esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The thermal decomposition product of magnesium hydroxide (MH) is magnesium oxide (MgO), which serves as the foundational material for fireproof layer construction in the condensed phase. However, the weak interaction force between particles of MgO generated by thermal decomposition leads to the insufficient strength and poor adhesion ability of the fireproof layer. The fireproof layer was easily damaged and detached in this study, resulting in the low flame-retardant efficiency of MH. In this work, polycarbosilane (PCS) and divinyl benzene (DVB) were used to modify MH, and EVA/MH/PCS/DVB composites were made via melt blending. The flame-retardant properties of EVA/MH/PCS/DVB were evaluated using the limiting oxygen index (LOI), vertical combustion (UL-94), and a cone calorimeter (CONE). The thermal stability of the composites and flame retardants was analyzed using a thermogravimetric analyzer. The char layer structure was observed and analyzed using scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS), respectively. The results indicate that the LOI of the EVA/MH/PCS/DVB with 50 wt.% flame retardants in total was as high as 65.1, which increased by 160% in comparison with EVA/MH. Furthermore, the total smoke production (TSP) of the EVA/MH/PCS/DVB composite decreased by 22.7% compared to EVA/MH/PCS; the thermal stability of the MH/PCS/DVB and EVA/MH/PCS/DVB improved to some extent; and the compact residual char after the combustion of EVA/MH/PCS/DVB had fewer cracks due to the adhesive effect induced by PCS/DVB.  
ethylene-vinyl acetate copolymer; magnesium hydroxide; polycarbosilane; divinylbenzene; cone calorimeter; total smoke production +
Footnote †: journal: polyymers  
Article  
Polycarbosilane/Divinylbenzene-Modified  
Hydroxide to Enhance the Flame Retardancy of Ethylene-Vinyl Acetate Copolymer. Polymers
## 4 Experiments
### System Efficiency Analysis and Performance Comparison
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Engine**} & \multicolumn{3}{c}{**Processing Time**} \\ \cline{2-7}
**Device** & **Type** & **Name** & \begin{tabular}{c} **Minimum** \\ **Required fps** \\ \end{tabular} & \begin{tabular}{c} **1 Frame** \\ **(ms)** \\ \end{tabular} & \begin{tabular}{c} **Total** \\ **Time (ms)** \\ \end{tabular} & \begin{tabular}{c} **Total** \\ **fps** \\ \end{tabular} \\ \hline \multirow{5}{*}{\begin{tabular}{c} Edge \\ (Jetsont NXavier) \\ \end{tabular} } & Decoder & NVDEC & - & 0.925 & \multirow{5}{*}{47.430} & \multirow{5}{*}{21.083} \\ \cline{2-7}  & Object Detection & YOLOv7-tiny TRT & - & 43.171 & & \\ \cline{2-7}  & Object Tracking & ByteTrack & - & 1.736 & & \\ \cline{2-7}  & Object Tracking & SORT & - & 1.112 & & \\ \cline{2-7}  & Abnormal Behavior & Assault & 20 & 0.352 & & \\ \cline{2-7}  & Detection & Fall & 20 & 0.134 & & \\ \hline \multirow{5}{*}{\
\begin{tabular}{c} Edge \\ (Jetson \\ AGX Orin) \\ \end{tabular} } & Decoder & NVDEC & - & 0.925 & & \\ \cline{2-7}  & Object Detection & YOLOv7-tiny TRT & - & 19.702 & & \\ \cline{2-7}  & Object Tracking & ByteTrack & - & 1.714 & & \\ \cline{2-7}  & Object Tracking & SORT & - & 1.143 & & \\ \cline{2-7}  & Abnormal Behavior & Assault & 20 & 0.345 & & \\ \cline{2-7}  & Detection & Fall & 20 & 0.169 & & \\ \hline \multirow{5}{*}{
\begin{tabular}{c} GPU \\ Server \\ \end{tabular} } & Decoder & FFmpeg & - & 1.133 & & \\ \cline{2-7}  & Object Detection & YOLOv7x & - & 7.441 & & \\ \cline{2-7}  & Pose Estimation & AlphaPose & - & 12.279 & 47.163 & 21.203 \\ \cline{2-7}  & Abnormal Behavior & Assault & 10 & 25.293 & & \\ \cline{2-7}  & Detection & Fall & 10 & 1.017 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overall processing time to process one frame of a video on the edge and server. Figure 8: Visualization of server module activation states in GPU servers within the experimental environment. The activation statuses of 6 server analysis modules loaded on 3 GPU servers to process real-time video streaming from 20 CCTV cameras for 1 h are shown. These data show that the proposed system is capable of real-time processing and can effectively cover 20 CCTV cameras with the selected configuration.
