esa-sceva/satcom-chunk-collection huggingface dataset:
# A Diagnosability-Integrated Design Approach Based on Graph Theory
## 3 Diagnosability Integrated Design Method Details
### Diagnosis Method Determination
#### 3.2.3 Posterior Probability Output of Classifier  
According to the approach shown in Section 3.2.1, a classifier can be determined for each maximal clique. In the design of classifiers, constructing a classifier to generate posterior probabilities is a better approach in the discrimination case. Combining multiple types of posterior probabilities is useful in the final decision, even if the discrimination result is chosen based on the maximum posterior probability, which is the most Bayesian decision for the equal loss case. The output of the posterior probabilities allows not only to determine the classification of the classifier but also to measure the classification performance of the classifier by the posterior probabilities.  
In this paper, we took the support vector machine (SVM) as an example to illustrate the output method of its posterior probability.  
Assume that the training sample data set is \(\{x_{i},y_{i}\}\). For a sample \(x\) in it, the output of the support vector machine takes the form  
\[f(x)=\text{sign}(f(x))=\text{sign}(h(x)+b) \tag{8}\]  
where \(\text{sign}(\cdot)\) denotes the symbolic function acting as a decision function.  
\[h(x)=\sum\limits_{i}y_{i}\alpha_{i}k(x_{i},x)\]  
\(h(x)+b\) is a quantity proportional to the distance of the interface, and it is also believed that the closer the point is to the interface, the less likely it is to be correctly classified; the farther it is from the interface, the more likely it is to be correctly classified.  
For a binary classification problem, based on the Bayesian formula, we can get  
\[P(y=1|f(x))=\frac{P(f(x)|y=1)P(y=1)}{\sum\limits_{i=\pm 1}P(f(x)|y=i)P(y=i)} \tag{9}\]  
By fitting the Equation (9), we can get  
\[P(y=1|f(x))=\frac{1}{1+\exp(Af(x)+B)} \tag{10}\]  
where \(A\) and \(B\) are the parameters to be estimated.  
Equation (10) was got from dataset \(\{f(x_{i}),y_{i}\}\) using maximum likelihood estimation.  
Construct the maximum likelihood function according to Equation (10)  
\[L=\prod\limits_{y_{i}=1}P(y=1|f(x_{i}))\times\prod\limits_{y_{i}=-1}P(y=-1|f(x _{i})) \tag{11}\]  
Turning Equation (11) into a log-likelihood function  
\[\begin{array}{l}\log L\\ =\log\Biggl{(}\prod\limits_{y_{i}=1}P(y=1|f(x_{i}))+\prod\limits_{y_{i}=-1}P(y =-1|f(x_{i}))\Biggr{)}\\ =\log\Biggl{(}\prod\limits_{y_{i}=1}P(y=1|f(x_{i}))\Biggr{)}+\log\Biggl{(}\prod \limits_{y_{i}=-1}P(y=-1|f(x_{i}))\Biggr{)}\\ =\sum\limits_{y_{i}=1}\bigl{(}\log(P(y=1|f(x_{i})))\bigr{)}+\sum\limits_{y_{i} =-1}\bigl{(}\log(P(y=-1|f(x_{i})))\bigr{)}\end{array} \tag{12}\]So  
\[\begin{array}{l}A^{*},B^{*}\\ =\underset{(A,B)}{\operatorname{argmax}}\log L\\ =\underset{(A,B)}{\operatorname{argmax}}\left[\begin{array}{c}\sum\limits_{y_ {i}=1}(\log(P(y=1|f(x_{i}))))+\\ \sum\limits_{y_{i}=-1}(\log(P(y=-1|f(x_{i}))))\end{array}\right]\end{array}\]
# Model Checking Resiliency and Sustainability of In-Vehicle Network for Real-Time Authenticity
## 3 Preliminaries
### CAN (Controller Area Network)  
A Controller Area Network (CAN) is a de-facto standard for an in-vehicle network. Basically, once a node using CAN releases a message onto CAN bus, CAN broadcasts the message to all nodes, and the message is selectively picked up by an ECU that is one of message's destinations. Table 1 shows the structure of the CAN packet frame.  
