esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analysis of the Volatile Flavor Compounds Using HS-SPME-GC-MS  
Each sample (1 mL) was mixed with a saturated NaCl solution and 20 \(\upmu\)L of internal standard (10 \(\upmu\)g/mL 3-Hexanone-2,2,4,4-d4) and extracted using HS-SPME for GC-MS analysis. GC-MS analysis was performed on an Agilent 8890-7000D system equipped with a DB-5 MS column. Headspace extraction was conducted at 60 \({}^{\circ}\)C for 15 min using a 120 \(\upmu\)m DVB/CWR/PDMS fiber, followed by desorption at 250 \({}^{\circ}\)C for 5 min. High-purity helium was used as the carrier gas at a flow rate of 1.2 mL/min. The temperature program started at 40 \({}^{\circ}\)C (held for 3.5 min), ramped to 100 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, then to 180 \({}^{\circ}\)C at 7 \({}^{\circ}\)C/min, and finally to 280 \({}^{\circ}\)C at 25 \({}^{\circ}\)C/min, where it was held for 5 min. Mass spectrometry was conducted in electron impact ionization (EI) mode. Metabolites were identified and quantified using a custom database, with accuracy ensured through retention time and selective ion monitoring (SIM).
# Making a Case for Hybrid GFRP-Steel Reinforcement System in Concrete Beams: An Overview
## 2 Background  
### Steel Deterioration in RC Beams  
Corrosion of steel reinforcement in concrete structures can lead to reduced structural integrity and service life. When steel reinforcement corrodes, it expands and can cause the concrete to crack and spall, leading to a loss of bond between the steel and concrete. This can result in reduced load-carrying capacity and increased risk of structural failure. The corrosion process of steel in concrete structures is facilitated by the presence of water and oxygen, as well as chloride ion contamination, which can come from sources such as seawater or de-icing salts. As the steel corrodes, the volume expansion accompanying the chemical products pushes on the surrounding concrete, leading to the formation of cracks and voids in the concrete cover. This can expose the steel to further corrosion, leading to a vicious cycle of deterioration.  
Concrete, on its own, because of its intrinsic mechanical property, is susceptible to cracking and is also a permeable material. The combination of intrinsic porosity and cracks through capillary action provides pathways for electrolytes to reach the embedded steel reinforcement in a concrete body. In a favourable atmospheric condition, dissolved ions of chlorides (Cl\({}^{-}\)) or carbonates (CO\({}_{3}^{-}\)) provide the electrolytic potential for corrosion [44; 45; 46; 47]. Concrete carbonation and the infiltration of chloride ions cause changes in the pore solution of concrete, present within the pores or voids in the material. Such conditions would aid the galvanic mechanism responsible for the corrosive attack on steel reinforcements, leading to significant corrosion damage. Additionally, carbonation results in acidification due to the penetration of atmospheric CO\({}_{2}\), and the presence of chloride ions can cause steel corrosion through pitting. Figure 1 presents the schematic illustration of the corrodent transport in RCs [48]. The diffusive travel of corrodents is resisted by the concrete cover, typically taken to be 30-50 mm. In several studies [49; 50; 51; 52; 53; 54], corrosion damage has been reported to be responsible for the reductions in service life and the ultimate capability of RC structures.
# Analytical TCP Model for Millimeter-Wave 5G NR Systems in Dynamic Human Body Blockage Environment
## 2 System Model  
In this paper, we consider a single 5G NR BS having the coverage of \(r\), such that UEs do not experience outage conditions when blocked. To calculate \(r\), we employ the standardized MCS set [12] and the 3 GPP UMi propagation model [13]. The height of the BS is \(h_{A}\). The spatial density of humans acting as dynamic blockers is \(\lambda_{B}\), and they move according to the random direction mobility (RDM) model [14] with the speed of \(v_{B}\) m/s by having the exponentially distributed run length with the mean of \(\tau_{B}\) m. Users are modeled as cylinders with a height of \(h_{B}\) and a radius of \(r_{B}\). The height of the UE is \(h_{U}\). Following the measurements of human-body blockage [4], the LoS occlusion by humans is assumed to result in 20 dB of additional signal strength degradation.  