# A Three-Dimensional Elastoplastic Constitutive Model for Geomaterials
## 4 Convexity of GMC-L Failure Criterion  
According to the [PERSON] the stabilized materials' failure surface must be convex [36]. Therefore, to ensure the failure surface's convexity, the GMC-L criterion needs to meet certain parameter conditions. Since the GMC-L criterion is linear on the meridian plane, it is only necessary to consider its convexity on the \(\pi\) plane.  
Similarly, combined with Equations (4) and (10), we can obtain the GMC criterion's expression using parameters \(\zeta,\rho\), and \(\theta\):  
\[\begin{array}{l}\zeta\sin\phi_{b}+\frac{\sqrt{2}}{4}\rho[(\cos\theta-\sqrt{ 3}\sin\theta)\sin\phi_{b}-(3\cos\theta+\sqrt{3}\sin\theta)]\\ +\sqrt{3}c_{b}\cos\phi_{b}=0\end{array} \tag{11}\]  
When \(\theta=0^{\circ}\), the geotechnical material is in the triaxial compression state; while \(b=0\), Equation (11) is as follows:  
\[\zeta\sin\phi_{0}+\frac{\sqrt{2}}{4}\rho(\sin\phi_{0}-3)+\sqrt{3}c_{0}\cos\phi _{0}=0 \tag{12}\]  
When \(\theta\)= \(60^{\circ}\), the material is in the triaxial extension state; while \(b=1\), Equation (11) can be written as follows:  
\[\zeta\sin\phi_{1}-\frac{\sqrt{2}}{4}\rho(\sin\phi_{1}+3)+\sqrt{3}c_{1}\cos\phi _{1}=0 \tag{13}\]  
Assuming the cohesion \(c_{b}=0\), according to Equation (12), the triaxial compression strength vector \(\rho_{c}\) of GMC-L on the \(\pi\) plane can be written as follows:  
\[\rho_{c}=\frac{2\sqrt{2}\sin\phi_{0}}{3-\sin\phi_{0}}\zeta \tag{14}\]  
According to Equation (13), the triaxial extension strength vector \(\rho_{e}\) of GMC-L can be provided in a similar manner:  
\[\rho_{e}=\frac{2\sqrt{2}\sin\phi_{1}}{3+\sin\phi_{1}}\zeta \tag{15}\]  
As shown in Figure 3, OP = \(\rho_{c}\) and OC = \(\rho_{e}\). To ensure the hexagon failure surface's convexity on the \(\pi\) plane, according to the geometric relationship, OB \(\leq\) OP \(\leq\) OA is obtained. According to the relationship, the strength parameters \(\phi_{0}\) and \(\phi_{1}\) must meet the conditions:  
\[\frac{\sin\phi_{1}}{3+\sin\phi_{1}}\leq\frac{2\sin\phi_{0}}{3-\sin\phi_{0}} \leq\frac{4\sin\phi_{1}}{3+\sin\phi_{1}} \tag{16}\]  
Figure 3: Convexity of GMC-L failure criterion in the \(\pi\) plane.  
Expanding and rearranging, Equation (16) becomes  
\[\frac{\sin\phi_{1}}{2+\sin\phi_{1}}\leq\sin\phi_{0}\leq\frac{2\sin\phi_{1}}{1+\sin \phi_{1}} \tag{17}\]  
Some studies [37; 38; 39] also show that the material's strength envelope surface should meet the convexity requirements. Therefore, when \(\phi_{0}\) and \(\phi_{1}\) satisfy Equation (17), the GMC-L criterion satisfies convexity.
