esa-sceva/satcom-chunk-collection huggingface dataset:
# Fourier Singular Values-Based False Data Injection Attack Detection in AC Smart-Grids
## 4 Case Studies
### Simulation Results of Assumed Case Studies
Figure 5:4 _Cont._  
Figure 5:5 Instability stems from manipulating the load’s current reference waveform: Manipulating shared current between agent II and III: (**a**) ISMG’s voltage; (**b**) Loads current; (**c**) Current waveform of DG\({}_{1}\); (**d**) Current waveform of DG\({}_{2}\); (**e**) Current waveform of DG\({}_{3}\); (**f**) FSVs of signals; (**g**) Time response of FSV’s scheme (FDIA is initiated at t = 0.5 s and is omitted at t = 1 s). Figure 5:6 _Cont._  
**Case study VIII: Load changing**  
In this case, the efficiency of the FSVs in FDIA detection is evaluated under several load scenarios such as linear/nonlinear loads. As exposed in Figure 12b, at t = 0.4 s, a resistance load is coupled to the system, and at t = 0.8 s, an inductive load connects, and at t = 1.2 s, a nonlinear load is coupled,  
Figure 5:7 Instability stems from manipulating on current sensor: Manipulating shared current between agent II and III: (**a**) ISMC’s voltage; (**b**) Loads current; (**c**) Current waveform of DG1; (**d**) Current waveform of DG2; (**e**) Current waveform of DG3; (**f**) FSVs of signals; (**g**) Time response of FSV’s scheme (FDIA is initiated at t = 0.5 s and is omitted at t = 1 s). and the other loads are disconnected. Figure 12a shows the output voltage of the ISMG. Figure 12c-e are depicted as the DG1, DG2 and DG3 current, respectively. Figure 12f depicted the FSVs of signal in the proposed expression. By choosing a threshold as 40 for cyber-attack diagnosing, it can be seen that the presented scheme is capable to successfully notice FDIA from loads changing. Figure 5:8 Cont. As shown in Figure 5c to Figure 9c, and from Figure 10f to Figure 12f, via choosing a threshold as 40, the suggested scheme can distinguish the false data injection attack. It is illustrated that the FSVs technique is able to identify FDI attack in several scenarios and distinguish the load changing from cyber-attack. Response time of the proposed scheme under FDIA in several scenarios are shown in Figure 5d until Figure 9d, and Figure 10g until Figure 12g. As shown, the speed of the  
Figure 5:9 Load changing: (**a**) ISMG’s voltage; (**b**) Load’s waveform current; (**c**) Current waveform of DG\({}_{1}\); (**d**) Current waveform of DG\({}_{2}\); (**e**) Current waveform of DG\({}_{3}\); (**f**) FSVs of signals. algorithm is very quick and can detect the attack less than 10 ms from the time that the FDIA has initiated.
# CFD-DEM Simulation of Dust Deposition on Solar Panels for Desert Railways
## 3 Numerical Simulation Results and Discussion
### Deposition Characteristics of Fine Sand Particles on Solar Panels
This phenomenon meant that the dust deposition on the photovoltaic panel was  
Figure 11: Movement of dust particles with a particle size of 100 \(\upmu\)m around solar panels when \(u_{*}=0.40\) m/s and \(K=1.05\times 10^{6}\) Jm\({}^{-3}\). (**a**) Particle streamlines. (**b**) Velocity distribution of particles at 2.0 s.  
Figure 12: Movement of dust particles with a particle size of 100 \(\upmu\)m around solar panels when \(u_{*}=0.40\) m/s and \(K=3.5\times 10^{6}\) Jm\({}^{-3}\). (**a**) Particle streamlines. (**b**) Velocity distribution of particles at 2.0 s.  
