esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Establishment of Coupled Vehicle-Bridge Vibration Equations  
### Vehicle Modelling  
Brazil and other countries have proposed an international smoothness index, IRI, by using a two-degree-of-freedom planar vehicle model for road surface unevenness tests and calculating the index from the vertical displacement of the vehicle as the two-degree-offreedom planar vehicle model passes over the roadway [27; 28]. As the vehicles travelling on the bridge in this paper are mainly integral four-axle transporters, this paper takes a four-axle, eleven-degree-of-freedom model [29]. The vehicle consists of eight wheels and a body with a total of nine rigid bodies. The four-axis space vehicle model includes the pitch motion of the vehicle body vibrating in the vertical direction, the roll motion of the vehicle body rotating in the longitudinal direction, the yaw of the vehicle body rotating in the transverse direction, and the motion of the eight wheels in the vertical direction of the vehicle body.  
As shown in Figure 1, \(a,\ b,\ c,\ d\) is the distance from each axle to the centre of mass of the vehicle body, \(I_{fy}\) is the vehicle body pitch moment of inertia, \(I_{cg}\) is the moment of inertia of the side roll of the vehicle, \(M_{1}\)\(\sim\)\(M_{8}\) is the weight of the wheels, \(M_{9}\)\(\sim\)\(M_{16}\) is the weight of each axle of the vehicle, \(M_{c}\) is the mass of the vehicle body.  
The vehicle system dynamics equations can be developed according to the D'Alembert principle:  
\[\big{[}M_{v}^{t}\big{]}\bigg{\{}\bar{Z_{v}^{t}}\bigg{\}}+\big{[}C_{v}^{t} \big{]}\bigg{\{}\dot{Z_{v}^{t}}\bigg{\}}+\big{[}K_{v}^{t}\big{]}\big{\{}Z_{v}^{ t}\big{\}}=\big{\{}F_{v}^{t}\big{\}} \tag{1}\]  
In Equation (1), \(\big{[}F_{v}^{t}\big{]}\) is the excitation force to which the vehicle is subjected, \(\big{[}Z_{v}^{t}\big{]}\) the displacement vector of the vehicle components, \(\big{[}M_{v}^{t}\big{]},\big{[}C_{v}^{t}\big{]},\big{[}K_{v}^{t}\big{]}\) the mass, damping and stiffness matrix of the vehicle.
## 2 Materials and Methods  
### System and Elements  
The hand rehabilitation system consists of a robotic hand orthosis, the NIRS system, and operating personal computer (PC) with a monitor, as shown in Figure 1a. A user, sitting on a comfortable chair in a relaxed manner, performs (or imagines) a hand motion based on the instructions provided on the monitor, located at a 50-60 cm distance from the user's eyes. The NIRS system measures local cortical activation in real-time and transmits the data to the operating PC. The operating program on the PC processes the data and classifies it into one of two hand motion states: hand-closing and hand-opening. Then, the classification output is wirelessly transmitted to the orthosis. The operation program is coded and implemented with MATLAB(r). A system demonstration is available in Video S1 (please see Supplementary Materials).  
Figure 1: System Configuration and Experimental Design: (**a**) System Configuration. The system consists of three basic components: a near-infrared spectroscopy (NIRS) system, a robotic hand orthosis, and an operating personal computer (PC). The NIRS system measures changes in local [Hb] in the motor cortex and sends them to the PC. The PC detects hand motor intentions from the NIRS data and wirelessly triggers the robotic orthosis; (**b**) Experimental Design. One session had a resting state period and a task period comprising 15 repeated unit trials. A unit trial was a sequence of ready, hand-closing, and hand-opening periods. The duration of the whole session was 810 s.
