esa-sceva/satcom-chunk-collection huggingface dataset:
# A Coalition Formation Game Approach for Efficient Cooperative Multi-UAV Deployment
## 4 Simulation Results and Discussion
### Convergence Performance  
In order to deeply investigate convergence performance, we choose \(\rho=1\times 10^{-5}\) as a contrastive index of both approaches. Figure 5(a) shows the diagram which shows the diagram of the convergence state of UAV deployment and coalition formation. Convergence performance of different algorithms is shown in Figure 5(b)-d. We observe that all the indicators converge after a certain number of iterations, which indicates that our proposed method can achieve a stable state solution of the system model. The coverage reward of the comparison algorithm converges to the same value. However, since there is no coalition formation mechanism for a comparison approach, its energy consumption and coverage utility converge to a worse level, which is consistent with the model analysis. It is an issue when facing complex environment, such as disaster, which requires UAVs to make accurate and fast self-decision for rapidly changing scenario parameters. Authors in [33] utilized reinforcement learning (RL) to solve the problem of anti-jamming communications in dynamic jamming environment. The application of RL can significantly help UAVs adapting to dynamic environment and make deployment and parameters adjustment accordingly. This gives us inspiration and will be our next step of the work outlook.  
Figure 4: Overall coverage utility \(U1\) in UAV network.  
Figure 5: Coverage performance considers the number of UAVs and weighting coefficient.  
To summarize, the simulation results illustrate that the proposed cooperative multi-UAV deployment model based on coalition formation can describe UAVs' cooperative behaviors well. Moreover, the setting of the weight coefficients can provide a theoretical reference for different task requirements. Finally, the proposed approach can achieve higher coverage utility compared with a comparison approach, reflecting the good performance of coalition formation mechanism and efficiency of the proposed model.
# Development of Traffic Scheduling Based on TSN in Smart Substation Devices
###### Abstract  
Smart substations are an important trend in substation construction. With increasing data traffic, it is difficult for the traditional Ethernet network to meet the real-time requirements of control information in smart substations. Hence, in this paper, a deterministic network architecture for substations based on time-sensitive networks (TSN) has been developed in order to realize the domain-wide time synchronization and efficient real-time communication of the \"three-layer and two-network\" model in smart substations. Furthermore, a design scheme for substation automation equipment based on TSN is proposed. The proposed device realizes the timely transmission of real-time control information packets by utilizing the Earliest TxTime First (ETF) Qdisc technology of Linux and the timing sending capability of Intel 210 NIC. Furthermore, it collaborates with the time-aware shaper (TAS) traffic scheduling mechanism of TSN switches to ensure the end-to-end deterministic delay of time-sensitive traffic. As a result, it provides efficient real-time communication services with low latency and jitter for smart substation automation systems.  
time-sensitive network; substation automation equipment; timing sending +
Footnote †: journal: Applied sciences
## 3 Dual-PRF Waveform Design and Signal Processing
### Signal Processing
#### 3.4.2 CS-Based Signal Processing  
According to CS theory, if a signal is sparse or compressible in a certain transform domain, it may be reconstructed using nonlinear methods by solving an optimization problem with high probability. It is clear that the designed waveform is sparsely sampled in the Doppler dimension. We also apply the CS-based sensing signal processing to the designed waveform, and its framework is illustrated in Figure 9.  
According to the Figure, we can see that the CS-based methods have the same processing steps on range spectrum, static clutter suppression, OS-CF  
Figure 9: CS-based signal processing.  
However, different from the DFT-based method, the CS-based methods do not need to solve velocity ambiguity. And here, we introduce two CS-based methods, namely the iterative adaptive approach (IAA) [38] and the orthogonal matching pursuit (OMP) [39] to reconstruct \(\mathbf{y}\).  
The IAA algorithm solves the following optimization problem  
\[\text{min}\ ||\mathbf{y}-\gamma_{k}\mathbf{a}(f_{k})||_{\mathbf{Q}_{k}^{-1}}^{2} \tag{28}\]  
where \(||\mathbf{x}||_{\mathbf{Q}_{k}^{-1}}^{2}=\mathbf{x}^{H}\mathbf{Q}_{k}^{-1}\mathbf{x}\), \(\mathbf{Q}_{k}\) denotes the covariance matrix of the clutter and noise, and \(|\gamma_{k}|^{2}\) is the power of the Doppler spectrum with frequency \(f_{k}\). And the IAA method operates in an iterative way. The OMP method also operates in an iterative way, and the solution estimated at each iteration requires the solution of a least-squares problem, given by  
\[\text{min}\Big{\|}\mathbf{A}_{\mathbf{\Omega}(p+1)}\mathbf{y}_{\mathbf{\Omega}(p+1)}-\mathbf{\hat{y }}\Big{\|}_{2}^{2}, \tag{29}\]  
where \(\mathbf{\Omega}(p+1)=\{b(p_{i}+1)|p_{i}=1,2,\dots,p\}\) denotes the set consisting of \(b(p+1)\) for each iteration. The details of the IAA and the method can be seen in reference [12].  
