esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Attributes, Utility-Based, Channel Quality Ranking Mechanism for Cognitive Radio Networks
## 4 Discussion
The selection of the channel with the lowest occupancy and the highest capacity enables secondary users to transmit with a high data rate during a long period. However, most of the existing algorithms rank channels based only on the availability, which can lead to the selection of a channel with low occupancy, but it can be of low quality. Some other algorithms such as the one given in [27; 28; 29; 30; 31] consider the quality of the channel, but this occurs only after the selection of the channel to adapt the parameters of transmission according to the condition of the selected channel.
## 2 Materials and Methods
### Experiments
#### 2.2.1 LOM Experiments  
_C. vulgaris_ and _Scenedesmus_ sp. were the selected microalgae species, and the inoculums were acquired from the strain collection of Banco Espanol de Algas (Las Palmas de Gran Canarias, Spain). The SWW used for microalgae cultivation was prepared according to the composition of a typical effluent from the primary decantation of an urban WWTP [62]: 7 mg NaCl, 4 mg CaCl\({}_{2}\), 2 mg MgSO\({}_{4}\cdot\)7H\({}_{2}\)O, 15 mg KH\({}_{2}\)PO\({}_{4}\), and 115.6 mg NH\({}_{4}\)Cl per litre of deionised water. Reactors in the LOM experiments were loaded with a combination of SWW and microalgae inoculum to achieve an initial biomass concentration of 0.1 g/L. Every reactor was spiked with 1 ppm of each EDC from a 10 ppm solution. Control experiments without contaminants were also performed. All experiments were conducted under continuous aeration using air pumps, and the air was pre-filtered through 0.45 \(\upmu\)m nylon membranes before reaching the cultures (Agilent Technologies, Santa Clara, CA, USA). Reactors were additionally irradiated using 12 V LED strips, offering an irradiance of 108 \(\upmu\)mol\(\cdot\)foton\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\). The photoperiod was established as 12 h of light followed by 12 h of darkness. This approach was cost-effective and sustainable, ensuring the health and resilience of the microalgae for consistent and stable pollutant-removal performance. Biomass growth was monitored daily. Dry cell weight (DCW) was determined by filtering 12 mL of microalgae culture through a pre-weighed nylon membrane (GVS, Bologna, Italy) and drying it at 56 \({}^{\circ}\)C for one week. The dried filters were weighed, and the DCW was calculated using a weight balance. Additionally, pH was measured daily using a pH meter (XS Instruments, Modena, Italy).
## 5 Conclusions  
To help solve the difficulty of feature drift in the process of AUV drift, a new method of AUV drift track prediction based on a modified neural network is proposed. The modified neural network realizes error correction in the deep ocean layer and the thermocline through two different modules of Proactive Modulation and Response Modulation. The experimental results show that the proposed method has better prediction performance than other time series prediction models in two ocean layers. MSE, RMSE, and MAE all have good improvement. In the deep ocean layer, the three error indexes are reduced by 3.484, 0.905, and 1.795, respectively, compared with the uncorrected prediction model, and it is reduced by 13.672, 3.306, and 2.373, respectively, in the thermocline.  
The prediction model proposed in this paper has achieved good prediction accuracy in the AUV track prediction of the crash. Nonetheless, this dataset is only in one ocean area. As a matter of fact, the AUV performs tasks in different and unknown ocean areas, and the data distribution of the influencing factors in the unknown ocean area changes greatly. How to improve the correction ability of the correction network and improve its generalization ability will be the focus of future research.  
Conceptualization, Methodology, software, investigation, data curation and writing--original draft preparation, Y.Y.; modification and writing--review and editing, Y.Y., J.Z. and T.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China under Grant No. 52171310, National Natural Science Foundation of China (Youth) under Grant No. 52001039, Funding of Shandong Natural Science Foundation in China under Grant No. ZR2019 LZH005, Research fund from Science and Technology on Underwater Vehicle Technology Laboratory under Grant No. 2021 JCJQ-SYSJ-LB06903.  
Not applicable.  
