esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Proof for Backscatter Signal Amplitude Deterioration  
In Section 3, it is described that a hardening and shrinking material can lead to microbending of the fiber. This phenomenon manifests itself in a diminishing backscatter signal amplitude. In turn, strain measurements obtained by the traditional method contain mostly the observed procedural noise. The magnitude of the backscattered signal is shown in Figure 5 as the received amplitude of the signal in dB/mm.  
The black curve in Figure 5 shows the backscatter amplitude signal of Measurement 16. As described in Section 5.1, the fiber at this time is still surrounded by liquid epoxy. Thus, it does not experience microbending, and within the borders of natural signal variations the backscatter amplitude signal is a straight line, not exhibiting any signs of a deteriorating amplitude (the step at ca. 15.7 m is due to the splice of the measurement fiber to an extension fiber, which is connected to the OBR instrument). The backscatter amplitude signal for Measurement 49 (red curve in Figure 2), however, shows a step structure, and the amplitude decrease can clearly be attributed to the passage of the fiber through the hardening and shrinking material. Within the material, the amplitude stays constant.  
As can be seen in Figure 2, the amplitude diminishes by more than 15 dB/mm, and the signal is only 4 dB/mm from the background signal amplitude. However, as Figure 3 shows, the running reference method is still able to retrieve reliable strain values.  
When the backscatter signal becomes undistinguishable from the background signal (e.g., after the fiber exits the material in Figure 5), the comparison of a random background noise record with the (any) reference record leads to the computing of truly random, and not procedural, noise. Since the  
Figure 5: Amplitude of the backscattered signal for different measurements. To be able to determine at which position the steps occur, the length in this figure is given as the absolute length from the OBR instrument and not as the relative length, as seen from the entry point of the fiber into the epoxy. Measurement times: \(\#16\to 200\,\mathrm{min}\), \(\#49\to 309\,\mathrm{min}\)  
background signal does not contain any information, not even the running reference analysis method can retrieve reliable strain values.  
These results show clearly that curing epoxy indeed treats the fiber so badly that by using the traditional method, no relevant strain data can be obtained any longer, even though the physical limits of the OBR instrument were not reached. How these shortcomings can be overcome without new measurements but by only utilizing the already available data shall be covered in the remainder of this article.
# Electronic and Magnetic Properties of Bulk and Monolayer CrSi\({}_{2}\): A First-Principle Study
## 4 Conclusions  
Electronic and magnetic properties of CrSi\({}_{2}\) were calculated using the first-principle methods based on density functional theory. The phonon dispersion curve, band structures with spin state, total and partial density of states (DOS), and spin density of bulk and monolayer CrSi\({}_{2}\) systems were systematically investigated. Both bulk and monolayer CrSi\({}_{2}\) were structurally stable at ground state. The results showed that the bulk CrSi\({}_{2}\) system is an indirect gap non-magnetic semiconductor with a band gap of 0.376 eV, whilst the monolayer CrSi\({}_{2}\) sheets were metallic and ferromagnetic (FM). Compared with previous literature, our results were consistent with [PERSON]'s results (ferromagnetism) and inconsistent with [PERSON]'s results (anti-ferromagnetism). We explain the reason why monolayer CrSi\({}_{2}\) had metallic behavior using [PERSON]'s theory, where surface and confinement effects play an important role in the metallic behavior of monolayer CrSi\({}_{2}\). Further analysis showed that total DOS and PDOS of the bulk CrSi\({}_{2}\) system were fully symmetric, and those of the monolayer CrSi\({}_{2}\) system were asymmetric, which may reveal the physical mechanism of magnetism for bulk and CrSi\({}_{2}\) nanosheets. In addition, we also elucidated the origin of magnetism considering the charge transfer model and [PERSON]'s rules, where magnetic moment of unit cell of 2D multilayer nanosheets with different layers was calculated. The results showed that the magnetism of materials is attributed to band energy between layers, as well as quantum size and surface effects. We also expect that our calculations may provide some helpful insight into further experimental investigations, and they show promise in device applications based on 2D CrSi\({}_{2}\) nanosheets.  
