esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries  
### Notations  
In this paper, we use the following notations and conventions. The security parameter is denoted by \(\lambda\). A function \(\textbf{negl}:\mathbb{N}\rightarrow\mathbb{R}\) is considered negligible if it decreases faster than any inverse-polynomial function. Formally, for every constant \(c>0\), there exists an integer \(N_{c}\) such that \(\textbf{negl}(\lambda)\leq\lambda^{-c}\) for all \(\lambda>N_{c}\), where \([n]=\{1,\cdots,n\}\) is the negligible function.  
The abbreviation PPT represents probabilistic polynomial-time algorithms. When sampling from a distribution \(\mathcal{X}\), we use \(x\leftarrow\mathcal{X}\) to denote the random value sampled from the \(\mathcal{X}\) distribution. For a set \(X\), the notation \(x\gets X\) indicates that \(x\) is sampled uniformly from the elements of set \(X\). Bold lowercase letters, such as \(\boldsymbol{v}\), represent vectors, while bold uppercase letters, such as \(\boldsymbol{M}\), represent matrices. By default, all vectors in this paper are assumed to be row vectors. In the context of matrices, the \(j\)-th row is denoted as \(M_{j}\), and \(M_{j}\) represents the submatrix of \(M\) composed of all rows indexed by \(j\in J\), where \(J\) is a set of row indexes. For a vector \(\boldsymbol{v}\), we use \(\|\boldsymbol{v}\|\) to denote its \(\ell_{2}\)-norm, and \(\|\boldsymbol{v}\|_{\infty}\) represents the \(\ell_{\infty}\)-norm of the vector.  
For an integer \(q\geq 2\), we let \(\mathbb{Z}_{q}\) denote the ring of integers modulo \(q\). We use \(\mathbb{Z}_{q}\) to represent integers in the range \((-q/2,q/2]\).
## 2 Safety Analysis Framework for Complex Electromechanical Equipment System Based on STAMP
### Principles of STAMP  
The safety of a complex system is more than component failure or reliability decline. In STAMP, safety is an emergent or system property, rather than a component property. In system theory, complex systems are viewed as a hierarchy of organizational levels. A hierarchical multilevel model of stakeholders is posited in STAMP, like the model of [PERSON] [13], but more expanded. Another basic concept in STAMP is safety constraints. In system theory, safety can be regarded as an emergent which is originated from interactions between components. The method of controlling the emergent is to impose constraints on the behavior of the components and the interactions between the components. Meanwhile, due to the constraint property of hierarchical structure, the accidents will happen if the higher layers cannot provide enough constraints or the lower layers violate the safety constraints when the high-level constraints control the behavior of the lower layers. With the development of the equipment toward automation and intelligent, the system becomes more and more complex, and the difficulty in identifying and executing safety constraints in design and operation has been increased. Figure 3 shows a general socio-technical system control structure from STAMP, which does not represent any particular system. Each node in the graph is a human or machine component in a socio-technical system. Connecting lines show control actions used to enforce safety constraints on the system and feedback that provides information to the controlling entity.  
Besides constraints and hierarchical models, a third basic concept in STAMP is that of process models. The process model is an important part of the control theory. A typical process model consists of the controller that issued the command and the controlled object that provided the feedback (controlled process), which is shown in Figure 4. Usually, the controller contains process model and control algorithm. When the controller sends a command to controlled process, the corresponding feedback which is generated by controlled process will be sent to controller to form a complete controlled process. Component interaction accidents can often be interpreted as process model errors. When the controller's process model does not match the controlled system or the controller issues an unsafe command, an accident will also happen.  
Based on three basic concepts of STAMP, the basic casual factors of a standard control loop under the STAMP framework was concluded in Figure 5 [19].  
Figure 3: General socio-technical system control structure from the system-theoretic accident model and process (STAMP) [15]. Reproduced with permission from [15], Elsevier, 2019.
## 5 Solving Strategy Based on NSGA3
### The Basic Process of NSGA3
#### 5.1.1 Population Classification into Nondominated Levels  
In order to select the best \(N\) solutions from population \(R_{t}\) into the next generation, \(R_{t}\) is first divided into several different nondomination levels using a nondominated sorting method. Then, a new population \(S_{t}\) is constructed by adding the solutions of each nondomination level to \(S_{t}\) from level 1 until the size of \(S_{t}\) is equal to or greater than \(N\) for the first time. Assuming that the last acceptable nondomination level is level \(L\), the solutions in level \(L+1\) are discarded, and the solution \(FL\) in level \(L\) is selected as the solution in the next population \(P_{t+1}\). The remaining individuals in \(P_{t+1}\) need to be selected from \(FL\).  
