esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries  
### Digraph  
In this paper, we study the consensus tracking problem via leader-following coordination control structure. The information flow among following agents forms a digraph \(G\)([PERSON] et al., 2017), and the leader is denoted by vertex \(0\).  
The digraph \(G=(V,E,\mathcal{A})\) composed of the following agents contains the vertex set \(V=\{1,\cdots,n\}\), the edge set \(E\subseteq V\times V\), and the adjacent matrix \(\mathcal{A}=\left[a_{ij}\right]\in R^{n\times n}\). A directed edge from \(i\) to \(j\) in \(G\) is denoted by \(e_{i,j}=(i,j)\in E\), meaning that the node \(j\) can obtain information from the node \(i\). Assume \(a_{j,i}>0\Leftrightarrow e_{i,j}\in E\) and \(a_{i,i}=0\) for all \(i\in V\). The set of neighbors of node \(i\) is denoted by \(N_{i}=\{j\in V:(j,i)\in E\}\). The Laplacian matrix of the digraph \(G\) is defined as \(L=D-\mathcal{A}=[l_{i,j}]\in R^{n\times n}\), where \(D=\text{diag}\{\sum_{j=1}^{n}a_{i,j},i=1,\cdots,n\}\) is the degree matrix. In the digraph \(G\), a directed path from node \(i_{1}\) to node \(i_{s}\) is a sequence of ordered edges of the form \((i_{1},i_{2}),\cdots,(i_{s-1},i_{s})\), where \(i_{j}\in V\). A digraph is said to have a spanning tree if there exists a node that forms a directed path from this node to every other node.  
Then, the interconnection among all agents is characterized by a compound digraph \(\tilde{G}=\{0\cup G\}\), and the coupling weight of the \(i\)th following agent to the leader is denoted by \(s_{i}\). \(s_{i}>0\) if agent \(i\) can obtain information from the leader; otherwise \(s_{i}=0\). Besides, let \(S=\text{diag}\{s_{i},i\in V\}\).  
Through this paper, we take into account the following topology.  
**Assumption 1**.: _The interconnection topology of the following agents and the leader contains a spanning tree with the leader being its root._  
Under Assumption 1, the eigenvalues of \(L+S\) all have positive real parts [24].
## 4 Skeletal Muscle Regeneration and Histone Modifications
Furthermore, H3.1 overexpression has been reported to replace H3.3 in the regulatory regions of skeletal muscle genes and induced a decrease in H3K4 me3 and an increase in H3K27 me3, resulting in the suppression of myogenic differentiation in C2C12 myoblasts [44]. They also showed that bivalent modification of H3K4 me3 and H3K27 me3 was frequently observed in H3.3-containing skeletal muscle genes in mouse embryos. Canonical and noncanonical histone-specific modifications and their selective incorporation into specific loci provide an appropriate balance of gene expression to maintain the lineage potential of muscle progenitors. Initiation of embryonic and fetal myogenesis is preceded by the proliferation of Pax3-positive myogenic precursors [45; 46]. During embryonic myogenesis, Pax3-positive cells migrate into the limb buds at E11-12, where most cells lack Pax7 expression [46]. Pax3 expression in the myogenic precursors is mostly downregulated before birth, and only a few cells in the lower leg muscles of adult animals appear to express Pax3 [47; 48]. Previous studies [49; 50] have also reported that Pax3- and Pax7-positive cells have a distinct function in regeneration. According to these observations, prenatal and postnatal cell sources might provide individual functions in the myonuclei after the differentiation into muscle fibers. We demonstrated that prenatally incorporated myonuclei in the transgenic mice with skeletal muscle-specific EYFP reporter expression driven by tetracycline administration persisted until at least 13 weeks of age, although these prenatal myonuclei were ablated by muscle regeneration after injury in adult mice [51]. We also tested the hypertrophic function in the regenerated fibers using the rat soleus muscle (summarized in Figure 5). Overloading by the tendon transection of synergistically failed to induce compensatory hypertrophy in the regenerated fibers of adult rats, whereas unloading by tail suspension normally induced fiber atrophy. Loss of hypertroplying function correlated with the lowered histone acetylation at the _Igf1r_ locus, which was one of the genes that did not respond to overloading in the regenerated muscle. These alterations in regenerated fibers were improved by the transplantation of cells harvested from juvenile soleus muscles of neonatal rats in association with enhanced histone acetylation at the _Igf1r_ locus. These results suggested that prenatal myonuclei have a unique epigenetic feature that might enhance gene responsiveness to physiological stimuli later in the life. Figure 5: **Hyertrophic function programmed by prenatal myonuclei. ** Myonuclei that are incorporated into muscle fibers during the development (prenatal myonuclei) persist in the adult muscle fibers. Muscle injury leads to a loss of the prenatal myonuclei. Although muscle fibers are regenerated via satellite cell proliferation and differentiation, the regenerated fibers fail to undergo hypertrophy in response to overloading. See the main text and [PERSON] et al. [51] for more details.