**Data Availability Statement:**[[http://mds.nmdis.org.cn/](http://mds.nmdis.org.cn/)]([http://mds.nmdis.org.cn/](http://mds.nmdis.org.cn/)) (accessed on 9 May 2022).  
**Acknowledgments:** The authors would like to thank the editors and reviewers for their advice.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Supporting Privacy of Computations in Mobile Big Data Systems +
## 2 A High-Level Overview of Our System
### Main Characteristics of Our System  
We highlight some of the main features of our secure cloud computing system in this subsection.  
1. Offloaded Computation: The client delegates the intensive computational tasks to the cloud servers of creating and evaluating the garbled circuit. The client only chooses the cloud servers, provides them with unique seed values, generates garbled inputs during evaluation, and interprets garbled outputs returned by the evaluator.
2. Compact Cipher Text: While Gentry's scheme has an extremely large cipher text size, the cipher text size can be as small as a few hundred bits with our scheme (Section 5.6). Thus, our proposed method is far more practical for cloud computing in mobile systems in comparison to FHE schemes.
3. Decoupling: The process of creating the garbled circuit is decoupled from the process of evaluating the garbled circuit. While the servers, \(p_{i}\) (\(1\leq i\leq n\)), interact with one another for creating the garbled circuit, the server \(p_{e}\) evaluates the garbled circuit, independently.
4. Advance Construction: Since evaluation of the garbled circuit requires no interaction among the servers, if several versions of the garbled circuit for a given computation are precomputed and stored at the evaluator, in advance, then it can readily carry out the requested computation. Thus, the client will only incur the relatively short time taken to evaluate the garbled circuit. In other words, precomputation will drastically improve the response time for the client.
5. Collusion Resistance: To interpret any garbled value, the evaluator, \(p_{e}\), must collude with each of the \(n\) servers, \(p_{i}\) (\(1\leq i\leq n\)). Thus, even if \((n-1)\) out of the \(n\) servers are corrupt and collude with the evaluator, the privacy of the client's inputs and the result of the computation are still preserved.
6. Verification of Outputs: The client has the ability to verify that the evaluator actually carried out the requested computation.
## 5 Experiment Results and Analysis
### Comparative Experiments  
To demonstrate the detection effect of the algorithm proposed in this paper visionally, the YOLOv8n model was compared with the confusion matrix of the proposed model in this paper in a comparison experiment, and the comparison results are shown in Figure 8. The true and predicted categories are represented by the rows and columns of the confusion matrix, respectively. The proportions of successfully predicted categories are represented by the values in the diagonal regions, while the proportions of wrongly predicted categories are represented by the values in the other regions.  
The comparison results in Figure 8 show that the diagonal region of the DEW-YOLO confusion matrix is darker than the diagonal region of the YOLOv8n confusion matrix, and the values in the DEW-YOLO diagonal region were also larger than those in the YOLOv8n diagonal region. This suggests that the model suggested in this paper is much better at accurately predicting the object categories. In addition, DEW-YOLO effectively reduced the false positive rate compared with YOLOv8n.  
As shown in Figures 9 and 10, the PR curve graphs depict a comparison of mAP values for YOLOv8n and DEW-YOLO, respectively. The PR curve graph reflects the mAP values for various defect categories, where \"all-classes\" denotes the average mAP value across all categories. Compared with the results before improvement, the overall mAP value  
Figure 8: (**a**) Confusion matrix plot of YOLOv8n. (**b**) Confusion matrix plot of DEW-YOLO.  
increased from 76.4% to 80.3%, showing an improvement of 3.9%. The results prove that the improved model can enhance the detection accuracy across multiple defect categories. Except for the rolled-in scale defect, which had a slight decrease of 0.1% in its mAP value, the other five defects had different levels of improvement, especially patches, which had a 6.7% increase in its mAP value from 83.9% to 90.6%, and scratches, which had the highest mAP value, reaching 97.3%.