Then, we discuss the computation complexity of the three signal processing method. According to the designed timing, the designed sensing waveform is time-division and the range dimension is uniform, and we use the DFT method to estimate range. Hence, we focus on discussing the computational complexity of the DFT-based, IAA, and OMP methods. In this paper, we use fast Fourier transform (FFT) to realize the DFT-based method with a computational complexity of \(O(L\text{log}(K))\). The complexity of IAA and OMP are \(O(L^{3}K^{3}))\) and \(O(L^{2}K))\), respectively, and we set the number of iterations as both not more than 10 times in this paper.
# Performance Development of Fluidic Oscillator Nozzle for Cleaning Autonomous-Driving Sensors
## 2 Theoretical Background and Analysis Method  
### Theoretical Background  
The Coanda effect is a phenomenon in which fluid moving around a wall sticks to the wall and flows along the wall without any additional equipment. The surface pressure along the curved wall changes depending on the conditions of the curved surface, and under certain conditions, the flow separates from the wall. The formula that expresses the fluid pressure and particle behavior of the Coanda effect is derived from the Bernoulli equation, and under the conditions shown in Equation (1), the pressure of the inviscid fluid becomes lower than the surrounding pressure.  
\[\frac{\rho U^{2}b}{a}\leq p_{\infty} \tag{1}\]  
Figure 2: Structure of patented “apparatus” for oscillating fluid injection”.  
Here, \(\rho\) is the density of the ejected fluid, \(U\) is the average flow speed, \(a\) is the radius of rotation of the curved surface, \(p_{\infty}\) is the atmospheric pressure, and \(b\) is the spacing of the ejection slot [19; 20]. During fluid ejection along the wall, the average velocity decreases and the surface pressure increases until the wall surface pressure becomes equal to atmospheric pressure. When the wall surface pressure \(p_{0}=p_{\infty}\), the flow separates from the curved surface. The Coanda effect can be explained by several physical parameters. Equation (2) was proposed by [PERSON] [22] as the equation for the flow along the cylinder under Coanda flow at high Reynolds numbers.  
\[\theta_{sep}=f[\Big{(}\frac{(p_{0}-p_{\infty})\!\cdot\!b\!\cdot\!a}{\rho\!\cdot \!v^{2}}\Big{)}^{\frac{1}{2}}] \tag{2}\]  
Here, \(\theta_{sep}\) is the angle at which the jet flow separates, \((p_{0}-p_{\infty})\) is the difference between the pressure of the wall surface and atmospheric pressure, \(b\) is the width of the slot, and \(a\) is the radius of curvature. It means that the flow separation angle \(\theta_{sep}\) is a function of pressure difference, cylinder geometry, and fluid characteristics [23; 24]. The slot width \(b\) and curvature radius \(a\) are important performance variables that affect the flow separation angle \(\theta_{sep}\). Since the pressure difference is generally a given operating condition, the main design variables of this study were selected as the slot width and radius of curvature near the outlet.
## 4 Analysis of Approaches to Modelling Agricultural Systems
### Agent-Based Approach  
The agent-based approach consists in modelling a system with autonomous decision-making entities interacting with their environment [98]. Agent-based models (ABMs) aim to reproduce real-world-like complexity (Figure 2). ABMs are usually based on a parsimonious paradigm [99] that leads to simple models with great explanatory power. This modelling approach is the only multi-level approach, allowing emergence of processes and feedback loops that mean behaviour adaptation of individuals or groups of individuals [99; 100; 101; 102]. This makes the ABMs very useful to simulate behaviour adaptation to new incentives, or new policy, especially for scenario testing [103].  
