esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The implementation of EVSs for indoor explosive-load-reduction purposes is efficient only in the case of deflagration explosions or in the case of explosive combustion of a gas-vapor mixture. In such cases, the visible flame-propagation rate is significantly lower than the explosive-combustion velocity (speed of sound). That is why in the case of a deflagration explosion, the quasi-static principle of excessive pressure is observed, whereby explosive pressure is unaffected by the spatial coordinate and is only a function of time. Considering the fact that the overwhelming majority of explosive accidents are of the deflagration type, the use of relief openings equipped with protective devices like EVSs appears to be the most effective way to reduce explosive pressure.  
Analysis of the results of theoretical and experimental studies in the sphere of blast resistance demonstrates that relief openings (windows) equipped with explosion-venting structures (EVS) allow for a reduction in explosion pressure to a safe level (2-4 kPa). Considering the fact that the maximum explosion pressure in EVS-equipped premises depends on the EVS start-to-open pressure, the response rate of the structure, and characteristic dimensions of the room, each particular building requires individual selection of EVS parameters, which provide a safe level of excessive pressure in the case of an explosive accident. To decrease the indoor explosion load related to the EVS response rate (which stipulates a rather slow clearing of the relief opening and thus an increase in excessive pressure), a modest upgrade of the venting structure was offered. The proposed upgrade provides an indoor pressure level corresponding to the EVS start-to-open pressure. The suggested innovation excludes the possibility of a significant increase in explosive pressure due to an EVS response delay. The efficiency of the suggested technical upgrade is backed up by numerical experiments and indirectly by experimental studies aimed at exploring the physical processes associated with the opening of EVSs after an explosive accident. The applicability of the upgraded EVS is provided by the fact that the maximum level of explosion load following an explosion event in an EVS-equipped room can be determined in advance.  
Conceptualization, A.A.K.; methodology, D.A.K.; software, A.D.K.; validation, N.V.G.; formal analysis, D.A.K.; investigation, A.D.K.; resources, D.A.K.; data curation, A.A.K.; writing--original draft preparation, D.A.K.; writing--review and editing, A.A.K.; visualization, N.V.G.; supervision, A.D.K.; project administration, D.A.K.; funding acquisition, D.A.K. 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.
# Mobility-Aware Hybrid Flow Rule Cache Scheme in Software-Defined Access Networks
## 4 Proposed Hybrid Flow Rule Cache Scheme
### Implementation Cost and Computational Complexity  
This paper considers that the implementation cost of each decision (i.e., whether to use the reactive or proactive flow rule cache) includes the processing cost, prediction cost, and transmission cost [37]. In the proposed scheme, the processing cost is dependent on the number of mobile flows multiplied by the number of ADs (i.e., \(M\times N\)). After the prediction based on LSTM, whose cost is dependent on the number of weights (i.e., \(w\)) [38], flow rules can be installed one by one in ascending order by delay constraint (i.e., \(\text{log}M\)) in the heuristic algorithm. On the other hand, they can be installed by comparing every flow with each other (i.e., \(M\)) in the ILP problem. Consequently, the computational complexity of the proposed scheme for the ILP problem and heuristic algorithm can be expressed as \(O(N\times w\times M\times M)\) and \(O(N\times w\times M\times\text{log}M)\), respectively.
# A Routing Optimization Method for LEO Satellite Networks with Stochastic Link Failure
## 2 Mathematical Model
### Graph Theory  
Satellites' operation is cyclical, so the Discrete-Time Dynamic Virtual Topology Routing (DT-DVTR) method can be used to divide the time. In each time interval, as illustrated in Figure 2, the satellite network topology can be approximately regarded as static.  