# Bounce: A High Performance Satellite-Based Blockchain System
## 3 Bounce Protocol
### Trust Model
#### 3.3.3 Notification Assumptions  
The following assumptions ensure that enough Broadcast Ground Stations behave correctly to take Merkle Tree transactions sent by Sending Stations or Commit Records sent by satellites and make such information widespread. If these assumptions do not hold, users of the blockchain will know because they will not receive Commit Records every few seconds.  
\(f\)**-failure assumption**: For some parameter \(f\), no more than \(f\) Broadcast Ground Stations will fail. However, a failing Broadcast Ground Station may fail traitorously (by commission in a Byzantine way) or by omission. In the later sections, starting with Section 4, when we refer to multisignatures, we assume that each multisignature will be derived from the signatures of at least \(f+1\) Broadcast Ground Stations.  
**Widespread Knowledge assumption**: Every working Broadcast Ground Station can send a message to enough sites that the message won't be lost and will be found if Mission Control polls for it. Information that has been broadcast this way is said to be _widespread knowledge_. Some sites will be Accumulator sites (see Section 4.3) that maintain wallets and prevent double-spending. The traitorous Ground Stations can't do any harm from a safety perspective, because only roots that are signed by some slot's satellite will be recorded on the blockchain.  
**Mission Control Assumptions**  
Mission Control sends messages very occasionally: only when \"Resets\" occur which happens only when a satellite fails. For that reason, the messages sent by Mission Control can be closely monitored by human users. Those messages assign slots to satellites and authenticate Sending Stations and Broadcast Ground Stations therefore are easy to parse. The protocol for resets is straightforward but lengthy so please see our book [3].  
The assumption of **exposure aversion** is that each administrator of Mission Control will avoid signing any message that embodies bad behavior. Bad behavior includes assigning the same slot to multiple satellites, validating bad Sending Stations, or validating bad Broadcast Ground Stations, all of which would be obvious by simple inspection of any \"Reset message\" which makes all these assignments.
## 3 Results
### Reactivity of anti-RSV IgG Antibodies with Mutated Ruc-RSV-mG Antigens  
RSV-G protein exists as a membrane bound protein in its native state. However, due to an alternative initiation of translation at the second Met (amino acid 48), a soluble form of G may also be present [27]. To rule out any potential interference of soluble G antigens with results obtained using Ruc-G\({}_{\rm A}\) or Ruc-G\({}_{\rm B}\) antigens, Ruc-mG\({}_{\rm A}\) and Ruc-mG\({}_{\rm B}\) constructs were generated and used to detect anti-RSV-G\({}_{\rm A}\) IgG and anti-RSV-G\({}_{\rm B}\) IgG antibodies. Samples were tested in parallel against native and mutated G antigens (Ruc-G\({}_{\rm A}\), Ruc-mG\({}_{\rm A}\), Ruc-G\({}_{\rm B}\), and Ruc-mG\({}_{\rm B}\)). Human immune globulin (BEI NR-21973), Mabs 1227 and 131-2G, or rabbit antisera (RSV-A2, -18537/G\({}_{\rm B}\) protein, -G\({}_{\rm A}\) protein, -LHWT-G\({}_{\rm A171-202}\) peptide, and -LHWT-G\({}_{\rm B171-202}\) peptide) had similar reactivities when tested against native versus mutated G proteins. Similarly, when adolescent sera were tested in the LIPS-G\({}_{\rm A}\) and -mG\({}_{\rm A}\) assays, 29/30 samples were positive using both Ruc-G\({}_{\rm A}\) and Ruc-mG\({}_{\rm A}\) antigens (Figure 9A). In contrast, when tested using Ruc-G\({}_{\rm B}\) and Ruc-mG\({}_{\rm B}\) antigens, 27 samples were positive in the LIPS-G\({}_{\rm B}\) assay whereas all 30 samples were positive in the LIPS-mG\({}_{\rm B}\) assay (Figure 9B). When the results of testing adolescent sera against Ruc-mG\({}_{\rm A}\) and Ruc-mG\({}_{\rm B}\) antigens were compared with the results obtained using the homologous subtype in a PRN test, no correlation was seen. Interestingly, the group mean RLU responses for Ruc-mG\({}_{\rm A}\) were significantly higher than those seen using Ruc-G\({}_{\rm A}\) (Ruc-G\({}_{\rm A}\) = 3.96 log\({}_{10}\) RLU versus Ruc-mG\({}_{\rm A}\) = 4.02 log\({}_{10}\) RLU with \(p\) = 0.0013). However, mean responses were similar for the tests using Ruc-G\({}_{\rm B}\) and Ruc-mG\({}_{\rm B}\) (Ruc-G\({}_{\rm B}\) = 3.94 log\({}_{10}\) RLU versus Ruc-mG\({}_{\rm B}\) = 3.90 log\({}_{10}\) RLU with \(p\) = 0.4893) (Figure 9C). Ig\(\Delta\) was used as the negative control and cutoffs for a positive response set as follows: 3.08 log\({}_{10}\) RLU for Ruc-G\({}_{\rm A}\), 3.13 log\({}_{10}\) RLU for Ruc-mG\({}_{\rm A}\), 3.10 log\({}_{10}\) RLU for Ruc-G\({}_{\rm B}\), and 2.96 log\({}_{10}\) RLU for Ruc-mG\({}_{\rm B}\).  
