esa-sceva/satcom-chunk-collection huggingface dataset:
# Simultaneous Sensing of Touch and Pressure by Using Highly Elastic e-Fabrics
## 2 Materials and Methods  
### Materials  
The polyester/spandex (PET/SP) fabrics were made by co-weaving spandex with polyester. The PET/SP spacer fabrics were placed by connecting two independent PET/SP fabric layers with a spacer layer (PET yarns) so that the fabrics had a three-dimensional structure. The structure of the PET/SP layers was composed of conventional PET/SP multifilament yarns with high elasticity and recovery. These fibers could be converted into conductive fibers via soaking, padding, and surface treatment.  
Figure 1: The manufacturing process of capacitive pressure sensor consisting of (**a**) preparing SWCNT ink and silver paste, (**b**) printing SWCNT rows of the capacitive sensor layer, (**c**) printing silver rows of the capacitive sensor layer, (**d**) printing the separation films at the cross-nodes of the capacitive sensor layer, (**e**) printing SWCNT columns of the capacitive sensor layer, (**f**) printing silver columns of the capacitive sensor layer, (**g**) the final capacitive sensor layer, (**h**) printing SWCNT rows on the top side of the capacitive-pressure sensor layer, (**i**) printing silver-rows on the top side of the capacitive-pressure sensor layer, (**k**) printing SWCNT columns on the bottom side of the capacitive-pressure sensor layer, (**l**) printing silver columns on the bottom side of the capacitive-pressure sensor layer, (**m**) the final capacitive-pressure sensor layer, (**n**) creating e-fabric-skin by combining the capacitive sensor layer and the capacitive-pressure sensor layer, (**o**) cross-section of the e-fabric-skin, and (**p**) application of the e-fabric-skin.  
Thus, the PET/SP fabrics and PET/SP spacer fabrics are very resilient, can withstand harsh levels of wear and tear, and are waterproof and show less wrinkling.  
In the research, we used the SWCNT ink and the stretchable silver paste in order to produce electrode lines on the fabrics. The SWCNT ink was prepared from KH Chemicals Co. Ltd., Seoul, Republic of Korea [35], and the stretchable silver paste was obtained from Dycotec Materials Ltd., Swindon, United Kingdom [36]. We used screen-printing technologies in order to apply SWCNT and silver paste on the PET/SP fabric and PET/SP spacer fabric, respectively. The silver paste layer helps the e-fabric skin to have conductivity, stretchability, recovery, and flexibility in a wearable device application. Besides, the SWCNT layer would guarantee constant and robust conductivity of the sensor structure under large deformations.
# Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks
## 3 Method
### Convergency and Complexity Analysis  
To solve the original problems, we handle two conflict object problems with the help of the CRMOP algorithm based on MOEA/DD to update the solutions until coverage. The convergence of the whole algorithm is influenced by the update process apparently. Since the PBI method acts as the leading role of our proposed algorithms, the objective function is constantly approaching the ideal object value as the iteration processes during the update process which indicates that the algorithm is guaranteed to converge [52]. The complexity of polynomial mutation and SBX in Algorithm 3 is \(O(2 DN)\), where \(D\) is the number of decision variables. Identifying the ideal point requires a total of \(O(2N)\) computations. As for the update process in Algorithm 5, the non-dominated sorting of a population of size 2N with 2-dimensional objective vectors requires \(O(N\text{log}N)\) computations [56]. Assuming that the number of solutions in the last non-domination level is \(L\). For each solution, the checking process needs \(O(L)\) computations. Inspired by the algorithm proposed in [52], the worst complexity of the complexity of one generation approximates \(O(N^{2}\text{log}N)\). The algorithm proposed in this manuscript provides a theoretical foundation and guidance for localization in practical settings.
