esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Modeling
### Computational Domain and Boundary Conditions  
The physical model was changed to a computational domain, as shown in Figure 2. As there is no change in velocity or temperature with the angular direction, the computational domain can be represented as an axisymmetric rectangular one. The tube diameter was kept the same as the physical one, at d = 10 mm. The computational domain has three sections: the inlet, test, and outlet sections. The inlet section is a smooth tube that expands for 20 d to allow for fully developed flow before the test section. Another benefit of this long inlet section is that testing is conducted using the fully developed flow of a smooth tube, as discussed below. The test section is approximately 9 d and comprises nine ribs. The first rib is a tall rib, followed by a short rib. This pattern of long and then short ribs is repeated along the test section. A 10 th rib is added at the end of the test section between the test and the exit sections to maintain the same flow pattern over the test section. The outlet section is 5 d long to prevent reverse flow during the computational simulations. The lengths of the inlet and outlet sections are the same as in [PERSON] et al. [24]. The test section of the present study has five tall ribs and four short ribs. The height of the tall ribs is 0.1 d and was kept fixed over the current study. The height of the shorter ribs was changed to allow its effect to be studied.  
The domain was discretized using a quad structural grid. The grid was clustered close to the tube wall and around the ribbed regions to capture the gradients of the variables more precisely, as in Figure 3. The boundary conditions were as follows. The lower boundary at r = 0 was axisymmetric. The upper boundary was at the tube wall, r = d/2, where the velocity components are zero. A constant heat flux was applied at the tube wall, normal to the axial direction. At the inlet section, x = 0, a uniform axial velocity component value was applied. The inlet temperature was equal to 300 K. A constant pressure value was applied at the outlet section.
## 2 Experimental
### Test Setup and Loading Procedure  
Figure 6 shows the testing setup in the lab. The \(X\) direction is the strong axial direction with relatively high stiffness, corresponding to the B and D faces, and the \(Y\) direction is the weak axial direction with relatively low stiffness in this paper, corresponding to the A and C faces. The displacement control method was used by cyclically applying lateral displacements on the top end of the bridge piers in both \(X\) and \(Y\) directions by means of an MTS (Mechanical Testing & Simulation) hydraulic actuator (Figure 6).  
Figure 4: Photos of strain gauges arrangement on (**a**) steel; (**b**) concrete.  
Figure 5: The experimental instruments: (**a**) The acquisition system of strain and displacement; (**b**) Crack width gauge.  
The loading actuator controls the top displacement values and the corresponding forces. The top of the sample was under compression when it was subjected to a hydraulic jack force on its top surface. The maximum loading capacity for the actuator and jack was 500 kN and 1000 kN, respectively; the maximum displacement capacity for the actuator and jack was 250 mm and 200 mm, respectively. The compression loads are respectively 105 kN, 210 kN, and 420 kN corresponding to the axial load ratios (_n_) of 0.05, 0.1, and 0.2.  
Two cycles were taken for each displacement loading level. The first displacement load was 2 mm for both \(X\) and \(Y\) directions, and the amplitude increased by 2 mm increments. When the steel stress reached yield strength, the displacement amplitude increments were increased to 3 mm until the horizontal force started to decline; then, the displacement amplitude increments were raised to 5 mm until the end of the test. A digital single-lens reflex camera was used to capture the damage variation of specimen surface during the cyclic loading process. The cracks appearing on the sample surface and other defects (i.e., steel bar yielding, spalling of concrete, pier collapsing) were recorded for quantitative analysis.  
The hysteresis curves for each specimen were recorded; their shape and the enclosed areas were used to determine the energy dissipation capacity of specimens (i.e., hysteresis energy) and their seismic performance. The hysteresis energy in each loading cycle in the \(X\) and \(Y\) directions were obtained, and the total hysteresis energy \(E_{tot}\) was calculated by:  
\[E_{tot}\ =\ \int P_{x}dx+\int P_{y}dy \tag{1}\]  
Note that the cumulative dissipated energy at each displacement level was calculated by the area of the two hysteresis loops at that displacement level.
