esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application of Hyperchaotic Sequence in Secure Communication
### Simulation Results  
In order to display the complex dynamic characteristics of the generated hyperchaotic sequence, we use three groups of chaotic models as the RM-DCSK scheme chaotic sequence generators. The chaos model of the first group is the classical logistic map, sine map, and tent map. The second group was the memristor model proposed in reference [27]. We generated two-dimensional memristor models based on the classical logistic map, sine map, and tent map, denoted as 2D-MLM, 2D-MSM, and 2D-MTM, respectively. The last group is three two-dimensional memristor models constructed in this paper. We use the above three groups of chaotic sequence generators to simulate the RM-DCSK scheme. The bit error rates (BERs) are calculated under different signal-noise-rate (SNR) and spread spectrum factor M. Note that each experiment transmits data in a randomly generated binary sequence of \(10^{5}\) bits. The initial conditions and control parameters of these models are listed in Table 4, which can make these models in a state of chaos.  
Firstly, the RM-DCSK scheme with different SNR are considered. By setting \(\text{SNR}\in\{0,1,2,\cdots,30\}\) and fixing \(\text{M}=300\), we simulated the RM-DCSK scheme using three groups of chaotic sequence, respectively. The BERs of the demodulated and modulated signal under different SNR is shown in Figure 9a. It can be seen that when the spectrum factor M is determined, when SNR is small, the models based on three groups of chaotic sequence generators can obtain almost the same BERs. However, the model based on the third group of the chaotic sequence can obtain smaller BERs than the first two groups of chaotic sequence with the increase of SNR. Furthermore, By setting \(\text{M}\in\{260,260,270,\cdots,350\}\) and fixing \(\text{SNR}=18\), we also simulated the RM-DCSK scheme using three groups of chaotic sequence, respectively. The BERs of the demodulated and modulated signal under different spectrum factor M is depicted in Figure 9b. As shown, the model based on the third group of chaotic sequence can obtain smaller BERs than the first two groups of chaotic sequence under different M. Chaotic system is used in secure communication. The distribution of chaotic output sequence is a key factor affecting transmission efficiency. Therefore, compared with the first two groups of chaotic sequence generators, the third group of chaotic sequence generators is more suitable for chaotic secure communication.  
Figure 8: Structure of the transmission signal in RM-DCSK.
## 2 Materials and Methods  
### Protocol and Eligibility Criteria  
The present systematic review follows the guidelines specified in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Statement, as detailed in reference [27]. Through the meticulous alignment of our research methodologies with the PRISMA Statement, we have established our systematic review as a model exemplifying meticulous planning, exhaustive data collection, rigorous analysis, and transparent reporting, leading to comprehensive and objective evaluation of the subject under scrutiny. The protocol of this systematic review was not registered in a public registry.  
The eligibility criteria are as follows.  
Question: Is there a difference in the treatment of peri-implantitis between air abrasion and other mechanical decontamination methods.  
Participants: Patients who received treatment for peri-implantitis.  
Interventions: The use of air abrasion techniques.  
Comparisons: Mechanical decontamination methods.  
The primary outcomes measured were probing depth, bleeding on probing, and alveolar bone loss. We excluded in vitro and animal studies, microbiological studies, and review articles. Moreover, literature reviews, interviews, case reports, case-control studies, retrospective study designs, and studies published in languages other than English were excluded.
## 5 Mechanical Quality Factor  
The mechanical quality factor \(Q_{m}\) (\(Q_{m}=1/2\)\(\zeta_{m}\)) is important to investigate. An increase in \(Q_{m}\) results in an increase in the tip displacement, which means an increase in the volume of motion. The second issue is that the half-power bandwidth is dependent on \(Q_{m}\). The investigations are carried out considering the properties of the PEH A in Figure 1. Two operating conditions are considered for the investigations: the corresponding \(R_{op\_res}\) and \(\omega_{n}\) (\(R_{op\_res}\) is dependent on \(\zeta_{m}\)), and \(R_{d}\) and \(\omega_{d}\) (\(R_{d}\) is independent on \(\zeta_{m}\)). Figure 8a displays the impact of the quality factor on the transfer function between the tip displacement and the input displacement. At the corresponding optimal load resistance \(R_{op\_res}\), the transfer function \(H_{xu}\) is directly proportional to \(Q_{m}\). At an applied load of \(R_{d}\), the increase in the transfer function is very slight for a large increase in \(Q_{m}\). Figure 8b displays the impact of the quality factor on the power output. At the corresponding \(R_{op}\), the power output is directly proportional to \(Q_{m}\). At an applied load resistance of \(R_{d}\), the increase in the power output is very slight for a large increase in \(Q_{m}\).  