Figure 10: Movement of dust particles with a particle size of 100 \(\upmu\)m around solar panels when \(u_{*}=0.40\) m/s and \(K=0.5\times 10^{6}\) Jm\({}^{-3}\). (**a**) Particle streamlines. (**b**) Velocity distribution of particles at 2.0 s.  
not uniformly distributed, which may have been caused by the rolling down of the fine sand grains from the upper part of the solar panel. It is well known that dust deposition can reduce the amount of sunlight that a PV cell senses, thus affecting the efficiency of photovoltaic cells to generate electricity. A non-uniform distribution of dust also acts as a local shield for photovoltaic cells, which can have a negative impact on the efficient operation of photovoltaic cells. The larger concentration at the lower part of the solar panel may have been caused by the continuous rolling down of sand particles from the upper part. When using compressed air to remove dust depositions from photovoltaic panels [31], one should pay attention to this phenomenon to ensure the efficiency of the dust removal. Figure 14 discusses the effects of the wind speed and the cohesion parameter, \(K\), on the deposition amount of fine sand on the solar panel surface. It can be seen that under a small wind speed, the total amount of particle deposition increased with the increase in cohesion, but eventually tended to a fixed value, while under a large wind speed, the total amount of particle deposition increased with the increase in cohesion. Figure 14b shows that the deposition of particles was significantly reduced with the increase in the wind speed with weak and moderate cohesion, which indicated that a low wind speed was more favorable for deposition on the photovoltaic panel surface, and the rebound ratio of particles on the solar panel surface was larger under a high wind speed. Meanwhile, for strong cohesion, the deposition rate was almost the same for the small and large wind velocities (32% and 30%). From Figure 14b, it can be seen that the settlement number of fine sand particles on the solar panel was about 72-165 per second, i.e., about 48.3-107 g/m\({}^{2}\) after a week. According to the attenuation curve of the radiation intensity of a solar panel covered with 80 um dust particles given by [PERSON] [9] the radiation intensity reduces by 10-20%, corresponding to the above amount of dust deposition.
# The Interaction between the LEO Satellite Constellation and the Space Debris Environment
## 3 Risk Analysis of Constellation Collision
### Phase 3: Take Derailment Disposal, Decelerate and Deviate from the Mission Orbit  
The power system of the Starlink satellite uses a krypton ion thruster, which triggers the active derailment device after the five-year operation mission, and the thruster con  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Flux (1/m\({}^{2}\)/a)** & **N** & \(P_{\geq 1}\) **(\%)** & \(N_{1628}\) & \(P_{\geq 1,1628}\) **(\%)** \\ \hline \(d\geq 1\) cm & \(3.16\times 10^{-5}\) & \(1.42\times 10^{-4}\) & 0.02 & \(2.31\times 10^{-1}\) & 20.60 \\ \(d\geq 6\) cm & \(2.56\times 10^{-6}\) & \(1.17\times 10^{-5}\) & \(<\)0.01 & \(1.91\times 10^{-2}\) & 1.89 \\ \(d\geq 10\) cm & \(1.60\times 10^{-6}\) & \(7.43\times 10^{-6}\) & \(<\)0.01 & \(1.21\times 10^{-2}\) & 1.20 \\ \(d\geq 1\) m & \(5.24\times 10^{-7}\) & \(3.30\times 10^{-6}\) & \(<\)0.01 & \(5.37\times 10^{-3}\) & 0.54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fluxes, numbers of impacts and probability for one or more collisions for constellation objects during Phase 1.  
Figure 3: Flux and satellites altitude over time during Phase 2 with objects \(\geq\)6 cm.  
tinues to apply small thrust to reduce the satellite's orbital altitude, and finally, enter the atmosphere to burn and destroy. The duration of this stage is about 9.7 months. The variation of the flux of the satellite encountering \(\geq\)6 cm objects with time and the evolution of the satellite orbital height are shown in Figure 4. At the beginning of satellite disposal, the flux of the object fluctuates from \(2.03\times 10^{-6}/\mathrm{m^{2}/a}\) to \(2.21\times 10^{-6}/\mathrm{m^{2}/a}\), the satellite orbit altitude reaches 350 km is affected by atmospheric resistance. The orbit height decreases rapidly and enters the atmosphere for combustion and destruction.  
