esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Discussion
### Results of Case Studies  
#### 4.3.1 Case 1  
In the first case, day-ahead optimization was conducted by taking advantage of TOU prices and real load data as well as predicted weather data according to details described in previous sections. The predicted output power of PV and WT were calculated based on the Equations (1)-(3). Optimization results are shown in Figures 11 and 12. Based on the results in Figure 11a,b, SB and PHEV were charged at separate hours during mostly off-peak and mid-peak periods. SB and PHEV also discharged the stored power during peak hours when the prosumer was responsible for providing the consumer load. Based on Figure 12, the prosumer imported less power from the utility grid in the hours between 7:00 and 11:00 because of sufficient production of the PV and WT system, which reduced the operation cost of the prosumer. It is worth mentioning that according to Figure 12, a noticeable decline had occurred at 14:00 since SB was discharging its power in order to supply the consumer load in a reasonable way. Furthermore, a considerable increase had occurred at 15:00, which is ordinary since the EMS was charging SB and PHEV to supply the contracted load in the hours between 14:00 and 16:00. Therefore, SB discharged at 14:00 to support the contracted power. Although PHEV was discharged at 13:00 with  
Figure 10: Contracted power that the prosumer is committed to providing to the consumer during the day.  
Figure 9: Prosumer electrical load profile during the day.  
a small amount of electricity (0.2163 kW), the significant injection had occurred at 16:00. Then, SB and PHEV were charged at 17:00 and 18:00, respectively, because the TOU prices were at a lower level in comparison to the peak hours. In the following, ESSs were discharged in peak periods for having a minimum operation cost. According to Figure 11b, PHEV was discharged at 19:00 and 20:00, while SB was discharged at 22:00. Finally, when the peak period ended at 22:00, EMS was making a proper plan in order to charge the ESSs after this time, which resulted in more power import from the grid by the prosumer based on the overall trend in Figure 12.  
Figure 11: Charging and discharging of a (**a**) stationary battery (SB) and (**b**) plug-in hybrid electric vehicle (PHEV).  
Figure 12: Import/export power from the utility grid.  
RESs are merely allocated to provide the prosumer load and consumer contracted power. The main reason is that the production of the RESs is much less than all electricity consumption, and the utility grid provides them to meet the customers' requirements. It is important noting that RESs do not inject electricity into the utility grid, and ESSs are charged through the utility grid instead of RESs due to economic benefits. This pattern may differ from one day to another and depends on input data such as weather and load and data. In this case, the objective function value was achieved at $20.353. In addition, revenues of contracted power were not considered in the objective function due to fixed costs of payments.
## 1 Introduction
### Other Related Work  
Balanced partitioning is a challenging problem to approximate within a constant factor [2; 24; 25] or to approximate within any factor in several distributed or steaming models [11]. As for heuristic algorithms for this problem in the distributed or streaming models, a number of recent papers have been written about this topic [4; 5; 21]. Our algorithms are different from the ones studied in these papers. The most similar related work are the label propagation-based methods of [PERSON] [4] and [PERSON] et al. ([PERSON]) [19] which develop a scalable distributed algorithm for balanced partitioning. Our random-swap technique is similar in spirit to the label-propagation algorithm studied in Reference [4], however, we also examine three other methods as a postprocessing stage and find out that these methods work well in combination. Moreover, Reference [4] studied two different methods for their initialization, a random initialization, and a geographic initialization. We also examine random ordering, and a Hilbert-curve ordering which is similar to the geographic initialization in Reference [4]. However, we examine two other initialization techniques and observe that even for map-based geographic graphs, the initialization methods based on hierarchical clustering outperform the geography-based initial ordering. Overall, we compare our algorithm directly on a LiveJournal public graph (the only public graph reported in Reference [4]), and improve the cut values achieved in References [4; 19] by a large margin for all values of \(k\). In addition, algorithms developed in References [5; 21] are suitable for the streaming model but one can implement variants of those algorithms in a distributed manner. We also compare our algorithm directly to the numbers reported on the large-scale Twitter graph by FENNEL [5], and show that our algorithm compares favorably with the FENNEL output, hence indirectly comparing against Reference [21] because of the comparison provided in Reference [5].  
Motivated by a variety of big data applications, distributed clustering has attracted significant attention over the literature, both for applications with no strict size constraints on the clusters [26; 27; 28] or with explicit or implicit size constraints [29]. A main difference between such balanced-clustering problems and the balanced graph-partitioning problems considered here is that in the graph-partitioning problems a main objective function is to minimize the cut size, whereas in those clustering problems, the main goal is minimize the maximum or average distance of nodes to the centers of their clusters.