Table 2 shows the individual frames of a CAN packet. Each node is given its own CAN ID, which plays a role as priority for CAN bus. Two or more nodes release messages into CAN bus at the same time, then one of them with the higher priority can transmit the message. In CAN bus, 0 (dominant bit) has a higher priority over 1 (recessive bit). That is, CAN controller permits 0 to flow over CAN bus rather than 1 when both are released at time same time. CAN causes various errors, such as bit, stuff, CRC, and ACK errors. Once a node on CAN bus encounters one of the errors, rest of nodes are informed the error simultaneously. Each node updates one of error counters, such as Receive Error Counter (REC) and Transmit Error Counter (TEC), according to error types error mode depending on error counter. For instance, a node transits into the passive error from the active error state when REC or TEC is over 126 (\(\geq\)127). A node under the passive error state goes to bus-off state when TEC is over 255 (\(\geq\)256), but the node is not driven to bus-off state by REC. Once a node is at bus-off state, it is enforced to leave the CAN bus for a specific time. TEC has different increasing and decreasing rates. Every time a transmission error happens, TEC increases by 8. Meanwhile, it decreases by 1 every time a transmission is successful.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**SOF** & **ID** & **Control** & **Data** & **CRC** & **ACK** & **EOF** \\ \hline
1 & 11 & 6 & 0–64 & 16 & 1 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CAN packet frame (Unit: bits).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Var** & **UPPAAL Var** & **Description** \\ \hline ECU\({}_{i}\) & nodid & ECU with id \(i\) \\ AUTH & & Authenticator \\ CANID\({}_{i}\) & canid & CAN id used by ECU\({}_{i}\) \\ Msg\({}_{i}\) & & Message with absolute sequence id \(i\) \\ AFR\({}_{i;i12}\) & & AFR message for message Msg\({}_{i}\) \\ Auth(Msg\({}_{i}\)) & & Authentication of message Msg\({}_{i}\) \\ Tran(Msg\({}_{i}\)) & & Transmission by CAN bus for message Msg\({}_{i}\) \\ Accept(Msg\({}_{i}\)) & & Action of accepting message Msg\({}_{i}\) by ECU \\ TxMsg\({}_{i}\) & txMsg[i] & Message released by CANID\({}_{i}\) \\ RxMsg\({}_{i}\) & rxMsg[i] & Message read by CANID\({}_{i}\) \\ CANBus & canstat & Predicate to indicate whether CAN bus is occupied or not \\ \hline \hline \end{tabular}
\end{table}
Table 2: Symbol and variable definitions.
## 2 Materials and Methods
We can conclude that with distancing of the tip \(A\) from the beam axis, the ultimate force increases. \[\widetilde{K}_{1}^{+}=\frac{\sqrt{l}l}{2}\big{(}2\widetilde{L}_{3}-\widetilde{d} \big{)}\big{(}2\widetilde{d}_{1}-\widetilde{l}\big{)}\widetilde{K}_{1}^{-}= \frac{\sqrt{l}l}{2}\big{(}2\widetilde{L}_{3}-\widetilde{d}\big{)}\big{(}2 \widetilde{d}_{1}-3\widetilde{l}\big{)}.\]4 Dependence of ultimate force \(Q^{*}\) on relative length \(\widetilde{l}\) of crack with particularly contacting faces at \(\widetilde{d}_{1}=0.03\). \[\widetilde{K}_{1}^{+}=\frac{\sqrt{l}l}{2}\big{(}2\widetilde{L}_{3}-\widetilde{d} \big{)}\big{(}2\widetilde{d}_{1}-\widetilde{l}\big{)}\widetilde{K}_{1}^{-}= \frac{\sqrt{l}l}{2}\big{(}2\widetilde{L}_{3}-\widetilde{d}\big{)}\big{(}2 \widetilde{d}_{1}-3\widetilde{l}\big{)}.