esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Theoretical Calculation of the Narrow Coal Pillar Width for Double-Roadway Excavation
### Theoretical Calculation of Narrow Coal Pillar Width  
Based on the above calculation results and the geological conditions of the 2109 and 2110 working face in Qingwa Mine, the calculation formula of the coal pillar supporting load was simplified considering roof cutting (\(l=0\)) and no support (\(q_{1}=0\)):  
\[q_{2}=\frac{3q_{0}(x_{0}+d+B_{c})^{2}-6M_{1}+R_{t}h^{2}}{6B_{c}\Big{(}x_{0}+d+ \frac{1}{2}B_{c}\Big{)}} \tag{8}\]  
where \(q_{0}\) is the overburden load, 5.5 MPa; \(x_{0}\) is the width of the plastic zone of the coal seam, 5.6 m; \(d\) is the roadway width, 5 m; \(B_{c}\) is the width of the coal pillar, m; \(R_{t}\) is the tensile strength of immediate roof, 4.75 MPa; \(h\) is the thickness of the immediate roof, 4.43 m.  
The supporting stress of the coal rib \(M_{1}\) is as follows:  
\[M_{1}=\frac{1}{6}k\gamma{Hx_{0}}^{2}=\frac{1}{6}\times 2\times 2.5\times 2.2 \times 5.6^{2}=57.5\ (\text{MN}) \tag{9}\]  
Substituting the data into Equation (8), the variation in the width of the coal pillar and the supporting stress of the coal pillar was determined, as shown in Figure 4. The support stress with the change in the coal pillar width was divided into three stages: the steep decline stage (0-4 m), the transition stage (4-7 m), and the stabilization stage (>7 m). In the stage of steep decline, the supporting stress of the coal pillar is greater than its ultimate strength, resulting in the plastic failure state of the coal pillar. If the coal pillar width is set in this range, and high-strength reinforcement measures need to be taken to increase the supporting load of the coal pillar exerted on the roof, the roadway is centrally supported at the same time; consequently, the deformation of the surrounding rock of the roadway is controlled, and the supporting pressure of the coal pillar is reduced simultaneously. In the transition stage, the supporting stress of the coal pillar is within the range of ultimate strength, and when the width of the coal pillar continues to increase to more than 7 m in the stabilization stage, the supporting load of the coal pillar is further reduced. The coal pillar in this width range has a certain strength margin, and the support stress can be further reduced after taking it into account to avoid the instability and damage of the coal pillar in a certain area and ensure the overall stability of the coal pillar, but the specific size of the coal pillar needs to be further explored.
## Open Issues in DTN Multicasting
### Node Integrity  
From the perspective of DTN nodes, they are vulnerable to attacks (e.g., Denial of Services (DoS) and spoofing) and might behave maliciously. Security mechanisms are a necessity in DTN, especially in MSN when the distribution of personal or classified information is involved. However, traditional security models designed for standard Internet Protocol are not suitable for DTN due to the sparse and intermittent nature of classified information. Since DTN is a resource-constrained network, in which bandwidth, energy, storage, and processing power are limited, a good security mechanism with minimal resource overhead shall be implemented. Due to this limitation, most of the proposed routing schemes do not consider the security aspects in DTN, where the security mechanism involves forwarding nodes (e.g., routers and gateways) in the authentication process. Forwarding nodes authenticate sender information to prevent unauthorized traffic at the earliest time and to conserve network resources. Some nodes might maliciously drop or tamper with messages forwarded to them, and we have to avoid relaying messages to such kinds of nodes. The trust level of each node can be calculated, and messages are relayed only to trustworthy nodes. There are available trust management schemes to detect and filter out malicious nodes in DTNs based on the forwarding pattern and the energy level of the node, such as in Reference [78]. Besides that, faulty node detection mechanisms are also important to detect and isolate faulty nodes that might produce erroneous data [79].  
In terms of multicast security, the present security architecture does not provide security measures to restrict any node to register with a specific multicast group Endpoint Identifier (EID). EIDs are used to identify the sender and receiver of a message bundle. Any node can register itself into a multicast group and receive traffic dedicated to that group. When a node receives a message in flooding-based routing, it will forward the message to every encountered node, including unintended recipients. For example, temperature nodes in environmental monitoring should receive a minimum number of messages intended for other groups of nodes to prevent high bandwidth consumption and long delays in message delivery. Furthermore, security mechanisms also prevent unintended receivers from performing malicious activities. Security mechanisms are essential in DTN multicasting to protect the integrity of the node [80].  