The power output at different quality factors is shown in Figure 9. At the corresponding \(R_{op\_res}\), a decrease in \(Q_{m}\) to the half causes a reduction in the power output to the half. For this significant drop in the power output, there is a small increase in the half-power bandwidth. At an applied load of \(R_{d}\), the decrease in the power output due to a decrease in \(Q_{m}\) is less than the decrease of the power output at the corresponding \(R_{op}\). At \(R_{d}\), there is no increase in the half-power bandwidth for a decrease in \(Q_{m}\). At low mechanical damping ratios, the power peak is sharp at \(R_{op\_res}\) and \(\omega_{n}\), while at \(R_{d}\) and \(\omega_{d}\) the peak of the power output is not as sharp as at \(R_{op\_res}\) and \(\omega_{n}\). The power output at \(R_{d}\) and \(\omega_{d}\) is more consistent.  
Figure 8: The impact of the quality factor on (**a**) the transfer function and (**b**) the power output at input excitation level of \(u=0.8\)\(\upmu\)m.  
Figure 7: Normalized power to the total occupied volume (beam, tip mass, and motion) at input displacement of (**a**) \(u=0.8\)\(\upmu\)m (**b**) \(u=180\)\(\upmu\)m (\(f_{n}\) constant at 155 Hz, free length of 9.62 cm, mass ratio varies from 0 to 0.52 for the corresponding total thickness from 0.382 to 0.682 cm, respectively, and \(\zeta_{m}=0.01\)).
## 2 Materials and Methods
### Mixture Proportion Optimization and Test Methods
#### 2.2.1 Mixture Proportions  
The mixture proportions, with flowability exceeding 200 mm, are presented in Table 3. The initial water content represents the proportion of water mass to dry soil mass (in percentage). The cement content is the proportion of cement mass to dry soil mass (in percentage). The PCE content is the proportion of PCE mass to the total mass of dry soil and cement (in percentage). T1 and T2 were designed to investigate the effect of PCE on both flowability and strength. T2, T3, T4, T5, and T6 were designed to examine the impact of a varying cement content. T2, T7, T8, and T9, corresponding to 38.8% (natural water content), 46.5% (1.5 \(w_{\text{L}}\)), 62% (2 \(w_{\text{L}}\)), and 80% (2.58 \(w_{\text{L}}\)), respectively, were designed to assess the influence of the initial water content. The selection of 80% water content was based on the observation that, at this initial water content (with a cement content of 8%), the flow value of the mixture could exceed 200 mm without the addition of PCE.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Natural Water** & **Clay** & **Silt** & **Sand** & **Gravel** & **Liquid** & **Plastic** & **USCS** & **Color** \\
**Content** & **Content** & **Content** & **Content** & **Content** & **Limit** & **Limit** & **USCS** & **Color** \\ \hline
38.8 & 2.9 & 11.5 & 85.1 & 0.5 & 31.0 & 14.6 & SM & Brown \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of excavated soil [in %].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **CaO** & **MgO** & **SO\({}_{3}\)** & **LOI \({}^{*}\)** & **CI\({}^{-}\)** & **Initial Setting** & **Final Setting** \\
24.99 & 8.26 & 4.03 & 51.42 & 3.71 & 2.51 & 3.31 & 0.04 & 172 & 234 \\ \hline \hline \end{tabular}  
* Loss on ignition.  
\end{table}
Table 2: Chemical composition and physical properties of cement [in %]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mix ID** & **Initial Water Content (\%)** & **Cement Content (\%)** & **PCE Content (\%)** & **Curing Time (d)** \\ \hline T1 & & 8 & / \\ T2 & & 8 & 0.52 \\ T3 & 38.8 (natural water content) & 10 & 0.55 \\ T4 & 15 & 0.59 \\ T5 & & 20 & 0.63 \\ T6 & & 25 & 0.67 \\ \hline T7 & 46.5 (1.5 \(w_{\text{L}}\)) & & 0.44 \\ T8 & 62 (2 \(w_{\text{L}}\)) & 8 & 0.33 \\ T9 & 80 (2.58 \(w_{\text{L}}\)) & & / \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mix proportions of soil-based CLSMs.
