esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Due to Taiwan's high-risk slopes on National Freeways 1 and 5, the PLAXIS 2D CE program was utilized in this study to simulate the slope stability of many existing slope protection schemes. The following conclusions were drawn from the numerical simulation results:  
* Because the gradients at Sites 1 and 2 were similar, the simulation results showed that, with a normal groundwater level, the force acting on the slope was mostly located at the position of the sliding surface. In the high groundwater level analysis, the force acting on the slope extended to the top.
* Due to the superior strength of the soil, Site 1 saw comparatively little displacement at both normal and high groundwater levels during the analysis.
* The displacement at the top of the slope increased as the groundwater level rose, regardless of whether the slope analysis was conducted with a normal or high groundwater level.
* According to the analysis results, in three different scenarios, the SF value of the slope of Site 1 was greater than the design specification value. This indicates that the slope protection project at Site 1 is working effectively.
* According to the analysis results, the effect of the slope protection project at Sites 2 and 3 needs to be improved.  
Conceptualization, S.-L.C. and C.-Y.C.; methodology, S.-L.C. and C.-Y.C.; software, C.-W.T. and Y.-H.T.; validation, S.-L.C. and H.-W.C.; formal analysis, C.-W.T.; investigation, S.-L.C. and H.-W.C.; resources, C.-Y.C.; data curation, S.-L.C., Y.-H.T. and H.-W.C.; writing--original draft preparation, S.-L.C. and C.-W.T.; writing--review and editing, C.-W.T.; visualization, Y.-H.T.;  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Scenarios**} & \multicolumn{3}{c}{**Safety Factor**} \\ \cline{2-3}  & **Simulation Result** & **Design Specification** \\ \hline Scenario 1: Normal groundwater level & 1.405 & \(\geq\)1.5 \\ Scenario 2: High groundwater level & 1.306 & \(\geq\)1.2 \\ Scenario 3: Normal groundwater level and & 1.021 & \(\geq\)1.1 \\ pseudo-static analysis & & \\ \hline \hline \end{tabular} Note: Initial value of safety factor for Site 3 at normal groundwater level without slope protection facilities = 1.032.  
\end{table}
Table 20: Safety factor for Site 3 in various scenarios.  
Figure 39: Site 3’s \(\Sigma\)_Msf_ in various scenarios.  
supervision, C.-W.T. and H.-W.C.; project administration, C.-W.T.; funding acquisition, S.-L.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology of Taiwan, grant number MOST 110-2221-E-027-025-MY2.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy concerns.  
Author [PERSON] was employed by the company ARBEL Technical Consultants, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Parallel Operation of the Power Converter
### Voltage Drop Method  
The voltage drop method relies on the drop characteristics of the output voltage to control the load sharing [17; 18; 19]. This method reduces the output voltage in proportion to the load current being shared, so that each module independently controls the load sharing without using the communication line of current information under the paralleled power converters. This method has poor current distribution characteristics in low power areas. Also, it is difficult to distribute current between parallel modules with different power ratings or poor load regulation characteristics [20; 21].