## 5 Establishment of Internal Variable Fatigue Model  
### Selection of Internal Variables  
The internal variable theory of continuum mechanics is a theoretical approach to describing the properties of materials based on their internal structural variables (internal variables) [35]. The damage of rocks is the formation and expansion of small pores and cracks in a continuous medium, which causes changes in the mechanical properties of rocks. The dynamic elastic modulus of rock under fatigue loading is greatly affected by residual deformation, and the internal variable model of rock can reflect the change of the deformation modulus of rock under fatigue loading. The model in this paper chose the residual axial strain as the internal variable and ignored the influence of the number of freeze-thaw cycles and the confining pressure on the deformation modulus. The influence of the crack inclination angle on the dynamic elastic mode was also considered.  
The dynamic modulus of elasticity \(E_{fc}\) under cyclic loading can be expressed as follows [36]:  
\[E_{fc}=A(\sigma^{-cr})^{n}\epsilon_{1r}^{m} \tag{1}\]  
\[\sigma^{-cr}=\frac{q_{p}-p_{p}\tan\beta}{1-\frac{1}{3}\tan\beta} \tag{2}\]\[p_{p}=\left(\sigma_{1p}+2\sigma_{3}\right)/3 \tag{3}\]  
\[q_{p}=\sigma_{1p}-\sigma_{3} \tag{4}\]  
\(A\), \(n\), and \(m\) are model parameters; \(E_{fc}\) is the residual axial strain; \(\overline{\sigma}^{cr}\) is the equivalent stress; \(\beta\) is the inclination of the equivalent stress line, related to the angle of internal friction of the rock sample; and \(\sigma_{1p}\) is the peak stress within the cycle.  
Based on the freeze-thaw-triaxial fatigue compression test, the relationship between the dynamic elastic modulus \(E_{fc}\), residual axial strain \(\varepsilon_{1r}\), and the number of cycles \(N\) is plotted as shown in Figure 9. As shown in the figure, the dynamic modulus and residual strain showed a negative correlation with the increase in the number of cycles. The larger the axial residual deformation, the smaller the dynamic modulus. The two had a good corresponding relationship.
## 4 Trust Evaluation in VANET
### Trust Level Computation  
The problem of trust computation for nodes in VANET can be formalized as follows:  
**Definition 4**: _Let \(N\) be a set of nodes in VANET and \(T_{l}\) a set of accepted values of trust levels. The trust function \(F_{T}\) maps the node from \(N\) to a value from the domain \(T_{l}\) as follows:_  
\[F_{T}(j)=\{T_{l},\ 0<T_{l}\leq 1\ 0,\ O.W\ \} \tag{3}\]  
In our work, each event in VANET takes two values: 1 means positive evidence, and a 0 value means bad evidence. Assume \(B\) is a random variable representing the events performed by a node in VANET. The Bernoulli distribution is a discrete distribution [22; 23] with two possible outcomes denoted by b = 0 and b = 1, with b = 1 (\"positive\") occurring with probability p and b = 0 (\"bad\") occurring with probability 1-\(p\), where \(0<p<1\)[22; 23]. As a result, it has a probability density function, which can be written as [22; 23]:  
\[P(b)=\{1-p,\ b=0\ p,\ b=1\ \} \tag{4}\]  
This function can be written as follows [22; 23]:  
\[(b,\ p)=p^{b}(1-p)^{1-b},\ \text{b}\in\{0,1\} \tag{5}\]  
where \(p\) is the probability that the evidence is positive. The variable \(B\) follows a Bernoulli distribution \(b\)(1, \(p\)). \(\text{E}(B)\) is the expectation of the variable \(B\), and it can be written as follows [22; 23]:  
\[\text{E}(B)=p \tag{6}\]  
\[\text{V}(B)=p(1-p) \tag{7}\]  
The corresponding variance is:  
Central Limit Theorem (CLT) and the law of large numbers predict that the mean of all variables selected from the same population will be nearly equal to the population's mean, given a big enough sample size from the population with finite variance. As the sample size increases, the variances of these samples tend to match those of the population as a whole, according to the Law of Large Numbers. The random variable B is the weighted average of \(n\) independent events that are performed by the \(i^{th}\) node. The events follow the same distribution and are independent because the probability that the event is positive is independent of the probability that the next event is positive. In our model, the following criteria will be considered for computing the trust level for a node in VANET:  
Recommendations from other nodes, including other RSUs.  
