esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Data Storage and Information Discovery in the WSANs-Based Edge Computing Systems
###### Abstract
In a nutshell, data are stored in WSNs mainly based on the following three models: (1) data are stored among the sensor nodes in a distributed manner [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]; (2) data are stored at the static Sink node/nodes intensively [29; 30; 31; 32; 33]; (3) data are collected and stored at some mobile elements [34; 35; 36; 37; 38; 39; 40; 41], such as mobile Sinks. For the first model, queries should be launched across the sensor nodes to search for and discover the needed information; for the second and third models, the needed information can be discovered directly at the static Sink node/nodes or the mobile elements. Although these models work well in WSNs, they may not be fit for WSANs. First of all, a storage model that allows all the sensing data to be stored among the sensor nodes may not be suitable for WSANs. Unlike WSNs, which mainly consist of sensor nodes, WSANs are heterogeneous and composed of not only the sensor nodes but also the actuators. The actuators have more resources and longer communication ranges than the sensor nodes. Thus, if all data are stored at sensor nodes, there can be a waste of the resources and the capabilities of the actuators. Moreover, the data-storage and information-discovery schemes based on this model in WSNs have not taken into account the problem of coordination between the sensor nodes and the actuators, which only exists in WSANs. Secondly, the centralized storage model, which relies on static Sink nodes, is not applicable to WSANs. The reason is because: on the one hand, the storage schemes proposed for WSNs based on this model still lack negotiation and the coordination mechanisms between the sensor nodes and the actuators; on the other hand, the actuators in WSANs are usually mobile and can respond to the events that may happen in other regions. Finally, the data collection model utilizing mobile Sinks or elements are not unsuitable for WSANs either. In WSANs, the actuators have to move to the locations where events occur to take some actions. Because the events may happen anywhere at any time, the destinations for the actuators to move to can be random, and the frequencies for actuators to move are also random. Moreover, because a quick response is required in WSANs to respond to the events occurring in the monitored field, the actuators need to move to the destinations directly as fast as possible. Thus, it is impossible to design the moving paths and the time duration to stay in each of the locations for the actuators to collect data, as the mobile elements typically do in WSNs. From the analysis presented above, we can see that it is non-trivial to migrate the data-storage and information-discovery schemes developed for WSNs directly to WSANs, and specially designed novel schemes need to be developed for the WSANs-based Edge computing systems.
## 3 Need for Dynamic Consent of Personal Data
### Dynamic Consent and Privacy Agentof Use of Analyzed Result  
By the mechanism explained above, if the information that can be extracted from personal data can expand due to machine learning or subsidiary statistics, consent to provide personal data must also change dynamically. Therefore \"dynamic consent\" is already proposed. However, the choice of consent is also growing and becoming complex rapidly. Therefore there is a need for the \"privacy agent\" to assist the data subject in managing such consent.  
Figure 2: Identification of individuals with Sherlock threat: In this case, with statistical distributions (**a**,**b**), statistical distribution of (y1, y2) in the mapped space (**c**) will be narrowed for corresponding \(x_{1}\) and attribute \(x_{2}\). Individuals p, q, r, and s (in the graph (**c**)) can be identified in this case.  
[PERSON]'s Personal AI agent might be helpful for the data subject to manage personal data. Those AI agents are not actually necessary to be AI as long as they represent the benefit of the data subject. However, it is not sufficient to manage the provision of personal data. Even if they are employed to manage the provision of the data subject, if the resulting information of the analysis once becomes public for permanent use, uncertainty about extracting privacy data from the public domain information does not go away.  
However, if the license to use personal data waits until the time of use of derivative information obtained from the provided data instead of the permission to provide data, and \"dynamic consent\" is made when it is clear what kind of derivative information is to be provided, to whom, and what kind of analysis, there is minimum uncertainty as to whether the results will be used; additionally, if the data subject feels uncomfortable providing such information, they can stop providing the data at any time. Therefore, the data subject can reduce anxiousness about their data from the use of unexpected analysis results in the future.
