esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Design and Method
### Fuzzy Hierarchy Analysis
#### 3.5.2 Determining the Weights of Complementary Judgment Matrices  
Let M\({}_{\rm i}^{1}\) (i = 1, 2, \(\ldots\), n) be the sum of the nth row of the judgment matrix in the dimension layer. M\({}_{\rm ii}^{2}\) (i = 1, 2, \(\ldots\), n) (j = 1, 2, \(\ldots\), m) is the sum of the nth row of the nth judgment matrix in the criterion layer.  
Let \(\omega_{\rm i}^{1}\) (i = 1, 2, \(\ldots\), n) be the nth weight for indicator for primary layer; \(\omega_{\rm ii}^{2}\)(i = 1, 2, \(\ldots\), n) (j = 1, 2, \(\ldots\), m) is the nth weight for secondary layer in the nth primary indicator.  
Figure 2: Indicator establishment flow chart.  
There are more than ten methods for calculating the weights of fuzzy judgment matrices, and the use of Equations (2) and (3) demonstrated in the literature [32] can reduce the amount of calculations while obtaining reasonable and effective results. Subsequently, \(\omega_{i}^{1}\) and \(\omega_{ii}^{2}\) are obtained according to Equations (2) and (3).  
\[\omega_{i}^{1}=\frac{M_{i}^{1}+\frac{n}{2}-1}{n(n-1)}(i=1,2,\ldots,n) \tag{2}\]  
\[\omega_{ii}^{2}=\frac{M_{ij}^{2}+\frac{n}{2}-1}{n(n-1)}(i=1,2,\ldots,n)(j=1,2, \ldots,m) \tag{3}\]  
Let \(W^{1}\) be the vector of primary indicator weights and \(W_{1}^{2}(i=1,2,\ldots,n)\) be the vector of secondary indicator weights. \(W^{1}=\left(\omega_{1}^{1},\omega_{2}^{1},\ \ldots,\omega_{n}^{1}\right)^{T}\), \(W_{1}^{2}=\left(\omega_{1i}^{2},\ \omega_{1i^{2}}^{2},\ \ldots,\omega_{im}^{2}\right)^{T}\).  
Finally, a consistency test was performed on the complementary judgment matrix. The consistency test helps to confirm that the expert's judgments are consistent when comparing the factors in pairs. For example, if the expert believes that A is more important than B and that B is more important than C, it is reasonable that A should be more important than C. In this study, if the difference between any row and the corresponding indicator data of other rows is the same constant, then it is a consistency matrix, i.e., it passes the consistency test.
## 3 Implementation of the OAC-DES
### ANN-Simulation  
Fitness function evaluation is the most complex task in the (1+1)-DES, because the quality of the solution cannot be calculated directly, but must be determined by evaluating its behavior in real environmental conditions. Due to the time complexity of this estimation, the fitness evaluation is instead performed using ANN-simulation, which presents the surrogate model. In our study, the behavior of each solution is actually predicted using ANN-simulation as depicted in Figure 7, from which it can be seen that the same two inputs used in ANN-learning also enter into the ANN-simulation. The former is obtained from the non-linear robotic mechanism and denotes the measured velocity \(\omega_{a}(k-1)\), while the latter shows the output from the (1+1)-DES and denotes the trial solution reference current \(i_{r}^{trial}(k+1)\) (actually \(x_{trial}^{(g)}\)), where \(trial=\{orig,init\}\) determines whether either the original or the initial solution is evaluated, respectively. The output from the ANN-simulation presents the predicted velocity \(\hat{\omega}_{trial}^{(g)}(k)\) that enters into the fitness function calculation. This predicted value is then subtracted from a reference velocity \(\omega_{r}(k)\) (i.e., desired value), and the difference is squared to obtain the fitness function value. In other words, the fitness function is defined as:  
\[f(\mathbf{x}_{trial}^{(g)})=(\omega_{r}(k)-\hat{\omega}_{trial}^{(g)}(k))^{2}, \tag{5}\]  
where the square of the difference between the reference \(\omega_{r}(k)\) and predicted velocity \(\hat{\omega}_{trial}^{(g)}(k)\) ensures that the difference is always positive. The task of the (1+1)-DES is to minimize the value of the fitness function, with actual \(\omega_{a}(k-1)\) and reference velocities \(\omega_{r}(k)\) held constant during the single sample time interval.  
