esa-sceva/satcom-chunk-collection huggingface dataset:
# An Iterative Optimization Algorithm for Planning Spacecraft Pathways Through Asteroids
## 5 Other Algorithms for the ARP
### Key Differences of FAT-RLS with Respect to UMM and CEGO  
The main difference between UMM and CEGO, on the one hand, and FAT-RLS, on the other, is that UMM uses a probability distribution to model a population of permutations, and CEGO adopts a distance-based probability model over permutations to define a surrogate function, whereas FAT-RLS does not use probability models over permutations at all. In contrast, FAT-RLS adopts a greedy randomized local search approach, which is not adopted either by UMM or by CEGO.  
Moreover, FAT-RLS operators have a negligible linear complexity, while those of CEGO and UMM require quadratic costs to perform statistical operations over permutations.  
Another difference is that FAT-RLS, in contrast to CEGO and UMM, is invariant to monotonic transformations of the objective function. This property is crucial in many practical contexts. For example, consider objective values which are monetary amounts or physical measurements. In real-world applications, it is essential that the algorithm's behavior--and thus the quality of the solution it produces--does not depend on the specific currency or unit of measurement under consideration.  
There are, however, a few similarities. For example, CEGO may use the Ulam distance over permutations to construct its surrogate model. This distance metric counts the number of insertions needed to transform one permutation into another. In this sense, CEGO employs the same type of perturbation move used in FAT-RLS, i.e., insertions of items within a permutation.  
Moreover, there are notable parallels with UMM. With this regard, let us first note that an insertion move that shifts an item by \(d\) positions results in a permutation that is \(d\) steps away from the current solution in terms of Kendall's-\(\tau\) distance (which counts the number of pairwise disagreements between two permutations). In this way, FAT-RLS and UMM, despite being fundamentally different types of algorithms, exhibit somewhat similar search behaviors: both iteratively perturb a permutation by moving to another one at a specific Kendall's-\(\tau\) distance, which is progressively reduced over iterations. However, there are key differences, as follows: (i) UMM perturbs the centroid of the Mallows distribution, while FAT-RLS perturbs the best solution to date; (ii) UMM performs a jump at an expected Kendall's-\(\tau\) distance in an isotropic manner, whereas FAT-RLS uses a more constrained insertion move; (iii) UMM linearly decreases the perturbation strength, while FAT-RLS uses a \"skewed S\"-shaped decay pattern.
## 2 Background
### Security Analysis and Threat Modelling
#### 2.2.2 Linddun  
LINDDUN is a privacy threat modelling framework designed to systematically identify and address privacy risks in IT systems [31]. The name LINDDUN is an acronym derived from the categories of privacy threats it aims to address:  
* Linkability refers to the risk that two or more pieces of data can be linked to derive additional information about a person.
* Identifiability describes the possibility of uniquely identifying a person from a dataset.
* Non-repudiation is the inability of an entity to deny having performed an action.
* Detectability refers to the risk that the existence of data or a system can be detected by unauthorised parties.
* Disclosure of Information is unauthorised access to private data.
* Unawareness relates to a lack of awareness or content about how data are collected or used.
* Non-compliance means violation of legal, regulatory, or contractual obligations regarding privacy.  
The application of LINDDUN consists of the following six steps, as depicted in Figure 2:  
1. _Data Flow Diagram (DFD) Definition_. To begin with, it is necessary to provide a high-level system description identifying included components, data flows, and processes.
2. _Mapping Threats to DFD_. Next, for each system component defined in Step I, it is necessary to identify related LINDDUN components.
3. _Identifying Scenarios_. During this step, potential threats and usage scenarios for each category should be identified.
4. _Prioritising Threats_. Further, evaluating the risks based on their likelihood and impact is required.
5. _Identifying Requirements_. During this step, privacy threats and requirements should be mapped.
6. _Mitigation Strategy Planning_. Finally, strategies to address or minimise the identified privacy risks should be proposed.