# Image-Based Feature Representation for Insider Threat Classification
## 4 Implementation
### Dataset  
We used CMU CERT insider threat dataset v4.2 [11] to test the proposed method. Availability of the data is indeed a challenge for insider threat detection because the data from the organizations are sensitive. These data are managed by the data owner and seamless data sharing remains an open challenge. CMU CERT data is a benchmark dataset for insider threat detection which has been widely used by researchers to evaluate their proposed methods [11]. This synthetic dataset generation uses various scenarios to define the malicious activities. The log files contain logon-logoff data,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **MobileNetV2** & **VGG19** & **ResNet50** \\ \hline
**Input shape** & (32,32,3) & (32,32,3) & (32,32,3) \\ \hline
**Weight** & Initialized to ImageNet & Initialized to ImageNet & Initialized to ImageNet \\ \hline
**Optimizer** & RMSProp & SGD & Adamax \\ \hline
**Loss function** & Binary cross entropy & Binary cross entropy & Binary cross entropy \\ \hline
**Classifier** & Softmax & Softmax & Softmax \\ \hline
**Epochs** & 15 & 15 & 15 \\ \hline
**Batch size** & 64 & 128 & 128 \\ \hline
**Dropout rate** & 0.3 & Nil & Nil \\ \hline
**Regularization** & Nil & BatchNormalization & L2 Regularization \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyper-parameters used in the MobilenetV2, VGG19, and ResNet50-based model.  
web browsing history, file access logs, emails, device usage, psychometric information, and LDAP data. Each dataset produced contains a small number of insider threat incidents, or scenarios. The malicious activities are categorized into three scenarios in this version. These scenarios were developed in consultation with counterintelligence experts. Each scenario is instantiated as synthetic data in the same form and scope as the normal background data generated for all users in the dataset. The dataset also consists of ground truth information needed to validate the approaches. The CMU CERT dataset is the most popular dataset being used in almost all the recent works in insider threat analysis [42; 43; 44; 45].  
We used the five event files: logon/logoff activity, http data, email communications, file operations, and usage of external storage device. The events from these log files are fed as input to derive the malicious and non-malicious instances used to create the feature vector and the representative images. Our feature extraction process extracts the various features and summarizes it per day per user to a feature vector of malicious and non-malicious instances. Table 3 gives an overview of the dataset.  
As seen in Table 3, the number of malicious instances is very small compared with the non-malicious ones. In the next section, we address the data imbalance issue.
## 4 Access Control Policy Conflict Detection and Resolution
### Modality conflict
** In the UCON model, subject attributes change during the access process affect the subject's authorization. Thus, the policy with higher priority for attributes in use is accepted. * **Recent task priority principle. ** In the TBAC model, the most prominent feature is that a task consists of multiple subtasks, and completing the task is given priority. Among them, the subtask with the latest time is given priority. * **High-level subject priority principle. ** In the RBAC model, access control policies focus on the permissions obtained by the subject role, so the role of the subject has higher priority, the subject has higher priority. When the subject priority levels of the two access control policies are inconsistent, the policy with the higher priority level is given priority. * **High sensitivity priority principle. ** In the ABAC model, it focuses on protecting the object resources, so the policy with a higher priority for the object sensitivity has higher priority. * **Negation priority principle. ** When the authorization results of the two policies are opposite, to protect the security of the object resources, denying authorization is given priority.
# Synchronizing TSN Devices via 802.1 AS over 5G Networks
###### Abstract  
The 3 GPP release 16 integrates TSN functionality into 5G and standardizes various options for TSN time synchronization over 5G such as transparent mode and bridge mode. The time domains for the TSN network and the 5G network are kept separate with an option to synchronize either of the networks to the other. The TSN time synchronization over 5G is possible either by using the IEEE 1588 generalized Precision Time Protocol (gPTP) based on UDP/IP multicast or via IEEE 802.1 AS based on Ethernet PDUs. The INET and Simu5G simulation frameworks, which are both based on the OMNeT++ discrete event simulator, are widely used for simulating TSN and 5G networks. The INET framework comprises the 802.1 AS based time synchronization mechanism, and Simu5G provides the 5G user plane carrying IP PDUs. We modified the 802.1 AS-based synchronization model of INET so that it works over UDP/IP. With that, it is possible to synchronize TSN slaves (connected to 5G UEs), across a 5G network, with a TSN master clock, present within a TSN network, that is connected to the 5G core network. Our simulation results show that 500 microseconds of synchronization accuracy can be achieved with the corrected asymmetric propagation delay of uplink and downlink between the gNodeB (gNB) and the User Equipment (UE). Furthermore, the synchronization accuracy can be improved if the delay difference between uplink and downlink is known.  
