esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reconfigurable Formation and Disjoint Hierarchical Routing for Reshapeable Bluetooth Networks
## 6 Scatternet Performance
### Scatternet Performance  
In the scatternet performance simulation, it was assumed that Bluetooth nodes were randomly located within a rectangular area of 40 \(\times\) 40 m\({}^{2}\), while a radio transmission range of 10 m was assumed. The number of simulated nodes ranged from 60 to 150. A set of performance metrics was calculated by averaging over 30 randomly generated topologies for each simulated node number. The simulated mesh-tree topology can be applied to non-uniform distribution applications, such as dense-sparse areas. In addition, the simulation results are shown as follows.  
An average hop length between any two nodes was also calculated for these networks. A larger average hop length implies that it would require a longer time to deliver a packet. As shown in Figure 9, the optimum mesh-tree method achieved significant performance improvements in terms of the average hop length, as compared to that of BlueHRT. More return links could be connected among the M/S nodes as the number of layers increased; thus, the average path length among nodes could be reduced. In addition, the optimum layer of the peak-search method achieved approximate performance in average hop length, compared with the ideal layer as the optimal topology. However, this peak-search method required the partial 72% topology to locate the optimum layer instead of four full topologies including two, three, four and five layers, in contrast to the ideal layer. From the  
Figure 8: Tree-subnet routing flow diagram.  
observation of simulation results, most of the optimum decision layer \(I\) is either the case of layer = 4 or layer = 5, since both cases almost achieve the lowest hop length for various numbers of nodes.  
As shown in Figure 10, the reconfigurable mesh-tree required more formation packets in terms of energy cost than BlueHRT did. The formation packets are counted as the total number of communication packets in the mesh-subnet and the formation packets of all the other tree subnets. However, this formation packet overhead was only generated during the scatternet formation phase.
## 1 Introduction
### Contributions  
We assume that machine learning model can learn to mine the specific association between the filters and the I/O context to prevent XSS attacks. Then, we can detect XSS vulnerabilities in PHP source code based on this association.  
In summary, the contributions of this paper are as follows.  
1. **Cross-Scripting Guardian**: A novel PHP source code vulnerability detector. According to our experimental evaluation, this detector performs better in vulnerability identification compared with related methods. It has guiding significance for the follow-up research of machine learning applied to XSS code audits.
2. **Algorithms for input-output pattern mining**: We analyzed the I/O patterns in XSS-related PHP source code and design an algorithm to identify the specific path of the data stream. With this algorithm, it will be more efficient to mine the I/O context and build the precise and consistent TOKEN sequence for representation.
3. **A novel adapted open-sourced PHP code parsing tool**[5]: We modify the source code of the VLD to enable it to output richer and more structured parsing information of the PHP source code, which can facilitate the work of parsing and serializing PHP source code.  
The following sections are organized as follows: in Section 2, we introduce the background of the research, the current work, and results related to the code audit. In Section 3, we develop the approaches and theoretical methods of the framework. In Section 4, the experimental goal, process, results, and evaluation are presented. In the last section, we summarize our work and list several prospects for future work.
# Efficient Algorithm for Providing Live Vulnerability Assessment in Corporate Network Environment
## 7 Conclusions  
The obtained results present a fully automated vulnerability management platform. It was demonstrated that the inclusion of an asset collector module has a major impact on the CVSS scores. This fact shows that it is possible to maintain and operate with an open software platform that uses a well known open scoring system presenting reliable results. The vertical scalability of the developed software was also studied and demonstrated that the developed VMC is capable of processing the increasing amount of data. The results obtained show also that the developed VMC supports VM automation by:  
* calculating CVSS Environmental vector component, reducing thereby the workload for stakeholders,
* fully scalable implementation, thus enabling processing large amounts of data in corporate environments [31],
* creating the dynamic metrics adjusted to corporate requirements.  
In conclusion, comparing to solutions presented in literature [37; 49; 50; 51] the developed VMC software package takes into consideration the value of assets and is adjusted to the increasing amount of data in cloud computing environment. Therefore, the developed VMC can be applied for every network infrastructure. Additionally, none of the presented [37; 49; 50; 51] and [6; 7; 8; 9] offer prioritization for CVSS 2.0 and CVSS 3.1 simultaneously.  