Figure 6: Effect of the different mutation step sizes on optimization results.  
Figure 7: The fitness function evaluation in OAC-DES.
## 7 Tests and Results
### WiMAX Test  
The previously described WiMAX system was used to perform several reliability tests of the WiMAX technology. Due to the decay of the signal intensity with the increase in the working distance or with the presence of obstacles leading to multi-path fading, the IEEE 802.16.1 standard [36] does not provide any data on the distance achievable with WiMAX devices.  
Because our goal is to also serve areas placed some kilometers away from the BS, we decided to test essentially Line of Sight (LOS) scenarios to verify our system by mounting the receiving antenna on top of an extendable tripod and placing the system on the roof of a building, whilst the BS was on the roof of a building itself. For the non-line of sight (NLOS) cases, three typical obstacle situations can be considered: when there is a hill between the BS and the client, when there are trees and when there is vegetation. As demonstrated by [37], vegetation and foliage in particular, cause unstable radio links and affect a lot the global throughput of the system obliging the CPE to work at lower modulations. However our aim is to verify the reliability of the radio link rather than the maximum bandwidth achievable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**XBEE STANDARD—Patch Antenna**} \\ \hline \multicolumn{2}{c}{**Sunny**} & **Rainy** \\ \cline{2-3}  & **up to 45 m** & **up to 45 m** \\ \hline No obstacles & 100\% & 100\% \\ \hline Vegetation & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: ZigBee reliability tests.  
The considered radio parameters are the received signal strength indicator (RSSI) and the carrier to interference plus noise ratio (CINR). The first is a measurement of the total received power of the frame preamble and is used by the subscriber station to determine the received signal level from the BS [38]. The latter represents a measure of the quality of the WiMAX signal. The higher the value of the CINR, the more throughput a link can maintain. The standard 802.16 supports the link adaptive modulation and the channel coding (AMC) and, in order to maintain a constant bit error rate (BER) of 1 part per million, it adapts to signal degradation by dropping to a lower modulation [39].  
The AMC is controlled BS side and it applies the Modulation and Coding Schemes (MCS) which defines the most suitable modulation rate for current radio channel, and leads to the highest data rate possible. WiMAX mainly uses quadrature amplitude modulation (QAM) with its more powerful 64-QAM5/6 in downstream; it is optional in upstream [26]. Maximum modulation in upstream is usually 16-QAM3/4. In Table 5 the test shows no packet loss until 12.9 km.
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 4 Design Issues in Gait, Balance and RoM Wearable Systems
### Thresholds  
A threshold is a limit used to trigger an action when that limit is surpassed. Various systems use thresholds to make decisions and conclusions about the data's behavior. The most common solution is to set thresholds that generalizes the data as much as possible. Additionally, there are studies that investigated the use of thresholds with filtering techniques to extract useful gait, balance, and RoM parameters from wearable sensors [32; 33; 44; 45; 54; 33; 46]. Another way to avoid setting thresholds is by using machine learning techniques. By using these techniques, the algorithms are trained to learn the most optimal threshold for a specific problem or measurement [51; 60; 61; 62; 66; 70; 85].  
Systems should provide the option to adjust thresholds since they should be optimized to the movements to give the most reliable and accurate measurements. Otherwise, the data yields misleading results, which may affect decisions made by researchers of professionals making rehabilitation decisions.
## 5 Conclusions  
This paper describes a novel method for separating PD signals based on selected bispectra. The characteristic parameters are selected from the bispectra of the UHF signals with Fisher-like class separation measures. The separation measures are  
Figure 8: Separabilities of the UHF signals collected in the substation. (**a**): Bispectra of UHF signals radiated by the PD simulator. (**b**): Bispectra of UHF signals radiated by the extra discharge.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(Q=5\) & \(Q=8\) & \(Q=10\) & \(Q=12\) & \(Q=15\) & \(Q=20\) \\ \hline
74\% & 77\% & 80\% & 82\% & 81\% & 82\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Separation rate of signals collected in the substation with different numbers of selected bispectra.  
Figure 9: Separabilities of UHF signals collected in the substation.  
separation measures. By the application of bispectra, the narrow-band periodical interferences as well as Gaussian noises blended in the UHF PD signals can be significantly suppressed. The phase information in the bispectra can be useful for further signal processing.  
