esa-sceva/satcom-chunk-collection huggingface dataset:
# Solar Extreme and Far Ultraviolet Radiation Modeling for Aeronomic Calculations
## 4 Discussion
It is possible to estimate the maximum values of V variability (the ratio of radiation fluxes for the maximum and minimum levels of solar activity at a given wavelength) from the archived LISIRD data. The results of model calculations of the variability of the FUV fluxes at different wavelengths are shown in Figure 9.  
Figure 8: Model spectra for extreme values of the N\({}_{\rm{L}\alpha}\) index. The dotted line in Figure 9 corresponds to the N\({}_{\rm L\alpha}\) values for 1947-2018. In view of the inertia of the processes in the upper atmosphere, individual extreme values occurring at relatively short (one or several days) time intervals are hardly advisable to use in model calculations or estimates of the parameters of the upper atmosphere. The solid line in Figure 9 corresponds to the extreme values of the time series obtained by calculating the moving average for three solar revolutions (81 days) of daily values of N\({}_{\rm L\alpha}\) (respectively, N\({}_{\rm L\alpha}\) = 3.5\(\cdot\)10\({}^{15}\) m\({}^{-2}\)s\({}^{-1}\) and 6.1\(\cdot\)10\({}^{15}\) m\({}^{-2}\)s\({}^{-1}\)). The use of such a time interval is typical for estimating the state of the ionosphere and the upper atmosphere and leads to more realistic estimates of variations. It can be seen from Figure 9, the intensity of the FUV-radiation at \(\lambda\) < 189 nm can vary by tens of percent in the activity cycles, but at long wavelengths the changes become insignificant, and at \(\lambda\) > 210 nm they are only about 4%.
# Optimizing Retransmission Threshold in Wireless Sensor Networks
## 3 Problem Description  
In this section, the problem of finding optimal retransmission thresholds for each node along a delivery path is defined and is formalized as a general integer optimization problem. We prove that the problem is NP-hard.
###### Abstract  
Whilst balance disturbances are common in people with advanced Parkinson's disease, it has not previously been possible to record vestibular evoked myogenic potentials (VEMPs), and thus otolithic function, during monopolar deep brain stimulation (DBS) due to an overwhelming number of signal artifacts. A \(\upmu\)VEMP device has been developed with parameters to allow VEMP recording during monopolar DBS. The aim of this proof-of-concept study was to ascertain whether, during DBS, VEMP responses could be accurately identified after signal filtering recordings from the \(\upmu\)VEMP device. Both cervical and ocular VEMP responses to taps and clicks were recorded with the \(\upmu\)VEMP device in five Parkinson's disease patients with monopolar deep brain stimulation. Additionally, VEMP responses were recorded in one patient whose deep brain stimulation was switched ON and OFF to allow a direct comparison of the signals. Customised post-filtering analysis allowed successful VEMP response extraction from signal noise in all five patients with deep brain stimulation ON. VEMP responses with deep brain stimulation ON after filtering were similar to VEMP responses with deep brain stimulation OFF, validating the filtering analysis. We present the first study to record VEMP signals with monopolar deep brain stimulation using a \(\upmu\)VEMP device coupled with customised post-filtering. This finding will allow patients to be assessed without requiring adjustment of their therapeutic deep brain stimulation.  
vestibular; Parkinson's disease; DBS; VEMP; signal filtering; noise artifacts +
Footnote †: journal: Medical Biomics Department, University of Melbourne, Melbourne, VIC 3002, Australia
## 3 Performance Evaluation of 2D Hyperchaotic Map
### Lyapunov Exponents Spectrum  
In a 2D system with two Lyapunov exponents, the system is considered chaotic when one or both exponents are positive. Conversely, it generally remains in a periodic or quasi-periodic state [29]. In chaotic image encryption, the Lyapunov exponent is an important measure for evaluating key sensitivity, initial value sensitivity, and the extent of chaos in the system. Favorable Lyapunov exponents suggest a higher level of security and reliability for the system.  
In the proposed 2D hyperchaotic map, with b held constant at 6 while a is varying within the range of [\(-\)40,40], the Lyapunov exponential spectrum is illustrated in Figure 5a. When a is fixed at 15 and b is varying within the range of [\(-\)40,40], the Lyapunov exponent spectrum is illustrated in Figure 5b. The Lyapunov exponents of Maps 1-4 as shown in Figure 5c-f are also tested, and the comparison reveals that both exponents of the system are greater than 0 and remain stable over a large range. Therefore, the system remains in a stable chaotic state, demonstrating intricate nonlinear dynamics and strong sensitivity to initial conditions.  
