esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Driven Waste Management in Innovating Space Exploration
## 7 Conclusions  
This research provides a novel, comprehensive evaluation of cutting-edge waste management technologies tailored explicitly for long-duration space missions. This study uniquely addresses the multifaceted challenges of sustainable waste management beyond low-Earth orbit by integrating artificial intelligence-driven sorting systems, microbial bioreactors, and thermal processing techniques. Among the key findings, plasma arc gasification demonstrates the highest waste volume reduction efficiency (up to 90%), but its high energy demand of 500-1000 kWh per ton poses significant operational constraints. Pyrolysis offers a balanced solution, reducing waste volume moderately at a lower energy range (200-500 kWh per ton), but it necessitates additional handling systems. Notably, efficient bioreactors consume the least energy (50-150 kWh per ton) and effectively convert organic waste into vital resources, such as oxygen and water, essential for prolonged missions. The analysis quantified current ISS recycling efficiencies, revealing a 90% water recycling rate with potential improvements of up to 98% using advanced methods. However, implementation faces challenges like microbial containment under microgravity conditions, computational limitations for onboard AI systems, and significant initial investment costs. This study underscores the critical balance between energy consumption, recycling efficiency, and operational practicality, proposing clear directions for developing sustainable, integrated solutions essential for future space exploration.  
Conceptualization, D.B.O. and B.I.O.; Methodology, O.Z.W., T.T.P., E.E., J.O.I. and B.I.O.; Software, D.B.O., O.Z.W. and J.O.I.; Validation, O.Z.W., E.E. and J.O.I.; Formal analysis, D.B.O., O.Z.W. and E.E.; Investigation, O.Z.W.; Resources, T.T.P. and J.O.I.; Data curation, D.B.O., J.O.I. and B.I.O.; Writing--original draft, D.B.O.; Writing--review & editing, T.T.P., E.E. and B.I.O.; Visualization, E.E.; Supervision, T.T.P. and E.E.; Project administration, J.O.I. and B.I.O.; Funding acquisition, T.T.P. and B.I.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Effect of Parameters on Vapor Generation in Ship-to-Ship Liquefied Natural Gas Bunkering
## 4 Results and Discussions
### Effects of Insulation Performance of LNG Bunkering Lines  
The effects of pipe insulation materials with different thermal conductivities on the vapor generation were analyzed. Three types of insulation materials were compared: cellular glass, multilayer insulation, and PUF. Cellular glass and PUF are selected because they are widely used in the marine and offshore industries especially for cryogenic services. Although multi-layer insulation is rarely used in the marine industry, for comparison purposes, it was selected in the present study.  
The properties of these insulation materials are listed in Table 6. The other design parameters remained constant, as shown in Tables 2-4.  
As can be seen in Figure 8, in all of the applied insulation materials, the temperature at the tank inlet increased over time, as the LNG temperature in the bunker tanks gradually increased. The PUF showed a temperature approximately 0.013 \({}^{\circ}\)C lower at the receiving tank inlet than the cellular glass, and the multi-layer insulation showed a temperature approximately 0.026 \({}^{\circ}\)C lower than the PUF. These results can be explained by the heat ingress rate for each applied insulation, as shown in Figure 8 (dotted lines). Overall, the temperature difference at the inlet of the tank was not large, and thus the type of insulation in the pipes did not significantly affect the vapor generation during bunkering, as tabulated in Table 7. However, it should be recognized that different results may be displayed depending on other factors, such as the length of the pipe and bunkering time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Cellular Glass** & \
\begin{tabular}{c} **Polyurethane Foam** \\ **(PUF)** \\ \end{tabular} &
\begin{tabular}{c} **Multi-Layer Insulation** \\ **(MLI)** \\ \end{tabular} \\ \hline Thermal conductivity (W/m-K) & 0.033 & 0.021 & 0.010 (at 133.3 Pa) \\ Overall heat transfer coefficient of & 2.632 & 1.730 & 0.849 (at 133.3 Pa) \\ pipe insulation (k/h-m\({}^{2}\)-K) & 2.632 & 1.730 & 0.849 (at 133.3 Pa) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Properties of insulation materials.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Cellular Glass** & \
\begin{tabular}{c} **Polyurethane Foam** \\ **(PUF)** \\ \end{tabular} &
\begin{tabular}{c} **Multi-Layer Insulation** \\ **(MLI)** \\ \end{tabular} \\ \hline Total amount of vapor return (kg) & 3991.22 & 4016.52 & 4036.62 \\ Receiving tank net vapor generation, & 409.15 & 434.83 & 459.35 \\ Net vapor generation (overall system), & 14.39 & 17.01 & 20.01 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Total amount of vapor return and net vapor generation with different insulation materials.  
