esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Impact Localization from Fluid-Pipe Coupled Vibration under Noisy Environment
## 2 Materials and Methods
### Continuous Wavelet Transform for a Coupled Wave
As a simple model, but without loss of generality, a single resonant frequency with damping can be formulated as follows:  
\[x(t)=Ae^{-\alpha(t-\tau)}\sin[2\pi f_{r}(t-\tau)] \tag{6}\]  
where \(A\) is the magnitude of the signal, \(\alpha\) is the damping coefficient, \(f_{r}\) represents the resonant frequency of the system, and \(\tau\) is the time delay. Suppose that we have a waveform (\(A=1\), \(\alpha=50\), \(f_{r}=70\)) that has a 0.1 s time delay (see Figure 3a). The frequency was chosen as a frequency below 100 Hz, which can be regarded as an almost non-dispersive frequency range for the 1200 mm pipeline in Figure 1. In other words, sound speed changes less than 1% with respect to the speed at 0 Hz under 100 Hz. As shown in Figure 3b, the maximum peak in CWT is at 0.1 s in the time domain, and small peaks follow it, similar to a coupled wave signal. Along the y-axis, the maximum peak has a scale parameter of about 730, which corresponds to the resonant frequency, 70 Hz. The modulus of the waveform in CWT is sharply localized in the time domain, but the scale parameter value ranges from 300 to 1200, corresponding to about 60% of the signal energy. Moreover,  
Figure 3: An impact signal (**a**) and its continuous wavelet transform (CWT) (**b**). Figure 2: Haar wavelet, wavelet function (**a**) and scaling function (**b**). it is also noteworthy that the maximum peak tends to extend to high scales by being continuously decreased.
# Electrospun Nanofibers for Integrated Sensing, Storage, and Computing Applications
## 7 Conclusions and Future Insights
Especially, artificial synapses and optoelectronics based on electrospun fibers were studied to reveal the potential of the electrospinning technique for developing multifunctional systems with low energy consumption. Nevertheless, to truly approach cost-effective, daily life applications or large-scale and efficient commercial production for electrospinning, there are still several remaining challenges. Firstly, considering the characteristics easily affected by the surrounding environmental parameters, the electrospun nanofibers tend to be varied in different production batches, which leads to the poor performance and inferior quality of the prepared devices. Consequently, the inconsistency and instability of electrospinning cannot ensure the reliability and efficiency of industrialization production. Moreover, the low output of the electrospinning process (0.01 g-2 g/h) owing to the use of single needle nozzles hinders the further development of the electrospun fiber-based applications. Although the volume of production will be increased through the use of needle-free methods [140], the needs of industrial production are still unsatisfied. Conceptualization, H.T. ; resources, Y.Q., T.C. and Y.G. ; writing--original draft preparation, Y.G. ; writing--review and editing, Y.G., Y.Q., T.C., F.W., S.J., Y.Y., H.T. and T.R. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (grant nos. 6202047, 61874065, U20A20168 and 51861145202), the National Key R&D Program (grant no. 2021 YFC3002200 and 2020 YFA0709800), The Beijing Natural Science Foundation (grant no. M22020), the Fok Ying-Tong Education Foundation (grant no. 171051), the Beijing National Research Center for Information Science and Technology Youth Innovation Fund (grant no. BNR2021 RC01007), State Key Laboratory of New Ceramic and Fine Processing of Tsinghua University (grant no. KF202109) and the Research Fund from Beijing Innovation Center for Future Chip, Center for Flexible Electronics Technology of Tsinghua University, Tsinghua-Foshan Innovation Special Fund (TFISF) (2021 THFS0217) and the Independent Research Program of Tsinghua University (grant no. 20193080047). Not applicable. The authors declare no conflict of interest.