Many security aspects related to node integrity in DTN are unexplored, such as encryption of multicast data, multicast routing protocol security, and trust management in DTN multicast. These issues still remain open for further research.
## 4 Discussion
### Depletion of Formaldehyde  
Exploration of the potential of plants to purify air from pollutants started in the early 1980s [23, 41], and, to date, several plant species have been studied and identified for use in formaldehyde removal. However, previous studies have tested extremely high concentrations of formaldehyde (over \(\sim\)2000 ug/m\({}^{3}\)) [21], higher than the concentrations that are usually found in common indoor environments [1]. This study presents the results of the uptake of formaldehyde with a concentration of 300 ppb (0.37 mg/m\({}^{3}\)), which is within the boundaries of the detection threshold of formaldehyde indoors (0.03 mg/m\({}^{3}\)-0.6 mg/m\({}^{3}\)) [1] and close to the guideline value based on sensory effects (0.1 mg/m\({}^{3}\)) [1] (WHO 2010). Furthermore, formaldehyde is soluble in water [1]; therefore, it may be depleted faster in wet environments [25]. In a study published in 2011, [PERSON] and [PERSON] reported that activated carbon alone showed the highest formaldehyde removal and the four plant species studied demonstrated similar abilities to remove formaldehyde [25]. During this set of experiments, the reduction of formaldehyde concentration inside of the chamber was faster when wet substrates were present, and the plant species had similar behavior in formaldehyde removal (\(\sim\)0.03 m\({}^{3}\)/h). However, activated carbon appeared to be a very unstable component. Activated carbon did not have an optimal performance in any of the cases. Figure 6 presents the results of the effect of dry activated (AC_D; \(n=3\)) and wet activated carbon (AC_W; \(n=2\)) on the depletion of formaldehyde in the chamber. The third sample of wet activated carbon was excluded because, instead of reducing the formaldehyde concentration, the wet activated carbon released it into the chamber. The third sample of the wet activated carbon came from a different package than the other samples. The packaging material was most likely polluted, which might have caused the unstable behavior of the selected substrate.  
Previous studies suggest that the depletion of formaldehyde also occurs due to photosynthesis and metabolism of the plant at daytime [42]. A growing light was used during this test to ensure the optimal conditions of the plant.  
Studies with potted plants in closed chambers continue to be useful for isolating factors that may enhance removal efficiency and contribute towards the improvement of plant-based systems (e.g., plant species and growth medium). Therefore, it is recommended to use the lessons learned from this study in creating plant-assisted botanical purifiers (\"biowalls\" or active green walls), which mechanically force the air to pass through the leaves and the roots [23, 43, 44].
## 5 Numerical Applications
### Software Application for the Pre-Earthquake Assessment of RC Buildings with Full Data Availability
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{4}{c}{**n\({}_{\text{v}}\) Ratios**} \\ \cline{2-6}
**Building** & \multicolumn{2}{c}{**Dir 1 (n\({}_{\text{v1}}\))**} & \multicolumn{2}{c}{**Dir 2 (n\({}_{\text{v2}}\))**} & \multicolumn{2}{c}{**Eccentricity e\({}_{\text{0}}\)**} \\ \cline{2-6}  & **min\({}_{\text{v1}}\)** & **max\({}_{\text{v2}}\)** & **min\({}_{\text{v1}}\)** & **max\({}_{\text{v2}}\)** & **min\({}_{\text{0}}\)** & **max\({}_{\text{0}}\)** \\ \hline SFExFy\_3B & 0.00 & 0.65 & 0.00 & 0.65 & 0.9014 & 1.352 \\ SFExFy\_5B & 0.35 & 0.65 & 0.00 & 0.65 & 1.3521 & 2.704 \\ SFExFy\_8B & 0.00 & 0.65 & 0.00 & 0.00 & 1.3521 & 2.704 \\ SFExFy\_3F & 0.00 & 0.65 & 0.00 & 0.65 & 0.9014 & 1.352 \\ SFExFy\_5F & 0.35 & 0.65 & 0.00 & 0.65 & 1.3521 & 2.704 \\ SFExFy\_8F & 0.00 & 0.65 & 0.00 & 0.00 & 1.3521 & 2.704 \\ SFExFy\_3P & 0.00 & 0.65 & 0.00 & 0.65 & 0.9014 & 1.352 \\ SFExFy\_5P & 0.35 & 0.65 & 0.00 & 0.65 & 1.3521 & 2.704 \\ SFExFy\_8P & 0.00 & 0.65 & 0.00 & 0.00 & 1.3521 & 2.704 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Assumed ranges of the values of the case study buildings’ unknown structural parameters. Finally, the CMs corresponding to the classifications performed using the input vectors **X****R** and the input vectors **X****i** that provide the min[\(\Delta\)(OA)] and the max[\(\Delta\)(OA)] of Figure 16 are presented in Figure 17. The study of