Figure 7: Variation of tank pressure at bunkering flow rates of 300, 500, and 1000 m\({}^{3}\)/h.
## Materials and Methodology
### Extraction of the Water Area  
The water area extracted from OLI was regarded as the ground-truth water area and was extracted using the threshold method. If the MNDWI value of a pixel is more than 0 and the normalized difference vegetation index (_NDVI_) value is less than 0.15, it is classified as water. The NDVI was computed from OLI based on the flowing equation:  
\[NDVI=\left({\rho_{nir}-\rho_{red}}\right)/{\left({\rho_{nir}+\rho_{red}}\right)} \tag{6}\]  
where \(\rho_{nir}\) represents the reflectance of the near-infrared band (B5 of OLI) and \(\rho_{red}\) represents the reflectance of the red band (B4 of OLI).  
The histogram frequency of the SWI imagery (on 27 September 2016) based on the ground-truth water and non-water is plotted in Figure 2 to facilitate visualization. Figure 2 demonstrates that the majority of the SWI water values are more than 0.40 and the majority of the SWI non-water values are less than 0.18 through visual judgment. However, an appropriate and unique threshold ranging from 0.18 to 0.40 needed to be determined by calculating the positive and negative errors. The threshold is regarded as appropriate when the classification errors of water and non-water are approximate and the overall accuracy derived from the confusion matrix is optimal (the sum of positive and negative errors comes to the minimal value). The result showed that the sum is minimal (0.02%) when the threshold is 0.20. In the present study, the water information of Poyang Lake was extracted using the threshold method from 24 May 2015 to 14 November 2016.
# Affective Stroking: Design Thermal Mid-Air Tactile for Assisting People in Stress Regulation
## 4 Discussion  
### Why Did the STAI Measurements Not Align with the Physiological Responses?  
The results showed participants from the SG significantly felt more relaxed than those from the CG based on STAI measurements. The physiological stress index, RMSSD, also increased more, and LF/HF also decreased more in the SG, compared to the CG. However, the physiological data were not significantly proven. Three possible reasons are as follows:  
* The novelty of the device may induce subjective preference in users: Some participants in the SG felt excited by using our novel device. They may have anticipated greater relaxation effects, potentially leading to elevated self-reported relaxation scores that do not necessarily reflect genuine physiological changes;
* The limits of the stress induction method: Real-world stressors are varied. The stress induction task mostly affects cognitive load. In real life, people are affected by lots of emotional-driven situations such as social stress caused by interpersonal relationships. Many participants noted that the anxiety induced by mathematical tasks is temporary, unlike ongoing life stressors. This made it easier for them (no matter which group they were in) to relieve anxiety during the relaxation phase, which may challenge the effectiveness of our design intervention;
* Not enough time for the affective touch to take effect on physiological response: Previous research has shown that the effect of affective touch on parasympathetic activation becomes significant only when participants remain in a stress-free environment for more than 16 min [47]. Our study only proceeded for 5 min for intervention. Future research should aim to conduct experiments either in the lab with a new stress induction method, or in real-world settings, tracking users' stress regulation behaviorsfor a period of time. We should also let participants become familiar with the device and then test them to avoid of preference or bias, creating enough time for an affective touch to impact the parasympathetic activity.