# MSmart: Smart Contract Vulnerability Analysis and Improved Strategies Based on Smartcheck
## 5 Experiment
### MSmart vs. Smartcheck on Specific Contracts  
All vulnerabilities discovered by the tool are manually marked as \"true positives\" (TP) or \"false positives\" (FP) (TP is the actual number of vulnerabilities, FP is the number of false positives). For each detection tool, the false discovery rate (FDR) was defined as the number of vulnerabilities for that tool divided by the number of all vulnerabilities reported by that tool. The false negative rate (FRN) is the number of FNs for the tool divided by the number of all true discoveries (by any tool or manually), which is the sum of the tool's TP and FN (FN is the number of false negatives. The calculation formula is as follows:  
\[\text{FDR}=\text{FP}/\left(\text{TP}+\text{FP}\right) \tag{1}\]  
\[\text{FNR}=\text{FN}/\left(\text{TP}+\text{FN}\right) \tag{2}\]  
Due to the fact that Smartcheck does not support delegatecall vulnerabilities, DOS vulnerabilities, and integer overflow vulnerabilities. Therefore, timestamp-dependent vulnerabilities and self-destruct vulnerabilities are compared here. There are 20 smart contracts in Project1, 10 of which are timestamp dependence vulnerabilities, and 10 of which appear to be timestamp-dependently vulnerable but are not. A total of 20 smart contracts make up project2, 8 of which have self-destruct vulnerabilities, and 12 of which appear to have self-destruct vulnerabilities but have no vulnerabilities. The analysis results are shown in Table 4.  
Table 4 shows that the FNR of these three smart contracts has decreased from 83.3%, and 75.0% to 50.0% and 25.0%, respectively, and that the FDR has increased from 9.1%, and 20.0% to 33.3% and 42.8%. Since the FNR has decreased more noticeably, MSmart has likely been successful in identifying contract vulnerabilities and reducing false negative. FDR has been risen compared to Smartcheck, which shows the rise of MSmart's accuracy rate.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Contract** & **MSmart** & **Smartcheck** \\ \hline Governmental\_survey.sol & & x \\ Lottopollo.sol & & x \\ Rouletle.sol & & x \\ Time\_crowd\_sale.sol & & x \\ Time\_sol & & x \\ Time\_Fame.sol & & x \\ \hline \hline \end{tabular}
\end{table}
Table 3: Timestamp issue statistics results.  
In conclusion, MSmart has significantly decreased the false negative rate when compared to Smartcheck. Considering the security of smart contracts, if the detection tool has false negatives, it is fatal to the security of smart contracts. Naturally, it cannot reach 100% detection due to a flaw in its detection technique, so some false positives are unavoidable.
## 2 Islanding Detection Methods
### Passive Methods
#### 2.1.3 Rate of Change of Active and Reactive Power Method (ROCOP)  
Because load variations will occur when the grid is lost, this approach tracks the change in DG power (dP/dt) for a certain time integral. When islanding happens, the power dP/dt changes significantly more than it does when it is measured beforehand. Over a few sample cycles, the dP/dt will be measured when the utility grid is cut off. Therefore, the DG will be shut off to stop powering the load when the change in power exceeds the permitted limits [9].  
The power discrepancy between the load and DG has no impact on the ROCOP's detection speed. To maintain the stability of the power system's functioning, it is also possible to quickly detect the unsynchronized reconnection of the utility supply to DG. Inthis scenario, this approach still has NDZ when the power between the load and DG is balanced. However, this method will become increasingly effective to identify the islanding condition as the power imbalance increases.
## 3 Results and Discussion
### The Influence of Various Factors on the Dynamic Performance of RFR
#### 3.2.2 The Effect of the Coordinate Deformation of the Tie Rods  
The change rate of natural frequencies of the RFR considering the coordinated deformation of the tie rods with the increase of the preload, corresponding to situation C, is shown in Figure 14.  
According to Equation (36), it is easy to know that the coordinated deformation of the tie rods enhances the local flexural stiffness of the rod-fastened shaft segment of the rotor. From Figure 14, it can be seen that the coordinated deformation of the tie rods increases the first-order and third-order natural frequencies of the rod-fastened rotor, while decreases the second-order natural frequency of the rod-fastened rotor. The reason is that the rotor system is a complex system composed of multiple degrees of freedom, and the fastened shaft segment is exactly at the node of the second-order mode shape of the rotor. With the  
Figure 14: The rate of change of natural frequencies of the RFR considering the coordinate deformation of tie rods.  
Figure 13: The rate of change of natural frequencies of the RFR considering the influence of preload on the displacement and load transmission of shaft segments.  
increase of preload, the effect of the coordinated deformation of the tie rods on the natural frequencies of the rod-fastened rotor is gradually reduced. The coordinated deformation of the rods has a greater influence on the first-order and second-order natural frequencies but has little influence on the third-order natural frequency. When the preload is smaller than 1e4N, the effect is relatively larger.