ABMs can be used as spatially explicit models. Using real (i.e., GIS layers or pixel grids) or virtual landscapes accounting for the spatial dimension, distance, and time concepts, ABMs allow coupling with LULCC models to analyse the processes causing the changes [104; 102]. For example, farming decisions depend on incentives and interactions at different levels of organisation, such as interactions with other farmers, institutions, associations, markets, or other networks [100; 101; 104; 105]. ABMs are also useful to test individual and collective adaptations to new policy, for example, GHG price effect [102], or adaptation capacity of winegrowers to climate change [106].  
**Strengths**:
* At a regional to national scale, the ABM approach is well designed to simulate society or stakeholders' behaviour facing new policy.
* ABM approach allows coupling of models (using additional physical deterministic models like crop or climate models).
* ABM approach allows for the development of conceptual models to test simple agent behaviours or the combination of behaviours under different incentives.
* ABM approach can simulate disruptions and agent adaptation capacities to a system at different organisation levels and scales.  
**Weaknesses**:
* Like SD, it is difficult to validate ABMs, but a range of tools can be used: the validation of the conceptual framework (by external experts or stakeholders), a robust sensitivity analysis to make sure conclusions fit with the model [104].
* ABMs are not suitable for prediction but powerful for scenario testing.
## Materials and Test Methods
### Optimum Asphalt Content  
According to the design method of PAC in China, the allowable range of asphalt content is determined based on the results of the Cantabro abrasion test and Schellenberg binder draindown test. The trial asphalt content was determined based on the assumed asphalt film thickness of 13 um and total aggregate surface area. Five trial mixtures of varying asphalt contents in 0.5% increment were prepared for the two tests, and each test was repeated three times at each trial asphalt content. As Figure 1 illustrates, the minimum asphalt content is determined as the content corresponding to the inflection point of the Cantabro loss curve, and the maximum asphalt content is determined as the content corresponding to the inflection point of the draindown ratio curve [12].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Items**} & \multicolumn{1}{c}{**PAC13**} & \multicolumn{1}{c}{**PAC10-C**} & \multicolumn{1}{c}{**PAC10-I4**} & \multicolumn{1}{c}{**PAC10-I6**} & \multicolumn{1}{c}{**PAC10-I8**} \\ \hline \multirow{4}{*}{Aggregate Composition (\%)} & 1\# (10\(-\)15 mm) & 44 & 0 & 0 & 0 & 0 \\  & 2\# (5\(-\)10 mm) & 40 & 84 & 75 & 75 & 75 \\  & 3\# (3\(-\)5 mm) & 0 & 0 & 21 & 19 & 17 \\  & 4\# (0.075\(-\)3 mm) & 12 & 12 & 0 & 0 & 0 \\  & Mineral filler & 4 & 4 & 4 & 6 & 8 \\ \hline \multirow{8}{*}{Percent Passing (\%)} & 16 mm & 100 & 100 & 100 & 100 & 100 \\  & 13.2 mm & 91.0 & 100 & 100 & 100 & 100 \\  & 9.5 mm & 62.1 & 99.1 & 99.2 & 99.2 & 99.2 \\  & 4.75 mm & 21.4 & 27.1 & 32.0 & 32.3 & 32.5 \\  & 2.36 mm & 13.3 & 13.4 & 5.0 & 6.9 & 8.8 \\  & 1.18 mm & 10.1 & 10.1 & 4.2 & 6.2 & 8.2 \\  & 0.6 mm & 7.5 & 7.5 & 4.0 & 6.0 & 8.0 \\  & 0.3 mm & 5.8 & 5.8 & 4.0 & 6.0 & 8.0 \\  & 0.15 mm & 4.8 & 4.8 & 3.7 & 5.6 & 7.4 \\  & 0.075 mm & 3.9 & 3.9 & 3.3 & 4.9 & 6.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PAC gradations for evaluation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Properties**} & \multicolumn{1}{c}{**Test Results**} & \multicolumn{1}{c}{**Technical Requirement**[19]} & \multicolumn{1}{c}{**Specification Method**[20]} \\ \hline Apparent specific gravity & 2.726 & \(\geq\)2.5 & T 0352 \\ Water content (\%) & 0.24 & \(\leq\)1.0 & T 0352 \\ Hydrophilic coefficient & 0.71 & \(<\)1.0 & T 0354 \\  & \(<\)0.6 mm & 100.0 & 100 \\ Gradation (\% passing) & \(<\)0.15 mm & 93.0 & 90\(-\)100 & T0351 \\  & \(<\)0.075 mm & 87.6 & 70\(-\)100 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Properties of mineral filler.  