\]5 Dependences of the ultimate force \(Q^{+}\) on the relative length \(\widetilde{l}\) for a crack with non-contacting faces at the tip \(B\) at various values \(\widetilde{d}_{1}\). The dependences of the ultimate force \(Q^{*}\) on relative length \(\overline{l}\) of cracks with perfectly contacting faces at various values of \(A\) are presented in Figures 6 and 7. \[\widetilde{K}_{1}^{+}=\frac{\sqrt{l}l}{2}\big{(}2\widetilde{L}_{3}-\widetilde{d} \big{)}\big{(}2\widetilde{d}_{1}-\widetilde{l}\big{)}\widetilde{K}_{1}^{-}= \frac{\sqrt{l}l}{2}\big{(}2\widetilde{L}_{3}-\widetilde{d}\big{)}\big{(}2 \widetilde{d}_{1}-3\widetilde{l}\big{)}.\]6 Dependences of ultimate force \(\widetilde{Q}^{+}\) on relative length \(\overline{l}\) for crack with particularly contacting faces. \[\widetilde{K}_{1}^{+}=\frac{\sqrt{l}l}{2}\big{(}2\widetilde{L}_{3}-\widetilde{d} \big{)}\big{(}2\widetilde{d}_{1}-\widetilde{l}\big{)}\widetilde{K}_{1}^{-}= \frac{\sqrt{l}l}{2}\big{(}2\widetilde{L}_{3}-\widetilde{d}\big{)}\big{(}2 \widetilde{d}_{1}-3\widetilde{l}\big{)}.\]7 Dependences of the ultimate force \(\widetilde{Q}^{+}\) on relative length \(\overline{l}\) for cracks with perfectly contacting faces at \(\overline{d}_{1}=-0,05\). \[\widetilde{K}_{1}^{+}=\frac{\sqrt{l}l}{2}\big{(}2\widetilde{L}_{3}-\widetilde{d} \big{)}\big{(}2\widetilde{d}_{1}-\widetilde{l}\big{)}\widetilde{K}_{1}^{-}= \frac{\sqrt{l}l}{2}\big{(}2\widetilde{L}_{3}-\widetilde{d}\big{)}\big{(}2 \widetilde{d}_{1}-3\widetilde{l}\big{)}.\]8 Dependences of ultimate force \(\widetilde{Q}^{*}\) on relative length for cracks with perfectly contacting faces at various values of \(\overline{d}_{1}\). In Figure 6, curve 1 corresponds to crack tip \(A\) that is closer to the beam axis, while curve 2 corresponds to remote tip \(B\). It is clear that beam failure begins simultaneously at both tips. These curves are built for constant crack length. Curve 1 corresponds to \(\widetilde{d}_{1}=-0,05\), while curve 2 corresponds to \(\widetilde{d}_{1}=-0,1\). It follows that with distancing of the crack tip \(A\) from the beam axis, the ultimate force decreases for a fixed relative length of the crack.
## 2 Proposed Method
### The Proposed Model
#### 2.2.4 Facial Synthesis Method  
In addition to generating images from random noise, as with other unsupervised GANs, our model can also be used to synthesize two images. StyleGAN introduced style mixing, which exploits two or more input vectors, but it was used on only random noise input, not existing images. To mix two existing images, an additional encoder needs to be trained to map the images onto the latent space of the input. However, our model does not require an additional network because our discriminator already has an encoder. By taking advantage of the auto-encoder structure of our discriminator, we present a method for mixing existing images. The encoder encodes an input image as two latent vectors, and they are exploited in different layers of the decoder. Reconstruction occurs when the decoder uses the two latent vectors from a single image. However, if the decoder exploits a combination of the two latent vectors from two different images, the output of the decoder is a mixed image.  