Figure 8: Twelve paired serum samples obtained at 9 and 15/18 months of age were tested against Ruc-G\({}_{\rm A}\) and Ruc-G\({}_{\rm B}\) antigens. The mean reactivities for individual samples tested against Ruc-G\({}_{\rm A}\) and Ruc-G\({}_{\rm B}\) antigens are shown in red and blue bars, respectively, and error bars represent standard deviations. Cutoff values for a positive signal were five standard deviations above the mean value of the negative control and are shown as horizontal red (Ruc-G\({}_{\rm A}\)) and blue lines (Ruc-G\({}_{\rm B}\)).
## 3 Adaptive Harmony Search with Differential Evolution
### Self-Adaptive PAR and F  
Inspired by the concept of the learning period of SGHS [21] to adaptively tune parameters HMCR and PAR, this paper employs a new modified scheme for \(PAR\) and \(F\).  
In addition, the parameters \(HMCR\) and \(PAR\) are dynamically adapted to a suitable range by recording their historic values corresponding to the generated harmonies entering the harmony memory. During the evolution, the values of \(HMCR\) and \(PAR\) for the generated successful harmony are recorded to replace the worst members in the harmony memory.  
First, both the means of \(PAR\) (\(PARm\)) and \(F\) (\(Fm\)) are initialized as 0.5. Second, parameters \(PAR\) and \(F\) are generated with a normal distribution. During the generations, the values of \(PAR\) and \(F\) are recorded when the generated harmony successfully replaces the worst member in the harmony memory. After Learning Period (\(LP\)), parameters \(PARm\) and \(Fm\) are recalculated with the weighted Lehmer mean formulas [42]. The weighted Lehmer mean \(mean_{wl}(S)\) uses the following deterministic Equations (8)-(10) to compute. The amount of fitness difference \(\Delta f_{k}\) is used to influence the parameter adaptation.  
\[mean_{wl}(S)=\frac{\sum_{k=1}^{|S|}w^{k}.S^{k2}}{\sum_{k=1}^{|S|}w^{k}.S^{k}} \tag{8}\]  
\[w^{k}=\frac{\Delta f^{k}}{\sum_{l=1}^{|S|}\Delta f^{l}} \tag{9}\]  
\[\Delta f^{k}=\left|f(x^{new})-f\Big{(}x^{worst}\Big{)}\right| \tag{10}\]  
where \(S\) includes either \(S_{PAR}\) or \(S_{F}\) and \(mean_{wl}(S)\) is the new value of \(PARm\) or \(Fm\). \(x^{new}\) is the newly generated solution in the current generation. \(x^{worst}\) is the worst solution in harmony memory. \(f(\star)\) denotes the fitness function.  