## 5 Conclusions  
We have integrated communication and sensor arrays to propose a mathematical model to route a collection of AUAS, without having a base station of communication but by communicating with each other using multiple spatio-temporal data. The theories of structured matrices, concepts in multi-beam beamforming, and sensor arrays were utilized to propose a swarm routing algorithm. We have addressed the routing algorithm's computational and arithmetic complexities, precision, and reliability. We have measured BER based on the number of elements in sensor arrays and beamformed output of the members of the swarm to authenticate and secure the routing for the decentralized AUAS networking. Finally, we have discussed future work on obtaining an ML-based low-cost AUAS swarm routing algorithm.  
Figure 7: We show how the beamformed output of the AUAS swarm routing model diverges from the ground-truth signal, especially based on a large number of elements in the antenna arrays, i.e., \(N=256\) (to show the worst possible case): (**a**) shows the expected ground-truth signal; (**b**) shows the imaginary component of the beamformed output signal; (**c**) shows the differences in (**a**) and (**b**); and (**d**) shows the magnitude of the difference between (**a**) and (**b**).  
**Author Contributions:** Conceptualization, S.M.P.; theory, S.M.P.; software, R.J.M.; validation, S.M.P., R.J.M., K.S. and K.B.; formal analysis, S.M.P. and R.J.M.; investigation, S.M.P. and A.M.; resources, S.M.P. and H.S.; data curation, S.M.P. and R.J.M.; writing--original draft preparation, S.M.P., R.J.M., K.S., K.B. and H.S.; writing--review and editing, S.M.P. and H.S.; visualization, R.J.M., K.S. and K.B.; supervision, S.M.P.; project administration, S.M.P. and H.S.; funding acquisition, H.S. and S.M.P. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by the National Science Foundation under Grant No. 2150213.  
Not applicable.  
In the future, we will provide data in the public domain using a GitHub account.  
The authors would like to thank [PERSON] for the discussions on BER analysis.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AUAS & Autonomous Unmanned Aerial Systems \\ BER & Bit-error-rate \\ DNN & Deep neural network \\ ML & Machine learning \\ CSI & Channel state information \\ DSP & Digital signal processing \\ MUSIC & Multiple Signal Classification \\ ESPRIT & Estimation of Signal Parameters via Rotational Invariance Technique \\ AI & Artificial Intelligence \\ MIMO & Multiple-input multiple-output \\ AODV & ad-hoc on-demand distance vector \\ \end{tabular}
## 4 WASPAS Multi-Objective Assessment  
WASPAS is a technique for optimizing responses to multiple-criteria queries. The total score for each potential solution is determined by assigning a weight to each criterion and then aggregating their individual scores. When some criteria are more important than others, or when the criteria have different units, WASPAS is useful [39]. Using this method, decision-makers can provide greater consideration to more significant factors without ignoring others. The WASPAS method algorithm comprises a series of consecutive stages [40].  
Step 1: Normalizing the decision matrix.  
Normalization is performed so that every response, including their dimensional units, is devoid of any abnormalities that may have occurred during measurement. For this, the responses are to be normalized using the highest-value criterion, i.e., Equation (1), or the lowest-value criterion, i.e., Equation (2).  
Highest-value criterion:  
\[\widetilde{\mathrm{x}}\mathrm{\ }_{\mathrm{ij}}=\frac{\mathrm{x}_{\mathrm{ij}}}{ (\mathrm{Max}_{\mathrm{i}})(\mathrm{x}_{\mathrm{ij}})} \tag{1}\]  
Lowest-value criterion:  
\[\widetilde{\mathrm{x}}\mathrm{\ }_{\mathrm{ij}}=\frac{(\mathrm{Min}_{\mathrm{i}})( \mathrm{x}_{\mathrm{ij}})}{\mathrm{x}_{\mathrm{ij}}} \tag{2}\]  
Step 2: Calculating the total relative importance matrices based on the WSM method.  
