esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Seismomagnetic Mechanism  
The performance and mechanisms of geomagnetic anomalies in the early stages of earthquakes are very complex, involving geomagnetic anomalies generated by the continuous changes of the source, regional stress fields, and other factors related to earthquakes [16; 26; 27]. It is necessary to separate earthquake-related anomalous signals from the collected geomagnetic anomaly signals, as these signals may be caused by earthquakes and other factors. Additionally, these measured geomagnetic signals mainly include the basic magnetic field and the changing magnetic field; the abnormal changes of the geomagnetism are also affected by these two parts. Therefore, it is necessary to filter out the geomagnetic anomaly caused by the changing magnetic field unrelated to the earthquake. Diurnal and secular variation corrections are made in observational data precisely to ensure the acquisition of reliable geomagnetic anomalies.  
Tectonic activities such as earthquakes and volcanoes are very complex physical processes, often accompanied by subsurface thermal convection, causing changes in temperature, pressure, and fluid movement. The physical mechanisms leading to the magnetic anomalies caused by these tectonic activities are not unique. Besides the piezomagnetic effect related to the change of rock magnetization caused by stress, they also include the electrokinetic effect caused by the existence of electric double layer at the solid-liquid interface, the thermal demagnetization effect caused by temperature, etc. [15; 20]. [PERSON] et al. calculated the piezomagnetic and electrokinetic effects probably produced by the Yangbi earthquake, and found that the changes at a distance of 20 km from the epicenter were \(-0.3\) nT and \(-0.2\) nT, respectively, which were much smaller than the actually observed change in the geomagnetic field [46]. Therefore, a single mechanism cannot effectively explain the geomagnetic field changes before and after the Yangbi earthquake, and it is necessary to synthesize a variety of data for quantitative analysis.
## 2 SFSE Double-Differenced GPS/Galileo Mathematical Model
### Integrated GPS/Galileo Code and Phase Observation Equations
#### 2.2.2 GPS/Galileo Tightly Combined Mode (TCM) with Differential Inter-system Biases (DISBs)  
Earlier research based on GPS data, when combined with data of the Galileo GLOVE satellites, demonstrated that DISBs are significant between the overlapping observations of L1/E1 and L5/E5a only when different types of receivers are used. DISBs are mainly caused by the different device delay in receivers with respect to different system satellite signals. This means that, if the baseline consists of identical types of receivers, the difference of device delay for different systems can be eliminated by the receiver difference technology. Therefore, DISBs must be taken account of (estimated or offset) when integrated GPS/Galileo data from different types of receivers are used. Assuming that the pivot satellite of both systems belongs to GPS, then, (3) and (4) turn into [25]:  
\[\begin{array}{l}\
abla\Delta P_{br,j}^{1G,\delta}=\
abla\Delta P_{br}^{1G, \delta}+\
abla\Delta e_{br,j}^{1G,\delta}\\ \lambda_{j}\cdot\
abla\Delta\phi_{br,j}^{1G,\delta}=\
abla\Delta e_{br}^{1G, \delta}+\lambda_{j}\cdot\
abla\Delta N_{br,j}^{1G,\delta}+\
abla\Delta\in_{ br,j}^{1G,g}\\ \
abla\Delta p_{br,j}^{1G,\epsilon}=\
abla\Delta p_{br}^{1G,\epsilon}+\
abla \Delta d_{br,j}^{1G,\epsilon}+\
abla\Delta e_{br,j}^{1G,\epsilon}\\ \lambda_{j}\cdot\
abla\Delta q_{br,j}^{1G,\epsilon}=\
abla\Delta p_{br}^{1G, \epsilon}+\lambda_{j}\cdot\left(\
abla\Delta N_{br,j}^{1G,\epsilon}+\
abla \Delta\delta_{br,j}^{1G,\epsilon}\right)+\
abla\Delta\in_{br,j}^{1G,\epsilon} \end{array} \tag{6}\]  
where \(\
abla\Delta d_{br,j}^{1G,\epsilon}\) and \(\
abla\Delta\delta_{br,j}^{1G,\epsilon}\) denote the code and phase DISBs between the overlapping frequency observations of GPS and Galileo, respectively. After linearization and simplification, (4) is transformed into:  
\[\left[\begin{array}{c}y^{G}\\ y^{GE}-\delta^{GE}\end{array}\right]=\left[\begin{array}{c}B^{G}x\\ B^{G}x+A^{G}N^{E}\\ B^{GE}x\\ B^{GE}x+A^{GE}N^{GE}\end{array}\right]+\left[\begin{array}{c}\xi^{G}\\ \xi^{GE}\end{array}\right] \tag{7}\]  
where \(\delta^{GE}\) consists of both the code and phase DISBs between the overlapping frequency observations of GPS and Galileo.
