esa-sceva/satcom-chunk-collection huggingface dataset:
# Fracture Prediction in Weldox 700E Steel Subjected to High Velocity Impact Using LS-DYNA
## 3 Results and Discussion
### Investigation of Fracture Behaviour
#### 3.3.5 Fracture Behaviour with Varying Projectile Mass  
Now, the model is applied to analyze the fracture behaviour with varying projectile mass. The target plate thickness is 4 mm, and projectile velocity is kept at 400 m/s. The investigation is performed with varying projectile mass viz. 6.4 g and 12.8 g, while the other process parameters remain the same. The material and fracture constants are given in Section 2.3.  
1. Plug formation  
Figure 33 shows the plug formation in the target plate with varying projectile mass. The plug formation is smoother in the case of the lower mass projectile compared to the higher mass projectile. The kinetic energy is greater with higher projectile mass, and therefore, the fracture behaviour is altered.  
Figure 32: Trend for temperature with varying projectile velocity.  
For the \"mid\" element, failure occurs earlier using higher projectile mass (Figure 34) due to a rapid increase in equivalent plastic strain (Figure 35). The von Mises effect has no significant effect on fracture (Figure 37), whereas the triaxiality values are almost zero while using higher projectile mass (Figure 36), indicating a shearing type of failure. In contrast, failure occurs due to the tensile effect, with triaxiality approaching a positive value, with lower projectile mass, as shown in Figure 36. Figure 38 shows that the increase in temperature is greater for the higher projectile mass than for the lower projectile mass.  
Figure 37: Trend for von Mises stress with varying projectile mass.  
Figure 36: Trend for triaxiality with varying projectile mass.  
Figure 38: Trend for temperature with varying projectile mass.  
due to the higher kinetic energy. In the mid element, due to the effects of plastic strain rate and temperature, the element withstands more load and elongation before failure occurs.  
For the \"bottom\" element, the equivalent plastic strain (Figure 35) and triaxiality (Figure 36) remain constant until 6 us. Due to the increase in temperature (Figure 38), the element elongates under constant loading during this period, as shown in Figure 37. However, due to the rapid increase in triaxiality while using a higher projectile mass, the element fails earlier than with the lower projectile mass, as shown in Figure 34.  
Overall, fracture in the target plate occurs earlier with the higher projectile mass than with the lower projectile mass.
## 4 Simulation Results and Discussion
### Simulation for One-Hop Setup
#### 4.1.3 Overall Accuracy  
Using the chosen values for parameters, we test the overall accuracy of our proposed localization solution using the measure of location estimation error given in (9). Figure 8 depicts the estimated locations from phase 1 (shown in hollow triangles) and the corresponding true locations (shown in solid triangles). The errors are obvious. In Figure 8, we show the estimated locations from phase 2 (shown in solid triangles) and the corresponding true locations (shown in hollow triangles). The refinement due to phase 2 is clearly illustrated with many close pairs of triangles and the matched shape of sensor deployment. Numerically, the average errors in phase 1 and 2 are 15.65 m and 6.93 m respectively. Normalizing to sensor transmission range \(R\), phase 2 estimation gives 0.35\(R\) in average.  
Figure 5: The convergence speed with different \(\tau_{1}\) and \(\delta_{1}\).  
In Figure 7 which shows the CDF of the location estimation errors, we further see that with the effort of phase 2, \(90\%\) of the location estimation are improved from more than 30 m to 10 m.  
Figure 6: The evolution of location estimation error for the observed sensor with different \(\tau_{2}\) and \(\delta_{2}\).  
Figure 7: CDF of the location estimation error from two phases.