Conceptualization, S.C.; Methodology, Y.C. and X.Q.; Software, S.C. and Y.C.; Formal Analysis, W.Y., S.Z., and W.X.; Data Curation, S.C.; Writing-Original Draft Preparation, S.C.; Writing-Review & Editing, S.C., L.L., and W.X.; Supervision, Y.C.  
This research was funded by [Key Projects of the Tripartite Foundation of Guizhou Science and Technology Department] grant number [[2015]7696], and by [Guizhou College Student Innovation Entrepreneurship Training Program] grant number [201710667017], and by [Major Projects for Creative Research Groups of Guizhou Province of China] grant number [2016]048], and by [Innovation Team of Anshun University] grant number [2015 PT02], and by [Natural Science Foundation of Science and Technology Department of Guizhou Province of China] grant number [[2010]2001].  
We are really grateful to the cloud computing platform at Guizhou University and the CNROCK HOLE BLACKHOLE high density computing platform for computing support.  
The authors declare no conflict of interest.  
Figure 6: Valence electron diagrams of Cr atom in (**a**) bulk and (**b**) monolayer CrSi\({}_{2}\) systems.  
Figure 7: The magnetic moment of unit cell of multilayer CrSi\({}_{2}\) nanosheets varies with different layers.
# Transient Responses of Repeated Transverse Impacts on Beams
## 6 Conclusions  
The transient behavior of a flexible beam, driven by repeated transverse impact of a spherical projectile, are investigated in this study. The eigenfunction expansion method is firstly presented to solve the transient structure responses of beams. Then the new method can be used to solve the repeated impact responses of beams by combining the local contact models. In order to verify the validity, the results of the new method as well as the spring-mass model are compared with the experimental data, where a simply-supported beam is repeatedly struck by a sphere. The main findings are concluded as follows.  
1. The eigenfunction expansion method could predict the wave propagation during repeated impacts on beams, and the results agree well with the experiments.
2. For transient impact behaviors related to energy dissipations, including the accumulated permanent indentation, the coefficient of restitution and the separation velocity of the sphere, the results of eigenfunction expansion method agree well with the experimental results, while the results of spring-mass method have large discrepancy with the experimental results.
3. For impact duration, the simulation results of eigenfunction expansion method and spring-mass method both agree well with the experimental results.
4. The differences for the eigenfunction expansion method and spring-mass method are that, for eigenfunction expansion method the energy can be dissipated by wave propagations, structural vibrations as well as local contact behaviors, while the spring-mass method only dissipates energy thorough structural vibrations and local contact behaviors.
5. The eigenfunction expansion method appears to be suitable for the transient responses of repeated transverse impacts on beams, especially for the study of impact-induced wave propagations.  