# Autonomic Network Management and Cross-Layer Optimization in Software Defined Radio Environments
## 2 State of the Art and Background Information
### Autonomic Network Management  
ANM encompasses self-management and cognitive functionalities, such as self-awareness, self-configuration, self-optimization, and self-healing [22], addressing the ability of networks to be aware of themselves and their environment, and thus self-govern their behavior to achieve specific goals. Accordingly, collection, modeling, reasoning, and distribution of context in relation to sensor data play a critical role in ANM [23].  
Based on the above, the ANM operation can be described by a number of closed control loops (i.e., Autonomic Control Loops--ACLs) [24], which can be grouped into three main classes: Information Collection, including monitoring (or sensing) changes in the network and its environment; Decision Making, including analyzing changes to achieve the goals and planning reconfiguration if goals cannot be achieved; and Execution, including the implementation of respective actions and observation of the results, (i.e., returning to monitoring) closing the general control loop. Accordingly, the operation of the control loops is enhanced by learning and reasoning processes. The aforementioned ACL components that characterize ANM are depicted in Figure 1.  
Specifically, ANM offers context-awareness and thus application-awareness and self- and environment-awareness, which can be combined with programmability and virtualization, making the  
Figure 1: ACL components.  
network architecture application-customized and network-capability-aware. As a result, computing, storage, and network resources can jointly be optimized [5]. Additionally, ANM enables the automatic deployment of NFV applications in the network infrastructure [25]. Utilizing higher level abstractions could orchestrate the functionality of the compilers, operating systems and middlewares towards local and global optimization goals, thus enhancing adaptability [1]. ANM could be the answer to covering the manageability requirements of novel network architectures towards a more distributed and flexible management of operations, covering the market demand for more flexible and extensible designs, presenting the ability of context-aware and QoS-aware network logic, and meeting the running applications demands [1].
# CI-UNet: Application of Segmentation of Medical Images of the Human Torso
## 2 Materials and Methods
### Loss Function  
The loss function in this paper is obtained by adding the Cross-entropy loss function and Dice loss function. The mixed-use of the loss function can improve the ability of the network to detect small targets and enhance the segmentation ability. The loss function in this paper is shown in Equation (1).  
\[loss=Cross-entropyloss+Diceloss \tag{1}\]  
The Cross-entropy loss function is the most commonly used in classification, which measures the difference in a probability distribution. \(y\) is the actual probability of sample \(x_{i_{f}}f_{\theta}(x)\) is the predicted fraction of a specific sample probability, and S denotes the Softmax function. The multiclassification Cross-entropy loss function is shown in Equation (2).  
\[Cross-entropyloss=-\sum_{i=1}^{n}y_{i}\log(S(f_{\theta}(x_{i}))) \tag{2}\]  
The Dice loss function was first proposed in V-net [42] and has been widely used in medical image segmentation. The Dice loss function is named after the Dice coefficient, a metric function used to evaluate the similarity of two samples. The Dice coefficient compares whether the predicted image labels are similar to the samples of the actual  
Figure 8: The structure of the Polarized Self-Attention mechanism.  
image labels, and an enormous value means that the two samples are more similar. The mathematical expression of the Dice coefficient is shown in Equation (3).  
\[Dice=\frac{2|x\cap y|}{|x|+|y|} \tag{3}\]  
The mathematical expression of the Dice loss function is shown in Equation (4).  