# Automatic Detection and Unsupervised Clustering-Based Classification of Cetacean Vocal Signals +
In the unsupervised network-based classification [28], an unsupervised learning strategy associating the CW clustering algorithm with the pruned network was developed to identify dominant vocal types based on vocal spectral shape and inter-click interval (ICI) distributions computed by discrete Fourier transform (DFT). Manual removal of false positives is operated by an analyst using detEdit, a custom graphical user interface (GUI)-based tool. In deep embedded clustering (DEC), [PERSON] et al. [29] manually labeled a dataset for classifying simulated signals into fish and whale clusters. DEC, GMM, and conventional clustering were tested, and DEC achieved the highest accuracy of 77.5%. The unsupervised methods mentioned above can only distinguish several specific types of cetaceans' vocal signals in small datasets and require manual operation to some extent. In this paper, our key motivation is to pick up valuable data (i.e., cetacean sound) from real-world recorded signals, where only a small portion of the signals is valuable. Since all kinds of cetacean sounds are of interest, we consider different type of vocalizations from different cetacean species. By doing this, we build a data set of cetacean sounds that are valuable for future research. Further, unsupervised classification is applied to validate the effectiveness of our built data set. This method mainly includes three parts: automatic detection, feature extraction, and unsupervised clustering-based classification. All the experiments are conducted in MATLAB2016a with the program package \"audio\". The workflow diagram for the whole process is shown in Figure 1.  
Figure 1: The workflow diagram for automatic detection and classification process.
# Review of Autonomous Intelligent Vehicles for Urban Driving and Parking
## 3 The Concepts of Autonomous Driving
### Moving Out  
On the Internet of Vehicle environment, finding an available parking lot can be solved [62]. This futuristic parking system provides a car park recommendation for the driver while taking the driver's preferences into account [63; 64; 65]. Such preferences may include parking fees, distance from the car park to the destination, driving time from the current location to the destination, and reservation reliability [64]. Supporting such a system will require a cooperative network of vehicles, parking lots, and a central server. The car park will first need to provide the correct occupancy status with the precise location and then update it into the central server. Once a driver makes a reservation in that particular car park, information must be updated to the central server. Another possible solution is to book the parking lot in advance through the use of short message service (SMS) [66] or an Android application [67].  
For an accurate occupancy status, several authors [68; 69; 70; 71; 72] have proposed to use image processing techniques as opposed to using counter-based or sensor-based techniques, which are relatively higher in cost [73]. Sensors deployed to detect occupancy include a passive and an active infrared sensor, an ultrasonic sensor, a magnetometer sensor, and a microwave sensor [74]. Sensors are usually embedded in parking lots to detect the presence of a vehicle. The idea of using the image processing technique is to first capture the parking lot's image using a camera installed in the car park, followed by using an image processing algorithm to either extract or enhance features in the image. Finally, a classifier is used to classify the occupancy status. Such a system brings the benefits of saving fuel time, as car cruising while waiting for an empty lot is reduced, improving the traffic flow in the car park [75; 76]. Furthermore, conflicts between drivers over the rights to the parking lot can be reduced [77].
## 4 Structural Optimization
### Optimization of Energy Transfer Efficiency  
In the structural design of the displacement reducer, to avoid energy loss within the structure, the output efficiency is further set as the desired optimization objective. According to the principle of energy conservation, when the output displacement decreases, the output force should increase as much as possible. The energy transfer efficiency is defined as [32]  
\[\text{Min}\ \eta\big{(}a_{x},\dots,h_{x},h_{y},w_{\text{ad}},\dots,w_{\text{ ef}}\big{)}=\frac{F_{\text{out}}\cdot u_{\text{out}}}{F_{\text{in}}\cdot u_{\text{in}}},\quad i=0,1,2,\dots,n, \tag{16}\]  
When \(\eta\) approaches 1, the energy loss within the structure is minimized. The constraints are given in Tables 2 and 3.  
After 1000 generations of iteration, the fitness values are as shown in Figure 13a. The results tend to converge after about 820 iterations, demonstrating the feasibility of the optimization algorithm. The optimized structure is shown in Figure 13b, where the overall structure has a flattened shape. Figure 13c shows the comparison of energy transfer efficiency before and after optimization. Compared to the previous design, the energy transfer efficiency has increased by approximately 50%.
## 2 Modeling and Experimental Validation
### Model Validation
#### 2.2.2 Validation of Velocity Field  
To accurately reflect the actual wind speed, a multipoint measuring approach and an average value were adopted. The specific layout of the measuring points is shown in Figure 6, and data were recorded every 2 s. All test data were averaged over three measures to minimize the inaccuracy caused by unintentional variables. The uncertainty of measured wind speed can be estimated as  
\[X_{i}=X_{i}(measured)\pm\delta X_{i}, \tag{10}\]  
\[\sigma=\sqrt{\frac{\sum_{i=1}^{n}\left(X_{i}-\overline{X}\right)^{2}}{n-1}}, \tag{11}\]  
where the value \(X_{i}(measured)\) is the mean value of three set of repeated experiments, \(\sigma\) is the standard deviation of repeated experiment data and \(\delta X_{i}=2\sigma\)[36].  
