esa-sceva/satcom-chunk-collection huggingface dataset:
# Routing in Mobile Wireless Sensor Networks: A Leader-Based Approach+
## 3 A Leader-Based Routing Approach (LBR)  
In this section, we describe our approach to route messages in MWSN, which includes the construction and maintaining of the spanning tree (described in Section 3.2) that is used for the routing of application data (described in Section 3.3).  
The construction and maintaining of the spanning tree, which is the core of our approach, is based on dynamic leader election, commonly studied in the field of fault tolerant distributed agreement [12; 22; 23]. We present two alternative protocols to implement this approach. The first one (Subsection 3.2.1) uses a reactive, asynchronous forwarding mechanism to propagate leader election messages. Differently, the second protocol (Subsection 3.2.2) piggybacks the messages on periodic heartbeats messages.
# Optimal Control of a Virtual Power Plant by Maximizing Conditional Value-at-Risk
## Nomenclature  
_Acronyms_  
CODIS  
CVAR  
DERs  
distributed energy resources  
DGs  
distributed generations  
DR  
demand response  
EBFA  
enhanced bacterial foraging algorithm  
ESS  
energy storage systems  
EP  
evolutionary programming  
GA  
genetic algorithm  
ISO  
independent system operator  
LSSVM  
least square support vector machine  
MAPE  
mean absolute percentage error  
PSO  
particle swarm optimization  
PV  
photovoltaics  
SWT  
stochastic weight trade-off  
VPP  
virtual power plant  
WT  
wind turbine  
_Constraints_  
_Profit\({}^{\text{DA}}\)_  
Maximizing risk income  
\(R(n)/C(n)\)  
Total income/cost in the context of \(n\) samples  
\(\mu^{\text{DA}}/\xi\)  
Aversion parameter/auxiliary parameter  
\(Z_{n}/R_{\text{exp}}\)  
The rate of return/expectation profit  
\(m/\beta\)  
Number of samples/confidence level  
\(\lambda^{t}_{\text{VPP}}/\lambda^{t}_{\text{Z}}/\lambda^{t}_{\text{S}}\)  
The sold price of VPP/WT/PV at time t  
\(P^{t}_{\text{load}}/P^{t}_{iso}\)  
The load supplied by VPP/the generation supplied by ISO at time \(t\)  
\(P^{t}_{\text{tr},\text{sel}}/P^{t}_{\text{s},\text{sel}}\)  
The generation of WT/PV at time \(t\)  
\(P^{t}_{\text{var}}/P^{t}_{\text{DR}}\)  
The load curtailed of VPP/user at time \(t\)  
\(\lambda^{t}_{\text{inc}}\)  
Rebate money of customers by VPP at time \(t\)  
\(\lambda^{t}_{\text{DR}}\)  
Rebate money of VPP by ISO at time \(t\)  
\(\lambda^{t}_{iso}\)  
The price sold of ISO at time \(t\)  
\(\theta^{t}\)  
Coefficient of elasticity at time \(t\)  
\(\lambda^{t}_{\text{ess}}\)  
The operating cost of battery at time \(t\)  
\(P^{t}_{\text{ess}}\)  
The capacity of battery at time \(t\)  
\(P^{t}_{\text{ess}}/P_{\text{ess},\text{max}}\)  
The initial/maximal capacity of battery at time \(t\)  
_Variables_  
Area covered by the rotor (m\({}^{2}\))  
\(A_{PV}\)  
The area of the \(PV\) array (m\({}^{2}\))  
\(C_{p}(\lambda,\theta)\)  
The performance coefficient of wind power  
\(\eta_{PV}\)  
The efficiency of \(PV\)  
\(p^{t}_{\text{tr},\text{sel}}\)  
The WT output power at time \(t\)  
\(P^{t}_{\text{s},\text{sel}}\)  
The \(PV\) power output at time \(t\)  
\(P^{t}_{\text{G}}\)  
The global radiation (w/m\({}^{2}\)) at time \(t\)  
\(P_{\text{det}}\)  
The probability of \"lethargy\"  
\(r_{1},r_{2}\)  
Random number between 0 and 1  
\(Sign\)  
_Freak Factor_  
\(v_{t}\)  
Wind speed (m/s) at time t  
\(v^{t}_{t}\)  
The current wind speed (m/s) at time \(t\)  
\(v_{start}\)  
The start wind speed (m/s)  
\(v_{full}\)  
The rated wind speed (m/s)  
\(v_{stop}\)  
The stop wind speed (m/s)  
\(\lambda\)  
Tip speed ratio  
\(\theta\)  
Pitch angle of the rotor blades (deg.)  
