esa-sceva/satcom-chunk-collection huggingface dataset:
# Graph Convolutional Networks for Privacy Metrics in Online Social Networks
## 3 Datasets and Notation
### Problem Description and Notation  
In social networks, privacy metrics evaluate various factors that may cause privacy disclosure and finally obtain a number that can map the user's privacy status, which represents the degree of the user's privacy disclosure. In this paper, we aim to integrate the attribute information, friend relationships, and behaviour characteristics of users on social networks through the introduction of GCNs to obtain the degree of privacy exposure and achieve the purpose of privacy metrics. In addition, our method can reduce subjective intervention and the difficulty of labelling samples.  
We define the sample space as X\(=\{x_{1},x_{2},x_{3}\cdots\cdots x_{N}\}^{T}\). The privacy scores are defined as numbers from 1 to 7, that is, the ultimate goal is to classify unlabelled samples into K\(=\{k_{1},k_{2},k_{3},k_{4},k_{5},k_{6},k_{7}\}\). The privacy score of a sample is the corresponding category number \(i=|k_{i}|\) and is ultimately classified into a certain category. In each category \(k\), there are few labelled samples \(X_{k}^{l}\), \(\left|X_{k}^{l}\right|=C\in\{1,5,10,20,50,100\}\) and an unlimited number of unlabelled samples \(X^{z}\). The feature set of the samples is \(V_{N}\)\(=[v_{1},v_{2},v_{3},\cdots\cdots,v_{d}]\), and finally, all the samples can be expressed as a matrix \(V\in\mathbb{R}\)\({}^{N\times d}\).  
To combine the graph structure information, we establish the adjacency matrix \(A\in\mathbb{R}\)\({}^{N\times N}\) of nodes, where \(a_{ij}\) indicates that the link of node i points to node j, and in a social network, it indicates that user i follows user j. It is worth noting that the adjacency matrix we establish is asymmetric-\(a_{ij}\) is not necessarily equal to \(a_{ji}\)--which means that the relationship between nodes is bilateral. Since the elements on the diagonal of the matrix \(A\) are all 0, to be able to back-propagate in the training of the deep neural network, we normalise the adjacency matrix \(A\) to obtain \(\widetilde{A}=A+I\), where \(D=diag((A+I)1)\) is the degree matrix of \(\widetilde{A}\) and \(\mathbf{1}\) is a vector for which the elements are all 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute** & **Sensitivity** \\ \hline Phone Number & 0.5669 \\ Email & 0.3260 \\ Hometown & 0.2253 \\ Birthdate & 0.2748 \\ Address & 0.4212 \\ Job Details & 0.2024 \\ Relationship Status & 0.1731 \\ Interests & 0.1255 \\ Education & 0.1575 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Attribute sensitivity.
# IoT-Based Smart Surveillance System for High-Security Areas
## 2 Materials and Methods  
The methodology behind SS-HSA is categorized into three main sections. The initial section provides a detailed explanation of how SS-HSA operates. The second section highlights the hardware features and specifications that are employed in SS-HSA. The final section delves into the application of machine learning techniques to evaluate the precision and authenticity of SS-HSA. SS-HSA is composed of six modules. The initial module is GMS, followed by Arduino UNO, GSM, and an end-user device, such as a cellular phone or similar device. The fifth and sixth modules concern the construction of an XLSX file that contains data on detected moving objects and testing their accuracy using machine learning algorithms. The block diagram of SS-HSA is shown in Figure 3.  
The SS-HSA was constructed in three phases: a setup phase, a detection-of-suspected-object phase, and a notification-of-a-user-in-real-time phase, the latter of which is carried out through a GSM call and SMS generated on the end-user's device. In the first phase, 5 V is supplied to activate Arduino UNO. In the second phase, the GMS module begins detecting an object. If it finds an object, sends a notification signal to Arduino UNO that an object has been detected. In the third phase, Arduino UNO coordinates with the GSM module and instructs it to send SMS and call alerts to the end user via their mobile device.  
Figure 3: Block diagram of the SS-HSA.  
Figure 2: Real-time SS-HSA.  
In this way, it is possible to take necessary action upon the detection of a suspected object, which, ultimately, contributes to mitigating the loss of property as shown in Figure 4.