The relationship between the average collision times, collision probability and objects diameter of a single satellite and all satellites in the orbital layer during the out-of-orbit disposal phase is shown in Table 4. In the out-of-orbit disposal phase, the collision probability of the orbital layer satellite with objects larger than 1 cm is 50.15%, and the collision probability with objects larger than 6 cm is 6.83%.
## 6 Conclusions and Future Works  
The problem of the optimal sizing of PV sources in AC distribution networks was addressed in this research through the application of a master-slave optimization approach. In the master stage, the NMA was implemented which was entrusted to determine the nodes where the PV sources must be located and their corresponding sizes using discrete-continuous codification, where the discrete component was associated with the nodes and the continuous part with the assigned PV sizes; in the slave stage, each combination provided by the NMA was evaluated using the successive approximation power flow approach. Numerical results in the IEEE 34- and IEEE 85-bus systems demonstrated that the proposed NMA reaches better numerical results than the classical CBGA using the same codification and an exact solver in GAMS; in addition, for the IEEE 85-node test feeder, the exact approach with the BONMIN solver did not converge towards any feasible solution. The proposed NMA reaches reductions of approximately 26.89% and 27.60% regarding the benchmark cases for the IEEE 34- and IEEE 85-node test feeders. Differences with respect to the DCCBGA were less than USD 50 per year in both test feeders, even if these differences are small, these confirm that the proposed approach is better regarding the final objective function value with the main advantage that minimum parameters are required in its tuning when compared with the DCCBGA.  
Figure 8: Slack generation output in the benchmark case and with the solution provided by the NMA: (**a**) IEEE 34-node test feeder; and (**b**) IEEE 85-bus system.  
Even if the proposed optimization algorithm is general regarding the optimal sizing and location of the PV sources, an interesting research work that can be derived would be that of evaluating multiple PV generation curves--especially for countries (regions) that present high variations on the weather conditions as is the case of the four seasons. This will be an important analysis since the expected annual reductions in the total grid operational costs will be drastically reduced when PV generation availability becomes lower than 50% of their nominal capacities for long periods of time. Additional future works would be: (i) the application of the NMA to determine the optimal location and size of the distribution static compensators and capacitor banks in distribution networks; and (ii) the extension of the proposed algorithm to the simultaneous combination of renewable generation based on wind power and PV sources including reactive power compensation.  
Conceptualization, methodology, software, and writing--review and editing, O.D.M.; L.F.G.-N.; L.A.-B.; A.A.-L. and C.A.-A. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Centro de Investigacion y Desarrollo Cientifico de la Universidad Distrital Francisco Jose de Caldas under grant 1643-12-2020 associated with the project: \"Desarrollo de una metodologia de optimizacion para la gestion optima de recursos energeticos distribuidos en redes de distribucion de energia electrica\".
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 3 Micromegas as a Tracker in CLAS12 and COMPASS  
Micromegas are commonly used for tracking applications. Their low material budget due to their small anode thickness and the gaseous medium compared to solid-state detectors is an advantage. A segmentation of the anode in strips or pads is necessary to preserve the position information of the crossing particles. Micromegas can be used for larger area coverage, and they can be placed in front of a calorimeter due to the relatively small material budget.