## 4 Experimental Validation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **FND** & **HND** & **LND** \\ \hline LEACH & 355 & 532 & 643 \\ EGRC & 505 & 711 & 799 \\ FBCPSO & 546 & 748 & 876 \\ FCMMPO & 578 & 796 & 896 \\ EECRP & 626 & 837 & 946 \\ IMCMR-UWSN & 698 & 875 & 970 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Network lifetime analysis of the IMCMR-UWSN technique. Figure 5: NLT analysis of IMCMR-UWSN technique with existing methods (**a**) FND; (**b**) HND; (**c**) LND. Table 3 and Figure 7 compare the IMCMR-UWSN method's number of packets received (NOPR) analysis to other methods in various iterations. The experimental results demonstrated that the LEACH approach achieved worse results with a lower NOPR. Following that, the EGRC technique achieved a slightly higher NOPR than the LEACH protocol. Following that, multiple rounds of the FBCPSO and FCMMFO techniques resulted in a moderately closer NOPR. While the EECRP strategy attempted to achieve a fair NOPR in comparison to the other approaches, the disclosed IMCMR-UWSN methodology outperformed them all with a greater NOPR. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **No. of** \\ **Rounds** \\ \end{tabular}  & \begin{tabular}{c} **LEACH** \\ \end{tabular}  & \begin{tabular}{c} **EGRC** \\ \end{tabular}  & \begin{tabular}{c} **FBCPSO** \\ \end{tabular}  & \begin{tabular}{c} **FCMMFO** \\ \end{tabular}  & \ \begin{tabular}{c} **EECRP** \\ \end{tabular}  & \begin{tabular}{c} **IMCMR-** \\ **UWSN** \\ \end{tabular}  \\ \hline
0 & 0 & 0 & 0 & 0 & 0 & 0 \\
50 & 922 & 2296 & 2296 & 2951 & 3212 &  
By examining the tables and figures above, it is clear that the IMCMR-UWSN technique has been demonstrated to be an excellent instrument for achieving maximum energy efficiency and lifetime in the UWSN environment. The preceding findings compare the IMCMR-UWSN method's number of packets received (NOPR) analysis against existing methods in various iterations. The experimental results demonstrated that the LEACH technique performed poorly with a lower NOPR in all rounds. Following that, the EGRC model achieved a somewhat higher NOPR than the LEACH methodology. Following that, across numerous rounds, the FBCPSO and FCMMFO approaches resulted in a moderately closer NOPR. With a larger NOPR, the proposed IMCMR-UWSN model achieved the highest performance.
# Short-Facelift Approach in Temporal Artery Biopsy: Is It Safe?
## 1 Introduction  
Giant cell arteritis (GCA) is a quite common panarteritis of the elderly that affects medium- and large-size arteries.  
This type of vasculitis commonly involves the superficial temporal branches of the external carotid artery and the ophthalmic branch of the internal carotid artery.  
GCA has its peak of incidence between 70 and 80 years and it is more common in females than in males, especially among the Northern European population [1].  
There is still an open debate regarding pathogenesis of this clinical condition; regardless, all theories are in agreement in affirming that the immunopathology of GCA is due to an active immune response towards the vessel wall. The immune response involves T-cells and macrophages that infiltrate the boundary of the media and intima, leading to a granulomatous inflammation that promotes the formation of giant cells, and this is the reason why this vasculitis is named giant cell arteritis [2].  
The classic symptoms are related to the local vascular damage resulting in vessel wall hyperplasia and arterial occlusion, and consist of headaches, jaw claudication, and visual impairments [3].  
Other clinical manifestations of GCA are related to a systemic inflammatory status and consist of fever, malaise, polymylgia rheumatica, anorexia, and weight loss.  
The major clinical complication of GCA is blindness, which can only be prevented by an early detection and treatment [4].  
Concerning physical examination, upon palpation, the superficial temporal artery can be increased in thickness, and become tender, erythematous, and with a decreased pulse.  
Nevertheless, 50% of patients affected by GCA present normal physical findings on palpation of the superficial temporal artery.  
The diagnostic algorithm also includes blood tests in order to determine erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP), which are non-specific markers of inflammation disease.  