# Flexible Antennas: A Review
## 4 Applications of Flexible Antennas under Different Frequency Bands  
Even the most conservative projection for the growth of the internet of things (IoT) shows that the global IoT industry set to reach over USD 363.3 billion by the year 2025 [130]. A significant portion of this market includes health monitoring and clinic therapeutic devices, medical microwave radiometry, wearables, vehicular navigation systems, etc. Because of the nature of these applications, the antenna used should be flexible, conformal, and stretchable to comply with the curvilinear surfaces and dynamic motions. Besides civilian applications, it also plays a vital role in the military domain. Most of the military devices are connected to a large ad-hoc network. Military personnel are required to carry a large amount of equipment with different sensors and health monitoring devices. Therefore, flexible, and lightweight antennas are desirable in the military sector to reduce the burden of the soldier. In this article, flexible antenna applications are delineated into two categories: below 12 GHz and above 12 GHz.
## 2 Data and Methodology
### Methodology
#### 2.2.3 Criteria for Differentiating Water Scarcity- and Heatwave-Induced Droughts  
Given the heterogeneity of vegetation coverage and the distinct topographical features of the Songnen Plain [34], it became imperative to undertake separate spatial and tempo  
Figure 2: A schematic representation of the method used to identify a flash drought event. The subfigure shows the development period of flash drought revealed by changes in soil moisture.  
ral examinations of heatwave-induced and water scarcity-induced flash droughs. The mechanism triggered by a flash drought can be classified into two categories [13; 17]: One is precipitated by short-term high-temperature weather, specifically heatwaves that intensify soil evaporation and precipitate a sharp decrease in soil water content. The second type of flash droughs is attributed to a regional precipitation deficiency, resulting in an insufficient soil water content, and weakened soil evapotranspiration capacity, coupled with abnormally high air temperatures. More precisely, when the air temperature is anomalously high, accompanied by low soil moisture content and evapotranspiration exceeding the norm, it is classified as a heatwave-induced flash drought. Conversely, when the air temperature remains abnormally high but the soil moisture content is low and precipitation falls below the standard range, it is identified as a water scarcity-induced flash drought [19; 35].  
The type of flash droughs was identified according to the following procedure:  
(i) Temperature condition judgment: Determine whether the temperature anomaly of a certain meteorological day is higher than 1.0 times the standard deviation.  
(ii) Determination of the ET conditions: If the air temperature is abnormally high, the soil moisture is low, and the ET anomaly is positive, it is a heatwave-induced flash drought; otherwise, if the air temperature is abnormally high, the soil moisture is low, and the ET anomaly is negative, it is a water scarcity-induced flash drought.
## 3 NARX Model on the Rotor-Bearing System
### Identification of the NARX Model
When the selection goes to step \(l\), define \(m\
eq S_{1}\cap m\
eq S_{2}\cap L\cap m\
eq S_{l-1}\), and based on the selected orthogonalized model term vectors \(\widetilde{w}_{1}\), \(\widetilde{w}_{2},\cdots\widetilde{w}_{l-1}^{(l)}\), the \(l\)th model term vector is given as: \[w_{m}^{(l)}=p_{m}-\sum_{i=1}^{l-1}\frac{<p_{m},\widetilde{w}_{i}>}{<\widetilde {w}_{i},\widetilde{w}_{i}>}\widetilde{w}_{i}\] (14) The ERR value of each vector and the one with maximum ERR value are obtained as: \[ERR_{m}^{(l)}=\frac{{(g_{m}^{(l)})}^{2}{<w_{m}^{(l)},w_{m}^{(l)}>}}{<y,y>} \times 100\%\] (15) Figure 5:0 (16)Based on (16), one obtains the \(l\)th model term vector of the orthogonalized matrix in (9), which means \(w_{l}=w_{S_{l}}^{(l)}\), and given \(\beta_{l}=p_{S_{l}}\). * The algorithm will stop selecting at the \(M^{\prime}\)th step when the model structure satisfies the following condition: Figure 5:1 (17) where \(\rho\) is the threshold; \(M^{\prime}\) represents the total number of the selected model terms. Based on (15)-(17), the \(M^{\prime}\)th selected model term is given as \(w_{M^{\prime}}=w_{S_{M^{\prime}}}^{(M^{\prime})}\). The orthogonalized NARX model is given as: Figure 5:2 (18)
* Step 3: Calculation of model coefficients: Based on (18), the final NARX model can be written as [15]: Figure 5:3 (19) where: Figure 5:4 (20)  
