esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Sensitivity Analysis
#### 3.3.1 Layers  
One of the most important parameters that have a significant impact on the accuracy and the training time of the ANN model is the number of hidden layers. For simplicity, most of the researchers consider only one layer for their ANN model. That said, in this work, the five different numbers of hidden layers, starting from the single-layer model to quintuple-layer one, have been tested to ensure that the best number of hidden layers for the dataset has been chosen for further steps. Also, it is good to note that the values of indices of the best setup of neurons in each number of hidden layers have been considered to perfectly make a decision on which one is the best for further steps of analysis. Also, Levenberg-Marquardt as the training function, Purelin-Tansig as the activation function, and 70% as the training ratio have been considered for this step according to [28].  
Electric Field  
Figure 3 demonstrates that for the CFNN, the more hidden layers a model has, the lower RMSE and the higher R-squared it has. This means the total accuracy of the CFNN model increases by adding more hidden layers, while for the FFNN, after three hidden layers, the RMSE climbs up and the R-squared drops. Therefore, for the FFNN, more than two hidden layers not only does not increase but actually decreases the model's accuracy. This is a good example of the necessity of sensitivity analysis for AI models.  
Figure 2: Flowchart of the sensitivity analysis process.  
Figure 3: Sensitivity analysis on the number of hidden layers in FFNN and CFNN for the electric field. (**a**) RMSE values; (**b**) R-squared values; (**c**) response time.  
In terms of the response time, for both models, it soars after three hidden layers. This means that with respect to the application of the model and the computing resources, an appropriate limit for the response time should be chosen. It is good to note that the reported response time in this paper is the time in which the model is able to predict the fields. Therefore, it may vary based on different computers. In this research, the models have been tested on a computer with Intel(r) Core(tm) i7-4710 HQ CPU with 12 GB RAM.
## 3 Results and Discussion
### Scenario Analysis
#### 3.4.4 Scenario 4 (6 Access Points)  
In this scenario, we obtained the following coverage results (Figure 8d):  
* 52% of the campus area is covered by a very good signal level greater than \(-\)70 dBm;
* 36% of the surface is covered by a good signal level between \(-\)80 dBm and \(-\)70 dBm.  
Figure 9: Methodology overview for best available technologies (BAT), including screening and ranking.  
These access points are active for a period of 10 h per day and consume 720 wh. As a result, 418 g of CO2 in the case where electricity is produced from natural gas and 39.6 g of CO2 in the case where electricity is produced from solar energy. Table 4 summarizes the results obtained for the four scenarios.
# A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services
## 4 Proposed Scheme
### Data Retrieval Process  
Since the process of the user registration in the LBSP is not the focal point of our scheme, we explain the data retrieval process based on the registered user who has obtained the key \(k\) of the pseudo-random function, the key \(k_{AES}\) of \(AES\), the set of \(l\) hash functions \(H\) from the LBSP, and a hash function \(h_{\varphi}\) that used to verify the consistency of the query result in the registration process. When an authenticated user wants to request a query service, s/he could specify a query region by the client-side installed on his/her mobile device (e.g., an app) and send the query region to the LBSP. Subsequently, the LBSP sends the identifier set of fog nodes contained in the query region and a guide set that is used to fix the proposed problems, such as preventing the fog nodes from obtaining accurate statistical information about the frequency of the encrypted outsourced data. Upon receiving the identifiers of fog nodes and the guide set, the user begins to communicate with the corresponding fog nodes and enjoys the outsourced data retrieval service. Herein, since the interaction process between the user side and the fog side is the same, we focus on explaining the interaction process between one user and one fog node \(g_{o}\). In what follows, we first introduce how the LBSP designs a guide set based on the one-to-n lookup table and then shows how the user uses the guide set to request the encrypted outsourced data.