\[Dicloss=1-Dice=1-\frac{2|x\cap y|}{|x|+|y|} \tag{4}\]
## 4 Experimental Results and Analysis
### CSCL-WVE-UDI Method Validity Experiment  
In order to validate the effectiveness of the CSCL-WVE-UDI method, experiments and analyses are conducted on the classic UNSW dataset for a scenario in which three unknown IoT devices coexist, and Precision, Recall, and F1 values are used to assess their recognition performance. The choice of three unknown IoT devices for testing in this experiment stems both from literature [7] that randomly selects three IoT devices from the UNSW dataset, and because three unknown IoT devices are sufficient to evaluate the performance of recognition  
Figure 17: Confusion matrix for identification of three unknown IoT devices.  
method [7], which balances the experimental complexity with the demonstration of the recognition results. The results of this experiment are shown in Figure 18.  
As can be seen from Figure 18, the CSCL-WVE-UDI method is more effective in recognizing the three unknown IoT devices, but there are also less-than-ideal recognition results, which are as follows: (1) for the Belkin Wemo Switch device, the Precision rate, Recall rate, and F1 value remain the same, and all of them reach 89.6% recognition, which indicates that the CSCL-WVE-UDI method is able to handle the recognition task of this unknown IoT device stably and effectively; (2) For the Canary Camera device, the CSCL-WVE-UDI method exhibits a high Precision rate and a low Recall rate, which is further shown by combining with the confusion matrix in Figure 17, which is due to the fact that only 2% of the Belkin Wemo Switch device is misidentified as the Canary Camera devices, making the false positive rate (FP) relatively low, but 24% of the Canary Camera devices are misidentified as the other two devices, resulting in an increase in the false negative rate (FN). Therefore, although the CSCL-WVE-UDI method shows a high Precision rate in recognizing Canary Camera devices, it fails to correctly identify all the Canary Camera devices, pulling down the Recall rate, but the F1 value still reaches 85.4%, which indicates that the CSCL-WVE-UDI method has a better overall performance and is capable of recognizing in all the Canary Camera devices to a certain extent the contradiction between Precision rate and Recall rate; (3) For Insteon Camera devices, the CSCL-WVE-UDI method has a higher Recall rate of 100%, but the Precision rate is 81.4%, which is relatively low, due to the fact that 8% of the Belkin Wemo Switch devices and 14% of the Canary Camera devices are misidentified as Insteon Camera devices, resulting in an increase in the false positive rate (FP) for this device. However, the CSCL-WVE-UDI method succeeded in correctly identifying all Insteon Camera devices, resulting in a false negative example rate (FN) of 0. Therefore, the Recall rate reached 100% and its F1 value reached 89.7%. In summary, the CSCL-WVE-UDI method shows good performance in recognizing three unknown IoT device coexistence scenarios, which validates the effectiveness of the CSCL-WVE-UDI method.  
Figure 18: Experiments on the validity of the CSCL-WVE-UDI approach.
## 2 Mathematical Model of SPMSM  
The stator voltage equation of SPMSM under the \(d\)-\(q\) axis:  
\[\left\{\begin{array}{ll}U_{d}&=&R_{s}I_{d}+L\frac{dI_{d}}{dt}-L\omega_{e}I_{q} \\ U_{q}&=&R_{s}I_{q}+L\frac{dI_{q}}{dt}+L\omega_{e}I_{d}+\psi_{f}\omega_{e}\end{array}\right. \tag{1}\]  
where \(U_{d}\) and \(U_{q}\) are the \(d\)-axis and \(q\)-axis components of stator voltage, respectively; \(I_{d}\) and \(I_{q}\) are the \(d\)-axis and \(q\)-axis components of stator current, respectively; \(R_{s}\) is the stator resistance; \(L\) is the stator inductance; \(\omega_{e}\) is the electric angular velocity of the rotor; \(\psi_{f}\) is the flux linkage of the rotor's permanent magnet.  
Rewriting Equation (1) into the state space equation with current as the state variable:  
\[\dot{I}=AI+BU+C \tag{2}\]  
where \(A=\begin{bmatrix}-\frac{R_{S}}{L}&\omega_{e}\\ -\omega_{e}&-\frac{R_{S}}{L}\end{bmatrix}\), \(B=\begin{bmatrix}\frac{1}{L}&0\\ 0&\frac{1}{L}\end{bmatrix}\), \(C=\begin{bmatrix}0\\ -\frac{\psi_{f}}{L}\omega_{e}\end{bmatrix}\), \(U=\begin{bmatrix}U_{d}&U_{q}\end{bmatrix}\), \(I=\begin{bmatrix}I_{d}&I_{q}\end{bmatrix}\).