Figure 5: Main measuring instruments and fire source device: (**a**) temperature and humidity logger, (**b**) thermocouple, (**c**) hot-wire anemometer, (**d**) portable weather station, (**e**) design of the fire source device.  
Before the alcohol was lit, the initial air temperature and wall temperature were measured. When the portable weather station detected an external wind speed of more than 2 m/s, the alcohol was lit, and the smoke cake created smoke as well. At this time, the smoke exhaust system of the station hall was opened, and hot-wire anemometers continuously recorded the smoke speed of the entrances and exits of the station hall, exhaust outlets, and staircases leading to the platform.  
Identical initial and boundary conditions as in the field test were used for the simulations, and velocity test values were used to validate the numerical model. The relative error between the experimental and numerical simulation results is defined as [37]  
\[\delta=\frac{\left|V_{num}-V_{exp}\right|}{V_{exp}}\times 100\%. \tag{12}\]  
As listed in Table 4, after activating the TSE system, the relative errors between the simulation and test values in passageways A, C, and D were 4.08%, 5.36%, and 8.57%, respectively. The errors were mainly caused by the simplification of the physical model. However, the maximum relative error was less than 10%, indicating that the numerical model is quite accurate in simulating smoke movement in the station hall.
## 4 Conclusions  
We found a new class of three-dimensional (3D) topological insulators (TIs) in Pb\({}_{2}\)BiBrO\({}_{6}\) and Pb\({}_{2}\)SeTeO\({}_{6}\) double perovskites by first-principles theoretical calculation. Our first-principles theoretical calculations show that Pb\({}_{2}\)BiBrO\({}_{6}\) and Pb\({}_{2}\)SeTeO\({}_{6}\) are \(Z_{2}\) nontrivial and their bandgaps are high at 0.390 eV and 0.181 eV, respectively. The topology comes from the band contact and band inversion at \(\Gamma\) point in the absence of spin-orbit interactions. This results in a larger bandgap for this new class of topological insulators than conventional TI. We also use a large supercell to calculate the surface metallic bands and we confirm that the topology-protected surface metallic bands come from the BiBrO\({}_{4}\) or SeTeO\({}_{4}\) surface. It means that one can build a transport device using Pb\({}_{2}\)BiBrO\({}_{6}\) or Pb\({}_{2}\)SeTeO\({}_{6}\) double perovskites with a PbO layer as the outmost surface protection layer. The mechanical stabilities such as bulk, shear, Young's moduli, Poisson's and Pugh's ratio, longitudinal, transverse, and average sound velocity, together with Debye and melting temperatures are also studied.  
Conceptualization, [PERSON]-[PERSON] and [PERSON]-[PERSON]; methodology; validation, C.-H.L.; formal analysis, [PERSON]-[PERSON], [PERSON]-[PERSON] and [PERSON]-[PERSON]; investigation, C.-H.L.; writing--original draft preparation, [PERSON] and [PERSON]-[PERSON]; writing--review and editing, [PERSON] and [PERSON]-[PERSON]; supervision, [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology (MOST), Taiwan, grant numbers 110-2218-E-A49-020-MBK, 109-2224-E-003-00, 107-2112-M-039-002-MY2.  
Computational studies were performed using the resources of the National Center for High Performance Computing, Taiwan, and the computing resources of the Institute of Atomic and Molecular Sciences, Academia Sinica, IAMS.  
The authors declare no conflict of interest.  
Figure 6: Surface electronic band structure of the Pb\({}_{2}\)BiBrO\({}_{6}\) and Pb\({}_{2}\)SeTeO\({}_{6}\) supercell, **(a,e)** total band structures, **(b–d)** project band structures of the top PbO, BiBrO\({}_{4}\) and top2 BiBrO\({}_{4}\) layer, **(f–h)** project band structures of the top PbO, SeTeO\({}_{4}\) and top2 SeTeO\({}_{4}\) layer, respectively. Note that only the Pb, Bi/Se, and Br/Te atoms’ orbitals are projected. The Fermi energy is set to be zero as a reference.