We model a selected UE located at the distance of \(d\), \(d<r\), from the BS, and provided with the bandwidth of \(B\). We are interested in the throughput achieved by a TCP source associated with the selected UE. The mean RTT that includes all of the delays both in a wired network and at the 5G NR interface is assumed to be \(D\). Similarly to other studies, for example, [15], we address the congestion control phase, where TCP relies upon the additive increase multiplicative decrease (AI/MD) mechanism. Accordingly, after each RTT, TCP increases the number of transmitted segments by one. Once the capacity is achieved, several segments may be lost, forcing the TCP source to cut down its rate by half. We also assume that the residual packet error rate, caused by imperfect error concealment at the NR physical and data-link layers, is negligible [11].
## 1 Introduction
Overall, the inverse analysis can be used to obtain the constitutive parameters, and then the empirical relationship between the constitutive parameters and the field test parameters can be established to calibrate the soil constitutive parameters in a simpler way. Although the inverse analysis method based on field-measured data is quite mature, there are drawbacks such as the cumbersome steps of parameter inversion, too many optimization parameters, and the established empirical relationships not being easy to obtain. Therefore, it is of great significance to propose a selection method for constitutive parameters with simple steps and high accuracy to satisfy the design requirements. To solve these limitations, this study takes a foundation pit excavation project adjacent to a subway in Hangzhou as an example. A step-by-step inverse analysis method was proposed, initially determining the ratio of important constitutive parameters and subsequently calculating the key constitutive parameters. Then, the empirical relationship between the optimized key constitutive parameters and the blow counts (\(N\)) of the in situ standard penetration test (SPT) was established. The selection method for constitutive parameters of typical silt-sandy soil in Hangzhou City was obtained. This study provides an important reference and insights for the assessment and design of the braced excavation by numerical simulation.
## 2 Materials and Methods  
### System Structure  
#### 2.1.1 Hardware Structure  
Compared with professional surveying and mapping grade boards and antennas, the price of commercial boards and antennas is lower. Figure 1 shows the hardware structure built in this paper. The difficulty of multi-antenna DOA calculation lies in the precise synchronization and data sharing among receivers. Frequency synchronization can be achieved between multiple receivers by locking the external clock, but time synchronization cannot be achieved, which requires compensation at the algorithm level. The data of the four receivers are shared with the processing unit for real-time calculation, and the calculation result are displayed on the computer through the upper computer. Each antenna is connected to the receiver through a long cable, and the delay of the signal in the cable needs to be considered. Low-cost antennas introduce additional measurement errors due to antenna phase center instability and susceptibility to multipath effects, which need to be corrected. Considering the DOA ambiguity of the dual antennas and introducing more geometric constraints, a special configuration of four antennas is designed, as shown in Figure 2. The multi-antenna configuration is closely related to the solution of carrier single difference and the performance of spoofing detection. More calculation details are described in detail in subsequent chapters.  
Figure 1: Schematic diagram of hardware structure. The I, II, III and IV represent the antennas of the four receivers, respectively.  
Figure 2: Four-antenna configuration. A, B, C, and D represent four antennas. \(\triangle ABC\) is an equilateral triangle.
# DAB Signal Preprocessing for Passive Coherent Location
## 2 Materials and Methods
### DAB Signal Preprocessing
#### 2.4.4 Duplication of MSC Symbol in Place of Null Symbol  
After finding the start of the frame (\(n_{0}\)) as seen previously, the signal samples carrying the Null symbol from both the reference and the surveillance channel are replaced by copies of one MSC symbol selected from the previous frame  
\[\begin{array}{c}x(n)x(n-N_{\rm s}),\\ x_{\rm s}(n)x_{\rm s}(n-N_{\rm s}),\\ n=n_{0}-N_{\rm margin}-N_{\rm null},\ldots,n_{0}\end{array} \tag{9}\]  
where \(N_{\rm s}\), as previously (8), is determined by the index \(N_{\rm C0}\) (\(5\leq N_{\rm C0}\leq 76\)) of the copied MSC symbol. The frame structure after this modification is shown in Figure 11.  
