esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Existing Routing Methods for the DTN
### How to Determine Whether a Bundle Can Reach Its Destination Area and Routing  
When a DTN is constructed, each relay node initializes its covered area. When two relay nodes come in contact with each other, they exchange information about covered areas, so that all relay nodes will gain knowledge about the connectivity of all covered areas in the network. The knowledge is propagated to all relay nodes in multiple hops.  
When a sending node sends a forwarding-bundle request to a relay node, the relay node checks whether the bundle can reach its destination area. Specifically, the relay node decides that the bundle can reach its destination area if the destination area overlaps with a covered area that is reachable for that relay node. Only when this has been confirmed, the relay node receives the bundle and sends it in multiple hops to the node that has the covered area that overlaps with the destination area. Suppose that the sending node sends a forwarding-bundle request to Node A in a case where covered areas and the destination area are laid out as shown in Figure 6. Node A knows that the covered areas of Nodes C and F overlap with the destination area. Therefore, Node A receives the bundle from the sending node and forwards it to Nodes C and D. All relay nodes except Nodes C and F discard the bundle after relaying it to another relay node. In contrast, Nodes C and F retain the bundle after relaying it to a receiving node because they may be able to relay it to other receiving nodes.  
Figure 5: Example of generating a vectorial covered area.  
Figure 6: Layout example of the destination area and covered areas.  
When the bundle has been relayed to the relay node that has a covered area that overlaps with the destination area, and this relay node comes in contact with a receiving node in the destination area, it relays the bundle to that receiving node. The receiving node relays the bundle to other receiving nodes within the destination area. This continues in multiple hops to propagate the bundle in the destination area.  
An example of the routing table held by the relay node of Covered Area A in Figure 6 is shown in Figure 7. The routing between relay nodes is determined based on the number of hops. The route with the smallest number of hops is selected. In the case of the routing table shown in Figure 7, the route from Covered Area A to Covered Area C goes through the relay node of Covered Area B, while the route to Covered Area F goes through the relay node of Covered Area E.
# Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
## 4 PIDL for UQ
### PIDL-UQ for TSE
#### 4.1.1 Physics-Informed Generative Adversarial Network (PhysGAN)
The PhysFlow (**b**) consists of a normalizing flow and a PICG. The PhysFlowGAN (**c**) consists of a normalizing flow, a discriminator, and a PICG. In each subfigure, the top blue box encloses the data-driven component, and the bottom red box encloses the physics component. (**a**) PhysGAN architecture. In the data-driven component, the observation is used to calculate the discriminator loss function \(Loss_{\Phi}\) using Equation (12) and the data loss of the generator \(L_{O}\) using Equation (13). In the physics-based component, the collocation points are used to calculate the residual \(r_{C}\) using Equation (10), which is then used to calculate the physics loss of the generator \(L_{C}\) with two different ways of incorporating the residuals. (**b**) PhysFlow architecture. In the data-driven component, the inverse flow function \(G_{\theta}^{-1}\) aims to map the observation to a prior that follows a Gaussian distribution. In the physics-based component, the flow function maps a Gaussian prior to the collocation points, and a PICG is used to calculate the physics loss \(L_{C}\) as in PhysGAN. (**c**) PhysFlowGAN architecture. This combines the architectures of PhysGAN and PhysFlow. **Mean-GAN** Ref. [97] incorporates stochastic differential equations into the physics components (illustrated as the PICG in Figure 11a), where physics parameters are assumed to follow Gaussian distributions. The randomness in the physics parameters is the source of the epistemic uncertainty, which leads to the randomness in the residual \(r_{c}\). The physics loss is calculated based on the square error in the mean of \(r_{c}\), i.e., \(|\frac{1}{N_{\text{k}}}\sum_{i=1}^{N_{\text{k}}}r_{c}|^{2}\), where \(N_{\text{k}}\) is the number of physics parameter samples. Then, the physics loss is included in the loss function of the PUNN using the weighted sum. Within the PICG in Figure 11a, we can also replace the parametric FD with ML surrogates, which is used in the **PI-GAN-FDL**[65; 98].