# Binaural Auralization of Room Acoustics with a Highly Scalable Wave-Based Acoustics Simulation
## 2 Theory
### Expansion Coefficient Calculation via Spatial Differentiation of the FE Shape Function
\begin{pmatrix}\frac{\partial\mathbf{N}}{\partial\zeta}\\ \frac{\partial N}{\partial\eta}\\ \frac{\partial N}{\partial\zeta}\end{pmatrix} =\mathbf{J} \begin{pmatrix}\frac{ \partial N}{\partial x}\\ \frac{\partial N}{\partial y}\\ \frac{\partial N}{\partial z}\end{pmatrix} , \tag{33}\]  
where \(\mathbf{J}\) is the Jacobian matrix expressed as  
\[\mathbf{J}= \begin{pmatrix}\frac{\partial x}{\partial\zeta}&\frac{\partial y}{ \partial\zeta}&\frac{\partial z}{\partial\zeta}\\ \frac{\partial x}{\partial\eta}&\frac{\partial y}{\partial\eta}&\frac{ \partial z}{\partial\eta}\\ \frac{\partial x}{\partial\zeta}&\frac{\partial y}{\partial\zeta}&\frac{ \partial z}{\partial\zeta}\end{pmatrix} . \tag{34}\]  
From the equations above, the FOA signal is calculable as  
\[A_{0,0}=\sqrt{\frac{1}{4\pi}}\mathbf{N}\mathbf{p}(t), \tag{35}\]  
and  
\[ \begin{pmatrix}A_{1,1}\\ A_{1,-1}\\ A_{1,0}\end{pmatrix} =\mathcal{F}^{-1}\left[\sqrt{\frac{3}{4\pi}}(\frac{1}{jk}) \mathbf{J}^{-1} \begin{pmatrix}\frac{\partial N}{\partial\zeta}\\ \frac{\partial N}{\partial\eta}\\ \frac{\partial N}{\partial\zeta}\end{pmatrix} \mathbf{p}(\omega)\right]. \tag{36}\]  
Therein, \(\mathbf{p}(t)\) denotes the time-domain nodal sound pressure vector on eight-node hexahedral FE. In addition, \(\mathcal{F}^{-1}\) expresses the inverse Fourier-transform operator. It is also notable that \(A_{0,0}\) is an omnidirectional component and that \(A_{1,-1}\), \(A_{1,0}\), and \(A_{1,1}\) are, respectively, bidirectional components in the \(y\), \(z\), and \(x\) directions. Although the present study computes only the first-order expansion coefficients, the proposed method is applicable to compute higher-order coefficients. For example, the second-order spatial differentiation of sound pressure in FE is calculated by applying the same procedure as that in Equation (36) after obtaining the first-order differentiation of nodal sound pressures.
## Materials and Methods
### GA-adaptive Template Matching  
In traditional TM, as described in Section 2.2, the template is selected as a cutout from one larger reference search image and compared to all the successive test search images using the cross-correlation. The position of maximum correlation testifies the match, proving the template detection. This obviously works very well in case of a fixed framing camera depicting an object which translates on a plane orthogonal to the optical axis of the camera. Nevertheless, in case of rotations, scale changes or non-rigid transformations (N.B., images are a projection of a 3D scene onto a 2D plane, so that movement on a plane non-orthogonal to the optical axis of the camera can lead to deformations), the method cannot be used unless some technical device is implemented, such as using multiple templates with different scales and rotations (e.g., eigenspaces) or using Deformable Part Models (DPM) or implementing Deformable Template Matching [37]. Nevertheless, in this particular work, the Genetic Algorithm was selected to deal with rigid transformations of the template. The GA was used for adapting a parametric template so as to get the maximum correlation (i.e., the best match). The complete cross-correlation function (i.e., correlation for all the delays or lags) is never computed in this case; the GA is exploited for optimizing at the same time not only the scale and the orientation but also the location of the parametric template in the search space, so as to obtain an hybrid of the template-based and feature-based approaches which allows to overcome the issues of the traditional TM (i.e., scaling and rotations).  
Notice that, in the SURVISHNO video, the framing is fixed, but the fan revolution occurs in a plane non-perfectly orthogonal to the optical axis, so that, during the revolution, the center of rotation of the fan moves in a small region, while the image undergoes slight deformations. These perspective-related deformations are neglected by the here-introduced algorithm, but relative translations of the locking screw head hexagon and the underlying hexagon (lying on two different planes) are allowed by breaking the template adaptation in three successive GA steps.  
Figure 5: Preprocessing scheme.