Figure 10: Autocorrelation of DAB signal after copying of MSC symbols (**a**) and its close-up (**b**).  
Figure 9: DAB frame structure with Null, PR, and FIC symbols replaced by MSC symbols.  
As mentioned previously, additional side peaks have been introduced this way in the autocorrelation function of the signal. Additionally, because of unchanged PR and FIC symbols, the periodical side peaks in the autocorrelation function of the signal are still present, as shown in Figure 12 (\(N_{\text{CO}}=69\)). Besides these drawbacks, this kind of signal modification is interesting in PCL signal processing and will be further analyzed. It should be noted that the only modified part of the frame was the Null symbol with no RF signal emission. Since the Null symbol position is known, and PR, FIC symbols are left unchanged, the DAB frames after this modification can still be demodulated and decoded, for example, to reconstruct the reference signal [8,24].
## 2 Materials and Methods
### Filtration Performance Test  
The pore size distribution and specific surface area of the PET nanofiber membrane, CuBTC powder, and CuBTC@PET composite nanofiber membrane were determined by using the fully automatic specific surface area and porosity distribution analyzer (BET) of ASAP2020 from Mack, Atlanta, GA, USA. The adsorption and desorption isotherms of nitrogen were measured at the temperature of liquid nitrogen, and the specific surface area and pore size distribution of the samples could be obtained directly according to the self-contained calculation model.  
The filtration performance of CuBTC@PET composite fiber membrane under different parameters was tested on the DR251 XL automatic filter tester from Suzhou Checker Instruments. A sample with a test area of 30 cm \(\times\) 30 cm was clamped between the upper and lower grippers, and the flow rate of the airflow through the sample was controlled by the flow control knob to be 32 L/min. The aerosol generator produces atomized NaCl particles with a random distribution of particle size, the number of upstream and downstream particles (N1 and N2, respectively) can be detected by the connected laser particle counter, and the computer will automatically give the filtration efficiencies of particles with particle sizes of \(\geq\)0.3 \(\upmu\)m, \(\geq\)0.5 \(\upmu\)m, \(\geq\)1 \(\upmu\)m, \(\geq\)3 \(\upmu\)m, \(\geq\)5 \(\upmu\)m, and \(\geq\)10 \(\upmu\)m according to the efficiency formula \(\upeta\) = (N1 \(-\) N2)/N1 \(\times\) 100%; the compression resistance can be obtained by pressure sensor. The pressure resistance can be obtained through the pressure sensor test.
###### Abstract  
A gantry crane located in a near-field earthquake-prone area is selected in this paper as an example, and the nonlinear finite element (FE) model is used considering the material nonlinearity including plastic hinges and the second order (P\(-\Delta\)) effect with a comprehensive consideration of the components including sill beams, support beams, legs, and trolley girders. The local displacement ratio (LDR) and deflection ratio (DR) are proposed as demand measures (DMs) of the gantry crane, which are utilized to construct a probabilistic seismic demand model (PSDM). Then, the capacity limit states for the gantry crane are defined in this study by performing pushover analysis (POA), known as serviceability, damage control, and collapse prevention, respectively. Moreover, the operating capacity of the crane during an earthquake is further investigated and quantified by operating seismic peak ground acceleration, which is defined as the maximum acceleration when the failure probability is 50%. Finally, the fragility curves and the failure probability of the gantry crane are derived by the above definitions, all of which are pioneering in the seismic design of gantry cranes subjected to near-field ground motions. Some major conclusions are drawn that the horizontal component of an earthquake has a more notable effect on the structural damage of the gantry crane compared to the vertical component, and incremental dynamic analysis can take seismic uncertainty into account and quantify the deformation of gantry crane in more detail.  