Typically, the optimum asphalt content (OAC) of the PAC mixture is selected between the minimum and maximum asphalt contents. However, to provide better durability, the maximum asphalt content can be selected to produce larger asphalt film thickness. In addition, the PAC should also meet the mixture design requirements, such as the minimum Marshall stability of 3.5 kN, the minimum tensile strength ratio (TSR) of 75%, and the target air void content, which was 21% in the study.
# A Multisensor Approach to Satellite Monitoring of Trends in Lake Area, Water Level, and Volume
## 5 Conclusions  
Both of the overarching objectives of this study were successfully addressed. An automated system (objective 1) was demonstrated for monitoring changes in the surface area, water level, and volume of lakes, using data from multiple earth-observation satellites. Surface area measurements were produced using an adaptive algorithm that estimates fractional (sub-pixel) estimates of water extent along lake shorelines, using a newly proposed Enhanced Lake Index (ELI), parallel to the Enhanced Vegetation Index (EVI). The results are consistent across two orders of magnitude in spatial scale. Water-level measurements and trends can be derived from satellite laser altimetry or from analysis of digital elevation models (DEMs) using two different methods. Changes in volume can be inferred from surface area trends and hypsographic curves, which in turn can be derived from DEMs during periods of low water. While presenting several methodological improvements, e.g., the use of ELI for mappingwater extent and comparison of two algorithms and two DEM data sources for water level estimation, this study also shows how these methods could be implemented in an end-to-end automated system for lake monitoring.  
Applied to the Toshka Lakes in southern Egypt (objective 2), these methods have provided the most comprehensive daily record of the formation, expansion, and decline of lakes over the past two decades, and have enabled the inference of the amount of groundwater recharge from each lake basin. With the development of new sources of wide-scale, high-temporal frequency imagery from constellations such as Sentinel and Planet Labs, and the recent launch of a new laser altimeter on ICESat-2, the next decade should see a rapid expansion in remotely sensed assessments of lake hydrology.  
conceptualization, J.W.C.; methodology, J.W.C.; software, J.W.C.; validation, J.W.C.; formal analysis, J.W.C.; investigation, J.W.C.; resources, J.W.C.; data curation, J.W.C.; writing--original draft preparation, J.W.C.; writing--review and editing, J.W.C.; visualization, J.W.C.; project administration, J.W.C.; funding acquisition, J.W.C.  
Portions of this work were funded by the NASA Terrestrial Hydrology Program through grant #NNG06 GE46G. The APC was funded by Dartmouth College's Open-Access Publication Equity Fund.  
MODIS and ASTER data were provided courtesy of NASA Land Processes DAAC, AVHRR data courtesy of NOAA, Landsat data courtesy of USGS EROS Center, ICESat-1 GLAS data courtesy of the National Snow and Ice Data Center, and SRTM data courtesy of the Jet Propulsion Laboratory and USGS EROS Center.  
The author declares no conflict of interest.
## 4 Analysis of Results and Discussion
### Divergence of DA and DAP According to Room Reflectance  
The third analysis addresses the variation of DAP with respect to DA depending on the reflectance of the inner surfaces of the room. Figure 6 represents the quantification of both metrics, considering the calculation model and boundary conditions described above and taking into account two scenarios; with high (B) and low (D) reflectance values, varying the qualities of the surfaces according to the study cases described in Table 2. As in the previous trial, first and second rows show the room sections with a small window, third and fourth rows show medium-size openings, and the last two rows describe the results for large windows. As in previous studies, labels located in the left-top of the room sections describe the calculation model in accordance with parameters defined in Table 2.  
As can be deduced from Figure 6, the divergence between the studied metrics depending on the room reflectance is lower than in the case of the previous trials. This is due to the fact that the room reflectance is a less decisive parameter in the determination of daylight metrics, taking into account the boundary conditions described in the methodology.  
According to the previous assertion, it can be noted that the mean deviation between DA\({}_{300}\) and DAP\({}_{300}\)[243; 244; 245; 246; 247; 248; 249; 250; 251; 252; 253; 254; 255; 256; 257; 258; 259; 260; 261; 262; 263; 264; 265; 266; 267; 268; 269; 270; 271; 272; 273; 274; 275; 276; 277; 278; 279; 280; 281; 282] corresponds to 5.1%, while this value increases up to 8.2% in the case of an illuminance threshold of 500 lx. As can be deduced, the presented deviations are lower than those observed in the trials above, also showing a standard deviation of 2.6% for bright rooms and of 4.7% for dark rooms. Another singularity of this part of the study is that the maximum difference between DA and DAP is not observed in the back of the room, as in the previous cases, but in the center of the room, at a distance between 2 and 4 m from the facade. As deduced from the study cases with windows facing South, both metrics tend to converge in the zone near the facade as well as in the back of the room.  