Supposing a satellite network is divided into \(N_{T}\) static topologies, which is composed of \(n\) satellites in period \(T\), the \(c\)th topology can be represented by a static directed graph \(\mathcal{G}(c)=(\mathcal{V},\mathcal{E}(c))\). Nonempty finite set \(\mathcal{V}(c)\)\(=\{v_{1},\cdots,v_{n}\}\) and \(\mathcal{E}(c)\subseteq\mathcal{V}\times\mathcal{V}\) represent the node and edge set, respectively. Matrix \(\mathcal{A}_{c}\)\(=\)\([a_{ij}^{c}]\in\mathbb{R}^{n\times n}\), \(a_{ij}^{c}\in\{0,1\}\) represents the adjacency matrix if \((v_{i},v_{j})\in\mathcal{E}\), \(a_{ij}=1\) (\((v_{j},v_{i})\in\mathcal{E}\), \(a_{ji}=1\)); otherwise, \(a_{ij}=0\)\((i,j=1,\ldots,n)\). The adjacent set of node \(v_{i}\) is defined as \(\mathcal{N}_{i}=\{j|(v_{i},v_{j})\in\mathcal{E}\}\). The in-degree matrix of \(\mathcal{G}(c)\) is denoted by \(\mathcal{L}\)\(=\)\(\mathrm{diag}\{l_{1},\cdots,l_{n}\}\), where \(l_{i}=\sum_{j\in\mathcal{N}_{i}}a_{ji}\), \(i=1,\cdots,n\) is the number of edges with \(v_{i}\) as the end node, which is called the in-degree of \(v_{i}\). Correspondingly, \(\mathcal{O}\)\(=\)\(\mathrm{diag}\{o_{1},\cdots,o_{n}\}\) is defined as the out-degree matrix, where \(o_{i}=\sum_{j\in\mathcal{N}_{i}}a_{ji}\), \(i=1,\cdots,n\) represents the number of edges with \(v_{i}\) as the starting node, which is called the out-degree of \(v_{i}\). The cost matrix is denoted by \(\mathcal{D}^{c}=[d_{i,j}^{c}]\in\mathbb{R}^{n\times n}\), \(d_{i,j}^{c}\in[0,+\infty)\), where \(d_{i,j}^{c}\) represents the cost of \((v_{i},v_{j})\).  
Figure 1: Simplified schematic diagram of Low-Earth Orbit (LEO) satellite network.  
Figure 2: Key diagram of Discrete-Time Dynamic Virtual Topology Routing (DT-DVTR) algorithm.
## 3 Results and Discussion
### Prediction Models  
The PLSR models were evaluated to determine their reliability and practical usefulness for each chemical compound and FA.  
Calibration, cross-validation, and validation statistics for the PLSR models related to proximate composition, fibre constituents and FAs are presented in the following subsections. In the same tables, the outlier and LV are reported for each compound. Additionally, in the Supplementary Materials, the best PLSR models for calibration and cross-validation analysis are provided in Figure S2a-d, while Figure S3 presents the plot of prediction analysis on an independent dataset.  
The outliers in PLSR analysis were identified and removed using statistical tools such as Leverage and Q residuals (or Squared Prediction Errors). Leverage measures the distance of a sample from the average in the predictor space.  
Q residuals (or Squared Prediction Errors) are statistical measures used in multivariate analysis, particularly in PLSR. Mathematically, Q residuals are defined as the sum of the squared differences between observed values and those estimated by the model. High Q residuals suggest that the sample contains variability not captured by the model, whether during calibration or validation. When both Leverage and Q residuals are significantly high, it is likely that the sample is an outlier. Removing these outliers can improve the model's robustness and reduce SEC.  
Figure 2: The trends in the spectral data with the pre-treatment applied. Note: (**a**) = untreated spectra; (**b**) = SNV; (**c**) = detrending; and (**d**) = first derivative (1-2-2-1).
# The Virial Effect--Applications for SF\({}_{6}\) and CH\({}_{4}\) Thermal Plasmas
## 3 Virial Coefficients
### Results and Discussion
#### 3.2.1 Choice of Parameters
It should be underlined that a dataset for \(\sigma\) and \(\frac{\varepsilon}{k_{B}}\), obtained from the experimental measurements for the given values of temperature and pressure, cannot be used without care over the whole pressure range. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & & & \\ \hline \hline \multicolumn{2}{c}{**Sources**} & \multicolumn{3}{c}{**[PERSON] et al. [19]**} & \multicolumn{3}{c}{**This Work**} \\ \hline \multicolumn{2}{c}{**T**} & \multicolumn{2}{c}{**B**} & \multicolumn{2}{c}{**C**} & \multicolumn{2}{c}{**P**} \\ \multicolumn{2}{c}{**[K]**} & \multicolumn{2}{c}{**[cm\({}^{3}\)/mol]**} & \multicolumn{2}{c}{**[cm\({}^{3}\)/mol]**\({}^{2}\)**} & \multicolumn{2}{c}{**[Bar]**} & \(\rho_{\text{fit}}\) [kg/m\({}^{3}\)]** & \(\rho_{\text{cal}}\) [kg/m\({}^{3}\)]** & **from B and C** & **\% Error** \\ \hline \multirow{2}{*}{300} & \multirow{2}{*}{\(-\)271.2 \(\pm\) 0.8} & \multirow{2}{*}{18,160 \(\pm\) 1250} & 100.1876 & 1505.417 & 1732.788 & 15.1\% \\  & & & 40.1390 & 1382.802 & 1532.088 & 10.8\% \\  & & & 20.0346 & 162.945 & 162.913 & 0.01\% \\ \hline \multirow{2}{*}{316} & \multirow{2}{*}{\(-\)241.1 \(\pm\) 0.8} & \multirow{2}{*}{17,850 \(\pm\) 1000} & 100.2052 & 1399.646 & 1467.539 & 4.9\% \\  & & & 40.6363 & 1152.050 & 1122.203 & 2.6\% \\  & & & 20.0334 & 142.452 & 142.439 & 0.0\% \\ \hline \multirow{2}{*}{324} & \multirow{2}{*}{\(-\)227.4 \(\pm\) 0.6} & \multirow{2}{*}{16,950 \(\pm\) 750} & 100.0588 & 1340.546 & 1410.368 & 5.2\% \\  & & & 40.6180 & 550.994 & 915.276 & 66.1\% \\  & & & 19.9775 & 134.527 & 134.572 & 0.03\% \\ \hline \multirow{2}{*}{340} & \multirow{2}{*}{\(-\)202.9 \(\pm\) 0.6} & \multirow{2}{*}{15,860 \(\pm\) 750} & 100.0395 & 1208.301 & 1235.266 & 2.2\% \\  & & & 41.2991 & 351.610 & 355.637 & 1.1\% \\ \cline{1-1}  & & & 19.8607 & 121.89 & 121.912 & 0.01\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the SF\({}_{6}\) mass densities between the experimental values [19] and the theoretical value calculated from B and C [19].
# Measuring Gas Flaring in Russia with Multispectral VIIRS Nightfire
## 2 Method
### Tagging of the Clusters with High-Resolution Daytime Imagery  
The annual gas flare contours were visually inspected using high-spatial resolution daytime satellite imagery to validate the presence and categorize the gas flares. The gas flares which had been successfully cross-matched with the contours from the previous annual datasets were automatically merged with and inherited the metadata of the previous flare IDs. New (i.e., not cross-matched) contours were divided into two categories: with a mean temperature higher than 1300 K (\"high-temperature\") and with a mean temperature lower than 1300 K but with 4 or more unique Nightfire observations (\"low-temperature\"). Both datasets were examined using high spatial resolution imagery and, if required (i.e., if the hotspot was not confirmed to be an outlier), following the steps described in Section 2.3.  
Google Earth usually contains less than 5 years old imagery from the WorldView satellites for urbanized or traditional oil and gas regions of Russia (such as Khanty-Mansi Autonomous Okrug). However, for some remote regions, relatively fresh satellite high-resolution images were found to be missing. To fill the gap for 2019-2020, we used the PlanetScope satellite imagery available at Planet Explorer ([[https://planet.com/explorer/](https://planet.com/explorer/)]([https://planet.com/explorer/](https://planet.com/explorer/)) (accessed on 27 July 2021)). PlanetScope imagery, while lower in spatial resolution and oversaturated if full snow cover is present, grants nearly daily coverage of the desired location, which allows detection of the gas flares at exploration facilities based on varying surroundings with the possibility to spot the flaring event itself.  
From the analysis of high-resolution images and metadata as described in Section 2.3, the \"high-temperature\" dataset was found to mainly consist of gas flares, while the \"low-temperature\" dataset mainly consisted of landfills (characterized by burning and smoldering), steel mills, chemical plants, sawmills, and, rarely, persistent forest fires and volcanoes. On a rare occasion, gas flares could be found in the \"low-temperature\" datasets, while other non-flare hotspots were present in the \"high-temperature\" dataset. Hotspots at coal chemistry plants, fertilizer plants, and landfills may correspond to gas flares burning methane (extracted from coal or natural gas) or biogas; however, as the focus of the study is the oil and gas industry, such hotspots were discarded. Examples of high-resolution images for different types of hotspots detected via VIIRS Nightfire are shown in Figure 2.
