esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Combination Weighting Method
#### 3.2.2 Analytic Hierarchy Process (AHP)  
The Analytic Hierarchy Process (AHP) was proposed by renowned mathematician [PERSON] and is a simple and feasible decision-making method with significant subjectivity [38]. The main advantage lies in the ability to determine the weights of various indicators at different levels in the system, which has unique advantages and is simple and convenient to calculate; therefore, it is widely used [39; 40]. The specific calculation steps are as follows:  
(1) Establish a tiered hierarchical structure model. The hierarchical structure is generally divided into three layers: target layer, criterion layer, and scheme layer.  
(2) Establish the judgement matrix. For different factors at the same level, establish a judgment matrix by comparing their impact on the target factors. The formula for constructing the judgment matrix is as follows:  
\[A=\left(a_{ij}\right)_{n\times n^{\prime}}a_{ij}\textgreater 0,\quad a_{ij}= \frac{1}{a_{ij}},\left(i,j=1,2,\ldots n\right) \tag{9}\]  
Among them, \(a_{ij}\) is the ratio of the influence degree of elements \(B_{i}\) and \(B_{j}\), usually represented by a scoring method of one to nine, as shown in the Table 4 below.  
(3) Calculate consistency indicator _CI_. By calculating the eigenvalues and eigenvectors of the judgment matrix, it can be represented as:  
\[CI=\frac{\lambda_{\text{max}}-n}{n-1} \tag{10}\]  
Among \(\lambda_{max}\) is the eigenvalue of matrix \(A\).  
(4) Calculate consistency ratio _CR_  
\[CR=\frac{CI}{RI} \tag{11}\]  
Among them, _RI_ is the consistency indicator of the judgment matrix (Table 5). When _CR_ < 0.1, it is judged that the matrix meets the consistency requirement. Otherwise, it is considered that the matrix does not meet the consistency requirement, and further adjustments are needed until the consistency check is met.
## 2 System and Mathematical Models
### Mathematical Model  
The main mathematical principle of RF cancellation in a CCFD system is the composition of vectors [31]. The following notation is utilized in developing a mathematical model for the problem of SI cancellation in the RF domain addressed in this paper: \(s(t)\) is the near-end transmitting signal, \(s_{i}(t)\) is the same phase component as \(s(t)\), \(s_{q}(t)\) is the orthogonal component obtained by \(s_{i}(t)\) through a 90 degree phase shifter, \(r_{1}(t)\) is the estimated SI signal, \(r(t)\) is the near-end receiving signal, and \(e(t)\) is the error signal. Assuming that the SI signal in the RF domain is a vector in the space rectangular coordinate system, the signal can be cancelled by synthesizing another vector with the same information characteristics as the vector. As shown in Figure 2, the near-end transmitting signal \(s(t)\) is divided into two branches, \(s_{i}(t)\) and \(s_{q}(t)\), after group delay, t. The delay block is the group delay of the estimate interference signal.  
Let \(S(t)=\left[s_{i}(t)\ s_{q}(t)\right]^{T}\), where \(T\) is the transpose of the matrix, and the weight vector is \(W(t)=\left[\omega_{i}(t)\ \omega_{q}(t)\right]^{T}\), that is the amplitude attenuation controlled by in-phase and orthogonal branch attenuator, which is used to adjust the amplitude and phase of \(r_{1}(t)\). Therefore, the basic process of cancellation mentioned above can be expressed by  
\[r_{1}(t)=\left[W(t)\right]^{T}\!\!S(t)=\left[\omega_{i}(t)\ \omega_{q}(t) \right]\left[\begin{array}{c}s_{i}(t)\\ s_{q}(t)\end{array}\right]=\omega_{i}(t)s_{i}(t)+\ \omega_{q}(t)s_{q}(t) \tag{7}\]  
\[e(t)=\left[r_{SI}(t)-r_{1}(t)\right]+r_{u}(t)+n(t)=\left\{r_{SI}(t)-\left[ \omega_{i}(t)s_{i}(t)+\ \omega_{q}(t)s_{q}(t)\right]+r_{u}(t)+n(t)\right. \tag{8}\]  
\[W(t+t)=\left[\omega_{i}(t+\Delta t)\ \omega_{q}(t+\Delta t)\right]^{T}=\left[ \omega_{i}(t)\ \omega_{q}(t)\right]^{T}+\left[\omega_{i,\Delta t}\ \omega_{q,\Delta t}\right]^{T}=W(t)+\ \mu\ e(t)S(t). \tag{9}\]  
Equation (9) is the iteration formula for updating the weight vector of the LMS algorithm, where \(\mu\) is a fixed step factor and \(\Delta t\) is the step interval of feedback control. The above mentioned cancellation  
Figure 2: Principle diagram of self-interference (SI) cancellation in the radio frequency (RF) domain.  
