esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Formulation
### Distributed Bayesian Filter Formulation
The predictive distribution of state \(x_{k}\) can be given by the Chapman-Kolmogorov equation,  
\[p(x_{k}|z_{k-1})=\int p(x_{k}|x_{k-1})p(x_{k-1}|z_{k-1})dx_{k-1}. \tag{6}\]  
The posterior can be given as:  
\[p(x_{k}|z_{k})=\frac{1}{\bar{c}}p(x_{k}|z_{k-1})p(z_{k}|x_{k}), \tag{7}\]  
where \(\bar{c}=\int p(x_{k}|z_{k-1})p(z_{k}|x_{k})dx_{k}\) denotes the normalization. However, for the Bayesian filter (6) and (7), the computational complexity of state estimation is usually intractable. A computationally-feasible approximation is provided by the cubature Kalman filter [13; 14], which uses cubature rules to compute numerical integration for multi-dimensional integrals. It has been shown that the CKF has better performance compared with EKF and UKF [13]. It can be seen from (6) and (7) that if one can access the global likelihood \(p(z_{k}|x_{k})\), the global estimate \(\hat{x}_{k}\) can be obtained. However, in our paper, each sensor only knows local likelihood function \(p(z_{i,k}|x_{k})\), and therefore, we have to propose a distributed approach to estimate \(x_{k}\). In Section 3, we proposed a distributed Bayesian filter based on the consensus of PDFs, which is obtained by the Kullback-Leibler divergence described in Section 2.2.
## 3 SSMA Broadcast Protocol
### Protocol Structure  
The protocol starts with _initial construction period_ (ICP) and repeats _broadcast period_ (BP) as shown in Figure 3. During ICP, time synchronization, tree construction, and BSS scheduling are performed. The BP starts with a globally synchronized time, \(sTime\). During BP, broadcast is performed, starting with the message broadcast of a sink at tree level _one_ and proceeding to next levels progressively. Given a tree of depth \(H\), BP is divided into one BS and H-2 BSSs since nodes at level \(H\) do not have to rebroadcast a message, and each BSS is further divided into a number of BSs. During the (\(i\)\(-\)1)th BSS, every node at level \(i\) is in broadcast mode and goes to sleep immediately after rebroadcasting until the end of BP, while a node at level \(i\)+1 is in receiving mode and goes to sleep immediately after receiving until the start of its own sending BSS (ith BSS).  
Figure 2: Comparison of average active time (\(AAT\)) per node for different approaches (assuming that \(len(BS)=1\), \(H=6\) and \(r\approx 0.5\)).
# APT Attack Detection Scheme Based on CK Sketch and DNS Traffic
## 3 Research Design
### Feature Extraction
The default TTL is currently given as 3600 s, and the value can be adjusted by itself as needed. [PERSON] [40] summarizes the DNS features used until 2018 and mentions the body of literature about TTL as a feature for detection. The TTL distribution of APT traffic has variability from normal traffic, so the TTL value is added to the classification vector as a feature dimension. The TTL values of normal traffic are mostly concentrated below 1000, but there are several values above 1000; the TTL values in the DNS dataset of APT are distributed at both ends, with one end distributed at 60 and below, and the other end distributed at around 100. * Domain Access Interval Under normal circumstances, the time interval between two visits to the same domain by internal hosts is not too small. The attacker has a long period of latency during the lateral penetration phase, in which the infected hosts rarely visit external servers to hide their behavior, so the longer the interval between domain accesses, the more suspicious the domain is. Among the above eight features, features 1, 4, and 5 do not need to be accumulated and can be calculated directly using domain names; features 2 and 3 are accumulated using counting; features 6, 7, and 8 are calculated and measured in the form of entropy.
# Energy-Efficient Spatial Query-Centric Geographic Routing Protocol in Wireless Sensor Networks
## 3 QTGrid Routing Protocol
### Cluster Setup
#### 3.1.1 Grid Position Encoding  
In QTGrid, the pre-encoding of sub-regions at each level can involve stacks of area identification codes. The identification codes of level-1 grids were expressed as 01 (northwest, NW), 11 (northeast, NE), 00 (southwest, SW), and 01 (southeast, SE), and the identification codes of level-2 grids can be expressed as 0100, 0111, and so on.  
The shaded portion grid X in Figure 1 is 010110, which was taken as an example. The first two bits \"01\" represent the upper left quarter level-1 sub-zone of the entire G. The middle two bits \"01\" represent the upper left quarter level-2 sub-zone, and the last two bits \"10\" represent the upper right quarter level-3 sub-zone.  