Conceptualization, H.W. and H.Z.; Data curation, H.W., S.M. and X.W.; Funding acquisition, H.W.; Investigation, H.W., S.M. and X.W.; Methodology, H.W.; Project administration, H.W.; Supervision, H.W. and H.Z.; Validation, H.W.; Visualization, H.W.; Writing--original draft, H.W.; Writing--review & editing, H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 11902143), the Postdoctoral Research Funding Program of Jiangsu province (2020Z056) and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX22_1281).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## Appendix A
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Instance**} & \multicolumn{3}{c}{**CII Heuristic (Phase 2)**} & \multicolumn{3}{c}{**CII Heuristic (Phase 3)**} & \multicolumn{3}{c}{**Other Heuristics**} \\  & \(V\) & **Opt?** & \(C(BKS)\) & \(C(T)\) & \(Error_{CII}\) & **Time** & \(C(T)\) & \(Error_{CII}\) & **Time** & **RAM** & \# & \(C_{min}(T_{H})\) & \(Error_{H}\) & **Time** & **Heuristic Id** \\ \hline lin105 & 105 & yes & 14,379 & 14,913 & 3.7\% & 1.2 ms & 14,440 & 0.4\% & 3.8 ms & 0.6 MiB & 3 & 14,379 & 0.0\% & 2.0 s & DFACO \\  & & & & & & & & & & 14,379 & 0.0\% & 2.0 s & ACO-3 Opt \\  & & & & & & & & & & 14,379 & 0.0\% & 2.0 s & ESAACO \\ \hline pr107 & 107 & yes & 44,303 & 45,730 & 3.2\% & 1.1 ms & 45,262 & 2.2\% & 18.1 ms & 0.6 MiB & 5 & 54,121 & 22.2\% & 0.9 ms & NN \\ \hline pr124 & 124 & yes & 59,030 & 62,193 & 5.4\% & 1.4 ms & 60,055 & 1.7\% & 5.3 ms & 0.6 MiB & 3 & 73,008 & 23.7\% & 1.3 ms & NN \\ \hline bier127 & 127 & yes & 118,282 & 121,544 & 2.8\% & 5.4 ms & 121,544 & 2.8\% & 5.6 ms & 0.6 MiB & 3 & 118,282 & 0.0\% & 47.0 s & DFACO \\  & & & & & & & & & & & 118,282 & 0.0\% & 56.0 s & ACO-3 Opt \\ \hline ch130 & 130 & yes & 6110 & 6676 & 9.3\% & 1.7 ms & 6190 & 1.3\% & 27.9 ms & 0.6 MiB & 9 & 6110 & 0.0\% & 13.0 s & DFACO \\  & & & & & & & & & & 6110 & 0.0\% & 16.0 s & ACO-3 Opt \\ \hline pr136 & 136 & yes & 96,772 & 102,934 & 6.4\% & 1.7 ms & 98,711 & 2.0\% & 9.9 ms & 0.6 MiB & 5 & 125,458 & 29.6\% & 1.2 ms & NN \\ \hline pr144 & 144 & yes & 58,537 & 60,625 & 3.6\% & 2.1 ms & 59,902 & 2.3\% & 6.8 ms & 0.6 MiB & 3 & 64,886 & 10.8\% & 1.4 ms & NN \\ \hline ch150 & 150 & yes & 6528 & 7038 & 7.8\% & 2.1 ms & 6746 & 3.3\% & 11.5 ms & 0.6 MiB & 3 & 6,528 & 0.0\% & 24.0 s & DFACO \\  & & & & & & & & & & & 6528 & 0.0\% & 17.0 s & ACO-3 Opt \\ \hline kroA150 & 150 & yes & 26,524 & 28,814 & 8.6\% & 2.2 ms & 27,230 & 2.7\% & 10.2 ms & 0.6 MiB & 5 & 26,524 & 0.0\% & 57.0 s & DFACO \\  & & & & & & & & & & & 26,524 & 0.0\% & 1.4 m & ACO-3 Opt \\ \hline kroB150 & 150 & yes & 26,130 & 27,476 & 5.2\% & 2.2 ms & 26,399 & 1.0\% & 26.4 ms & 0.6 MiB & 5 & 26,130 & 0.0\% & 7.0 s & DFACO \\  & & & & & & & & & & & 26,130 & 0.0\% & 9.0 s & ACO-3 Opt \\ \hline pr152 & 152 & yes & 73,682 & 76,952 & 4.4\% & 2.3 ms & 74,605 & 1.3\% & 19.0 ms & 0.6 MiB & 5 & 86,906 & 17.9\% & 1.4 ms. & \\ \hline ul59 & 159 & yes & 42,080 & 47,591 & 13.1\% & 2.6 ms & 46,875 & 11.4\% & 15.7 ms & 0.6 MiB & 3 & 53,918 & 28.1\% & 1.6 ms & NN \\ \hline rat195 & 195 & yes & 2323 & 2569 & 10.6\% & 3.7 ms & 2485 & 7.0\% & 16.2 ms & 0.6 MiB & 4 & 2826 & 21.7\% & 2.0 ms & NN \\ \hline d198 & 198 & yes & 15,780 & 16,862 & 6.9\% & 3.9 ms & 16,119 & 2.1\% & 32.6 ms & 0.6 MiB & 4 & 15,780 & 0.0\% & 6.5 s & ESACO \\ \hline kroA200 & 200 & yes & 29,368 & 31,792 & 8.3\% & 3.9 ms & 30,767 & 4.8\% & 17.5 ms & 0.6 MiB & 5 & 29,368 & 0.0\% & 2.8 m & DFACO \\  & & & & & & & & & & & 29,379 & 0.04\% & 3.5 m & ACO-3 Opt \\  & & & & & & & & & & 29,368 & 0.0\% & 4.7 s & ESACO \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cont.