## 4 Data and Methodology
Delayed data of the selected index are then selected as the input vector for the fuzzy model, up to a delay of three days, as described here:  
\[(y_{t-3},\,y_{t-2},\,y_{t-1},\,y_{t}) \tag{18}\]  
The delay of up to three days proved to be the most accurate in an earlier survey by [38], which is why the application is also performed here. The whole practical part is processed using MATLAB 2021a software and a special fuzzy logic toolbox was used to create the fuzzy model. The fuzzy model for stock market research consists of three input variables (value at time \(t\)-3, \(t\)-2 and \(t\)-1), one rule block and one output variable (value at time \(t\)).
## 1 Introduction  
### Background and Motivation  
In recent years, with the advancement of urbanization, the rapid climate change caused by fossil fuel consumption has become increasingly prominent. There is an urgent need to deal with the challenges brought up by climate change, as its consequences have seriously affected our society, with projections of even worse consequences in the near future [1]. For these reasons, governments and organizations around the world have accelerated the deep adjustment of the energy pattern and turned to a more sustainable development model to meet the large-scale global energy demand.  
A possible solution to improve sustainability is to develop high-efficiency energy systems with a high penetration of renewable energy resources, which are abundant and are clean substitutes to traditional fuels. However, renewable energy has the disadvantages of intermittency and volatility, which will lead to complex prediction methods and energy management strategies [2]. To overcome these challenges, distributed energy systems (DESs have been proposed.  
DESs for supplying electricity, heating, or cooling have been widely employed for industrial and residential applications in many countries [3], with high energy utilization efficiency [4]. Different from the traditional energy system, DES involves on-site production and consumption. Therefore, a DES can make full use of local resources and be customized according to the users' features, which can obtain better economic and environmental benefits. In addition, DESs can be used to exploit waste energy to improve the efficiency of the system, so as to minimize carbon dioxide emissions. DESs can further be used to deal with climate change. However, although DESs possess many advantages, the design of a DES is not a simple task, which has become the bottleneck restricting the development of DESs. Owing to the great differences among users in the region [5], a DES needs to comprehensively consider all energy supply processes, mainly including source, transmission, and consumption. However, few designs have fully considered the above factors and most lack integrity and a systematic nature, resulting in poor system efficiency and economic benefits. Therefore, it is very necessary to realize the collaborative planning of energy stations and networks of DES, which can save investment and improve the economy of DES to the greatest extent.
## 3 Results and Discussion
### Experimental Results  
Experiments were performed on an SGSRT to verify the proposed criterion and the simulation results. The equipment is shown in Figure 10. This systems mainly consists of a single-axis rotation hub, a flexible aluminium beam (\(2480\times 200\times 2.5\) mm), a torque mode reaction wheel (\(T_{\mathrm{max}}=0.065\) Nm) whose rotation axis is coincident with the hub, a periodic rotation dish with an eccentric mass to create the disturbance, and equipment on the hub for measuring, control and data exchange.  
The hub is suspended by a gas bearing. A zero-gravity environment is required, and the table is limited to rotate on the horizontal plane. The residual vibration is measured by an accelerometer fixed on the tip of a flexible beam. The model of the accelerometer (\(\pm 2\ g,0-400\ Hz\)) is 2220-020, produced by Silicon Designe Inc. (Kirkland, WA, USA). Eccentric mass is chosen to be 0.4 kg and 0.25 kg for different frequency ratios, and the periodic rotation dish is placed 0.6 m from the rotation axis of the system. This is the best setting to generate sinusoidal disturbance whose amplitude is within the limits of the reaction wheel.  
Frequencies of the experimental equipment have a close relationship with the experimental results. To identify the frequencies of the experimental equipment, free vibration tests were performed, and the fast Fourier transform (FFT) was applied. Figure 11a,b shows the measured tip acceleration data and the FFT result.  
Figure 11: Tip free vibration and FFT analysis: (**a**) Measured tip free vibration; (**b**) FFT of tip free vibration.  
Figure 10: Experimental setup of a single-axis rotary table.  
Results show that the first two vibration orders are obvious. The measured and the calculated frequencies are shown in Table 1.  