## 4 Results and Discussion
### Results of Different Modulations
The DQPSK modulation was not as promising as the other three modulations. However, the results were also acceptable, and the DE estimator could be utilized with DQPSK and can be worked as a candidate. Figure 8: Bit error rate statistics of demodulation results in four cases: BPSK (blue), QPSK (red), DQPSK (purple), and 16 QAM (black).
# Biochar from Agro-Forest Residue: Application Perspective Based on Decision Support Analysis
## 2 Materials and Methods
### Decision Support Analysis
#### 2.5.1 Pareto Dominance Analysis  
The concept of dominance as a viable approach for comparing several criteria or objectives was first introduced in solving multi-objective optimization problems [52].  
In a decision problem, the complexity exists when finding a solution that is not exclusively dependent on one criterion (e.g., search for the solution with the highest HHV), but is rather dependent on several criterion simultaneously. In such cases, there can be better solutions in terms of one criterion that are worse in another. Thus, the question is which is preferential. Normally, several solutions are possible, but in a trade-off perspective. However, it is possible to analyze whether a solution is better than others with respect to several criteria, or in most criteria, and this relies on the definition of dominance. In a set of \(N\) solutions \(x_{n}=\{x_{1},x_{2},\ldots,x_{N}\}\) of a problem with \(p\) decision criteria \(f_{p}=\{f_{1}(x_{n}),f_{2}(x_{n}),\)\(\ldots,f_{M}(x_{n})\}\), solution \(x_{1}\) dominates solution \(x_{2}\) if and only if \(x_{1}\) is not worse than \(x_{2}\) in all criteria and \(x_{1}\) is strictly better than \(x_{2}\) in at least one criterion (Equation (3)).  
\[x_{1}\preceq x_{2}iff\big{\{}f_{i}(x_{1})\leq f_{i}(x_{2})\forall_{i}\in 1, \ldots,p\exists_{j}\in 1,\ldots,pf_{j}(x_{2})<f_{j}(x_{2}) \tag{3}\]  
The signs \(\leq\) and \(<\) (Equation (3)) represent the desire to minimize all the criteria (e.g., for simplicity, all criteria in Table 1 were mathematically translated into minimization criteria, whereas for the maximization of HHV, the inverse was applied).  
A solution \(x_{n}\) is called non-dominated if it is better in all criteria than all other solutions. On the other hand, a solution has weak dominance if it is better in at least one criterion or strong dominance if it has better or equal results in all criteria. The goal of this paper was to find the set of variables that produces the best solutions possible for the specified applications, e.g., non-dominated (or the least dominated possible).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Biofuel (12 Variables)**} & \multicolumn{4}{c}{**Catalyst (6 Variables)**} & \multicolumn{4}{c}{**CO2 Sequestration and Soil**} & \multicolumn{4}{c}{**Supercapacity (\(\emptyset\) Variables)**} \\ \hline
## 4 Blockchain for Smart Campuses and Universities  
Both academia [11; 12; 13] and public entities like the European Commission [46] have recently considered the improvement of the architectures described in the previous section by using Distributed Ledger Technologies (DLTs) like blockchain. Such a technology can be used for implementing higher education and smart campus applications, due to their ability to provide data exchanges among entities that do not necessarily trust each other [47]. In addition, the use of blockchain enhance smart campus applications that need transparency, data immutability, privacy, and security. Furthermore, blockchain allows for developing Decentralized Apps (DApps) based on Peer-to-Peer (P2P) transactions whose processes can be automated through the use of smart contracts, which can execute pieces of code in an autonomous way [48].  