process, the amplitude of the in-phase component, and the orthogonal component can be changed by adjusting the value of \(W(t)\), and then the SI signal can be reconstructed. Therefore, the weight vector is the key factor affecting the performance of SI cancellation. The adjustment of the weight vector based on the minimum mean square error (MMSE) criterion is expressed as  
\[\min\left\{E\left(\left|e(t)\right|^{2}\right)\right\}=min\left\{E\left(\left| r_{0}(t)-r_{1}(t)\right|^{2}\right)\right\}. \tag{10}\]
## 4 Results and Discussion
### Land/Water Boundary Delineation  
Land/water discrimination based on the seeded region growing algorithm achieved the highest overall accuracy of more than 99% with clear visualized land/water boundaries. The land/water boundary was delineated from region growing results produced by combining C1 with C3 (RG_C1C3_bound) and C2 with C3 (RG_C2C3_bound). The labeled points were converted into raster images with a pixel size of 1 m, and contour lines were automatically drawn using the embedded function in ArcGIS \"_Contour list_\". The output contour lines were compared with the contour line produced from labeled point clouds of C3 based on elevation (H_C3_bound) as shown in Figure 10. The misclassification at the dataset edges caused disconnection and delineation errors of the land/water boundary.  
The RMSE was calculated from nine transects distributed perpendicular to the land/water boundary as shown in Figure 10. The tenth transect was excluded from calculations as the land/water boundary was not delineated from _RG_C2C3_bound_. If nine transects are considered, the RMSE of RG_C1C3_bound and RG_C2C3_bound are 4.81 m and 0.87 m, respectively. The high values are due to the discrimination errors at the land/water interface at the edges. Therefore, we also excluded transects at the edges (the first, second, and ninth transects). If only six transects are considered, the RMSE of RG_C1C3_bound and RG_C2C3_bound improved to 0.42 m and 0.60 m, respectively.  
Figure 9: Average overall accuracies based on different point features and seeded region growing.
# Laser Fabrication of Titanium Alloy-Based Photothermal Responsive Slippery Surface
## 3 Results and Discussion
### Wettability  
Figure 7a is the static contact angle histogram of the sample at room temperature. It can be seen that the initial contact angle of the sample is 58.4\({}^{\circ}\). In the sample after laser processing, the contact angle measured from the direction perpendicular and parallel to the groove was 54.2\({}^{\circ}\) and 65.2\({}^{\circ}\), respectively. These results show that the titanium alloy sample before and after laser treatment are both hydrophilic. To fabricate the photothermally responsive slippery surface, paraffin was placed on the laser-treatment surface. After heating, the paraffin was melted and coated the laser-treatment surface. As shown in the Figure 7b, the liquid can slide on the slippery surface upon light irradiation.  
Figure 6: (**a**) Thermal imaging of initial titanium alloy and laser-treated titanium alloy substrate. (**b**) Dependence of the surface temperature on irradiation time for initial titanium alloy and laser-treated titanium alloy substrate.  
Figure 8a is the schematic diagram of the droplet sliding on the sample surface under light irradiation [33]. There is a high adhesion between water droplets and paraffin. The water droplets can be pinned on the paraffin surface. When exposed to light, the laser-treated titanium alloy will undergo photothermal conversion to melt the paraffin on the surface. Therefore, when irradiated by light, the paraffin will melt. The material surface will become a slippery surface. The adhesion of water droplets to the substrate surface will be reduced, resulting in water droplet sliding (Figure 8b).
# Authentication Based on Non-Interactive Zero-Knowledge Proofs for the Internet of Things
## Security Proofs
### Attacks on the Graph Problem  
The specific problem under the ZKP is fundamental for the security of the scheme. The implemented scheme uses the two graph problems of the graph isomorphism and of the Hamiltonian cycle.  
Regarding possible attacks to the problem of the graph isomorphism, some efficient algorithms for some specific graphs have been proposed. For example, in [36], several probabilistic algorithms were discussed; and in [37], some algorithms for determining whether two graphs are isomorphic or not have been described. However, the proposed scheme can use graphs of different sizes, and depending on the size of these graphs, the security can be increased. The generated graphs are completely random, and therefore, none of the proposed algorithms are useful for finding the secret. Therefore, under the hypothesis of using well-chosen instances of the problem, the graph isomorphism problem can be considered NP-complete for the particular execution of the proposal.  