The proposed method was used to process UHF signals mixed with fixed-frequency interference and Gaussian white noise with different SNRs. The result showed that signals generated by multiple PD sources could be effectively separated. When SNR \(>\) 10 dB, the signal separation rate can exceed 80%. The separation rate of the algorithm based on the selected bispectra is related to the number of characteristic parameters. Therefore, it is easy to determine the number of characteristics.  
To verify the validity of the proposed method for separating PD sources in a substation, the method was used to process UHF signals radiated by different discharge sources and received by an antenna array in a 500 kV substation. The signal separation rate exceeded 80% when more than 10 characteristic parameters were selected, which verified the effectiveness of the proposed method.  
Conceptualization, Z.H.; Data curation, Z.H.; Formal analysis, Z.H.; Funding acquisition, H.H.; Investigation, H.H.; Methodology, H.H.; Project administration, H.H.; Resources, H.H.; Software, Y.Z.; Supervision, Y.Z.; Validation, Y.Z.; Visualization, Y.Z.; Writing--original draft, G.S.; Writing--review & editing, X.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Program of China under Grant 2017 YFB0902705.  
The authors declare no conflict of interest.
# Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region
## 2 Study Area and Datasets
### SAR Satellite Data  
Sentinel-1 is an important component of the Global Monitoring for Environment and Security (GMES) [35], a joint initiative of the European Commission and the European Space Agency (ESA), consisting of two satellites of the constellation. Sentinel-1A was launched on 3 April 2014, while Sentinel-1B was launched on 25 April 2016 [3]. Sentinel-1A provides C-band images in both singular and dual polarization with a 12-day repeat cycle [36], and has four imaging modes: strip map  
Figure 1: The study area is located in the southwest of Jiangsu Province, China and an overview of the SAR data (R: 12 November 2015 VH polarization, G: 21 March 2016 VV polarization, B: 8 May 2016 VH polarization).  
(SM), interferometric wide swath (IW), extra wide swath (EW), and wave (WV), each with different resolutions. In this study, six Sentinel-1A images in IW mode with a dual polarization scheme (single-look complex (SLC) products) were acquired from the ESA (European Space Agency), as shown in Table 1.
# Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks
## 5 Conclusions  
In this paper, we investigated a UAV-assisted DFRC system in the ocean monitoring scenario to simultaneously realize data collection and target localization. To maximize the communication throughput while minimizing the CRB of localization at the same time, we formulate a two-objective optimization problem under constraints of buoy transmit power threshold and the available UAV's location. A joint communication and radar sensing many-objective optimization algorithm is proposed to solve this problem. Numerical results revealed the superiority of our proposed algorithm in the ocean monitoring data collection system which significantly improves both the network throughput and the localization accuracy. This proposed design can be used for complex marine activities that require multiple tasks to be performed simultaneously and will improve the efficiency and performance of different tasks. However, in real-world scenarios, controlling all factors becomes more challenging due to external influences, unpredictable events, and limitations in experimental setup. In the future, we will consider additionally exploring the impact of other variables, such as UAV operation or wireless environments, as well as the effects of energy consumption and multi-target detection capability on the DFRC system in marine scenarios.  
Conceptualization, Y.L. and F.H.; methodology, Y.L.; software, Y.L.; validation, Y.L., F.H. and M.C.; formal analysis, H.J.; investigation, H.Z.; resources, S.Z.; data curation, S.Z.; writing--original draft preparation, Y.L.; writing--review and editing, M.C.; visualization, S.Z.; supervision, S.Z.; project administration, H.J.; funding acquisition, S.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by National Key R&D Program of China 2019 YFB2102300 and 2019 YFB2102301, in part by National Natural Science Foundation of China under Grant 61936014, in part by National Natural Science Foundation of China under Grant 6201388, in part by Shanghai Municipal Science and Technology Major Project No. 2021 SHZDZX0100, in part by Shanghai Science and Technology Innovation Action Plan Project 22511105300, in part by Natural Science Foundation of Shanghai: 222R1463400, and in part by Fundamental Research Funds for the Central Universities.  
Not applicable.  
Data Availability Statement: Data is unavailable due to privacy restrictions.  
The authors declare no conflict of interest.