\(\rho\)  
Air density (kg/m\({}^{3}\))
# Multi-Wavelength Digital-Phase-Shifting Moire Based on Moire Wavelength
## Appendix A  
The intensity distribution \(I_{\text{O}}\) of the captured image when a binary grid is projected onto an object surface is:  
\[I_{\text{O}}(x,y)=b_{0}+\sum\
olimits_{m=1}^{\infty}b_{m}\cos\left(\frac{2\pi m }{p}y+\phi(x,y)\right), \tag{1}\]  
where \(p\) is the grid pitch, and \((x,y)\) are the image coordinates. Moire patterns are generated digitally by first producing a synthetic grid \(I_{\text{S}}\) with the same pitch as in the captured frame, when the binary grid is projected onto a flat plate.  
\[I_{\text{S}}(x,y)=a_{0}+\sum\
olimits_{n=1}^{\infty}a_{n}\cos\left(\frac{2\pi n }{p}y\right). \tag{2}\]  
The synthetic grid \(I_{\text{S}}\) is then superimposed over the captured image of the deformed grid on the object surface, \(I_{\text{O}}\), to generate the following image which contains the moire pattern as well as grid noise and background intensity:  
\[\begin{array}{c}I_{\text{M}}\,=a_{0}b_{0}+a_{0}\sum\
olimits_{m=1}^{\infty} b_{m}\cos\left(\frac{2\pi m}{p}y+\frac{2\pi dz(x,y)}{pH}\right)+b_{0}\sum \
olimits_{n=1}^{\infty}a_{n}\cos\left(\frac{2\pi n}{p}y\right)+\\ \sum\
olimits_{n=1}^{\infty}\sum\
olimits_{m=1}^{\infty}a_{n}b_{m}\left(\cos \left(\frac{2\pi n}{p}y\right)\cos\left(\frac{2\pi m}{p}y+\frac{2\pi dz(x,y)} {pH}\right)\right),\end{array} \tag{3}\]  
where \(H\) is the camera-projector to object distance, and \(z\) is depth. The first term \(a_{0}b_{0}\) is the background intensity, and the second and third terms are the grid noise, composed of the unwanted high-frequency deformed and straight grids, respectively. The fourth term represents the moire patterns that are encoded with surface-depth (height) information.  
A combined stationary-wavelet Fourier transform (SWT-FFT) grid-removal technique [19] can be applied to Equation (3) to remove the unwanted high-frequency deformed (curved) and straight grid lines (second and third terms, respectively), and thus extract pure moire patterns. The application of SWT to an image containing grid-noise makes it possible to use a Daubechies wavelet with a low number of vanishing moments to decrease smoothing and distortion in the reconstructed de-noised image. The application of FFT and damping the wavelet coefficients related to grid noise improves SWT in removing highly curved grid lines, without increasing the decomposition level and smearing of the moire pattern. The combination of SWT and FFT can thus remove both straight and curved grid-noise lines, while minimizing artifacts in the de-noised image, and preserving the moire pattern without blurring and degradation. Further details of the method are described in Reference [19].
