esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### 3D Reconstruction of the Jaws  
The CT (Computerized Tomography) is the starting point of this procedure. The DICOM (Digital Imaging and Communications in Medicine) images from the CT scan of the involved zones, in this case the jaws, were imported in the open-source software 3D Slicer (3D Slicer, Open source software--[[https://www.slicer.org](https://www.slicer.org)]([https://www.slicer.org](https://www.slicer.org)), Harvard Medical School, Boston, USA) and used to realize the segmentation.  
3D Slicer allows one to return to information regarding CT; in particular, the CT concerning the patient who was the subject of the study for the elaboration of the methodology had the following characteristics:  
Scan option: spiral CT;  
1. distance source to detector (distance in mm between source and detector) is equal to 949.075;
2. distance source to patient (distance in mm from the source to the isocenter (center of the FOV) equal to 541;
3. reconstruction diameter (diameter in mm of the region in which the data was used to create the image reconstruction) equal to 250;
4. slice thickness: 1.25 mm;
5. spiral pitch factor: 0.5625.  
The segmentation allows the definition of the 3D model of the jaws in order to design the template afterwards. Teeth 3D reconstruction is carried out using a laser surface scanner; this step is required because the CT/CBCT imaging data do not provide enough details of the dentition [33], and thus a high-resolution scan is required. The best practice asks for high-resolution scans (to create 3D models) of the facial skeleton, teeth, and soft tissues; a facial model created by aligning and merging digital dental models into a maxillofacial CT is called a 'composite model' [4] This step, although extremely important for a correct reconstruction of the anatomical elements of interest, was not carried out in this phase as one of the primary purposes of this work was in the definition of the methodology for the design of the aid templates for the surgeon.  
Coronal, sagittal, and transverse planes, that are the characteristic planes of the human body, are considered coincident with those of the CT scans [31]. Following this kind of operations, 3D models are primarily converted, using the same software, into STL (Solid To Layer) ones and then, using Geomagic software (Design X rel., 3D Systems, Rock Hill, SC, USA) into IGES (Initial Graphics Exchange Specification) models that are modelled by surfaces. Furthermore, is possible to reconstruct in detail the structure of the condyles. Mandibular's structure processing, through Geomagic, at this step, has allowed to evaluate the peculiarities of the three-dimensional model defined on Computed Tomography. In particular, for the case in exam, the mandibular structure presents 846 faces, 1692 corners and 400,000 mesh triangles. After that, the models are imported into Rhinoceros (Robert McNeel & Associates, Seattle, WA, USA) for preparing the data for template design.  
Reported errors among the real tissues/bones geometries and the reconstructed-by-CT ones are less than 0.06 mm while segmentation errors are less than 0.05 mm [34].
## 2 Materials and Methods
### Platform Monitoring  
The platform reliability is evaluated thanks to a dedicated testbench permitting simulated faults injection (Figure 5). The Cortex-R5 is used to monitor the proper functioning of the application running on the Cortex-A53 and the programmable logic. It also executes the fault injection task allowing the efficiency of the mitigation mechanisms to be measured.  
Figure 4: Custom Mitigation Procedure.  
Figure 3: Fault Classification.  
On this monitoring processor, a watchdog timer is used to ensure that the Cortex-A53 is still alive and a task periodically receives the processing result from the application processor (Figure 6). If the processing result is erroneous or if the watchdog expires, the monitoring processor concludes that a failure has occured and stops the testbench for the current run. The elapsed time is used to measure the system reliability before the occurrence of a critical failure and the system is reset thanks to the Power Management Unit (PMU) contained in the MPSoC. Information exchange between the three cores is done via the Inter-Processor Interrupt (IPI) mechanism.  
The Cortex-R5 also monitors the radiation level using the information provided by the SEM, especially the number of soft errors detected by the configuration memory scrubber.  
Figure 5: Prototype Testbench.  
Figure 6: Monitoring Diagram.  
This information is compared to a threshold that should be defined regarding the number of soft errors that could lead to a system failure. The number of soft errors is periodically reset in order to obtain an error rate. If this rate is greater than the threshold, the application is stopped and the platform is reconfigured to switch to the Safe mode, and the application is resumed. When this rate decreases below the threshold, the platform returns to the High-Performance mode.