QTGrid encodes each grid using the Morton code [43; 46] to convert the 2D coordinate into an M number. The M coding method is not explained in detail in this paper, but the M code of the entire  
Figure 1: Construction of grids.  
monitoring area is shown in Figure 2. Combining Figures 1 and 2, the shaded portion in Figure 1 (010110) can be converted into M code (22) to represent its location. After encoding, the M code of a grid and the intra-cluster ID (\(N_{\mathrm{id}}\)) of a sensor node form the sensor node's identification code format, which is \((M,N_{\mathrm{id}})\).  
Numerous GRs use the 2D coordinate \((m,n)\) to identify the location of a grid, where \(m\) is the row number and \(n\) is the column number. If \(m,n\) are represented by double-precision values, then 8 bytes of space is consumed. In contrast, QTGrid uses M code, which only needs to occupy 2 bytes, thereby reducing the data size. Moreover, each node can deduce its neighbor CHs and other levels' CHs by using the M code without communication and election between nodes (details in Section 3.2). This method decreased the data transmission energy consumption while recording the hierarchical structure of the network and the topological relationship between nodes.
# A Survey on Immersive Cyber Situational Awareness Systems
## 2 Research Methodology
### Study Selection  
We identified, selected, and extracted the existing state-of-the-art ICSA systems through a six-step process. The phases of the study selection process are briefly described as follows.  
* _Automatic Search:_ We ran our search string on the Scopus search engine to identify existing literature on ICSA systems. As a result, we retrieved 3536 potential studies.
* _Title-Based Selection:_ We analyzed the title of the 3536 studies. If a paper title was relevant to the research questions of this survey, we included that paper. In case we were not sure about the relevance of a paper, that paper was transferred to the next phase. At the end of this phase, we had 327 papers.
* _Duplication Removal:_ As we only consulted one database (i.e., Scopus) to retrieve the existing literature, no duplicate study was found during our study selection process.
* _Abstract-Based Selection:_ We thoroughly read the abstracts and conclusions of the remaining 327 studies to check their relevance to our research questions. Here, we also applied the inclusion and exclusion criteria (Table 2) to the abstracts of papers. Consequently, this phase reduced the pool of papers from 327 to 139.
* _Full-Text-Based Selection:_ We read the full text of 139 studies, and applied the inclusion and exclusion criteria on them. As a result, we obtained 36 relevant studies.
* _Snowballing:_ We performed forward and backward snowballing [26] on the 36 studies to identify more literature on ICSA systems. This gave us 7 potentially relevant studies that were then passed through our inclusion and exclusion criteria. Consequently, we finalized 43 relevant studies for this survey.
## 2 Grid-Connected Solar Photovoltaic (SPV) System
### Direct Current-Direct Current (DC-DC) Boost Converter Model  
As the voltage of PV arrays is not sufficient as input into the inverters, a DC-DC boost converter should be connected in order to increase the DC voltage under certain conditions. The structure of the boost converter is shown in Figure 2.  
The input-output voltage relationship of the DC-DC converter is (1):  
\[\mathrm{V_{in}}=\mathrm{V_{o}}(1-\mathrm{D}) \tag{1}\]  
where D is the duty cycle, Vin is the input voltage, and \(\mathrm{V_{0}}\) is the output voltage.  
Figure 1: Schematic diagram of the grid-connected photovoltaic (PV) system. VSC: voltage source converter; DCL: DC-link; MPPT: Maximum power point tracking.  
The ripple voltage is:  
\[\mathrm{V_{rip}}=\Delta\mathrm{V_{o}}/\mathrm{V_{o}} \tag{2}\]  
To reduce the ripple voltage, the pulse-width modulation (PWM) frequency of the pulse generator was reduced. However, we could not increase the frequency beyond a certain value because a higher frequency produces large energy losses in the circuit.