In the original NSGA2, a solution with a large crowding distance in \(FL\) is preferentially selected. However, the crowding distance is not suitable for solving multiobjective optimization problems of three or more objectives. Therefore, the NSGA3 no longer uses the crowding distance but adopts a new selection mechanism, which analyzes the individuals in \(St\) more systematically through the provided reference points and selects partial solutions in \(FL\) into \(P_{t+1}\).
# Performance and Measurement Devices for Membrane Buildings in Civil Engineering: A Review
## 3 Performance
### Energy Consumption  
Statistics show that the building sector accounts for about 30% of overall energy requirements [114]. In contexts where the concept of energy-saving is receiving increasing attention, it is a trend to improve the energy performance of buildings.  
The design of buildings plays a major role in energy efficiency. [PERSON] et al. [101] assessed the energy consumption disparities between three membrane structure designs: single-layer, double-layer of cushions, and double-layer with a continuous air gap. The energy savings of the second and third designs were found to be 11% and 18%, respectively, compared to the single-layer design. However, the single-layer structure might accomplish the same result by adding a low-emissivity coating or lowering infiltration losses. Furthermore, utilizing natural light through the light transmission of membrane material helps lower the energy necessary for artificial lighting [115].  
Incorporating photovoltaic (PV) panels into the membrane structure converts solar energy into electrical energy to provide the membrane with the ability to maintain itself and reduce the need for additional energy. The conversion efficiency of photovoltaic panels is approximately 5-19% [116; 117]. [PERSON] et al. [118; 119] found that the average power consumption to output ratio of ETFE cushion-integrated PV panels was greater in sunny weather than in sunny-to-cloudy weather. It was confirmed that the cushion could operate appropriately without an external power source. The relationship between energy usage and output is depicted in Figure 20.  
Figure 19: Temperature fluctuating with time [6].
# Electromagnetic Imaging for Buried Conductors Using Deep Convolutional Neural Networks
## 5 Conclusions  
We have presented the two-dimensional inverse scattering reconstruction for perfect conductors buried in half-space via DCNN. We use the scattered field for the initial estimation. DCNN is employed in the frequency domain for reconstructing the accurate size and position of the shapes. According to our numerical simulation results, DCNN is highly reliable for reconstructing the perfect conductors. Based on our testing and training processes, the validation range of the DCNN could be determined. In addition, DCNN can recover better reconstruction results even when 5% noise is added to the scattered field. The method proposed here neither requires any regularization procedure such as direct methods or the traditional iterative method nor requires any initial guesses, which may be a big bottleneck for some algorithms, especially those that based on Newton iteration or backpropagation. Furthermore, the image can be reconstructed in just a few seconds once the training procedure is completed. It is also worth highlighting that the method is quite efficient, even under noise environments. Although simple images are being investigated in this paper, one future goal is to reconstruct images with complex dielectric distributions. Meanwhile,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Example** & **Method** & **RMSE** & **SSIM** \\ \hline Case 1 & 2.95\% & 95.91\% \\ \hline Case 2 & 3.11\% & 94.18\% \\ \hline Case 3 & 17.81\% & 80.01\% \\ \hline Case 4 & 15.10\% & 83.82\% \\ \hline Case 5 & 14.14\% & 89.27\% \\ \hline Case 6 & 15.24\% & 86.73\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: RMSE and SSIM reconstructed images for case 1 to case 6.  
Figure 10: The train losses versus epoch.  
we will also try to innovate different neural network architectures simultaneously so that more accurate electromagnetic images can be reconstructed promptly.  