# A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks
## 5 Simulation Results  
To evaluate the performance of LDGM on balancing energy consumption, prolonging network lifetime as well as reducing coverage redundancy and transmission delay, a series of simulations are carried out with the help of VC++6.0 and Matlab 8.5. We also compared LDGM with some typical cluster based data gathering methods with mobile Sinks, e.g., MST, MWST and so on. Values of the parameters in these experiments are shown in Table 1.
# A Survey of Forex and Stock Price Prediction Using Deep Learning
## 5 Discussion
### Analysis Based on the Method Used
#### 5.1.6 Other Deep Learning Methods  
In the section of other deep learning method-based models, the following conclusions could be drawn:  
1. According to the datasets used, 4 papers made use of sentiment and news analysis to predict the stock. The rest of them used the method of technical analysis only.
2. For the variables, five of the other deep learning methods models used the multivariable input and only one paper used candlestick charts alone as input.
3. It could be found that there were 5 different models in the other deep learning methods. The only model that appeared three times was HAN, which consisted of an ordinary HAN model and two modified HAN models. The rest of the models were not duplicated.
4. Because the metrics used in each paper were different, there were 9 metrics used in the measurement performance of this section.
5. Multiple articles selected accuracy, Sharpe ratio, and return rate; we found that paper [92] had the highest accuracy. Paper [84] had a much higher Sharpe ratio than paper [88].
6. For return rate, paper [89] had the highest return rate followed by papers [84,88], and paper [87] had the lowest return rate.
## 3 Results
### Computational Data
This was also experimentally confirmed. On the other hand, CPME was proved to be an optimum ether of choice ascribed to its lowest \(\chi\) values in comparison to the other three ethers throughout the entire examined temperature range (50-500 K), provided that it shares low \(\chi\) values, even in the case of temperature reduction, as low as 86 K (Figure 5D). Thus, supporting its optimum compatibility with the CTC resin, which has translated in bypassing osmotic shock and reserving the morphological characteristics of its beads. Furthermore, analysis of Emix charts also showed the CPME has the lowest Emix values and, hence, the best swelling capacity over the entire examined temperature range (Figure 6A-D). Figure 5: Chart displaying Chi (\(\chi\)) dependence on temperature (50–500 K): (**A**) DEE; (**B**). 2-MeTHF; (**C**) MTBE; and, (**D**) CPME. Chi (\(\chi\)) values at the working temperature in this study (280 K): DEE; 55.5 \(>\) 2-MeTHF; 49.3 \(>\) MTBE; 42.8 \(>\) CPME; 33.2. Secondly, an analysis of the swelling free energy dependence on the temperature was performed over the range (50-500 K). As the case with \(\chi\) and Emix relationship with the temperature, it was also observed that CPME has occupied the lowest swelling free energy values over the examined temperature range, which indicates that this ether is thermodynamically the most favored one among the others analyzed in this study (Figure 7A-D). Furthermore, in the case of CPME, the swelling free energy at the working temperature (280 K) is 4.2 Kcal/mol, which is the lowest value among the ethers. However, it went up to 7.4 Kcal/mol in the case of DEE. The swelling free energy has provided an additional explanation for the unexpected behaviour of 2-MeTHF. Its swelling free energy is 6.5 Kcal/mol, which is considered to be a high value and less thermodynamically favoured. In addition, this value is even higher than that of MTBE (5.6 Kcal/mol), as was also observed in the trend of its \(\chi\) and Emix values. In conclusion, the swelling free energy data are in line with the experimental findings and they have provided an explanation of the excellent performance of CPME, as well as the observed adverse effect of the 2-MeTHF on the morphological characteristics of CTC resin and the high chance of osmotic shock occurring. Figure 6: Chart displaying Emix dependence on temperature (50–500 K): (**A**) DEE; (**B**) 2-MeTHF; (**C**) MTBE; and, (**D**) CPME. Emix values at the working temperature in this study (280 K): DEE; 30.9 > 2-MeTHF; 27.4 > MTBE; 23.8 > CPME; 18.5. Figure 7: _Cont._
## 1 Introduction  
The heterogeneous signal processing platform is a comprehensive processing platform that integrates processing units from a variety of different architectures. Due to its rich hardware and software resources, high degree of heterogeneity, parallelism, and efficiency, it is widely used in signal processing. How to rationally allocate tasks to processing units with different computational power to achieve the shortest schedule length is one of the key issues affecting the performance of heterogeneous systems. This problem has been proven to be NP-hard [1]. Currently, task scheduling techniques are mainly modeled by the directed acyclic graph (DAG), which simulates tasks with different communication dependencies and different computational requirements. However, with the rapid development of wireless communications and the Internet, the size of the signal continues to grow. As a result, the number and complexity of tasks after DAG abstraction is increasing. Therefore, designing a reasonable and effective scheduling algorithm will greatly improve the computational performance of the heterogeneous platform.