Parameters \(PARm\) or \(Fm\) use the framework in Algorithm 3 to update their values, where generation counter \(lp=1\). The difference between the weighted Lehmer mean and the arithmetic mean is described as follows. Arithmetic mean means all the recorded successful parameters of \(PARm\) or \(Fm\) have the same weights. On the other hand, the weighted Lehmer mean shown in Equations (8)-(10) means that all the recorded successful parameters of \(PARm\) or \(Fm\) have the self-adaptive weights based on their fitness improvements. It is very possible that the weighted Lehmer mean outperforms the arithmetic mean statistically. However, we will not analyze their difference in this paper due to paper length restrictions, and instead cite the reference [42] directly. The detailed information of weighted Lehmer mean can be found in reference [42].  
In general, the values of \(PAR\) and \(F\) are regenerated as the following equations.  
\[\left\{\begin{array}{c}PAR=normrnd(PARm,0.1)\\ F=normrnd(Fm,0.1)\end{array}\right. \tag{11}\]  
If \(PAR\) is larger than 1, it is truncated to be 1; if \(PAR\) is less than or equal to 0, it will be assigned 0.001. The same action is executed on \(F\).
## 2 Model Setup
### Model Establishment  
#### 2.2.1 General Concept  
As mentioned previously, the MRM and BSM methods are the most commonly used calculation methods for shield tunnels in practice. Unfortunately, the MRM method requires a number of empirical parameters, which have to be determined by experience. Moreover, this method does not allow for the precise calculation of the actual distribution of the bending moments [13; 14; 15]. Since the BMS method seems the most promising approach, its applicability in special-section tunnels is investigated in this paper.  
As the tunnel construction adopts a staggered assembly, with alternating linings of pattern A and pattern B, the calculation model applied in the current research contains three rings: a front half ring (Pattern B), a middle full ring (Pattern A) and a back half ring (Pattern B). In this way, the effect of the neighboring lining structures is simulated with a minimum length for this kind of staggered assembly. We consider the joints between the segments in one ring as longitudinal joints, and the joints between the neighboring rings as circumferential joints. As shown in Figure 3, both rotational and radial shear springs are considered in the longitudinal joints, while radial and tangential shear springs are considered in the circumferential joints. The segments and columns are simplified as beam elements, with areas and moments of inertia determined according to their specific size and amount of reinforcement.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 6 Machine Learning for Enhancing PHY Layer Security
### Reccurent Neural Networks and Long Short-Term Memory
The schema of vanilla Transformers is depicted in Figure 12. At the core of the Transformer architecture is the self-attention mechanism, commonly extended to multi-head self-attention, which allows the model to weigh the importance of different parts of the input data, enabling it to process sequences in parallel and capture complex, long-range dependencies more effectively than its predecessors [138]. This characteristic is particularly beneficial for RF fingerprinting, where capturing the intricate temporal and spatial relationships within signal data can enhance identification accuracy and robustness in LTE and 5G networks. The ability of Transformers to efficiently handle large data sequences while maintaining context awareness makes them a promising option for RF fingerprinting methodologies. As far as we are aware, there has yet to be a recent publication on research focusing on RF fingerprinting utilizing 5G/LTE signals. However, a few recent studies have explored similar concepts using alternative technologies. The approach from [139] used LoRa devices, where the input of the model consisted of spectrograms obtained via _Short-Time Fourier transform_ (STFT). The collected signals were preprocessed, and the preprocessing compromised four stages: (1) Synchronisation, (2) Preamble Extraction, (3) Carrier Frequency Offset (CFO) Compensation, and (4) Normalization. Figure 11: The fundamental building cells of (**a**) RNN and (**b**) LSTM networks. \(c_{t}\) denotes the cell state, \(h_{t}\) is the current hidden state, and \(x_{t}\) is the input data. \(h_{t-1}\) and \(c_{t-1}\) are the previous hidden and cell states, respectively. The yellow blocks are component-wise and the red blocks are layers. LSTM has marked the _Forget Gate_, _Input Gate_, and _Output Gate_.