# Hybrid Dual-Polarization Synthetic Aperture Radar
## 4 Performance in Earth Applications
### Results from Appropriate Methodologies
The authors of one investigation [44] concluded that the difference between quad-pol and hybrid dual-pol was \"negligible\" for their application, and those of another [45] found that the respective results were \"comparable\". That pattern is reinforced with similar endorsements: \"1% to 3% differences between the HCP and FP classifications\" [46]; \"almost the same\" [47]; \"almost the same\" [48]; and \"equally well\" [49]. A multi-disciplined investigation for a variety of Canadian applications [25], compiled in preparation for their RCM mission (whose main operational modes are all hybrid dual-polarimetric [50]), concluded that the classifications from hybrid dual-pol data differed by no more than a few percent with respect to those available from a quad-pol system. [PERSON] et al. [51] found very good retrieval of rice biophysical parameters using a variety of classification methods and parameters on emulated HCP data, including one example for which \(\alpha^{0}_{\mathrm{RH}}\), a self-contaminated metric, turned out to be the best discriminator. (This result suggests that there may be applications in which one is interested in the total backscatter observed seen through one of the receiver's linearly-polarized channels.) [PERSON] and [PERSON] [52] found that HCP data supported sea ice classifications that were comparable to QP Pauli decompositions. Results were \"similar\" from emulated HCP and QP observations of ships and oil spills [53], and [PERSON] et al. [54] concluded that CP and QP sea ice classification results were comparable over two test sites (96% vs. 99%, and 97% vs. 99%, respectively). [PERSON] et al. [55] showed that HCP does as well as QP for oil slick classifications, hence noting that HCP is the preferred polarimetric observation mode because of its wide coverage.
## 3 Results  
### Wind Tunnel Tests  
#### 3.1.1 Phase 1: Effect of Biopolymer Concentration on Wind-Induced Soil Erosion  
_Medium-grained sand._ Figure 4 shows the wind-induced soil losses of medium-grained sand samples treated with different biopolymer types and concentrations (numerical values are appended in Table A1, Appendix A). All the biopolymer treatments significantly reduced the wind-induced soil loss compared to that of the control (C). The soil loss decreased at increasing concentrations until reaching a 'plateau concentration', beyond which only a marginal reduction of soil loss occurred. At lower concentrations, the polysaccharides (XG, CS, and CMC) tended to perform better than the tested proteins did (FBPC and WP). Treatments with XG and CS resulted in very low soil losses at the lowest tested concentrations (XG = 0.05 wt% and CS = 0.13 wt%), with higher concentrations leading only to marginal improvements. Conversely, treatments with FBPC, WP, and CMC displayed higher soil losses at 0.13 wt%, which gradually reduced as the concentration increased. The WP- and CMC-treated samples showed no improvement in soil losses at 0.50 wt%, while FBPC plateaued at 0.75 wt%.  
Figure 4: Total soil loss (g/m\({}^{2}\)) of medium-grained sand samples treated with different biopolymer concentrations. Tests were performed in triplicate (n = 3). Note, numerical data (mean (M) and standard deviation (SD)) are appended in Table A1. Error bars representing the SD have been deliberately omitted for better legibility.  
Fine-grained silica sandThe wind-induced soil losses of biopolymer-treated fine-grained silica sand samples are shown in Figure 5 (numerical values are appended in Table A1). Compared to the control, which experienced substantial soil loss, all the biopolymer treatments significantly reduced the wind-induced soil losses. As observed for medium-grained sand, the soil loss decreases as the biopolymer concentration increases, until it reaches a 'plateau concentration'. Again, the protein amendments (especially FBPC) showed noticeably higher soil losses at lower concentrations than the polysaccharide-treated samples did. Samples treated with XG displayed low soil loss at 0.05 wt%, reaching a stagnation concentration at 0.13 wt%. By contrast, the CS, CMC, and WP treatments showed moderately higher soil losses at 0.13 wt%, which decreased at higher concentrations. While CS stagnated at 0.25 wt%, CMC stagnated at 0.50 wt% and WP stagnated at 1.25 wt%, respectively. Lastly, amendments with 0.13 wt% FBPC displayed the highest soil loss, but these values reduced significantly as the concentration increased until they plateaued at 1.0 wt%.
# Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform
## 4 Experimental Design
### Data Description  
Three time series wind power data from three different wind farms were selected for experiments in this paper. Every time series covered 12 months in order to make experiments more supportive. The time interval between two neighboring power data was 15 min (35,000 points in total). The parameter of the wind power data was MW. Three wind farms were located in inner Mongolia (farm 1), The Netherlands (farm 2), and Yunnan of China (farm 3), respectively. These wind farms were far away enough to ensure that the three time series wind power data were different. The three different data are shown in Figure 5.