Nowadays, there are many blockchain platforms, like Ethereum [49], Hyperledger Fabric [50], or the popular Bitcoin [51], that can be used in multiple practical applications [52; 53; 54; 55]. However, it is important to emphasize that blockchain is not the best technology for every application that needs to perform trustworthy data exchanges. For example, in many cases where smart campus applications are deployed in a private network, a traditional database is powerful enough and usually provides faster transactions than a blockchain. Therefore, to decide whether a blockchain is necessary, smart campus developers may use a decision framework [56] and, thus, detect certain necessary features, like the need for decentralization, transaction transparency, cybersecurity (e.g., data redundancy and protection against Denial-of-Service (DoS) attacks), or the lack of trust among entities (including respect to government agencies and banks).  
Due to the previously mentioned benefits, different authors have proposed the use of blockchain and other DLTs for developing applications for smart campuses and universities. Specifically,  
Figure 2: Smart campus architecture evolution.  
blockchain has been suggested for guaranteeing education certificate authenticity [57; 58], managing digital copyright information [59], verifying learning outcomes [60; 61], or enhancing e-learning interaction [62]. More potential smart campus applications and a deeper analysis on their characteristics can be found in [11; 12; 13; 46].
## 5 Conclusions  
The validation results confirm that this method utilizing TFME-LC-MS/MS is suitable for the selective, sensitive, accurate and precise analysis of chosen beta-lactams in both plasma and BAL samples. The simultaneous high-throughput analysis of as many as 96 samples is possible, substantially reducing the time and cost of analysis. The application of TFME extraction ensures not only a very low matrix effect, enabling the highly specific determination of analytes in complex biological material, but also the direct analysis of a sole unbound fraction of antibiotics. The validated method was successfully applied to determine the penetration of piperacillin and imipenem into BAL fluid, demonstrating that measured unbound concentrations of both antibiotics substantially differ from the values estimated by recalculation from total concentrations using published protein-binding data. Additional evidence for substantial variability in nonlinear plasma-protein binding, dependence on creatinine clearance, and poor permeability to the site of infection may lead to inadequate subtherapeutic concentrations of piperacillin and, to some extent, to imipenem. Therefore, the presented study provides a valuable analytical tool offering a step towards personalized therapeutic drug monitoring.  
Conceptualization, R.W. and W.F.; Formal analysis, K.Z. and W.F.; Funding acquisition, W.F.; Investigation, R.W., K.Z. and W.F.; Methodology, K.Z. and W.F.; Project administration, W.F.; Resources, W.F.; Supervision, R.W. and W.F.; Validation, K.Z. and W.F.; Visualization, K.Z.; Writing--original draft, R.W., K.Z. and W.F.; Writing--review and editing, K.Z. and W.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Centre, Poland under the Project No.: 2017/26/D/NZ6/00136.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of the Ludwig Rydygier Collegium Medicum in Bydgoszcz at Nicolaus Copernicus University of Torun (Poland) under the approval number KB-218/2018 (date of approval 27 March 2018).  
The authors declare no conflict of interest.  
Samples of the biological material from VAP patients are not available from the authors.