the CMs extracted using these **X****i** led to the conclusion that the vast majority of the recall (R-index) and the precision (P-index) indices are close to the corresponding values of the CMs extracted using **X****R** as input vectors. Thus, the classifications of the three case study buildings to the three pre-defined DS extracted using the input vectors **X****i** are not significantly different from the corresponding classifications extracted using the input vectors **X****R** with the actual values of the structural parameters \(\mathrm{n_{v1}}\), \(\mathrm{n_{v2}}\), and \(\mathrm{e_{0}}\). However, as mentioned above, the approach based on the use of input vectors **X****i** (if the input vectors **X****R** are unknown) can be significantly improved  
Figure 16: OA index values of the classifications extracted by the used MFPNNs considering input vectors with assumed values for the unknown structural parameters: (**a**) for BB, (**b**) for FIB, and (**c**) PB. if the users' expertise can lead to a more reliable estimation of the unknown structural parameters n\({}_{v1}\), n\({}_{v2}\), and e\({}_{0}\) than the one presented in Table 11. To this end, the software is developed open to including user-defined values.
# O-Band Multimode Interference Coupler Power Combiner Using Slot-Waveguide Structures
## 4 Conclusions  
In this study, we have shown that it is possible to gain a higher power level using a SiN MMI combiner in a slot waveguide structure. Using the FV-BPM algorithm, it is proven that the SiN MMI coupler can combine two coherent Gaussian laser sources at the operating wavelength of 1310 nm under TE mode polarization with high efficiency, over a light propagation of 109.81 \(\upmu\)m. The mode solution shows that TE mode has a large space of 794 nm over the \(y\)-axis, which can improve the light coupling between the Gaussian laser source mode and the input waveguide taper modes. The device can function with a high intensity range of 94.8-97.6% of the total power over the O-band range. Thus, this device has good sensitivity to the laser wavelength drift that can happen in transmitter systems that work at the O-band range because of the heating laser process.  
This study shows that is not necessarily efficient to use a laser with a higher power level (more than 100 milliwatts) to overcome the power losses that happen because of using a high-speed modulator such as MZM. Instead, it is better to use the proposed power combiner that efficiently combines two standard lasers into one waveguide source with the same level of power of more than 100 milliwatts. Thus, the transmitter system does not need to deal with nonlinear effects that can happen because of using a laser with higher-level power, and the proposed solution is cheaper due to the fact that a complex laser is an expensive cost.  
From the results, it is proven that the width and the length of the MMI coupler can overcome large deviations in the dimensions, which makes this coupler very robust for the fabrication process. Furthermore, FDTD was used to show the advance of using SiN slot waveguides and taper waveguides to obtain a very low back reflection of 44.94 dB. Thus, this study can be used for combining two coherent laser sources that function at the O-band and are integrated into a high-speed transmitter system for improving the channel performances. In future research, this study can be utilized to understand how to combine multiple coherent or non-coherent laser sources at the O-band window to obtain a higher level of power in transmitter systems. The problematic issue with non-coherent sources can be dealt with using a phase-shifter or heater, which can be added to the waveguide for solving the phase matching.  
[PERSON] envisioned the project. [PERSON] provided guidance. S.K. and N.K. designed the device. S.K. and N.K. performed simulations with the support of [PERSON]. [PERSON], S.K. and N.K. wrote the paper and the reply letter, S.K. and N.K. made the figures, and all authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Algorithm Development for the Non-Destructive Testing of Structural Damage
## 5 Results and Discussion  
### Estimating the Performance Indicator for Traditional and Developed SVM  
Experimental data analysis was carried out, considering the signals obtained by SAs mounted on the concrete and RC beams. The signals were recorded every minute throughout the loading period. Table 2 shows the comparative analysis between the traditional and the developed SVM of this study.  