## 2 Mathematical Model of the Hydrocooler
### Thermal Resistance of Ice-on-Coil  
Ice-on-coil is a simple and effective TES method with good discharge characteristics. Direct evaporative TES refrigeration system has simple structure and good applicability to small equipment. The structure diagram of evaporation coil icing of the cold storage tank is shown in Figure 2. The coil is horizontal, and the icing state in the TES tank is represented by the evaporation coil per unit length.  
During the charging process, the refrigerant absorbs heat by evaporation and cools the water outside the coil. Both the thermal resistance between coil and water changes and the heat flow of coil section will change with the increase in ice thickness. According to the fundamental law of heat transfer, the heat flux of the evaporating coil can be expressed using the following equation [31]:  
\[q_{l}=(T_{osi}-T_{eva})h_{com}=\frac{T_{osi}-T_{eva}}{R_{com}}=\frac{T_{osi} -T_{eva}}{R_{src}+R_{coi}+R_{ico}+R_{osi}+R_{cic}} \tag{10}\]  
Figure 2: Structure diagram of evaporation coil icing.  
The refrigeration system uses R22 refrigerant [32]. The boiling thermal resistance of refrigerant in the coil per unit length can be expressed as [33]:  
\[R_{src}=\frac{1}{2\pi r_{ics}h_{ics}} \tag{11}\]  
The heat conduction resistance of the evaporated coil per unit length can be expressed as [33]:  
\[R_{coi}=\frac{1}{2\pi\lambda_{coi}}ln\bigg{(}\frac{r_{coi}}{r_{isc}}\bigg{)} \tag{12}\]  
The heat conduction resistance of ice layer per unit length can be expressed as [33]:  
\[R_{ice}=\frac{1}{2\pi\lambda_{ice}}ln\bigg{(}\frac{r_{osi}}{r_{coi}}\bigg{)} \tag{13}\]  
The thermal resistance of heat convection between ice and water per unit length can be expressed as [33]:  
\[R_{osi}=\frac{1}{2\pi r_{osi}h_{osi}} \tag{14}\]  
The contact resistance between the ice and coil per unit length can be expressed as:  
\[R_{cic}=\frac{1}{2\pi r_{osc}h_{cic}} \tag{15}\]  
One-dimensional steady-state heat conduction analysis is used to solve the heat transfer process in the cylinder at any time instant. From Equations (11) and (12), it can be seen that increasing the coil radius can reduce the refrigerant evaporation thermal resistance and coil thermal resistance under constant coil thickness. It can be observed from Equations (13) and (14) that \(R_{ice}\) increases and \(R_{silv}\) decreases with increasing ice thickness when the coil radius remains unchanged.
# A High-Resolution Spectral Analysis Method Based on Fast Iterative Least Squares Constraints
## 3 Model Test  
To demonstrate the effectiveness of the proposed method, Figure 1a shows a single seismic trace with a signal-to-noise ratio (SNR) of 20 dB, containing four seismic wavelets with central frequencies of 15 Hz, 20 Hz, 25 Hz, and 30 Hz. Figure 1b-d display the time-frequency spectra obtained using wavelet transform, short-time Fourier transform, and generalized S-transform, respectively. And the window length for all the compared methods is all 40 ms. As seen from the figures, the time-frequency resolution obtained by wavelet transform is the lowest, making it difficult to effectively distinguish the frequency-domain characteristics of the seismic waves. Compared to wavelet transform, both short-time Fourier transform and generalized S-transform are able to capture the seismic wave characteristics, but with very low resolution, failing to accurately extract the dominant frequency information of the seismic waves. In contrast, as shown in Figure 1e, the time-frequency spectrum obtained using our method can extract the dominant frequencies of seismic waves with high resolution, which is of significant importance for identifying gas-bearing formations.  
To further illustrate the noise robustness of the proposed method, we reduced the SNR of the single seismic trace shown in Figure 1a to 10 dB and extracted the corresponding time-frequency spectra using the same methods. A comparison between Figures 1e and 2e shows that, even under extremely low SNR conditions, our method is still able to accurately extract the time-frequency characteristics of the seismic waves, while other methods are greatly affected by noise, significantly compromising the accuracy of the time-frequency spectra.  