## 2 Methods
### NSGA-II Application
#### 2.3.2 Fitness Function  
Two fitness functions were established by the performances calculated in the RL environment with surrogate models. The genes of NSGA-II were the 18-dimensional design variable \(s\in\mathbb{S}\), which is also the state of the RL environment. The two fitness functions expressed in Equation (5) aimed to improve the pressure ratio and efficiency.  
\[\begin{array}{l}f_{1}=\hat{\pi}-\hat{\pi}_{0}-a_{1,1}N_{bad}-a_{1,2}\delta f _{L}-a_{1,2}\delta f_{W}\\ f_{2}=\hat{\eta}-\hat{\eta}_{0}-a_{2,1}N_{bad}-a_{2,2}\delta f_{L}-a_{2,3} \delta f_{W}\end{array} \tag{5}\]  
where \(f_{1}\) and \(f_{2}\) were the fitness functions and \(\hat{\pi}_{0}\) and \(\hat{\eta}_{0}\) were the reference values of the integrated pressure ratio \(\hat{\pi}\) and integrated efficiency \(\hat{\eta}\), respectively. \(N_{bad}\) was the number of dimensions of \(p_{i}\in\mathbb{P}\) that were worse than the reference value \(p_{i}\). The parameters \(a_{i,j}\) (\(i=1\), \(2\), \(j=1\), \(2\), \(3\)) were constants indicating the strength of the correct terms.  
\[\delta f_{L}=-\frac{1}{2}\|s-s_{0}\|_{2}^{2} \tag{6}\]  
The correction term \(\delta f_{L}\) provided L2 normalization of the design variables, as expressed in Equation (6), where \(s\) was the design variable and \(s_{0}\) was the reference value. Normalization ensured that the design variable modifications would decay if they were located in dimensions that could not significantly influence the fitness function.  
\[\delta f_{W}=a_{W}\big{|}\hat{m}_{W}-\hat{m}_{W,0}\big{|} \tag{7}\]  
The correction term \(\delta f_{W}\) constrained the working point of the optimized rotors to change only minimally. As expressed in Equation (7), \(\hat{m}_{W,0}\) was the working point of the reference rotor, and the constant \(a_{W}\) defined the strength and shape of \(\delta f_{W}\). If \(\hat{m}_{W}\) was modified excessively, \(\delta f_{W}\) increased and reduced the fitness functions.
## 5 IETF ROLL Working Group
### Leveraging upon RPL to Realize the IoT
#### 5.3.2 Loop-free Repair Mechanisms  
When link quality decreases and/or failure of a parent occurs, the repair mechanism of RPL, can introduce DODAG loops. In _Loop Free DODAG Local Repair_ (draft-guo-roll-loop-free-dodag-repair-00) [75] an adaptation to the repair mechanism is proposed. In _Loop Free RPL_ (draft-guo-roll-loop-free-rpl-01) [76] adaptation to the rank mechanism and objective function are presented. These drafts envision a loop free repair mechanism.
## 4 Empirical Results
### Impact of Vehicle Density  
In this simulation, to analyze the impact of vehicle density on the performance of the proposed protocol, the number of vehicles was varied from 100 to 500. The results depicted in Figure 11a indicate that the SLD-GEDIR protocol has lowest end-to-end delay in comparison with the state-of-the-art protocols. The impact of vehicle density on end-to-end delay of the proposed protocol is insignificant due to its unique NHV selection process. The state-of-the-art protocols show higher end-to-end delays since they select more than one NHV. It is also noteworthy that the state-of-the-art protocols show nearly equal end-to-end delays since the protocols minimize the forwarding area and select all the vehicles within the forwarding region as NHVs. Thus, it is clearly noted that SLD-GEDIR outperforms other considered state-of-the-art protocols in terms of end-to-end delay with increasing density. The results depicted in Figure 11b indicate that the throughput of the proposed protocol is nearly equal to the considered state-of-the-art protocols with 100 vehicles in the segment area, but, as soon as the vehicle density increases, the throughput of the state-of-the-art protocols decreases rapidly with higher rates as compared to the proposed protocol. This is due to the fact that the proposed protocol almost eliminates duplication of packets whereas in case of state-of-the-art protocols, duplication of packets increases with the increase in vehicle density. Therefore, SLD-GEDIR performs better than the state-of-the-art protocols in terms of throughput.  
The results are depicted in Figure 11c. It clearly shows that the number of one-hop link disconnections in SLD-GEDIR is lower as compared to the considered state-of-the-art protocols. It is also noteworthy that the link disconnection of the proposed protocol remains the same with increasing vehicle density whereas in case of state-of-the-art protocols link disconnections decrease marginally with increasing vehicle density. This can be attributed to the fact that the proposed protocol always selects a single NHV whereas in case of state-of-the-art protocols the number of NHVs increases with increasing vehicle density. The rate of one-hop link disconnection is 5% for the proposed protocol whereas it varies from 22% to 33% in the case of state-of-the-art protocols. The state-of-the-art protocols show approximately equal link disconnections for each vehicle density considered. Thus, it can be noted that the proposed protocol shows lower link disconnection as compared to the state-of-the-art protocols.