Figure 4: (**a**) Picture and schematic of (**b**) the top view, with the embedded disks indicated, (**c**) the cross-sectional side view at Plane 1, and (**d**) the cross-sectional side view at Plane 2.  
Figure 5: Cross-sectional side view (upper) and schematic of the top view without the rubber layer (lower) of the parts of aluminum plate with (**a**) notch 1 and (**b**) notches 1 and 2.  
The classifier learnt from the training features [40] was used to make a decision when the testing data was presented. The performance accuracy was computed through an average of 4-fold cross-validation for 100 runs, and performed for 29 observations. The performance of the algorithms was obtained through computing the average accuracy.  
According to the above results, the analysis conducted through an estimation of the performance of the developed SVM obtained an accuracy of 87.22% rather than the traditional SVM, with an accuracy of 84.72%. The suggested method could outperform the benchmark. Further analysis of this performance was carried out by computing the \(p\)-value of the \(T\)-test, for the average of the accuracies (Table 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Accuracy (\%)**} \\ \hline \multirow{2}{*}{**No. Observations**} & \multicolumn{2}{c}{**Concrete Beam**} & \multicolumn{2}{c}{**RC Beam**} \\ \cline{2-5}  & **Simple SVM** & **Developed SVM** & **Simple SVM** & **Developed SVM** \\ \hline
1 & 84.72 & 87.23 & 85.26 & 86.19 \\
2 & 84.63 & 87.19 & 85.42 & 87.14 \\
3 & 84.81 & 87.22 & 85.39 & 86.19 \\
4 & 84.76 &  
The obtained \(p\)-values in the above cases demonstrate that the difference between the accuracies of the simple SVM and the developed classifier are statistically significant.
## 3 Results
### FTIR (Fourier-Transform Infrared Spectroscopy) Study  
Infrared spectroscopy was utilized to explore any chemical interactions between the drug and any other substances included in the formulation as shown in Figure 9. The spectra exhibited distinctive peaks for muiprocin, blank muiprocin, and muiprocin-loaded NEG. Observed spectra of pure muiprocin corresponding to the major peaks were found at 3480 cm\({}^{-1}\) (O-H), 3309 cm\({}^{-1}\), 2934 cm\({}^{-1}\) (alkyl C-H), 1729 cm\({}^{-1}\), 1712 cm\({}^{-1}\) (C=O), 1231 cm\({}^{-1}\), and 1222 cm\({}^{-1}\) (C-O). Upon observing the FTIR spectra for Blank-NEG and muiprocin NEG, it was found that there is strong broadband around 3300 cm\({}^{-1}\) due to O-H stretching that results from intermolecular hydrogen. Furthermore, the existence of a distinctive peak between 1700 and 1500 cm\({}^{-1}\) in both muiprocin NEG and blank NEG formulations suggested that the peaks of muiprocin and tea tree oil overlapped. This demonstrated that muiprocin was successfully trapped within the tea tree oil without any indication of a major chemical interaction between the drug and the oil. This result was consistent with [PERSON] et al. who stated that there were no interactions or interferences between the inclusion of muiprocin and the essential oil nanoemulsion [51].  
Figure 8: Profile of in vitro release of muiprocin from the optimized NE and NEG formulations in phosphate buffer pH 5.5 at 32 \({}^{\circ}\)C. The results from three experiments are presented as mean \(\pm\) SD. * \(p\) \(<\) 0.05 compared with muiprocin-loaded NE.
# A Review of Research on Secure Aggregation for Federated Learning
## 4 Aggregation Techniques for Federated Learning
### Secure Aggregation Algorithms
#### 4.2.4 Secure Aggregation Based on Blockchain
In this paper, blockchain-based secure aggregation is classified by the consensus algorithms employed, as summarized in Table 4.
## 2 Materials and Methods  
### Configuration of the Folded Dipole  
Figure 1 depicts the configuration of the proposed folded dipole element with optimized sizes, as shown in Figure 1. It consists of a DSPSL and a folded long dipole with two gaps. A SIW is used to feed the antenna as a broadband balun. The folded long dipole is composed of two identical rectangular strips printed on the bottom and top surfaces of the substrate, respectively. The rectangular strips are connected by shorted metal vias. By etching the gaps at the zero current points, the folded long dipole can work at half-wavelength mode and three half-wavelength mode. Thus, the working bandwidth can be broadened. The metal vias is used to improve cross polarization.  