Given the typically large volume of seismic data, computational efficiency is a critical factor in practical applications. As shown in Table 1, we have evaluated and compared the computational efficiency of our method with other time-frequency analysis techniques. The results in Table 1 demonstrate that our proposed method outperforms the others in terms of efficiency, ensuring its practicality for real-world use.  
Figure 1: (**a**) A single waveform with four wavelets with noise of 20 dB using (**b**) CWT, (**c**) STFT, (**d**) ST, and (**e**) our new time spectrum method.
## 4 Experiment and Analysis
### Performance of the Models  
#### 4.2.1 Overall Performance of the Models  
Figure 7 shows the quantitative evaluation results and scatter diagrams of the 10-fold cross-validation performance of the three machine learning models compared with MLR. In model fitting, R values range from 0.44 to 0.88, and RMSEs from 0.25 to 0.46. In the cross-validation results, a similar trend appears with no obvious overfitting phenomenon, which proves the validity and applicability of the trained models. Compared to traditional MLR, the RMSE values of the machine learning methods are less than 0.037 and R values are greater than 0.7, but the R (RMSE) of MLR is only about 0.4 (0.046). The machine learning methods show obvious superiority, as they are better to simulate the complex nonlinear relationship and the hidden features within the datasets. When comparing the three machine learning methods, we find that RF performs the best, with the R of RF greater than 0.80 and the RMSE less than 0.03, followed by GRNN and BPNN. From the scatter diagrams, the models somewhat overestimate the NMRI when the NMRI values are low, and underestimate when the NMRI values are at a higher degree; this phenomenon is particularly evident in MLR. Among all the methods, the RF model obtains the best results, the point distribution is the densest near the fitting line, and the maximum slope is obtained. This is followed by BPNN and GRNN with a more dispersed scatter diagram. Similarly, the results of MLR are still the worst.  
Figure 6: Statistical box chart of the correlation coefficients among the VWC-related indices.
## 4 Conclusions
The new C2C+LCA endpoint weighting method provides an environmental, social and economic evaluation in a single procedure, with different indicators organized into levels of action in design, evaluation and management:  
* Level of evaluation of the product life cycle, which includes the Environmental Life Cycle Assessment (E-LCA) and Social Life Cycle Assessment (S-LCA). Results are collected in the Cause Effect Inventory (CEI), with eco-effectiveness design data and resources used (health, material reutilization, renewable energy and carbon management, water stewardship, social fairness and information). At the interpretation stage, these data help to identify strategies for minimizing and controlling environmental and social impacts. * Level of product design and redesign. Results are collected in the Quality Effects Inventory (QEI), which integrates data of the level of intervention of the damage causes. These are obtained from the new weighting with LCA+C2C endpoint indicators. In the interpretation stage, these data help to determine the quality level of the solution and to propose specific improvements in the input groups. * Level of management of the product life cycle, in the Continuous Improvement Plan (CPI). Results are collected in the Future Effects Inventory (FEI), with data of sustainable global performance with macro-indicators of efficiency, consistency and sufficiency that can aid in: (1) selecting life cycle management strategies; (2) defining strategies for product redesign, taking into account economic, environmental and social requirements; and (3) establishing new improvement objectives and planning their implementation, thanks to obtaining a quantified value of the improvements between two consecutive time periods, and the tendency to change since the implementation of the CIP. The following benefits are achieved individually for each method (life cycle assessment and cradle to cradle). Firstly, the LCA results are completed with the identification and grouping of problem cause types (material, energy, water, land, human and information) and the calculation of cause-effect influence. This facilitates the interpretation of the results and speeds up the identification of the problem. Secondly, a quantitative process is provided to the C2C techniques. Additionally, the S-LCA data improve the level of detail and reduce the error of the C2C results in the social category, by being able to identify the set of inputs that generate direct, or indirect, social impacts (externalities). Finally, integrated assessment helps to find a balance between eco-effective and eco-efficient strategies. Conceptualization, M.E.P. and N.A. ; Formal analysis, M.E.P. and N.A. ; Investigation, M.E.P. and N.A. ; Methodology, M.E.P. and N.A. ; Resources, N.A. ; Supervision, M.E.P. ; Validation, M.E.P. ; Visualization, V.M.S. ; Writing--review and editing, M.E.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors declare no conflict of interest.