## 2 Materials and Methods
If a sudden manoeuvre is performed by the pilot or an external perturbation occurs, such as a strong gust of wind, the slow response of the ICU to provide the new additional power is dampened by using the energy stored in the capacitors array. Once flight conditions demand less energy, the FPGA activates the two-way converter to recharge the SCs. This process continues during the whole flight. The charging of SCs can be done extremely quickly in comparison to batteries, and these devices are capable of operating for a million cycles. The use of a backup battery in case of an ICU fault is not possible if the hybrid generator, as in this case, provides a high output power. Unfortunately, no commercial 12S LiPo batteries exist that are capable of providing 155 A without an unsustainable voltage drop. Moreover, such a battery's weight would be unacceptable. More than 300 tests were performed in our lab on all types of Li-Ion polymers and Li-Ion batteries. Only FoxtechFPV's solid batteries can deliver this level of current. They are only manufactured for large capacities from 16 Ah to 30 Ah. Thus, their use in this application was impossible. Regarding this issue of Li-Ion polymer batteries, Figure 8 shows the results obtained during a test at 90 A discharge on an 18 Ah 65 battery. The selected manufacturer was the best and most known, GENSAGE(tm) (Genspow GmbH Ottostr.11 41352 Korschenbroich, Germany) [29]. The tested battery belonged to their third generation of UAS batteries with integrated electronics. A huge voltage drop can be observed in the graph, which clearly prevents its use as a backup system. In fact, after two years of tests on batteries, we could conclude that, for capacities above 12 Ah, the maximum discharge rate, with an acceptable voltage drop and temperature increase, is around 6 \({}^{\circ}\)C.  
A simple redesign of the power balance in the prototype, increasing stored energy with the generation of SCs and adding a fast charge prior to flight during the heating up of the motor, might provide the pilot with enough energy to perform a safe emergency landing. The prototype presents two operation modes: the manual selection of the flight envelope, when the pilot decides the maximum level of throttle variation, and adaptative control. The first is included to decrease or even suppress the variation of throttle range for flights under very harsh conditions, such as those at high altitude or with strong winds. The system is completed with a 433 MHZ on-board transmitter. An XBEET(tm) SX 868 module  
Figure 8: Discharge test of a third-generation GENSAGE battery. Constant 90 A, 5 \({}^{\circ}\)C equivalent. The impossibility of this being used as a propulsion battery, even at 5 \({}^{\circ}\)C, is clear, although the manufacturer’s specifications indicate a maximum discharge of 15 \({}^{\circ}\)C.  
was selected, since it is low-cost, light, small and long-range. This communication system was installed in the ground control station to receive information about the temperature, fuel consumption and level of charge of the energy storage system by means of an OSD system.
# The SF12 Well in LoRaWAN: Problem and End-Device-Based Solutions
## 2 LoRaWAN Main Features
### LoRaWAN Overview  
LoRaWAN is a wireless technology that offers long range (often in the order of kilometers) [6] while supporting low energy consumption (e.g., allowing multiyear lifetime for battery-operated devices) [14]. As in other LPWAN technologies, long range is achieved at the expense of reduced communication capacity. However, this feature does not pose a problem for many IoT use cases.  
The LoRaWAN network architecture comprises three main types of network entities: end-devices (EDs), gateways, and a network server (NS). These elements are organized in a topology known as the _star of stars_[15]. The EDs typically correspond to constrained devices such as sensors. The EDs transmit LoRaWAN messages to the NS as the destination through one or more gateways. This type of message transmission is known as uplink transmission. In the opposite direction, i.e., in the downlink, the NS may transmit LoRaWAN messages to the EDs through only one gateway. Communication between the EDs and the gateways is carried out by means of a physical layer called LoRa. The gateways and the NS are connected by means of an IP-based network, while the gateways forward LoRaWAN messages between the EDs and the NS (Figure 1) [14].  
LoRaWAN defines three classes in terms of supported features and functionality: class A, class B, and class C. Class A, which is also referred to as basic LoRaWAN, is mandatory for all LoRaWAN devices. In this class, and for the sake of energy saving, downlink transmission is only allowed in time intervals called receive windows, which are subsequent to an uplink transmission. Class B is defined on the basis of class A, offering additional downlink transmission opportunities at times which may be scheduled a priori. In contrast with class A and class B, class C offers no constraints for downlink transmission. However, the class C EDs cannot turn off their radio interface and are not suitable for devices with a constrained energy source. Most LoRaWAN devices implement only class A features as the other classes are optional.  
This paper assumes that class A is used in a LoRaWAN network. The next two subsections overview the main features of the class A LoRaWAN physical layer and the MAC layer, respectively.