Let \(X\) and \(Y\) denote the two images to be mixed; the output of the encoder when the input is \(X\) and \(Y\) can be expressed as  
\[[z_{X_{1}}^{*}\ z_{X_{2}}^{*}]=Enc(X),\ [z_{Y_{1}}^{*}\ z_{Y_{2}}^{*}]=Enc(Y). \tag{7}\]  
If the decoder decodes the image using \(z_{X_{1}}^{*}\) and \(z_{X_{2}}^{*}\), it reconstructs \(X\), and if it uses \(z_{Y_{1}}^{*}\) and \(z_{Y_{2}}^{*}\), it reconstructs \(Y\); i.e.,  
\[X^{*}=Dec\big{(}z_{X_{1}}^{*},\ z_{X_{2}}^{*}\big{)}\to\textit{ Reconstruction of }X, \tag{8}\]  
\[Y^{*}=Dec\big{(}z_{Y_{1}}^{*},\ z_{Y_{2}}^{*}\big{)}\to\textit{ Reconstruction of }Y, \tag{9}\]  
where \(X^{*}\) and \(Y^{*}\) denote the reconstructed images of \(X\) and \(Y\), respectively, and \(Dec(.)\) denotes our decoder. To synthesize \(X\) and \(Y\), the decoder needs to take the latent vectors from the two images as input. A mixed image of \(X\) and \(Y\) is acquired by exploiting a combination of the latent vectors from the two images (e.g., \(z_{X_{1}}^{*}\) and \(z_{Y_{2}}^{*}\)), as illustrated in Figure 3. The two blue boxes in Figure 3 represent the two parts of the decoder; i.e., one is involved in generating a \(32\times 32\) feature map from a given latent  
Figure 2: Progressive training of the proposed generator and discriminator. Our model starts with a \(32\times 32\) image in the first stage, and the size of the training images is doubled in the next stage.  
vector, and the other is involved in generating a \(128\times 128\) image from a given \(32\times 32\) feature map. Therefore, the synthesis process can be expressed as  
\[I_{X,Y}=Dec\big{(}z_{X_{1}}^{*},\ z_{Y_{2}}^{*}\big{)}\to Synthesis\ of\ X\ and\ Y, \tag{10}\]  
where \(I_{X,Y}\) is an image that has the structural or coarse-scale characteristics of X and the details or fine-scale characteristics of Y.
# Fatigue Crack Growth in Managing Steel Obtained by Selective Laser Melting
###### Abstract  
Selective Laser Melting (SLM) is an additive manufacturing technology, ideal for the production of complex-shaped components. Design against fatigue is fundamental in the presence of cyclic loads, particularly for these materials which typically have significant porosity, high surface roughness and residual stresses. The main objective here is to study fatigue crack growth (FCG) in the 18 Ni300 steel obtained by SLM. Typical da/dN-AK curves were obtained in C(T) specimens, indicating that cyclic plastic deformation may be the controlling mechanism. A complementary analysis, based on plastic CTOD range, showed a relatively low level of crack tip plastic deformation, and consequently a reduced level of plasticity induced crack closure. The curve da/dN versus plastic CTOD range is clearly above the curves for other materials.  
fatigue crack growth; Selective Laser Melting (SLM); Crack tip Opening Displacement (CTOD); crack tip plastic deformation; AK +
Footnote †: journal: Applied Sciences
# An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph
## 2 System Overview  
In this section, the hardware structure of the MUAV system is described. The MUAV has the characteristics of small size and light weight. A multi-sensor navigation system plays a key role in the MUAV system. According to the characteristics of different navigation sensors, the hardware is designed to enhance the expandability of the system. Considering the flexibility and high efficiency of the hardware structure, dual-redundant sensors are used in order to improve performance of the navigation system. The redundant sensors are beneficial for monitoring the health of each sensor if one sensor fails to work. The hardware also consists of the Cortex-M4 core ARM processor, RC signal processing, motor drive circuit, SD card data storage, and the communication interface.  
The MUAV is equipped with a variety of sensors, including the GPS receiver, optic flow sensor, sonar sensor, Xbee wireless communication module, electron speed regulator, and motors, etc. Different sensors and their performance are shown as follows.  