The proposed antenna is designed on RT/Duriod 5880 substrate with the thickness of 0.508 mm and its permittivity and loss tangent are 2.2 and 0.0009, respectively. The center frequency of the proposed antenna is 34 GHz. The proposed antenna was optimized to  
Figure 1: (a) Configuration of the proposed SIW-fed folded dipole antenna (b) top view and bottom view of the folded dipole.  
achieve a high gain and a wideband operating bandwidth by using the ANSYS HFSS. The optimized parameters for the final design are given in Table 1.
## 5 Experiments and Performance Evaluation  
### Performance of Monte Carlo Simulation  
Based on the major components of typically battery-powered sensor nodes (BPSN), the sensor node reliability is evaluated by its radio component and microcontroller system and battery [35]. In order to reflect real scenarios, failure rate \(\lambda_{\text{BPSN}}\) practically acquired by statistics in normal, bad and extreme weather is 9.151 \(\times\) 10\({}^{-5}\), 1.363 \(\times\) 10\({}^{-2}\) and 3.376 \(\times\) 10\({}^{-2}\) failures per hour, respectively. We consider some realistic values of the parameters (\(p\), \(q\), \(r\), \(s\)) = (0.4448, 2, 1000, 300) for 100 Monte Carlo experiments to show the reduction in the number of input combination that should be applied to estimate the network reliability. Assuming the failure of senor node is subjected to exponential distribution [35], the calculated sensor node reliability considering accidental failure after 24 h exposure time is 0.9978, 0.7224 and 0.4448 in normal, bad and extreme weather. The simulations are run on an Intel(R) Core(TM) i5-3470@ 3.20 GHz machine (Intel, Wuhan, China).  
Table 2 illustrates the advantages of using Monte Carlo over an exhaustive experiment for different percentage errors. If we can tolerate 0.1 percent error for network reliability, around ten thousand input combinations have to be applied. If we can tolerate one percent error for network reliability, we have to apply a total of around a thousand input combinations. In addition, when the number of towers is very high (greater than 15), Monte Carlo proves to be very effective. In the following cases, we apply 10\({}^{5}\) input combinations, which is enough to meet the accuracy for optimal arrangement.
## 5 Traffic Models and Utility Functions
### Utility-Based Bandwidth Management
passive bandwidth that is reserved for NSIS-MIP flows that will come in the current AP (passive-NSIS-MDP). As regards the time complexity of the proposed scheme, assuming there are \(n\) admitted users, an update of the normalized gap must be made, with a complexity of O(n), then the AP has to sort the users list (it can be assumed to have a complexity of O(nlogn)). When a BRS operation needs to be made, the benefactor and the beneficiary are found with a single list scan in O(n) time. Comparing the complexity terms, we can conclude that the algorithm performs with a time complexity of O(nlogn) in the \"worst case\". The CAC algorithm is different for the two classes of service (NSIS-MIP, NSIS-MDP). For NSIS-MIP class, the flow is admitted if:  
\[\sum_{c=1}^{C}p_{0-MIP,c}\leq C\cdot p_{outage-MIP} \tag{10}\]  
where \(C\) is the number of cells that mobile host will visit (that will be determined through a dedicated algorithm as in the next sections) and \(\text{P}_{\text{outage-NSIS-MIP}}\) is the outage probability of the wireless system. As regards NSIS-MDP users, the condition in Equation (10) is verified only for the current cell. When a new user makes a service request, the CAC calculates \(p_{0,c}\) and verifies if it is less or equal \(p_{outage}\) for each cell: in this way the new request can be admitted or rejected (if it is not satisfied). The outage probability \(p_{0}\) for user \(i\) belonging to the \(CLASS\) service class at any time is:  
\[p_{0-CLASS}=\text{Pr}\Bigg{\{}\sum_{i=1}^{n}\frac{r_{i,\text{min}}}{1-D_{i,m_{ i}}}>R_{CLASS}\Bigg{\}} \tag{11}\]  
where \(m_{i}\) is user \(i\)'s link state, \(p_{mi}\) is the probability of the user \(i\)'s link to being in state \(m\), \(n\) is the total number users (with the new one) belonging to \(CLASS\) and \(R_{CLASS}\) represents the bandwidth dedicated to \(CLASS\) services. For a deeper analysis of the proposed CAC and channel model with degradation states to see [30].