Figure 4: Bifurcation diagram.
## 3 Methodology  
The complete flowchart is illustrated in Figure 1, showing the four key steps in the proposed methodology: room segmentation, floorplan extraction and regularization, structural model reconstruction, and 5G signal simulation. For the room segmentation, the door position and the simulated visible point clouds of sample trajectories are used to establish the initial space, while the global optimization of the indoor space is solved by the energy minimization function via multi-label graph cuts. For the floor extraction and regularization, the line elements are processed, which included the following steps: (1) the 3D point cloud slices are transformed into a binary image, and the line elements are extracted from the image; (2) the correction of line elements are based on global optimization; and, (3) similar lines are clustered to remove redundant line elements. The three-dimensional structural models are reconstructed via multi-label graph cuts, with room segmentation, 2D line elements, and 3D surfaces as semantic constraints. Finally, the signal intensity of 5G small base station is simulated using the structural models in the indoor environment.  
Figure 1: Flowchart of the proposed method.
## 3 Design of the Wide Passband SIW-SSPP Filters with Multi-Sub-Bands
### The Wide Bandpass Filter with Multi-Sub-Bandpass  
After loading the Archimedean spiral, the single wide bandpass filter demonstrated significant performance improvement. However, in wireless applications, unwanted signals often require suppression. Therefore, based on the single-band design, this section achieves band segmentation by loading CSRRs, enabling effective rejection of undesirable signals.  
Figure 5: Single-bandpass filter loaded with Archimedean spiral: (**a**) schematic diagram; (**b**) surface current; (**c**) S-parameters.
# Design of Warship Simulation Using Variable-Chromosome Genetic Algorithm
## 4 Case study: Optimal Tactics Generation
### Initialization  
The number of generations in the evolution governed by the GA is set to a total of 10, because there are almost no changes after the seventh generation. The number of existing populations in a generation is set to 50, because there are almost no differences of more than 50. The 50 populations have 10 rules (chromosomes) in the first generation. Subsequently, decisions are made as to which populations survive to the next generation, and breeding takes place depending on the fitness values. Five groups survive to the next generation.  
* The top 10 fitness value populations from the previous generation are determined.  
Figure 5: Mission scenario.  
* The 10 offspring are created from breeding between the second best fitness value population and a random selection from the top 10 fitness value populations from the previous generation.
* The 10 offspring are created from breeding between two random selections from the top 10 fitness value populations and a random selection from the lowest 10 fitness value populations from the previous generation.  
The mutation rate and chromosome attachment rate are set at 10%. The three measures of effectiveness (MOEs) being used for the fitness value are the force survival rate, the enemy damage rate, and the goal achievement rate. Equation (1) shows how the fitness value (f) is determined from the three MOEs. The force survival rate is expressed as the surviving number of warships (s\({}_{\text{ours}}\)) over the total number of warships (n\({}_{\text{ours}}\)). The enemy damage rate is expressed as the surviving number of enemy submarines (s\({}_{\text{enemy}}\)) over the total number of enemy submarines (n\({}_{\text{enemy}}\)). The goal achievement rate is expressed as the lineal distance from the HVU to the destination goal at the end of the simulation (d\({}_{\text{istmoved}}\)) over the lineal distance of the HVU to the destination goal at the beginning of the simulation (d\({}_{\text{isttotal}}\)). The sum of the \(\omega 1\), \(\omega 2\), and \(\omega 3\) weight values must always be 1.  
\[\text{f}=\ \omega_{1}.\frac{dist_{moved}}{dist_{total}}+\omega_{2}.\frac{s_{ours }}{n_{ours}}+\omega_{3}.\frac{s_{enemy}}{n_{enemy}} \tag{1}\]
# Trustworthiness of Situational Awareness: Significance and Quantification
## 3 Security and Trust of Situational Awareness
### Security of Common Operating Picture (COP)  
COP is used to display SA to commanders. UDOP is an evolution of of COP, which makes it possible to dynamically present an operator's or commander's view of the information. The COP is generally a display shared by more than one command teams, where each command team is in control of their operating picture. The availability of COP helps mission command by enabling all the participants to see the overall operation and their contributions to it as the operation proceeds [14]. The COP information is obtained from various sources as shown in Figure 1. The COP also includes situation reports, which are compiled by a commander's line staff, based on their comprehension of available information. However, SA is susceptible to security attacks, and the trustworthiness of COP can be negatively effected by faults (in sensors, hardware, and software), security attacks, and/or misprojection of information.