# Dynamic Cost-Aware Routing of Web Requests
## 3 Problem Definition  
Assume an enterprise that manages multiple datacenters (DC) located at different regions. Each location operates under the control of a local Regional Transmission Organization (RTO), so that electricity pricing varies from location to location and possibly also with time, depending on the specific policy in use. A total of \(N\) servers take part in running a common web application across \(L\) locations. In the notation that follows, we use superscripts to denote location and subscripts to indicate elements within a location. Each location has \(n^{i}\) servers, \(1<i<L\). If \(p_{j}^{i}(t)\) is the average power (in kW) consumed by server \(j\) at DC \(i\) and obtained at time \(t\) since a specific epoch \(t_{0}\), the average energy cost per hour is:  
\[E^{i}(t)=\sum_{j=1}^{n^{i}}p_{j}^{i}(t)\ \gamma^{i}(t), \tag{1}\]  
where \(\gamma^{i}(t)\) is the electricity price (rate) at DC \(i\) at \(t\) which remains constant during the last evaluation period (i.e., since the epoch \(t_{0}\) to \(t\)). The average power \(p_{j}^{i}(t)\) may include pro rate the contribution of indirect sources, such as lighting, cooling, and other servers. The average energy cost per server and per hour related to running the web application and observed at time \(t\) is:  
\[x(t)=\frac{1}{N}\sum_{i=1}^{L}E^{i}(t). \tag{2}\]  
While the general problem is to minimize the energy cost \(x(t)\), it is also crucial to consider that long request response times can have a detrimental impact on enterprise revenue. A simple example is an electronic commerce application where a slow system directly impacts transaction rates and, therefore, revenue. However, a similar rationale could be applied to other services. In addition to content quality, system responsiveness can affect the user perception of service quality and therefore impact the size of the user base over time. The exact definition of revenue loss due to response delay depends on application specifics and the needs of the enterprise. We formulate a basic expression for the purposes of our study assuming that revenue loss increases linearly with the average response time observed at each DC \(i\):  
\[D^{i}(t)=\delta\ d^{i}(t), \tag{3}\]  
where \(\delta\) is the revenue loss factor and \(d^{i}(t)\) is the average response time observed at location \(i\). The average revenue loss of the system per server between the epoch and \(t\) is therefore:  
\[y(t)=\frac{1}{N}\sum_{i=1}^{L}D^{i}(t). \tag{4}\]  
The combined cost is then described as:  
\[z(t)=x(t)+y(t). \tag{5}\]  
The problem is how to achieve the optimal cost through routing web request to the \(N\) servers available. Iterative or direct methods may not be efficient enough to deal with the highly dynamic nature of this system where the electricity prices, energy demand and response time are all variable. We need a rapid decision-making system to make autonomous routing decisions based on the performance of the previous decisions in a dynamically changing environment. This provides us motivation to approach the problem with a learning method.
# A Numerical Study of Chemical Compatibility of GCLs
###### Abstract  
A series of COMSOL numerical models were established to study the chemical compatibility of GCLs (geosynthetic clay liner). The effect of chemistry on the mesoscopic structure and the hydraulic conductivity of GCLs was investigated. The factors, including the initial mobile porosity, the swelling ratio, the pore size, and the ionic strength, were discussed as well. The mesoscopic mechanism of the physical and chemical processes of GCLs was explored by the COMSOL models. The hypothesis that the final mobile porosity and the final pore size are the key factors of the hydraulic conductivity of GCLs was proven by the simulation. Meanwhile, when the ionic strength increased from low to medium, the changes in pore size, mobile porosity, and hydraulic conductivity were obvious. However, when the ionic strength increased from medium to high, the changes of these parameters tended to be gentle, and the changes in hydraulic conductivity were not obvious. Moreover, a theoretical model considering the effect of the initial particle size, the initial mobile porosity, and the ionic strength was developed to predict the hydraulic conductivity of GCLs in a chemical solution. This theoretical model was verified by experimental data. A good agreement was obtained.  
chemistry compatibility; COMSOL; mesoscopic mechanism; GCLs; hydraulic performance
### State of the Art  
#### 1.1.1 Nominal Stability Analysis  
So far, many research works have been conducted for the small signal stability analysis of PEPS, either using the eigenvalue method or the impedance-based method.  
Eigenvalue analysis clarifies essential information such as stable, unstable, and oscillation modes and participation factors of each system variable on the system modes. However, eigenvalue analysis is based on the system state-space representation and needs complete system information, which may not be available due to confidentiality rules of different vendors [8; 9; 10; 11; 12].  