## 3 Experiments and Results
### Tooth Identification through YOLOv5  
For integrating the AIHub and CBNUH data, the following procedures were performed: First, the varying image sizes of these data were adjusted to the standard size. As the AIHub data are in the JSON format containing the pixel coordinates (x, y) and the CBNUH data are in the csv format of YOLOv5, the integration required conversion to the YOLOv5 annotation format, the result of which is presented in Figure 6.  
To calculate the tooth number and tooth length, the YOLOv5 algorithm was used. As shown in Figure 7, each tooth was numbered, and through the semantic segmentation of each tooth, instance segmentation was performed. Assuming the image height at 100 mm, the tooth length was converted and then calculated as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** & **Loss** & **Validation Accuracy** & **Validation Loss** \\ \hline PBL & 0.9856 & 0.0132 & 0.9813 & 0.0306 \\ \hline CEJ & 0.9891 & 0.0096 & 0.9864 & 0.0213 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Predictive accuracy and loss of PBL and CEJ using U-Net.  
Figure 5: Accuracy of the CEJ boundary detection model.  
Figure 6: Annotation conversion from JSON to YOLOv5 for AIHub and CBNUH data integration.  
Using the YOLOv5 algorithm, a model was built for the identification of the tooth number and tooth length for the AIHub data. For the detection of the PBL and CEJ on the CBNUH data through U-Net, the semantic segmentation to define the category of each pixel was performed. After integrating the models, the stage of each tooth was classified based on the lengths of the PBL and CEJ and tooth length.
## 3 Model and CFD Methods
### Simulated Restraint Model Test
#### 3.4.2 Pure Heaving  
The pure heaving motion of a submarine refers to the motion along its centerline on the vertical plane being uniform while simultaneously having a vertical displacement. The submarine's heading remains unchanged and undergoes a sinusoidal motion with zero pitch angle. Figure 12 illustrates the concept of pure heaving motion.  
Figure 11: Oblique towing test results. (**a**) Angle of attack (\(\alpha\)) against vertical force (Z); (**b**) angle of attack (\(\alpha\)) against pitching moment (M).  
Figure 12: Model trajectory during the pure heaving test.  
The expression for simulating pure heaving motion is as follows:  
\[\left\{\begin{array}{l}\zeta=a\sin\omega t\\ \theta=\dot{\theta}=0\\ w=\dot{\zeta}=a\omega\cos\omega t\\ \ddot{w}=-a\omega^{2}\sin\omega t\end{array}\right. \tag{26}\]  
where \(\zeta\) represents the vertical displacement of the submarine at any given moment, \(a\) is the amplitude of the motion, \(\omega\) is the circular frequency of the motion, \(\theta\) is the pitch angle, and \(\dot{\theta}\) is the angular velocity. Additionally, \(w\) and \(\dot{w}\) represent the vertical velocity and acceleration of the submarine.  
The submarine model has a motion with a frequency of \(f\) = 0.2 Hz and an amplitude of a = 0.15 m. The simulation calculated data for 6 complete cycles and focused on the stable data from the 2 nd to the 3 rd cycle. The vertical force (Z) and the pitching moment (M) acting on the underwater body were monitored, with Figure 13 presenting the non-dimensional time history curves for Z and M in pure heaving motion.
