esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodological Framework
### Scenario Definition
#### 3.1.1 Building Configuration and Renovation Action
\begin{table}
\begin{tabular}{c c} \hline
**Component** & **Details** \\ \hline Heating System & Heat Pump Air-to-Water \\ Thermal Rating (kW) & 9.6 \\ Heating Efficiency & 2.5 \\ Lifetime (years) & 20 \\ Heating Purchase Cost (EUR) & 6720.00 \\ Heating Maintenance Cost (EUR/year) & 490.56 \\ \hline Cooling System & Basic Air Conditioning \\ Thermal Rating (kW) & 7.5 \\ Cooling COP & 2.5 \\ Lifetime (years) & 20 \\ Cooling System Purchase Cost (EUR) & 1298.08 \\ Cooling System Maintenance Cost (EUR/year) & 51.92 \\ \hline Insulation Material & Extruded Polystyrene \\ Thickness (mm) & 100 \\ Surface Area (m\({}^{2}\)) & 110.3 \\ Coverage & All Walls \\ Lifetime (years) & 100 \\ Insulation Purchase Cost (EUR) & 3131.90 \\ \hline Glazing Type & Double Glazing (Double/Float 4–16–4 (Air)) \\ Glazing Opening Surface (m\({}^{2}\)) & 160.61 \\ Frame Material & Aluminium Frame (20\% Frame Coverage) \\ Glazing Lifetime (years) & 40 \\ Glazing Purchase Cost (EUR) & 51,652.18 \\ Glazing Maintenance Cost (EUR/year) & 25.83 \\ \hline Tank for Domestic Hot Water & Copper Tank \\ Tank Capacity (L) & 600 \\ Electricity Rating (kW) & 5 \\ Lifetime (years) & 20 \\ DHW Purchase Cost (EUR) & 1681.79 \\ DHW Maintenance Cost (EUR/year) & 16.82 \\ \hline \end{tabular}
\end{table} 
Table 2: Building Components and Systems. \begin{table}
\begin{tabular}{c c} \hline
**PV System Parameter** & **Details** \\ \hline Mounting & Root-slanted PV System \\ PV Panel Material & Polycrystalline-Si \\ Reference Capacity (kWp) & 20.14 \\ PV Purchase Cost (EUR) & 19,334.40 \\ Lifetime of PV System (years) & 30 \\ \hline \end{tabular}
\end{table} 
Table 3: Energy Upgrade Details (PV system). Table 5 presents the CO\({}_{2}\) equivalent (CO\({}_{2}\)-eq) emission factors for Austria, as officially reported for each EU Member State, from the dataset provided by the Joint Research Centre (JRC) of the European Commission for the years 1990 to 2021 [49]. Since VERIFY dynamically adjusts the CO\({}_{2}\)-eq emission factor in its analysis every year, a Logistic Regression model was developed to estimate the corresponding emission factors beyond 2021 based on historical values. This model fits the historical data and generates future projections (extrapolations) of emission factors based on the characteristic trend observed. The model is based on the logistic function, as shown in Equation (1) [50]:  
\[f(x)=\frac{L}{a-\exp(-k)\cdot(x-median(x)))} \tag{1}\]  
where the parameters \(L\), \(k\), and \(\alpha\) are estimated through curve-fitting on the historical data. The curve fitting was performed using the nonlinear least squares method, minimizing the sum of squared residuals between the observed and predicted values. The algorithm was implemented using Python 3.8. The analysis period for the kindergarten spans 25 years, during which the CO\({}_{2}\)-eq emission factor for years beyond 2024 is assumed to remain consistent with the 2024 value.
## 3 Application and Results
### Case Study V: The MW9 Problem
Nevertheless, the ALMO approach still demonstrates significant computational savings compared to a traditional NSGA-II run without active learning. There are minor fluctuations in the percentage of feasible solutions and overall savings due to inherent randomness in the population generated by the algorithm. Finally, the impact of the ALMO approach on constraint evaluation savings over different optimization intervals is studied. As with the MW1 problem, the savings tend to  
Figure 20:2 Case study V: Pareto-front solution found using ALMO and two different configurations. Figure 20:3 Case study V: Pareto-front solution found using ALMO. Figure 20:4 Case study V: (**A**) percentage of infeasible solutions found in the optimal Pareto front; (**B**) overall savings in constraint evaluations in the optimization with different values of \(\phi\) and \(\beta\). increase as the optimization proceeds, with the active learner becoming more confident in its predictions. Figure 26 illustrates the increasing percentage savings in constraint evaluations across five equal optimization intervals.