## 3 Results
### Changes in LST with VIs  
Summertime Landsat-derived LST (\({}^{\circ}\)C) for all non-permanent water (all land) provided a baseline for comparison to (a) areas of significant (\(p<0.05\)) TS greening for EVI (EVI+) and (b) areas of no EVI greening but significant NDVI greening (NDVI+ minus EVI+) (Figure 10). Only positive trends were considered here, as browning trends constituted a negligible proportion of the study area. All three trendlines (\(p<0.001\)) show increasing LST. The \"all land\" (red) baseline condition exhibits the warmest temperatures throughout, with a slight warming trend of 1.67 \({}^{\circ}\)C per 18 yrs (slope: 0.093 \({}^{\circ}\)C/yr). In comparison, the EVI+ (blue) trendline lies slightly below the baseline condition but follows a similar warming pattern with a slightly non-significant trend difference of 1.74 \({}^{\circ}\)C per 18 yrs (slope: 0.097/yr). Areas showing only NDVI greening (NDVI+ minus EVI+ green line) were \(\sim\)1 \({}^{\circ}\)C colder than either the baseline or EVI greening areas, but the rate of warming over exclusively NDVI greening areas was significantly greater at 2.02 \({}^{\circ}\)C per 18 yrs (slope: 0.11 \({}^{\circ}\)C/yr).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Survey Area**} & \(\Delta\)**P90 CHM** & **NDVI Trend** & **EVI Trend** & \(\Delta\)**P90 CHM vs. NDVI Trend** & \(\Delta\)**P90 CHM vs. EVI Trend** \\ \cline{2-7}  & \multicolumn{3}{c}{**Mean Rate of Expansion m/yr (SD)**} & **Difference** & \(p\)**Value** & **Difference** & \(p\)**Value** \\ \hline A & 3.3 (3.1) & 5.5 (3.2) & 2.4 (3.3) & \(-\)2.2 & 0.00 & 0.9 & 0.24 \\ B & 2.6 (3.0) & 5.5 (5.0) & 1.9 (3.0) & \(-\)2.9 & 0.00 & 0.7 & 0.39 \\ C & 2.4 (0.3) & 7.2 (4.7) & 3.6 (3.8) & \(-\)4.8 & \(<0.001\) & \(-\)1.2 & 0.04 \\ D & 1.5 (1.2) & 4.1 (2.7) & 1.7 (3.3) & \(-\)2.6 & \(<0.001\) & \(-\)0.2 & 0.62 \\ \hline All & 2.2 (2.3) & 5.4 (4.1) & 2.3 (3.8) & \(-\)3.2 & \(<0.001\) & \(-\)0.1 & 0.79 \\ \hline \hline \end{tabular} SD: Standard Deviation.  
\end{table}
Table 2: \(t\)-test (independent samples, 2-tailed) between \(\Delta\)P90 CHM and VI trends per survey areas and overall rate of ecotonal expansion (m/yr).
## 5 Experimental Results and Discussions
This approach ensured that, even if the shape of an object was not clearly distinguishable in some frames due to printing artifacts or surrounding structures, the system achieved very high accuracy and reliability in determining the presence of foreign particles by making a comprehensive judgment based on observations from other perspectives. The performance of foreign particle detection observed in previous research is shown in Table 1, alongside our results. All prior studies involved rigid body forms such as bottles, vials, or ampoules, which are generally small in size. These small fluid containers allow foreign particles to remain within a focal range, clearly showing the shape of the moving object in every sequential frame. In contrast, the flexible fluid bags used in this study have a thickness of 5 to 6 cm in the center, resulting in areas that are out of the camera's focus. This often causes the moving objects to appear blurry and unclear depending on their position. Additionally, unlike the clean surfaces of small bottles that do not have printed content and do not fold or crumple, the flexible fluid bags have crumpled surfaces and printed text, leading to lens distortion. This makes it extremely challenging to achieve a good detection performance for foreign particles in flexible fluid bags. Despite these difficulties, we compared our research results with those of previous studies. Even under these challenging conditions, the test results of the proposed algorithm demonstrated comparable or superior performance in terms of accuracy compared to existing algorithms, proving the effectiveness of the proposed algorithm.