gantry crane; incremental dynamic analysis; fragility analysis; seismic capacity; near-field ground motion
# Jointly Production and Correlated Maintenance Optimization for Parallel Leased Machines
## Appendix B Service rate Constraint Transformation  
From the previous proof, the variance of inventory variable is defined by \(V(I_{k})=k\cdot\sigma_{d}^{2}\). This inventory variable depends linearly on the random demand variation. It is possible to consider the inventory variable as a random variable following a normal distribution defined by:  
\[I_{k}=\hat{I}_{k}+X_{k}\times\sqrt{V(I_{k})}\Leftrightarrow I_{k}=\hat{I}_{k} +X_{k}\times\sqrt{\hat{k}}\times\sigma_{d}\]  
with \(X_{k}\propto N(0,1)\) is a standard Gaussian deviate.  
\[Prob(I_{k}\geq 0)\geq\beta\]  
\[Prob\Big{[}\hat{I}_{k}+X_{k}\times\sqrt{\hat{k}}\times\sigma_{d}\geq 0\Big{]}\geq\beta\]  
\[Prob\Big{[}\hat{I}_{k-1}+X_{k}\times\sqrt{\hat{k}}\times\sigma_{d}+p_{k}-\mu_{ k}^{d}\geq 0\Big{]}\geq\beta\]  
\[Prob\Big{[}X_{k}\times\sqrt{\hat{k}}\times\sigma_{d}\geq-\hat{I}_{k-1}-p_{k}+ \mu_{k}^{d}\Big{]}\geq\beta\]  
\[Prob\Bigg{[}X_{k}\geq\frac{-\hat{I}_{k-1}-p_{k}+\mu_{k}^{d}}{\sqrt{\hat{k}} \times\sigma_{d}}\Bigg{]}\geq\beta\]  
\[1-Prob\Bigg{[}X_{k}\leq\frac{-\hat{I}_{k-1}-p_{k}+\mu_{k}^{d}}{\sqrt{\hat{k}} \times\sigma_{d}}\Bigg{]}\geq\beta\]  
Assuming that \(\varphi\) is the repartition function of this variable \(X\).  
\(\varphi\) is strictly increasing, indefinitely differentiable and therefore we conclude that \(\varphi\) is invertible:  
\[1-\varphi\Big{[}\frac{-\hat{I}_{k-1}-p_{k}+\mu_{k}^{d}}{\sqrt{\hat{k}}\times \sigma_{d}}\Big{]}\geq\beta\]  
\[\hat{I}_{k-1}+p_{k}-\mu_{k}^{d}\geq\sqrt{\hat{k}}\times\sigma_{d}\times\varphi ^{-1}(\beta)\]  
\[p_{k}\geq\sqrt{\hat{k}}\times\sigma_{d}\times\varphi^{-1}(\beta)+\mu_{k}^{d}- \hat{I}_{k-1}\]
# On the Statistical Characterization of Lightning-Induced Voltages
## 4 Numerical Applications
### Effect of the Countermeasures to Mitigate the Induced Overvoltages  
Further simulations were performed by introducing an overhead ground wire as a countermeasure to mitigate the induced overvoltages. The ground wire's height is 11 m and grounding resistance is 20 \(\Omega\). Again, the statistical characterization was carried out for \(\beta\) = 0.3 and by using both the step-function current model 1 and the linearly-rising current model 2. In both cases, the presence of the ground wire has been taken into account through Equation (4).  
Figure 15 shows the distribution of the extracted values of \(V_{peak}\) and selected fitting parametric pdfs in the case of the step-function current model. The resulting values, along with the parameters' estimates corresponding to the two distributions, are reported in Table 4.  