## 3 Test Setup, Loading Protocol, and Instrumentation  
The experimental test setup is shown in Figure 4. A hydraulic actuator APD-1000 (Anco Engineering, USA) with a 1000 kN capacity and a stroke length of 250 mm was used to apply the cyclic lateral displacements at the top of the beam. This actuator was operated in displacement control and horizontally held to the strong wall of the laboratory. A steel plate was used in the space between the specimen and the actuator for a smooth transfer of the actuator load at the beam level. Pinned boundary conditions at each end of the columns were simulated using two supports. No restraint was provided against rotation along any axis.  
A constant axial load of 800 kN was applied and maintained throughout the test at the end of the column by a hydraulic actuator (CGS250P250, Europress, Italy). Then, the lateral displacement was imposed cyclically, in a quasi-static way, at the end of the beam. The loading cycle consisted of a series of reversed-displacement cycles of increasing amplitude (Figure 5). The first steps were at a 0.25% drift story, followed by steps at 0.5%, 0.75%, 1%, 1.25%, 1.5%, 1.75%, 2%, 2.25%, 2.5%, 3%, 3.5%, 4%,  
Figure 4: Test setup.  
4.5%, 5%, and 5.5%. Each drift step consisted of two cycles of push and pull. Note that the drift of the specimen was calculated as the ratio of the total relative horizontal displacement of the steel beam at the loading point to the distance between this point and the axis of the column.  
To collect the information regarding the joint distortion and the yielding of the components, several different instruments were used when testing the specimens. The arrangement of the strain gauges and rosettes is presented in Figure 6. During the loading, the test results were recorded every second. At the joint region, six strain gauges (KFGS-5-120-C1, Kyowa, Japan) were placed on both transverse and longitudinal reinforcements of the column. There were also four strain gauge rosettes (KFGS-10-120-D17-11, Kyowa, Japan) arranged in the embedded steel profile.
## 4 Model Validation
### Oxygen Storage Submodel  
The same calibration protocol was used for the reactions involving cerium, which, as mentioned, is mainly responsible for the accumulation/release of oxygen (OSC). Cerium is normally present in high quantities in the washcoat (around 30% by weight, i.e., 1000 g/ft\({}^{3}\) or 35.31 kg/m\({}^{3}\)). Cerium stabilizes the washcoat layer, enhances precious metal activity, and improves thermal resistance. Oxygen storage is due to cerium's ability to form 3- and 4-valent oxides [7]. The following reaction represents cerium oxidation and the corresponding oxygen capture:  
\[\frac{1}{2}O_{2}+Ce_{2}O_{3}\to 2 CeO_{2}\]  
This reaction stands for the storage of an oxygen atom by increasing the cerium oxidation state. Because of the significant presence of carbon oxides and hydrogen in the exhaust gas, additional important pathways are:  
\[CO+2 CeO_{2}\leftrightarrow CO_{2}+Ce_{2}O_{3}\]  
\[H_{2}+2 CeO_{2}\leftrightarrow H_{2}O+Ce_{2}O_{3}\]  
Figure 5: CO and CH\({}_{4}\) conversions with the uncalibrated (GT-Suite default) kinetic scheme from [17]. Gas Temp. \(\approx\) 760 \({}^{\circ}\)C. (1900 RPM, 100% load).  
As demonstrated in previous works [7; 11], cerium also interacts with nitrogen oxides, according to the following reaction:  
\[NO+Ce_{2}O_{3}\rightarrow\frac{1}{2}N_{2}+2 CeO_{2}\]  
which has been added to the initial kinetic scheme to better characterize the dynamics of TWC during fast transient \(\lambda\) sweep. It also provides a mechanism for NO reduction under lean conditions, which can be particularly important under real-world driving conditions. Finally, the importance of this pathway was confirmed by the typical delay in the NO release at the TWC outlet, also detected in the present experimental activity.