Conceptualization, C.-C.C. and W.C.; methodology, K.-X.Y. and P.-H.C.; software, K.-X.Y. and P.-H.C.; validation, E.H.L. and W.C.; formal analysis, C.-C.C. and K.-X.Y.; investigation, P.-H.C.; resources, W.C.; data curation, P.-H.C. and E.H.L.; writing--original draft preparation, K.-X.Y.; writing--review and editing, P.-H.C.; visualization, C.-C.C.; supervision, E.H.L.; project administration, E.H.L.; funding acquisition, W.C. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Optimization of Cycle Times
#### 3.2.1 Cycle Time  
It is clear from Figure 4 that the highest cooling capacity values are attained at the cycle time between 200 and 400 s. When the cycle time is less than 200 s, the cooling capacity decreases abruptly because the adsorption/desorption process does not effectively occur within this short period of time. For cycle times longer than 400 s, the adsorption/desorption process reaches its equilibrium state and so further useful cooling is not foreseen and, as a result, cooling capacity decreases with longer time. For COP, it is observed that COP increases monotonically with cycle time although COP increases with the adsorption/desorption cycle time; the cooling capacity slowly decreases and therefore a 300 to 400 s period was chosen to be the optimum adsorption/desorption cycle time.  
Figure 3: Comparisons of chiller experimental and model cooling capacity. (Model Results are at the Chiller Nominal Operating Conditions shown in Appendix C).  
Figure 2: Experimental validations of outlet temperatures for hot bed, cold bed and chilled water (model results are at chiller nominal operating conditions shown in Appendix C).
## 4 Discussion  
Table 2 shows that the modelling approach used in this study is validated by comparing the results with other similar studies. Additionally, the smoothness of the curves presented in Figures 4 and 5 shows that the optimization of the process parameters for Cases 5a, 6a, and 6b has been made on a consistent basis giving confidence in the trends illustrated in the results.  
The variation of performance with ambient temperature presented in Figure 4 shows that Case 6a is the process that consumes the least energy across most of the temperature range studied. There is, however, a trade-off point around 45\({}^{\circ}\)C, where Case 5a becomes the lowest energy consuming case. Case 6a does not offer the minimum energy consumption in any cases, but it does outperform Case 5a when the cooling temperature is below 20 \({}^{\circ}\)C. These trends support the original assertion of [PERSON] [6] that open-cycle CO\({}_{2}\)-based systems outperform NH\({}_{3}\)-based refrigeration at low temperatures.  
It is worth noting that the comparison between Case 5a and 6b is not entirely a fair one since the machinery required in Case 6b is significantly more complicated than that required in Case 5a. The potential benefit of a turbo expander-based process would ultimately be determined by the optimum balance between operating and capital costs, which in-turn will vary between projects. Also worth noting is that Case 4b offers similar performance to Case 6b with a small reduction in process complexity. Although the life-cycle costing of these schemes falls outside the scope of this study, the results provided here suggest that all of the Cases 4b, 5a, 6a, and 6b would merit consideration in a techno-economic study of the CO\({}_{2}\) liquefaction process.  
Cases 5a and 6a could be considered \"conventional\" and are also similar in the level of process complexity involved. Case 6a does, however, have a potentially significant advantage over Case 5a because of the possibility of condensing water out of the CO\({}_{2}\) stream at pressure levels above the final liquefaction pressure. This will reduce, and possibly eliminate, the requirement for an additional dehydration step in the process. Therefore, in Case 5a an additional pressure-drop associated with a dehydration unit located prior to the liquefaction step may be necessary, representing a small additional energy penalty for this process. Again, the level of dehydration required and the energy penalty this represents would be expected to vary on a project-by-project basis.
## 3 Results and Discussion
### Basic Physical Properties Test
#### 3.1.3 Collapsibility Test  
To evaluate the effectiveness of CL combined with MgO carbonated solidified in improving the collapsibility of loess, indoor water immersion confined compression tests were conducted under various loading pressures (50 kPa, 100 kPa, 150 kPa, and 200 kPa). The results, illustrated in Figure 8, indicate that under all loading conditions, the collapse coefficient of the samples decreased significantly as the stabilizer content was increased. Further analysis revealed that when the stabilizer content reached 1.0%, 1.5%, and 2.0%, the collapse coefficient (\(\delta_{\text{s}}\)) of the samples remained below 0.015 across all tested loading pressures. Compared to the moderate collapsibility (Grade II) of the original remolded loess, the improved carbonated solidified loess exhibited slight collapsibility (Grade I), effectively demonstrating the stabilizer's enhancement effect. The improvement mechanism underlying this phenomenon can be attributed to two primary factors. First, CL enhances soil structure by reinforcing the cementation between soil particles, thereby increasing structural integrity. Second, the synergistic effect of magnesium hydroxide (Mg(OH)\({}_{2}\)), produced through the hydration of reactive MgO, along with carbonation products, efficiently fills soil pores. This process reduces porosity and simultaneously strengthens the bonding between soil particles, significantly decreasing the soil's susceptibility to collapse. In summary, the results confirm that carbonated solidified soil technology, combining CL and MgO, offers substantial engineering application potential for the treatment and stabilization of collapsible loess.  
