esa-sceva/satcom-chunk-collection huggingface dataset:
# Wall-Climbing Mobile Robot for Inspecting DEMO Vacuum Vessel
## 5 Adsorption Control Analysis  
### Modelling of Adsorption Systems  
As shown in Figure 14, the WMR must be attached to the wall using the thrust generated by the impeller fan's high-speed rotation as it moves around the inside of the circular vacuum chamber. The adsorption force generated by the motor's rotation must not be less than the force of gravity; otherwise, it will cause the robot to slip. Furthermore, the adsorption force cannot be much greater than gravity, or it will waste energy and shorten the use time requiring the control of the impeller fan speed to keep the pressure provided at a specific value to ensure the stable operation of the robot.  
For the design of the robotic adsorption control subsystem, we obtained a mathematical model of the adsorption device by analysing the dynamics of the impeller fan. We achieved adaptive adjustment of the WMR's adsorption pressure by designing a closed-loop controller for the adsorption system. Figure 15 shows the closed-loop control system's framework for the adsorption of the WMR. The input value was set according to the actual situation during the WMR operation. The system compares the input value with the negative feedback signal provided by the sensor to find the error, which the controller then corrects.  
We assumed that the impeller fan motor satisfies the following conditions:  
1. The waveform of the counter-electromotive force is an ideal flat-topped trapezoidal waveform;
2. The magnetic circuit of the motor is not saturated during rotation;
3. The eddy current and hysteresis losses of the motor are negligible.  
By neglecting the current fluctuations in the motor and the diode voltage drop, the armature voltage balance equation for a rotating DC motor is:  
\[U=Ri+L_{0}\frac{di}{dt}+K_{e}w \tag{10}\]  
Figure 14: WMR simulation process. (**a**,**b**) The WMR moves on the interior walls of the vacuum chamber.  
Figure 15: Closed-loop control system for adsorption.  
where \(U\) is the motor armature voltage; \(R=2r\) is the equivalent resistance; \(i\) is the current in the motor; \(K_{e}\) is the counter-electromotive force coefficient of the motor; \(w\) is the equivalent output pressure corresponding to the motor speed; \(L_{0}=2(L-M)\) is the equivalent inductance [34,35].  
The equation for the torque balance of a DC motor is:  
\[\left\{\begin{aligned} T_{e}-T_{l}=J\frac{dw}{dt}+K_{b}w\\ T_{e}=K_{T}i\end{aligned}\right. \tag{11}\]  
where \(T_{e}\) is the electromagnetic torque; \(K_{T}\) is the torque coefficient; \(K_{b}\) is the damping coefficient; \(J\) is the rotational inertia; and \(T_{l}\) is the load torque.  
This provides the differential equation for motor voltage and equivalent output pressure as follows:  
\[\frac{d^{2}w}{dt^{2}}+a\frac{dw}{dt}+bw=cU \tag{12}\]  
where \(a=\frac{K_{b}L_{0}+RJ}{L_{0}J},b=\frac{K_{b}R+K_{T}K_{e}}{L_{0}J},c=\frac{K_{ T}}{L_{0}J}\).
# Identifying Hyper-Heuristic Trends through a Text Mining Approach on the Current Literature
## 4 Results
### Most Frequent Words Reported  
Figure 4 demonstrates the word clouds at the 90 th percentile of the most-cited research papers related to hyper-heuristics. We exclude the words heuristic and hyperheuristic, as they are part of the query used to search for the articles. Figure 3(a) shows the highest-frequency words in the abstract. The most used abstract words are \"optimization\", \"scheduling\", \"metaheuristic\", \"search\", \"evolutionary\", and \"benchmark\". Figure 3(b) depicts the most popular words in the title, which include \"scheduling\", \"optimization\", \"timetabling\", \"evolutionary\", \"genetic programming\", and \"multiobjective\". Figure 3(c) shows that the most frequent author keywords are \"optimization\", \"genetic programming\", \"timetabling\", \"evolutionary\", \"scheduling\", and \"metaheuristic\". Finally, in Figure 3(d), we can observe that the most popular index keywords are \"scheduling\", \"optimization\", \"evolutionary\", \"genetic programming\", \"timetabling\", and \"software\". Hence, the most frequently mentioned terms in the hyper-heuristic literature involve evolutionary algorithms, optimization problems, and scheduling.  
Figure 4: Word clouds for elements of interest in the publications.  
Figure 3: Distribution of citations and number of documents of the top ten journals with the most citations for documents on hyper-heuristics (obtained on 5 April 2022).  