## 4 Example Analysis
### The Time-Varying Global Reliability Analysis Results during the Design Reference Period  
Similarly, by following the above process, the reliability index and the corresponding PDF of \(t_{r}\) (\(r\) = 0, 10, 20, \(\ldots\),50) can be calculated, respectively, as shown in Figures 5 and 6. Figure 5 indicates that after 50 years of service, the reliability index of the transmission tower decreases from 4.32 to 3.39, with a decrease of 0.93, which shows a significant reduction  
\begin{table}
\begin{tabular}{c c c c} \hline  & \(s\) = 2 & \(s\) = 4 & \(s\) = 6 \\ \hline \(\beta\) & 3.466 & 3.701 & 3.802 \\ \(\varepsilon(\%)\) & 8.74 & 2.55 & 0.11 \\ \(H_{\mathrm{s}}\) & 18.4827 & 18.4822 & 18.4822 \\ \hline \end{tabular}
\end{table}
Table 4: Evolution of the estimated reliability index and its Shannon entropy with the variation in s.  
Figure 4: PDF curve of the proposed method and MCS method at t = 30.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **MCS** & **Traditional MEM** & **The Proposed Method** \\ \hline \(\beta\) & 3.798 & 3.390 & 3.802 \\ \(\varepsilon(\%)\) & — & 10.72 & 0.11 \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of estimated reliability index for different approaches.  
in the reliability index of the in-service transmission tower. Therefore, it is necessary to analyze the time-varying reliability of the in-service transmission tower. Moreover, the reliability index decreases faster nearly 10 years later, because the corrosion life \(t_{L}\) of the in-service transmission tower is taken as 10 years, which also indicates that the in-service transmission tower exceeding the corrosion life must be treated with corrosion protection to slow down the degradation process. Figure 6 shows that the PDF gradually shifts to the left over time, which denotes an increase in the failure domain, and the change in failure probability is consistent with the gradual decrease in the reliability index of the in-service transmission tower over time.
# TransECA-Net: A Transformer-Based Model for Encrypted Traffic Classification
## 2 Related Work
### Machine-Learning-Based Methods  
During the 2010s, there was a notable emergence of machine-learning methodologies in the domain of network traffic classification, wherein researchers systematically implemented traditional machine-learning algorithms. Prominent algorithms in this context encompass Decision Trees, Random Forests, Support Vector Machines (SVMs) [11], and K-Nearest Neighbors (KNN) [19]. These methodologies exhibit a high degree of versatility and possess the capability to manage intricate and ever-evolving network traffic. Nonetheless, they necessitate an extensive volume of labeled data for effective training, and the precision of the resulting models is contingent upon the quality of the training datasets utilized.
# Design of Cislunar Navigation Constellation via Orbits with a Resonant Period
## 3 Design Methodology  
The cislunar space offers a diverse selection of operational orbits with significant variations in orbital periods. The orbits of different periods exhibit distinct dynamic characteristics and performance advantages in specific regions. If the orbits within a constellation demonstrate resonant characteristics, the navigation and positioning performance metrics for targets will follow periodic variation patterns. This feature not only provides theoretical insights into the evolutionary mechanisms of constellation performance but also significantly streamlines the daily management, maintenance procedures, and long-term optimization strategies of the constellation system.  
The cislunar resonant constellation design method proposed in this study was developed based on a lunar-periodic orbit library, comprising three main phases--orbit library  
Figure 1: Cuboidal Spatial Gridding.  
Figure 2: Spherical Spatial Gridding.  
preprocessing, resonance combination generation, and resonant constellation formation--as illustrated in Figure 3. During the orbit preprocessing phase, the characteristic period extraction and sorting of orbit families is implemented to isolate suitable reference orbit families for subsequent operations. The resonance combination generation phase initiates with the selected reference orbits, focusing on matching resonant orbits as core elements. Given the computational complexity caused by the large number of generated resonance combinations, a type-based merging and deduplication process is applied to orbit compositions. Finally, resonant constellations are constructed from the combinations according to predefined scale requirements, with category-based merging implemented to reduce the computational load. The optimal constellation configuration is determined through exhaustive evaluation iterations.  