Direct trust.  
Accuracy of the information provided.
# Technology Identification, Evaluation, Selection, and Optimization of a HALE Solar Aircraft
## 2 HALE Solar Aircraft Concepts
### Reference Flight Condition  
Solar aircrafts using solar energy as their source of power are greatly affected by environmental factors during their missions. If the solar energy reaching the earth is regarded as 100%, 49% of it is lost in the troposphere (under 11 km) but 22% is lost in the stratosphere (11-50 km) because of the atmosphere. Therefore, the cruising altitude was set at 18 km, and place of flight was Anheung (latitude: 34.65\({}^{\circ}\), longitude: 126.19\({}^{\circ}\)), Republic of Korea.  
The solar attenuation factor takes into account the environmental factors such as reflection in the atmosphere or absorption when the light of the sun reaches the earth. This factor varies according to altitude and time of sunrise. As shown in Figure 4, the lower the altitude, the more the attenuation is as the light of the sun passes through the atmosphere, whereas the higher the altitude, the lower the attenuation. The solar attenuation factor were derived from experiments [21] and newly plotted.  
Figure 4: Solar attenuation factor.  
Figure 3: SPHALE aircraft aerodynamic analysis using XFLR5.  
The date of flight is one of the factors greatly affecting the energy procurable from the sun. In this study, the summer solstice (06/22) was chosen, because this is when the time available to obtain solar energy is the longest in the year. Atmospheric turbidity indicating atmospheric state impacts the solar power per unit area and was set at 100%. The flight conditions and design parameters of the reference model for evaluating flight performance are shown in Table 3.
# The Path Planning of Mobile Robots Based on an Improved Genetic Algorithm
## 3 Improved Genetic Algorithm
### Population Initialization
#### 3.1.3 Generating Initial Population Individuals  
Since the path generated by the dichotomy method is discontinuous, a multi-step approach is required to connect adjacent grid cells, thereby generating a continuous path. This ensures the coherence and feasibility of each individual in the path planning process.
## 4 Discussion
### Discussion on Development Planning Recommendations
#### 4.4.1 Protection Zoning Based on Fractal Characteristics  
The zoning must be based on objective data and multiple variables. Evidently, cluster analysis is well-suited for integrating various variables for classification. Additionally, cluster analysis can classify settlements according to similar morphological patterns, revealing local characteristics and spatial heterogeneity.  
In this study, subspace fractal dimensions, slope influence factors, and elevation influence factors were selected as clustering factors. Z-score standardization was applied to eliminate the effects of dimensional differences. K-means clustering was used for the analysis, requiring the determination of the value of K. The elbow method was employed to calculate the sum of squared errors (SSE) for K values ranging from 2 to 8 (Table 6). A line chart was plotted (Figure 10), and the inflection point was identified at K = 3.  
Based on the above calculations, the clusters were divided into three categories. Cluster analysis was conducted, resulting in cluster centroids (Table 7) and an ANOVA table (Table 8). The number of samples in each category was reasonable and statistically significant.  
In addition, the clustering analysis retained the classification results of each subspace region and visualized them (Figure 11).  
All subspace regions are grouped into three categories. According to the results, the first category is characterized by low fractal dimensions, the highest slope influence factor, and a relatively low elevation influence factor. This corresponds to areas with low-complexity building layouts, significantly influenced by slope, and located in the steeper  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**K** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline SSE & 83.275802 & 57.619362 & 52.125689 & 45.686388 & 39.778818 & 36.945142 & 34.558358 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Sum of Squared Errors (SSE) corresponding to different K values.  
Figure 10: Elbow curve.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Clustering**} \\ \cline{2-4}  & **1** & **2** & **3** \\ \hline Z-score: fractal dimension & \(-1.35942\) & 0.43210 & 0.25622 \\ Z-score: slope & \(1.26056\) & 0.24549 & \(-1.14809\) \\ Z-score: altitude & \(-0.24554\) & \(-0.76269\) & 1.23095 \\ Effective & 14 & 31 & 22 \\ Invalid & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Final cluster centers.  
northeastern part of the settlement. The second category exhibits moderately low fractal dimensions, a moderately low slope influence factor, and the lowest elevation influence factor. These correspond to areas with medium-complexity building layouts, situated in relatively flat transitional elevation zones. The third category is marked by the highest fractal dimensions, the lowest slope influence factor, and the highest elevation influence factor. These areas correspond to high-complexity building layouts, significantly influenced by elevation, with dense and complex building arrangements.