Table 1 displays the most mentioned compound words in the abstracts of selected articles about hyper-heuristics. The two-word compounds most often used in the literature were \"combinatorial optimization\", \"simulated annealing\", \"examination timetabling\", and \"job shop\". In addition, the most common three-word compounds were \"particle swarm optimization\", \"capacitated arc routing\", \"arc routing ucap\", and \"ant colony optimization\".  
Therefore, Figure 4 and Table 1 allow us to identify the words and compound words that represent the most dominant subjects in the selected literature related to hyper-heuristics. The main words and topics include \"genetic algorithm\", \"optimization problems\", \"scheduling\", \"combinatorial optimization\", \"evolutionary algorithm\", \"swarm optimization\", \"particle swarm\", \"metaheuristic\", \"timetabling\", and \"machine learning\".
# Early Gapping and Platoon Merging Strategies for Autonomous Vehicles using Local Controllers
## 3 Results  
### Vehicle Dynamics in a Position-Time Space  
Figure 3 introduces two examples of vehicle dynamics along the road section of the main lane. The positions of the main lane vehicles are presented (with blue lines) as a function of time. Red lines indicate merging vehicles. Figure 3a shows the short-term dynamics (vehicles 1-100 for 3 min) of a case with 10% zipper merging, and Figure 3b shows a longer-term (vehicles 1-800 for 18 min) of a 30:2 platoon size ratio. Note that the vehicles in the preparation section try to comply with the gap profile provided, and accumulated gaps are added up to form platoons with distinguished large gaps between them. This method utilizes fractions of gaps gained during the maneuvering section by contributing them to larger gaps. For example, small gaps marked by circle A in Figure 3a are translated to the larger gap between two dense platoons, leaving vehicles without excessive gaps in B. This optimizes the potential for a larger number of vehicles to merge in each gap and avoids situations where a fraction of gaps is \"wasted\" without merging vehicles.
# Infrared All-Dielectric Metasurface Beam Splitter Based on Transflective Structures
Metasurfaces are artificial, sub-wavelength, two-dimensional, planar structural materials. Due to their significant advantages in compactness, easy integration, and capacity to arbitrarily modulate electromagnetic waves, which have been extensively employed in various research fields, including spectrum imaging, neural networks, thermal radiation cladding, absorbing material, and so on [19; 30; 31]. In general, metasurfaces can be classified into metallic metasurfaces and dielectric metasurfaces according to their constituent materials. Both kinds of metasurfaces have made considerable contributions to beam-dividing devices. Not only have many advances been made in PBSs [32; 33; 34; 35], but [PERSON] et al. recently also used a symmetric Si nanoring structure to construct a near-infrared band splitter ratio tunable (0.5:1-1:1) all-dielectric metasurface BS [36]; [PERSON] et al. demonstrated a free-space optical multiport amorphous Si metasurface beam splitter with an arbitrarily predetermined output port number (2-7) by a gradient-descent-based iterative algorithm [37]. Compared to metallic metasurfaces, dielectric metasurfaces have easily tunable scattering and high transmission efficiency while having almost no absorption loss. They make dielectric metasurfaces an excellent choice for BS designs. Currently, metasurface BSs are usually designed to act only on transmitted or reflected light, which makes it necessary to attach additional light path conversion devices when the light path back from the splitting beam is used as a feedback signal, hindering further integration of optical systems. In this context, we propose a transflective all-dielectric metasurface beam splitter based on a single-layer, incompletely transmissive Si nanocolumn structure. It could achieve a beam-splitting function by sequentially arranging nanocolumns of different radii for arbitrary polarization lights with a splitting ratio of 59%:41%, a total splitting efficiency (TES) of 93.3%, and an angular bandwidth of about 32\({}^{\circ}\). In this work, the light transmitted through the metasurface and reflected from the nanocolumns and substrate intersections both pass through the unit cell once, and then the transmitted and reflected lights are subjected to an isophase modulation. Therefore, it shows a phenomenon in which the transmitted and reflected light are separated on the same side of the normal at a similar emergence angle. This transflective metasurface BS can obtain a feedback signal while splitting the beam to correct the operating parameters of the optical system, which is hopeful to be used in the design of feedback links for optical detection, interferometers, and other integrated optical systems.
## 4 Conclusions  
In this study, the 3D four-step braided composites were aged in a cyclic hydrothermal environment, and the microscopic damage morphologies of the composites were examined by SEM, to determine the damage evolution with cyclic hygrothermal aging days. A split Hopkins pressure bar (SHPB) apparatus was employed, to determine the dynamic mechanical properties of the composites under three high strain rates, when subjected to the longitudinal compression. The main findings are summarized in the following:  
1. The saturated moisture absorption of 3D four-step braided composites was not reached over the full duration of 210 cyclic hygrothermal aging days, the maximum moisture absorption was 0.6944%, and the composites did not show very much non-linear moisture behavior, compared with the T700/TDE85 laminated composites in our previous work.