## 5 Test and Simulation  
Extensive tests of the MWR payload were done in the laboratory environment, conducted in a precise moving platform, as shown in Figure 7. The moving platform can perform six degree of freedom (6-DOF) movement with an accuracy of microns in position and milliarcseconds in attitude orientation. The test work was divided into two stages: first, the assessment of MWR ranging accuracy by simply using K-band microwave signal processing, through transmitting and receiving. The output of this stage is the knowledge of the best accuracy that MWR can provide, which is considered as a fundamental specification for the next step. The second stage is hardware-in-the-loop (HIL) simulation. To conduct an in-depth analysis of the relative navigation filter performance using real MWR measurement equipment, an HIL simulation was run with all of the payloads fixed in the 6-DOF platform that will be deployed on-board in future SFF missions. Relative navigation errors that are provided by different measurement payloads and different filter algorithms were obtained at this stage, and the trade-off analysis for SFF mission reward and measurement capability was determined.
## 2 Materials and Methods
### Pearson Correlation Analysis  
To quantitatively analyze the pixel-by-pixel correlation between NDVI and SPEI in the West Liao River Basin, this study employed Pearson correlation analysis to reflect the sensitivity of NDVI changes to drought. The Pearson correlation coefficient ranges from [\(-1\), \(1\)], where \(-1\) indicates a perfect negative correlation, \(1\) indicates a perfect positive correlation, and \(0\) indicates no correlation. A significance level of \(\alpha=0.05\) was used to assess the statistical significance of the correlations.  
To further analyze the seasonal response characteristics of NDVI to SPEI, the study divided the time period into four seasons: spring (March-May), summer (June-August), autumn (September-November), and winter (December-February). Seasonal NDVIs were correlated with seasonal SPEIs (for example, spring NDVI--spring SPEI) to reveal seasonal differences in vegetation responses to drought. We also analyzed the correlation coefficients between NDVI and SPEI at time scales of one month and six months. To examine vegetation's lagged response to drought, we analyzed correlations between NDVI (2001-2018) and one-year-lagged SPEI (2000-2017), where SPEI(t1) corresponds to NDVI(t). The formula for the Pearson correlation coefficient is as follows:  
\[p_{xy}=\frac{cov(x,y)}{\sigma x\sigma y} \tag{6}\]  
In the formula, the numerator represents the covariance of the two variables, and the denominator represents the product of the standard deviations of the two variables.
## 3 Proposed Graph Compression Scheme
### Graph Manager and Reference Pattern Generator
Subsequently, the results are sorted in descending order to determine the reference pattern candidates. For each pattern, the reference pattern score (RPS) is calculated to update the pattern dictionary if it is isomorphic to the subgraph, \(\alpha\), of graph \(Gg\). Equation (1) calculates the pattern score. Here, \(\alpha\) is a value between 0 and 1. \[RPS=\textit{edgesize}\cdot\alpha+\textit{frequency}\cdot(1-\alpha) \tag{1}\]
# Adaptive Emergency Call Service for Disaster Management
## 5 Simulation Results
### Performance Analysis
#### 5.2.2 Impact of Emergency Call Duration  
The impact of ETI on success rate and \(EEL\) is observed in Figures 8 and 9. It is observed that when the emergency call arrivals are spread over a longer ETI, the amount of successful transmissions increases. As the relay nodes become overcharged due to a shorter ETI value, the probability that a node ignores an incoming request, due to the buffer saturation, increases. In contrast, when the ETI is longer, the number of idle relay nodes available to cater to each relay request is higher.  
Further, it is seen that with the increment in ETI, the success rate and \(EEL\) of 5G-SOS is significantly improved compared to M-HELP and FINDER. The adaptive parameters in 5G-SOS enhance the responsiveness to a large number of emergency calls occurring in a shorter ETI. The parameters, \(R5_{\text{th}}\) and \(T_{\text{max}}\), are adaptive in 5G-SOS compared to M-HELP, where such parameters are fixed to a specific value. The reason for the success  
Figure 8: Comparison of successful rate against emergency time interval (ETI) under 5G-SOS, M-HELP, and FINDER. Parameters: ETI: 600–1800 s, NoU: 15,000, NEC: 5000.  