# Decision Support Models for Operations and Maintenance for Offshore Wind Farms: A Review
## 7 Discussion
### Types of Maintenance  
The different types of maintenance discussed in the review are (a) preventive maintenance, (b) condition monitoring, (c) condition-based maintenance and (d) corrective maintenance.  
1. Preventive maintenance is conducted preventively on a fixed schedule to prevent failures from happening. It is included in the models and analyses by [16; 17; 20; 21; 22; 26; 32; 36; 46].
2. Condition monitoring can be used to monitor the performance of a wind turbine or component and can aid in predicting failures before they occur. Condition monitoring is studied by [63; 67; 70; 71; 72; 73].
3. Condition-based maintenance is similar to preventive maintenance conducted to prevent failures. It is, however, based on the condition of the system instead of a fixed schedule. Condition-based maintenance is (i) considered in the models by [16; 22; 40]; and has been (ii) reviewed by [35; 75].
4. Corrective maintenance must be carried out after a fault to return the component to a state in which it can perform its required function. Corrective maintenance is included in the models by [12; 16; 17; 20; 22; 26; 36].
# Decentralizing Private Blockchain-IoT Network with OLSR
## 6 Conclusions and Future Works  
This paper introduces a method of decentralizing the underlying network of the private blockchain-IoT system using OLSR. The private blockchain-IoT networks will benefit from the flexibility of the routing protocol by operating over multiple ad hoc IoT nodes. Moreover, the blockchain connection well recovers from failures, which may happen in various IoT application scenarios. We built a real Ethereum blockchain-IoT network that works as a monitoring application. The network collects PM monitoring data and transfers them over the ad hoc network with the OLSR protocol. The evaluation results show that the blockchain connection recovers from the underlying network failures within 22.3 s on average. Besides, we also evaluated and compared the latency performance in ad hoc and infrastructure networks. The results show that the blockchain connection over an ad hoc network has a better latency for block propagation than transaction propagation. Meanwhile, the infrastructure network disseminates information more quickly than the ad hoc network through the same path.  
In the future, we will investigate and evaluate the recovery time when varying other parameters such as different validity time, traffic conditions, etc., aiming to find the optimal recovery time. Moreover, we plan to conduct a comparative study of different ad hoc routing protocols in blockchain-IoT networks.  
Conceptualization, X.C. and K.N.; methodology, X.C. and K.N.; software, X.C.; validation, X.C. and K.N.; formal analysis, X.C.; investigation, X.C., S.T. and K.N.; resources, X.C.; writing--original draft preparation, X.C. and K.N.; writing--review and editing, S.T., K.N. and H.S.; supervision, K.N. and H.S.; project administration, K.N.; funding acquisition, K.N. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Ping (ms)** & **Lost Rate (\%)** & **Throughput (Mbit/s)** \\ \hline Link 1 & 1.991 & 0.009 & 22.4 \\ \hline Link 2 & 2.165 & 0.01 & 21.8 \\ \hline Link 3 & 10.643 & 1.76 & 2.49 \\ \hline Link 4 & 9.975 & 1.82 & 2.13 \\ \hline Path 1 & 13.599 & 1.79 & 2.09 \\ \hline Path 2 & 13.370 & 1.85 & 1.93 \\ \hline Path 2 in infrastructure mode & 11.834 & 0.93 & 1.56 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Link evaluation.  
Figure 4: Latency performance between the sender and the receiver.  
**Funding:** This work was supported by JSPS KAKENHI Grant Numbers 19K20251 and 20H04174. Additionally, [PERSON] is supported by the Leading Initiative for Excellent Young Researchers (LEADER) program from MEXT, Japan.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Reservation-Based 3D Intersection Traffic Control System for Autonomous Unmanned Aerial Vehicles
## 7 Discussion of the Scheduling Algorithm
### Effect of the Cube Size to the Efficiency in the Intersection  
As discussed in the complexity analysis of the scheduling algorithm, the complexity of the scheduling increases as the size of the cube decreases. However, there is an advantage to using smaller cube size. For example, in Figure 12a, the size of each cube is 2.5 m and there are two UAVs with radius of 1 m. Since there can be only one UAV in a cube at a time, the minimum distance between them is the size of the cube, which is 2.5 m. On the other hand, if the size of each cube is 1 m, as shown in Figure 12b, then the minimum distance between the UAVs becomes 1 m. Hence, as we decrease the cube size, more UAVs can be in the intersection, thus increasing the efficiency in the intersection. Therefore, there is a  
Figure 11: y-z view of Figure 10.  