## 4 Case Study
### Feature Extraction from Motor Control Signal
The rest of the figures explain the process of estimating them. For example, in the case of the HD, the frequencies of the unique scores for all of the features are shown in Figure 11c. Using this information, the weighted finite differences defined by Equation (12) are computed, as shown by the curves in Figure 11e, from which the local peaks defined by Equation (14) are identified by the triangles and corresponding percent values. Among these, the index and its score, with a maximum of 450%, are found to be 76 and 0.87, which are marked with a red circle in Figure 11e and  
Figure 9: Wavelet coefficients of (**a**) normal condition; (**b**) HD at \(Lv_{2}\) fault condition; (**c**) TB at \(Lv_{3}\) fault condition; and (**d**) both HD and TB at \(Lv_{2}\) fault condition. constitute the threshold in Figure 11a,c. Through this process, four features are selected for the HD, which are those higher than the threshold in Figure 11a. Figure 11: Threshold for (**a**) HD fault score and (**b**) TB fault score. Unique score frequency of (**c**) HD fault and (**d**) TB fault. Weighted difference of (**e**) HD fault score and (**f**) TB fault score. Figure 10: Wavelet energy of each decomposition band, where HD fault severities are divided by vertical dotted lines and TB fault severities are indicated by the color of the dots. In the final step, the redundancies between the selected features are checked by computing the Pearson correlation for a combination of pairs, in which those with a correlation higher than 0.9 are removed from the selection. As a result, the features are reduced to only one, which is W1-STD of the current signal, namely, the STD of the wavelet coefficient in the frequency band (62.5-125 Hz). The same process is applied to the TB to obtain Figure 11b,d,f, from which the features without redundancy are reduced to four features (W0-SF of the velocity, W7-MF, W7-Peak, and W7-SE of the current signal). To illustrate the importance of the feature selection, plots of the best and worst features in view of the score are shown in Figure 12. In the case of the HD, the number of selected features is one, where the best is W1-STD of the current and the worst is W3-Mean of the velocity. Their histograms are shown in Figure 12a,b. In the case of the TB, the number of features is four, which are W0-SF of the velocity, W7-MF, W7-Peak, and W7-SE of the current. In this case, for the sake of visual comparison, t-distributed stochastic neighbor embedding (t-SNE) is used for the selected features, which is useful for visualizing the high-dimensional data by embedding the n-dimensional original feature space into the two-dimension [30]. The best four features are compared against the worst ones: W2-CF, W5-SK, W7-Mean, and W1-CF of the current signal, as shown in Figure 12c,d, respectively. The results of the HD and TB features all indicate that the selection of good features is important to the aspect of fault severity identification.
## 3 Results Simulation Analysis and Optimization
### Optimization of Multi-Ring Elliptical Core Parameters
To obtain a larger min \(\Delta n_{\text{eff}}\) and better-designed tolerances, we chose the points \(a_{1}\) = 3.3 \(\upmu\)m (i.e., \(b_{1}\) = 2 \(\upmu\)m) and \(a_{3}\) = 9 \(\upmu\)m (i.e., \(b_{3}\) = 4.45 \(\upmu\)m) in the region as the parameters of the elliptical core. This parameter is marked by a red dot in Figure 5a. The effective refractive index difference between adjacent eigenmodes is greater than 2.58 \(\times\) 10\({}^{-4}\). Figure 3: Variations of (a) the min \(\Delta n_{\text{eff}}\) and (b) mode number as a function of \(a_{2}\) and \(e\). Figure 2: Schematic cross-section of the BT-MREC-MMF. We investigated the influence of the core parameter \(\Delta n_{1}\) on \(\Delta n_{\text{eff}}\) at \(a_{1}\) = 3.3 \(\upmu\)m, \(a_{2}\) = 6.1 \(\upmu\)m, \(a_{3}\) = 9 \(\upmu\)m, \(a_{4}\) = 12 \(\upmu\)m, \(\Delta n_{2}\) = 0.018, \(\Delta n_{3}\) = 0.005, \(r_{1}\) = 20 \(\upmu\)m, \(h\) = 8.9 \(\upmu\)m, \(\theta\) = 90\({}^{\circ}\), and \(e\) = 1.65, as shown in Figure 6. It can be seen that the min \(\Delta n_{\text{eff}}\) first increases and then decreases with increasing \(\Delta n_{1}\). Min \(\Delta n_{\text{eff}}\) is larger than 2.3 \(\times\) 10\({}^{-4}\) within the range of 1% \(\leq\Delta n_{1}\leq\) 1.48%. Here, we took \(\Delta n_{1}\) = 1.3% to keep supporting 22 fully separated eigenmodes in the whole C+L+U band. This corresponds to a 13 % mol fraction of GeO\({}_{2}\) doped in SiO\({}_{2}\). Adding a trench structure can effectively reduce bending loss and obtain a larger design tolerance at the same time [32]. We fixed \(a_{1}\) = 3 \(\upmu\)m, \(a_{3}\) = 9 \(\upmu\)m, \(a_{4}\) = 12 \(\upmu\)m, \(\Delta n_{1}\) = 0.012, \(\Delta n_{2}\) = 0.018, \(\Delta n_{3}\) = 0.005, \(r_{1}\) = 20 \(\upmu\)m, \(h\) = 8.9 \(\upmu\)m, \(\theta\) = 90\({}^{\circ}\), and \(e\) = 1.65, and adjusted \(a_{4}\) and \(\Delta n_{3}\) accordingly, to optimize min \(\Delta n_{\text{eff}}\) between adjacent eigenmodes. The effect of \(a_{4}\) and \(\Delta n_{3}\) on min \(\Delta n_{\text{eff}}\) and the number of supported eigenmodes are shown in Figure 7. Within the region of 10.25 \(\upmu\)m \(\leq a_{4}\)\(\leq\) 13 \(\upmu\)m and 0.0055 \(\leq\Delta n_{3}\)\(\leq\) 0.009, the proposed fiber supports 22 eigenmodes, and min \(\Delta n_{\text{eff}}\) between adjacent eigenmodes is larger than 2.3 \(\times\) 10\({}^{-4}\). The trench has a significant effect on lifting min \(\Delta n_{\text{eff}}\) between adjacent eigenmodes. Considering the feasibility of the fabrication process, we chose the parameters of the red dot (\(a_{4}\) = 11 \(\upmu\)m and \(\Delta n_{3}\) = 0.7%) for further optimization.