With respect to the Hamiltonian cycle problem, under the condition of the defined scheme, the problem can be also considered NP-complete. This is achieved with the use of non-planar graphs, because through the theorem shown in a previous section, we can ensure that in the implementation, none of the graphs that are generated in the scheme are planar. Again, under the hypothesis of using well-chosen instances of the problem, the Hamiltonian cycle problem can be considered NP-complete for the particular execution of the proposal. Thus, a real attack based on the used graph problems is not possible because the complexity of the chosen problems can be considered NP-complete.
## 4 Analysis of Computational Complexity
### Simulation of a Single-Channel ANC System
Table 9 shows the number of additions and multiplications required to perform each algorithm by processing a Gaussian noise signal. In this case, our proposal with fixed and dynamic scaling factor expend approximately 2.45, 2.39, 8.24, and 8.057 times fewer multiplications and 3.11, 3.11, 7.66, and 7.66 times fewer additions than do the FXAPL-I and FXA-AP algorithms, respectively. In addition, our proposal exhibits similar convergence speed when compared with FXAPL-I algorithm, as shown in Figure 4. On the other hand, our proposal with fixed and dynamic scaling factor require approximately 2.54 and 2.60 more multiplications and 2.42 and 2.43 more additions than are required by the FXLMS algorithm. Despite using the minimal number of operations, the FXLMS algorithm shows slower convergence speed when compared with our proposal. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Multiplications** & **Additions** \\ \hline FXLMS & 660,000,000 & 652,000,000 \\ FXAPL-I & 4,122,000,000 & 4,922,000,000 \\ FXA-AP & 13,842,000,000 & 12,124,000,000 \\ Proposed algorithm with fixed scaling factor & 1,601,824,300 & 1,436,306,450 \\ Proposed algorithm with dynamic scaling factor & 1,631,122,340 & 1,502,695,020 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Total number of multiplications and additions of the FXAPL-I, FXLMS, FXA-AP and the proposed algorithms for a single-channel ANC system using a multimodal noise input. Figure 3: Learning curves for the FXAPL-I algorithm, the FXLMS algorithm, the FXA-AP algorithm and the proposed algorithm for a multimodal input in a single-channel system.
# Malware Analysis in IoT & Android Systems with Defensive Mechanism
## 3 Type of Attacks on IoT and IIoT Devices
### Network Attacks  
Network attacks are performed by the maneuvering of IoT networks to cause damage. This may be launched from a remote location. Some common network attacks are Traffic Analysis attacks, RFID Spoofing (Radio Frequency Identification), RFID Unauthorized Access, Routing Information Attacks, Selective forwarding, Sinkhole attacks, Wormhole attacks, Sybil attacks, Man-in-the-Middle Attacks (MiTM), and Replay attacks. Network attacks may lead to serious consequences, and ultimately, may face a shutdown of the system and services. The denial of one service, such as OTP in banking services, makes other services inaccessible and useless.  
**Countermeasures against Network Attacks:** The framework is named EPIC (Efficient and Privacy-preserving traffic obfuscation), a multi-hop routing protocol to achieve strong privacy. It protects commercial applications used for home automation or smart homes. Three basic properties, referred to as CIA (Confidentiality, Integrity, Authentication), are to be maintained for secure communication and to defend against Reply attacks. IBC (Identity-Based Cryptography), which is a signcryption technique, efficiently satisfies CIA. The IBC combines encryption and signature schemes and also eliminates the need to access a trusted third party to fulfill the authentication process that survives against replay attacks.  
Work conducted by [17] in the direction of IDS for IoT proposed three different models. The first model is based on the unsupervised machine learning approach K-means to find wormhole attacks, while the second is the supervised approach based on the decision tree. Both models are dependent on the clause that if any router tries to add a new node outside of its cluster or safe distance, it is declared as a wormhole. The third model is based on the hybridization of the K-mean and decision-based tree [18; 19]. The RPL protocol (Routing Protocol for Low-Power and Lossy Networks), i.e., SecTrust-RPL, may be used to handle Sybil attacks.  