# Design and Evaluation of Dynamic Topology for Mega Constellation Networks
## 3 Problem Analysis
### Dynamic Link Selection Algorithm
Input: Connectivity threshold C_M\(\leftarrow\)4 for each satellite, initial satellite \(S_{1}\)  
\(S\leftarrow\{\{1,\{S(1,1),\cdots,S(24,66)\}\},\cdots,\{66,\{S(1,1),\cdots,S(24,6 6)\}\}\)  
\(C\leftarrow\{1,\{3,\cdots,3\},\cdots,\{66,\{3,\cdots,3\}\}\}\) // specify the connection degree at the initial time  
Output: Set of satellite link results for each time slice LISL_SET  
```
1:ISL_SET\(\leftarrow\)\(\{\}\), TempMap\(\leftarrow\)\(\{\}\), entry \(\leftarrow\)\(\{\}\)
2:for t \(\leftarrow\) 1 to 2 / / first calculate connectivity in the first two time slices of \(S_{1}\)
3:SNN\(\leftarrow\) selectNonNonT\(\{S_{1},S_{2}\}\) // take the set of satellites in non-adjacent orbits to \(S_{1}\)
4:for i \(\leftarrow\) 1 to 66,\(i\gets\) 1 to 24, SNN \(\leftarrow\)\(S(i,j)\)
5:d \(\leftarrow\)\(d_{12}=\sqrt{R_{1}^{2}+R_{2}^{2}-2\times R_{1}\times R_{2}\times\cos\theta}\) // calculate the ISL distance between \(S_{1}\) and \(S_{2}\)
6: TempMap.add\((S_{2},d)\) // the address key is satellite and the value is \(d_{12}\)
7:end
8:SortByDistance(TempMap) // sorted by call distance from smallest to largest
9: For entry IN TempMap // define entry as address key
10:IFC(T, entry) > C_M Then
11:S(T).remove(entry)
12:Else
13:LISL_SET.add((T,S1).entry) // t time slice \(S_{1}\) and entry.key satellite establish link
14:C(T,S1) \(\leftarrow\)4; C(T,entry) \(\leftarrow\)4
15:Next entry
16:Next T
17:For K \(\leftarrow\) 3 to 66 // dynamic links for the remaining time slices of \(S_{1}\) are inferred from the dynamic link pattern
18:ISL_SET\(\leftarrow\) DLSRGenerate(S1,LISL_SET,K)
19:Next K
20:For P\(\leftarrow\)3 to 23 // all dynamic links based on combined dynamic link selection strategy and track change pattern
21:ISL_SET\(\leftarrow\) DLSRAndCATS(ISL_SET,C,S,P)
22:Next P
```  
**Algorithm 1** Pseudocode for the dynamic link selection  
The advantage of the designed dynamic link selection algorithm is that once the non-adjacent orbit satellites connected to each satellite in the first two time slices are calculated, the algorithm can directly determine the dynamic links established by all satellites in that orbit at any time slice. The links established by the corresponding non-adjacent orbit satellites in the remaining time slices can also be obtained accordingly, completing the link connections of all satellites in different time slices during the cycle. Moreover, the resulting network topology is relatively regular, facilitating future routing research. In addition, compared with the static topology consisting of four fixed links, the introduction of dynamic links provides more path options and shorter inter-satellite distances, resulting in smaller communication delays. Additionally, traffic can be quickly transmitted in various directions, significantly reducing the number of routing hops during inter-satellite communications. Therefore, the network performance is better in a large-scale constellation with many hops.
## 2 Theoretical and Numerical Scheme
### Phase Field Equations  
The phase-field method is of high accuracy, stability and wide applicability. It can accurately describe the distribution and interfacial evolution of different phases of a two-phase fluid by introducing continuous phase-field variables. The method can also accurately simulate the dynamic behavior of complex interfaces. Therefore, when considering the interfacial problem of two-phase flow, the Cahn-Hilliard equation is particularly suitable for describing the interfacial motion of two phases that do not mix with time. The specific equations are formulated as [34]:  
\[\frac{\partial\Phi}{\partial t}+u\cdot\
abla\Phi=\
abla\cdot\left(\frac{ \gamma^{\lambda}}{\varepsilon^{2}}\right)\
abla\psi \tag{6}\]\[\psi=-\
abla\epsilon^{2}\
abla\Phi+\left(\Phi^{2}-1\right)\Phi+\left(\frac{\epsilon ^{2}}{\lambda}\right)\frac{\partial f}{\partial\Phi} \tag{7}\]  
In the above equations, \(\gamma\) is the mobility, which denotes the moving speed of the flowing interface per unit driving force. \(\epsilon\) denotes the capillary width with respect to the interface thickness. \(\lambda\) denotes the mixing energy density. \(\Phi\) is the phase field variable, which is used to characterize the distribution of the different phases in the system. \(\Psi\) is the auxiliary variable of the phase field, and \(\bigtriangledown\) is the Laplace operator, which is used to describe the diffusion process in the phase field. These parameters are related to the surface tension \(\sigma\) as a function of the surface tension as follows [35]:  
\[\sigma=\frac{2\sqrt{2}}{3\epsilon}\lambda \tag{8}\]  
The mobility rate \(\gamma\) is a function of the mobility adjustment parameter \(\chi\) and the interface thickness control parameter \(\epsilon\). The mobility rate determines the diffusion time scale of the Cahn-Hilliard equation. Moderate migration rates can maintain a constant interface thickness and prevent excessive suppression of convective terms. The mobility \(\gamma\) is expressed as [36; 37]:  
\[\gamma=\chi\epsilon^{2} \tag{9}\]  
In COMSOL Multiphysics(tm), the effect of the wettability of the walls of a porous media on the two-phase flow interface is characterized by the following equations [38]:  
\[u=0 \tag{10}\]  
\[n.\frac{\gamma\lambda}{\epsilon^{2}}\
abla\psi=0 \tag{11}\]  
\[n.\epsilon^{2}\
abla\Phi=\epsilon^{2}\cos\theta_{w}\mid\
abla\Phi\mid \tag{12}\]  
where \(n\) is the normal vector in the Cahn-Hilliard equation indicating the direction of the phase field gradient. \(\theta_{w}\) is the surface contact angle, which is used to characterize the wettability condition of the porous media.