In the impedance-based method, the power system can be divided into two separate parts: a load-equivalent part and a source-equivalent part. The system stability can then be analyzed by the impedance ratio of the load and the source using the Nyquist criterion. This method needs less information about the system and analyzes system stability at the point of connection of each inverter. However, this method is unable to identify oscillation modes and participation factors of each system state [13; 14; 15; 16; 17; 18].  
These methods are popular due to the effectiveness and simplicity of their application. However, they have been developed for linear time-invariant (LTI) systems around a certain steady-state operating point. Therefore, they cannot investigate the impact of multiple uncertain parameters and operational point changes on the system's stability and performance. Eventually, without properly incorporating these potential system uncertainties, the stability assessment may lead to conservative or even inaccurate results.
## 5 Experiments
### Experiment 4: Static Splitting Factors with Stopping Criteria
#### 5.4.1 Time Criterion  
Upon analyzing the simulation outcomes, it becomes evident that low to medium-splitting factors result in more effective solutions. Another key (and anticipated) remark is that as the pollution factor increases the success rates of the employed splitting strategies are dropping. Moreover, despite the high splitting factors typically leading to low success percentages, they demonstrate considerably fewer demands for resources, given the 15 min constraint. The results are illustrated in Figure 10 and detailed in Tables 8-10. Figure 10: Comparison of the efficiency of the proposed scheme when considering various splitting steps. The pollution factors considered are 1% (**top**), 5% (**middle**), and 10% (**bottom**). This case considers a time criterion set to 15 min. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Num. of connections** & **SF = 2** & **SF = 4** & **SF = 6** & **SF = 8** & **SF = 10** & **SF = 12** & **SF = 14** & **SF = 16** & **SF = 18** & **SF = 20** \\ \hline
1000 & & & & & & & & & \\
2000 & & & & & & & & & \\
3000 & & & & & & & & & 98.9 & \\
4000 & & & & & & & & 98.2 & 98.6 & \\
5000 & & & & & & & 97.8 & 98.0 & 98.5 & \\
6000 & & & & & & 96.9 & 97.8 & 98.2 & 98.7 \\
7000 & & & & & & 96.3 & 97.4 & 98.2 & 98.5 \\
8000 & & & & & & & & 96.9 & \\
9000 & & & & & & & 96.3 & 97.4 & 98.2 & 98.5 \\
10,000 & & & & & & & & \\ \hline
1000 & & & & & & & & & \\
2000 & & & & & & & & \\
3000 & & & & & & & & \\
4000 & & & & & & & & \\
5000 & & & & & & & & \\
6000 & & & & & & & & \\
7000 & & & & & & & & \\
8000 & & & & & & & & \\
9000 & & & & & & & & \\
10,000 & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Detection rate (top) and resources consumption (bottom) when considering a pollution factor of 1%. Highlighted are the worst (red) configurations. Empty cells indicate that all benign connections (99% of all connections) were saved Specifically, with regard to Figure 10 and Table 8, when examining a pollution factor of 1%, splitting factors ranging from 2 to 12 achieve complete solutions within the 15 min constraint across all tested connection pools. Consequently, the corresponding cells in Table 8 remain empty as they exhibit identical values for time and resources as Table 3 from the experiment conducted without no criteria applied, as discussed in Section 5.2. On the other hand, splitting factors ranging from 14 to 20 occasionally yield suboptimal
## 6 Future Prospects and Outlook for Grain-Drying Machine Control Systems
### Advanced Technology Integration and Intelligent Automation Sector
#### 6.1.2 Integration of IoT and Remote Monitoring  
Secondly, the burgeoning of IoT technology is outlining a blueprint for the intelligent interconnection of grain dryer control systems. The rapid development of IoT technology will greatly enhance the remote monitoring and management capabilities of grain dryers. The operating parameters of the dryer can be transmitted to the cloud platform in real-time through wireless sensor networks. Users can monitor and adjust the drying process anytime, anywhere, through mobile applications or web interfaces, which not only simplifies the operation process but also significantly reduces manual intervention and improves production efficiency.