## 5 Conclusions  
To conclude, we found that absorption dominates high EMI shielding efficiency of nanometrically thin PyC films. Specifically, a free standing PyC film of 110 nm thickness allows less than 20% of incident power to penetrate through because of 50% of absorption and more than 25% of reflection losses. This allows one to propose PyC films as an alternative to graphene-based EMI shields and filters [4,9]. At the optimal thickness, the absorption losses in the free standing film can be as high as 50%; however, interference effects can make losses even higher in the binary structures, when the PyC film is placed onto the back of the dielectric substrate. This opens opportunities for prototyping EM devices with enhanced absorption by using PyC and other conductive ultra-thin films. It is also worth noting that the absorbance, reflectance and transmittance of the PyC films deposited on silica substrate show very weak dependence on the frequency within the K\({}_{\text{a}}\)-band.
## 4 Results and Discussion
### Real-Time Digital Simulation  
Furthermore, the Simulink model of SST has been built and executed in real-time simulation using the OPAL-RT simulator. The specification of the real-time digital simulator is OP5707 XG, Intel(r) Xeon(r) processing cores with the power of a Xilinx(r) Virtex(r)-7 FPGA. The RT lab 2020 is installed on our laptop with a specification of 13 th Gen Intel(R) Core(TM) i7-1355U 1.70 GHz, 64-bit operating system.  
This key action marks a major advancement in the pursuit of accuracy and real-world application. The incorporation of OPAL-RT as real-time simulation is crucial and often employed for system development, analysis, and validation prior to the physical implementation. The proposed SST model has been developed using OPAL-RT software synchronization, creating an environment closely resembling real-world situations.  
Figure 11 represents the real-time synchronization setup, where the SST model has been developed using RT Lab (Figure 11a), synchronized through the OPAL-RT simulator (Figure 11b), and the output can be observed on an oscilloscope (Figure 11c). Furthermore, the performance of the real-time digital model of SST is presented in Figure 12. The input voltage and current are displayed in Figure 12a,b, while the output voltage and current are shown in Figures 12c and 12d, respectively. The DAB output can be observed in Figure 12e. Based on the results, it can be concluded that it exhibits the exact performance of the Simulink model, as presented in Figure 10. The implementation of the proposed SST Simulink model with OPAL-RT for real-time simulation has been successfully validated, representing a significant accomplishment in our research and development work. This makes the proposed SST model more credible and ensures that our considerations are not limited to the virtual world but have relevance to the complexities of the actual world.  
Figure 11: Real-time digital simulation setup for SST model validation. SST model has been developed using RT Lab (**a**), synchronized through the OPAL-RT simulator (**b**), and the output can be observed on an oscilloscope (**c**).  
Figure 10: (**a**) Source voltage. (**b**) Source current, (**c**) Load voltage (**d**) Load current, (**e**) DC-link voltage, (**f**) DAB output voltage.
## 1 Introduction
### Background
[19] developed a method to model phase spectra near the source region, taking into account the source rupture mechanisms, propagation path, and local soil conditions. Moreover, [PERSON] et al. [20; 21] presented a simple method for modeling phase characteristics based on GDT and wavelet analyses. [PERSON] [22] used the stochastic differential equation to simulate the GDT. Additionally, [PERSON] et al. [23] developed a method to apply the stochastic differential equation in relation to earthquake magnitude, epicenter distance, and local soil conditions. [PERSON] et al. [24] used the GDT concept to model phase spectra according to the compact support of the Meyer wavelet. The non-stationary features of design ground motions synthesized using a stochastic model of the Fourier phase were presented by [PERSON] and [PERSON] [25]. Implication of the model in engineering application to synthesizing design accelerograms was then discussed. Also, there is other method can be used to generate a DRSCEM by selecting the phases of the ground motions from particular observed earthquakes [6; 26; 27]. Many statistical studies have been conducted over the years to evaluate the appropriate stochastic characteristics of earthquake motions. Some researchers used actual recorded ground motions [28; 29; 30]. Other researchers used simulated ground motions [31; 32] to take into account the uncertainty of ground motion precisely. Because the uncertainty of the time history of earthquake motions affects the response characteristics of structural systems, strong fluctuations are commonly seen in response spectra calculated from observed earthquake motions. Due to this phenomenon, design-response spectra are usually defined through a stochastic evaluation. On the other hand, earthquake motion uncertainties are decomposed to uncertainties of the Fourier amplitude and phases. It is commonly understood that a design-response spectrum is a type of amplitude indicator for earthquake motion and it is used to control the amplitude of DRSCEMs. Although the uncertainty of Fourier amplitude has already been taken into account in linear response spectra, the uncertainty of Fourier phases has not been precisely considered thus far.
