esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Future Directions  
As it is often repeated, routing is really a process located at a vital point of communication. The fact that routing techniques can be managed with full efficiency is a revolution in communication. However, it has always been a difficult process to manage. Although there are existing and used routing protocols, there is a need for new routing approaches that canboth better adapt to new technologies and better integrate. With increasing demand, there is an explosion in communication technologies. The demands on the user, the width of the coverage area, accessibility from anywhere, accessibility at any time, and the integration of 3+ areas into each other show that the importance of both 6G networks and satellite networks will increase rapidly. For this reason, there is no suspicion that 6G networks and satellite networks will not become the main hot topic areas of research in the future. In order for the networks that will be majorly considered to meet the needs, the topics of routing in 6G and satellite networks will also be one of the hot research topics. It is essential that routing networks contain the following parameters as a priority:  
* Adaptability of the dynamic alteration of network anatomy: Both 6G networks and satellite networks are networks with a dynamic network topology. For this reason, routing algorithms need to be developed to adapt to this dynamic structure in order to ensure stability.
* Invulnerability: Both 6G networks and satellite networks are complicated networks. These networks can be exposed to various attacks. It is possible that this will remain the case in the future. In the future, it is envisaged that many processes will be carried out based on satellite networks. In order for the routing protocols to be prepared for possible adverse situations, survivability must be taken into account.
* High efficiency: Efficiency is becoming increasingly important in all areas. In order to better manage resources, improvements should be made to make the system work more efficiently.
* Meeting multiple QoS needs. The key commitments of 6G networks are end-to-end low latency and data transmission at high speeds, as well as mobility. So, the future networks are very diverse in terms of QoS. For this reason, routing protocols should also be able to cover this diversity.
* Adaptability to changes: Routing protocols need to be more visionary to meet the future. As of the 2000s, space studies have gained rapid momentum. Routing networks also need to be managed effectively and flexibly in the face of developments.  
The methods and approaches that can meet the needs of networks that will require smarter, more efficient, sustainable, wide coverage, low latency, fast, and flexible routing in the future are detailed below.
# Effects of Wave-Induced Processes in a Coupled Wave-Ocean Model on Particle Transport Simulations
## 2 Methods  
### Models and Set-Up  
The [PERSON] coupled coastal model system (GCOAST) [30; 31] was built upon a flexible and comprehensive coupled model system, integrating the most important key components of regional and coastal models. GCOAST encompasses (i) atmosphere-ocean-wave interactions, (ii) dynamics and fluxes in the land-sea transition, and (iii) coupling of the marine hydrosphere and biosphere. In our study, we used the GCOAST circulation, wave and drift model components to investigate the role of coupling in particle transport simulations in the North Sea. Those particles can be considered, for example, as simple representations of either oil fractions, fish larvae or search-and-rescue objects [32; 33]. The wave-current interaction processes are momentum and energy sea state dependent fluxes, wave-induced mixing and Stokes-Coriolis forcing.
## 3 Preliminaries
### Imbalanced Energy Consumption in HCR  
In this section, we use simulations to exploit the imbalance of energy consumption in HCR. The residual energy ratio is used to demonstrate the degree of energy imbalance, which is defined as the ratio of residual energy to initial energy when the first dead node appears. At first, simulations are implemented in the network with its area varying from 200 \(\times\) 200 m\({}^{2}\) to 400 \(\times\) 400 m\({}^{2}\), and the node density is set as \(0.01\) node/m\({}^{2}\). The sink resides at coordinate \((0,0)\). \(R_{t}\) is set as \(70\) m, and \(R_{c}\) varies from \(20\) m to \(70\) m. The simulation results are given in Figure 1.  
As shown in Figure 1, the residual energy ratio increases with the growth of network area and decreases as the \(R_{c}\) enlarges. When the side length of the network is \(400\) m and \(R_{c}=70\) m, the residual energy ratio is near to \(90\%\). When the side length is \(200\) m and \(R_{c}=70\) m, there is still over \(50\%\) energy left in the network when the first dead node appears. This indicates that there is a severe imbalance of energy consumption in HCR.  