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
## 4 Network Resource Allocation Algorithm Design for the HSN
### Bursty Traffic Allocation Scheme
#### Reordering  
The bursty traffic is reordered in descending order based on the priority level of the emergency traffic and consolidated with traffic with the same priority level. The transmissions are reordered in descending order based on the level of priority of the unexpected transmissions and reconsolidated with transmissions with the same priority. \(Bur\_Traffic_{i}[p]\) denotes the collection of bursty traffic whose priority is \(p\), \(p\) is a positive integer, and \(q\) denotes the number of traffic types with the same priority level.  
\[Bur\_Traffic_{k}=\{Bur\_Traffic_{k}[1],\cdots,Bur\_Traffic_{k}[P_{max}]\} \tag{22}\]  
\[P_{max}=MAX[Bur\_Traffic_{k}\_priority] \tag{23}\]  
\[\begin{split}& Bur\_Traffic_{k}[p]\\ &=\big{\{}Bur\_Traffic_{k}[p][1],Bur\_Traffic_{k}[p][2],\cdots,Bur \_Traffic_{k}[p]\big{[}Q_{p,max}\big{]}\big{\}}\end{split} \tag{24}\]  
\[Q_{p,max}=MAX[q_{p}] \tag{25}\]
## Appendix A  
For the system (24), we choose the Lyapunov function candidate:  
\[V(\tilde{x}_{n},t)=\tilde{x}_{n}^{T}P\tilde{x}_{n}. \tag{101}\]  
Here, \(P=P^{T}>0\).  
For a given scalar \(\rho>0\), define \(\gamma:=\sqrt{\rho}\). Introducing \(\gamma\) into the matrix inequality (25), it follows:  
\[\left[\begin{array}{ccc}AX+BW+(AX+BW)^{T}&B_{1}&(CX+DW)^{T}\\ B_{1}^{T}&-I&D_{1}^{T}\\ (CX+DW)&D_{1}&-\gamma^{2}I\end{array}\right]<0. \tag{102}\]  
Multiply (102) with \(diag\{I,I,\gamma^{-1}I\}\) in the right and left and yield:  
\[\left[\begin{array}{ccc}AX+BW+(AX+BW)^{T}&B_{1}&\gamma^{-1}(CX+DW)^{T}\\ B_{1}^{T}&-I&\gamma^{-1}D_{1}^{T}\\ \gamma^{-1}(CX+DW)&\gamma^{-1}D_{1}&-I\end{array}\right]<0. \tag{103}\]  
By (26) and (28), (103) can be written as:  
\[\left[\begin{array}{ccc}AP^{-1}+BK_{1}P^{-1}+(AP^{-1}+BK_{1}P^{-1})^{T}&B_{1 }&\gamma^{-1}(CP^{-1}+DK_{1}P^{-1})^{T}\\ \gamma^{-1}B_{1}^{T}&-I&\gamma^{-1}D_{1}^{T}\\ CP^{-1}+DK_{1}P^{-1}&\gamma^{-1}D_{1}&-I\end{array}\right]<0. \tag{104}\]  
Multiply \(diag\{P,I,I\}\) in the right and left of (104), and consider (23); we have:  
\[\left[\begin{array}{ccc}\bar{A}^{T}P+P\bar{A}&PB_{1}&\gamma^{-1}\bar{C}^{T} \\ B_{1}^{T}P&-I&\gamma^{-1}D_{1}^{T}\\ \gamma^{-1}\bar{C}&\gamma^{-1}D_{1}&-I\end{array  
Consider the zero initial condition,  
\[\bar{x}_{n}^{T}(T)P\bar{x}_{n}(T)+\gamma^{-2}\int_{0}^{T}\bar{y}_{n}^{T}(t)\bar{y} _{n}(t)dt<\int_{0}^{T}\bar{w}^{T}(t)\bar{w}(t)dt. \tag{104}\]  
Let \(\mathrm{T}\to\infty\),  
\[\gamma^{-2}\|\bar{y}_{n}(t)\|_{2}^{2}<\|w_{n}(t)\|_{2}^{2}. \tag{105}\]  
By (105), the transfer function \(T_{w_{n}\bar{y}_{n}}(s)\) of System (24) satisfies:  
\[\left\|T_{w_{n}\bar{y}_{n}}(s)\right\|_{\infty}=\frac{\left\|\bar{y}_{n}(t) \right\|_{2}}{\left\|w_{n}(t)\right\|_{2}}<\gamma. \tag{106}\]  
By (107), it is easy to know that \(A^{T}P+PA<0\). As \(P>0\), System (24) is asymptotically stable. Therefore, it can be concluded that (26) is a \(H_{\infty}\) control of System (24), and (106) is its Lyapunov function. Theorem 1 has been proven.