As for model validation, there are two different methods, defined as OSA (One Step Ahead) and MPO (Model Predicted Output). It is difficult to give a general conclusion as to method should be used in a specific situation [32]. The concept of OSA validation can be explained by using a simple second-order NARX model:  
Figure 5:5  
Assume that a number of values if system input \(u(k)\) and \(y(k)\) are available. The OSA validation processes, starting from step 3, are then given as:  
\[\left\{ \begin{array}{c}\hat{y}(3)=ay(2)+by(1)+cu(2)y(2)\\ \vdots\\ \hat{y}(k)=ay(k-1)+by(k-2)+cu(k-1)y(k-1)\end{array} \right. \tag{22}\]  
By comparison, the calculation of model output by the MPO method is completely different from the OSA method. Consider again the NARX model (21), for which the MPO can be defined as:  
\[\left\{ \begin{array}{c}\hat{y}(1)=y(1)\\ \hat{y}(2)=y(2)\\ \vdots\\ \hat{y}(k)=a\hat{y}(k-1)+by(k-2)+cu(k-1)\hat{y}(k-1)\end{array} \right. \tag{23}\]In this paper, the results are evaluated by time domain and spectrum graph, as well as RMSE (root mean square error (RMSE). The RMSE is given as:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}(\hat{y}_{i}-y_{i})^{2}}{N}} \tag{24}\]  
where \(\hat{y}_{i}\) and \(y_{i}\) are the predicted output and actual output, respectively; \(N\) is the number of sampling observations.
## 3 Results and Discussion
### Tip Leakage Flow in the Ducted Propeller  
The tip clearance between the propeller tip and the duct inner wall is necessary for the regular operation of the DP. However, it can pose some flow features, such as complicated vortical structures, which are usually undesirable. These vortical structures are often generated by the tip leakage flow and its interaction with surrounding boundaries and can cause rotational instabilities and blockage in the flow passage, which affects the overall performance [17].  
Figure 9: The locations of the different sections on the duct (**a**) and comparisons of pressure distributions in the section at the propeller’s (**b**) leading edge (Sec-L), (**c**) middle plane (Sec-M), and (**d**) trailing edge (Sec-T) between the RDT and the ducted propeller (DP) at J = 0.1.  
Figure 10 displays the velocity vector field at the blade tip region with the pressure contours as background for the DP and the RDT at J = 0.1 and J = 0.5 at Sec-M, as defined in Figure 9a. It is shown in Figure 10a,b that when approaching the tip clearance region, the boundary layer on the duct's inner surface starts to separate due to an adverse pressure gradient, and the separation occurs earlier at J = 0.1 than at J = 0.5, as a heavier loading condition means a larger pressure gradient. On the other hand, the flow on the propeller tip separates at the pressure side of the propeller and interacts with the separated boundary layer on the duct's interior side. It can be clearly observed that vortical structures are formed in this region and, with increasing fluid momentum, these vortices are forced to move to the propeller suction side and become smaller. In contrast, as shown in Figure 10c,d, the flow field near the blade tip for the RDT is less complex.  
According to their features, You [17] roughly classified vortical structures into three categories, namely the tip leakage vortex, the tip separation vortex, and the induced vortex. The tip leakage vortex is strongly influenced by the pressure difference between the propeller's pressure and suction sides, and the induced vortex is generated alongside the predominant tip leakage vortex. The tip separation vortex is formed by the separated flow from the propeller tip. As a result, these vortical structures interact with each other and cause viscous loss in development, which influences the performance of the propeller.  
The Q-criterion for vortex identification [18] is introduced to help visualize the vortical structures in the tip gap flow. Figure 11 displays the three characteristic vortical structures at J = 0.1 and J = 0.5. As expected, the vortex scale at J = 0.5 is smaller than the one at J = 0.1, and it can also be observed that in the downstream direction from the trailing edge the tip separation vortex starts to interact with the tip leakage vortex.
## 4 Methods and Heuristic Algorithm
### Nonlinear Models
#### 4.1.3 OSELM  
An Extreme Learning Machine (ELM) is a simple and effective single hidden-layer feed-forward neural network proposed by [PERSON] et al. [38]. In many practical applications, however, learning is a continuous process. For this reason, [PERSON] et al. proposed an online learning algorithm, the OSELM [39].  