# Thermal Characteristics Study of the Bump Foil Thrust Gas Bearing
## 2 Numerical Simulation Research
### Bearing Load Capacity and Friction Loss Power  
Load capacity (\(\overline{F}\)) and friction loss power (\(\overline{W}\)) are the main static characteristics to measure bearing performance, their calculation formulas are as follows:  
\[\overline{F}=\frac{F}{p_{a}r_{2}}^{2}=\int\limits_{0}^{\beta}\int\limits_{r_{ 1}/r_{2}}^{1}(\overline{p}-1)\overline{r}d\overline{r}d\theta \tag{10}\]  
\[\overline{T_{r}}=\frac{T_{r}}{p_{a}h_{2}r_{2}}^{2}=\int\limits_{0}^{\beta}\int \limits_{r_{1}/r_{2}}^{1}\frac{\overline{r}h}{2}\frac{\partial\overline{p}}{ \partial\theta}+\frac{\Lambda}{6}\frac{\overline{r}^{3}}{\overline{n}}d \overline{r}d\theta \tag{11}\]  
\[\overline{W}=\frac{2\pi n}{60}\times\overline{T_{r}} \tag{12}\]  
where \(n\) is the rotational speed of the thrust plate, \(\overline{T_{r}}\) is friction torque.  
Figure 3: Gas film thickness distribution diagram.
# New Detection Paradigms to Improve Wireless Sensor Network Performance under Jamming Attacks
## 4 Analysis of Results
### Comparison with Another Jamming Detection Algorithm
* Experimentation time (sec): 100. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Feature** & **Proposed Mechanism in This Work** & **Algorithm Cited in [19]** \\ \hline New type of nodes & Marked nodes & Homcycles \\ \hline Function of nodes & Encircle the jammer node & Try to attract jammers \\ \hline Protection of the coordinator node & Isolate the attack until the jammer node decreases its energy & Hybrid proactive and reactive frequency selection \\ \hline Operation & Proactive and reactive jamming & Proactive and reactive jamming \\ \hline Detected attack & The nodes affected by their performance metrics isolate the jammer node selection of the next frequency \\ \hline \end{tabular}
\end{table} 
Table 7: Contributions of each algorithm. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Algorithm** & **Jammed Duration (sec)** & **Message Overhead(\%)** & **PDR(\%)** & **Energy (D)** \\ \hline Connected algorithm & 2.2 & 100 & 94.0 & 2.345 \\ Extended algorithm & 1.7 & 80 & 95.1 & 2.027 \\ Algorithm cited in [19] & 2.1 & 94 & 96.3 & 2.158 \\ \hline \end{tabular}
\end{table} 
Table 8: Comparison of the algorithms: presented in this work and the cited in [19]. Table 8 shows a great similarity between performance metrics, taking into account that originally our proposed algorithm aimed at a scenario of static nodes. We compared some performance metrics with a literature algorithm to analyze the benefits of our best evaluated scheme in this study: the Extended Mechanism. However, the Extended Mechanism proposal shows very good performance in terms of overhead (approximately 17% less overhead than the other two proposals) and the jamming duration (approximately 18% less duration than the other two proposals). Likewise, the proposal of the Extended Mechanism has a performance lower than the algorithm cited in [19] of approximately 6%. Regarding energy consumption metric, we measure in both cases the total network consumption during the experimentation time. The best scenario that our work presents is the Extended Mechanism, which has a consumption of 7% less energy with respect to the algorithm evaluated in [19].
# Tracking of Moving Targets Through Asynchronous Measures
## 1 Introduction
In particular, we derive the best linear estimator for the update of a 3D position given a single new range measurement. This approach can fix many of the issues encountered by other algorithms and, in order to validate it, we will investigate the effect of asynchronous measurements, latency between the anchors, and line-of-sight unavailability for some of the anchors. We believe the proposed algorithm might represent a simple yet effective tool for improving ToA-based 3D positioning accuracy, which will be crucial in many promising fields such as UAV navigation and aerial communications. The paper is structured as follows: in Section 2, we present our novel algorithm and its derivation, while in Section 3 we briefly discuss two widely employed tracking algorithms based on EKF and PF, which will be used as benchmarks to compare the results. Then, in Section 4, we show and discuss the numerical results, and, finally, Section 5 summarizes the work, its limitations and future steps.