The MiTM attack has significant potential to harm the IoT system, as it can bypass the control of the attackers. This attack can exploit the possible path of industrial robots, which leads to potential damage to the assembly line of a system. To solve MiTM issues, the work performed by [20] proposed MQTT (Message Queuing Telemetry Transport) and MQTT-SN (MQTT Sensor Network) protocols. These protocols maintain end-to-end communication. The MQTT protocol uses a key policy, whereas MQTT-SN uses an attribute-based encryption policy and elliptic curve cryptography to defend MiTM.
# Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation
## 4 Illustrative Examples
### Example 1  
Now, a univariate nonstationary growth model (UNGM) that is often used as a benchmark example for nonlinear filtering is considered, whose state and measurement equations are given by  
\[x(k)=\alpha_{1}x(k-1)+\alpha_{2}\frac{x(k-1)}{1+x(k-1)^{2}}+\alpha_{3}\cos \left(1.2(k-1)\right)+q(k-1), \tag{46}\]  
\[y(k)=\frac{x(k)^{2}}{20}+r(k). \tag{47}\]  
The parameters are set to \(\alpha_{1}=0.5,\alpha_{2}=25,\alpha_{3}=8\).  
First, we consider the case in which all the noises are Gaussian  
\[\begin{array}{l}q(k-1)\sim N(0,1),\\ r(k)\sim N(0,1).\end{array}\]  
In this example, the parameters are chosen as \(K=500\), \(M=100\). Table 1 illustrates the MSEs of \(x\), defined in Equation (41), in Gaussian noise. Since all the noises are Gaussian, the UKF gives the smallest MSE in all filters. Moreover, it is noted that when the kernel bandwidth is small, the MCUKF may result in a worse estimation; in contrast, when the kernel bandwidth becomes larger, its performance will approach that of the UKF. Actually, it has been proved easily that when \(\sigma\rightarrow\infty\), the MCUKF will reduce to the traditional UKF (see Theorem 1). Therefore, one should choose a larger kernel bandwidth in Gaussian noises.  
Second, we change the observation noise into a heavy-tailed non-Gaussian noise, with a mixed-Gaussian distribution  
\[\begin{array}{l}q(k-1)\sim N(0,1),\\ r(k)\sim 0.8N(0,1)+0.2N(0,500).\end{array}\]Table 2 shows the MSEs of \(x\) in non-Gaussian measurement noise. As one can see, in this case, when kernel bandwidth is too small or too large, the performance of MCUKF will be not good. However, with a proper kernel bandwidth (say \(\sigma=2.0\)), the MCUKF can outperform the UKF, achieving the smallest MSE. Again, when \(\sigma\) is very large, MCUKF achieves almost the same performance as the UKF.
# A Survey on Parameters Affecting MANET Performance
## 3 Simulation in MANETs
### MANETs Simulators  
Simulators are software tools used to create a virtual environment that supports researchers to set up and test a network's performance under different conditions. Simulators are GUI-driven tools used to set up a network environment and then perform different attacks on the defined network, or make comparisons between a standard routing protocol and a newly proposed protocol. Using the defined evaluation metrics, a simulator is also capable of collecting the network's results and evaluating the overall performance [30].  
There is another method for developers to define and test MANETs using testbeds. Testbeds are experimentation in-lab networks that researchers can set up using dedicated hardware sets for this purpose. Testbeds lack the flexibility to define a MANET network, as MANETs are dynamic networks where nodes continuously join and leave the network. Additionally, the cost is much higher than software simulations to define a MANET using testbeds.  
To be able to select a suitable simulator, the researchers need to know the simulator's key features [31]. Table 5 is a comparison between the widely used simulators in MANETs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**OLSR Parameter** & **Default Value** \\ \hline Willingness & WILL\_DEFAULT (3) \\ TCInterval & 5 s \\ RefreshInterval & 2 s \\ NeighbHoldTime & 6 s \\ TopHoldTime & 15 s \\ HelloInterval & 2 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: OLSR parameters’ default values.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Simulator Name** & **Languages Supported** & **Platform Support** & **License** & **Advantages** & **Disadvantages** \\ \hline \hline \multirow{4}{*}{OPNET} & \multirow{4}{*}{C, C++} & Windows, Sun Solaris, RedHat Linux & Commercial, Free Educational License & - User-friendly and easy to use. & - Limited wireless mobility. \\  & & & & use. & - Not open source and supported protocols are \\ \cline{1-1}  & & & & - Provides additional & limited. \\ \cline{1-1}  & & & & supportive tools. & - Expensive. \\ \cline{1-1}  & & & & - Lack of energy model. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between simulation tools in MANETs.