# Blockchain-Based UAV-Assisted Mobile Edge Computing for Dual Game Resource Allocation
## 3 Model of a Drone-Assisted Mobile Edge Computing System
### DSO Algorithm Two-Stage Equalisation Analysis
Then the optimal strategy is set and the utility functions of the bidders are analyzed and it is found that the optimal strategy is to use the true valuation as the bidding price. This strategy is optimal in the Vickrey auction because deviation from this strategy leads to lower utility. This results in an equilibrium solution where all bidders bid at their true valuations, and this combination of strategies is a Nash equilibrium, i.e., no bidder is able to increase utility by changing strategies. The highest bidder then wins, but pays the second highest bid. Next, we analyze the auction process for drones and users. In the auction algorithm, the user utility function can be defined as Equation (17). \[U_{i}=\sum_{j=1}^{M}x_{ij}\big{(}V_{i,j}-p_{j}\big{)} \tag{17}\]  
The utility function of the UAV can be defined as Equation (18). \[U_{j}=\sum_{i=1}^{N}x_{ij}p_{j}-C_{j} \tag{18}\]  
Here N denotes the number of users, M is the number of drones, \(B_{i,j}\) is user i's bid for drone j, \(p_{j}\) is the service price of drone j, determined by the second highest bid, \(x_{i,j}\) indicates the variable, \(x_{i,j}=1\) if user i chooses drone j, otherwise \(x_{i,j}=0\), \(V_{i,j}\) user i's valuation of drone j, and \(C_{j}\) is the cost of drone j's service. The user's goal is to maximize their utility function, which can be expressed in Equation (19). \[\underset{x_{i,j}}{max}\;U_{i}=\sum_{j=1}^{M}x_{i,j}\big{(}V_{i,j}-p_{j}\big{)} \tag{19}\]  
User i will choose the UAV that maximizes its utility, which can be represented by Equation (20). \[x_{i,j}=\left\{ \begin{array}{ll}1&if\;V_{i,j}-p_{j}\geq 0\\ 0&otherwise\end{array} \right. \tag{20}\]  
For each drone j, the price is determined by the second highest bid. The drone adjusts its service strategy according to its service cost and the revenue it receives. The objective of the drone is to maximize its utility function, which can be expressed in Equation (21). \[\underset{x_{i,j}}{max}\;U_{j}=\sum_{i=1}^{N}x_{i,j}p_{j}-C_{j} \tag{21}\]  
Drone j selects the user with the highest bid and takes the second highest bid as the price, which can be expressed in Equation (22). \[p_{j}=\underset{k\
eq i}{max}\;b_{k,j} \tag{22}\]  
In equilibrium, the combination of the user's and drone's strategies maximize both drone utility and user utility. This means that under the current strategy, drones and users have no incentive to unilaterally change their strategies. User i will choose the drone j that maximizes its utility, i.e., the drone of choice. Drone j will select the user with the highest bid based on the user's bid and use the second highest bid as the service price [44; 45; 46; 47]. This ensures that the drone maximizes its revenue. In equilibrium, all users and drones are chosen such that their utility functions are maximized and no unilateral strategy change can increase their utility.
# A New One-Dimensional Compound Chaotic System and Its Application in High-Speed Image Encryption
## 2 The Proposed New One-Dimensional Chaotic Map
### Logistic-Tent Map (LTM)
#### 2.3.5 NIST Statistical Test Analysis  
The NIST statistical test suite (version 2.1.1, National Institute of Standards and Technology, Gaithersburg, MD, USA) was used to evaluate the randomness of the bit sequence that was produced by the proposed system. The suite involves 15 tests which assess the randomness that might occur in a sequence. Each test result is converted to a \(p\)-value for judgement, and when applying the NIST test suite a significance level \(\alpha=0.01\) is chosen for testing. If the \(p\)-value \(\geq\alpha\), then the test sequence is considered to be pseudo-random. Setting 1000 different initial state values of the logistic tent map system (4) and iterating it 1,000,000 times for each initial state value generates 1000 sequences, 1 million bits each. The parameters used in the test are set as: \(a=4\), \(b=0.35\). For the first sequence, \(x_{0}=0.11\), and each time a 1,000,000 bits sequence is generated, the initial value of chaotic state variable increases by 0.001. Finally, each real number of state value \(x\) of the sequence is transformed into a binary number \(bx\) according to the following Algorithm 1:  
```
\(\text{Intx}=\text{mod(floor($x\times 10^{12}$),256$)}\); \(\text{If Intx}<128\) then \(bx=1\); Else \(bx=0\); End if
```  
**Algorithm 1** An algorithm for converting real numbers into binary numbers.  
Table 1 presents the results of NIST statistical test for 1000 sequences generated by the LTM, which indicates that the NIST test is effectively performed: All \(p\)-values among 1000 sequences used for testing are evenly distributed in the 10 subintervals, while the pass rate is also acceptable. The average pass rate is 99.01%, with one minimum pass rate of 98.1%. Experimental results show that the sequences generated by the logistic-tent map proposed in this paper have good pseudo randomness. Therefore, the compound chaotic system can be used as a pseudo-random number generator.  