## 1 Introduction
### Silica Nanoparticles  
Despite the ubiquitous presence of silica in the earth's crust, it is commercially produced using synthesis processes that use a variety of sources, including agricultural wastes [27]. The resulting SNPs are used for several industrial applications, ranging from electronic components to targeted drug delivery. As per recent trends, a market share of goods consisting of silica nanoparticles was estimated at around USD 8.8 billion in 2020 [28], showing significant future market trends. Silicon (Si), as one of the elements in silica, is normally available in crystalline, polycrystalline, or amorphous form, and it is available as silicon dioxide (SiO\({}_{2}\)) and is commonly used for various applications in industry, including the applications, such as plant development to eliminate biotic and abiotic stresses [29]. Overall, silica is produced using chemical sources such as tetramethyl orthosilicate (TEOS) or sodium silicate; however, since silica naturally occurs in sand, flint, or quartz in various forms, it requires a lot of energy to complete the synthesis process, which is usually quite complicated. It should also be noted that high energy consumption and temperature requirements for manufacturing are often exceedingly expensive, unsustainable, and ecologically unfriendly. As a result of this concern, several researchers are working on ways to develop new processes for producing silica nanoparticles that are simpler, less expensive, and more ecologically friendly using principles of green chemistry [13]. Quite independently, the production of solid waste poses a huge environmental concern, especially as the landfills are filling up rapidly with associated GHG issues, including the release of solid waste in aquatic environments. Hence, it is critical to find new ways to minimize the quantity of solid waste. Agricultural waste, or solid waste derived from agricultural goods, is an important contender for producing silica nanoparticles. Rice, corn, barley, wheat, sugarcane, and other agricultural crops create solid waste on Earth; thus, the amount of solid waste left on the ground should be decreased by using them as an alternate source for producing other critical resources. Several products with high silicon content are utilized to make silica nanoparticles, which in turn are also employed to solve an environmental problem since they are waste products that have been turned into valuable resources. Since converting wastewater into a usable water resource has become an area of great interest to many researchers, we have ventured into using rice husk, wheat straw, barley grass, and other agricultural wastes utilized as sources to make silica nanoparticles [19; 20; 30; 31].
# A Review of Multimodal Interaction in Remote Education: Technologies, Applications, and Challenges
## 4 Keyword and Correlation Analysis
### Multimodal Interaction Across Educational Levels
#### 4.3.2 Higher Education  
In higher education and online learning, EEG and speech analysis track students' cognitive and emotional states in real time. EEG patterns and speech feedback help assess comprehension and detect emotional changes. Instructors can adjust teaching speed and offer personalized support to improve online learning. [PERSON] et al. [60] used deep neural networks to combine EEG and speech data, showing its potential to enhance online education.
## 3 Results  
_Hybrid Network Adjustment and Transformation Results_  
In the first stage, control adjustment of the DLN and GTN networks was carried out using a robust method to identify possible outlier observations (Table 4). Two outlier observations were identified in the DLN network, identical to Figure 1, which shows two unclosed loops of the double-leveling network with the permissible error exceeded. These observations were removed from the set. Then, both DLN, GTN and hybrid networks were aligned using the least squares method. The error distribution is shown in Figure 8.  
The average errors obtained from the equalization of the hybrid network in version 2 are closest to the normal distribution. For the DLN network, there is a left-sided skewness, and for the DTN network, there is a clear kurtosis. In the transformation process (version 1), three methods were used: isometric (6 parameters), conformal (7 parameters) and affine (12 parameters), all in the variant without and with Hausbrandt correction. As a result of the transformation from the primary system to the secondary system, all adjustment points received new coordinates. The purpose of the Hausbrandt correction is to leave the coordinates of the adjustment in the secondary \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Version 1** & **Version 2** \\ \hline Total number of observations & 632 & 632 \\ Redundancy observations & 362 & 323 \\ Network points & 270 & 309 \\ Reliability index z & 0.57278 & 0.51108 \\ Otrebski parameter \(p\) & 0.42722 & 0.48892 \\ Mean observation error reduction factor q & 0.65362 & 0.69923 \\ number of common points between & & \\ DLN—GTN networks & 50 & 11 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Characteristics of the approach in version 1 and version 2 (source: own study). Figure 8: The error distribution in DLN and GTN networks (source: own study). system unchanged. For the remaining points, corrections were calculated using interpolation formulas. The number of common points was 50, and the transformation errors are shown in Table 5. In version 2, the average errors did not exceed 0.1 mm/yr, except for in one case, where 0.11 mm/yr was observed. In the transformation process (version 2), the same transformation methods were used as in version 1. The number of common points was 11, and the transformation errors are shown in Table 5.  