## 3 Results and Discussion
### Validation of CIM  
Uncertainties in the material properties of the rotor and the supporting properties of the bearing can result in an uncertain gravity response in the generator rotor system. To validate the accuracy and efficiency of the proposed method, the results obtained by the proposed method and the scanning method are compared.  
The scanning method is a straightforward method to evaluate the uncertain gravity response based on FEM. To carry out the scanning method, the interval variable is divided into a number of equidistant samples. Then, the upper bound and lower bound can be obtained from the FEM results of all the samples. The scanning method is easy to carry out, which does not involve any new algorithm. However, a large number of samples should be used to ensure the convergence of the bounds predicted by the scanning method, which limits the application of the scanning method to a high-dimensional  
Figure 3: The gravity response amplitude and the relative error of reduced-order model (ROM) versus rotational speed: (**a**) gravity response amplitude; (**b**) relative error.  
Figure 2: The whirl speed versus rotational speed in the rotating frame.  
uncertain problem. Therefore, the scanning method is used in this paper to validate the accuracy of the proposed method.  
Figure 4 shows the uncertain gravity response amplitude versus the rotational speed by using the scanning method and the proposed method. The uncertain gravity response of the left disk is evaluated. In this figure, the uncertainty in the bearing's stiffness is considered. The deviation of the bearing's stiffness is 10%, which means that \(\varepsilon_{k}^{l}=\left[4.5\times 10^{8},5.5\times 10^{8}\right]\) N/m. As shown in Figure 4, the results (lines) calculated by the scanning method and the results (red-filled) calculated by the proposed method are in good agreement with each other. In the application of the scanning method, 1000 equidistant samples are used. The order of Chebyshev polynomial expansion is 10, so that the number of CIM samples is 11. The samples used in the proposed method are just 1.1% of that used in the scanning method. A conclusion can be made that CIM can give an accurate prediction of an uncertain gravity response of the generator rotor system with fewer samples than the scanning method. A higher order of Chebyshev polynomial expansion can result in larger computational effort. Therefore, in the following analysis, an 11-order CIM is adopted. Under the influence of the uncertainty in the bearing's stiffness, there is a local large-amplitude band at each resonance peak, which satisfies the phenomenon in the previous studies concerning uncertain problems [24; 32]. The local large amplitude occurs when the uncertain factor that affects the resonance speed is taken into account. The resonance speed increases with the increase of the bearing's stiffness, which contributes to the occurrence of the local large-amplitude bands in Figure 4.
## 2 Materials and Methods
### Results  
The calibration results of simulations are presented in Table 3. The data calculated based on simulation A (without tag detection, only end-effector position tracking) show high-precision identification with a very small difference in comparison to the expected results (difference \(\Delta\) is shown in the brackets). Such an accuracy could be achieved using, for example, a laser tracker in reality. For simulation B, when the tags were detected using simulated camera, the error was higher (maximum 2.59 mm for \(x_{0}\)), which gave us an idea about how accurate the system might be, so the noise from the environment was lowered, but the camera parameters were kept.  
In Table 4 are the results of the experiment A. The best values were obtained when all the results of the three cameras were combined and analyzed together. The error for the base frame (\(x_{0},y_{0},\) and \(z_{0}\)) was maximally 7.61 mm in the \(z_{0}\) direction. We performed other measurements following the same strategy; they all provided similar results, which made it made clear that the position of a camera has an influence on the detection accuracy, which is supported by results of [PERSON]'s research [25].  
In Table 5 are the results of the experiment B. It proves the possibility of placing the base frame freely with respect to the robot.  
The trajectories are compared in Figures 6-10. Measured cameras/measured path are plotted points that were obtained by camera/end-effector position sensor; robot path is a self-check plot of the end-effector path after the identified parameters of \(X\) were applied in the transformation matrix. Points of origin are points where the world base frame was determined for every point on the path. In Figures 11 and 12 we can observe the error values in box plots.  