# Extensions and Enhancements to \"the Secure Remote Update Protocol\"
## 3 Enhancements and Improvements to the Original Protocol
### Additional HTTP Error Handling  
Whilst the original version of the protocol provided only a means to signal that there had been an error with the HTTP server when attempting to retrieve an update, an enhancement has been added to provide the ability to send further details of the mode of failure (should these be required for a given system or implementation). Rather than a SRUP_MESSAGE_TYPE_RESPONSE status that just signals an error, a further response-type has been added (SRUP_UPATE_FAIL_HTTP_ERROR) to signify that the detail of the HTTP error will be encapsulated as two separate SRUP data messages containing the HTTP status code and the contents of the server's response.
# Research on Path and Sequence Planning for Multi-Cabin Onboard Transportation of Large Cruise Ships
## 3 Improved A-Star Algorithm Designing
### Improved A-Star Algorithm  
The flow chart of an improved A-Star algorithm is shown in Figure 4.  
Figure 4: Improved A-Star algorithm flow chart.  
In the improved A-Star algorithm presented in this paper, obstacles are categorized into two types: grid obstacles and line obstacles. Grid obstacles are primarily used to describe obstacles at grid points, such as the non-prefabricated cabins commonly found in cruise ships. Line obstacles, on the other hand, are used to describe obstacles whose thickness can be ignored, but whose length cannot, such as the longitudinal and transverse bulkheads in a cruise ship.  
In traditional A-Star algorithms, grid points can directly be defined as grid obstacles at the early stage of the algorithm. When handling grid obstacles, they are treated as impassable nodes, and their cost is set to infinity to avoid selecting obstacles as part of the path during the search process. In the improved A-Star algorithm in this paper, line obstacles are treated similarly; they can be seen as boundaries between two grid points, A and B. The prefabricated cabin can only pass through point A or point B, but cannot pass between the two points, A and B.  
Therefore, when handling obstacles, the constraint expression in this paper can be written as follows:  
\[D=\left\{\begin{array}{ccc}A&,Point\ A\ is\ impassable\\ B&,Point\ B\ is\ impassable\\ \left(A_{x}+B_{x}\right)/2&,Impassable\ between\ points\ A\ and\ B\ in\ the\ x\ - direction\\ \left(A_{y}+B_{y}\right)/2&,Impassable\ between\ points\ A\ and\ B\ in\ the\ y\ - direction\end{array}\right. \tag{6}\]  
where \(D\) represents the obstacle, and \(A\) and \(B\) represent two adjacent grid points on the grid map.  
In addition, in the large cruise ship multi-cabin transportation problem, it is necessary to consider that when a cabin is moved to its destination, it is treated as an obstacle in the grid. This newly introduced obstacle can affect the transportation paths of subsequent cabins; as the number of obstacles increases, the number of required turns for transporting the remaining cabins also rises, further complicating the process. Research shows that incorporating a turn penalty mechanism into the A-Star algorithm can significantly reduce the number of turns required--by approximately 36.6% in environments with 20% obstacles, and about 27.7% in environments with 30% obstacles [23]. Therefore, this paper further integrates the number of turns in cabin transportation as an optimization objective. Specifically, when calculating the cost of adjacent nodes, an additional unit of path cost is added if the direction of cabin movement changes.
# A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model
## 2 Dynamic Modeling
### Hybrid Models  
* Agent-based modeling (ABM): ABM is a new approach used to model distributed and intelligent systems. It is a decentralized model, highly preferred for complex systems and characterized by the diversity of its abstraction level. ABM was tested and used in different application as supply chain [32], air transport [33; 34], health and spread of pandemics [35], and evacuation plan in a fire situation with obstacles [36], but its application for risk engineering science is very limited. ABM is considered a simple modeling tool for complex system representation by modeling only the individual units named agents and simulating their interaction to get the behavior of the whole system [16].
* Logical-combinatorial approach (LCA): It is mainly used for supervised and unsupervised dynamic pattern recognition problems. It aims to classify a set of classes as normal or deviated [37]. The majority of the papers developed using LCA focused on three problems: feature selection, supervised classification, and unsupervised classification [38]. This approach can be used to perform dynamic risk analysis as it can show two different categories of behavioral modes: Normal and Abnormal.