# Studies on Seismic Performance of Precast Concrete Columns with Ground Splice Sleeve
## 3 Test Results and Discussion
### Discussion on Test Results
#### 3.2.4 Energy Dissipation Capacity  
When the structure gets into the elastic-plastic state, its seismic performance mainly depends on the energy dissipation capacity. Figure 14 shows one loop of the hysteretic curve, by which the structural nonlinear energy absorption and dissipation capacity can be directly reflected from the shaded area ABCDA. Hence, the area that the \(P\)-\(\Delta\) hysteresis curve envelops represents the structural ductility and energy-dissipating ability. The larger area that the hysteretic curve envelops, the more energy that structure dissipates, which is conducive to structural earthquake-resistant behavior.  
The viscous damping coefficient \(h_{e}\) is one of the main indexes that reflects the structural ability to dissipate energy. Specifically, it can be calculated by Equation (3) [42]:  
\[h_{e}=\frac{1}{2\pi}\cdot\frac{A_{rea}(ABC+CDA)}{A_{rea}(OBE+ODF)} \tag{3}\]  
Figure 14: Schematic diagram of computing the viscous damping coefficient.  
Figure 13: Stiffness degradation curves of the specimens.  
The viscous damping coefficients \(h_{e}\) of six specimens in terms of certain loading cycles are shown in Table 6, by which the following conclusions can be drawn:  
1. Since force-controlling loading is adopted, at the same level of the load, the stiffness reduces as the circle times increases, leading to a smaller viscous damping coefficient and a worse capacity of energy dissipation.
2. The energy dissipation capacity of the specimens changes significantly in the first stage of low cyclic repeated loading and the specimens tend to lose the ability to dissipate energy in the whole second stage.
3. The specimen 5# grouted successfully with little grout leaking out has the largest viscous damping coefficients. Generally speaking, the prefabricated specimens have larger viscous damping coefficients than that of the cast-in-situ specimens, which demonstrates that the prefabricated columns have better performance on dissipating energy.
## 5 Discussion
### Limitations and Future Research  
This study has several limitations. First, the IVR collaboration space developed in this study lacks stability owing to technical glitches, such as the loss of the player's position in virtual space, which had negative effects on the collaboration experience. Future research needs to improve the stability of the IVR space to boost the relevance of study results on collaboration. Second, because the empirical investigation was conducted only once in this study, students' experiences in the IVR collaboration space may have been influenced by the novelty effect. Future researchers are recommended to conduct longitudinal studies to enhance the credibility of the empirical findings. Third, this study measured collaboration experience by collecting data only through a self-reported questionnaire, which tends to be subjective and less accurate. Subsequent studies could use more diverse data-collection instruments, such as observation protocols, location heatmaps, or the Kinect sensor to collect multimodal data for a more accurate and comprehensive analysis of students' collaboration behaviors and experiences. Fourth, note that the overall prediction capacity of the regression models was low, suggesting the absence of key influencing factors. Future studies should extend the scope of the investigation to include a more comprehensive list of predictors for collaboration experience in IVR. Lastly, although we believe our research findings based on the collaborative IVR game can still inform real work collaboration in IVRs, future research studies are needed to systematically investigate their applicability with more authentic collaboration tasks.