* MPU-6000 inertial sensor, including a three-axis MEMS gyroscope and a three-axis accelerometer.
* HMC5983 magnetometer enables 1\({}^{\circ}\) to 2\({}^{\circ}\) compass heading accuracy with temperature compensation.
* MS5611 barometer module, with an altitude resolution of 0.1 m.
* URM37 sonar module provides 0.04 m-5 m non-contact measurement function, the ranging accuracy can reach to 1 cm.
* Ublox LEA 6H GPS receiver, with the position accuracy of 2 m.
* Optical flow sensor processing the pixel resolution of 752 \(\times\) 480 at 120 (indoor) to 250 Hz (outdoor).  
The ground control station used is the advanced open-source ground Control Station software, which can realize 2/3D aerial maps with drag-and-drop waypoints. The SPI bus is used for interfacing the sensor to our microcontroller which makes the measurements available to our ground control station. The hardware structure of the MUAV is shown in Figure 1.
###### Abstract  
_Aims:_ This study evaluated the relationship between pulse pressure (PP) and periodontal disease in Korean adults with or without hypertension (classified as HTN and non-HTN, respectively). _Methods:_ Data on 3496 adults (\(\geq\)20 years) were obtained from the Korean National Health and Nutrition Examination Survey (2015). We classified those with HTN as SBP \(\geq\) 140 mmHg, DBP \(\geq\) 90 mmHg, or current use of anti-hypertensive medications. _Results:_ There were a few key findings: when logistic regression analysis was applied for periodontal disease (community periodontal index score \(\geq\) 3), the odds ratios (OR) were significantly higher in the high PP (PP \(>\) 60 mmHg) than in the normal PP (PP \(\leq\) 60 mmHg) in the HTN group (OR, 2.131; 95% confidence interval (CI), 1.579-2.876). However, periodontal disease was not associated with high PP in the non-HTN group and was not significant (OR, 0.866; 95% CI, 0.494-1.518). _Conclusions:_ In Korean adults, periodontal disease was positively associated with PP in the HTN group but not in the non-HTN group.  
periodontal disease; community periodontal index scores; blood pressure; pulse pressure; hypertension +
Footnote †: journal: Applied Sciences  
the relationship between PP and periodontal disease in Korean adults with or without HTN. Data from the 6 th Korean National Health and Nutrition Examination Survey (KNHANES VI-3) were used.
###### Abstract  
The distribution systems operate radially with meshed topologies thanks to tie-switches and sectionalizing switches consisting of the systems. The power distribution systems are undergoing evolutions strongly toward active distribution systems for reliability and quality of service enhancements through reconfiguration. The distributed generations are also one effective solution for the above objectives. A consumption time is one of the considerable issues that many existing methods have not been achieved for reconfiguration purpose. This paper brings an adaptive solution called modified sequential switch opening and exchange (MSSOE), which is carried out with MATLAB and MATPOWER tool, to overcome these issues. The fundamental loop has been proposed for the MSSOE method to reduce search space in the iteration process. In each step of the searching process, MSSOE observes that if any switches within the same loop of the selected tie-switch are deleted to avoid opening those switches in the next iteration. The process of MSSOE is done when the radial topology is given. The proposed method is tested with the standard IEEE 33-bus, IEEE 69-bus, and IEEE 119-bus distribution systems to observe the effectiveness of the MSSOE algorithm. Comparison to existing algorithms in terms of global solution and computation time conclude that the MSSOE is the best method.  