Figure 8: Wetting collapse coefficient of carbonated solidified loess.  
Figure 7: Compaction curves of uncarbonated solidified loess with CL contents.
## 3 Inversion Algorithm of Atmospheric Temperature in MLT
### Optimal Estimation Method
After the \(i_{th}\) iteration, the new target state vector is  
\[x_{i+1}=x_{a}+\left(S_{a}{}^{-1}+K_{i}  
where \(I\) represents the radiance, \(T\) represents the atmospheric temperature, \(\Delta\) represents the difference without and with the perturbance, and \(i\) and \(j\) represent the number of rows and columns of inversion, respectively. The typical weighting functions of the six central emission lines in the observed Band 2 are shown in Figure 9 below. In Figure 9, a-f is the weighting function matrix of the atmospheric temperature of six emission lines in Band 2 with respect to the limb radiance. Different colored lines represent different lines of the weighting function \(K_{n}\), and the weighting function of each specific tangent height is the line whose peak value is at the corresponding tangent height. Based on the observed radiance data, the above optimal estimation method is conducted for the synchronous inversion of atmospheric temperature in the whole range of tangent height. The Gauss-Newton iterative algorithm was used for multiple iterations, and the results of the first five iterations were shown in Figure 10. The bias between the temperature and the actual temperature obtained through 1-5 iteration inversion and  
\[y_{\varepsilon}=F(x)+\varepsilon \tag{5}\]0 Weighting function matrix of target emission line with respect to temperature. Subgraph (**a–f**) correspond to the weighting function of the six emission lines in figures, respectively. Different colors represent rows of K, where the weighting function for a specific tangent height is the row that peaks at that altitude. the mean absolute bias under different iteration steps were shown in Figure 11a,b. By comparing the cost function corresponding to the result of each iteration, it is found that when the number of iteration step \(i=3\), the cost function reaches the minimum value, so \(x_{3}\) is regarded as the optimal estimated value. Meanwhile, this conclusion can also be verified in Figures 10 and 11. When the number of iteration steps \(i=3\), the bias between the retrieved temperature and the actual temperature, and the mean absolute bias under different iteration steps both reached the lowest level.
## 3 Materials and Methods
### Data Preprocessing
#### 3.2.3 Normalization  
Normalization is defined as a specific form of feature scaling that transforms the feature range into a standard scale. Normalized data enhances the model performance and improves model accuracy. It helps classification algorithms based on distance measurements, preventing larger scale features from dominating the learning process. Normalization boosts stability in the optimization process, promoting faster convergence during gradient-based training. Normalization facilitates the understanding and interpretation of data. Min-max scaling, which is also used in this study, is one of the most used normalization techniques and normalizes features to a certain range according to the given lower and upper bound parameters. In this study, the range 0-1 has been selected as the lower and upper bounds [45].
## 3 FE Analysis
### Material Modeling
#### 3.2.2 Tension Behavior  
[PERSON] [21] presents the tensile nonlinear behavior of concrete, which is depicted by the stress-crack opening relationship in Equation (7):  
\[\frac{\mathrm{f_{t(w)}}}{\mathrm{f_{tm}}}=\left[1+\left(\mathrm{c_{1}}\frac{ w}{\mathrm{w_{c}}}\right)^{3}\right]\mathrm{e}^{-\mathrm{c_{2}}\frac{w}{ \mathrm{w_{c}}}}-\frac{\mathrm{w}}{\mathrm{w_{c}}}(1+\mathrm{c_{1}^{3}}) \mathrm{e}^{-\mathrm{c_{2}}} \tag{7}\]  
Here, \(\mathrm{c_{1}}=3\), \(\mathrm{c_{2}}=6.93\), and \(\mathrm{w_{c}}\) represent the crack opening, which can be determined using the fracture crack opening given in Equation (8).  
\[\mathrm{w_{c}}=5.14\frac{\mathrm{G_{F}}}{\mathrm{f_{tm}}} \tag{8}\]  
\(\mathrm{G_{F}}\) is the fracture energy which is determined by \(\mathrm{G_{F}}=0.073\mathrm{f_{cm}^{0.18}}\).  