## 2 New Production Scheduling Methodology
### Optimization Algorithm
Only the exact placement of an operation on the timeline (start and end time) is performed deterministically on the basis of the feasibility criterion. Its aim is to obtain the most compact schedule possible while meeting this criterion. Despite this deterministic element, the algorithm satisfies the postulate of ignoring the results of previous iterations. This means that the subsequent versions of entire schedules are not affected by previously generated schedules. In this way, we obtain protection against getting stuck in the local minimum. In the case of any optimization algorithm, the selection of its parameters is always crucial. Therefore, in order to avoid the impact of the selection of parameters, it decided to use a laboratory algorithm with a simple structure, which can be classified as a brute-force algorithm. The main advantage of the algorithm is, apart from the resistance to getting stuck in local minima described in the paper, its complete non-parametric character. The analysis of its flowchart (Figure 3) and the above description shows that both in the case of the selection of resources for the technological operation and the order of inserting orders and individual operations into the schedule, a randomization is used, in which the probability of individual variants is identical and is not controlled by any external parameters. Contrary to such heuristic algorithms as, e.g., genetic algorithms [3; 41], PSO [7] or GWO [8] we do not need to choose parameters such as mutation and crossover (or their equivalent) probability, selection determinism, or population size. This limits the subjective influence of the algorithm user on the results obtained. The only parameter that must be selected is the stop condition. In order to maximize the probability of finding the optimal solution, theoretically, the optimization time should come as close as possible to the postulate of an infinite number of trials. Ensuring a sufficiently long optimization time should significantly increase the chances of obtaining a global minimum for given technological conditions during research. A convenient stop criterion is the maximum period of no progress in minimizing the criterion. Setting this period in relation to the period of minimization progress additionally facilitates the determination of the threshold value. The algorithm itself is not an integral part of the proposed solution, and the universal methodology developed will ensure significant profit for any optimization algorithm implemented correctly. The difference from the lab algorithm will perhaps be getting the schedule faster. However, this simple laboratory algorithm has one practical advantage. It has unlimited parallelization capabilities (due to the complete independence of iterations). Today, we have the option to rent almost unlimited public cloud resources only for the duration of optimization. In the case of a commercial application of the proposed methodology, the use of this brute-force algorithm may prove to be economically acceptable.
## 3 Implementation and Evaluation of the Method  
To validate the proposed method, several tests were accomplished, focused on evaluating the performance of the hash function in terms of: (i) resistance to collisions, which implies determining if two different inputs produce the same output; (ii) runtime used by the mobile device to calculate the hash code with respect to the duration of the signal; and (iii) sensitivity of the code with respect to changes in the input signal.  
For the evaluation of the aforementioned parameters, two mobile devices with the characteristics mentioned in Table 1 were used.  
Figure 2: Block diagram of the proposed integrity verification module.  
In each mobile device, 70 audio signals of different duration were recorded. To structure the results, seven ranges of duration were defined, with ten recordings in each range per device. The first range includes recordings of up to 10 s, while the second includes recordings between 10 and 20 s, doubling the duration limit in each new range. In this sense, there are 140 total recordings, as shown in Table 2.  
Immediately after recording the audio signals, collision resistance, computational cost and sensitivity were evaluated. For the tests performed, stored metadata (Microsoft Azure) of each recording were used, such as recording name, creation date, hash code, and recording duration and compile time. On the other hand, seven audio recordings (one for each time range) were randomly selected, which were used to make minor modifications that later allowed generating and comparing the hash code of the modified signal with respect to the original one.
# Safety Control for Satellite Formation Flying via High-Order Control Barrier Functions
## 5 Conclusions  
The safety control problem of satellite formation flying is investigated in this paper. A high-order control barrier function-based safety control scheme is proposed to guarantee not only stability but also safety. By considering the characteristics of the satellite formation flying model, a high-order control barrier function is constructed, based on which the safety set of satellite formation flights is defined. By adding the designed high-order control barrier function as a constraint, an optimization problem is formulated; solving this, the safety controller is obtained and the forward invariant property of the safety set is guaranteed. Finally, simulation results are provided to validate the proposed safety control scheme in this paper. When there exist unknown uncertainties and an imperfect communication network [30, 31, 32], the proposed safety control scheme cannot be directly used since the exact value of the Lie derivative of the constructed control barrier function cannot be computed. To solve such a problem, neural networks or unknown input observers will be introduced to estimate these unknown uncertainties, and a model will be provided to describe the imperfect communication network. By combining these methods, a safety control design problem under these complicated situations will be considered as future work.  