## 2 Calculation Model and Method
### Process of Numerical Calculation  
In this paper, a developed ARTAP code is applied in comparative analysis of the safety characteristics between the LBE-cooled ADS and CNES, which is comprised of a steady-state analysis module and a transient analysis module. The steady-state analysis module couples a one-dimensional neutron diffusion equation and a thermal-hydraulics single-channel model [26]. The single channel model includes the heat conduction in the fuel element and the heat transfer from cladding to coolant. A flowchart of the neutronics and thermal-hydraulics coupled calculation is shown in Figure 2.  
Before the assembly calculation starts, the geometric arrangement and detailed composition of all materials (fuel, cladding, coolant, spallation target, reflector, control rod, etc.) for the ADS and CNES reactors need to be determined through the existing literature and technical reports. After obtaining these basic data and empirical correlation, the calculation of neutron source distribution in the ADS spallation target, the processing of macroscopic cross sections for the subcritical core of ADS and the critical core of CNES, and material thermophysical properties introduced into the thermal-hydraulics model of both reactors could be carried out during the process of simulation calculation and comparative analysis, as shown in Figure 2. When macroscopic cross sections and spallation neutron source are obtained, steady-state neutron diffusion equations of these two reactors could be solved, respectively, by the power iteration method. According to the power distribution by the  
Figure 2: Numerical calculation flowchart.  
neutronics calculation, the thermal-hydraulics analysis is performed for both reactors, and the obtained distributions of fuel temperature and coolant density are selected as the feedback parameters to update their nuclear cross sections. Then the neutron diffusion calculations are carried out for both reactors, respectively, to update the power distributions again. This coupling iterative process continues until some criterion for convergence is met. The steady-state parameters of reactor neutronics and thermal-hydraulics are provided as initial parameters for dynamic simulation. The numerical calculation of dynamic models is divided into two steps, i.e., the spatial discretization with the finite difference method and the numerical solution of nonlinear time-dependent differential equations with a backward differentiation formula (BDF) method [23].
## 4 Performance Tests
### UPDATE Statement  
Simple Update Operation by Id  
In order to test the update operation, a query will be performed that aims to change different values of a car announcement based on its unique identifier. The update statement that realizes a simple update operation by _id_ is presented in Table 7, and the obtained performance results are described in Figure 13.  
In the update process, Elasticsearch will retrieve the existing document in order to apply the changes, and after that it will index the new document received during the update operation. At the end of the operation, the old document will be marked for deletion.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**MySQL Document Store** & _schema.getCollection(CARS).find(“carMake = ‘Audi’ and carModel = ‘A4”)_ \\  & _.sort(\"carAnnouncementNo”).limit(10).execute();_ \\ \hline \multirow{4}{*}{**Elasticsearch**} & _[ _“size”.10,_ \\  & _“query”.]_ \\  & _“bool”.]_ \\  & _“must”.]_ \\  & _[ _“match”.]_ \\  & _[ _“match”.]_ \\  & _[ _“match”.]_ \\  & _[ _“sort”.]_ \\ \hline \hline \end{tabular}
\end{table}
Table 6: SELECT statements with two search criteria without index on MySQL Document Store.  
Figure 13: Simple update operation by _id_.  
In MySQL Document Store, the update operation will filter the collection to find the document which has the corresponding id, and after that it will update the document value.  
For both alternatives, Elasticsearch and MySQL Document Store, the existing indexes will be used to find and change the value of the document.  
Based on the results obtained after performing the data modification operation, it can be seen that Elasticsearch is faster, but considering that the processing times are at the level of milliseconds, it can be also stated that MySQL Document Store was very fast in the update operation.