The future work will focus on developing machine learning algorithms that predict each CVSS 3.1 vector component derived from public CVE databases. This issue is very important because all the vulnerabilities have not been evaluated using CVSS 3.1 metric and most of the available scores are based on CVSS 2.0 [18] metric only. However, the predicted vector components of CVSS 3.1 allow calculating the environmental score for all vulnerabilities coming from scanning software. This may further reduce time-to-vulnerability-remediation and total-vulnerability-exposure and facilitate the network administration by bringing focus to genuine deficiencies present within the infrastructure.  
All authors have read and agreed to the published version of the manuscript.  
Figure 5: The time consumption in proportion to the number of processing module used and proposed \(P\).  
**Funding:** Wroclaw University for Science and Technology, grant No CYBERSECIDENT/381690/ II/NCBR/2018 from National Centre for Research and Development within the CyberSecident-Cybersecurity and e-Identity program.  
**Acknowledgments:** The authors wish to thank Wroclaw University of Science and Technology (statutory activity) for financial support and [PERSON] for proofreading and translation. This publication was created as a part of the Regional Security Operations Center (RegSOC) project (Regionalne Centrum Bezpieczeristwa Cybernetycznego), cofinanced by the National Centre for Research and Development as part of the CyberSecident-Cybersecurity and e-Identity program.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Energy Efficient Multicast Communication in Cognitive Radio Wireless Mesh Network
## 3 Network Model and Problem Formulation
The total energy consumption \(E[k]\) per bit for a multicast session \(k\) is the sum of the energy consumed by each transceiver involved in the local forwarding operation in the multicast tree \(T_{k}\), which can be expressed as  
\[E[k]=\sum_{\forall i\in T_{k}}E_{i} \tag{2}\]  
Let \(T_{G}\) be a set of possible multicast trees for a session \(k\), with each multicast tree \(T_{k}\) having different relay nodes and multicast links. In this work, our focus is to select appropriate relay nodes and establish multicast links in such a way that the minimum energy multicast tree \(T_{k}^{*}\) is constructed for each session \(k\). This can be mathematically expressed as  
\[T_{k}^{*}= \begin{matrix}min\\ \forall i\text{T}_{k}\in T_{G}\end{matrix} \sum_{\forall i\in T_{k}}E_{i} \tag{3}\]  
While selecting the relay nodes and establishing the multicast links for a tree \(T_{k}\), we need to assign channels for each forwarding node which involves interference and scheduling constraints that needs to be obeyed. Let us define a binary variable for the channel assignment  
\[u_{ij}^{h}= \begin{cases}1&\text{if channel $h$ is used for link $i$ }\to j\,;\\ 0&\text{Otherwise}\end{cases} \]  
To avoid interference between hyperlinks, a hyperlink is established at a forwarding node \(i\) over channel \(h\) with \(j\) as one of the receiving nodes. Another hyperlink with forwarding node \(p\) and \(q\) as one of the receiving nodes cannot be established over the same channel if \(d_{pj}<R_{T}\) because the transmission of node \(p\) over the same channel may cause interference to the reception at node \(j\). This can be mathematically expressed as  
\[u_{ij}^{h}+\sum_{p\in Q_{j}}u_{pq}^{h}\leq 1 \tag{4}\]  
where \(Q_{j}\) is the set of nodes whose distance to node \(j\) is less than \(R_{T}\) and which has channels in common with node \(j\). Mathematically, \(Q_{j}\) can be expressed as  
\[Q_{j}=\{\text{P}:d_{pj}<R_{T},\text{and, }C_{j}\cap C_{p}\
eq\O\}\]  
To summarize, we are a given set of sessions \(K\). For each session \(k\in K\) we need to select the relay nodes and perform channel assignments to establish multicast links in such a way that a minimum energy multicast tree is formed for each session. Hence, our optimization problem has the objective function in (3) subject to the constraint in (4) and can be expressed as  
\[\textbf{Find}:T_{k}^{*}= \begin{matrix}min\\ \forall i\text{T}_{k}\in T_{G}\end{matrix} \sum_{\forall i\in T_{k}}E_{i} \tag{5}\] \[\textbf{Subjectto}:u_{ij}^{h}+\sum_{p\in Q_{j}}u_{pq}^{h}\leq 1\]  
According to [21], the problem of finding a minimum energy consumption-based multicast tree is an NP-complete problem. Thus, this study proposes a heuristic algorithm to solve this optimization problem.