Despite the increasing role of imaging with advancing technology, the gold standard for the diagnosis of GCA is still the temporal artery biopsy [5].  
The length of biopsy specimens varies from 2 cm to 3-5 cm in patients with and without signs of temporal arteritis, respectively [6].  
Skip lesions are frequent in GCA, thus an adequate specimen is crucial in order to prevent missing the affected portion of the vessel and to avoid a false negative result.  
A described complication of superficial temporal artery biopsy (STAB), for which incidence is not clear, is the accidental damage of the frontal branch of the facial nerve [7].  
In every case of facial nerve impairment after STAB described in the literature, incisions were placed within the known course of the frontal branch of the facial nerve.  
In this paper, we describe the short-scar facelift surgical approach for STAB and a case series study.
## 3 Mathematical Model for Optimal Trajectory Planning
### Constraint Function Design
#### 3.3.3 Turning Angle Constraint (TAC)  
The turning angle explains the maneuverability of a UAV during the flight from the previous and current directions [1; 35; 36]. The Path for UAVs should be adequately smooth to maneuver through easily [37]. Therefore, the turning angle of the UAV is required to be less than the maximum turning angle [35]. This constraint can be formulated as follows,  
\[TAC=0,TAC=\sum_{i=2}^{N_{w}-1}TAC_{i} \tag{21}\]  
where,  
\[TAC_{i}=\left\{\begin{array}{cc}P,&if\theta>\theta_{max}\\ 0,&otherwise\end{array}\right.\]  
where \(\theta\) defines the turning angle of the UAV in 3-D directions (\(x_{i}\), \(y_{i}\), \(z_{i}\)), and \(\theta_{max}\) is the maximum tolerable turning angle.
# Multi-Device to Multi-Device (MD2 MD) Content-Centric Networking Based on Multi-RAT Device
## 4 Performance Evaluation and Analysis
### Testbed for Performance Evaluation  
The specifications of the device used for the test are as listed in Table 1. The device is a Raspberry Pi module running the Raspbian OS and is equipped with WLAN and Bluetooth interfaces. Figure 12 shows the testbed used for testing the connection between devices. The device is equipped with two WLAN interfaces and one Bluetooth interface. As shown in Figure 12, the device satisfying the user requirements incurs a backhaul by connecting an AP and supporting CCNx. Devices transmit the Bluetooth Beacon, which includes information on the backhaul and CCNx support. A device satisfying the user requirements establishes a wireless relay mode. One WLAN interface is used to connect to the  
Figure 11: Process of network configuration between devices.  
AP, while the other is used to operate the AP mode by using the hostapd package. Furthermore, by simultaneously using the BlueZ package, Bluetooth Beacon signals are transmitted. The transmission cycle for the Bluetooth Beacon signal was set to 0.1 s.
## 2 Related Studies and Conceptual Framework
### Machine Learning Algorithm
[45], who also applied deep learning to evaluate and model customer preferences to improve customer relationship management. The research adopted deep learning to enable catering businesses to be more productive and prevent waste. Although alternative models exist, [PERSON] et al. [46] and [PERSON] et al. [47] highlighted how deep learning benefits domains with massive amounts of data and outperforms shallow machine learning techniques in most applications. Another tool that has been utilized is the Random Forest Classifier (RFC). It comprises a decision tree and a Bagging algorithm [48]. Compared with alternative methods, the RFC tool has fewer errors and higher accuracy, making it suitable for solving problems without preceding knowledge [49]. [PERSON] et al. [48] also highlighted its difference and advantages compared to the basic decision tree and provided justification on its usage. [PERSON] et al. [50] utilized three machine-learning techniques to predict the customer's behavior, which gave good accuracy. Their study used the k-nearest neighbor algorithm, which resulted in 68% accuracy, the logistics regression algorithm which resulted in 55% accuracy, and the RFC algorithm, which resulted in 94% accuracy. Based on the results, the RFC algorithm provided the best accuracy in predicting the probability of an individual purchasing a specific product [51]. Therefore, among other classification tools, the RFC algorithm was considered due to its prominent results. An advantage of the RFC algorithm is the higher accuracy rate and better result output compared to other tools. However, [PERSON] et al. [33] explained that the RFC algorithm has disadvantages in terms of coding as the user dictates all the parameters needed to be used, data processing, and data arrangement. With that,it has heavy preparation requirements for analysts. In addition, the interpretation would be dependent on the arrangement made by the analyst. With more effort spent on the analysis and interpretation, the RFC tool would still provide better output compared to other classification tools [27]. In addition, [PERSON] et al. [49] expounded how the number of decision trees (DTs) in the RFC model is the key to the result of the model, as the number of DTs is directly proportional to the performance of the RFC algorithm. With that, it was concluded that the RFC algorithm is the best model for predicting consumer behavior, having an accuracy of 94.3% with 19 decision trees in total. Furthermore, [PERSON] et al. [52] showed that the results and predictions of the RFC algorithm with a 96% accuracy rate. This concluded that customer activity has the strongest influence, causing customer retention implying that consumers' behavior influenced their purchase intention. Thus, employing different MLAs to determine factors affecting consumer behavior has become viable in recent studies.