TSN; time synchronization; 5G; 802.1 AS +
Footnote †: journal: Electronics Electronics  
1 Faculty of Engineering and Computer Science, University of Applied Sciences Osnabrueck, 49076 Osnabruck, Germany; [EMAIL_ADDRESS]  
2 Chair of Communication Networks, University of Technology Chemnitz, 09126 Chemnitz, Germany; thomas.  
Performance simulation for TSN time synchronization in a 5G handover scenario;
* Analysis of factors impacting synchronization accuracy in 5G network.  
The sections are structured as follows. Section 2 describes the basics of TSN along with the time synchronization procedure of the TSN standard. Section 3 gives an overview of the current research in the field of TSN time synchronization using the 5G network as a communication link. The integration of TSN and 5G, with a focus on time synchronization, is given in Section 4. It also discusses the implementation of various models and simulation scenarios created, using those models to evaluate the performance of TSN time synchronization using encapsulation over the 5G system (5 GS). Section 5 analyzes the result of the simulations. And finally, Section 6 concludes the paper.
# Fault ride-Through Control Strategy for Variable Speed Pumped Storage Unit with Full-Size Converter
## 3 Analysis of Fault Ride-Through Characteristics
### HVRT Characteristics  
Based on the FSC, the EESG and the power grid are completely isolated. When grid voltage rises, the GSC is the first one to be affected. The generator/motor passively responds to the influence of the RSC perturbation, and the GSC is capacitively coupled to the RSC through the DC capacitor. Therefore, maintaining the stability of the DC bus voltage is essential for uninterrupted operation when the system voltage rises.  
The current relationship of the GSC in the \(dq\) rotating coordinate system can be expressed as Equation (10):  
\[\left\{\begin{array}{l}L_{g}\frac{d\dot{u}_{q}}{dt}=e_{d}-R_{g}\dot{l}_{d}+ \omega L_{g}\dot{l}_{q}-u_{gd}\\ L_{g}\frac{d\dot{l}_{q}}{dt}=e_{q}-R_{g}\dot{l}_{d}-\omega L_{g}\dot{l}_{d}-u_{ gq}\end{array}\right. \tag{10}\]  
where \(e_{\rm d}\) and \(e_{\rm q}\) are the d-axis and q-axis components of the grid voltage; \(u_{\rm gd}\) and \(u_{\rm gq}\) are the d-axis and q-axis components of the output voltage on the AC side of the GSC; \(i_{\rm d}\) and \(i_{\rm q}\) are the d-axis and q-axis components of the input current of the GSC; \(R_{\rm g}\) and \(L_{\rm g}\) are the incoming resistance and filtering inductance of the GSC, respectively; \(\omega\) is the grid angular frequency.  
When the voltage vector of the power grid is oriented along the d-axis, \(e_{\rm d}=E\), \(e_{\rm q}=0\), \(i_{\rm d}\) and \(i_{\rm q}\) respectively represent the active and reactive components of the current, where \(E\) is the peak phase voltage of the power grid. During normal conditions, both \(i_{\rm d}\) and \(i_{\rm q}\) are DC components, and the differential term is zero. According to (10), the control equation of the GSC during normal conditions can be obtained as shown in Equation (11).  