Figure 5: Experiment B setup.  
Figure 8: Paths measured by cameras in comparison with the real robot path of experiment A.  
Figure 6: Measured path in comparison with the real robot path of simulation A.  
Figure 7: Measured path in comparison with the real robot path of simulation B.  
Figure 11: Errors for the identified values based on simulation B.  
Figure 10: Path measured by a camera in comparison with the real robot path of experiment B.  
Figure 9: Paths measured by cameras in comparison with the real robot path of experiment A, xy plane.
# Tracking Mobile Sinks via Analysis of Movement Angle Changes in WSNs
## 4 Performance Evaluation
### Comparison between DAMST and SinkTrail  
To prove the effectiveness of DAMST, we compare DAMST with SinkTrail on energy consumption, lifetime and overhead, respectively, in a random pattern. The scenario is set in a 500 m \(\times\) 500 m region with one mobile sink and 2500 sensor nodes. The interval to appoint footprints is set to 3 min. The movement speed of mobile sink is set to 5 m/min. The communication radius of each node is set to 50 m. It is shown in Figure 15 that DAMST outperforms SinkTrail in energy efficiency, the reason being that, unlike SinkTrail, DAMST does not need to consistently broadcast the location information of mobile sinks. Propagating trail messages and updating tail references in SinkTrail brings considerable overhead in the network. However, DAMST produces less overhead via forwarding packets in the routing path established by the DAMST points and relay nodes. It has been verified in Figure 16 that the overhead of DAMST is much less than that in SinkTrail.  
Figure 14: Average ratios of unconnected adjacent DAMST points with different footprint appointment intervals.  
Figure 13: Average overhead with different footprint appointment intervals.  
From the above comparison results, it can be seen that DAMST is able to prolong network life time due to its less overhead and energy consumption. Based on this, we further compare DAMST and SinkTrail in a simulation scenario with same parameters at the same period of time and record the number of survived nodes in the entire process. The simulation result in Figure 17 shows that, compared with SinkTrail, the DAMST delays the first node death time and extends the network lifetime.  
Figure 16: Total overhead in DAMST and SinkTrail.  
Figure 17: Numbers of survived nodes in DAMST and SinkTrail.  
Figure 15: Total energy consumption in DAMST and SinkTrail.
## 3 Methodology
### Hierarchical and Optimized Clustering for Superpixels
#### 3.3.2 Topological Description for Superpixels with RAG  
Theoretically, the clustering of superpixels based on the regional dissimilarity needs an exhaustive comparison between superpixels, the complexity of which is \(O\big{(}n^{2}\big{)}\), here \(n\) means the number of superpixels. However, only the adjacent superpixels are possible to be merged. Therefore, this paper introduces RAG to describe the topological relation between superpixels, on the basis of which only adjacent superpixels would be compared, and the complexity is effectively reduced to linear. Figure 9a,b respectively shows an example of superpixel segmentation result for an image as well as the corresponding RAG.  
The RAG of the superpixel set \(\{S_{1},S_{2},\cdots,S_{n}\}\) could be described by nodes and edges. Each node represents a superpixel, and each edge connects two adjacent superpixels. An example of RAG composed of nine superpixels is shown as Figure 10. In Figure 10b, \(\{V_{1},V_{2},\cdots,V_{9}\}\) means the set of nodes, i.e., the set of superpixels, and \(\{E_{1},E_{2},\cdots,E_{9}\}\) means the set of edges. In Figure 10a, the superpixel \(S_{1}\) corresponds to the node \(V_{1}\) in Figure 10b; adjacent superpixels of \(S_{1}\) is \(S_{2}\), \(S_{5}\) and \(S_{6}\), and \(V_{2}\), \(V_{5}\) and \(V_{6}\) are corresponding nodes, and edge \(E_{1}\), \(E_{2}\) and \(E_{3}\) respectively connect \(S_{1}\) to \(S_{2}\), \(S_{5}\) and \(S_{6}\).  
Figure 9: An example of superpixel segmentation for an image: **(a)** Overlay display of the origin image and the corresponding superpixel segmentation result; **(b)** The RAG corresponding to superpixels.  