# Novel Methodology for Condition Monitoring of
## 3 Methodology
### Fault Identification  
Once the most significant and discriminant information has been highlighted by LDA, all the considered conditions are represented in a 2D space, where the linear separation between classes is retained as much as possible. After that, automatic fault identification is carried out by means of an Artificial Neural Network (ANN); as a result, the final diagnosis outcome results in the classification of four different conditions; HTL, and 25%, 50%, and 75% wear in the gears. Since the classification task is facilitated by considering the dimensionality reduction through LDA, the structure of the ANN classifier is based on a classical structure, which only considers the input layer, a single hidden layer, and the output layer.
## 2 The Establishment of CWM-UM Evaluation Modeling
### Determination of Index Weight
#### 2.1.3 The Method of Calculating Combination Weight  
1. Rationality analysis of combination weighting  
Before combining the subjective weight \(\alpha_{i}\) and the objective weight \(\beta_{i}\), their rationality should be analyzed to ensure the scientific and rational weights of the construction quality evaluation indicators for concrete structures in hydraulic tunnels. The specific process is as follows:  
The subjective and objective weights of evaluation indicators are sorted, as shown in Table 2, where \(\alpha_{i}^{\prime}\) is the sorted value of \(\alpha_{i}\) and \(\beta_{i}^{\prime}\) is the sorted value of \(\beta_{i}\). The sorting values range from 1 to \(n\). The index with the largest weight value is sorted as 1, and the index with the smallest weight value is sorted as \(n\).  
The correlation between the two sets of variables can be tested using [PERSON]'s consistency coefficient \(\rho\)[19] as shown in Equation (14).  
\[\rho=1-\frac{6}{n(n^{2}-1)}\sum\limits_{i=1}^{n}\left(\alpha_{i}^{\prime}-\beta_ {i}^{\prime}\right)^{2} \tag{14}\]  
where the value range of \(\rho\) is \([-1,1]\). When \(\rho\in[-1,0)\), it means that there is a lack of consistency between subjective and objective weights; when \(\rho=0\), it means that the correlation between subjective and objective weights is \(0\); and when \(\rho\in(0,-1]\), it indicates that there is consistency between subjective and objective weights, which can be combined and assigned.  
Calculating the combination weights  
The weights of evaluation indexes calculated based on the IAHP method and the CRITIC method are \(\alpha_{i}\) and \(\beta_{i}\), respectively. In order to make the combination weight \(\omega_{i}\) as close as possible to \(\alpha_{i}\) and \(\beta_{i}\), the combination weights \(\omega_{i}\) are calculated based on the MIE principle. The calculation model is as follows [20]:  
\[\left\{\begin{aligned} & min\;J(\omega)=\sum\limits_{i=1}^{n}\left( \omega_{i}\ln\frac{\omega_{i}}{\alpha_{i}}+\omega_{i}\ln\frac{\omega_{i}}{ \beta_{i}}\right)\\ & s.t.\sum\limits_{i=1}^{n}\omega_{i}=1,\omega_{i}\geq 0,i=1,2, \cdots,n\end{aligned}\right. \tag{15}\]  
By solving this optimization model based on the [PERSON]'s algorithm, the combination weights can be obtained as follows:  
\[\omega_{i}=\frac{\sqrt{\alpha_{i}\beta_{i}}}{\sum\limits_{i=1}^{n}\sqrt{ \alpha_{i}\beta_{i}}} \tag{16}\]
# A Coordinated Supply Contract for a Two-Echelon Supply Chain Considering Learning Effects
## 3 Basic Supply-Chain Models without Learning-Curve Effects
In this strategy, the manufacturer sets the wholesale price, \(w\), considering production costs and their desired profit margin. It is well known that the wholesale contract cannot lead to channel coordination due to the double marginalization problems. (The coordination can be achieved only for the case of \(w=c\), which is not reasonable in real industries). Double marginalization occurs when each entity in the supply chain makes decisions to maximize its own profit, without considering the overall supply chain's welfare. In wholesale contracts, the manufacturer sets the wholesale price to maximize its profit, and the retailer, in turn, determines the order quantity to maximize its profit. This sequential decision making leads to reduced total supply-chain profit and decreased consumer welfare. To overcome the double marginalization problems, various coordinated supply contracts have been proposed, among which revenue-sharing contracts are one of the most prominent contract models. In the revenue-sharing contract, the sales revenue is shared between the retailer and the manufacturer. Two parameters need to be designed by the manufacturer: wholesale price, \(w\), and the revenue sharing, \(\phi\). At the end of the season, the retailer disposes of unsold products for a salvage price, \(s\). It has been well known that channel coordination can be achieved by the revenue-sharing contract with the parameter set of \(w\) and \(s\) if the relationship \(\frac{\partial p-w}{\partial p-s}=\frac{p-c}{p-s}\) holds [4]. Existing revenue-sharing contracts assume that the per-unit production cost is constant regardless of the production quantity. Supply contracts based on such static assumptions fail to provide channel-coordinated solutions when production costs dynamically vary with increases in production quantity. It will be seen in the forthcoming numerical illustration section that additional benefits can be realized for both manufacturers and retailers when we design supply contracts considering production cost dynamics due to learning-curve effects.