Conceptualization, X.Z.; methodology, X.Z.; validation, H.W., D.L. and Z.J.; formal analysis, H.W.; investigation, X.Z.; writing--original draft preparation, X.Z.; writing--review and editing, H.W.; visualization, D.L.; supervision, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Defense Science and Technology Key Laboratory Fund, Research on HighPrecision Satellite Formation Control Technology in Space Microgravity Environments of funder grant number 2020Y12066.  
Not applicable.  
The data are not publicly available due to the project is still in progress.  
The authors declare no conflict of interest.
## 5 Relationship between Connectivity and Capacity
### Improving Capacity by Denser Connectivity  
Another approach has been taken to studying the capacity of sensor networks, in the classic work [68] presented in \(1978\) by [PERSON] and [PERSON], which sought to maximise throughput in networks using the ALOHA medium access control scheme. They considered a packet radio network with Poisson distributed terminals and homogeneous transmission ranges. In their model, the one-hop progress of a packet in the direction from a source to a destination is maximised by setting the transmission range to give an average of six neighbors for each node. [PERSON] and [PERSON] revised this result, and gave a new optimal nodal degree eight [69]. Other nodal degrees were also obtained under different transmission protocols and inhomogeneous transmission ranges [69; 70; 71].  
This work either implicitly or explicitly assumed that the network is connected. For example, [68] considers only networks up to size 150, and concludes that an average degree of 4 is sufficient for connectivity. Among others, [PERSON] _et al._[33] have proved that with a constant average nodal degree, a sufficiently large network is almost surely disconnected, dispelling the notion of a \"magic number\" [68]. However, the work on maximising throughput remains interesting in that it demonstrates that, at least for small networks, it may be optimal to use a larger number of neighbours than the minimum required for connectivity.
## 9 Future Research Directions and Challenges
### Orientation Estimation  
Deployment of a multi-array antenna system paves the way for orientation estimation. However, attitude estimation would be challenging, especially in a 3D space while the UAVis flying. Therefore, integrating RF signals with other data to improve attitude estimation in 3D seems to be an attractive research topic which has been left untouched.
## 3 Results and Discussion  
### XRD  
Figure 1 shows the powder X-ray diffraction pattern of ZnO. The XRD peaks had a specific line sharpness, indicating that the formed material particles had a good crystal structure. The presence of characteristic diffraction peaks in ZnO was proved by XRD patterns. Figure 1 depicts the X-ray diffraction (XRD) pattern required to validate the structure of ZnO NPs with a wurtzite structure. The crystalline nature of ZnO NPs is indicated by the intensity of peaks diffraction. The diffraction peaks at 31.69\({}^{\circ}\), 34.34\({}^{\circ}\), 36.18\({}^{\circ}\), 47.51\({}^{\circ}\), 56.53\({}^{\circ}\), 62.78\({}^{\circ}\), 67.84\({}^{\circ}\), and 68.99\({}^{\circ}\) indicate (1 0 0), (0 0 2), (10 1), (1 0 2), (1 1 0), (1 0 3), (1 1 2) and (2 0 1) planes, respectively, of the ZnO similar to the standard data of the JCPDS card number: 36-1451. The calculated average crystalline size of the ZnO NPs was determined using [PERSON]'s formula [24]:  
\[\text{t}=0.9\lambda/\beta\text{Cos}\theta \tag{1}\]  
where \(\lambda\) is the wavelength, \(\beta\) is the full width at half maximum (FWHM), and \(\theta\) is the diffraction angle. The calculated crystalline size of the ZnO NPs, the strain, and cell parameter are listed in Table 1. The crystallite size of prepared ZnO nanoparticles was calculated around 33 nm; additionally, the lattice parameters (a, c) were found to be about 3.25 and 5.2, respectively.
## 3 Materials and Methods
### Proposed Framework  
The fusion of multiple models can improve the network's performance by combining clustering methods such as k-means, k-medoids, and random clustering with Gradient Boosted Tree, Decision Tree, Random Forest, Naive Bayes, and Deep Learning classifier, integrating ensemble machine learning algorithms such as Voting, bagging, boosting, and stacking. Below are the steps involved in the proposed method.  