To further exploit the reason for the imbalanced energy consumption, we decided to study the distribution of residual energy with different \(k\) and \(d_{toEdge}\) (the definitions are given in Section 3.2.1). In this simulation, the side length of the network is set to \(200\) m, and the clustering range \(R_{c}\) is set as \(70\) m. The results are given in Figure 2.  
Figure 2 demonstrates an interesting result that the imbalance of energy consumption in HCR almost appears in \(\mathcal{R}(1)\). In \(\mathcal{R}(1)\), the residual energy ratio varies from \(67\%\) to \(28\%\) with the growth of \(d_{toEdge}\). While in other rings, the residual energy ratio keeps stable at about \(52\%\) and has little relation to \(d_{toEdge}\). This is caused by the gradient routing strategy adopted in HCR. Each CH has to forward its data to  
Figure 1: The residual energy ratio with different \(R_{c}\).  
the CH, which has a smaller gradient. For the CHs with gradient \(k\geqslant 2\), their transmission ranges are relatively stable based on the gradient field. However, all of the CHs with gradient \(k=1\) have to transmit their data to the sink directly. Due to the difference in node location, the transmission range in \(\mathcal{R}(1)\) is variable, and that results in the imbalance of energy consumption.
## 2 Study Area and Data Sources
### Selection of Indicators and Data Sources  
#### 2.2.1 Selection of Indicators  
Eco-efficiency reflects the transformation of input factors such as capital, labor, energy, and land into expected indicators related to economic growth and non-expected indicators such as pollutant emissions [48]. [PERSON] et al., selected human capital, industrial structure, import and export dependence, economic growth level, urbanization rate, and FDI level as input indicators [49]. [PERSON] et al., selected energy, materials, equipment, R&D, and services of various production systems as input indicators, and waste and product benefit data and other manufacturing system-related data as output data [50]. [PERSON] et al. selected input indicators including labor, capital, land, energy, and water resources; expected indicators were economic output and general public budget revenue; and wastewater, waste gas, and solid discharge were taken as non-expected output [51].  
In the data calculation, the input indicators selected in this study include labor force, energy, land, and water resources. The labor force is represented by the number of employed people in each city at the end of the year, the energy is represented by the electricity consumption of the whole society, the water resource is represented by the water supply of the whole city, and the land is approximately represented by the urban construction land. The expected output is the GDP deflator of each city. With reference to [PERSON]'s practice [45], the year 2003 of the study area is taken as the base period for deflating. Considering that industrial wastewater is the main pollutant discharged in cities [52], industrial wastewater is introduced as an undesirable indicator, and SO\({}_{2}\) discharged by various cities is introduced as an undesirable indicator to reflect air pollution.  
In terms of influencing factors, with reference to existing research results [53; 54], [PERSON] et al. found an inverted U-shaped relationship between per capita income and per capita land consumption using a panel quadratic log function regression model [55], and [PERSON] et al., found an inverted U-shaped or N-shaped relationship between per capita GDP and sulfur dioxide and wastewater emissions using panel quadratic and cubic function fixed effects models [56]. Therefore, the level of economic development (logarithm of per capita GDP), industrial structure (proportion of output value of tertiary industry in GDP), cultural and educational level (proportion of education expenditure in GDP), and scientific and technological development level (proportion of scientific research expenditure in GDP) were selected as influencing factors to explain the time evolution law and spatial  
Figure 1: Overview map of the study area.  
distribution of ecological efficiency in the process of urban development. The data features and unit information are shown in Table 1.
## 5 Simulation Results
### VND Method  
In this section, the proposed VND3 BA and VND2 BA are compared with VNDH12, VNDH22, and VNDH32, proposed by [PERSON] et al. These are 3 different versions of the VND method using H12, H22 and H32 to generate the initial solution. The VND method uses product local search to improve the product sequence and part local search to improve the part sequence for each product. Tables 13 and 14 show the computational results and CPU time for the 5 VND algorithms.  