Figure 16 shows the distribution of the extracted values of \(V_{peak}\) and selected fitting parametric pdfs in the case of the linearly-rising current model. Table 5 reports the resulting values, along with the parameters' estimates corresponding to the two distributions.  
Figure 14: Distribution of the experimental induced voltages reported in [23] (**a**) and of those resulted from the application of the proposed procedure (**b**).  
Figure 16: Distribution of the extracted values of \(V_{peak}\) and selected fitting parametric pdfs (linearly-rising current model, \(\beta\) = 0.3).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{3}{c}{**Fitting Distribution**} \\  & \multicolumn{3}{c}{**Generalized Extreme Value**} \\ \hline Log Likelihood & Det. Coeff. & k & \(\sigma\) & \(\mu\) \\ \hline \(-\)114,827 & 0.9993 & 0.489427 & 15,166.9 & 23,092.8 \\ \hline \multicolumn{5}{c}{**Fitting Distribution**} \\  & \multicolumn{3}{c}{**Lognormal**} \\ \hline Log Likelihood & Det. Coeff. & \(\sigma\)\({}^{1}\) & \(\mu\)\({}^{1}\) \\ \hline \(-\)114,941 & 0.9945 & 0.756103 & 10.3548 \\ \hline \end{tabular} \({}^{1}\)\(\mu\) and \(\sigma\) are the mean and variance of the associated normal distribution, respectively.  
\end{table}
Table 4: Results of Fitting.  
Figure 15: Distribution of the extracted values of \(V_{peak}\) and selected fitting parametric pdfs (step current model, \(\beta\) = 0.3).  
In this case too, the selected fitting distributions seem to reproduce the data distribution reasonably well. The effect of the countermeasure is apparent in terms of the mitigation of the induced overvoltages.
# On Blockchain-Enhanced Secure Data Storage and Sharing in Vehicular Edge Computing Networks
## 3 Proposed Architecture
### Procedures of Proposed Scheme
#### 3.3.1 Vehicle Registration and Initialization  
Before joining the network, every vehicle needs to register to TA that grants the vehicle identity legitimation. After passing identity authentication, a legitimate vehicle \((V_{i})\) with true identity \((ID_{i})\) generates its public keys \((PK_{l}D)\), private keys \((SK_{l}D)\), and certificates \((Cert_{l}D)\) to get access to the network service. Meanwhile, the cryptography algorithm (e.g., ECDSA) will guarantee the vehicle identity anonymity and communication security in VECNets.
# MDFULog: Multi-Feature Deep Fusion of Unstable Log Anomaly Detection Model
## 3 Methods
### Classification Based on Informer  
In MDFULog, the Informer-based classification model is used for detection. Compared with Transformer, Informer reduces the computational and space complexity resulting from the conventional self-attention. Moreover, it has the advantages of high speed, less memory, and more perception. The classification in the MDFULog model includes the Informer decoder layer, pooling layer, and MLP classification layer.  
**Informer decoder layer:** The classification model stacks multiple Informer decoder layers to increase the robustness. The structure of the Informer decoder is shown in Figure 4. It includes the ProbSparse self-attention and the fully connected layer. This layer receives \(X=\{V,X_{0}\}\) as the input (the target part of the prediction was set to 0). The ProbSparse self-attention layer first handles this. Secondly, it interacts with the initial vector \(V\) through multi-head attention. Finally, the fully connected layer is used to connect multiple outputs. The calculation process is as follows:  
\[Q=XW^{Q}, \tag{8}\]  
\[K=XW^{K}, \tag{9}\]  
Figure 3: Bert input sequence diagram.  