Figure 9: Comparison of end–end latency against ETI under 5G-SOS, M-HELP, and FINDER. Parameters: ETI: 600–1800 s, NoU: 15,000, NEC: 5000.  
rate being lower in the FINDER protocol is due to devices relaying the data only to the cluster head (CH). The CH aggregates the received data and sends them to nearby CHs. Such an aggregation results in a high traffic concentration on CHs and reduces the success rate. Moreover, under each protocol, the _EEL_ increases against the duration of ETI. As the emergency calls occur over a shorter ETI, the amount of idle relay nodes is low. Further, since relay nodes do not buffer the simultaneously received calls in M-HELP, only a few calls are successfully transferred to the gNB. Hence, initially the corresponding latency in M-HELP is low. The _EEL_ in 5G-SOS is initially higher than M-HELP since the success rate of calls is higher and hence the corresponding latency increased. As the adaptive parameters in 5G-SOS allow the quick transferring of calls, the amount of waiting at the relays decreases and hence results in a lower _EEL_ compared to both M-HELP and FINDER. In FINDER, the delay in allocating a cluster head and communicating calls via multiple cluster heads causes the increment in _EEL_, when the amount of successful calls increases.
# Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm
## 6 Conclusions  
The coverage optimization, considering multiple objectives such as network connectivity, coverage, and energy consumption, is a focal point in current research on WSNs coverage optimization. In this paper, we have designed a multi-objective coverage optimization model and proposed an enhanced multi-objective optimization algorithm (EMSSA) based on non-dominated Sorting-based. We applied the proposed algorithm to coverage optimization in complex environments with obstacles. Different optimization configuration schemes were obtained by adjusting the coverage threshold according to different application requirements, improving the applicability of WSNs in different scenarios. It is evident that the proposed algorithm performs well and is better suited for the deployment model considered in this paper compared to the MSSA and NSGA-II algorithms. However, there are still some issues that need further investigation and exploration in this research. For example, in this paper, we employed a grid-based approximation to handle obstacles, which might enlarge the actual obstacle areas, leading to errors in the actual coverage range of the monitoring area. Our next steps will involve exploring more reasonable methods for obstacle handling, minimizing node coverage errors, and striving for alignment with actual deployment requirements as much as possible.  
Conceptualization, D.-D.Y.; Validation, D.-D.Y.; Formal analysis, D.-D.Y.; Investigation, D.-D.Y. and W.W.; Writing--original draft, D.-D.Y.; Writing--review & editing, Y.-J.Z.; Supervision, Y.-J.Z., X.H. and Y.X.; Project administration, M.M.; Funding acquisition, Y.-J.Z. and X.H. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Objective**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**Coverage Thresholds**} \\ \cline{3-5}  & & **90\%** & **95\%** & **100\%** \\ \hline \multirow{3}{*}{\(f_{1}\)} & NSGA-II & 0.9004 & 0.9479 & 0.9861 \\  & MSSA & 0.8988 & 0.9448 & 0.9804 \\  & EMSSA & 0.9068 & 0.9503 & 0.9908 \\  & NSGA-II & 0.19 & 0.29 & 0.37 \\  & MSSA & 0.21 & 0.25 & 0.335 \\  & EMSSA & 0.18 & 0.22 & 0.25 \\  & NSGA-II & 0.6342 & 0.6245 & 0.6193 \\ \(f_{3}\) & MSSA & 0.6769 & 0.6353 & 0.6103 \\  & EMSSA & 0.6029 & 0.6051 & 0.5892 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation Experimental Result.  
**Funding:** This research is supported by the General Program of the National Natural Science Foundation of China (62702004) and the General Program of the Anhui Provincial Natural Science Foundation (1908085 MF214).  