As the table shows, VND3 BA and VND2 BA perform significantly better than VNDH12, VNDH22 and VNDH12 in terms of solution quality and computational effort. The best overall average RPI value of 15.233% for all instances obtained by the existing VND algorithm is more than three times higher than the best overall average RPI value obtained by VND3 BA and VND2 BA. For all values of \(n\), \(m\), \(f\) and \(s\), the proposed VND algorithm produces better results than the existing VNDH12, VNDH22, and VNDH32. The VND method shows stable advantages without considering the different \(n\), \(m\), \(f\), and \(s\) involved. The proposed VND algorithm is also very fast, with an overall average CPU time of 0.009 and 0.057 s for VND3 BA and VND2 BA, respectively, compared with an overall average CPU time of more than 5 s for the existing algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**RPD**} & \multicolumn{6}{c}{**Algorithms**} \\ \cline{2-7}  & & **VNDH12** & **VNDH22** & **VNDH32** & **VND3 BA** & **VND2 BA** \\ \hline \multirow{4}{*}{\(n\)} & 100 & 19.177 & 14.919 & 16.791 & 4.656 & 4.365 \\  & 200 & 16.752 & 16.180 & 19.497 & 4.957 & 4.423 \\  & 500 & 12.746 & 14.599 & 18.129 & 4.743 & 4.074 \\ \hline \multirow{4}{*}{\(m\)} & 5 & 17.606 & 15.753 & 18.614 & 4.181 & 3.977 \\  & 10 & 16.616 & 15.345 & 18.731 & 5.015 & 4.520 \\  & 20 & 14.463 & 14.599 & 17.072 & 5.151 & 4.365 \\ \hline \multirow{4}{*}{\(f\)} & 4 & 13.095 & 11.824 & 13.706 & 4.462 & 3.919 \\  & 6 & 16.859 & 15.627 & 18.449 & 4.821 & 4.317 \\  & 8 & 18.731 & 18.246 & 22.262 & 5.063 & 4.627 \\ \hline \multirow{4}{*}{\(s\)} & 30 & 14.608 & 14.928 & 18.246 & 5.160 & 4.695 \\  & 40 & 15.850 & 15.423 & 18.449 & 4.860 & 4.326 \\  & 50 & 18.217 & 15.355 & 17.722 & 4.336 & 3.841 \\ \hline \multicolumn{7}{c}{average} & 16.226 & 15.233 & 18.139 & 4.784 & 4.287 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Average RPD value of VND method.  
Figure 3: Mean plot and 95% LSD interval of the bat algorithm at \(C=60\).
## 1 Introduction
In terms of the real-time measurement of the spatial target position and pose using monocular cameras, there have been many studies [27; 28] on the pose measurement of robots and aircraft, including studies on target design, the feature extraction of marker points, methods of measuring the positions and attitude angles of cooperative targets, and methods of measuring the positions and attitude angles of non-cooperative targets. Research on visual pose measurement technology in the literature [29] has mainly focused on algorithms, with much success. An algorithm was successfully applied to the Chinese space laboratory Tiangong-2, accurately guiding the robotic arm to complete various spatial experimental tasks, such as grasping floating objects, disassembling insulation materials, screwing electrical connectors, and screwing electric tools. The NASA Insight Mars probe [30], equipped with BMW markers on a seismometer to measure the attitude of the Mars probe relative to a seismometer, was launched in May 2018. The above research has mainly been applied to attitude measurements of aircraft and spacecraft, and there have been few reports on attitude measurements of agricultural machinery. Reference [2] analyzed the mechanism and modeled the dynamics of a rigid-flexible integrated leveling shovel assembly of a paddy-field laser leveling machine. Two high-speed cameras and matching high-speed image analysis software were used to measure the center of mass position and attitude angle of the leveling shovel, thus verifying the dynamic model simulation results. Although this method provides parameter measurements, it requires two high-speed cameras. In addition, owing to the limitations of high-speed image analysis software, the method can simultaneously measure the motion changes of only five points, which is far from meeting the requirements of parameter measurements. Against the above background, this article combines the foundations of previous research on pose measurement to theoretically derive a method of measuring the absolute pose of a moving rigid body and presents a complete implementation of the method. The method is validated in terms of its feasibility and experimental repeatability for different positional accuracies using a high-speed camera with a variable focal length.