According to this, it can be concluded that the reflectance of the inner surfaces is not a decisive parameter to determine the difference between both metrics.
# Low-Profile Antenna System for Cognitive Radio in IoST CubeSat Applications
## 2 Antenna System Structure
### Wideband Antenna  
The sensing antenna is a semi-hexagonal-shaped slot antenna with a perimeter \(\approx\)_of_\(0.85\:\lambda_{g}\) at the center of the required band (0.7 GHz). The loaded capacitor at the middle of the slot's baseline with a value of 0.38 pF (see Figure 2) results in the slot's small physical length and improves the impedance-matching bandwidth. The width of the slot is 3 mm.  
Figure 3 shows that the sensing antenna operates from 1.3 GHz to 1.62 GHz before loading the capacitor, while it is reduced to cover a band from 0.57 GHz to 0.95 GHz in case of loading the capacitor. The slot is fed by two tapered transmission lines, the left feed (P-1) excites LHCP, and the right feed (P-2) excites RHCP. The axial ratio of the proposed antenna has been presented versus frequency at different capacitor values in Figure 4. The axial ratio with a capacitor value of 0.38 pF confirms the circular polarization of the antenna.  
Figure 3: S-parameters of the sensing antenna (**a**) with and without capacitive loading, (**b**) for various capacitive loadings.  
Figure 2: The antenna system structure (all dimensions in mm). (**a**) Top view and (**b**) bottom view.  
To better understand the CP generation in the left- and right-hand sense, the surface current distributions are provided in Figure 5. When P-1 is excited, the current distributions are plotted at angles of 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 180\({}^{\circ}\) at 0.6 GHz using High-Frequency Structured Simulator (HFSS), as depicted in Figure 5a. It is observed that the current distribution varies in the clockwise direction, which produces left-handed circular polarization. Similarly, when the antenna is excited from P-2, the current directions vary in the counterclockwise direction (see Figure 5b), demonstrating the antenna's ability to radiate as right-handed circular polarization.  
Figure 4: Axial ratio curves for various capacitance values.  
Figure 5: _Cont._
## 3 Results and Discussion
### Data Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Members** & **Correct** & **1** & **2** \\ \hline
**1** & 27 & 100\% & 27 & 0 \\ \hline
**2** & 22 & 100\% & 0 & 22 \\ \hline
**Total** & 49 & 100\% & 27 & 22 \\ \hline
**Fisher’s prob.** & \(4.5\times 10^{-14}\) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Misclassification table from OPLS-DA modeling regarding species’ origin of milk for all the data obtained from \({}^{1}\)H-NMR spectra and the region 1900–400 cm\({}^{-1}\) from FTIR; 1—cov origin, 2—goat-and-sheep-origin samples. Figure 4: Random permutation test for the OPLS-DA model depicted in Figure 3 that took place by using 100 permutations. In the study by [PERSON] and [PERSON] [6], only three types of cheese, namely Cheddar, Kefalotyri, and Halloumi, were studied by using the same spectroscopic techniques and chemometric method. The fact that three types of cheese were compared in that study and they were classified into three groups (i.e., the type of cheese) was the first finding and the initiator of the current study. Thus, in this study, an increase in the types of cheese samples took place as well as taking out the Halloumi cheese group to test Kefalotyri and Cheddar with various other cheese products not made in Cyprus. Therefore, the use of various cheese types gave discrimination of the samples based on the milk species' origin. Subsequently, it can be concluded that the number of different types of samples in a model is very important. For instance, a few different types of samples in a study may discriminate based on the types of samples. However, if various types of samples are used without a balanced number in each category, the discrimination may occur in terms of another parameter of the set (here, the milk species origin), which has fewer variations and discrepancies (here, cow or goat and sheep). Considering all the outcomes, it would be appropriate for future studies if all the new types of cheese in this study were enriched with up to 10 samples per category (like Cheddars) and tested with the same spectroscopic techniques. Moreover, it is still a great challenge for our group to discriminate goat and sheep samples, which always end up in the same group after chemometric treatment. It also seems necessary to use one type of cheese and avoid any cow-origin samples to focus on the milk species (goat and sheep).