## 3 Results and Discussion
### Judgment Results, Back-Propagation Neural Network
#### 3.3.2 Back-Propagation Neural Network  
This paper's five focal weather events were entered into SPSS simultaneously for reverse-transfer neural network analysis, with 70% of the data used for training and 30% for verification. Table 14 presents the findings of such analysis. The verification sample's interpretation accuracy rate was 91.5%, and that of the training sample was 94.1%, making for an overall accuracy of 93.3%. Table 15 shows the relative importance of each factor to accurate debris-flow disaster prediction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Classification Result**} & \multicolumn{1}{c}{**Total**} \\  & **Category** & **0** & **1** & **Total** \\ \hline Training sample & 0 & 77 (97.5\%) & 2 (2.5\%) & 79 (100\%) \\ (118 rows) & 1 & 5 (12.8\%) & 34 (87.2\%) & 39 (100\%) \\ Correct discrimination rate = [(77 + 34)/(79 + 39)] \(\times\) 100\% = 94.1\% & Validation & 0 & 36 (97.3\%) & 1 (2.7\%) & 37 (100\%) \\ sample (47 rows) & 1 & 3 (30.0\%) & 7 (70.0\%) & 10 (100\%) \\ Correct discrimination rate = [(36 + 7)/(37 + 10)] \(\times\) 100\% = 91.5\% & Overall sample & 0 & 113 (97.4\%) & 3 (2.6\%) & 116 (100\%) \\ (165 rows) & 1 & 8 (16.3\%) & 41 (83.7\%) & 49 (100\%) \\ Correct discrimination rate = [(113 + 41)/(116 + 49)] \(\times\) 100\% = 93.3\% & & \\ \hline \hline \end{tabular}
\end{table}
Table 14: Results of the analysis of the back-propagation neural network.  
The back-propagation neural network predicted the potential area of slope disaster up to more than 90%, and the results were similar to those of past scholars [56; 57; 58].
## 4 Results and Discussion
### Outcomes of Solid Volume Fraction (\(\phi\))  
The simulation results toward the influence on the (left) streamlines, (middle) isotherms, and (right) nanoparticle concentration with solid volume fraction (\(\phi\)) at \(N=4\), \(A=0.1\), and \(Ra=10^{6}\) in the \(X-Y\) plane is presented in Figure 14. This stands apparent of the streamlines the occurrence from the two circulations inside each wavy portion remains almost the same in size and strength. Aforementioned does assign into a particular buoyancy force (\(Ra=10^{6}\)), adding nanoparticles up to 4% appears not to have an important influence toward the flow field. In this case, the density difference due to variable volume fraction concerning nanoparticles is suppressed by the buoyancy effect. More importantly, increasing solid volume fraction of nanoparticle up to a certain level (4%) is very effective in heat transfer enhancement. The physics behind this is that the nanofluid has higher thermal conductivity than the conventional fluid and heat transfer occurs at a higher rate in the high thermal conductivity medium. For adding more nanoparticles, the sedimentation process starts in the fluid and reduction of flow occurs which is not efficient in heat transfer in the desired rate.  