# Disaster-Resilient Optical Network Survivability: A Comprehensive Survey
## 4 Disaster-Aware Network Survivability
They analyzed the model for different numbers of data centers, multiple disasters, and optical reach. Figure 6: Grid based Spatiotemporal Impact as Risk Profile. (**a**) Network Area; and (**b**) Grid Model. GeoPath diversification in optical networks is essential to establish reliable, high capacity, and resilient connections between two network nodes. In References [98; 99], [PERSON] et al. introduced GeoPath diversification mechanisms where routing decisions considered to be a physical network topology. Performance of the proposed mechanism and that of Open Shortest Path First (OSPF) compared when the network was subject to area-based challenge. A new metric as compensated Total Geographical Graph Diversity (cTGGD) introduced to distinguish the GeoPath diversity of different network topologies. [PERSON] et al. [100] proposed an integer linear program which computes a pair of minimum path length such that all intermediary nodes and links are at least \(D\) km apart from each other. In this way, a disaster having coverage diameter less than \(D\) km cannot influence the backup path and result in terms of network resilience against disaster-based failures. They also consider the vulnerability regions to assess the susceptible components (nodes and/or links) of \(D\)-geodiverse paths pair between two network nodes belonging to the same region. Similarly, a geographical location-aware algorithm proposed by [PERSON] et al. in Reference [101] that calculates multiple paths separated by at least a given spatial distance, known as pre-defined distance threshold \(r\). Two variants of the algorithm based on a penalty adjustment are proposed and tested. [PERSON] et al. evaluated the performance using the proximity factor and concurrent failures to measure the resilience of the algorithm. The number of failures depends upon topology and failure generation. The proximity factor is the geographical correlation between paths of the computed pair. Table 1 shows the close comparison of survivability techniques. Figure 7: Link Representation and their Minimum Separation Distance. \begin{table}
\begin{tabular}{c c c c} \hline
**Ref** & **Techniques/Formulation** & **Objectives** & **Assessments/Limitations** \\ \hline \hline \multirow{4}{*}{[\(\mathcal{P}\)6]} & & & Characterizing the impact of WMD attacks in WDM networks. Re-provisioning algorithms, multi-path routing, data replication, WDM-risk-aware virtual-network mapping \& re-mapping and service recovery. Addresses the network preparedness for upcoming attacks. \\  & & & Addressed two NP-hard problems and proposed the integer programming formulations as their solutions. \\  & & & Also proposed corresponding greedy algorithms to approximate the optimal solutions. \\ \hline \multirow{4}{*}{[\(\mathcal{P}\)6]} & & & Assessed the network survivability in the event of geographically correlated failures. \\  & & & \\
## 3 Relative Position Measurement Calculation Method Using IRA Output  
### Simple Method  
According to existing research results, when roll and pitch angles are \(0^{\circ}\), the relative position of the closest point can be calculated very simply by using the following formula [8].  
\[p=\left[\begin{array}{c}p_{e}\\ p_{n}\\ p_{u}\end{array}\right]=\left[\begin{array}{c}R\sin\theta\sin\varnothing\\ R\sin\theta\cos\varnothing\\ -R\cos\theta\end{array}\right] \tag{3}\]  
where \(R\) is slant range, \(\theta\) is look angle, and \(\varnothing\) is heading angle.  
Figure 5 shows the measurement model for IRA used in reference [8]. This calculation method is very intuitive and has the advantage of allowing the reader to understand how to calculate the relative position of the closest point. It is also very simple to calculate the relative position of the closest point in a TRN simulation that does not consider the influence of wind. This formula was useful for simulation development before flight testing.  
The existing paper [8] does not show how to calculate the closest point when pitch exists, but that point can be calculated using the following formula.  
\[p=\left[\begin{array}{c}p_{e}\\ p_{n}\\ p_{u}\end{array}\right]=C_{b}^{n}(\gamma+\theta,\delta,-\psi)\left[\begin{array} []{c}0\\ 0\\ -R\end{array}\right] \tag{4}\]  
Figure 7: Example on TRN using miscalculated \(p_{k}\): (a) Measured terrain profile; (b) DEM; (c) position correction value.  
where \(\gamma\), \(\delta\), and \(\psi\) are roll, pitch and heading angle of the aircraft, respectively.  