* Initially, clustering algorithms are employed, and select top clustering algorithm. We used 2 clusters in the clustering algorithms. In our experiments, the k-medoids technique outperforms k-means and random clustering.
* Next, a single classifier including GBT, DT, RF, NB, and DL classification algorithms is implemented.
* Afterward, k-medoids and single classifier-based model are implemented. This hybrid technique attained superior performance in comparison to single clustering or classification algorithms.
* Then, k-medoids clustering and hybrid classifiers-based model are designed and results are evaluated in terms of accuracy, recall, precision, and F-measure.
* Finally, ensemble models such as bagging and stacking are incorporated with the best previous hybrid model which achieves top results in a contrast to all the above experiments.
* It is clear from the evaluation that the proposed combination of clustering and ensemble model has achieved the highest prediction accuracy as compared to other methods.  
Map Clustering on the Label  
After clustering, mapping is applied to produce TP, TN, FN, and FP from Orange and Cell2 Cell datasets. Cluster 0 is mapped to churners, while cluster 1 is mapped to non-churners. Let's say we have a table with three columns: cluster0, cluster1, and churn. Mapping will produce a fourth column, prediction (churn), which will include classes 0 and 1. Class 0 and 1 are used as a mapping for cluster 0 and 1 data, allowing TP, TN, FN, and FP to be produced. cluster 0 and cluster 1 are the two groups that emerge from applying clustering to the dataset. The values of the predicted class are compared to the clusters to see if they fall within any of them. Therefore, clusters are examined by first mapping them using prediction class. A TN is produced if the values of cluster 0 falls into prediction class 0, and a TP is produced if the values of cluster 1 fall into prediction class 1. To the same extent, FP is produced if the values of cluster 0 falls into prediction class 1, and FN is produced if the values of cluster 1 fall into prediction class 0. Hence, mapping is used for cluster analysis.
## 3 Results and Discussion  
### Quantitative Analysis  
In the first set of experiments, the fluorescence spectra for a set of soil samples prepared as described in the previous section were detected. The typical fluorescence spectra of diesel oil before and after preprocessing by baseline correction are shown in Figure 2. One can observe from Figure 2 that the fluorescence peak of diesel oil is significantly influenced by the soil organic matter, polycyclic aromatic hydrocarbons, and so on. In order to eliminate the influence of fluorescence spectra by soil background, the data that follows in this paper are all the fluorescence spectra of diesel oil for removing soil background.  
Figure 3 shows the fluorescence spectra of diesel oil and the change rule of fluorescence intensity in different soil moisture with the concentration of 0.5%. Five measured spectra for each sample were averaged into an analytical spectrum. As shown in Figure 3, the fluorescence intensity change of diesel oil is smaller when the percentage of soil moisture content is between 0% to 10%, and the fluorescence intensity of diesel oil changes greatly with the increasing of soil moisture, the deviation of multiple  
Figure 1: Schematic diagram of the laser induced fluorescence (LIF) experimental setup.  
Figure 2: The typical LIF spectra of diesel oil before and after preprocessing.  
measurements also increases. This may be due to the changes of the fluorescence emission efficiency in different soil moisture, therefore the analysis of fluorescence spectra should consider the soil moisture content. The soils beside the roads or near gas stations are the hot spots of diesel oil spilled and the soils are usually dry, so in the following analytical process, all the soil samples were in low relative soil moisture.  
The calibration curve between the fluorescence peak intensities of diesel oil and the known concentrations of the diesel oil are shown in Figure 4. It notes that the square correlation coefficient value is 0.99, which verifies that the fluorescence peak intensity of diesel oil can be used to predict the concentration of diesel oil in soils.
## 12 Future Research Directions and Identifying Research Gaps  
As we conclude this study on electrical steels, it is important to highlight the areas where further research is needed and identify the gaps in our current understanding. By addressing these research lacunas, we can pave the way for future investigations that will contribute to a more comprehensive understanding of electrical steels and their applications.  