## 4 Analysis of Slope Deformation Failure Process  
According to geological engineering survey data, the geological cross section shown in Figure 3 was used to represent the quarry slope and simulated by DDA. As the strike of the strata is almost parallel to that of the slope surface, two-dimensional simulation is very suitable for this slope.  
Discontinuities were considered in DDA modeling, including fault F12, F17, F18, F90, and F113, as well as the interlayer shear zones buried in different depths. Rock mechanic parameters were derived based upon the rock mechanical test, and they are listed in Table 2. The control parameters used in the calculation of DDA are shown in Table 3. A kinematic damping coefficient of 0.99 was adopted to simulate the energy dissipation during seismic loading in order to account for energy loss mechanisms in DDA. The range of this coefficient is in good agreement with the conclusions of [32]. Additionally, choosing a small allowable maximum step displacement ratio (\(5\times 10^{-3}\) in Table 2) is beneficial in order to promote a precise DDA calculation [31; 32; 36].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Rock Material**} & **Unit Weight** & **Deformation** & **Poisson’s** & **Cohesion** & **Frictional** & **Tensile Strength** \\  & **(kN\(\cdot\)m\({}^{-3}\))** & **Modulus (GPa)** & **Ratio** & **(MPa)** & **Coefficient** & **(MPa)** \\ \hline Rock strata & 26.0 & 20.0 & 0.25 & 1.0 & 1.0 & 0.6 \\ \hline Interlayer shear zone & / & / & / & 0.05 & 0.36 & 0.0 \\ \hline Joint, small fault & 23.5 & 20 & 0.167 & 0.1 & 0.47 & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical parameters of the rock strata.  
Figure 9: Pre-stressed cable connects blocks i and j.  
The simulation results show that the slope will fail if excavation is completed without support, and the failure process is shown in Figure 10. Under the effect of gravity, the upper strata of slope initially slip. From the results in Figure 10b, we can find that the middle and upper strata slip along the interlayer shear zones and consequently transmit force to the lower part of strata, causing an active extrusion effect on the lower strata. The lower strata subsequently slip and crack along the joints or small faults and gradually extrude outward, as shown in Figure 10c,d. Finally, in the lower position of the slope, buckling failure occurs, as shown in Figure 10e,f. This is a typical buckling failure mode characterized by upper-slipping followed by lower-buckling. DDA can adequately reveal this mechanism and slope failure process.  
Figure 10: The failure process of the slope simulated via DDA.  
The deformation failure mechanism can be attributed to two reasons: one is that the sedimentary rock strata form a mechanical multiple slab structure with densely-developed interlayer shear zones in the strata; the other is that the dip of strata is steep and close to the angle of the excavated slope, forming a favorable condition for slip-buckling deformation.
# Innovation and Application of the Type III Secretion System Inhibitors in Plant Pathogenic Bacteria
## 2 Regulation of T3 SS in _D. dadantii_
### Post-Transcriptional Regulators Control T3 SS  
Post-transcriptional regulation is an essential mechanism to control gene expression in bacteria [95; 96]. Progress made in _D. dadantii_ has identified several post-transcriptional regulators that control the expression of T3 SS. Polynucleotide phosphorylase (PNPase) is one of the post-transcriptional regulators conserved in bacteria and eukaryotes [97; 98; 99]. Known as an exoribonuclease, PNPase is majorly involved in RNA decay [100]. The PNPase homologues in _Yersinia_ spp. and _Salmonella_ spp. have been reported to control T3 SS [101; 102]. The deletion of _ppnase_ in _D. dadantti_ significantly increased the transcriptional activities and mRNA levels of _hrpA_, _hrpN_, and _DspE_, suggesting that PNPase downregulates T3 SS through HrpL in _D. dadantti_[103]. Further analyses confirmed that PNPase negatively regulates the stability of _rpoN_ mRNA, which in turn affects the transcription of _hrpL_; PNPase also stimulates the decay of _hrpL_ mRNA by reducing the amount of available RsmBtranscripts (Figure 1) [103]. In _Salmonella_ and _E. coli_, PNPase has been reported to be essential for the RNA decay of RsmB homologues CsrB [104; 105].  