## 2 Theoretical Background  
### Criteria for Vortex Induced Vibration Formation  
Vortex induced vibration (VIV) occurs only with the fulfillment of the following few criteria. Critical wind speed, \(v_{\text{crit}}\) is the minimum wind speed for VIV generation and the vortex effect is insignificant when \(v_{\text{crit}}\) < 1.25 \(v_{\text{m}}\), where \(v_{\text{m}}\) is the mean flow velocity [43]. For a certain vibrational mode \(r\), critical wind speed is defined as the wind velocity that creates a vortex shedding frequency equal to the natural frequency of that mode. The formula is given as below:  
\[v_{\text{crit},i}=\ b\times n/\text{St} \tag{1}\]  
where \(b\) is the fluid-structure contact width, St is the Strouhal number and \(n\) is the natural frequency of the model of certain mode \(r\).  
Scruton number, Sc, is a dimensionless parameter describing the damping and mass ratio of the structural model. A model with a low Scruton number signifies lower damping and mass ratio value, which encourages VIV occurrence. It was discussed by [PERSON] that a Scruton number less than 15 is optimum for producing significant VIV [44]. The formula for Scruton Number calculation is shown below [45]:  
\[\text{Sc}=\ 2\times\delta_{s}\times m_{r,e}/\rho\times b^{2} \tag{2}\]  
where \(\delta\) is the logarithmic decrement structural damping, \(m_{r,e}\) is the mass per length ratio of the model and \(\rho\) is the fluid density.  
Lastly, the aspect ratio represents the ratio between the width and chord length of the model. Vortex quality is heavily dependent on the aspect ratio value as a vortex tends to be more visible with a larger aspect ratio value. Research conducted by both [PERSON] et al. and [PERSON] et al. showed that a model with an aspect ratio value of 10 to 20 has a clear and strong definition of a vortex, an aspect ratio value of 5 to 0.5 shows moderate vortex shedding, while an aspect ratio with a value ranging from 0.2 to 0.1 shows a faint sign of vortex [46; 47].
## 4 Simulation and Experimental Results
### Energy Deviation  
The energy deviation considers the difference between the sensor node with the highest residual energy and the sensor node with the lowest residual energy. This is an indicator of load balancing. If the energy deviation is high, then the energy expenditure is not balanced among sensor nodes. The unbalanced energy expenditure causes premature node death. Consequently, the network lifetime is reduced. For a better load balancing, it is expected to reduce the highest peak.  
Figure 6 shows the energy deviation of the reported protocols. From the figure, it can be observed that the highest energy deviation is reached by LEACH, while the lowest is achieved by DEEC and YSGAP. However, the YSGAP protocol reaches the highest peak after 230 rounds, while DEEC after 180 rounds. The later the highest peak is reached, the better. Therefore, we can conclude that YSGAP outperforms its opponents in terms of the energy deviation.  
Figure 4: Network lifetime: percentage of dead nodes.  
Figure 5: Total residual energy.
# A Quantitative Detection Method for Surface Cracks on Slab Track Based on Infrared Thermography
## 3 Quantitative Detection Method of Crack Area
### Pixel Calibration in Crack Region  
Another important problem that needs to be solved is the false detection of DCJ, which is the designed component of the slab track, rather than the surface crack, as shown in Figure 9. Because the DCJ is more consistent and the boundary distinction is more obvious, the false joint area can probably be detected as the surface crack on the slab track. To distinguish DCJ from actual surface crack, this paper proposes an identification method for DCJ. Since the spacing between adjacent DCJs on the track slab is a fixed value, the pixel spacing between the centroids of adjacent connected domains at the DCJ is a fixed value \(H_{real}\). Therefore, the pixel distance \(H_{pixel}\) between adjacent connected areas at the DCJ is obtained by calculating the centroid coordinates of the connected areas at the two DCJs. The conversion coefficient \(K_{pixel}\) between a single pixel and the physical length in the infrared thermal image is obtained by comparing the actual distance \(H_{real}\) and the pixel distance \(H_{pixel}\) between adjacent DCJs in the infrared thermogram:  
\[K_{pixel}=\frac{H_{real}}{H_{pixel}} \tag{18}\]  
where \(H_{real}\) is the actual distance between adjacent DCJs, \(H_{pixel}\) is the pixel distance between adjacent DCJs, and \(K_{pixel}\) is the physical distance of a single pixel. The conversion factor \(K_{pixel}\) can be used to convert the pixel length, local pixel width, average pixel width, and pixel area of the crack area into the actual crack length, actual crack width, and actual crack area.