## 2 Convolutional Neural Network
### Convolution Layer  
The convolution operation expression for discrete data is given as follows:  
\[s(t)=(x\times w)(t)=\sum_{a=-\infty}^{\infty}x(a)\times w(t-a) \tag{1}\]  
where \(x\) is the input and \(w\) is the kernel function. Here, the \(s\) output is called the feature map. Equation (1) is a convolution operation for 1-D data. When the input data are a multi-dimensional array, the parameters of the kernel are usually a multi-dimensional array (features) and trained by the learning algorithm. For example, the formula for 2-D convolution with a 2-D convolution kernel of a 2D image is as follows:  
\[S(i,j)=(I\times K)(i,j)=\sum_{m}\sum_{n}I(i+m,j+n)K(m,n) \tag{2}\]  
where \(S(i,j)\) is the pixel value in the output image \(S\) at the point \((i,j)\), and \(K(m,n)\) is the parameter value of the convolution kernel at the point. Figure 1 shows an example of the convolution operation on a 2-D tensor.  
The convolution operation makes the CNN have sparse interactions, parameter sharing, and equivariant presentations, etc. These properties make the convolution operation very suitable for processing time series data collected by reciprocating compressors.  
Each convolutional layer may contain more than one convolution kernel and each convolution kernel corresponds to a channel of output data. The output after the convolution operation is necessary to add a bias term and is then activated by a nonlinear activation function. The output of the convolution layer is described as the following:  
\[z_{i}^{[l]}=K_{i}^{[l]}\times a^{[l-1]}+b_{i}^{[l]} \tag{3}\]  
\[a_{i}^{[l]}=g(z_{i}^{[l]}) \tag{4}\]  
where \(K_{i}^{[l]}\) is the \(i\)-th convolution kernel in layer \(l\), and \(a^{[l-1]}\) is the output of layer \(l\)\(-\)1. The offset term corresponding to the \(i\)-th convolution kernel in layer \(l\) of \(b_{i}^{[l]}\). \(z_{i}^{[l]}\) is not the activated output of the channel \(i\) in layer \(l\). \(g\) is the nonlinear activation function. \(a^{[l]}\) is the output of the channel \(i\) of the \(l\)-th layer and it is the input data of the next layer.  
Among them, the activation function enables the model to learn the nonlinear mapping of the input data, which improves the model's learning features ability. Activation functions mainly include the sigmoid function, tanh function, linear rectification function (ReLU), leakage rectification  
Figure 1: Schematic of the 2-D convolution operation.  
function, parametritis modified linear unit [25], softmax function, etc. Because ReLU does not easily disappear when using the error back propagation algorithm for model training, it can make shallow parameters easier to learn. Thus, the process of model training is accelerated, and the model converges better. Therefore, ReLU is also employed as the activation function in the convolutional layer and the expression of ReLU is as follows:  
\[g(z)=\max(0,z) \tag{5}\]  
For the convolutional layer, the parameters of each convolution kernel need to be obtained through training.
## 5 Conclusions and Future Work  
In this paper, the open-source CFD toolbox waves2 Foam based on Open FOAM is employed to numerically simulate breaking waves past a standing cylinder with different transverse inclined  
Figure 16: Free surface elevations around the inclined cylinder (\(\theta\) = 45\({}^{\circ}\)) with horizontal velocity contours (left). Dynamic pressure on the cylinder from the wave propagation view (right): (**a**) \(t/T\) = 30.375, (**b**) \(t/T\) = 30.555, (**c**) \(t/T\) = 30.7125, and (**d**) \(t/T\) = 30.855.  
angle \(\theta\). The present numerical results of the free surface elevations and the horizontal breaking wave forces are in good agreement with the published data. The variation of \(\theta\) has a significant effect on the characteristics of the wave forces and the free surface elevations.  