distributed generations; distribution systems; optimization; reconfiguration; sequential switch opening and exchange method +
Footnote †: journal: Applied Sciences  
Article  
Optimal Reconfiguration in Distribution Systems with Distributed Generations Based on Modified Sequential Switch Opening and Exchange
## 4 Experiments and Discussion
### Result and Discussion
In addition, we used modularity to evaluate the performance of the IDBESW model and other different models on three real datasets. Table 2 shows the comparison between our model and the other models. Each row in the table represents the modularity accuracy of different models under different datasets. In order to ensure the stability of the results, we simulate 100 times and put the average value as the final result. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Facebook** & **Twitter** & **Epinions** \\ \hline IC & 0.54 & 0.47 & 0.03 \\ \hline GAD & 0.82 & 0.76 & 0.15 \\ \hline PSO & 0.83 & 0.81 & 0.08 \\ \hline MSIP & 0.84 & 0.82 & 0.26 \\ \hline IDBESW & 0.87 & 0.84 & 0.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the proposed model with four rival models. Figure 6: Comparison of the IDBESW model with traditional models and real diffusion propagation. (**a**) Comparison between our model with the traditional model using the Twitter dataset. (**b**) Actual information propagation trends of the Ferguson event in the Twitter dataset. As shown in Table 2, the IDBESW model is superior to other models because it considers the common neighbor nodes. The more common the neighbors are, the closer the relationship between nodes, and the higher the probability of accepting information. The IC model is the weakest in terms of reflecting the community structure. The nodes activated by the IC model have only one chance to spread information, and the probability of information transfer among nodes is completely random. However, for the Epinions dataset, all the model results are worse than the results for the other two datasets. Through the analysis of the datasets, we found that, owing to the sparse network structure of the Epinions, the average node degree is only 6.71, while the average node degree in the Facebook and Twitter datasets is 43.7 and 43.5, respectively.
## 2 Remote Sensing Technologies for the Monitoring of Coastal and River Delta Regions
The remainder of this section provides technical details on the principal RS technologies, including both optical and microwave methods, emphasizing their role in the monitoring of major coastal regions and river deltas, and considers various disaster risk conditions, such as ground subsidence, flooding, coastal erosion, SLR, and extreme climatic events. Figure 1 shows the worldwide geographical distribution of major coastal regions and the major river deltas considered here.
## 4 Contribution  
There are a large number of scatterers such as buildings and trees in a complex urban environment. These scatterers have different distribution densities in different positions. And as the height changes, the density of the scatterers decreases. In emergency management, when the UAV interacts with the ground, the performance of the propagation channel plays an important role in the reliability and stability of communication. According to the different channel characteristics of UAVs in different regions, this section proposes to divide the channel into ultra-low altitude (0-100 m) and low altitude (100-1000 m) for modeling.
## 3 Experiment and Results  
### Fabrication of the Proposed Waveguides  
The exact reproduction of the designed structure requires tremendous efforts to optimize the parameters of the facility. Fortunately, 3D printing is a process of making prototype parts directly from computer models, which opens up almost unlimited possibilities for rapid prototyping [13]. In this study, waveguides with the gap distance (\(g\)) set as 500 \(\upmu\)m, 1000 \(\upmu\)m, and 2000 \(\upmu\)m, respectively, were printed using the SLA 3D printing mechanism. Figure 3a shows an overview of the entire 3D printing methodology [25; 26]. Figure 3b-e shows a cross-section of the optical electron microscopy (OEM) images of the waveguide (e.g., the gap distance \(g\) is 500 \(\upmu\)m). The fabricated error of the waveguide structure was measured using a microscope camera to evaluate the accuracy of the dimensions [27]. The OEM results proved that the morphology of the 3D-printing was consistent with the design. According to the measurement result, as shown in Figure 3f, \(d\) = 800 \(\pm\) 14.96 \(\upmu\)m, \(D\) = 1500 \(\pm\) 19 \(\upmu\)m, \(L\) = 750 \(\pm\) 18.74 \(\upmu\)m, \(g\) = 500 \(\pm\) 14.23 \(\upmu\)m. It is noted that the difference between the measured and designed dimensions was within the allowable printer error (<50 \(\upmu\)m). Therefore, the fabrication method of the proposed waveguide had the advantages of a low-cost and easy fabrication, and flexible design. These properties are desirable for applications of various terahertz devices [30].