## 3 Experimental Results
### Acoustic Emission Characteristics
#### 3.4.2 Damage and Crack Evolution  
Among the acoustic emission parameters, two waveform parameters, the rising angle (\(RA\)) and average frequency (\(AF\)), can be used to determine the type of crack [29]. The RA value is equal to the ratio of the rise time to the maximum amplitude, and the \(AF\) value is equal to the ratio of the ringing count (number of times the acoustic emission signal exceeds the detection threshold) to the duration. Tension cracks are characterized by a low rise time and high frequency, while shear cracks usually result in longer waveforms, as well as longer rise times and lower frequencies [30]. The \(RA\)-\(AF\) distribution of the acoustic emission events in the composite rock layer of the specimen is shown in Figure 11a, in which the data points in the central region are magnified. It can be seen that both tension cracks and shear cracks formed during the loading process of the sample, and the proportion of the tension cracks was higher than that of the shear cracks.  
To obtain the time-varying characteristics of the microcrack development during the different loading stages, the \(AR\) value was defined as \(AR=AF/RA\). A larger \(AR\) value denotes tension cracks and a smaller \(AR\) value denotes shear cracks. In addition, the damage variable \(D\) is defined as follows:  
\[D=\frac{\sum_{0}^{t}N_{t}}{\sum_{0}^{T}N_{t}}, \tag{1}\]  
where \(N_{t}\) is the number of rings at the current time, \(t\) is the time, and \(T\) is the total experimental time.  
The evolution of the cracks and damage in the specimen at different loading times is shown in Figure 11b. The tension cracks mainly clustered in the AR value range of 0.006-1.2 \(\mathrm{dB}/\mathrm{\SIUnitSymbolMicro s}^{2}\), while the shear cracks mainly clustered in the AR value range of \(1\times 10^{-5}\)-0.001 \(\mathrm{dB}/\mathrm{\SIUnitSymbolMicro s}^{2}\). The damage to the rock specimen generally occurred in three stages, namely, the nonlinear increase stage (0-46.7 s), the accelerated increase stage (46.7-49.1 s), and the stable increase stage (49.1-60 s), which corresponded to the bending, fracturing, and post-peak deformation stages of the rock, respectively. In the bending deformation stage of the rock, both the shear cracks and tension cracks propagated, and, before the specimen fractured, the proportion of shear cracks gradually increased, indicating that the degree of shear dislocation between the layers continued to increase. In the fracture deformation stage of the rock, the AR value of the acoustic emission events was low, and the proportion of shear fractures was higher than that of the tensile fractures, which was opposite to the situation when the tensile fractures dominated in a single-layer rock.  
Figure 11: Crack and damage evolution of acoustic emission: (**a**) crack classification; and (**b**) variations in the D and AR values with the loading level.
## 3 Results
### Comparison between Different Methods  
This article uses three methods (only using UAV, only using TLS, and a combination of the two to form a registration point clouds). The comparison index includes horizontal error, inclined error and _RMSE_ (Equation (22)). Horizontal error is the error between actual displacement and measure displacement of monitoring point on a horizontal plane. Inclined error is the error between actual displacement and measure displacement of monitoring point on an inclined plane.  
The comparison results are shown in Table 5. The horizontal error and inclined error is the absolute error between actual displacement and measure displacement.  
\[RMSE=\sqrt{\frac{1}{m}\sum_{i=1}^{m}\left(l_{i}-l_{i}\right)^{2}} \tag{22}\]  
Figure 19: The correlation between variables. (**a**) is correlation between flight altitude and monitoring point size. (**b**) is correlation between monitoring point size and measurement error. (**c**) is correlation between UAV flight altitude and measurement error.  
Figure 18: The layout of site datum points and monitoring point.  
where, \(l_{i}\) is the measured value of \(i\)th monitoring point displacement, \(\hat{l}_{i}\) is the actual value of \(i\)th monitoring point displacement and \(m\) is the number of monitoring points.  
When only using UAV for displacement measurement, the number of monitoring points identified is 10, which is more accurate when measuring the displacement of the monitoring points arranged on the horizontal plane and can be basically controlled within 10 mm, but when measuring the displacement of the monitoring points on the inclined plane, the direction error is relatively large, about 20-30 mm. The \(RMSE\) is 1.2 mm.  
When only using TLS to measure the displacement of a slope surface, due to the limitation of the viewing angle, the horizontal monitoring points arranged on the step slope platform cannot be scanned. Finally, the number of monitoring points identified is 7, the displacement measurement accuracy of the 7 monitoring points on the inclined surface in all three directions (\(x\), \(y\) and \(z\)) can be measured. The \(RMSE\) is 0.48 mm.  