## 2 Materials and Methods
### Numerical Simulation and Fabrication Processes  
The fabricated patterns were simulated using Ansys Maxwell software to study the magnetic interactions between permanent magnets. In the first stage, three different patterns were studied. The first pattern consisted of the use of permanent magnets in an alternating configuration; the second pattern consisted of the utilisation of permanent  
Figure 1: Overview of the workflow carried out.  
magnets in a design where the surface of the magnets had the same polarity; and finally, the third pattern consisted of a Halbach array.  
After observing that the MWCNT response under a static magnetic field compares to the ANSYS simulation, the three different electrode patterns were simulated (rectangle, zig-zag shape, and parallel lines). These three patterns were implemented using permanent magnets in a configuration of alternating polarity to allow the formation of continuous patterns.  
The magnetic patterns were fabricated in the laboratory to confirm the simulation results. Tests were carried out to characterise: (1) the electrical percolation threshold of the nanocomposites, (2) the magnetic patterns produced, and (3) the fabrication of the sensor's electrodes. The samples prepared for each study were rectangular with the following dimensions and MWCNT concentrations: for test (1) 2 \(\times\) 1 \(\times\) 0.5 cm\({}^{3}\), with MWCNT concentration in the range 0.01-5 wt.%; for test (2) 2 \(\times\) 1 \(\times\) 0.5 cm\({}^{3}\) and MWCNT concentration of 0.1 wt.%; for test (3) 4 \(\times\) 2 \(\times\) 0.2 cm\({}^{3}\) with MWCNT concentration of 0.5 and 1 wt.%.  
The fabrication process of all the MWCNT/PDMS nanocomposites is schematically illustrated in Figure 2. First, the MWCNTs were dispersed into the PDMS under constant manual stirring for 2 min. Then, the curing agent, in a 1:10 ratio, was added to the MWCNT/PDMS mixture and manually stirred for another 2 min. Then, the mixtures obtained were placed in rectangular laser-cut acrylic moulds, on top of the magnetic patterns, for 15 h. The samples were cured during the 15 h to make sure that the PDMS was solid enough to prevent the movement of the MWCNTs before the final curing process. The final curing process was done in an oven at 110 \({}^{\circ}\)C for 30 min without magnetic fields. If the curing process is too fast, there is not enough time for the particles to settle in the right position, affecting the patterning quality.  
The electrical contacts were fabricated by cutting a thin layer of MWCNT/PDMS on opposite sides of the sample and by placing silver conductive epoxy (MG Chemicals 8331S Silver Conductive Epoxy) on the MWCNTs' surface. After curing the conductive epoxy, the electrical contacts were covered with copper tape to facilitate the connection to the measurement equipment.
# Charge-Line Dual-FET High-Repetition-Rate Pulsed Laser Driver
## 3 System Implementation
### Delay Line and Charging Resistor  
In the presented solution, approximately 25 cm of a high quality RF coaxial cable was used to obtain the maximum pulse width of approximately \(\mathrm{t_{FWHM\,MAX}}\) = 2 \(\cdot\) DL = 2 ns. The characteristic impedance of the transmission line is of the commonly used standard, i.e., \(Z_{0}\) = 50 \(\Omega\). If the boundary values of parameters in Equation (2) are considered, the maximum lasing current is described by Equation (3).  
\[\mathrm{I_{LAS\,MAX}}\ =\ \frac{U_{+HV}-\ U_{F\,LAS}}{R_{on\ Q1}+Z_{0}}=\frac{U_{ DS\,MAX}-\ U_{F\,MAX}}{R_{DS\,ON}+Z_{0}}=\frac{84\ V-20\ V}{2\ \Omega+50\ \Omega}=1,23\ \mathrm{A} \tag{3}\]  
Each generated pulse discharges the delay line, whereas the recharging process takes place during successive repetitions. The \(\mathrm{R_{C}}\) resistor supplies the delay line with a time constant \(\mathrm{\tau_{line}}\) = \(\mathrm{R_{C}}\) \(\cdot\)\(\mathrm{C_{line}}\), where \(\mathrm{C_{line}}\) is approximately 23 pF. In order to ensure a stable pulse amplitude in a wide range of repetition rate (DC to 20 MHz), \(\mathrm{R_{C}}\) should fully charge the line in the shortest period between the two following pulses at Trg1 and Trg2 (i.e., \(\mathrm{\tau_{min}}\) = 46 ns). If the time constant relation of Equation (4) is assumed, the line will charge to \(>\)98% of the final voltage.  