As shown in the second column of Figure 14, isothermal contours have no notable changes for the variation of solid volume fraction. Though the isothermal lines are nearly the same, increasing nanoparticle volume fraction increases the thermal conductivity regarding the nanofluid, which generates higher energy transport from heated to cooler portion, which is clear in Figure 12. From the last column of Figure 14, it is apparent that the nanoparticle volume fraction has a significant influence on the nanoparticle concentration field. At lower values of \(\phi\), the concentration contours mostly remain near the walls indicating a higher concentration gradient. With the increasing \(\phi\)  
Figure 14: Variation of the streamlines (**left**), isotherms (**middle**), and nanoparticle distribution (**right**) evolution by solid volume fraction (\(\phi\)) for \(N=4\), \(A=0.1\), and \(Ra=10^{6}\) in the plane (\(X,Y\)).  
more lines appear in the domain. Thus, higher nanoparticle volume fraction reduces the concentration difference between the walls and the nanofluid. Consequently, less particle distribution will take place.
## 2 Materials and Methods
### Method for Spectrum Assignment Using Multi-Level Formats and Optical Frequency Combs--RMLSA  
In this application, we design an RMLSA algorithm to control the pulse shaper based on the bandwidth and distance information of each request in the network, to find an appropriate optical comb carrier for the transmission process. In designing this algorithm, we propose a heuristic methodology that efficiently assigns wavelengths while respecting several constraints. These constraints include the request-required transmission distance, the transmission distance of the wavelength based on its modulation configuration, and the number of lines needed per modulation. Furthermore, we ensure that the assigned super-channels are contiguous and adjacent, without any gaps. The primary goal of this allocation is to optimize the spectrum. Our algorithm employs a flexible grid ranging from [12.5, 200] GHz to select the modulation format that requires the smallest spectrum region while ensuring that the format can transmit at the required distance. This grid structure allows for the flexible allocation of resources. It enhances the spectrum efficiency by assigning small bandwidths on small grids or utilizing all spectrum wavelengths when creating a super-channel for a large bandwidth requirement. To illustrate this, Figure 7 depicts the spectrum organization on both rigid and flexible grids, as proposed by our algorithm. Some strategies for spectrum allocation have been proposed. However, these strategies are unsuitable for MRR use, and some entail high computational costs. Therefore, we propose the spectrum allocation algorithm depicted in Algorithm 1. This algorithm maximizes the utilization of network resources, specifically comb lines, while minimizing the number of rejected requests. Firstly, the algorithm calculates the bandwidth matrix (\(bw_{mod}\)) and the matrix of required carrier counts for each modulation format (\(VL\)) for each bandwidth request. Then, it determines the necessary OSNR for each modulation format to achieve a desired bit error rate (BER) and computes the distance at which each request can be placed on the optical comb when considering fiber attenuation. Figure 6: Scheme for spectrum allocation in a PON. Figure 7: Spectrum assignment on rigid and flexible grids.
# Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction
## 3 System Modeling  
### Network Model  
We consider a network architecture consisting of mobile users, UAVs, terrestrial base stations (TBSs), and satellites. TBSs are deployed in coastal areas to provide communication services to users in coastal waters. The broadband coverage of TBSs is usually limited due to high non-line-of-sight path loss. Outside the TBS coverage area, satellites deployed in space orbit provide communication links. Ships equipped with expensive high-gain antennas can be guaranteed broadband service. However, for low-end ships without high-gain antennas, it is still difficult to enjoy broadband service even within the satellite coverage area. To fill this gap, we use drones to provide broadband services. More specifically, if a mobile user requires a high-rate communication service (e.g., videoconferencing) from \(t_{s}\) to \(t_{e}\), the communication request will be sent from the mobile user to the nearest TBS and then transmitted to the central processor. The central processor selects an idle UAV and prepares the idle UAV to serve the mobile user. The UAV will fly along the optimized trajectory to serve the user from \(t_{s}\) to \(t_{e}\). After completing the high-speed communication service, the mobile user will associate with the nearest TBS at the moment \(t_{e}\), and the UAV will return to the coast. Consider a target coverage area with network services from an LEO satellite, a terrestrial base station, and m drones, and there are n user ships in the area, which are serviced with \(m\in\{1,2,3,\cdots,\ M\}\), and \(n\in\{1,2,3,\cdots,\ N\}\), as shown in Figure 1. The satellite serves as the main base station, which mainly handles computing and communication tasks for the more concentrated users at sea. Each UAV is equipped with a computational processor that can handle simple computational tasks. The transmission time is time-slotted, and it is assumed that one information-transmission cycle is divided into \(T\) time slots, each denoted by \(t\), where \(t\in\{1,2,3,\cdots,\ T\}\). The user moves at a speed of \(u_{speed_{n}}\), which satisfies \(\ 0\leq u_{speed_{n}}\leq V_{max}\), where \(V_{max}\) denotes the maximum speed of vessel movement.