The width of the crack at the mesh size \(\mathrm{l_{eq}}\) is determined by Equation (9):  
\[\mathrm{w}=\mathrm{l_{eq}}\mathrm{\varepsilon_{t}^{ck}}=\mathrm{l_{eq}}( \mathrm{\varepsilon_{t}}-\mathrm{\sigma_{t}}\mathrm{E_{c}^{-1}}) \tag{9}\]  
The tension damage variable, \(\mathrm{d_{t}}\), is determined by the portion of normalized energy dissipated by damage in the following Equation:  
\[\mathrm{d_{t}}=1-\frac{1}{2+\mathrm{a_{t}}}\Big{[}2(1+\mathrm{a_{t}})\exp(- \mathrm{b_{t}}\mathrm{\varepsilon_{t}^{ck}})-\mathrm{a_{t}}\exp(-2\mathrm{b_{ t}}\mathrm{\varepsilon_{t}^{ck}})\Big{]} \tag{10}\]  
\[\mathrm{a_{t}}=2(\mathrm{f_{tm}}/\mathrm{f_{t0}})-1+2\sqrt{(\mathrm{f_{tm}}/ \mathrm{f_{t0}})^{2}-\mathrm{f_{tm}}/\mathrm{f_{t0}}} \tag{11}\]  
\[\mathrm{b_{t}}=\frac{\mathrm{f_{t0}}\mathrm{l_{eq}}}{\mathrm{G_{F}}}\Big{(}1 +\frac{\mathrm{a_{t}}}{2}\Big{)} \tag{12}\]  
\(\mathrm{\varepsilon_{t}^{ck}}\) is the cracking strain. \(\mathrm{f_{t0}}\) is the tensile strength at zero cracking strain, and \(\mathrm{f_{tm}}\) is determined by \(\mathrm{f_{tm}}=\frac{\mathrm{f_{t0}}\left(1+\mathrm{a_{t}}\right)^{2}}{4 \mathrm{a_{t}}}\).  
From the above equations, combined with Model Code recommendations [22], the curves show the stress and damage variables in the compression and tension behavior of the concrete used in the modeling model, which is presented in Figure 4.  
Figure 4: The compressive and tensile behavior of concrete: (**a**) compressive stress-crushing strain; (**b**) compressive damage variable-crushing strain; (**c**) tensile stress-cracking strain curve; (**d**) tensile damage variable-cracking strain curve.
## 6 Conclusions
### Limitations and Further Research  
There are several limitations to this study. First, the study did not consider the impact of different platforms or devices on the expression of emojis since [PERSON] [18] noted that \"the exact appearance of emojis is not fixed, but varies between platforms or devices, in the same way that normal typefaces do\" and because emojis may accrue unexpected meanings. Therefore, in a next step, we can discuss the impact of the use of emojis on online recovery performance based on different devices and different platforms. Second, the study discussed the online recovery scenario only for Wi-Fi rental services. Future research can extend the research area to airline ticket online booking, hotels, and visa services and identify more factors that affect online service recovery performance. Third, this study did not explore the moderating role of different service failure types; however, different values are assigned to different types of failures (process vs. outcome) [45]. Thus, service types will influence customers' different labeling of their service, and further research can expand on this point. Fourth, the collected data used in the study has been gathered in China, and future studies are advised to test the proposed theory-driven framework considering different populations in diverse cultures. Fifth, the sample size of this study was small, and most of the respondents were aged 20-35 years; thus, further study can expand the sample number to increase the degree to which the empirical results are convincing.  
The authors declare the following roles during the development of the present paper. Conceptualization, Y.Z. and B.S.; Methodology, Y.Z.; Formal Analysis, Y.Z.; Investigation, Y.Z.; Data Curation, Y.Z.; Writing-Original Draft Preparation, Y.Z.; Writing-Review & Editing, B.S.; Supervision, B.S.; Project Administration, B.S.; Funding Acquisition, B.S.  
Funding: This research was funded by the National Social Science Foundation of China grant number [No.14 AGL023].