2. The damage evolution of 3D four-step composites during the whole cyclic hygrothermal aging days mainly included epoxy hydrolysis and interfaces debonding.
3. The dynamic compressive stress-strain curves for the 3D four-step braided composites both behaved in a similar way, whether cyclic hygrothermal aging or not, and a linear response in the initial stage had no significant yield before reaching the peak stress.
4. The peak stress of the 3D four-step braided composites still behaved as a strain rate effect after cyclic hygrothermal aging, however, the dynamic stiffness modulus clearly had no specific regularity.
5. The peak stress and the dynamic stiffness modulus of 3D four-step braided composites after 210 cyclic hygrothermal aging days almost decreased by half when subjected to longitudinal compression.  
Conceptualization, L.L.; methodology, K.X. and W.C.; software, K.X.; validation, K.X., G.L. and Z.Z.; formal analysis, K.X.; investigation, K.X. and L.L.; resources, L.L.; data curation, K.X., G.L. and Z.Z.; writing--original draft preparation, K.X.; writing--review and editing, W.C. and L.L.; visualization, K.X.; supervision, W.C.; project administration, L.L.; funding acquisition, L.L. and W.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 51975279, and 51605218) and Equipment Pre-research field Foundation of Equipment Development Department of China (61409220203).  
The authors would like to thank [PERSON] for his suggestions about image processing.  
The authors declare no conflict of interest.
# An Improvement to the 2-Opt Heuristic Algorithm for Approximation of Optimal TSP Tour
## 4 Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Opt.**} & \multicolumn{3}{c}{**R\&R**} & \multicolumn{3}{c}{**2-Opt++ Algorithm**} \\ \cline{3-8}  & & **Best** & **Err \%** & **Sec** & **Best** & **Err \%** & **Sec** \\ \hline berlin52 & 7542 & 7820.80 & 3.70 & 5.7 & 7544.26 & 0.03 & 0.12 \\ \hline bier127 & 118,282 & 127,550.90 & 7.84 & 30.2 & 119,228.26 & 0.8 & 0.58 \\ \hline ch150 & 6528 & 6712.87 & 2.83 & 52.4 & 6683.37 & 2.38 & 1.00 \\ \hline d198 & 15,780 & 15,937.39 & 1.00 & 74 & 16,160.30 & 2.41 & 1.85 \\ \hline d493 & 35,002 & 35,923.41 & 2.63 & 258 & 36,479.08 & 4.22 & 14.21 \\ \hline eil101 & 629 & 662.89 & 5.39 & 15.2 & 666.93 & 6.03 & 0.31 \\ \hline eil51 & 426 & 444.42 & 4.32 & 2.5 & 432.35 & 1.49 & 0.08 \\ \hline eil76 & 538 & 573.28 & 6.56 & 6.6 & 553.01 & 2.79 & 0.17 \\ \hline fl417 & 11,861 & 13,094.38 & 10.40 & 207 & 12,061.45 & 1.69 & 8.46 \\ \hline gil262 & 2378 & 2422.93 & 1.89 & 108 & 2462.66 & 3.56 & 2.63 \\ \hline kroa100 & 21,282 & 21,559.42 & 1.30 & 15.8 & 21,522.49 & 1.13 & 0.31 \\ \hline kroa150 & 26,524 & 26,952.53 & 1.62 & 46.8 & 26,635.40 & 0.42 & 0.77 \\ \hline kroa200 & 29,368 & 30,231.95 & 2.94 & 73 & 30,031.72 & 2.26 & 1.39 \\ \hline krob100 & 22,141 & 22,746.89 & 2.74 & 15 & 22,295.99 & 0.7 & 0.33 \\ \hline krob150 & 26,130 & 26,237.26 & 0.41 & 48 & 26,568.98 & 1.68 & 0.76 \\ \hline kroc100 & 20,749 & 21,154.50 & 1.95 & 15 & 20,815.40 & 0.32 & 0.36 \\ \hline krod100 & 21,294 & 21,842.21 & 2.57 & 17.5 & 21,813.57 & 2.44 & 0.31 \\ \hline kroe100 & 22,068 & 22,413.32 & 1.56 & 14.8 & 22,299.71 & 1.05 & 0.34 \\ \hline pr107 & 44,303 & 63,228.97 & 42.72 & 19 & 46,066.26 & 1.08 & 0.41 \\ \hline pr124 & 59,030 & 70,108.17 & 18.77 & 26 & 59,667.52 & 1.24 & 0.71 \\ \hline pr136 & 96,772 & 104,690.06 & 8.18 & 36 & 97,971.97 & 2.25 & 0.84 \\ \hline pr144 & 58,537 & 69,368.47 & 18.50 & 42 & 59,854.08 & 0.46 & 0.84 \\ \hline pr152 & 73,682 & 79,805.00 & 8.31 & 46 & 74,020.94 & 2.11 & 0.96 \\ \hline pr226 & 80,369 & 88,804.29 & 10.50 & 99 & 82,064.79 & 0.82 & 2.28 \\ \hline pr264 & 49,135 & 58,608.78 & 19.28 & 105 & 49,537.91 & 3.78 & 3.75 \\ \hline pr299 & 48,191 & 53,950.30 & 11.95 & 116 & 50,012.62 & 3.27 & 4.71 \\ \hline pr439 & 107,217 & 120,717.01 & 12.59 & 188 & 110,723.00 & 1.83 & 11.63 \\ \hline pr76 & 108,159 & 110,467.08 & 2.13 & 6.5 & 110,138.31 & 0.94 & 0.23 \\ \hline rat195 & 2323 & 2378.49 & 2.39 & 67 & 2344.84 & 6.68 & 1.80 \\ \hline rat99 & 1211 & 1231.29 & 1.68 & 13 & 1291.89 & 1.65 & 0.40 \\ \hline rd100 & 7910 & 8046.29 & 1.72 & 14.8 & 8040.52 & 1.12 & 0.38 \\ \hline st70 & 675 & 693.37 & 2.72 & 5.2 & 710.51 & 1.96 & 0.21 \\ \hline ts225 & 126,643 & 142,681.07 & 12.66 & 82.5 & 129,125.20 & 1.38 & 2.28 \\ \hline tsp225 & 3916 & 4371.03 & 11.62 & 83.2 & 3970.04 & 3.52 & 2.27 \\ \hline u159 & 42,080 & 49,734.95 & 18.19 & 47 & 43,561.22 & 2.51 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of 2-Opt++ algorithm with _R_& \(R\) in time and error.
# Sensing and Device Neighborhood-Based Slot Assignment Approach for the Internet of Things
## 4 Proposed Neighborhood-Enabled TDMA Approach
### Device Mobility in the Operational IoT Network
Additionally, every server \(S_{j}\) is bounded to make sure that a fair slot allocation policy is adopted for every contestant device \(C_{i}\). The proposed TDMA-enabled algorithm that is presented below has built in support for the mobility of member devices \(C_{i}\) from the coverage area of one server \(S_{j}\) to another server \(S_{j+1}\) in the operational IoT network. ```
0: TDMA for Mobile Device \(C_{i}\)
0: T Allocation of Free Slots to Devices (Mobile) \(C_{i}\)
1:\(Member-Devices\leftarrow\textbf{Null}\)
2:\(NonMember-Devices\leftarrow\textbf{Null}\)
3:\(\bigtriangleup\gets 30\) ms
4:\(j\) (Variable)\(\leftarrow\) Server Module \(S_{j}\)
5:\(i\) (Variable)\(\leftarrow\)\(C_{i}\)
6:for Server \(S_{j}\in IoMTs\)do
7: Generate \"\(MSG_{Q}\)\"
8: Set value \"\(S:0,M:0,L:0,J:0\)\"
9: Broad-Cast \"\(MSG_{Q}\)\"
10:endfor
11:for every member or neighbor \(C_{i}\in S_{j}\)do
12:if\(C_{i}\) has data to send then
13: Update \"\(MSG_{Q}\)\"
14: Set value \"\(S:1,M:0,L:0,J:0\)\"
15:endif
16:if\(C_{i}\) needs multiple slots then
17: Update \"\(MSG_{Q}\)\"
18: Set value \"\(S:0,M:1,L:0,J:0\)\"
19:endif
20:if\(C_{i}\) is interested to move out then
21: Update \"\(MSG_{Q}\)\"
22: Set value \"\(S:o,M:0,L:1,J:0\)\"
23:endif
24:if\(C_{i}\) has moved and is interest to send data then
25: Update \"\(MSG_{Q}\)\"
26: Set value \"\(S:0,M:0,L:0,J:1\)\"
27:endif
28: Set Destination \"\(S_{j}\)\"
29: Backoff Timer = rand(0-100 ms)
30: Broad-Cast \"\(MSG_{Q}\)\"
31:endfor
32:for every \(S_{j}\in IoT\) do
33:if (\(MSG_{Q}:m\)) then
34: Assign multiple slots if available
35:elseif (\(MSG_{Q}:J\)) then
36: Assign slot if available
37:elseif (\(MSG_{Q}:L\)) then
38: Slot is available
39:endif
40:endfor
41:return Time Slots based on Neighborhood Information for Mobile Devices
```  
**Algorithm 2** Proposed TDMA algorithm for mobile devices in the IoT Networks
## 2 Methodology
### Proposed Tracking Strategy  
The present proposal increases the useful life of solar tracking systems by measuring parameters and making decisions in real-time, where the integrated system must consist of a two-axis solar tracker in azimuth-elevation configuration, a monitoring device, and a photovoltaic system. Figure 2 shows the flow chart of the tracking strategy. The monitoring device is responsible for recording the real-time energy parameters of the photovoltaic system and the tracking process. In addition, it calculates the energy balance with the recorded data. The solar tracking system is responsible for maximizing energy capture by the photovoltaic system due to its alignment with the sun's position throughout the day and the reduction of tracking error by implementing control strategies and actual measurement with a solar pointing sensor. Based on real-time measurement, the decision-making process allows for maximizing the energy gain and reducing the number of steps to move simultaneously, considering the energy consumption due to the monitoring and tracking action. The decision-making process is described in Algorithm 1 and begins with the calculation of the solar path with an existing algorithm such as the Solar Position Algorithm [33], PSA [34], ENEA [35], and Cooper Equations [36], among others. In the general form, the location of the sun can be calculated in terms of azimuth angle (\(\beta\)) and elevation angle (\(\gamma\)), see Figure 3, expressed as follows:  