## 5 Experimental Results
### Ablation Experiment of Offsets Estimation  
When estimating the initial 3D box, our algorithm estimates the offsets of the keypoints of the 3D box relative to the center point of the 2D box twice, which can increase the constraints of the loss function and improve the precision of the initial 3D object detection. The results of the ablation experiments are shown in Table 6.
# OGSM: A Parallel Implicit Assembly Algorithm and Library for Overlapping Grids
## 3 Parallel Implicit Assembly Method
### Treatment of Defective Cells  
As shown in Figure 1, the _overlapping_ cells come from the volume cells adjacent to the external boundary surfaces or from the _inactive_ cells adjacent to the _transitional_ cells.  
Figure 10: Partial coincidence between hole-cutting boundaries and partition boundaries.  
Equation (2) suggests that the lattice center of the latter case is displaced relative to the equidistance surface of different components. However, we may find some _overlapping_ cells that cannot share strict coverage relation with the donor cells under certain conditions: (1) for a vanishing overlapping region, certain lattice centers of the _overlapping_ cells may locate outside the overlapping regions; (2) when the gap between components is extremely narrow, the lattice centers of the _overlapping_ cells may penetrate the solid wall boundary surfaces of the target grid. We call these _overlapping_ cells _defective_ cells.  
To deal with the _defective_ cells, proper mesh refinement of the overlapping regions can diminish the deviation of the overlapping cell centers, thereby reducing or even eliminating the defective cells. However, for the multi-body motion problems, the density of the mesh in the overlapping regions is difficult to control at each physical time step. Instead, we search the donor cells corresponding to the _defective_ cells within an optimal grid-center distance. On the source grid \(G^{i}\), an extra layer of cells is constructed. Based on the minimal distance between the centers of _overlapping_ cells and the _extra-layer_ cells, we can obtain a single donor cell within the source grid.  
1. On each process hosting the source grid \(G^{i}\), the donor cells for the relevant _defective_ cells are independently selected.
2. In the non-blocking communication mode, the information about the above selected donor cells is gathered and then broadcasted to those processes (denoted by \(p_{t}\)s) containing the distributed target grids.
3. In each process of \(p_{t}\)s, the _defective_ cells are independently screened, and the nearest _flow-field_ cells are taken as donor cells.  
On completion of treating the _defective_ cells, full information of the overlapping assembly can be provided to the numerical simulator.
## 1 Introduction
As NPQ and SiF are both closely linked to photosynthetic efficiency, an increase/decrease in the amount of energy directed to any of them willlead to a decrease/increase in the energy available for the remaining two photosynthetic pathways. Therefore, if we can accurately estimate SiF and NPQ, it is then possible to have a measure of the level of efficiency of carbon uptake by plants. While both the PRI and SiF-based approaches were successfully applied at the plant and small canopy levels [23; 24; 25], their application to larger spatial scales using airborne and satellite data remains challenging [26; 27; 28; 29; 30; 31; 32; 33; 34]. However, recent articles have highlighted, with varying degrees of success, the ability to extract SiF from orbiting platforms (e.g., from the Japanese Greenhouse gases Observing SATellite, GOSAT, and from the Global Ozone Monitoring Experiment-2, GOME-2) for estimating GPP [27; 35; 36; 37]. These findings highlighted that spatial and temporal patterns of satellite retrieved SiF were highly correlated with GPP at the biome and global levels [33], but such correlations weaken at individual higher temporal and spatial resolutions [38] or during extreme climatic events [39]. Field based studies are continuing to emerge [29; 40; 41; 42; 43; 20; 44] though long term data sets, covering multiple seasons, are still rare [45; 46; 47]. Additionally, many of these studies have been restricted to crop or shrub canopies (e.g., References [47; 48; 49; 50]) though one recent study has been in a deciduous forest [25]. In this study, we obtained a high frequency time series of canopy SiF, from early spring, during physiological inactivity, to peak physiological activity in summer, and decrease into autumn, over an evergreen boreal scots pine canopy, alongside measurements of PAM fluorescence, GPP from eddy covariance and environmental variables. We aimed to address the following questions: (1) How does SiF change over the growing season in a boreal evergreen ecosystem experiencing modest changes in APAR? (2) Is the SiF-GPP relationship as good as previously reported for crops and deciduous forests? (3) Is SiF able to capture changes in canopy physiological dynamics (LUE)?