## 2 Materials and Methods
### Experimental Design  
A Box-Behnken design was implemented in random run order (Table 1), generating 15 experimental conditions tested (Table 2). As per this design, three factors were evaluated at three different experimental levels of temperature (40-60 \({}^{\circ}\)C), pressure (20-40 MPa), and percentage of ethanol as a co-solvent (0-8% v/v). The effects of the factors on different responses, including extraction yield (Y), total carotenoids content (TCC) and its recovery (TC recovery), total phenolic content (TPC), antioxidant activity (TEAC assay), and fatty acids profile (FAMEs) were studied. The experimental design and data analysis were carried out using response surface methodology (RSM) with Statgraphics Centurion XVI()(StatPoint Technologies, Inc., Warrenton, VA, USA) software.  
In a design that involves three factors X1, X2, and X3, the mathematical relationship of the response with these factors is approximated by the quadratic polynomial equation named second degree Equation (1) described below:  
\[Y=\beta_{0}+\beta_{1}X_{1}+\beta_{2}X_{2}+\beta_{3}X_{3}+\beta_{12}X_{1}X_{2}+ \beta_{13}X_{1}X_{3}+\beta_{23}X_{2}X_{3}+\beta_{11}X_{1}^{2}+\beta_{22}X_{2}^{ 2}+\beta_{33}X_{3}^{2} \tag{1}\]  
where Y = estimate response; \(\beta\)0 = constant; \(\beta\)1, \(\beta\)2, and \(\beta\)3 = linear coefficients; \(\beta\)12, \(\beta\)13, and \(\beta\)23 = interaction coefficients between the three factors; \(\beta\)11, \(\beta\)22, and \(\beta\)33 = quadratic coefficients. Multiple regression analysis is done to obtain the coefficients and the equation can be used to predict the response.  
The effects of the independent factors on the response variables in the separation process were assessed using pure error, considering a confidence level of 95% for all the variables. The effect of each factor and its statistical significance, for each of the response variables, were analyzed using ANOVA and standardized Pareto chart. The response surfaces of the respective mathematical models were also obtained, and their significance was accepted at \(p\leq 0.05\). A multiple response optimization was carried out through the combination of experimental factors, to maximize the desirability function for the responses in the extracts. The desirability function method was applied to generate optimum conditions having some specific desirability value (close to =1 indicate that the setting achieves favorable results for all responses). All variables were obtained with the same equal weight = 1 (maximizing response).
# Development of Communication Protocols between BIM Elements and 3D Concrete Printing
## 4 Computer Procedure for Connection between BIM and 3D Concrete Printing
#### 4.1.5 Management Phase of the Resulting Element  
Once the element under study has been printed, it is necessary to know its real dimensions to compare the virtual design with what is obtained in practice. This procedure requires the use of a Leica BLK360 model laser scanner. This laser was chosen because of its interaction with RevitTM, which projects a 3D model of the scanned item. In this way, measurements are made to determine its real dimensions, which makes it possible to carry out a comparative analysis between design and execution, facilitating the study of the different heights of the printing cordon in each layer of the printed element.  