# Receiver-Initiated Handshaking MAC Based on Traffic Estimation for Underwater Sensor Networks +
## 7 Traffic Estimation for TERI-MAC
```
whilenew record \(x_{M+1}\) comes do if\(S_{WIN}\geq K\)then  Move out \(x_{M-K+1},...,x_{M}\) in the sampling window;  Resample from \(\{\hat{x}_{1},...,\hat{x}_{M^{\prime}},x_{M-K+1},...,x_{M}\}\) endif  Add \(x_{M+1}\) in the sampling window;  Build PDF with \(\{\hat{x}_{1},...,\hat{x}_{M^{\prime}}\}\) and records in sampling window; endwhile
```  
**Algorithm 1** Traffic estimation in TERI-MAC. Depending on the traffic estimation designed in Algorithm 1, the receiver estimates how much data to request from each sender and assigns time slots for their data transmissions. We simulated an experiment to show the average traffic of one sender and the channel resource assignment with traffic estimation at the receiver side. In this simulation, we tested the ability of the traffic estimation mechanism used in our work to capture the dynamic traffic and perform accurate predictions. A sequence of random traffic was generated following a Bernoulli distribution and the average traffic load varied from 0 to 2 to manipulate the traffic load variation over time in real applications. The traffic load was the average number of data packets generated per time slot. In addition, we set a sampling window size of \(K=10\) and resampled \(M^{\prime}=5\) from the historical records each time. Figure 8 illustrates that even though a lag existed from the moment of the traffic change and the slot assignment, the traffic estimation followed the trend very well. Furthermore, we computed the channel utilization by calculating the ratio of the time slots utilized for data transmission over the total amount assigned by the receiver. Based on the premise of \(P_{del}=50\%\), the simulation results verified that an overall \(88\%\) channel utilization was achieved when trying to assign the slots to guarantee a \(50\%\) probability of packet coverage in the transmission. This simulation verifies the efficiency of the traffic estimation mechanism.
###### Abstract  
To ensure the security of information exchange in software-defined optical networks, quantum key distribution (QKD) based on quantum mechanics is introduced. However, the slow and valuable process of generating quantum key resources contradicts the high-speed data transmission requirements of optical networks. To address this issue, this paper proposes the CPSR-HQKDN scheme, which takes into account factors such as security requests, key demand, key residual, and key update rates for trusted and untrusted links. This approach improves resource utilization and service efficiency by optimizing the processing order of key requests. Moreover, the routing strategy dynamically adjusts based on the network resource environment, thereby increasing the success rate of key requests. Through simulation experiments comparing the performance of the CPSR-HQKDN routing scheme with existing schemes, it is observed that in high-concurrent scenarios, the CPSR-HQKDN routing scheme can improve the success rate of key requests by at least 5%.  