The differences between the values from the hybrid network adjustment and individual transformation variants from versions 1 and 2 were calculated (Table 6). In the second version, the hybrid network was aligned in the same way as in the first version based on 11 connecting points (pseudo-nodal points); these points were not identified as suspicious, i.e., those that show their vertical movements [5], and, at the same time, in the transformation process, these showed the smallest differences compared to the vertical movements from the hybrid network: max. 0.2 mm/yr. The adjustment errors determined (Figure 9) did not differ significantly from those obtained based on 50 common points.
## 3 Influence of Face Advancing Rate on Roof Caving Interval
### Simulation of Immediate Roof Initial Caving Intervals Under Different Advance Rates  
Field measurements indicated that the initial caving interval of the immediate roof during the initial mining stage was 12.4 m. Based on this empirical value, numerical simulations were conducted to investigate the initial caving interval under different face advancing rates. The caving patterns of the immediate roof under various advancing rates are illustrated in Figure 5, with corresponding initial caving parameters detailed in Figure 6.  
Notably, the horizontal distance between immediate roof caving positions and the coal wall remained consistent across all advancing rates, with collapse zones consistently located directly above the coal wall. The initial caving interval exhibited a progressive increase with higher advancing rates: from 12 m at 4 m/d to 20 m at 10 m/d. This phenomenon is attributed to insufficient time for stress redistribution within the immediate roof strata under rapidly advancing conditions, thereby extending the caving interval.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sequential Strata** & **Joint Surface** & \begin{tabular}{c} **Normal Stiffness** \\ **(GPa)** \\ \end{tabular} & \begin{tabular}{c} **Tangential** \\ **Stiffness (GPa)** \\ \end{tabular} & \begin{tabular}{c} **Cohesion (MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Internal Friction** \\ **Angle/\({}^{c}\)** \\ \end{tabular} &
\begin{tabular}{c} **Tensile Strength** \\ **(MPa)** \\ \end{tabular} \\ \hline
17 & Sandy mudstone & 5.5 & 4.27 & 4.5 & 25 & 0.6 \\  & Coal seam & 3.8 & 2.4 & 3.2 & 24 & 0.25 \\  & 2-1 lower & & & & & \\
15 & Sandy mudstone & 5.5 & 4.27 & 4.5 & 25 & 0.6 \\
14 & Siltstone & 4.2 & 4.27 & 4.8 & 26 & 0.2 \\
13 & Medium-grained & & & & & \\  & sandstone & 8.5 & 5.2 & 9.2 & 32 & 1.0 \\
12 & Sandy mudstone & 5.5 & 4.27 & 4.5 & 25 & 0.6 \\  & Coal seam & & & & & \\
11 & 2-2 upper & 3.8 & 2.4 & 3.2 & 24 & 0.25 \\
10 & Sandy mudstone & 5.5 & 4.27 & 4.5 & 25 & 0.6 \\
9 & Siltstone & 4.2 & 4.27 & 4.8 & 26 & 0.2 \\
8 & 2-2 middle & 3.8 & 2.4 & 3.2 & 24 & 0.25 \\
7 & Sandy mudstone & 5.5 & 4.27 & 4.5 & 25 & 0.6 \\
6 & 2-2 lower & 3.8 & 2.4 & 3.2 & 24 & 0.25 \\
5 & Sandy mudstone & 5.5 & 4.27 & 4.5 & 25 & 0.6 \\
4 & Conglomeratic & 6.2 & 5.2 & 8.7 & 28 & 1.2 \\  & sandstone & 5.5 & 4.27 & 4.5 & 25 & 0.6 \\
3 & Sandy mudstone & 3.8 & 2.3 & 3.2 & 24 & 0.23 \\
2 & Coal seam 3-1 & 3.8 & 2.3 & 3.2 & 24 & 0.23 \\
1 & Sandy mudstone & 5.5 & 4.27 & 3.5 & 25 & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical parameters of coal/rock joints.