The expression of RsmB was recently shown to be regulated by Hfq, an RNA chaperone [106; 107], via a feed-forward signaling circuit in _D. adandtii_ (Figure 1) [108]. Hfq and the Hfq-dependent small regulatory RNA (sRNA), ArcZ, repress the translation of _pecT_. PecT auto-inhibits its own transcription [109] and, more importantly, downregulates the transcription of _rsmB_ that contributes to the Hfq-mediated regulation on the T3 SS and Pel. As the PecT homologue HexA is also known to repress RsmB in _P. caroovorum_[110], it is reasonable to speculate that this regulation occurs at the level of transcription. RsmB is also post-transcriptionally regulated by Hfq since the deletion of _liftq_ elevated the intracellular c-di-GMP levels owing to the increased productions of two DGCs, GcpA and GcpL. Both DGCs are required for the Hfq-mediated T3 SS regulation. In _E. amylovora_, [PERSON] et al. found that Hfq and ArcZ were required for the virulence in host plants and the T3 SS-dependent HR in non-host tobacco plants [111; 112], but the mechanism has not yet been reported.
# Optimization Dubins Path of Multiple UAVs for Post-Earthquake Rapid-Assessment
## 2 Problem Modeling
### Mathematical Model  
To solve the MURARP, the objective function is designed to maximize the total expected profit of the routing scheme. The mathematical model of MURARP is formulated as  
\[Max\sum_{i=1}^{N}w_{i}\times[1-\prod_{k=1}^{K}\left(p_{k}\right)^{y_{i}^{k}}] \tag{6}\]  
_s.t._:  
\[\sum_{i=1}^{N}x_{D_{k}i}^{k}=\sum_{i=1}^{N}x_{i,D_{k}^{\prime}}^{k}=1;\ \forall k\in\mathbf{U}, \tag{7}\]  
\[x_{D_{k}i}^{k}+\sum_{i=1}^{N}x_{hi}^{k}=\sum_{j=1}^{N}x_{ij}^{k}+x_{iD_{k}^{ \prime}}^{k}=y_{i}^{k};\ \forall i\in\mathbf{T},k\in\mathbf{U}, \tag{8}\]  
\[t_{R_{k}}\leq T_{\max}^{k};\ \forall k\in\mathbf{U}, \tag{9}\]  
\[V_{l-1}^{k}\
eq V_{l}^{k};\ \forall k\in U,l=2,\cdots,r, \tag{10}\]  
\[V_{l}^{k}\
eq V_{l+1}^{k};\ \forall k\in U,l=1,\cdots,r-1, \tag{11}\]  
\[x_{ij}^{k}\in\{0,1\};\ \forall i,j\in\mathbf{T},k\in\mathbf{U}. \tag{12}\]  
Equation (6) is the objective function, corresponding to maximizing the expected profit of the multi-UAV post-earthquake rapid-assessment routing scheme, where \(w_{i}\) is the weight of target \(i\), \(p_{k}\) is the error probability of the sensor carried by UAV \(k\), \(y_{i}^{k}\) is times that target \(i\) is visited by UAV \(k\). Equation (7) ensures that all UAV must departure from their own depot and ultimately return to the same depot. Equation (8) ensures the connectivity of each UAV's route. Equation (9) is the endurance constraint, that is, the task execution time of the UAV \(k\) cannot exceed its maximum safe flight duration. Equations (10) and (11) are the constraints of an overnight stay being forbidden, thus ensuring that the UAV cannot stay at the same potential target, and a target can be revisited only after the UAV visits another target. Equation (12) involves the values of the binary decision variable whether UAV \(k\) flies to target \(j\) after leaving target \(i\); if so, \(x_{ij}^{k}=1\), and \(x_{ij}^{k}=0\) otherwise.