\[\sin\beta\ =\ \cos L\cos\delta\cos H+\sin L\sin\delta \tag{7}\]  
\[\sin\gamma\ =\ \frac{\cos\delta\sin H}{\cos\delta} \tag{8}\]  
where \(L\) is the system latitude, \(\delta\) is the declination angle, which varies from year to year, and \(H\) is the hour angle. The trajectory must be segmented into equal steps from sunrise to sunset as described in the previous section, according to [29]. The number of points will depend on the acceptance angle, based on [37], and the reduction of the electrical power at the maximum point can be neglected for angles of incidence less than \(5^{\circ}\) in photovoltaic technology. Thus, for the path segmentation is recommended to use \(1^{\circ}\) to \(2^{\circ}\) for each step. Figure 2: Flowchart of the proposed tracking strategy for lifetime improvement of solar tracking systems. Therefore, the segmented trajectory is expressed by the vectors \(\bar{\beta}~=~{}[\beta_{1},\ldots,\beta_{k},\ldots,\beta_{n}]^{T}\) and \(\bar{\gamma}~=~{}[\gamma_{1},\ldots,\gamma_{k},\ldots,\gamma_{n}]^{T}\), where \(n\) is the last point of the solar path. Figure 3 shows the solar path schematic considering the azimuthal and elevation angles for the \(k\)-point.
# An Improved Forwarding of Diverse Events with Mobile Sinks in Underwater Wireless Sensor Networks
## 6 Performance Evaluation  
We have considered the deep underwater environment of volume 100 m \(\times\) 100 m \(\times\) 1000 m in which 224 nodes are randomly deployed. Analytical calculations are programmed in MATLAB using channel model equations. Generally in UWSN applications can be divided into two major groups on the basis of traffic generation. In some applications only a fraction of all deployed nodes are event sensing nodes which can generate data packets. The rest of the nodes are used to forward or broadcast that data. In other group all the deployed nodes can sense and communicate equally. The number of nodes in former group can be fixed and preselected based on their depth, or can be varying and adaptively selected based on some threshold value of sensed attribute. We have chosen the second group in our simulations in which all the deployed 224 nodes are data generating nodes and also act as forwarder to the received data packets. In simulation scenario each node is allowed to generate exactly one packet in one round.  
Simulation setup for random event generation of ESDR can be with many different possibilities. This number can be fixed or varying (as in present case we have chosen fixed number of node in each type). For example in this scenario nodes are selected randomly as critical, very critical and normal. This election is repeated after 100 rounds; however, it is ensured that total number of nodes in each type remains constant. So every time, a network ratio of very critical, critical and normal nodes is constant and it remains uniform for the whole lifetime of network. One can argue about varying numbers of delay sensitive nodes because who knows how many events can be delay sensitive. However, in order to simplify the analysis and simulation setup, so far a constant ratio of delay critical nodes is used.  
We have compared the performance of ESDR with DBR, EEDBR and iAMCTD. In order to ensure fair comparison all the comparing schemes are implemented in same sensing volume. Necessary parameters with their values are given in Table 4.