For the WSM method, the total relative importance for each cycle can be calculated using Equation (3):  
\[\mathrm{Q}_{\mathrm{i}}^{(1)}=\sum_{\mathrm{j}=1}^{\mathrm{n}}(\mathrm{x}_{ \mathrm{ij}})(\mathrm{W}_{\mathrm{j}}) \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **DF** & **Seq-SS** & **Adj-MS** & **F** & **\(p\)** & **\% Impact** & **Significant** \\ \hline ap & 3 & 27.0136 & 9.0045 & 152.58 & 0.001 & 33.00 & Yes \\ s & 3 & 19.5626 & 6.5209 & 110.49 & 0.001 & 23.89 & Yes \\ v & 3 & 12.0609 & 4.0203 & 68.12 & 0.003 & 14.73 & Yes \\ q & 3 & 23.0560 & 7.6853 & 130.22 & 0.001 & 28.16 & Yes \\ Error & 3 & 0.1770 & 0.0590 & & & & \\ Total & 15 & 81.8701 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: ANOVA findings for cutting noise.  
Figure 18: Interaction effects of input variables on cutting noise.  
Step 3: Calculating the total relative importance matrices based on the WPM method. For the WPM method, the total relative importance for each cycle can be calculated using Equation (4):  
\[Q_{i}^{(2)}=\prod_{j=1}^{n}(x_{ij})^{W_{j}} \tag{4}\]  
Step 4: Calculating Cumulative Relative Relevance Index (5).  
A Cumulative Relative Relevance Index is calculated from each individual measurement. We compute this using Equation (5) and then arrange the results in a sequential fashion. The optimal response corresponds to the highest possible value of (Q\({}_{i}^{(2)}\)):  
\[Q_{i}=\lambda\bullet Q_{i}^{(1)}+(1-\lambda)\bullet Q_{i}^{(2)} \tag{5}\]  
In order to proceed from Equation (3), we need the values of weights. In this paper, weights are calculated using the entropy method.
## 4 Evaluation Study
### Quantitative Findings
#### 4.4.2 Factors Influencing the Collaboration Experience in IVR  
Next, to determine the relationships among all of the variables, a correlation analysis was performed. Table 6 shows the preliminary results for the descriptive statistics and Pearson's correlation coefficients. First, an extroverted personality and collaboration competence were weakly positively correlated with spatial presence (\(r\) = 0.226 and 0.375, respectively) and social presence (\(r\) = 0.227 and 0.302, respectively). In addition, spatial presence and social presence had a medium correlation (\(r\) = 0.706). Additionally, VR readiness was not correlated with collective efficacy, while an extroverted personality (\(r\) = 0.303) and collaboration competence (\(r\) = 0.297) were weakly correlated with collective efficacy, and spatial presence (\(r\) = 0.459) and social presence (\(r\) = 0.521) were moderately positively correlated with collective efficacy. Moreover, an extroverted personality and collaboration competence were weakly correlated with social experience, while spatial presence and social presence were moderately correlated with social experience. To find the most parsimonious model for predicting collective efficacy and social experience, we used the backward-elimination multiple linear regression analysis approach. Using collective efficacy as the dependent variable, the final model included two independent variables (IVs): extroverted personality and social presence. The regression analysis suggested a significant final model (\(F_{(2,92)}\) = 19.577, \(p\) < 0.001) in which the IVs explained 28.3% of the variance in collective efficacy (\(R^{2}\) = 0.283), and only social presence (\(\beta\) = 0.473, \(p\) < 0.001) was a significant predictor of the collective efficacy. In terms of social experience,
## 2 Materials and Methods
### GEDI Data and Processing  
The GEDI instrument, which is attached to the International Space Station (ISS), collects data globally between latitudes of 51.6\({}^{\circ}\) north and 51.6\({}^{\circ}\) south latitude. This data represent the highest resolution and largest sampling density in on-orbit optical detection and LiDAR instruments data [26]. The main objective is to enhance our ability to describe the impacts of climate change and land use on ecosystem structure and dynamics. The GEDI instrument consists of three lasers that are splitting and beam jittering, producing eight beam ground cross-sections at 600 meter intervals in the cross-orbit direction of the Earth's surface. Each beam has footprint samples with a diameter of approximately 25 meters and an interval of about 60 meters along the track, covering a width of about 4.2 kilometers across the track [27]. Figure 2 illustrates the GEDI beam sampling mode [26]. This study used GEDI-derived Level 2B (L2B) data; it included eight beams and one metadata. The metadata contains comprehensive information, such as acquisition time, purpose, file name, and creation time. Additionally, each beam contains a total of one hundred and fifty-six fields that encompassed geographic location, land cover data, and more.  