It is important to note that the resonant constellation design proposed in this study adheres to the following principles: (1) no more than one orbit from each orbital family is included in the constellation configuration, and (2) each orbit accommodates only one satellite.
# Comparative Study of the Presence of Heavy Metals in Edible Vegetable Oils
## 2 Analysis of Publications and Trend of Heavy Metals in Edible Vegetable Oils
### Results of the Analysis of Heavy Metals in Edible Vegetable Oils
#### 2.3.4 Iron (Fe)  
Fe currently lacks specific international legislation for edible vegetable oils. It is also considered an accelerator of oxidation processes and, although it refers to consumer protection, the maximum permissible levels for the _Codex Alimentarius_ are marked as quality criteria for oils [74]:  
Refined fats and oils: 1.5 mg/kg;  
Virgin fats and oils: 5.0 mg/kg;  
Cold-pressed fats and oils: 5.0 mg/kg.  
Table 6 shows the value issued by Chinese regulations in the case of olive oils established for Fe at 3.0 mg/kg (Table 2), and olive oil from Saudi Arabia is above this toxicity threshold with a value of 7.861 mg/kg [91]. In the case of Spanish legislation, which establishes 10.0 mg/kg as the maximum permissible limit for Fe in edible vegetable oils (Table 2), Chinese walnut oil is above this limit with a value of 11.2 mg/kg [92].  
It is necessary to indicate that overexposure to Fe, even in trace amounts and for prolonged periods of time, makes this heavy metal cause adverse risks to human health, such as risks of Parkinson's, Huntington's, Alzheimer's, cardiovascular diseases, Diabetes Mellitus, hyperkeratosis, pigmentation changes, kidney, liver and respiratory tract disorders, and neurological disorders [93].
## 3 Results and Discussion
### Types of Study: Simulaiton or Real Experiment  
Over the past few decades, a wide variety of highly accurate and effective MR imaging simulation tools have been developed and implemented, such as the BrainWeb Simulated Brain Database (SBD) [64]. Since the development of these tools, the study of image processing algorithms, including noise reduction technology, has begun to proceed simultaneously with simulated and real experiments. Therefore, for this systematic review, we analyzed the study type on the application of the NLM noise reduction algorithm to MR images. Figure 6 shows the overall publication numbers with respect to the types of study (real, simulated, or both). Table 4 describes the characteristics for the types of study of the 25 included papers. Of the 25 papers identified, the \"both\" category, based on simulation and experiment, represented the largest number of studies (\(n=17\)) [39; 44; 45; 47; 48; 49; 50; 51; 53; 54; 56; 57; 58; 59; 60; 61; 62; 63], followed by the solely experimental category (\(n=5\)) [40; 41; 42; 52; 59], and finally the solely simulation category (\(n=3\)) [43; 46; 55]. Papers related to the application of the NLM noise reduction algorithm in both simulation and experimental studies were first published in 2008. After analyzing the number of publications by year, it was determined that while experiment-based research has been conducted since the early stages in 2008, research that includes simulations has been actively increasing over the years. One of the major benefits of simulation studies is the ability to verify the accuracy and effectiveness of a given developed tool. As shown in Figure 5(a), the most widely used simulation tool is BrainWeb, developed with a well-validated process using images acquired in-vivo and using ground truth data [64]. This tool has the advantage of being able to set the modality and noise levels in a user-friendly fashion based on the SBD and acquired 3D images. BrainWeb is used as the gold standard for research on the application of image processing algorithms to MR images of the brain. As a result, 18 of the 25 total included papers used the BrainWeb tool. Recently, MR imaging simulation tools with improved computational speed have been developed; a representative example is MRiLab [65]. This tool was developed to improve speed and to make use of various image acquisition parameters based on the available graphic processing units (GPUs). It is expected that these developments will enable various pulse sequence models, coil designs, and various simulation studies.