## 4 Performance Evaluation
### Accuracy Evaluation  
A cumulative distribution function (CDF) plot was selected to gain a deeper insight into the distribution of estimation errors across various Doppler values and SNR levels. Figure 13 shows the CDF plot of these estimation errors. The CDF was calculated based on randomly generated Doppler values from the low (100 Hz-2 kHz), medium (2-10 kHz),and high (10-15 kHz) ranges, with SNRs spanning from \(-12\) dB to 0 dB in 1 dB increments, and the calculations were repeated 1000 times each for a total of 39,000 trials.  
A steeper slope in the CDF curve indicates more occurrences of smaller errors, particularly for values less than 100 Hz, with a 99.5% probability. This reflects the algorithm's ability to achieve precise Doppler estimation. Additionally, the plot reveals instances of larger errors occurring less frequently, with values of up to 1000 Hz having a 0.5% chance of occurring. This, however, can be addressed via an ensemble average over a few extra pulses.  
To further gauge the accuracy of the proposed module, we rely on box plots. These plots allow us to examine the central tendency and spread of Doppler estimation errors at low, medium, and high Doppler values with a fixed SNR of \(-3\) dB. In each case, the Doppler frequency to estimate is randomly selected and then tested 100 times. Figure 14 shows the results obtained. Remarkably, our algorithm consistently exhibited excellent performance across all three categories, with the absolute median estimation error not exceeding 30 Hz. It is important to note that the proposed algorithm has the ability to detect both positive and negative differences in all cases. The algorithm is designed to consistently check for these differences to prevent subtracting a value from a negative result, which could otherwise lead to an increase in the output error.  
Table 2 provides a quantitative representation of the overall accuracy achieved by incorporating the proposed module into the radar system. The accuracy assessment was conducted based on the predetermined threshold value of 250 Hz (see Section 4.2), representing the maximum allowable frequency deviation to ensure the extraction of the main signal features without distortion. Additionally, the module was tested across a range of SNR values between \(-12\) dB to 0 dB to provide a more comprehensive evaluation of the system's performance. The obtained results demonstrated notable accuracy levels, with a 100% accuracy rate being achieved at low Doppler values, 98.23% at medium Doppler values, and 96.32% at high Doppler values. These findings indicate the efficacy of the proposed module in maintaining high accuracy levels across a wide range of Doppler scenarios and SNR values, thereby validating its suitability for the intended application.  
Figure 13: Cumulative probability of the estimation errors at different SNR values ranging from \(-12\) dB to 0 dB with \(1\) dB increments. The dash line, representing the theoretical CDF of the normal distribution, is used as a reference.
## 3 Case Study
### Performance Comparison Analysis of Different Solar Irradiance Forecasting Models
#### 3.5.1 Comparison Analysis of Sunny Days
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Forecasting Models** & **MAE** & **RMSE** & **R** \\ \hline DWT-CNN-LSTM (WD level 1) & 23.174 & 36.548 & 0.991 \\ CNN-LSTM & 22.560 & 36.226 & 0.992 \\ CNN & 22.773 & 36.763 & 0.992 \\ LSTM & 24.497 & 37.049 & 0.990 \\ Manually extracted features-ANN & 43.045 & 54.796 & 0.985 \\ ANN & 23.533 & 36.888 & 0.989 \\ Persistence forecasting & 30.271 & 41.742 & 0.987 \\ ARIMA & 32.148 & 40.174 & 0.988 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The performance comparison of different sunny day’s forecasting models using the dataset of Elizabeth City State University. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Forecasting Models** & **MAE** & **RMSE** & **R** \\ \hline DWT-CNN-LSTM (WD level 1) & 17.131 & 34.299 & 0.992 \\ CNN-LSTM & 16.573 & 32.411 & 0.993 \\ CNN & 16.222 & 33.178 & 0.993 \\ LSTM & 17.032 & 33.294 & 0.992 \\ Manually extracted features-ANN & 30.187 & 44.101 & 0.981 \\ ANN & 17.869 & 34.783 & 0.990 \\ Persistence forecasting & 21.034 & 38.341 & 0.984 \\ ARIMA & 20.433 & 37.781 & 0.987 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The performance comparison of different sunny day’s forecasting models using the dataset of Desert Rock Station. Figure 7 shows the actual and forecasted solar irradiance curve on sunny day pattern using dataset of Elizabeth City State University.