## 3 Rational for Optimization in WSNs
### Co-existence of Energy Efficiency, QoS, and Security  
After all this discussion the question may arise on the coexistence of these three issues in a WSN application. Our conjecture is that it mostly depends on the application type. As an example Body Area Network (BAN) applications have significant legal, financial, privacy, safety, and real time implications. Hence, data freshness/timeliness, privacy, confidentiality, authentication, authorization, and integrity are their most fundamental requirements. Moreover, being a type of wireless sensor network, energy efficiency in most applications of BAN is a prerequisite, so the coexistence of energy efficiency, QoS (timeliness), and security is necessary, but as we discussed earlier in most cases these are conflicting issues which require a trade-off between them and optimization can be a useful tool in making these trade-offs.  
From the pyramid view of Figure 3, we can easily notice the self contradicting nature of the three areas of a wireless sensor network in three layers. In the lowest two pyramid view, it is clear that if we want to ensure high amount of QoS then eventually the other two parameters will be affected. In the middle pyramids we can see the same scenario with security as the most important issue. Like the previous one, here QoS and energy needs to be compromised. In the upper two pyramids energy efficiency is the most prioritized issue, so QoS and security parameters are highly compromised.
## 2 Materials and Methods  
### Interferometric SAR Principles  
The principle of the InSAR technique relies on the measurement of the phase difference between two or more SAR images, acquired from different positions and/or at different times [36; 37]. The interferometric phase, related to any point seen within the illumination footprint, is expressed as  
\[\phi=\frac{4\pi}{\lambda}(R_{M}-R_{S})\approx\frac{4\pi}{\lambda}B\cdot sin( \theta-\alpha) \tag{1}\]  
where \(R_{M}\) and \(R_{S}\) are the sensor-target distance for the reference and secondary acquisitions, respectively, \(\lambda\) is the radar wavelength, and \(B\), \(\theta\), and \(\alpha\) are the spatial baseline, the incidence look angle, and the tilt angle, respectively.  
The spatial baseline is very important in any InSAR application. As depicted in Figure 1, \(B\) represents the spatial separation between the two acquisitions, which can be decomposed into parallel baselines \(B_{\parallel}\) and perpendicular baselines \(B_{\perp}\), which are respectively highlighted in green and blue. \(B_{\perp}\) is an important factor because it defines the system sensitivity for elevation or topographic mapping. In the InSAR literature it is common to measure the interferometric performance for elevation mapping by means of the Height of Ambiguity, and it can be derived from Equation (1)  
\[Z_{2\pi}=\frac{\lambda Rsin(\theta)}{2B_{\perp}}. \tag{2}\]  
where \(R\) is the slant range distance, which can be either \(R_{M}\) or \(R_{S}\) from Equation (1). In the PolInSAR literature, the system sensitivity to height is usually defined as vertical wavenumber \(\kappa_{z}\), which expresses the ratio of change in the interferometric phase with the variation of the target height [15]  
\[\kappa_{z}=\frac{4\pi B_{\perp}}{\lambda Rsin(\theta)}. \tag{3}\]  
Regardless of the metric used to measure the interferometric sensitivity, \(Z_{2\pi}\) or \(\kappa_{z}\), it is important to work with the appropriate \(B_{\perp}\) according to the range of elevation values to map. In spaceborne systems there is little possibility of selecting or controlling the spatial baseline, unlike airborne systems that can acquire in more experimental and flexible contexts. This is a constraint that needs to be accounted for when estimating canopy height from spaceborne instruments.  
Finally, when working with interferometric pairs, it is very important to know what is the critical baseline of the system, which is normally defined as  
\[B_{\perp,crit}=\lambda\frac{B_{R}}{c}Rtan(\theta-\zeta). \tag{4}\]  
\(B_{\perp,crit}\) is the value of \(B_{\perp}\) beyond which the spectral shift between two acquisitions is higher than the system range bandwidth \(B_{R}\), leading to a zero geometric decorrelation. \(\zeta\) is the terrain slope, but normally a good approximation of \(B_{\perp,crit}\) can be obtained by using the incidence angle \(\theta\) only.