# Angle-Only Cooperative Orbit Determination Considering Attitude Uncertainty
## 3 Attitude and Orbit Determination Method with Angle-Only Measurements
### Case II: The Orbit of \(S_{1}\) Is Known, and the Attitude of \(S_{1}\) Is Unchanged
The initial covariance matrix \(\mathbf{P}_{0}\) is given as \(\mathbf{P}_{\boldsymbol{r}_{1}\boldsymbol{r}_{1},0}=\mathbf{P}_{\boldsymbol{ r}_{2}\boldsymbol{r}_{2},0}=100\mathbf{I}_{3\times 3}\) km\({}^{2}\), \(\mathbf{P}_{\boldsymbol{r}_{1}\boldsymbol{r}_{0},0}=\mathbf{P}_{\boldsymbol{ r}_{2}\boldsymbol{r}_{2},0}=\mathbf{I}_{3\times 3}\,m/s^{2}\), \(\mathbf{P}_{\boldsymbol{r}_{0},0}=\left(\cos 2.5^{\circ}-1\right)^{2}\mathbf{I}_{1 \times 1}\), \(\mathbf{P}_{\boldsymbol{q}_{1},0}=\mathbf{P}_{\boldsymbol{q}_{2},0}=\mathbf{P} _{\boldsymbol{q}_{3},0}=\left(\sin 2.5^{\circ}/\sqrt{3}\,\right)^{2}\mathbf{I}_{1 \times 1}\) and \(\mathbf{P}_{\boldsymbol{\omega},0}=0.001^{2}\mathbf{I}_{3\times 3}\,(\,\circ/s\,)^{2}\). Suppose spacecraft \(S_{1}\) could track \(S_{2}\) using equal equipment, where possible, with a 1 s measurement. The angle-measurement error in Equation (17) is assumed to be Gaussian white noise with a standard deviation (STD) of \(0.01^{\circ}\) (equal to \(0.6^{\prime}\)).
# The DLR FireBIRD Small Satellite Mission: Evaluation of Infrared Data for Wildfire Assessment
## 1 Introduction
Data acquisitions need to be triggered for each specific incident by human interaction. Comparisons with other data sources are therefore only possible for selected occasions. Equivalent to HSRS, the spectral bands of MODIS, VIIRS, and SLSTR also allow the derivation of the FRP. This information is included in the MODIS MOD14A1 [12], VIIRS VNP14A1 [15], and the SLSTR FRP products, respectively. All three datasets are based on approaches developed by [PERSON] et al. [5; 16] to derive the FRP, which primarily use information from the MWIR spectral range. To allow comparability of FireBIRD data with the reference data sources, this study uses the above-mentioned method to derive FRP information from FireBIRD data. Additionally, a further method developed by [PERSON] [17] is tested and compared to the approach by [PERSON] et al. This latter method uses information from both the MWIR as well as the LWIR range.
# Hybrid Chaotic-Based PRNG for Secure Cryptography Applications
## 2 Known 1D Chaotic Maps
### Sine Map  
The chaotic sine map is a nonlinear function that exhibits complex and dynamic behaviors. It is derived from the sine function defined in Equation (3):  
\[x_{n+1}=\sin(\omega x_{n}) \tag{3}\]  
where \(\omega\) is a control parameter that determines the behavior of the map (Figure 1a). The chaotic sine map has been widely explored for its potential applications in cryptography and image encryption due to its ability to generate sequences of numbers that are apparently random and unpredictable.