# A Channel Measurement-Based Listen-Before-Talk Algorithm for LTE-LAA and WLAN Coexistence
## 3 Proposed CM-LBT Scheme
### Clear Channel Assessment at LTE-LAA eNB
\begin{cases}\alpha(R_{I}-R_{cca})+R_{cca}-r_{u},& \text{for}\ \theta=0,\\ \{\alpha(R_{I}-R_{cca})+R_{cca}\}\cdot\frac{\sin{(\theta-\theta^{\prime})}}{ \sin{\theta}},&\text{for}\ 0<\theta<\pi,\\ \alpha(R_{I}-R_{cca})+R_{cca}+r_{u},&\text{for}\ \theta=\pi,\end{cases}  \tag{13}\]  
where \(\theta^{\prime}=\arcsin(\frac{r_{u}\sin{\theta}}{a(R_{I}-R_{cca})+R_{cca}})\) for \(0<\theta^{\prime}\leq\frac{\pi}{2}\). When \(r_{u}<R_{cca}\), we can calculate \(P_{I}(R_{cca})\) by driving the area element \(vdvd\theta\) to be infinitesimal, as follows:  
\[P_{I}(R_{cca})\simeq 2(\lambda_{a}P_{a}+\lambda_{s}P_{s})K\cdot\int_{0}^{\pi} \int_{0}^{1}\
u(R_{cca},\alpha,\theta)^{-\eta+1}(R_{I}-R_{cca})d\alpha d\theta. \tag{14}\]  
Here, we use the relation \(dv=(R_{I}-R_{cca})d\alpha\). Additional care should be taken if LTE-LAA device \(u\) is located outside the CCA range of LTE-LAA eNB. We then consider two cases. In _Case1_, \(\alpha(R_{I}-R_{cca})+R_{cca}<r_{u}\). Otherwise, it is _Case2_. For _Case1_, we use the interfering area with the partial ring area defined by the range \([R_{cca},r_{u}]\) and angle \([0,\omega]\) as shown in Figure 3b, where \(\omega=\arcsin{(\{\alpha(R_{I}-R_{cca})+R_{cca}\}/r_{u})}\) and \(\frac{\pi}{2}\leq\theta^{\prime}<\pi\) in Equation (13). For _Case2_, we can calculate the interference for the range \([r_{u},R_{I}]\) in the same way as in Equation (14). Thus, when \(r_{u}\geq R_{cca}\), the cumulative interference is as follows:  
\[ \begin{split} P_{I}(R_{cca})\simeq 2(\lambda_{a}P_{a}+\lambda_{s}P_{s})K \cdot&\left(\int_{0}^{\omega}\int_{0}^{\frac{\eta-R_{cca}}{R_{I}-R_ {cca}}}\frac{\pi}{\omega}\cdot\
u^{\prime}(R_{cca},\alpha,\theta)^{-\eta+1}(r_ {u}-R_{cca})d\alpha d\theta\right.\\ &\left.+\int_{0}^{\pi}\int_{\frac{R_{cca}}{R_{I}-R_{cca}}}\
u(R_ {cca},\alpha,\theta)^{-\eta+1}(R_{I}-r_{u})d\alpha d\theta\right).\end{split}  \tag{15}\]where \(\
u^{\prime}(\cdot)\) can be obtained by substituting \(\frac{\sin{(\theta+\theta^{\prime})}}{\sin{(\theta)}}\) for \(\frac{\sin{(\theta-\theta^{\prime})}}{\sin{(\theta)}}\) in Equation (13) when \(0<\theta<\pi\). In the case of \(\theta=0\), \(\
u^{\prime}(\cdot)=-\
u(\cdot)\). We consider the Shannon capacity as the LTE-LAA rate function, denoted by \(R_{U}(\cdot)\). For practical LTE-LAA networks, we employ a closed-form approximation for the LTE-LAA rate function by using Equation (12) as follows [19; 20]:  
\[R_{U}(\gamma(R_{cca}))\approx\kappa_{bw}\cdot\kappa_{c}\cdot B\log_{2}(1+ \gamma(R_{cca})/\kappa_{sim}). \tag{16}\]  
where \(B\) is the bandwidth of an LTE-LAA subchannel and \(\kappa_{bw}\) is the system efficiency that accounts for various system-level overheads. The parameters \(\kappa_{c}\) and \(\kappa_{sim}\) jointly adjust for the SINR implementation efficiency of LTE-LAA modulation and coding schemes [20].