Figure 5: The time-series diagram and cobweb graph of LTM system. (**a**) The time-series diagram; (**b**) the cobweb graph.
## 3 Results and Discussion
### Analysis of PET Degradation
#### 3.2.2 Differential Scanning Calorimetry Analysis
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**O04** & **Tg 1** & **Tm 2** & **Tc 3** & \(\Delta\)**Hm 4** & \(\Delta\)**Hc 5** & **Wc 6** \\ \hline T0a & 79.44 \(\pm\) 0.01 & 264.06 \(\pm\) 0.85 & 252.22 \(\pm\) 0.81 & 24.84 \(\pm\) 0.08 & 19.92 \(\pm\) 0.06 & 6.85 \\ T3 & 83.44 \(\pm\) 0.01 & 263.72 \(\pm\) 0.85 & 252.52 \(\pm\) 0.80 & 21.71 \(\pm\) 0.07 & 20.05 \(\pm\) 0.06 & 6.89 \\ T6 & 94.44 \(\pm\) 0.01 & 257.19 \(\pm\) 0.83 & 252.29 \(\pm\) 0.76 & 26.28 \(\pm\) 0.08 & 22.59 \(\pm\) 0.07 & 7.76 \\ t-Student (\(\alpha\) = 0.05) T0a vs. T3 & 0.00 & 0.65 & 0.67 & 0.00 & 0.07 & \\ t-Student (\(\alpha\) = 0.05) T0a vs. T6 & 0.00 & 0.00 & 0.92 & 0.00 & 0.00 & \\ t-Student (\(\alpha\) = 0.05) T3 vs. T6 & 0.00 & 0.00 & 0.75 & 0.00 & 0.00 & \\ \hline
**O1** & **Tg** & **Tm** & **Tc** & \(\Delta\)**Hm** & \(\Delta\)**Hc** & **Wc** \\ \hline T0a & 79.61 \(\pm\) 0.10 & 255.93 \(\pm\) 0.82 & 249.79 \(\pm\) 0.80 & 27.83 \(\pm\) 0.09 & 26.42 \(\pm\) 0.08 & 9.08 \\ T3 & 82.43 \(\pm\) 0.58 & 254.89 \(\pm\) 0.82 & 251.35 \(\pm\) 0.81 & 26.19 \(\pm\) 0.08 & 20.78 \(\pm\) 0.07 & 7.14 \\ T6 & 91.44 \(\pm\) 4.62 & 252.27 \(\pm\) 0.81 & 252.71 \(\pm\) 0.81 & 19.17 \(\pm\) 0.06 & 17.53 \(\pm\) 0.06 & 6.02 \\ t-Student (\(\alpha\) = 0.05) T0a vs. T3 & 0.01 & 0.20 & 0.08 & 0.00 & 0.00 & \\ t-Student (\(\alpha\) = 0.05) T0a vs. T6 & 0.13 & 0.01 & 0.01 & 0.00 & 0.00 & \\ t-Student (\(\alpha\) = 0.05) T3 vs. T6 & 0.38 & 0.02 & 0.11 & 0.00 & 0.00 & \\ \hline \hline \end{tabular}  
* Tg: Glass transition temperature (\({}^{\circ}\)C). \({}^{3}\)Tc: Crystallization temperature (\({}^{\circ}\)C). \({}^{4}\)A:Hm: Enthply change in melting (\(\mathrm{J/g}\)). \({}^{5}\)A:Hc: Enthalpy change in crystallization (\(\mathrm{J/g}\)). \({}^{6}\)Wc: Crystallinity percentage (%).  
\end{table} 
Table 10: Thermal properties of PET in mild (O04) and intense olive oils (O1). thermal behavior of the analyzed samples [69; 70]. A shift in Tg toward an increase in temperature occurred in both treatments compared to the T0a, being more evident in the T6 treatment for both oils. These increases align with those obtained by other authors who studied similar thermal treatments, obtaining values close to 80 \({}^{\circ}\)C [70; 71].