# Improving Waste Management System Efficiency and Mobility with Efficient Path MANET
## 4 Experiment Results and Discussion
### Experiment 1
#### 4.1.2 Scenario 2  
The results of this scenario demonstrated the effectiveness of the proposed intersection GA-based routing algorithm when subjected to priority constraints. The results of the same experiments showed that the selected paths from the source node 0 to other nodes in the same network for different network topology sizes are shown in the results for the same experiments. The available paths from node 0 to other nodes are shown in Figure 8. The calculated intersection activation values were presented based on constraint priority.  
Figure 8: Calculated intersection activation values based on the constraint priority. (**a**) Available paths with 20 nodes; (**b**) available paths with 30 nodes; (**c**) available paths with 40 nodes; (**d**) available paths with 50 nodes.  
Similar to Scenario 1, Scenario 2 exhibited an increase in the number of available paths when the number of nodes was increased in the network. However, the calculated intersection activation values in Scenario 2 were less than those in Scenario 1. The efficient path has a low activation value. Thus, all the available paths in Scenario 2 are more efficient than those in Scenario 1.  
The constraint values that can produce efficient paths with priority are shown in Figure 9. With respect to different network sizes, these values represent the bare minimum cost of all available paths to all network nodes starting from the source node 0 for each network size.  
Figure 9: Constraint values that can produce efficient paths with priority. (**a**) Available paths with 20 nodes; (**b**) available paths with 30 nodes; (**c**) available paths with 40 nodes; (**d**) available paths with 50 nodes.  
The two scenarios in experiment 1 showed that the number of available paths to each destination node is increased when the network size (the number of devices) is increased and may offer a good quality connection amongst the neighbors. The selected paths using intersection GA-based routing algorithm without priority have 50% of the maximum constraint value. However, the efficiency of path selection in Scenario 2 with intersection GA-based routing algorithm with priority is increased by 20% compared with that in Scenario 1. Therefore, the efficiency of the selected path is increased as the number of nodes is increased, and the algorithm performs effectively. Moreover, the findings indicated that the intersection GA-based routing algorithm is reliable in finding the most efficient path with other alternative efficient paths based on multiple constraints when chromosome intersection is employed.
## 2 Network Model and Problem Description
### Closed-Loop Control Model of an Awareness Combat Network  
In a specific geographical area, nodes perform closed-loop topology control, discovering and maintaining the routing according to the needs of the transmitted data packet and its link state. The information transmitted in the awareness combat network is regarded as a continuous fluid, the principle of the routing algorithm is studied by using the control theory, and a closed-loop routing control model is formed, as shown in Figure 1.  
Figure 1: Closed-loop routing control model of the awareness combat network.  
In Figure 1, \(\mathrm{r(t)}\) represents the existing routing, \(\mathrm{y(t)}\) represents the routing discovered and maintained by the awareness combat network using the routing algorithm designed in this paper, \(\mathrm{e(t)}\) represents the routing error, and \(\mathrm{u(t)}\) represents the result of the routing algorithm, and they are all used for controlling the awareness combat network. The nodes in the network use the routing algorithm designed in this paper to calculate the transmission power required by the node according to the routing error so as to control the topological structure of the awareness combat network within a specific range. The routing under this topological structure is negatively fed back to the input of the routing algorithm and the routing algorithm is adjusted online accordingly according to the QoS policy, finally forming a closed-loop control structure.  
This is a conceptual model, including perception by combat network dynamic, real-time routing changes, so there must be an adjustment strategy, but adjustment is gradual, as reflected by the effect of the routing computation load.