The vertical cylinder (\(\theta=0^{\circ}\)) is subject to the largest normalized horizontal wave force \(F_{T}\) and normalized high-order wave force \(F_{H}\). The high-order wave force has a large contribution to the total horizontal breaking wave force. The minimum ratio of the normalized high-order wave forces to the normalized total horizontal forces is \(51.95\%\) when \(\theta=45^{\circ}\). Concurrently, the minimum normalized \(F_{H}\) occurs for the case of the cylinder with \(\theta=45^{\circ}\). The normalized low-order wave force, \(F_{L}\), slightly decreases as the \(\theta\) increases. For the free surface elevations in front of the cylinder, the largest value is obtained when the breaking wave past a vertical cylinder and the minimum value is observed for the cylinder with \(\theta=45^{\circ}\). The secondary load cycle phenomenon is not observed when the cylinder is placed with \(\theta=30^{\circ}\) and \(45^{\circ}\).  
In future work, the breaking wave past two cylinders in tandem will be conducted to study the effect of the distance between two cylinders and transverse inclined angles on the forces. Meanwhile, the breaking wave past the structures with different shapes will be also investigated.  
Conceptualization, S.Q., S.L. and M.C.O.; Methodology, S.Q., S.L., S.S., M.C.O. and H.R.; Software, S.Q. and S.L.; Validation, S.Q., S.L., S.S. and M.C.O.; Formal analysis, S.Q. and S.L.; Funding acquisition, S.S., M.C.O. and H.R.; Writing--original draft, S.Q. and S.L.; Writing--review and editing, S.Q., S.L., S.S., M.C.O and H.R. All authors have read and agree to the published version of the manuscript. This research was supported in part with computational resources provided by the Norwegian Metacenter for Computational Science (NOTUR), under Project No: NN9372K; The National K&D Program of China (2018 YFC0310500) and Fundamental Research Funds for the Central Universities (3072019 CP0104). The authors declare no conflicts of interest.
# Energy-Efficient Resource Provisioning Strategy for Reduced Power Consumption in Edge Computing +
## 5 Resource Provisioning Strategy
### Edge Node Virtualization Model  
For each edge node, we suppose that there are two different modes that are active and sleep. We use \(P\) to denote the power of one edge node, which includes two modes, active mode \(P_{a}\) and sleep mode \(P_{s}\). Active mode is a high power mode of one edge node. Only the device in the active state can execute the task, and the task scheduling activity is also a kind of activity, which needs the support of the active state. The power consumption under the active mode is linear depending on the CPU usage, i.e., \(P_{a}=k\cdot C+b\), where \(b\) is the power of the edge node under the idle and \(k\) is a coefficient that fits the power curve of the edge node. Sleep mode is a very low power mode of one edge node. A device under this mode typically only reserves power for memory and related devices that can be used to wake up. Commonly used wake-up devices are a network card, mouse, keyboard, power button, and so on. Because sleep power is processor-independent, sleep power is the same on all three platforms. In general, the power consumption of the sleep state is extremely low. For a normal PC, this power consumption is about 4.5 W [32]. In this paper, we use this parameter to simulate the power consumption during sleep.  
We used three different edge devices. We regard Config 1 as an original device. In order to reduce the power consumption of the edge nodes, we split the original device into two devices, Configs 2 and 3. To ensure fairness, we guarantee that the sum of MIPS of Configs 2 and 3 is equal to Config 1. We refer  
Figure 3: Power consumption of the device in different configurations.  
to a device combination with only one Config 1 configuration as a non-master-slave, and each has a combination of Configs 2 and 3 called master-slave. We set the master device to Config 2 and the slave device to Config 3. The master device is used to task scheduling and execution, and the slave device only executes tasks and goes to sleep when idles. The subsequent experimental part will be based on this configuration. The specific experimental steps and results can refer to Section 6. Figure 3 is the power consumption value of the edge node under two different strategies, where the horizontal axis is the occupancy rate of the edge node and the vertical axis is the instantaneous power consumption of the node. Figure 3 shows that the power consumption of the combination of master-slave is much lower than the non-master-slave when it is less than 1400 MIPS. This is because the device of Config 2 is running and the device of Config 3 is in the sleep state. In master-slave, devices that do not receive a task can go to sleep when the task that needs to be calculated is none. A device that is in a sleep state can be woken up by other devices through a network command. In a non-master-slave, the device cannot enter the sleep state because there is only one device in the node. The detailed configuration of the simulator is shown in Table 2.