Figure 5 is a flowchart that schematically shows the procedure for printing a construction element:  
Step-by-Step Development of the Computer Procedure That Communicates a Model Designed in BIM and 3D Printing Concrete Elements  
This section aims to show a summary of the development of the computer procedure required to communicate a model designed in a BIM software, and 3D concrete printing elements, by means of using DynamoTM, a plugin of Revit. It is important to emphasize that one of the most important contributions of the procedure presented in this research is the possibility of easily replicating it in other plugins or software such as GrasshopperTM and others. The entire step-by-step procedure is shown in Appendix A.  
1. Designing the initial construction element in RevitTM.
2. Exporting process of a construction element from RevitTM to DynamoTM.
3. Preparation of the control code for the printing process with the KUKATM robot.  
* To obtain the surfaces to create the points cloud of the element to be printed.
* Creation of the point cloud that describes the contour of the figure.
* Creation of the printing trajectory from the points cloud.
* Transfer of the points cloud, within the coordinate plane, to a position within reach of the robotic arm.
* Control code of the KUKATM robotic arm.
4. Calculations and printing costs.
# Effects of Night Ventilation on Indoor Air Quality in Educational Buildings--A Field Study
## 2 Materials and Methods
### Measurements
#### 2.4.1 Measuring Instruments  
Table 3 shows the measuring instruments. The pressure differences over the building envelope, i.e., over the external wall, were measured by conducting a Sensirion manometer. The manometer sent data to the cloud service at 30-min recording intervals. The air distribution of supply air terminal devices was monitored using either the Sensirion manometer or the Swema 3000 manometer, in which a logging interval was 5 min. The indoor air temperature and CO\({}_{2}\) were measured by conducting the Tnytag loggers that were installed at the height of 1.1 m. The recording interval of Tnytag loggers was 5 min. TVOC measurement was performed by the metal oxide semiconductor method using a Nuvap IEQ monitor that was usually located on a shelf. A sampling was normally 3-4 times per hour.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Physical Quantity** & **Accuracy** \\ \hline Swema 3000 & pressure difference & \(\pm\)0.3\% \(\pm\) 0.3 Pa \\ Sensirion SDP816-125 Pa & pressure difference & \(\pm\)3\% \(\pm\) 0.08 Pa \\ Tnytag plus 2 TGP-4500 & air temperature and humidity & \(\pm\)0.5 °C, \(\pm\)3.0\% RH \\ Tnytag TGE-0011 & CO\({}_{2}\) & \(\pm\)3\% \(\pm\) 50 ppm \\ Nuvap IEQ monitor & TVOC & \(\pm\)15\% \\ Trotec PC220 & PM2.5, PM10 & \(\pm\)30\%, efficiency 50\% for 0.3 \\  & & \(\upmu\)m and 100\% for \(>\)0.45 \(\upmu\)m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measuring instruments.  
Figure 2: Typical air distribution methods in measured indoor environments: (**a**) wall air distribution of playroom in kindergarten 2; (**b**) duct air diffuser of classroom in primary school 1; (**c**) wall air distribution of playroom in kindergarten 3; (**d**) duct diffuser of classroom in primary school 4; (**e**) ceiling air distribution of classroom in primary school 2; (**f**) duct diffuser of classroom in secondary school.
# Delay Management in Regional Railway Transport
## 2 Materials and Methods
### Waiting Time Issue  
The reason for waiting times is to maintain the integrity of a transport chain during delays in connecting services. In the cases of isolated lines (or isolated operations, such as subways) and short-interval lines (e.g., S-Bahn daily services and public transport in big cities), it is not generally necessary to deal with waiting for services because the subsequent services are managed in a relatively short time; waiting due to delayed services would bring more negative influences for non-transfer passengers than positive influences for transfer passengers. However, in the Czech Republic, long intervals in railway transport are common. Many lines (generally long-distance lines or regional lines outside metropolitan areas) are operated in 2-h intervals, at least during off-peak hours, so waiting times for transfer connections in the Czech Republic are relatively high.  
The basic thesis for determining transfer waiting times is that delays and anomalies that are spread across a network should have limited impact and predetermined maximum allowable waiting times, affecting as few passengers as possible.