\[\left\{\begin{array}{l}u_{gd}=e_{d}-R_{g}\dot{l}_{d}+\omega Li_{q}\\ u_{gq}=-R_{g}\dot{l}_{q}-\omega Li_{d}\end{array}\right. \tag{11}\]  
When the power factor angle of the GSC operates at a certain value, there is a certain constraint relationship between the maximum output voltage of the GSC and the DC voltage. Based on voltage modulation theory, the modulation ratio \(m\) should satisfy Equation (12).  
\[m=\frac{\sqrt{u_{gd}^{2}+u_{gq}^{2}}}{U_{dc}/2}\leq\frac{2}{\sqrt{3}} \tag{12}\]  
Therefore, the DC bus voltage \(U_{\rm dc}\) satisfies Equation (13).  
\[U_{dc}\geq\sqrt{3}\sqrt{\left(e_{d}-Ri_{d}+\omega Li_{q}\right)^{2}+\left(-Ri_ {q}-\omega Li_{d}\right)^{2}} \tag{13}\]
# SDNPS: A Load-Balanced Topic-Based Publish/Subscribe System in Software-Defined Networking
## 3 Architecture
### Event Routing
#### 3.5.2 Intra-Cluster Routing  
By abstracting clusters into nodes, we get a cluster-level topology, which lays the foundation for the inter-cluster routing mentioned above. However, every cluster may have multiple border switches to connect with other adjacent clusters, so the routing table obtained from inter-cluster routing must be mapped into flow entries on switches.  
When a broker receives an event published by a sensor or other terminal device, which is connected to it, it firstly identifies the type and the topic (\(t\)) of the event, then searches the topic tree for its binary code. Following this, it multicasts this event using UDP by encapsulating the type and topic code into the packet header as the IPV6 address. This IP address is the IPV6 multicast address for topic \(t\). Finally, it delivers this event to the representative broker by reliable transmission, called TCP, aiming at ensuring that every event can be transferred to the representative broker. When a broker receives an event from another broker or switch, it checks its subscription list to decide whether to receive it or not.
# Efficient Massive MIMO Detection for M-QAM Symbols
## 5 Conclusions  
In this work, we consider massive MIMO detection as a quadratic optimization problem and propose a solution based on an iterated BOXDCD algorithm with negative diagonal loading regularization. Negative diagonal loading regularization maximizes the energy of  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Method** & **Complexity Order** \\ \hline MMSE & \(10^{6}\) (complex multiplications and additions) \\ iterated MMSE & \(9\times 10^{6}\) (complex multiplications and additions) \\ BOXDCD & \(40\times 10^{6}\) (real additions) \\ INL-BOXDCD & \(1.6\times 10^{6}\) (real additions) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average complexity comparison of the detection methods.  
Figure 5: MER performance comparison among MMSE, iterated MMSE (\(V=1\)), BOXDCD (\(N_{u}=10^{3}\)) and INL-BOXDCD (\(N_{u}=10^{3}\), \(V=1\)): 16 QAM, \(100\times 100\) MIMO configuration.  
the solution to the box boundary. We examine the tradeoff between the computational complexity and the performance among MMSE, iterated MMSE, BOXDCD and INL-BOXDCD and demonstrate that the proposed detector INL-BOXDCD outperforms the more advanced counterparts and provides much greater adaptivity in a fully loaded massive MIMO system. In this work, we only consider a single user case MIMO scenario; performance investigation of the proposed method in multiuser MIMO systems is left for future work.  
Conceptualization, Z.Q., J.L., H.Z. and L.J.; methodology, Z.Q. and L.J.; writing--original draft preparation, Z.Q.; writing--review and editing, J.L., H.Z. and L.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grant U1604160.  
Not applicable.  
The authors declare no conflict of interest.
# Enhanced Efficiency and Reduced Side Lobe Level Convex Conformal Reflectarray
## 2 Convex Conformal Reflectarrays  
The CRA considered here is that already described in [20]: its main characteristics are summarized, for the sake of clarity, in the following. The design was made to operate in the Ka-band, in a frequency interval centered at \(f_{0}=30\)  GHz; the re-radiating elements are square patches, located on a rectangular grid, whose unit cell has a size of 0.45\(\lambda\) at \(f_{0}\), and are printed on a substrate of Diclad 527, 0.8 mm thick, with \(\epsilon_{r}=2.55\) and \(\tan\delta=0.0022\).  