## 1 Introduction
The outcome of CS-SR is two-fold. (a) Offering a QoS-based service selection, (b) reducing overall execution time required to find optimal service;
2. The filtration phase will reduce unnecessary comparison by filtering out candidate services;
3. The proposed approach makes use of quantitative and qualitative attributes that will improve the overall efficiency of our selection and recommendation approach;
4. The proposed CS-SR is compared with the analytical hierarchy process (A.H.P.) [38], efficient non-dominated sorting-sequential search (ENS-SS) [39], and best-worst method (B.W.M.) [40] on the performance parameter: total execution time and the energy consumption used in selecting and recommending the cloud service. The result shows that CS-SR outperforms the compared method. The three existing algorithms are chosen because all three algorithm deals with multi-criteria decision making and finds the optimal solution among the list of available solutions. The research is based on providing energy-efficient cloud service selection recommendation system. The proposed work, in general, is a step toward saving energy through the cloud servers at large and a step toward efficient cloud service selection and ranking system. The research paper's main objective is to improve the performance of the proposed algorithm CS-SR in two areas; energy consumption and execution time. Multiple studies in the past have presented negative impacts of data centers on climate change. It is believed that the carbon footprint of data centers is equivalent to that of the aviation industry. According to a report, there will be approximately 7.2 million operational data centers by 2021 across the world. Talking in terms of heat emission, data centers could contribute 3.2% of the total worldwide carbon emission by 2025 [41]. Therefore, the energy-efficient mechanism proposed in our manuscript ensures a reduction in carbon emission and supports sustainable smart cities. This paper is planned as follows: Section 2 briefly discusses different approaches proposed by authors. In Section 3, the CS-SR architecture is proposed. Section 4, the CS-SR algorithm, is presented. In Section 5, an illustration of the working of the CS-SR approach with an example is given. In Section 6, the implementation and analysis of the proposed algorithm are discussed. Finally, Section 7 provides an inference drawn, limitation, and future research ideas.
###### Abstract  
Due to their exceptional electrical properties, carbon nanotubes (CNTs) can be applied as conductive fillers to develop self-sensing cement-based matrices. In order to obtain an adequate self-sensing response, CNTs must be evenly dispersed through the cement matrix in a volume sufficient enough to create an electric percolation network. This is challenged by the difficulty of dispersing CNTs; therefore, there is a demand for an efficient dispersing agent that can be filled by superplasticizers, which are products of known compatibility with cement and high availability. This research explores the use of four commercial superplasticizers available in Brazil, both naphthalene and ether polycarboxylate-based, as dispersing agents for CNTs in water. Ultrasonic energy was applied to aqueous solutions containing CNTs and superplasticizers. UV-Vis spectroscopy and \(\xi\)-potential measurements were used to investigate which superplasticizer was more effective to disperse the CNTs. Cement pastes were produced with the CNT dispersions and their electrical resistivity was measured. It was found that only superplasticizers without aliphatic groups in their structure were capable of dispersing CNTs in water. It was concluded that second-generation naphthalene-based superplasticizers were more efficient dispersing agents for CNTs than third-generation ether polycarboxylate-based ones for self-sensing applications.  
carbon nanotubes; dispersion; superplasticizer; resistivity Article