The errors of the first two frequencies between measured and calculated results are 2.781% and 2.19%, respectively. In view of the difference between the experimental equipment and mathematical model, these errors are acceptable and negligible.  
According to the previous results, there are 2 typical varying laws in the amplitude-ratio domain. For situations \(\lambda<0.5\), variation is not obvious. This is because the torques are too small for flexible systems. Similarly, the torques are too large when \(\lambda\) is too high. Considering those situations and the output ability of the experimental equipment, three frequency ratios 1.3, 0.9 and 0.6 were selected for the experiment. Thus, three cases of the experiment corresponding to three selected frequency ratios were provided. On the other hand, simulations illustrated that the optimal amplitude ratio for most frequency ratios is near 0.7. Thus, three amplitude ratios near 0.7 were also selected for experimental cases.  
Simulations were performed with real parameters in the experiment. Zero initial conditions were provided for all the experiments. Square compensation torques from the reaction wheel and sinusoidal disturbance were started at the same time. The measured accelerations of the flexible beam tip are shown in Figures 12-14.
# SPR-Optical Fiber-Molecularly Imprinted Polymer Sensor for the Detection of Furfural in Wine +
## 3 Results
### Sensor Response in Water and in Wine-Mimicking Solution  
Water is a well-suited solvent for measurements with the platforms here described since its RI matches the operative range for the proposed SPR sensors [36]. The spectra can be recorded directly in the sample, since 2-FAL standards in water are easily prepared. When the 2-FAL concentration increases, the SPR wavelength is shifted to higher values, as seen in Figure 6a. As an example, a standardization curve of 2-FAL in water is shown in Figure 6b, and is obtained by reporting the wavelength variation \(\Delta\lambda\) vs. 2-FAL concentration. The experimental data are well fitted by Equation (1), as expected when the sorption takes place according to the Langmuir model. A plateau corresponding to \(\Delta\lambda_{\text{max}}\) is obtained for concentrations higher than 1 mg\(\cdot\)L\({}^{-1}\) due to the receptor's sites saturation. The parameters, evaluated by a non-linear regression method, are \(K_{\text{aff}}\), 9.4 L\(\cdot\)mg\({}^{-1}\) (9.0 10\({}^{5}\) M\({}^{-1}\)) and \(\Delta\lambda_{\text{max}}\) = 3.3 (0.447) nm. The sensitivity at low concentration (i.e., in the linear part of the curve) is 31.6 nm\(\cdot\)mg\({}^{-1}\cdot\)L. The saturation was reached at about 1 mg\(\cdot\)L\({}^{-1}\).  
The response of the sensor to 2-FAL concentration in wine-mimicking solution is reported in Figure 6c,d. In this case, the spectra were recorded in water, after equilibration with the sample and washing, according to the method of the solvent exchange.  
Figure 5: Comparison of the spectra of an SPR-MIP sensor (C1-3) in water–glycerol solutions with different refractive indices and the spectrum of an SPR-bare platform in water.  
The dose-response curve (\(\Delta\lambda\) vs. 2-FAL concentration) in wine-mimicking solution followed the Langmuir model (see Equation (1)) and the parameters obtained are: \(K_{\text{aff}}=75.6\) L\(\cdot\)mg\({}^{-1}\) and \(\Delta\lambda_{\text{max}}=3.3\) (0.247) nm. The sensitivity at low concentration is 254.9 nm\(\cdot\)mg\({}^{-1}\cdot\)L, and the limit of detection (LOD) is 0.004 mg\(\cdot\)L\({}^{-1}\). The saturation was reached at about 0.6 mg\(\cdot\)L\({}^{-1}\).  
The affinity constant is about 8 times higher in wine-mimicking solution than in pure water; consequently, the LOD is lower than in water.
# UAV-BS Site Planning Based on Circular Coverage Strategy
## 4 Methodology
### UAV-BS Full Coverage Network Model  
Considering the site planning of an aerial base station in a target area with radius R, this paper adopts a UAV equipped with aerial base stations to deploy over the target area to achieve full coverage of ground users in the target area, as shown in Figure 1. The flight altitude is H, and the UAV-BS forms a forward link with ground users. The Aircraft Control Center (ACC) forms a return link. Considering computing power and cost, it is assumed that a maximum of N UAVs can be accommodated in ACC.