**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.
## 6 Conclusions  
In this paper, we propose a forest point cloud real-time reconstruction method with a single-line lidar based on visual-IMU fusion, and we construct a low-cost point cloud acquisition device consisting of a monocular camera, IMU, and a single-line lidar. We adopt a nonlinear optimization method to obtain the pose estimation and then transform laser data using projection, densification, and loop closure correction to form a 3D point cloud map. Meanwhile, we create 3D point cloud maps of a single tree, a row of trees, and part of a forest region. The experimental results show that the average relative error of the proposed method is 3.41% compared with real values, and the root mean square error is reduced by 4.65% compared with the Kalman filter fusion algorithm. The average time cost is 903 us and reaches the real-time requirement. The method in this paper is optimized based on all historical data, which makes it easier to achieve the optimum.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Time Cost (us)**} \\ \cline{2-4}  & **Transform to the World Frame** & **Fill Point Cloud** & **Loop Elimination Error** \\ \hline Kalman filter & 147 & 951 & / \\ \hline Nonlinear optimization & 158 & 571 & 174 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average time cost.  
Figure 9: Comparison of reconstruction results of different methods: (**a**) 3D reconstruction based on Kalman filter; (**b**) 3D reconstruction based on the method proposed in this paper.  
**Author Contributions:** Methodology, C.Y.; software, C.Y. and K.L.; validation, C.Y. and K.L.; investigation, C.Y.; data curation, C.Y.; writing--original draft preparation, C.Y.; writing--review and editing, J.Z. and C.H.; visualization, C.Y. and K.L.; project administration, J.Z.; funding acquisition, J.Z. and C.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was financially supported by the National Natural Science Foundation of China (Grant No. 61703047).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available on request due to restrictions e.g., privacy or ethical. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to [forest resource protection].  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Conclusions  
A unique approach to evaluate the reduction of skin friction drag with an emphasis on the turbulent scales was applied to boundary layer profiles for a reference smooth wall surface and a riblet wall surface measured at the low-turbulence wind tunnel at the JAXA Chofu Aerospace Center. The riblets were fabricated from aircraft paint with a three-dimensional design. The riblet sheet was attached on the surface of a flat-plate test model. The boundary layer profile represented by the \(U\) velocity profile was measured by hot-wire anemometry and pitot-rake measurement. The measured raw output signal by the hot-wire anemometry was converted to the \(U\) velocity with appropriate temperature correction. Based on the measured boundary layer profiles, the following conclusions with regard to turbulence statistics and the validity of the proposed method evaluating the reduction of skin friction drag were drawn:1. The riblet surface made from aircraft paint successfully demonstrated a reduction of skin friction drag.
2. The riblet surface increased both the integral time scale and the length scale by 30% from those for the smooth wall at a freestream velocity of 41.7 m/s (Mach 0.12) and a chord length of 67% (\(\chi\)/\(\chi_{chord}\) = 2/3) from the model's leading edge.
3. The proposed method evaluated the skin friction drag for the riblets at a freestream velocity of 41.7 m/s (Mach 0.12) and a chord length of 67% (\(\chi\)/\(\chi_{chord}\) = 2/3) from the leading edge of the flat-plate model by 2.8%. This is consistent with that obtained by the momentum integration method based on the pitot-rake measurement of 2.9%.  
[PERSON] is responsible for the entire work from conceptualization, establishing methodology, data analysis, and writing the original manuscript entirely; M.K. managed the overall project and reviewed; [PERSON] and S.K. worked on wind tunnel experiments and raw data processing.  
This research received no external funding.  
This study was conducted as part of the Flight Investigation of Skin-Friction Reduction Eco-Coating (FINE) project by the JAXA Aeronautical Technology Directorate. The authors wish to thank the aerodynamics research unit and [PERSON] at the JAXA Aeronautical Technology Directorate for assistance with the wind tunnel experiment, and [PERSON] for help with the hot-wire anemometry. The authors also wish to thank [PERSON] for CFD simulation prior to the wind tunnel experiment. As for the riblet design, the authors acknowledge [PERSON] and [PERSON] at the Tokyo Metropolitan University, and [PERSON] at the Owell Incorporated. Also, the authors acknowledge [PERSON] and [PERSON] at the Tokyo University of Agriculture and Technology for carrying out the experiment and for insightful help on research collaboration.  
The authors declare no conflict of interest.