For different N training sample \(Z\), \(Z=\{(x_{i},\ t_{i})|i=1,\ldots,N\}\), where \(x_{i}=[x_{i1},\ x_{i2},\cdots,x_{\text{in}}]\in R^{n}\), \(t_{i}=[t_{i1},\ t_{i2},\cdots,\ t_{\text{in}}]\in R^{m}\), ELM with \(L\) hidden layer nodes, and \(g(x)\) activation function can approximate arbitrary \(N\) samples with zero errors by arbitrarily specifying \(a_{j}\) and \(b_{j}\), which can be expressed as follows:  
\[O_{i}=\sum_{j=1}^{L}\beta_{j}g(a_{j},b_{j},x_{i})=\ t_{i} \tag{8}\]  
Figure 1: The flow chart of hybrid Back-Propagation Neural Network (BPNN).  
where \(a_{j}\) is the input weight, \(b_{j}\) is the threshold of hidden layer nodes, \(x_{i}\) is the input vector, \(O_{i}\) is the output vector, and beta \(\beta_{j}\) is the output weight \(\beta_{L\times m}\)= \(\left[\beta_{1},\ \beta_{2},\ \cdots,\ \beta_{L}\right]^{T}\).  
Formula (8) can be simplified as:  
\[H\beta\ =\ T \tag{9}\]  
where, \(\beta_{L\times m}\)= \(\left[\beta_{1},\ \beta_{2},\ \cdots,\ \beta_{L}\right]^{T}\), \(T_{N\times m}\)= \(\left[T_{1},\ T_{2},\ \cdots,\ T_{N}\right]^{T}\), and \(H\) is hidden layer output matrix.  
\[H(a_{1},\cdots,a_{L};\ b_{1},\cdots,b_{L};x_{1},\cdots,x_{N})=\left[\begin{array} []{cccc}g(a_{1},\ b_{1},\ x_{1})&\cdots&g(a_{L},\ b_{L},\ x_{1})\\ \vdots&\ddots&\vdots\\ g(a_{1},\ b_{1},\ x_{N})&\cdots&g(a_{L},\ b_{L},\ x_{N})\end{array}\right]_{N \times L}\.\] Column \[J\] of \[H\]  
represents the output of the \(j\) hidden layer node for input \(x_{1},\ x_{2},\ \cdots,x_{N}\).  
After the pseudoinverse matrix is incorporated, the least-squares solution of the above linear system is:  
\[\hat{\beta}=(H^{T}H)^{-1}H^{T}T \tag{10}\]  
Based on a recursive least-squares algorithm, the algorithm flow of OSELM can be described as follows:  
(1) Initialization Stage  
Given activation function \(g\), the number of hidden layer nodes is \(L\). A small training set is given to initialize the network and obtain the initial value. The output weight value is \(\beta^{(0)}\). Take \(k=0\), where \(k\) is the number of data segments sent to the network, which is represented.  
(2) Online Learning Stage  
Given the \(k+1\) data segment, calculate output weight beta \(\beta^{(k+1)}\), take \(k=k+1\), then return to the online learning stage, and constantly update the calculated output weight until data learning is completed.
Sensors_**2012**, _12_, 5486-5501; doi:10.3390/s120505486  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Localization with a Mobile Beacon in Underwater Acoustic Sensor Networks**  
**[PERSON] and [PERSON] ***  
School of Information and Mechatronics, Department of Nanobio Materials and Electronics, WCU, Gwangju Institute of Science and Technology (GIST), 1 Oryong-dong, Buk-Gu, Gwangju 500-712, Korea; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 21 March 2012; in revised form: 23 April 2012 / Accepted: 23 April 2012 / Published: 27 April 2012_  
**Abstract:** Localization is one of the most important issues associated with underwater acoustic sensor networks, especially when sensor nodes are randomly deployed. Given that it is difficult to deploy beacon nodes at predetermined locations, localization schemes with a mobile beacon on the sea surface or along the planned path are inherently convenient, accurate, and energy-efficient. In this paper, we propose a new range-free Localization with a Mobile Beacon (LoMoB). The mobile beacon periodically broadcasts a beacon message containing its location. Sensor nodes are individually localized by passively receiving the beacon messages without inter-node communications. For location estimation, a set of potential locations are obtained as candidates for a node's location and then the node's location is determined through the weighted mean of all the potential locations with the weights computed based on residuals.  