# Synchrotron X-ray Radiation (SXR) in Medical Imaging: Current Status and Future Prospects
## 3 Synchrotron X-ray Radiation in Medical Imaging
### Bone and Joint Imaging
#### 3.4.1 Microstructural Features  
Bone cells osteocytes, connected with one another and with the vascular network with the dendritic process, are studied using SXR micro CT. The parameters investigated in these studies are vTMD of bone and lacunar volume, density, and orientation [14]. The mechanical characteristic of the bone metal implant interface has been investigated using SXR. When compared to lab X-ray sources, synchrotron radiation with a high flux photon beam allows for higher resolution and faster imaging times. Shorter exposure times are particularly appealing for in situ loading investigations that require multiple repeated scans because they reduce the total duration of the test. This reduces the amount of drying  
Figure 5: CT with SXR for excised breast. (**A**) transaxial, coronal, and sagittal view of the excised breast and (**B**) 3D rendered image of the same breast. The parenchymal distortion and speculations are clearly visible along with the tumor into the skin. Reprinted with permission from [23].  
Figure 6: Dual-energy KES CT imaging with SXR. (**A**) Control rabbit, (**B**) lung morphology, (**C**) animal with methacholine (MCH)-induced bronchoconstriction at baseline and (**D**) animal with methacholine (MCH)-induced bronchoconstriction at baseline during the challenge. Reprinted with permission from [22].  
of the test specimen (maintaining mechanical qualities) and sample relaxation during each scan (thus enabling tests that are more comparable to standard tests)  
With a 3.6 m isotropic voxel size, samples were subjected to in situ pullout utilizing high-resolution synchrotron X-ray tomography at the Tomcat beamline (SLS, PSI, Switzerland) at 30 keV with a 25 ms exposure duration, resulting in a total acquisition period of 45 s per scan. Screws were pushed out at 0.05 mm increments using a custom-made loading mechanism positioned inside the beamline, acquiring several images until the sample ruptured. The field of view (FOV) of the detector was 2.8 \(\times\) 7.2 mm\({}^{2}\) (height \(\times\) width) [40].
# A New Maximum Likelihood Estimator Formulated in Pole-Residue Modal Model
## 3 Simulated Data Analysis
### Five-DOF System
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Estimator**} & \multirow{3}{*}{**Mode**} & \multicolumn{3}{c}{**Sample Std.**} & \multicolumn{3}{c}{**Mean Over the Std. Estimates**} & \multicolumn{3}{c}{**Rel. Difference (\(d_{\sigma,\rho}\))**} \\ \cline{3-8}  & & \(\sigma_{f_{\star}}\) & \(\sigma_{g_{\star}}\) & \(\hat{\sigma}_{f_{\star}}\) & \(\hat{\sigma}_{g_{\star}}\) & \(d_{\sigma_{f_{\star}}}\) & \(d_{\sigma_{f_{\star}}}\) & \(d_{\sigma_{f_{\star}}}\) & \(d_{\sigma_{f_{\star}}}\) \\  & & **(Hz \(\times\)10\({}^{3}\))** & **(\% \(\times\)10\({}^{2}\))** & **(Hz \(\times\)10\({}^{3}\))** & **(\% \(\times\)10\({}^{2}\))** & **(\%)** & **(\%)** & **(\%)** \\ \hline \multirow{4}{*}{MLE-PMM} & 1 & 3.04 & 1.20 & 3.10 & 1.18 & 1.74 & 2.23 \\  & 2 & 3.80 & 1.03 & 3.80 & 1.04 & 0.05 & 0.38 \\ \cline{1-1}  & 3 & 8.83 & 1.75 & 8.97 & 1.73 & 1.60 & 1.05 \\ \cline{1-1}  & 4 & 6.74 & 1.20 & 6.81 & 1.21 & 1.12 & 1.20 \\ \cline{1-1}  & 5 & 7.04 & 1.11 & 7.02 & 1.12 & 0.29 & 0.51 \\ \hline \multirow{4}{*}{MLE-MM} & 1 & 3.01 & 1.19 & 3.06 & 1.16 & 1.53 & 2.15 \\  & 2 & 3.70 & 1.01 & 3.70 & 1.01 & 0.20 & 0.33 \\ \cline{1-1}  & 3 & 8.07 & 1.59 & 8.15 & 1.58 & 1.01 & 0.49 \\ \cline{1-1}  & 4 & 5.34 & 0.98 & 5.47 & 0.97 & 2.39 & 0.35 \\ \cline{1-1}  & 5 & 6.37 & 1.01 & 6.35 & 1.00 & 0.36 & 0.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Monte Carlo simulation results obtained from the simulated EMA of the five-DOF system: comparison between the sample standard deviations and those predicted in a typical Monte Carlo run with the proposed MLE-PMM after 10 iterations. Figure 9: Monte Carlo simulation results for the identified natural frequencies (**a**) and damping ratios (**b**): sample mean and standard deviation (black line) and mean and standard deviation predicted after 10 iterations of MLE-PMM (red line) from a typical Monte Carlo dataset.