# Analysis and Suppression of Self-Excited Oscillations in Pressure Servo Valve System
## 2 Dynamic Model of the PSVS
### Working Principle of the System  
The hydraulic schematic diagram of PSVS is shown in Figure 1. The hydraulic cylinder is used to simulate the load, and the relief valve is used to adjust the backpressure of the system.  
The structure of the pressure servo valve is shown in Figure 2.  
When the main valve spool moves to the left, the high-pressure oil from port P enters port B through the throttle window on the right side of the middle shoulder. At this time, the high-pressure oil will enter the hydraulic cylinder rod less cavity, pushing the piston to move against the spring force. At the same time the output pressure is formed, which acts on the left of the little valve spool, forming negative feedback to the main spool. On the contrary, when the main spool moves to the right, the oil in the hydraulic cylinder rod less cavity will enter the backpressure cavity through the throttle window on the left side of the middle shoulder (as \(p_{b}>p_{h}\)), resulting in the pressure increase of the backpressure cavity.
## 1 Introduction  
Developing automatic driving assistance systems that can detect driving environments and avoid possible collisions has attracted much interest lately. The rapidly falling costs and increased performance of the sensors involved have contributed to the continuous growth of this field. One of the most critical problems in automatic driving assistance systems is detecting moving objects. The most common approach to moving object detection in this field is using active sensors (e.g., radar, lidar) [1]. However, when a number of vehicles with the same sensors are on the road simultaneously, interference among sensors may cause the system to malfunction. Moreover, active sensors have several drawbacks, such as high computation cost, low spatial resolution, and equipment expense. On the other hand, an optical sensor, which is usually referred to as a passive sensor, is generally cheaper than an active sensor. Moreover, the visual information from optical sensors is very important in various applications, such as traffic sign recognition, object classification, or lane detection. Therefore, motion detection using optical sensors has been studied in various parts of the world.  
The optical sensors used in motion detection can be divided into two types, stereo cameras and mono cameras. A stereo camera uses a disparity map to acquire the depth information of the objects viewed. However, a stereo camera works well only if its parameters have been accurately estimated, which is difficult to achieve from a moving vehicle. When a stereo camera is on a quickly moving vehicle, vibrations from the vehicle can disrupt the stability of its parameters and thus finding the correct correspondences for the stereo camera can be a great challenge [2]. Therefore, many existing methods prefer the mono camera approach for detecting moving objects.  
One reason that detecting moving objects from image sequences obtained by a moving camera is a difficult task is that the images include the motion of a static background. The motion in the image due to the moving camera is called egomotion. Numerous methods for moving object detection from a moving camera have been studied and proposed.
## 3 Case Study
### Performance Comparison Analysis of Different Solar Irradiance Forecasting Models  
The proposed DWT-CNN-LSTM forecasting model is different from the previous traditional solar irradiance forecasting models. The key characteristics of the DWT-CNN-LSTM forecasting model are the perfect combination of the following parts: (1) DWT based solar irradiance sequence decomposition; (2) CNN based local feature extractor; and (3) LSTM based sequence forecasting model. In addition, the solar irradiance forecasting models are individually established under sunny, cloudy, rainy and heavy rainy days. Given this fact, the relevant performance comparison analysis is also shown and discussed under the above four weather types. The involved three error indexes (i.e., RMSE, MAE, and R) are considered as the basis of the following performance comparison analysis of different forecasting models.