The GEDI data in this study can be download free from the Earthdata website ([[https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)]([https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)), accessed on 7 November 2022). Based on the vector boundary of Shangri-La, all beams in the study area are selected. There was a total of thirty-two GEDI orbit tracks data (Figure 3a), ranging from 23 April 2019 to 17 October 2019. At the same time, we used five indicators (quality_flag, degrade_flag, solar_elevation, leaf_off_flag, and sensitivity) to screen out high-quality, full-information light spots. If a value of quality_flag is 1, it meant that the waveform satisfies a specific criterion of high quality, so the footprints with a value of 1 were retained. A degrade_flag value of 1 demonstrated that the satellite was in orbit descent, and that the data were inaccurate. The sensitivity indicated the waveform penetrable to the largest tree covering in the canopy, with a value range from 0 to 1. When the data were closer to 1, it indicated high quality. Usually, the value was set to be greater than 0.95 in dense forest areas [28]. After quality filtering, 52,366 footprints were obtained (Figure 3b).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Species** & **Sample Size** & **Minimum** & **Maximum** & **Average** & **SD** \\ \hline _Quercus aquifloioides_ & 52 & 5.36 & 131.98 & 41.79 & 30.23 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ground inventory sample information of _Quercus aquifloiides._  
Figure 2: GEDI beam sampling mode.
## 2 System Description
### Filters  
In order to recover the transmitted symbols, \(s_{I}(t)\) and \(s_{Q}(t)\), with improved SNR, the receiver filter should be a matched filter for \(p(t)\)[51]. However, there is no implementation of an analog matched filter for a raised cosine filter since its impulse response is non-causal. Nevertheless, the recovery of the symbols can be achieved by applying a filter that has approximate characteristics to the raised cosine filter. Thus, the filter stage should be properly designed in order to maximize the system performance. Since the receiver filter has more implementation constraints, compared to the digital transmission filter, it will be first analyzed in the following subsections.
## 5 Results and Discussion
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Vehicle (\(k\))** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline \(j=1\) & 1 & 2 & 2 & 2 & 3 & 3 & 4 & 17 & 17 & 17 & 17 & 17 \\ \(j=2\) & 1 & 2 & 6 & 2 & 3 & 3 & 11 & 12 & 17 & 17 & 25 & 17 \\ \(j=3\) & 1 & 2 & 14 & 6 & 11 & 3 & 12 & 16 & 25 & 17 & 33 & 17 \\ \(j=4\) & 5 & 6 & - & 11 & 14 & 3 & 16 & - & 33 & 17 & - & 17 \\ \(j=5\) & 13 & 10 & - & 15 & - & 11 & - & - & - & 17 & - & 21 \\ \(j=6\) & - & 13 & - & - & - & 15 & - & - & - & - & 21 & - & 30 \\ \(j=7\) & - & - & - & - & - & - & - & - & - & - & 30 & - & 34 \\ \(j=8\) & - & - & - & - & - & - & - & - & - & - & 34 & - & - \\ \(j=9\) & - & - & - & - & - & - & - & - & - & - & - & - \\ \(j=10\) & - & - & - & - & - & - & - & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Routes of certain vehicles produced as a result of the optimization (\(DS2\)). average speed (\(\overline{VN}\)), and in parallel, this scenario exhibited the highest standard deviation of the average speeds of the vehicles over the entire examined time interval (\(\sigma_{VN}\)). The example has demonstrated the applicability of the presented concept to manage the traffic of large vehicles, taking into account the relevant safety constraints, with the use of several alternative grids simultaneously.