The weight of each edge is calculated according to the regional dissimilarity function. Suppose attitudes of two adjacent superpixels \(S_{i}\) and \(S_{j}\) are denoted as \(\left(\theta_{i},\alpha_{i}\right)\) and \(\left(\theta_{j},\alpha_{j}\right)\), the number of pixels which contain are respectively denoted as \(m_{i}\) and \(m_{j}\). Let  
\[\eta_{ij}=\left(m_{i}\cdot m_{j}\right)/\left(m_{i}+m_{j}\right), \tag{31}\]  
The calculation formula of weight for edge \(E_{k}\) connecting two adjacent superpixels is written as Equation (32).  
\[W_{E_{k}}=\frac{1}{\eta_{ij}\cdot\left(\left(\theta_{i}-\theta_{j}\right)/ \Delta_{\theta_{\max}}+\left(\alpha_{i}-\alpha_{j}\right)/\Delta_{\alpha_{ \max}}\right)}, \tag{32}\]  
where \(\Delta_{\theta_{\max}}\) and \(\Delta_{\alpha_{\max}}\) respectively means the maximum of the dip angle and the dip between all adjacent superpixels.  
For the RAG in Figure 10b, if the length of the edge is used to describe its weight, it can be concluded that the weight of the bold black edge \(E_{11}\) is minimum, which indicates that the node \(V_{5}\) and \(V_{6}\), i.e., the superpixel \(S_{5}\) and \(S_{6}\) should be merged. The updated RAG is shown as Figure 11.
## 1 Introduction
Since one needs a signature with multiple peaks (e.g., resonance) to successfully identify damage, up to 100 kHz of frequency range might be not enough and could result in an impedance signature without any peak, resulting in failing to detect any damage. Especially for composite materials, due to its non-homogenous property, an impedance signature without any resonance can be commonly seen. To overcome this problem, the concept of sandwiching a metal disc between the PZT transducer and the host structure was proposed in [16], where its reliability was tested using glass fiber epoxy laminates. To create a portable impedance measuring system, the way of attaching the PZT transducer must be changed. Since the EMI technique requires the PZT patch to be permanently attached to the host structure, one must alter this so that the PZT transducer can be temporarily attached and detached for multiple EMI measurements. The easiest way to achieve this would be to use a double-sided tape with the PZT-metal EMI transducer; the reliability and performance of this idea are tested in this study. In addition to the test, simulation studies on using PZT in smaller sizes are introduced.
# Evaluation of IMERG Precipitation Products in the Southeast Costal Urban Region of China
## 2 Study Area and Datasets  
### Study Area  
The study area is located in the coastal region of southeast China between 111.2\({}^{\circ}\)-114.5\({}^{\circ}\)E and 21.3\({}^{\circ}\)-24.2\({}^{\circ}\)N (Figure 1). The region covers a total area of 56,000 km\({}^{2}\), and more than 80% is urban, including the nine cities of Guangzhou, Shenzhen, Zhuhai, Foshan, Huizhou, Dongguan, Zhongshan, Jiangmen and Zhaoqing in Guangdong Province, and two special administrative regions of Hong Kong and Macau. The regional gross domestic product (GDP) reached US$ 1.7 trillion in 2020, with a GDP per capita of over US$ 20,000 and a population of over 80 million. The study area has a well-developed transport infrastructure, with around 4000 km of highways, more than 6000 km of navigable rivers, and a dense network of coastal ports and airports.  
The elevation here varies from \(-\)10 to 1614 m, with some mountains in the north and extensive plains in the center and south. The study area has a distinct subtropical monsoon climate, with high temperatures and simultaneous rain and heat throughout the year. The average annual temperature is about 19-24 \({}^{\circ}\)C [56] and the average annual precipitation is about 1300-2500 mm [57]. 80% of the regional annual precipitation is concentrated in the wet season (April to September). Precipitation during the dry season (October to March) and the pre-wet season (April to June) is mainly influenced by frontal troughs, while precipitation during the post-wet season (July to September) is dominated by tropical weather systems and local convection. Orographic precipitation in this area is triggered by the northern mountains and some south-facing elevated terrain. During the wet season, extreme precipitation is frequent and can lead to severe flooding, as well as casualties and  
Figure 1: The (**a**) location, (**b**) topography and positions of the rain gauge stations, and (**c**) land cover of the study area.  
economic losses [58]. Therefore, timely and reliable precipitation information for this area is essential to ensure regional security and development.