# Global Power Dynamics in the Contemporary Space System
## 4 System Actors--Space Powers
### The Rise of Space Actors  
During the Cold War, the number of nation-states engaged in space activities was restricted to the two superpowers and a select group of nations that employed space-related activities with limited exploratory, commercial and military implications, such as Japan, France, Israel, China, Canada, and India. The 1990s witnessed a paradigm shift towards a novel space system in the 21 st century, which precipitated a surge in interest within the space sector. Nation-states engaged in outer space activities have exhibited renewed interest in the domain, while other states are expressing initial interest. The majority of national space programs have concentrated on public interest activities, including national security and defense, economic development, social applications, science, education, and national prestige. Nonetheless, despite the increasing involvement of new non-state actors in space activities, governments continue to play a pivotal or catalytic role in articulating the majority of activities in the space domain.  
As of January 2025, 90 nation-states have active involvement in activities related to outer space and space exploration [39]. Presently, 80 countries have registered a satellite in orbit, and at least 10 of these 80 have full launch capability and can access orbit [40]. In addition to the traditional space powers of the 20 th century (the United States, Russia, Europe, China, India, and Japan) that remain important, there are emerging space powers with ambitious space plans (Iran, Turkey, Pakistan, Brazil, South Korea, the United Kingdom, the United Arab Emirates, etc.), and a significant group of nations that are also developing stronger space programs (Canada, Argentina, Indonesia, and Egypt, among others).
# The NEWA Ferry Lidar Experiment: Measuring Mesoscale Winds in the Southern Baltic Sea
## 4 Processed Dataset
### Uncertainty Estimation for Ship Lidar Data  
Generally, there are different ways to assess the uncertainty of (wind speed) measurements. In terms of metrology, and to trace back the uncertainty to available standards, a verification or calibration with respect to a suitable reference measurement is required. This principle is also followed in Reference [21] outlining the procedure of how to verify a ground-based vertically profiling Doppler lidar (or more general remote-sensing) instrument for applications in the wind industry. For a ship lidar system, such a verification test can only be realized under high effort, if at all. An attempt was undertaken in Reference [11] with the outcome referred to in Section 2. This result could be used to estimate a verification uncertainty component as one of the most relevant parts of the ship lidar measurement uncertainty. However, in the present case, we believe that, first, the dataset of the verification test was not big enough to derive robust results, and second, the ship lidar configuration in the verification may not have been similar enough to that in the final application; in particular, different types of vessels were used with different motion patterns.  
Furthermore, the spatial aspect of the ship lidar measurements needs to be taken into account; the ship lidar measurements are not representative for a single spot in space as, e.g., the measurements of a met mast. The spatial variability of the measurements within the spanned 10-min interval are to be considered just as the temporal variations. This aspect becomes relevant when comparing the ship lidar data with other more \"spatial\" data (see Section 5). In this sense, we do not think that we could have assigned a single uncertainty figure to the ship lidar data, but rather include uncertainty considerations in the discussion of the data quality and precision in relation to reference datasets.  
In this respect, it should also be paid attention to that since the ferry takes one trip per day with recurring departure and arrival times, there was a strong correlation between the location of the ferry, and therefore also the measurements, and time-of-day.  
Figure 7: LLJ events observed in the NEWA Ferry Lidar Experiment: (**left**) vertical wind profiles for the period 25–27 February 2017; (**right**) events as markers on the ferry’s route with latitude/longitude coordinates, where the top plot is for the month of February and the bottom plot for June in comparison.  