## 3 Modeling of Dam Dynamics
### Setting of Boundary Conditions and Damping  
In the static analysis, horizontal displacement constraints are applied to the model, while three-way displacement constraints are set on the bottom surface. The slope surface is left unconstrained, serving as a free surface. When conducting a dynamic analysis, the use of fixed or elastic boundaries can cause outward waves to reflect back into the model, resulting in distorted dynamic calculations. To address this issue, boundary conditions in 3 DEC are primarily set as free-field boundaries and viscous boundaries [27; 28]. The viscous boundary is positioned at the bottom of the model, surrounded by a free-field boundary (refer to Figure 4). Additionally, the seismic wave must be converted into a stress-time scale and applied to the bottom of the model using the transformation formula [16]:  
\[\sigma_{n}=-2\rho c_{p}v_{n} \tag{1}\]  
\[\tau_{n}=-2\rho c_{s}v_{s} \tag{2}\]  
where: \(\sigma_{n}\), \(\sigma_{n}\) are the normal and tangential stresses applied to the viscous boundary, respectively; \(\rho\) is the density; \(c_{p}\), \(c_{s}\) are the longitudinal and transverse wave speeds, respectively; \(v_{n}\), \(v_{s}\) are the normal and tangential velocity components on the model boundary.  
In the dynamic computational analysis of 3 DEC, three common forms of damping are Rayleigh damping, local damping, and Maxwell damping. However, local damping is not  
Figure 4: Uranium tailings dam profile and distribution of monitoring points.  
Figure 3: Accelerogram of input seismic wave in z-direction.  
Figure 2: Accelerogram of input seismic wave in x-direction.  
suitable for simulating dynamic calculations under seismic loading as it cannot effectively attenuate the high-frequency components of complex waveforms. Both Rayleigh damping and Maxwell damping effectively prevent self-oscillation in systems and manage complex waveforms. Maxwell damping schemes with three components that produce relatively constant damping over the frequency range are relevant to most seismic deformation analyses. The numerical computation time required for Maxwell damping is much less than that required for Rayleigh damping [29; 30].
# Practical Approach for Smart and Circular Cities: Chatbots Used in Waste Recycling
## 4 An Analysis of Five Representative Chatbots That Support the CE
### Waste Management Chatbot for the Environmental Organization--No Waste Ukraine  
No Waste Ukraine, a non-profit environmental organization focused on social impact, required an automated aid to assist new individuals with waste sorting. Due to the underdeveloped municipal recycling system, insufficient recycling infrastructure such as bins and stations within the city, and ineffective sustainability initiatives coupled with flawed state regulations, cultivating a recycling culture in Kyiv was already quite a challenge. Implementing a digital assistant would streamline the process, reducing the workload and time invested by volunteers in answering inquiries through Telegram. Additionally, it would offer information on waste sorting with faster response times, improved flexibility, and easier maintenance. In March 2019, No Waste Ukraine and Infopulse unveiled the Waste Management Chatbot, which was immediately accessible on Telegram. Designed to offer guidance on waste sorting, this AI-powered chatbot project was crafted by students from NTUU KPI under the supervision of Infopulse professionals, with the following characteristics:  
* An expanding database featuring categorized waste types and a map showcasing nearby recycling stations and bins that is continually being developed for user accessibility.
* The development of an educational chatbot using the Dialogflow development platform to facilitate question-and-answer interactions.
* The chatbot offers support in Ukrainian and Russian languages, ensuring accessibility for a broader user base.
* A convenient topic search.
* The incorporation of prompts, suggestions, and a compilation of helpful links that guide users through waste recycling techniques and offer guidance to reach the nearest recycling station.  
The chatbot is designed to be user-friendly and intuitive, helping users understand the fundamentals of waste management, providing swift advice on waste sorting, locating the nearest waste disposal station, and offering information on its operating hours. It serves as a central hub for all queries related to waste recycling, significantly reducing response times and automating routine support tasks. This frees volunteers to focus on other crucial recycling program endeavors, ultimately fostering a more sustainable community. Accessible through Telegram, the waste sorting chatbot simplifies embracing eco-friendly practices (see Figure 4) [22].  
As [PERSON], the head of No Waste Ukraine, said, \"We are very happy about the chatbot and eagerly await its launch, since each day we have to answer dozens of questions about waste sorting and environmental management. We hope that this bot will make information more accessible and useful for people while simplifying our daily work\".