## 3 Results  
### Stress in Implants  
Figure 2 displays the distribution of von Mises stress (in MPa) in the implants that have contact with cancellous bone and the highest values of the stresses acting on the implants are reported in Table 2. It was observed that the cervical area next to the cortical bone was found to have the highest concentration of stresses. Additionally, it was noticed that the weak bone generated more stress in the implants than the normal bone. Furthermore, it was observed that the maximum stresses obtained for the implants of partial osseointegration were lower than those of full osseointegration. The maximum stresses ranging between 13.07 MPa and 19.90 MPa, were observed in the weak bone with full osseointegration case. The minimum stresses were observed in normal bone with partial osseointegration,  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Young’s Modulus** & **Yield Strength** & **Poisson’s Ratio** & **Porosity** & **Elements** \\  & **(E, GPa)** & **(Ys, MPa)** & **(v)** & **(\%)** & **(C3D10)** \\ \hline Crown & 40 & - & 0.3 & - & 2056 \\ \hline Cortical bone * & 13.7 & - & 0.3 & - & 10,834 \\ \hline Cancellous bone (Normal) * & 1.37 & - & 0.3 & 68 & 13,891 \\ \hline Cancellous bone (Weak) ** & 0.26 & - & 0.3 & 86 & \\ \hline \hline \end{tabular}  
* The values 13.7 GPa and 1.37 GPa have been frequently used for Young’s moduli of cortical and cancellous bone [40].
** Based on the given value of 0.26 GPa for weak cancellous bone, it can be inferred that the bone is assumed to be in an osteoporotic state, which is typically associated with a high level of bone porosity, reaching up to 86% [4].  
\end{table}
Table 1: Cont.  
ranging from 10 MPa to 15.90 MPa. Moreover, the stress in bulk Ti implants was higher than in porous implants. However, the magnitudes of the highest stresses (Table 2 and Figure 2) were below the yield stress in all cases (P76 with a yield stress of 760 MPa, P53 with 528 MPa, P31 with 305 MPa, P18 with 176 MPa, and P9 with 88 MPa) as reported in [23]). It was also observed that the stress distribution in the axial direction of implants was reduced as the porosity of implants was increased in the case of normal bone. However, in weak bone, the stresses remained similar.
## 4 Discussion
### Limitations  
Every piece of research is burdened with limitations and uncertainties. Among those potentially occurring within the herein study, the following can be listed:  
1. Relatively small research sample--it has been decided by the authors that only persons directly involved in previous research (in academia or industry) on the development of MASS were involved in the herein expert study. This was done in order to avoid uninformed answers, as well as to improve their quality. However, such a condition caused significant narrowing of the research sample, as MASS remains a rather niche specialization within the entire maritime industry. Thus, the number of the experts that could qualify as participants was very limited. This factor could render the results unrepresentative. Moreover, the scarcity of data did not allow for a more advanced statistical analysis;2. Ambiguity of questions asked--the fact that the MASS experts were preferred for the study over persons involved in human factors analyses might bring about some misunderstandings of the survey. The explanation of how particular causal categories are defined was presented to the respondents before filling the questionnaire and was available to them at any moment of the survey. However, certain inevitable limitations are always embedded within the expert study such as this [43]; and
3. Erroneous framework--the research used the maritime-oriented variant of a widely used HFACS framework as a foundation. However, HFACS-MA was developed in 2013 [31] and may not accurately reflect the subsequent development of the maritime industry in the direction of autonomy.  
All in all, the biggest limitation of the herein study lies within the size of the sample. With a limited number of the worldwide experts available in the domain of autonomous shipping, this issue may only be overcome once MASS becomes fully operational. However, the analysis of the factors affecting their safety is required before that happens, which is what this study aims to cover.
## 7 Superframe Duration Allocation Schemes
### Proportional Allocation Scheme Based on the Message Load
 \tag{31}\]  
After allocating a superframe duration for all cluster-heads, the protocol and timing constraints (Equations (10) and (26)) must be verified (steps 4 and 5 in Algorithm 1, lines 12-20). Case the protocol or the timing constraints are not satisfied, it means that the system may not be schedulable and it is necessary to modify the configuration of the network and/or the set of supported messagesstreams. Unfortunately, IEEE 802.15.4 standard provides a reduced flexibility to modify the values of the superframe duration and beacon Interval parameters. The main reason is that these parameters are described by SO and BO parameters, which are related to each other by a power of two. Thus, any adjustment of SO or BO parameters can significantly modify the values of SD and BI, respectively.