\[\mathrm{\tau_{line}}=4\cdot\mathrm{R_{C}}\cdot\mathrm{C_{line}}<\mathrm{t_{ min}} \tag{4}\]  
\(\mathrm{R_{C}}<\) 500 \(\Omega\)
# Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications
## 2 System Model  
### Signal Model  
As illustrated in Figure 1, this paper considers an IRS-assisted downlink LEO satellite communication system in IoRT networks. The LEO satellite is equipped with an array-fed reflector antenna, which comprises \(M\) feeds and can mostly provide \(M\) beams. Within the coverage area of a satellite beam, there are \(K\) randomly distributed single-antenna mobile users. The direct link between the satellite and users suffers severe attenuation due to heavy shadowing, thus, an IRS is implemented to assist the communications. The IRS is composed of \(N=N_{t}\times N_{t}\) reflecting elements and is attached to a smart controller for tuning phase shifts at each reflecting element. \(N_{t}\) represents the number of array elements uniformly placed along the axis. The channels from the LEO satellite to IRS and user \(k,k=1,\cdots,K\) are denoted by \(\mathbf{G}\in\mathbb{C}^{N\times M}\) and \(\mathbf{h}_{k}^{d}\in\mathbb{C}^{1\times M}\), respectively, while that from the IRS to user \(k\) is denoted by \(\mathbf{h}_{k}^{r}\in\mathbb{C}^{1\times N}\). As such, we let \(\mathbf{\Theta}=\text{diag}[e^{\beta_{1}},\cdots,e^{\beta_{N}}]\) be the phase shift matrix of IRS, where \(\theta_{n}\in[0,2\pi]\) is the phase shift of the \(n\)-th reflection element.  
This paper considers that the LEO satellite carries out superposition coding before broadcasting signals to the users. Thus, the transmitted signal for all users at the satellite at time \(t\) is written as \(x(t)=\sum_{k=1}^{K}w_{k}x_{k}(t)\), where \(w_{k}\in\mathbb{C}^{M\times 1}\) and \(x_{k}(t)\) represent the precoding vector and transmitted symbol for the \(k\)-th user, respectively. Hence, the signal received by the \(k\)-th user can be given as [15]:  
\[y_{k}(t)=\Big{(}\mathbf{h}_{k}^{d}+\mathbf{h}_{k}^{r}\mathbf{\Theta}\mathbf{G}\Big{)}\sum_{i=1 }^{K}w_{i}x_{i}(t)+n_{k}(t) \tag{1}\]  
where \(n_{k}(t)\) is additive white Gaussian noise (AWGN) at the \(k\)-th user with a zero mean and unit variance. Accordingly, the signal-to-interference plus noise ratio (SINR) of user \(k\) can be expressed as:  
\[SNIR_{k}=\frac{\left|\Big{(}\mathbf{h}_{k}^{d}+\mathbf{h}_{k}^{r}\mathbf{\Theta}\mathbf{G} \Big{)}w_{k}\right|^{2}}{\sum\limits_{i\
eq k}\left|\Big{(}\mathbf{h}_{k}^{d}+\bm {h}_{k}^{r}\mathbf{\Theta}\mathbf{G}\Big{)}w_{i}\right|^{2}+\sigma_{k}^{2}} \tag{2}\]In addition, we assume that the Doppler shift caused by the LEO satellite and users' mobility can be perfectly compensated at the received end. Therefore, we ignore its influence in the following.
## 4 Case Study
### Multi-Scale Information Collaborative Management in Xiong'an New District  
Just as the BIM and GIS based multi-scale information collaborative management method was introduced in the third section, the fourth section will introduce the application process of the method from micro-meso and meso-macro through the real engineering case of Xiong'an New District. Among them, the realization of micro-meso information collaborative management is divided into three aspects: format uniformity, data translation, and contract constraint. The realization of meso-macro information collaborative management is divided into three aspects: model analysis, data integration, and institutional guarantee.
## 3 SadHoa-Wsn
### SADHoA-WSN Structure  
SADHoA-WSN adopts the SDN paradigm by separating the control and the data plane. As shown in Figure 10, its static structure is thus divided into three different levels: physical, communication, and control. The physical layer gathers the elements of the wireless sensor network, as follows:  
* The _CommunicationNode_ represents the physical node, inserted into the communicating material while the _CommunicationNetwork_ represents the whole WSN, fully embedded into a _CommunicatingMaterial_, i.e., the material equipped with it.
