the prediction accuracy and the convergence rate as compared to other techniques. For classification, a modified DE-trained Pi-sigma network (PSN) is presented [48]. The presented technique is an enhanced version of a novel mutation and standard DE/rand/1/bin algorithm and, in addition to this, crossover approaches utilized with regards to both exploitation and exploration. The presented approach for pattern classification is validated through three famous real-world classification problems taken from the UCI repository. The experimental results of the presented method is verified with two well-known algorithms: chemical reaction optimization and DE/rand/1/bin algorithms. The results depict that the presented approach is superior.
## 2 Channel Propagation Characteristics
### Path Loss in Wireless Communications  
Path loss describes the attenuation or loss a wave (or electromagnetic signal) goes through as it moves from the transmitter to the receiver [21]. The received power is therefore less than the broadcast power [21]. Nevertheless, several pertinent factors come into play, including antenna gains, operating frequency, transmitted power, and the separation distance between the transmitter and the receiver [22]. Decibels (dB) are the most popular unit of measurement for path loss [23].  
Fortunately, the short wavelengths of mm-wave signals allow for the integration of numerous antenna elements into a small area, resulting in significant spatial processing improvements that, in principle, can at least offset the related isotropic path loss [10].  
Using a combination of computational algorithms [24] and approximations based on empirical measurements obtained from channel-sounding experiments, path loss models are created. In general, propagation path loss rises with both frequency and distance, shown as follows [25]:  
\[P_{l}=10\text{log}_{10}\bigg{(}\frac{16\pi^{2}d^{n}}{\lambda^{2}}\bigg{)}, \tag{1}\]  
where \(P_{l}\), \(d\), \(n\), and \(\lambda\) represent the path loss, length of the path between the transmitter and receiver, path loss exponent [26], and the free space wavelength in meters [25], respectively.  
As we can see, (1) can thus be transformed into (2), as follows [27]:  
\[P_{l}(f,d)=32.5+10n\text{log}_{10}(d)+20\text{log}_{10}(f) \tag{2}\]  
where \(d\) and \(f\) represent the distance between the Tx and the Rx in km and the frequency of operation in MHz [28].  
In the mm-wave spectrum, a variety of propagation mechanisms contribute to multipath propagation [29], but their significance differs from that in frequency bands below 6 GHz [30]. The measured path loss can thus be written as follows [30]:  
\[Path\ loss,\ P_{l}\ in\ dB=EIRP-R_{p}, \tag{3}\]  
where \(R_{p}\) and \(EIRP\) are the received power in dBm and the effective isotropic radiated power, which can thus be written as follows [30]:  
\[EIRP=P_{T}+G_{T}+G_{R}-C_{l}-A_{l}-A_{fl}\, \tag{4}\]  
Figure 2: Propagation mechanism effect along a terrestrial path.  
where \(P_{T}\), \(G_{T}\), and \(G_{R}\), are the transmitting power in dBm, the gain of the transmitting antenna, and the gain of the receiving antenna, and \(C_{I}\), \(A_{I}\), \(A_{I1}\) represent the losses that comprise the feeder cable, antenna, and filter, respectively.  
5G cellular has been the focus of considerable study and development over the past few years because it promises to greatly enhance data throughput, has very low latency, and offers connectivity capabilities to support and connect a new array of devices. Trial and commercial system deployments are happening right now in various regions of the world, especially at frequencies below 6 GHz.
## 2 Materials and Methods
### Data Acquisition
#### 2.2.1 Pulsed Thermography (PT)  
Figure 2a presents a view of the experimental setup for the PT tests in transmission mode (used for the estimation of the thermal diffusivity of the samples as discussed in Section 2.3.4). For the reflection mode (not shown) the flashes are placed between the camera and the specimen. A high-definition cooled IR camera (FLIR X8501 sc, NedT < 20 mK, InSb, 3-5 \(\upmu\)m, 1280 \(\times\) 1024 pixels, linear Sterling cooling, lens focal length = 50 mm) and two xenon flashes (Balcar, 6.2 kJ per flash, pulse duration 2 ms @ FWHM) are used and synchronized through a PC. The camera is positioned 60 cm from the specimens and the flashes are placed at a distance of 25 cm from either the back or the front side of the specimens depending on the test mode (transmission or reflection, respectively) forming a 45\({}^{\circ}\) angle with respect to the sample surface. Samples were inspected one at a time (S44, S45, S48), in reflection and transmission modes. In all cases, thermographic sequences of 2000 frames at 42 fps were collected and processed as described in Section 2.3.1.