\[C_{1}(\phi_{z})=\left[\begin{array}{ccc}\cos\phi_{z}&\sin\phi_{z}&0\\ -\sin\phi_{z}&\cos\phi_{z}&0\\ 0&0&1\end{array}\right] \tag{5}\]  
\[C_{2}(\phi_{x})=\left[\begin{array}{ccc}1&0&0\\ 0&\cos\phi_{x}&\sin\phi_{x}\\ 0&-\sin\phi_{x}&\cos\phi_{x}\end{array}\right] \tag{6}\]  
\[C_{3}\big{(}\phi_{y}\big{)}=\left[\begin{array}{ccc}\cos\phi_{y}&0&-\sin\phi _{y}\\ 0&1&0\\ \sin\phi_{y}&0&\cos\phi_{y}\end{array}\right] \tag{7}\]  
\[C_{b}^{n}\big{(}\phi_{z},\phi_{x},\phi_{y}\big{)}=\ C_{1}(\phi_{z})C_{2}(\phi_ {x})C_{3}\big{(}\phi_{y}\big{)} \tag{8}\]
## 2 Materials and Methods
### Measurements
#### 2.4.4 Mechanical Properties  
For flexural tests, bar-specimens were prepared by filling a Teflon mold (2 mm \(\times\) 2 mm \(\times\) 25 mm) with unpolymerized paste in accordance with ISO 4049 [48]. The mold assembly was irradiated by overlapping on both sides with the abovementioned LED polymerization unit. Each overlap irradiation lasted for 40 s. Ten specimen bars (\(n\) = 10) were prepared for each nanocomposite. The specimens were stored in water for 7 days at 37 \(\pm\) 1 \({}^{\circ}\)C after curing. Afterward, they were bent in a three-point transverse testing rig with 20 mm between the two supports (3-point bending). The rig was fitted to a universal testing machine (Testometric AX, M350-10 KN, Testometric Co. Ltd., Rochdale, UK). All bend tests were carried out at a cross-head speed of 0.5 mm-min\({}^{-1}\) until fracture occurred. The load and the corresponding deflection were recorded. The data were collected and processed by means of the software WinTest Ananlysis CX Version 3.5.30.10. The flexural modulus (E) in GPa and flexural strength (\(\sigma\)) in MPa were calculated according to the following equations:  
\[\mathrm{E}=\frac{\mathrm{F_{1}I^{3}}}{4\mathrm{bd_{1}h^{3}}}10^{-3}\ \mathrm{and}\ \sigma=\frac{3\,\mathrm{F_{max}\,1}}{2\mathrm{bh}^{2}}\]  
where: \(\mathrm{F_{1}}\) is the load recorded in N, \(\mathrm{F_{max}}\) is the maximum load recorded before fracture in N, \(\mathrm{I}\) is the span between the supports (20 mm), b is the width of the specimen in mm, h is the height of the specimen in mm, and d\({}_{1}\) is the deflection (in mm) corresponding to the load F\({}_{1}\).
## 4 Experimental Results
### Effectiveness of RC_LCI  
Synthetic brain MRI with 7% noise and 80% intensity inhomogeneity obtained from BrainWeb were used to demonstrate the effectiveness of RC_LCI, and the results of the comparison with two state-of-the-art models are shown in Figure 7. The segmentation results of MICO, LIC [26], and RC_LCI are shown in the first to third rows, respectively. MICO could not effectively remove noisy pixels from the original image. The bias field estimated by LIC could not ensure its slowly varying property. RC_LCI used three-order orthogonal basis functions as the basis functions, which not only ensured the property of the bias field but also obtained accurate segmentation results. In addition, it can be intuitively observed that the boundaries between different tissues could be clearly distinguished.  
The visual results cannot fully demonstrate the performance of RC_LCI. Thus, the JS coefficient was used to quantitatively evaluate the segmentation results and show the discrepancies between the segmentation results of each model and GT. It is noteworthy that the better match between results and GT will lead to higher values for JS. The JS of GM, WM, and CSF obtained from LIC, MICO, and RC_LCI in Figure 7 are displayed in Table 2. The JS coefficients of RC_LCI were markedly superior to those obtained by LIC and MICO, which reveals that RC_LCI can effectively segment brain MRI with both noise and intensity inhomogeneity. Therefore, both visual results and objective evaluations can demonstrate the effectiveness of RC_LCI.  
Figure 7: Comparison of the segmentation results of local intensity clustering (LIC), MICO, and RC_LCI: (**a**) original image; (**b**) estimated bias field; (**c**) bias field correction images; and (**d**) segmentation results.