* The _RoutingLink_ is the set of active routes used to send data all over the communicating network, whereas the _BridgeLink_ is the set of possible routes that may be used.  
The Communication Layer is a layer that realizes communication with the physical layer. It then acts as an interface between the physical and the control layers to (1) gather all the needed information for the control, and (2) reduce the impact of SADHoA elements in case of changes in the physical layer impacting the cyberphysical data exchange. This layer can be considered as a digital twin of the underlying communication network, composed of the following entities:  
* The _CommunicationAgent_ is an agent making the interface between the _CommunicatingNode_ and its _ControlAgent_. It can manage the physical node (for the update process) and collect the needed data from the node and for the control architecture.
* The _BridgeAgentLink_ and the _AgentLink_ are entities representing the real _RoutingLink_ and _BridgeLink_.  
Finally, the control layer regroups all the entities needed to control the physical communication network, as follows:  
* The _D-Agent_ is a decision agent, taking part in the reconfiguration process. It has different functions required to support the reconfiguration process depicted in Figure 8. The _D-Agent_ has two subtypes, the _ControlAgent_ and the _Holon_. The first one is an elementary _D-Agent_, directly linked to one or several _CommunicationNodes_. The second one acts as a supervisor and can be seen as the equivalent of a GDE in Pollux Architecture. It manages a set of _D-Agents_ by setting different governance parameters for each of them. Moreover, it also has the capability to elaborate new control strategies.  
Figure 9: (**a**) Internal composition of a decisional entity and (**b**) structure of a general control architecture [25].
## 4 Experimental Validation
### Empirical Tracking Algorithm
#### 4.3.1 Empirical Source Rotation Positioning
The positioning results after the SVD-STKF optimization are shown in Figures 9 and 10. The positioning accuracies of the point (0, 0) in the \(x\)- and \(y\)-direction were approximately 0.12 m and 0.104 m, respectively. Compared with the results before the optimization, the accuracies in the \(x\)- and \(y\)-direction were improved by approximately 0.004 m and 0.003 m, respectively. The  
As shown in Figures 14 and 15, for the point (0, 0), which was directly below the antenna, due to the strong signal, the positioning accuracy of the system was high, and the correction effect of the KF on it was not obvious. At this time, the difference between the SVD-STKF and strong KF tracking was small. However, for the distant point (2, \(-\)1.5), the stability of the signal decreased compared to the point (0, 0). Under the process of source rotation, the measurement error of some angles was large, and although the STKF improved the smoothness, the improvement in the accuracy was not obvious. The SVD-STKF provided a more obvious correction of the positioning results compared to the STKF. The overall positioning accuracy was improved by about 0.03 m, and the tracking ability for the abrupt state was stronger. The maximum error was reduced from 0.595 to 0.51, and the overall smoothing effect was good. The specific statistical results are given in Table 2.  
To present the positioning results more intuitively, the distribution of anchor points is presented in Figure 16, where the horizontal axis represents the x-coordinate, and the vertical axis represents the z-coordinate. In Figure 16, the point represented by the red circle denotes the original output; the point represented by the blue asterisk denotes the output position obtained by the STKF; the point represented by the green plus sign denotes the output position of the SVD-STKF. As shown in Figure 16, after the STKF processing, the deviation degree between the anchor point and actual position decreased, and the accuracy of the maximum error point was improved to a certain extent, which was closer to the real position. The SVD-STKF showed the same effect, but the correction effect was better for the point position, especially for the mutation of the outliers. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Point** & **Direction Component** & **Filter Method** & **Maximum Error (m)** & **RMS** \\ \hline \multirow{8}{*}{(0, 0)} & & Raw Output & 0.21765 & 0.124 \\  & x & STKF & 0.215 & 0.122 \\  & & SVD-STKF & 0.215 & 0.12 \\  & & Raw output & 0.21765 & 0.109 \\  & z & STKF & 0.191 & 0.107 \\  & & SVD-STKF & 0.189 & 0.104 \\  & & Raw output & 0.541 & 0.255 \\  & x & STKF & 0.532 & 0.249 \\  & & SVD-STKF & 0.47 & 0.223 \\  & & Raw output & 0.247 & 0.125 \\  & z & STKF & 0.227 & 0.119 \\  & & SVD-STKF & 0.204 & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Source rotation test error statistics. Figure 15: The SVD-STKF positioning results under the source rotation for the point (2, \(-\)1.5). (**a**) Error in the \(x\)-direction. (**b**) Error in the \(z\)-direction.