The size of the equivalent planar reflector is \(D=\overline{A^{\prime}B^{\prime}}=193.5\) mm and it was subsequently bent to a cylinder with a radius of curvature \(R=20\lambda\), as shown in Figure 1, where the lateral view of the CRA is sketched. The antenna radiates a pencil beam at \(\theta_{max}=25^{\circ}\) in the \(zy\) plane; the position of the feed is specular to the direction of maximum radiation and \(f/D=1\), being that \(f\) (\(\overline{FO}\) in Figure 1) is the distance from the feed to the center of the reflector.  
The results reported in [20] relevant to the experimental characterization of a CRA prototype, using a standard rectangular horn as feed, show a gain of 29.7 dB at \(f_{0}\), a measured efficiency of almost 21%, a 1 dB bandwidth of about 7% and relatively high SLL (see Table 1), especially in the E-plane, where the effect of the curvature is more remarkable and tends to scatter the radiated field in many directions instead of focusing it in a desired one. Note that the relatively narrow bandwidth is also due to the difference between the phase of the incident field in correspondence with the upper and lower edges of the CRA requiring the use of re-radiating patches with different sizes that are characterized by a different frequency dependence of their phase. The different path between the feed and the upper or lower edge occurs also in planar RAs, but here, it is enhanced by the curvature. For what concerns the efficiency, it is worth to observe that the curvature reduces the effective area with respect to the planar case.  
The aim of the work in [20] was to prove the feasibility of a CRA and, therefore, its performance was not optimized. However, those results reveal the need for some further optimization for improving the antenna performance, specifically for convex RAs for which the techniques commonly adopted for planar reflectarray, such as the use of multilayer re-radiating elements [21], are not applicable.  
For this reason, an innovative approach is here introduced that considers the antenna system in its entirety, and the feed is a further degree of freedom to be exploited for enhancing the CRA performance.
# Matrix Adaptation Evolution Strategy with Multi-Objective Optimization for Multimodal Optimization
## 1 Introduction
To address this issue, this paper proposes an improved algorithm based on the MA-ES called the matrix adaptation evolution strategy with multi-objective optimization algorithm (MA-ESN-MO). The main contributions of this paper are summarized as follows:  
1. The MMOP is transferred into two objective optimization problems with strong objective confliction; the advantage of multi-objective optimization can be fully used to ensure the diversity of the population. 2. The information of the population landscape and the fitness of the objective function are employed to construct two conflicting objective functions instead of utilizing classical niching strategies. Moreover, the archive is employed to save better individuals, which are helpful to ensure the convergence of the algorithm. In this manner, the exploration and exploitation abilities of the algorithm are balanced effectively. 3. The population is divided into several subpopulations in MA-ES instead of using one population in CMA-ES. In this way, the algorithm can explore and exploit in parallel within these subpopulations to find multiple optimal solutions for the given problem. Moreover, the niching method is employed to improve the diversity of the population. 4. Systematic experiments conducted to compare the algorithms including CMA-ES, MA-ES, CMA-ES-Niching (CMA-N), CMA-ES-Niching-MO (CMA-NMO), CMA-ES Niching with Mahalanobis Metric [20] (CMA-NMM), CMA-NMM-MO, MA-ES-Niching (MA-ESN), and MA-ESN-MO on CEC2013 multimodal benchmark problems [21] are described. CMA-NMO and CMA-NMM-MO are obtained by introducing the proposed method into CMA-ES and CMA-NMM. The experimental results show that the proposed method is promising for solving multimodal optimization problems. The rest of this paper is organized as follows. In Section 2, the related work on multimodal optimization problems is reviewed. Section 3 introduces variants of CMA-ES and the framework of MA-ES. The proposed matrix adaptation evolution strategy with the multi-objective optimization algorithm (MA-ESN-MO) is presented in Section 4. Section 5 reports and discusses the experimental results. Finally, the conclusions and possible future research are drawn up in Section 6.