Figure 10: 3D view of the graph to be searched for a UAV entering from the left-most entrance on mode 2 where UAV can change layer only at the start and end of the intersection.  
tradeoff between the effect of the cube size to the scheduling run time and the efficiency in the intersection.
## 2 Related Works
### AMSGrad  
In the recent paper \"On the convergence of Adam and beyond\" [18], the authors indicated that Adam might suffer from non-convergence issues. Long-term memory should be emphasized to reduce the influence of non-informative gradients and eliminate unreasonable large learning rates. Therefore, the AMSGrad is proposed.  
The AMSGrad differs at two points from the Adam algorithm. The first difference is that the AMSGrad only uses the EMA indicator of gradients \(m_{t}\) and EMA predictor of square gradients \(v_{t}\), and omits the bias corrections for both \(\hat{m}_{t}\) and \(\hat{v}_{t}\). Note that \(m_{t-1}\) and \(v_{t-1}\) are the last corresponding EMA indicators. The second difference concerning Adam is that the denominator calculation is replaced by taking the maximum value \(\hat{v}_{t}\) between the last indicator \(v_{t-1}\) and the current indicator \(v_{t}\). In this way, a sort of long-term memory is incorporated into large gradients, which results in a non-increasing step size and avoids the pitfalls of Adam. However, the denominator increases monotonically because only the biggest variable is concerned, leading the slow updating or non-updating circumstances like AdaGrad. The formal notations of AMSGrad are described as follows Equations (12) to (15). The parameter setting is the same as Adam, where \(\alpha\) is 0.001, \(\beta_{1}\) is 0.9, and \(\beta_{2}\) is 0.999. The epsilon \(\varepsilon\) is \(1\times 10^{-8}\).  
The general updating rule for weights in AMSGrad:  
\[\theta_{t+1}=\theta_{t}-\frac{\alpha}{\sqrt{\hat{v}_{t}+\varepsilon}}\times m _{t} \tag{12}\]  
Calculating the EMA indicator of gradients:  
\[m_{t}=\beta_{1}\times m_{t-1}+(1-\beta_{1})\times g_{t} \tag{13}\]  
Calculating the EMA indicator of square gradients:  
\[v_{t}=\beta_{2}\times v_{t-1}+(1-\beta_{2})\times g_{t}^{2} \tag{14}\]  
Taking the maximum operation for the denominator:  
\[\hat{v}_{t}=max(v_{t-1},v_{t}) \tag{15}\]
# Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning
## 4 Discussion
The second criterion involved comparing the deforestation rates obtained for each patch size with official reports and previous studies conducted in the same area using Sentinel-2 data and deep learning methodologies, which estimated deforestation rates between 2% and 5% [20; 32; 131; 132]. For instance, a study employing Convolutional Neural Networks (CNN) with 32 \(\times\) 32 patches estimated a deforestation rate of 2.56% between 2015 and 2020 [20], while another study using U-Net with 16 \(\times\) 16 and 32 \(\times\) 32 patchesreported a 2.59% deforestation rate between 2015 and 2022 [32]. The 9 \(\times\) 9 patch aligned most closely with this range, producing results that balanced noise reduction and fine-scale deforestation detection. The 1 \(\times\) 1 and 3 \(\times\) 3 patches significantly overestimated deforestation, making them unreliable. Therefore, the 9 \(\times\) 9 patch size provided the best overall performance, preserving detail while maintaining classification stability (Figure 15). This study highlights the importance of optimizing patch selection strategies to enhance deforestation detection, particularly in regions where ground truth data is limited. Despite the progress made, several challenges remain. The first challenge arises from the model's occasional difficulty in distinguishing between organ trees and soil, leading to errors in organ detection. Argan trees, native to arid and semi-arid regions of southwestern Morocco, have small, oval, leathery leaves spaced to conserve water in harsh conditions. The result is less dense foliage than trees from temperate or tropical climates, which tend to have larger and denser leaves. In addition, the paraled, twisted, and widely spreading branches of the organ contribute to this effect. The low canopy density allows sunlight to reach the ground (Figure 2), which makes the spectral signatures of the organ tree and the ground similar, making it challenging for machine learning models to differentiate between the two. The model's ability to accurately detect organ trees could be improved by using images with higher spatial resolution than those provided by Sentinel-2, ideally  
Figure 14: Deforestation maps by patch size in study area (Region 2). Figure 13: Deforestation maps by patch size in study area (Region 1). with a resolution of less than one meter, such as drone or airborne data. Alternatively, experimenting with other models, especially deep learning models, may yield better results. The second challenge lies in the phenomenon of \"same spectra but different objects, same objects but different spectra\", which limits classification accuracy when relying on single-date data [21; 25]. This study mitigated this issue by employing multi-temporal data, demonstrating the value of temporal information in improving classification results. While challenges remain, our findings provide a solid foundation for further advancements in the detection and management of organ forests.