When using TLS and UAV to generate a registration point clouds for slope surface displacement measurement, the number of monitoring points identified is 10, the measurement accuracy can reach less than 10 mm and the \(RMSE\) is 0.73 mm. The displacement of 10 monitoring points are shown in Figure 20.  
As shown in the Figure 20, the displacement accuracy of horizontal monitoring points higher the inclined monitoring points by UAV. The displacement curves measured by UAV + TLS are similar to the actual displacement curves and have a high degree of coincidence. The displacement accuracy measured of the inclined monitoring points by UAV + TLS is higher than by TLS alone. The error of horizontal monitoring displacement larger than the error of inclined monitoring displacement by UAV + TLS.
## 3 Results
### Analysis of the Second Point at 100 \(\mu\)m Subsurface from the Border of the Root Canal Lumen
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Section** & **Group** & **N** & **Mean** & **Std. Deviation** & **Minimum** & **Maximum** & \(p\)**-Value** \\ \hline \multirow{6}{*}{Coronal} & Control & 10 & 48.70 \({}^{\text{a}}\) & 6.62 & 39.38 & 60.20 & \\ \cline{2-7}  & UN & 10 & 43.02 & 6.08 & 30.87 & 52.72 & \\ \cline{2-7}  & UI & 10 & 47.30 & 15.78 & 32.07 & 89.82 & \\ \cline{2-7}  & MSI & 10 & 38.74 & 9.82 & 17.70 & 53.62 & \\ \cline{2-7}  & MUI & 10 & 42.60 & 6.47 & 31.16 & 52.05 & \\ \cline{2-7}  & EDTA & 10 & 31.91 \({}^{\text{a}}\) & 8.78 & 19.54 & 43.80 & \\ \hline \multirow{6}{*}{Middle} & Control & 10 & 46.70 & 8.40 & 33.70 & 64.64 & \\ \cline{2-7}  & UN & 10 & 41.35 & 7.25 & 29.44 & 53.05 & \\ \cline{2-7}  & UI & 10 & 39.41 & 7.21 & 26.36 & 50.30 & \\ \cline{2-7}  & MSI & 10 & 41.18 & 8.60 & 29.84 & 57.33 & \\ \cline{2-7}  & MUI & 10 & 45.50 & 11.51 & 33.30 & 66.33 & \\ \cline{2-7}  & EDTA & 10 & 37.50 & 7.60 & 26.64 & 53.62 & \\ \hline \multirow{6}{*}{Apical} & Control & 10 & 45.94 & 10.47 & 34.62 & 63.74 & \\ \cline{2-7}  & UN & 10 & 39.57 & 8.77 & 25.19 & 51.41 & \\ \cline{2-7}  & UI & 10 & 45.37 & 5.17 & 37.05 & 53.81 & \\ \cline{2-7}  & MSI & 10 & 44.00 & 8.03 & 32.97 & 59.77 & \\ \cline{2-7}  & MUI & 10 & 41.79 & 7.09 & 30.24 & 51.95 & \\ \cline{2-7}  & EDTA & 10 & 37.33 & 7.88 & 30.46 & 56.83 & \\ \hline \end{tabular}
\end{table} 
Table 2: Descriptive data of groups’ dentine microhardness in each section at a depth of 100 \(\upmu\)m subsurface from the root canal lumen after using different techniques with different materials. level of dentin microhardness value in the apical section, followed by the middle section, and a lower level in the coronal section than the control group (\(p<0.05\)). Analysis of the Fourth Point at 1 mm Below Midpoint of Each Third of the Root Section at the Lumen Surface  
A significant difference (\(p<0.05\)) was only observed in the middle section, where the mean value of dentin microhardness was lowest in the EDTA group, followed by the MSI group (magnetic field and endodontic needle, agitated IONPs irrigation), the UI group (ultrasound device, agitated IONPs irrigation), and the UN group (ultrasound device, agitated normal saline irrigant). The MUI group (magnetic field and ultrasound device, agitated IONPs irrigation) showed higher mean values than the control group. However, all other groups had lower mean values than the control group, except for the MUI group (magnetic field and ultrasound device, agitated IONPs irrigation). The data are shown in Table 4. Compared with the control group, the MUI group showed a higher mean value of dentin microhardness in the middle section, followed by the apical section, with a lower level in the coronal section.