packet security requirements; routing scheme; quantum key distribution (QKD) +
Footnote †: journal: Applied sciences
# Analysis of Feed Inlet and Optimal Feeding Amount of Waste Ground Film Impurity Removal Equipment
## 1 Introduction
They investigated the laws of bonding and breaking of the internal parts of the agglomerates during collision as well as the effect of collision velocity and other factors on the degree of agglomerate disaggregation. [PERSON] et al. [11] explored the various force relationships between particles as well as between particles and airflow at the microscopic scale. [PERSON] et al. [12] studied morphological changes in sieved material during wet agglomeration and determined the elemental conditions that must be achieved in the relevant operating steps. The agglomeration of residual film-impurity mixtures is a more complex multi-material bonding process than the agglomeration of microscopic particles. During disaggregation, the soil and straw detach from the residual film, ensuring that the residual film remains clean. Therefore, for analyzing the depolymerization of miscellaneous residual film-impurity mixtures, the study of traditional microscopic particle depolymerization is not suitable. During separation in a residual film-impurity mixture wind separator, a large amount of residual film-impurity mixture is not depolymerized during continuous operation. Herein, we conducted simulations and presented insights for addressing this problem. Based on the principle of agglomeration and depolymerization of the residual film-impurity mixture, the key factors affecting the depolymerization were analyzed. Using CFD-DEM fluid-solid coupling simulation, the location of the maximum collision force was determined, and force analysis was conducted for this stage. The maximum collision force was influenced by the feeding volume and inlet structure. Simulations were performed for different types of inlet structures. The maximum collision force of the residual film-impurity mixture agglomerates on the tremmel screen was used as the evaluation index, and the optimum inlet structure and feed rate were determined. Figure 3 shows the flow chart of the complete research process for some mixtures that are difficult to disaggregate. The findings of this study provide theoretical insights for determining the optimal inlet structure and feed rates of various cleaning devices. Figure 3: Flow chart of research content.
# An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks
## 2 Related Work
### Resource Scheduling Method Based on Machine Learning  
Based on the satellite time window characteristics, many researchers design algorithms to solve the scheduling problem of the visual time window constraints [12; 13; 14]. The resource scheduling process can generally be divided into task planning and resource allocation. Some scholars analyze the constraints and build corresponding models for these two aspects [15]. Paper [16] proposes an intelligent resource scheduling system based on the deep neural network (DNN, deep neural network) to solve the long solution time, low efficiency, and high computational cost problem of other scheduling algorithms. A study [17] proposes a learning-based approach (LBA, learning-based approach) to solve the problem of agile earth observation satellite in-flight scheduling. It uses offline training-on-board scheduling mode and uses a large amount of historical data to train classification on the ground offline. Moreover, it embeds the classifier into the onboard greedy construction algorithm. Researchers [18] studied the scheduling of Earth observation satellite tasks with specific time requirements, proposed an automatic scheduling algorithm for state equations based on linear temporal logic (LTL, linear temporal logic), and introduced LTL semantics to automate constraints and time specifications and to formulate the parameters of the task specification appropriately. The research [19] on small satellite networks expands the traditional dynamic programming algorithm based on it. It proposes a finite embedded infinite two-layer dynamic programming framework, transforming the scheduling optimization problem into a discrete Markov decision process (MDP). Papers [20; 21; 22; 23] considered the complex constraints faced in the process of satellite resource scheduling, combined with computational geometry and other theories for modeling, and verified the effectiveness of the model based on data from many instances. Ref. [24] proposed a heuristic algorithm based on deep reinforcement learning which automatically learns multi-satellite scheduling strategies and problem representation. Ref. [25] proposes a graph-based joint scheduling strategy which combines the information in the sensing and transmission phases, makes full use of the satellite network's observation and transmission resources, and maximizes resource utilization.  
Although machine learning methods have better resource scheduling performance, such methods usually incur high time consumption and require more computational resources during the learning process. The methods are difficult to adapt to the demand of high timeliness of resource-constrained spatial information networks.