Figure 2: Bert structure.  
\[V=XW^{V}, \tag{10}\]  
\[\mathrm{H_{i}}=\mathrm{Softmax}\Bigg{(}\frac{\overline{Q}_{\mathrm{i}}\mathrm{k} \mathrm{t}_{\mathrm{i}}^{\mathrm{T}}}{\sqrt{\mathrm{d_{k}}}}\Bigg{)}\mathrm{V_{i}}, \tag{11}\]  
\[\mathbf{O}=\mathit{Concat}(H_{1},H_{2},...,H_{n-1},H_{n})W^{O}, \tag{12}\]  
where \(W^{Q},W^{K},W^{V}\) are the learnable parameter matrices of the model and \(d_{k}\) is the dimension of the key vector. \(\mathit{Concat}(\cdot)\) uses the parameter matrix \(W^{O}\) to connect the outputs of each header.  
**Pooling layer:** The classification model uses the pooling layer to prevent information redundancy and overfitting. Input the output matrix \(O\) of the Informer decoder layer into the pooling layer for pooling. The main feature vectors are extracted to lay the foundation for subsequent processing.  
\[Z=\mathrm{pool}(O). \tag{13}\]  
**MLP classification layer:** The multi-layer perceptron is used for classification. The Softmax function is used to calculate the probability of abnormality so as to determine abnormality.
## 4 Measured Results and Discussion
### Discussion  
Table 5 is a performance comparison between the proposed multilayer antenna array and other reported milliliter-wave antenna arrays. In terms of size and BW, the profile size of the antenna designed in this paper is only 0.1016\(\lambda\), which is one-quarter to one-half of the same type of antenna, and the frequency bandwidth and gain are equal to or higher than others. However, compared with the efficiency, the proposed antenna is slightly insufficient, which can be explained by the fact that, although the multilayer can effectively expand the BW, it also aggravates the energy loss of the multilayer plates. Therefore, compared with other millimeter-wave antenna, the proposed one has attractive properties of miniaturization, broadband, high gain, and easy fabrication and integration.
## 1 Introduction
It is worth noting that, in addition to the study of MMs with high Q-factor and modulation depth, considering the tunability and flexibility of metadevices in various practical applications, such as optical switches, optical sensors, and ultra-sensitivity spectroscopy, regulation of high Q-factor Fano resonance is also urgently needed. Researchers have focused on tunable THz MMs with high Q-factor and modulation depth, and proposed different types of asymmetric Fano resonance structures, such as dipole bars [14], metal strip [15], ring structure [16], and planar defective [17]. The resonance frequency, modulation depth and linewidth of the Fano resonance can be tuned to a limited extent by adjusting the geometric parameters of the MMs structures in a passive way. Recently, a small number of research findings have preliminarily demonstrated that if the novel active materials are integrated into MMs, such as semiconductors [18] and graphene [19; 20; 21] with the assist of external stimulation, the Fano resonances will achieve precise active control at additional level, which is of great value to practical applications of metadevices. As a kind of active material, VO\({}_{2}\) has a temperature-sensitive insulator-metal transition (IMT) characteristic. Under the external thermal excitation, its conductivity can be rapidly increased by 4-5 orders of magnitude and is only one order of magnitude lower than that of gold [22; 23]. Therefore, VO\({}_{2}\) has also attracted great attention of researchers in development of high-performance active tunable THz MM devices, such as resonators [24], modulator [25], polarization controller [26], optical switch [27; 28], beam controller [29], and absorber [30]. In this paper, we demonstrate an active switching of extremely high-Q Fano resonances using VO\({}_{2}\)-implanted THz asymmetric double C-shaped MM structure. The simulation results show that the sharp Fano resonance with the high Q-factor can be switched using very low external thermal excitation (68 \({}^{\circ}\)C), which is only slightly higher than the room temperature. The simulated surface current and electric field distributions of MM structures are employed to describe the resonance mechanism of the thermally excited Fano MMs. By optimizing the asymmetric parameters (offset length), the Q-factor of Fano resonance can achieve 98, which is one order of magnitude higher than that of the traditional MMs. This kind of high Q-factor MMs have tremendous potential in the fields of ultra-sensitive sensors, ultra-narrow band filters, and optical switches.