# A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming
## 4 Potential Game Based Uplink Allocation
In order to maximize the total QoE utility, UAVs not only have to compete with each other, but also need to cooperate partially. **Theorem 2**. : _In potential game \(G=[\Omega,\{\mathcal{K}_{i}\}_{i\in\Omega},\{\mathcal{U}_{i}\}_{i\in\Omega}]\), if the potential function can be expressed as \(I=\Omega\cdot\sum\limits_{i=1}^{N}\{a+b\cdot\sqrt{\frac{r_{i}}{c}}(1-\frac{c}{r _{i}})\}-\eta\cdot\sum\limits_{i=1}^{N}r_{i}+N\cdot\Lambda\), then the correlated equilibrium of the game exists and is unique._  
Proof. : From Equation (6), we can get the first-order partial derivative of the potential functionA  
\[\frac{\partial I}{\partial r_{i}}=\Omega\cdot(\frac{b}{2\sqrt{cr_{i}}}+\frac{ b\sqrt{c}}{2\sqrt{r_{i}}^{3}})-\eta. \tag{8}\]  
Obviously, Equation (8) is a continuous function in the strategy space, so the potential function \(I\) is continuous and differentiable. Since \(\Omega\cdot(\frac{b}{2\sqrt{cr_{i}}}+\frac{b\sqrt{c}}{2\sqrt{r_{i}}^{3}})\) and \(\eta>0\), then there must be an extreme point \(r_{i}\)=\(\tilde{r}_{i}\) that can make Equation (8) identical to 0. Then, the second-order partial derivative of Equation (6) can be solved as:  
\[\left.\frac{\partial^{2}I}{\partial\tilde{r}_{i}^{2}}\right|_{r_{i}=\tilde{r}_ {i}}=-\Omega\cdot[\frac{b}{4\sqrt{c}\sqrt{r_{i}}^{3}}+\frac{3b\sqrt{c}}{4 \sqrt{r_{i}}^{5}}]. \tag{9}\]  
Because \(\frac{b}{4\sqrt{c}\sqrt{r_{i}}^{3}}>0\) and \(\frac{3b\sqrt{c}}{4\sqrt{r_{i}}^{5}}>0\), we can get \(\left.\frac{\partial^{2}I}{\partial\tilde{r}_{i}^{2}}\right|_{r_{i}=\tilde{r}_ {i}}<0\). The above results show that the potential function constructed in the potential game \(G\) is a continuously differentiable convex function in the strategy space. According to [53], potential game \(G\) has a correlation equilibrium, and the result is unique. In the process of rate allocation, UAVs compete for bandwidth as game players. When anyone's single strategy change can no longer bring increase to the total utility function, the system reaches a stable and unique equilibrium state. At this point, the extreme rate strategy \(\tilde{r}_{i}\) is the ultimate bandwidth choice for the \(i\)-th UAV. Thus, all the N UAVs will keep their own extreme rate strategies and the stability won't be broken as long as the transmitting environment and the video characteristics remain unchanged.
# Radiometric Comparison within the Sentinel-1 SAR Constellation over a Wide Backscatter Range
## 2 Materials and Methods  
### Sentinel-1 SAR Data Products  
For the current study, SAR data products of S-1A and S-1B are used. The radar brightness \(\beta^{0}\) is derived from the pixel intensity of calibrated level 1 slantrange complex (SLC) products using annotated look-up tables and the annotated calibration factor. These SAR data products are freely available, e.g., on the Copernicus open access hub ([[https://scihub.copernicus.eu](https://scihub.copernicus.eu)]([https://scihub.copernicus.eu](https://scihub.copernicus.eu))).  
Sentinel-1 can be operated in different modes - each offers a specific swath width and spatial resolution [20]. The highest resolution is given for the Stripmap mode with a resolution of 5 m by 5 m in azimuth and range and a swath width of about 80 km. The largest swath width of about 400 km is available for the extra-wide swath mode (EW) which provides a reduced geometric resolution of 20 m (range) by 40 m (azimuth). However, the main mode of Sentinel-1 is the interferometric wide-swath mode (IW) with a swath width of about 250 km and a medium geometric resolution of 5 m (range) by 20 m (azimuth). For the latter, in both modes the antenna is electronically steered within five and three subswathes for EW- and IW-mode, respectively. Dual-polarization products are available for Stripmap, EW-, and IW- mode; they offer co- and cross-pol images for a given transmit polarization H or V. The current study focuses on IW mode as the main mode using dual-polarization products (VV+VH or HH+HV).