# A Game Theoretic Approach for Energy Sharing in the Italian Renewable Energy Communities
## 3 Game Theoretic Approach for Value Redistribution
### Energy Community as a Coalitional Game  
The model of energy community presented in this paper can be seen as a cooperative game. The players are the producers (i.e., the financers of the generation plants) and the consumers. The economical value generated hour by hour depends on the presence and the interaction between load and production, in particular on the quantities of energy produced and shared. The players can decide to take part to the community or not. Both producers and consumers want to take advantage from the participation to the community. Producers can be generically considered as external actors that can invest in the installation of new generators and wants to get a return from the investment. In real applications, they can be external financers or the community itself (with the financial participation of the community members). In any case, even if external and internal financers could require different rates of return, it is important to notice that they are both expecting a positive return of the investment.  
From the methodological side, who the financier does not impact. On the other hand, when considering consumers, they have a certain contractual power as they are necessary to generate the profit (mainly based on the incentive for shared energy). As a consequence, they can require an appropriate return for their participation in the energy community. The objective of studying this game is to find a stable and fair allocation rule that gives an adequate payoff to the players, so that each one is encouraged to take part to the EC.
# Durability of High Volume Glass Powder Self-Compacting Concrete
## 3 Results  
### Compressive Strength  
The compressive strength results for different mixes from 28 to 545 days, along with the T\({}_{500}\) times, are summarized in Table 4. The early age compressive strength for all the fly ash and glass powder mixes were lower than the GP control. The 20G30% and 20G40% replacement levels together with the 40G30% replacement had the three lowest 28 day strengths with values between 43 and 47 MPa. As hydration continued the gap between the GP control and FA and glass powder mixes narrowed. The glass powder mixes shows the slowest strength development taking more than one year time to attain similar strength values. In particular, the glass powder mixes with the largest particle size were the slowest to gain strength. Figure 1 shows the compressive strength of mixes normalized with respect to GP mix at the respective age. The continued strength development is attributed to the formation of C-S-H arising from the pozzolanic activity of glass powder. The observations show the importance of long-term investigation for such binder systems that have slower pozzolanic activity. Similar, compressive strength characteristics have been reported for glass powder concrete mixes in literature [11; 27; 28].  
The fresh properties of concrete can have a significant impact up on the long-term durability of a structure. The presence of voids, as a result of compaction difficulties for instance, can be just as important as either the type of binder or w/c ratio. The primary goal of this investigation was to examine the effects of different glass powder replacement levels on the durability properties of the concrete. As such, the target flow for all the mixes was set at 700 mm and the amount of superplasticizer was allowed to vary to maintain the flow and minimize differences in the workability. It can be seen from the T\({}_{500}\) times reported in Table 4, that there was relatively little variation with the times ranging from 3.1 s (FAF30%) to 5.5 s (GP) with most of the glass powder mixes between 4 and 5 s. The differences between either the yield shear stress as indicated by slump flow (700 mm) or plastic viscosity as indicated by T\({}_{500}\) for the different mixes therefore were relatively small.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Compressive Strength at Age (Days)**} & \multicolumn{2}{c}{**T\({}_{500}\)**} \\ \hline  & **28** & **90** & **180** & **365** & **545** & \\ \hline
**GP** & 65.2 & 74.8 & 84.6 & 89.9 & 94.5 & 5.5 \\
**FAF30\%** & 49.6 & 72.2 & 75.8 & 81.6 & 92.8 & 3.1 \\
**FAC30\%** & 68.8 & 72.7 & 80.4 & 92.3 & 100.6 & 4.0 \\
**10G30\%** & 59.5 & 66.4 & 71.1 & 75.0 & 91.8 & 4.0 \\
**20G20\%** & 62.5 & 66.6 & 71.8 & 76.3 & 96.3 & 3.8 \\
**20G30\%** & 43.5 & 55.5 & 62.7 & 72.5 & 83.3 & 4.2 \\
**20G40\%** & 46.4 & 51.8 & 53.3 & 58.9 & 79.5 & 4.9 \\
**40G30\%** & 45.8 & 51.3 & 55.2 & 62.0 & 75.9 & 4.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Compressive strength (MPa) at different ages and flow time (Sec).