# Validation of Drifting Buoy Data for Ocean Wave Observation
## Appendix B Bootstrap Method  
The effective sample sizes \(N_{e}(k)\) were evaluated for each buoy using the method of Reference [32], where \(k\) is the buoy number (\(k=1,\dots,N_{B}\)), and the \(N_{B}\) is the number of drifting buoys for comparison. An effective sample size cannot be greater than the sample size. A pair of wave data \((H_{e},H_{b})\) is resampled. The normal random numbers \(\Pi(H_{b}+\epsilon,S_{d}(H_{b}+\epsilon))\) are generated. The parameter \(\epsilon\) is a uniform random number, \(-0.05\) m \(<\epsilon<0.05\) m, which considers that the resolution of the buoy-observed wave height is \(0.1\) m. For the observed value of \(H_{b}\), the actual drifting buoy wave height is \(H_{b}+\epsilon\)A number \(\Pi(\langle Y\rangle,S_{d}(Y))\) is a normal random number of which the mean is \(\langle Y\rangle\), and the standard deviation is \(S_{d}(Y)\equiv\langle(Y-\langle Y\rangle)^{2}\rangle^{1/2}\). The standard deviation \(S_{d}(H_{b}+\epsilon)\) is estimated from \(S_{d}(H_{1/3})\langle H_{1/3}\rangle^{-1}\) in Figure 11 (black line), which is described in Appendix A. The number of \(N_{e}(k)\) pairs of \((H_{e},\Pi(H_{b}+\epsilon,S_{d}(H_{b}+\epsilon)))\) was generated for each \(k\), and the skill metrics was evaluated from the wave parameters of the \(\sum_{k=1}^{N_{B}}N_{e}(k)\) pairs. Skill metrics, such as \(r_{c}(H_{g},H_{b})\), \(R_{d}(H_{g},H_{b})\), and \(SI(H_{g},H_{b})\), were compared with \(r_{c}(H_{g},H_{e})\), \(R_{d}(H_{g},H_{e})\), and \(SI(H_{g},H_{e})\). These comparisons were made \(10^{3}\) times. The probabilities of the accuracy deterioration of the ERA5 wave data were evaluated by comparing \(10^{3}\) pairs of skill metrics.
## 2 Mathematical Model
### Entropy Generation Analysis  
Based on the EGM technique, the \(\hat{S}_{\text{gen}}\) (entropy generation rate of the air-to-air exchanger) is expressed in terms of the temperature and pressure using the ideal gas model on both sides ((\(R_{\text{a}}\), \(c_{\text{p}}\))\({}_{\text{f}}\) and (\(R_{\text{a}}\), \(c_{\text{p}}\))\({}_{\text{e}}\)), as shown in Equation (1) [31]:  
\[\hat{S}_{\text{gen}}=\left(\dot{m}c_{\text{p}}\right)_{\text{f}}[\ln\frac{T_{ \text{fo}}}{T_{\text{fi}}}-(\frac{R_{\text{a}}}{c_{\text{p}}})_{\text{f}}\ln \frac{P_{\text{fo}}}{P_{\text{fi}}}]+\left(\dot{m}c_{\text{p}}\right)_{\text{e} }[\ln\frac{T_{\text{eo}}}{T_{\text{ei}}}-(\frac{R_{\text{a}}}{c_{\text{p}}})_{ \text{e}}\ln\frac{P_{\text{eo}}}{P_{\text{ei}}}] \tag{1}\]  
where \(R_{\text{a}}\) is ideal gas constant, \(c_{\text{p}}\) is specific heat at constant pressure, \(\dot{m}\) is mass flow rate. \(T\) and \(P\) are temperature and pressure, respectively. Subscripts i, and o mean inlet and outlet, respectively. Subscripts f, e, mean fresh air, exhaust air, respectively. Equation (1) indicates that \(\hat{S}_{\text{gen}}\) is generated by two behaviors of the heat exchanger: heat transfer and fluid flow.