Dynamic Behaviour under Variable Conditions: While much has been explored about the static properties of electrical steels, their behaviours under varying environmental and operational conditions remains less explored. Investigating how electrical steels respond to dynamic loading, temperature fluctuations, and exposure to different gases or liquids can provide valuable insights into their long-term performance.  
Multi-Scale Modelling and Simulation: The integration of advanced modelling techniques, such as multi-scale simulations, can bridge the gap between microstructural features and macroscopic properties. Developing accurate models that consider various length scales will help in predicting materials' behaviours more precisely, aiding in design optimization and performance prediction.  
Emerging Coating Technologies: While coatings have shown promise in enhancing electrical steel properties, their long-term performance and interactions with different environments require deeper investigation. Understanding the mechanisms behind these coatings and their effects on material behaviour over time can lead to more effective and durable solutions.  
Advanced Characterization Techniques: As new characterization methods emerge, their application to studying electrical steels can provide a wealth of information. Techniques like in situ microscopy, advanced diffraction methods, and high-resolution imaging can offer insights into electrical steels behaviours under specific conditions.  
Tailoring for Specific Applications: Electrical steels find applications in diverse fields, each with unique requirements. Tailoring electrical steels to meet the specific demands of applications like electric vehicles, renewable energy systems, and high-frequency devices requires a deeper understanding of how different material properties influence performance in these contexts.  
By addressing these research gaps and focusing on the aforementioned directions, future studies can contribute significantly to the advancement of electrical steel technology. These investigations will not only expand our understanding of material properties but also drive innovation, resulting in more efficient and effective electrical steel materials for a wide range of applications.  
Conceptualization, E.E. and F.A.; methodology, E.E.; software, E.E.; validation, E.E. and F.A.; formal analysis, E.E.; investigation, E.E.; resources, E.E.; data curation, E.E.; writing--original draft preparation, E.E.; writing--review and editing, E.E.; visualization, F.A.; supervision, E.E.; project administration, F.A.; funding acquisition, F.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## Cross-Layer Active Predictive Congestion Control Protocol for Wireless Sensor Networks  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 1 and [PERSON] 1**  
1 School of Instrument Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics (Beihang University), Beijing 100191, China; E-Mails: [EMAIL_ADDRESS] (R.J.F.); [EMAIL_ADDRESS] (Y.F.W.)  
2 School of Computer Science and Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; E-Mail: [EMAIL_ADDRESS] (X.F.X.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-10-82338089; Fax: +86-10-82339889.  
_Received: 29 July 2009; in revised form: 18 September 2009 / Accepted: 25 September 2009 / Published: 20 October 2009_  
**Abstract:** In wireless sensor networks (WSNs), there are numerous factors that may cause network congestion problems, such as the many-to-one communication modes, mutual interference of wireless links, dynamic changes of network topology and the memory-restrained characteristics of nodes. All these factors result in a network being more vulnerable to congestion. In this paper, a cross-layer active predictive congestion control scheme (CL-APCC) for improving the performance of networks is proposed. Queuing theory is applied in the CL-APCC to analyze data flows of a single-node according to its memory status, combined with the analysis of the average occupied memory size of local networks. It also analyzes the current data change trends of local networks to forecast and actively adjust the sending rate of the node in the next period. In order to ensure the fairness and timeliness of the network, the IEEE 802.11 protocol is revised based on waiting time, the number of the node's neighbors and the original priority of data packets, which dynamically adjusts the sending priority of the node. The performance of CL-APCC, which is evaluated by extensive simulation experiments. is more efficient in solving the congestion in WSNs. Furthermore, it is clear that the proposed scheme has an outstanding advantage in terms of improving the fairness and lifetime of networks.  
**Keywords:** wireless sensor networks; congestion control; dynamic priority; cross-layer protocol