**Keywords:** localization; range-free; mobile beacon; underwater acoustic sensor networks
## 3 Results and Discussion
### Effects of Divergent Length on Cavitation Characteristics
#### 3.2.2 Analysis of Cavitation Collapse Strength  
Figure 10 depicts the profiles of the turbulence kinetic energy (TKE) along the axis of a Venturi tube for various inlet pressures, which reveals two conspicuous spikes in TKE within the Venturi tube, corresponding to the inception of cavitation at the throat and the subsequent collapse of bubbles in the divergence section. Notably, the surge in the TKE during the collapse of cavitation bubbles surpasses that during the throat cavitation inception, which is indicative of a pronounced release of energy during bubble collapse. Upon comparing Figure 10a-c, it is evident that changes in the length of the divergence segment do not significantly impact the fluctuation behavior of the first turbulent kinetic energy inside the Venturi tube, which can be attributed to the fact that variations in the length of the divergent section have no impact on the cavitation characteristics at the throat of the Venturi tube. Nonetheless, there are noticeable differences in the variations of the second turbulent kinetic energy within the tube due to changes in the length of the Venturi tube divergence under various inlet pressures.  
To gain further insights into the fluctuation characteristics of the turbulent kinetic energy, Figure 11 exhibits the trends in the variation at two distinct locations of peak turbulent kinetic energy across diverse pressure conditions. It can be seen that the turbulent kinetic energy peak values at the throat of the Venturi tube are consistent for different divergence lengths at different inlet pressures, and the peak values show a linear relationship with the inlet pressure. In addition, as the inlet pressure increases from 0.4 to 1.2 MPa, the turbulent kinetic energy peak in the divergence section of the Venturi tube undergoes two stages of growth. In the first stage, a short divergence section results in fast peak growth. In the second stage, the slow growth of the peak value can be attributed to the fact that as the cavities develop closer to the exit straight section under higher pressure conditions, the variation in the cross-sectional area at the diffuser section is minimal, leading to a slow enhancement of the cavitation collapse intensity. Furthermore, the maximum diameter that the cavities can achieve imposes a limitation on further improvements in the cavitation collapse intensity.
_Article_  
B4C-Al Composites Fabricated by the Powder Metallurgy Process  
[PERSON], [PERSON], [PERSON], [PERSON] * (c), [PERSON] and Xinggui Long  
Institute of Nuclear Physics and Chemistry, China Academy of Engineering Physics, Mianyang 621900, China; [EMAIL_ADDRESS] (L-Z.); [EMAIL_ADDRESS] (J.S.); [EMAIL_ADDRESS] (C.S.); [EMAIL_ADDRESS] (S.P); [EMAIL_ADDRESS] (X.L)  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-816-2497405  
Received: 27 August 2017; Accepted: 19 September 2017; Published: 29 September 2017  
**Featured Application: Neutron absorber materials are used in spent-fuel storage racks, storage, and transportation tasks to control the reactivity of spent nuclear fuel, allowing the storage and shipment of spent fuel at maximum packing density. Due to the large thermal neutron absorption cross-section of the nuclide B-10 and the excellent mechanical performance of aluminum, B4C-Al composites have the potential to serve as the neutron absorber as well as the structural function in fuel racks. This work provides an efficient method to fabricate the homogeneously mixed B4C-Al composites with superior yield and tensile strength.**  
**Abstract: Due to the large thermal neutron absorption cross section of \({}^{10}\)B, B4C-Al composites have been used as neutron absorbing materials in nuclear industries, which can offer not only good neutron shielding performance but also excellent mechanical properties. The distribution of B4C particles affects the mechanical performance and efficiency of the thermal neutron absorption of the composite materials. In this study, 15 wt % B4C-Al and 20 wt % B4C-Al composites were prepared using a powder metallurgy process, i.e., ball milling followed by pressing, sintering, hot-extrusion, and hot-rolling. The yield and tensile strengths of the composites were markedly increased with an increase in the milling energy and the percentages of B4C particles. Microstructure analysis and neutron radiography revealed that the high-energy ball milling induced the homogeneous distribution of B4C particles in the Al matrix and good bonding between the Al matrix and the B4C particles. The load transfer ability and mechanical properties of the composites were consequently improved. The results showed the high-energy ball milling process is an appropriate fabrication procedure to prevent the agglomeration of the reinforcement particles even if the matrix to reinforcement particle size ratio was nearly 10.**  
**Keywords: B4C-Al composites; high-energy ball milling; mechanical property; neutron radiography**