Uncertainty components that could be well estimated are the uncertainties of the input data to the motion correction (i.e., heading and velocity of the ship) and their direct impact on the corrected wind estimates. But since the applied motion measurements were very precise, this contribution was assumed to be negligible in the presented context.
# Special Issue on Novel Applications of Artificial Intelligence in Medicine and Health
###### Abstract  
The problem of detecting the presence of a positive value of the  
of cardiovascular events. Coronary artery calcification (CAC) and muscle mass evaluation have shown diagnostic and prognostic power for cancer patients, and they can be quantitatively evaluated using whole-body positron emission tomography (PET)/computed tomography (CT) which is a standard medical proceeding of patients with malignancy. The work _\"Quantification of Coronary Artery Atherosclerotic Burden and Muscle Mass: Exploratory Comparison of Two Freely Available Software Programs\"_ made an exhaustive and objective comparison of two semiautomatic and free independent software programs that can be applied to the medical image to measure both CAC score and muscle mass [3], and concluded that, for the measurement of the two parameters, both softwares seem to be interchangeable.  
Lung cancer is the type of cancer that is still leading the mortality among cancer death, and for this reason, several works are dedicated to this disease, which is clear in this special issue [4; 5]. The decision support systems could help clinicians with early detection and better treatment plans. Usually, those systems benefit from the selection of the region of interest, reducing the unuseful information to be processed. The segmentation of the anatomic structures comprises a fundamental step of the preprocessing of medical imaging. This task is difficult due to the variability that can be found in the human body, but also by the heterogeneities induced by the different levels of severity and, sometimes, their co-occurrence. The work _\"Lung Segmentation in CT Images: A Residual U-Net Approach on a Cross-Cohort Dataset\"_ designed on a cross-cohort dataset of CT and uses a combination of two structures, a U-Net and a ResNet34 for lung segmentation [4]. The cross-cohort dataset allows the development of a robust segmentation model even for the most difficult pathological cases. On the other hand, with the development of target therapies, there is a need for a genotype characterization of lung cancer. The work _\"Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer\"_ was dedicated to studying the use of machine learning-based approaches to detect the mutational status of the most relevant oncogene for lung cancer, Epidermal Growth Factor Receptor (EGFR) on the CT images [5]. The analysis was performed using the lungs where the nodules were located based on the relevance of the region for the clinical assessment. The combination of multiple feature selection techniques with several ML methods showed that it can be possible to use imaging information contained in the CT to predict EGFR mutational status of lung cancer.
# 5G Price Competition with Social Equilibrium Optimality for Social Networks
## 2 The Model
### Assumptions about the Model  
To focus on the problem of social welfare optimization, we make hypotheses, ignoring some unnecessary factors in the supplier competition game. Here, we outline our model assumptions.  
**Assumption 1**.: \(u_{n}(y)\) _is increasing, differentiable, and strictly concave in \(y\) for each user \(n\in\mathcal{N}\). In the network literature, this is how resilient data applications typically are modeled._  
**Assumption 2**.: _We draw offset \(c_{nm}\) for the channel's quality from continuous, different probability distributions. \(c_{nm}\) are independent of each other, and evidently, different \(c_{nm}\) cannot be equal. \(c_{nm}\) indicates that a user will have different results if it buys the same commodity quantity from different suppliers._  
As Example 1 shows, \(c_{nm}\) is a function of \(h_{nm}\), which is the channel gain between a supplier and user. Given that \(h_{nm}\) is drawn from the independent continuous-probability distributions, these assumptions can be fulfilled. In the next section, we study a related socially optimal resource-allocation problem to analyze the supplier competition game. Additionally, we show the solution based on a user's unique demand. In Section 4, we return to the supplier competition game. We find that the socially optimal, unique solution resembles the supplier competition game's unique equilibrium. Here, even when suppliers and users are selfish, the game remains as efficient as previously.