## 5 Conclusions  
The problem of copyright protection in multimedia content, both audio and video, is currently a very popular issue analyzed by both researchers and commercial institutions developing ready-made DRM systems. Among the solutions used, watermarking is the dominant strategy, especially with the use of neural network algorithms, enabling the improvement of key watermarking paradigms, i.e., transparency, resistance, and bit capacity, to values impossible to achieve when using only classical methods of watermarking.  
This article presents an algorithm for marking video signals based on the architecture of convolutional networks and the architecture of the GAN network, characterized by high transparency (SSIM above 0.93 and PSNR above 30) and robustness (BER metric value at the level of several percent for almost all analyzed variants). The main advantage of the presented algorithm is the use of an information mapper based on entropy that allows the embedding of complex, multi-bit binary signatures of up to 16,384 bits. Increasing the entropy of the watermark made it possible to obtain the high transparency of the algorithm, with a very high capacity at the same time. Each variant of the watermark signature (each pair of parameters N and n) was treated as a separate algorithm, for which the appropriate values of the weighting coefficients of the complex loss function were empirically selected, which allowed optimal results to be obtained.  
The capacity of the tagging algorithm is important in the context of the commercial application of the method. The protection of copyright or content distribution rights requires marking the content with a complex watermark containing information about both the content and the owner. To encode such complex information, it is necessary to send a large number of bits.  
The developed algorithm is the basis for further work in the field of watermarking. The next stage of work will be devoted to making the algorithm resistant to lossy compression using the latest video codecs, i.e., H.264 and H.265.  
Investigation, M.B.; Methodology, M.B. and Z.P.; Resources, M.B.; Software, M.B.; Visualization, M.B.; Supervision, Z.P.; Validation, Z.P.; Writing--original draft, M.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Centre for Research and Development, grant number CYBERSECIDENT/381319/II/NCBR/2018 on \"The federal cyberspace threat detection and response system\" (acronym DET-RES) as part of the second competition of the CyberSecIdent Research and Development Program--Cybersecurity and e-Identity.  
Not applicable.  
Data available in a publicly accessible repository The PASCAL Visual Object Classes (VOC).  
The authors declare no conflict of interest.
## 4 Linearized Dynamics and the Proposed Control Law
### State-Space Formulation  
We define the state vector as \(\mathcal{X}=\left[\begin{array}{c}\phi\\ \dot{\phi}\end{array}\right]\). Therefore, the nonlinear dynamic model (21) may be represented in the subsequent state-space representation:  
\[\dot{\mathcal{X}}=\mathcal{AX}+\mathcal{B}\Delta u+\mathcal{D}_{1}\Phi_{1}( \mathcal{X})+\mathcal{D}_{2}\Phi_{2}(\mathcal{X})+\mathcal{B}\Delta_{t} \tag{22}\]where the following equations define the functions \(\Phi_{1}(\mathcal{X})\) and \(\Phi_{2}(\mathcal{X})\):  
\[\Phi_{1}(\mathcal{X}) =\text{sgn}(\phi) \tag{23a}\] \[\Phi_{2}(\mathcal{X}) =\text{sin}(\theta_{d})\phi+\text{cos}(\phi+\theta_{d})-\text{cos }(\theta_{d}) \tag{23b}\]  
The matrices \(\mathcal{A}\), \(\mathcal{B}\), \(\mathcal{D}_{1}\), and \(\mathcal{D}_{2}\) are represented by the subsequent expressions:  
\[\mathcal{A}=\left[\begin{array}{cc}0&1\\ -\frac{a}{v_{*}}\sin(\theta_{d})&-\frac{f_{*}^{2}}{v_{*}}\end{array}\right], \quad\mathcal{B}=\left[\begin{array}{cc}0\\ \frac{1}{v_{*}}\end{array}\right],\quad\mathcal{D}_{1}=\left[\begin{array}{ cc}0\\ -\frac{f_{*}}{v_{*}}\end{array}\right],\quad\text{and}\quad\mathcal{D}_{2}= \left[\begin{array}{cc}0\\ \frac{a}{v_{*}}\end{array}\right] \tag{24}\]