## 4 Robust Learning-Based GWO Algorithm (RLGWO)
### Robust Tolerance-Based Adjust Searching Direction Mechanism (RTASDM)
\[P_{adjust}=\frac{exp(h)-1}{exp\left(10+\frac{k\times 10}{MaxIt}\right)-1} \tag{17}\]  
As can be seen from Figure 2, the \(P_{adjust}\) is not fixed throughout the course of iterations; rather, its value is updated according to \(h\) and \(k\). When the value of \(P_{adjust}\) is larger than a random number with a range of [0,1], the leader alpha (beta or gamma) will be replaced by another candidate solution, which will continue to guide the hunting group. Algorithm 1, below, shows the details of the approach. It is clear from Figure 2 and Algorithm 1 that the \(P_{adjust}\) depends on \(h\) and \(k\), especially the tolerance value \(h\). When the value of \(h\) is small, \(P_{adjust}\) has a high probability of becoming smaller than the random number; then, the first three best leaders continue to guide the hunting group, and their leading ability will be useful for the next several iterations. When \(h\) increases to the threshold, \(P_{adjust}\) increases dramatically. This indicates that the number of solutions has still not improved over the next several iterations, and the hunting group is more likely to become trapped in a local optimum. Therefore, the value of \(P_{adjust}\) is probably greater than the random number, and a new leader will be used to lead the omega search direction. ```
1:At iteration \(k\)th;
2:Initialize \(h=0\)
3:if\(\sum_{n=1}^{N}\left(f(X_{\omega}^{n})^{k}\right)-\sum_{n=1}^{N}\left(f(X_{ \omega}^{n})^{k+1}\right)\in[-\varphi,\varphi]\) then
4:\(h=h+1\)
5:endif
6:generate a random number in the range of [0,1];
7:if\(\left(\frac{exp(h)-1}{exp\left(10+\frac{k-10}{k\text{min}}\right)-1}>rand()\right)\) then
8:Finding another candidate solution to replace alpha (beta or gamma)
9:endif
```  
**Algorithm 1** Robust tolerance-based adjust searching direction mechanism  
On top of that, \(P_{\alpha}\) is more likely to get close to the global optimum when the number of iterations is increased, especially with numbers of iterations greater than half, as can be seen in Figure 3. Therefore, to ensure convergence, the value of \(h\) should be increased to get rid of the omega and change its search direction.
## 2 Methodology
### Numerical Simulation
#### 2.3.2 Grid Independence Tests  
The ICEM was used to mesh the model with structured grids, which consumed less time and increased the accuracy of the calculation compared to unstructured grids. In addition, the grids near the supply air inlet, exhaust outlet, and the area around the occupants were refined. The grids are shown in Figure 5. The height of the first layer of the grid was determined according to the non-dimensional wall distance (y\({}^{+}\)) [45].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Boundary** & **Boundary Condition** \\ \hline Supply air inlet & Velocity inlet \\ Exhaust outlet & Outflow \\ Window, walls, and ceiling & Constant wall temperature, 28.4 \({}^{\circ}\)C \\ Floor & Constant wall temperature, 27.2 \({}^{\circ}\)C \\ Occupant & Constant heat flux, 60.24 W/m\({}^{2}\) \\ Light & Constant heat flux, 638.89 W/m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Boundary conditions for simulation calculation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Governing Equations** & \(\mathcal{O}\) & \(\Gamma_{\mathcal{O}}\) & \(\mathcal{S}_{\mathcal{O}}\) \\ \hline Mass equation & \(1\) & \(0\) & \(0\) \\ \hline Momentum equation & \(u_{i}\) & \(\mu+\mu_{i}\) & \(-\partial_{p}/\partial_{x}-\rho\beta_{i}\beta(T-T_{0})\) \\ \hline Energy equation & \  
For the grid-independence tests, CFD models with 1,110,000 (coarse), 2,600,000 (moderate), and 3,110,000 (fine) hexahedron grids were selected. With air velocity, temperature, and air inlet spacing of 1.62 m/s, 22.8 \({}^{\circ}\)C, and 650 mm, the temperature and velocity along Sampling Line 1 were compared for the three grids, as shown in Figure 6. It was found that when the grid was changed from moderate to fine, the differences in velocity and temperature were insignificant, whereas the differences between the moderate and coarse grids were large. Thus, the moderate grid was used considering both the prediction accuracy and calculation cost.  
Figure 5: Mesh of computational domain: (**a**) 3D mesh; (**b**) \(Z=1.1\)m.