# Development and Control of an Electro-Hydraulic Actuator System for an Exoskeleton Robot
## 1 Introduction
Because the hydraulic circuit is simple, it is easy to manage leakage, and manufacturing costs are reduced. Therefore, it has been applied to various fields [10; 19; 20]. Therefore, the EHA system applied to the exoskeleton robot can solve the problems of the electric exoskeleton robot and the hydraulic exoskeleton robot. Because the robot's knee joint is equipped with a cylinder, not a motor, it does not need an additional reducer. Also, the hydraulic circuit is simple. As a result, the size and weight can be reduced, and the maintenance is easy. Therefore, in order to solve the problems of the conventional hydraulic exoskeleton robots, it is necessary to study an EHA that is applicable to robots with a bidirectional piston pump. In this paper, we developed an EHA system for an exoskeleton robot considering volume, size, and weight. We designed the controller and verified its performance. First, a bidirectional piston pump was designed to overcome the limitation of the gear pump. The piston pump is advantageous for high-speed rotation and can obtain high pressure compared with the gear pump. Because the wearer lifts heavy weight on the back and moves, the hydraulic system needs to be minimized in weight and volume. For this development, we analyzed the gait cycles of ordinary people using data relative to gait analysis. Based on the results, we determined the stroke length of a hydraulic cylinder and the discharge flow rate of the pump. Also, a hydraulic pump was designed considering the pulsation rate, which affects the performance of the hydraulic system. In addition, we designed the hydraulic circuit with a simple structure and made one manifold in many valves to make them smaller and lighter. Therefore, the EHA system was developed by integrating the motor, pump, and hydraulic valves into one system. Because this hydraulic system has many uncertainties, robust control is required to minimize the effect of nonlinearity. As a result, we proposed sliding mode control for this and designed the control based on the mathematical modeling of the EHA system. So, the flow of the paper is shown in Figure 1 and its composition is as follows. The developed axial piston pump is presented in Section 2, and the hydraulic circuit is presented in Section 3. Additionally, we modeled the system mathematically, and this is presented in Section 4. Finally, we designed a controller and verified the performance of the proposed EHA system via experiments, which is presented in Section 5.
# A One-Class-Based Supervision System to Detect Unexpected Events in Wastewater Treatment Plants
## 2 Materials and Methods
### One-Class Techniques
#### 2.3.3 The K-means Algorithm  
The K-means algorithm, an unsupervised technique widely employed in various domains like machine learning, image processing, and pattern recognition [39], aims to partition the dataset into clusters containing data points with similar characteristics [39].  
Figure 4: Cluster division depending on the value of \(K\).  
Given a set \(X=x_{1},x_{2},\ldots,x_{N}\), where \(x_{i}\in\mathbb{R}^{n}\), the K-means algorithm partitions the data into \(K\) subsets \(G_{1},G_{2},\ldots,G_{K}\), each associated with centroids \(C=c_{1},c_{2},\ldots,c_{K}\), where \(c_{j}\in\mathbb{R}^{n}\). This partitioning is performed based on a clustering error criterion [40], typically computed as the sum of Euclidean distances between each point \(x_{i}\in\mathbb{R}^{n}\) and its centroid \(c_{j}\).  
The application of this algorithm using a one-class approach is based on the distance from test instances to their nearest centroid. It is considered anomalous if this value is greater than the maximum distance registered for that cluster during the training stage. Figure 6 shows an example in \(\mathbb{R}^{2}\) where the training set is grouped into two clusters. Then, the green dot represents a test instance that is labeled as normal because the distance to its nearest centroid is below the maximum distance of that cluster. On the contrary, the long distance from the red dot to its centroid reveals the anomalous nature of the test instance.