## 2 Results and Discussion
### XRD Analysis  
The XRD patterns of pure silica WMS and NR/WMS nanocomposites prepared without and with the functionalization of an aminopropyl group are shown in Figure 4. All samples clearly exhibited a single reflection at 2\(\theta\) around 1.5\({}^{\circ}\)-3.0\({}^{\circ}\), corresponding to the (100) plane of wormhole-like mesostructured silicate framework [33]. The introduction of the organic species, either the aminopropyl group or NR, into the WMS decreased the structural arrangement [3; 17]. The peak intensity of the NR/WMS-NH\({}_{2}\) samples was lower than that of the pristine NR/WMS and decreased with increasing the APS loading level. This result can be explained by either a lower diffraction contrast or a loss of structural order when the amino-functional groups were incorporated into the mesostructured silica. Interestingly, the characteristic peak was still retained even at 15 mol% of APS loading onto the NR/WMS. Some structural data obtained from the XRD analysis are summarized in Table 2. The WMS-NH\({}_{2}\) gave higher \(d_{100}\) and \(a_{0}\) than the WMS due to the expansion of pore size (see Section 2.6). An increase in the pore wall thickness (\(W_{\text{t}}\)) after functionalization indicates that the amino-functional groups were anchored on the inner surface of the mesoporous silica [34].  
Compared to the pure silica WMS, the \(a_{0}\) and \(W_{\text{t}}\) values were expanded in the case of the NR/WMS (Table 2). These results indicated not only that the rubber molecules were trapped in the mesostructured silica framework [13; 16], but also the presence of the less condensed silicate framework of the NR/WMS nanocomposite. The amine-functionalization onto the NR/WMS induced the contraction of \(a_{0}\), while enhancing an expansion of \(W_{\text{t}}\). This result indicates that the amine group promoted the hydrolysis and condensation of TEOS, resulting in a thicker silicate wall. It can be rationalized from these results that the mesoporous structure of both the WMS-NH\({}_{2}\) and NR/WMS-NH\({}_{2}\) was functionalized with the aminopropyl group.
## 6 Simulation Experiment and Analysis
### Simulation Results  
#### 6.2.1 OFDM Signal Detection Simulation  
In this section, two OFDM signal detection algorithms are investigated through simulation, including the burst signal detection algorithm based on autocorrelation and the proposed burst signal detection algorithm based on higher-order cumulants.  
Both of these algorithms demonstrate detection success rates for continuous and burst OFDM signals under AWGN channels, as illustrated in Figure 6.  
The analysis in Figure 6 reveals that under an AWGN channel, the burst signal detection performance of the higher-order cumulant algorithm is optimal, yet its performance for continuous signal detection is poor. At SNR = \(-\)4 dB, the correct detection rate of burst signals reaches 99%, and at SNR = 0 dB, the correct detection rate of continuous signals reaches 99%. This is because the higher-order cumulant can extract higher-order statistical information from the signal, which is affected by noise at low SNRs. This makes it prone to misidentifying continuous signals as burst signals. However, the overall performance is superior to that of the autocorrelation method and can well adapt to the low-orbit satellite channel.  
The use of higher-order cumulant features can simultaneously distinguish between OFDM and single-carrier signals, as shown in Figure 7. These features are insensitive to noise, resulting in a good distinction effect.  
Figure 6: Simulation performance of OFDM burst signal detection.  
Figure 7: OFDM versus single-carrier signal \(d_{20}\) value.
## 5 Numerical Results
### Radar Detection Performance Analysis
#### 5.1.4 Integration for Multi-Targets  
In this subsection, we present numerous simulation experiments to evaluate the multi-target integration performance of the proposed method when \(SNR=10\) dB.  
Scenario 1 Targets with the same distance and different velocities and acceleration are considered. Targets A and B are positioned at an initial distance of 300 range cells, corresponding to 9000 m. Target A has a velocity of [60 m/s, 30 m/s\({}^{2}\)] and Target B has a velocity of [150 m/s, 10 m/s\({}^{2}\)]. Figure 8a illustrates the trajectories of targets A and B. Figure 8b-d depict the integration results on the range-velocity, range-acceleration, and velocity-acceleration planes, respectively, demonstrating the effectiveness of the proposed algorithm in signal integration.  
Scenario 2 Targets with the same initial velocity and different ranges and acceleration are considered. The initial velocity of target C and target D is 150 m/s, with initial ranges and accelerations of [9000 m, 30 m/s\({}^{2}\)] for target C and [14,400 m, 10 m/s\({}^{2}\)] for target D, respectively. Figure 9a depicts the trajectories of targets C and D. Figure 9b-d show the integration results on the range-velocity, range-acceleration, and velocity-acceleration planes, respectively, demonstrating the outcomes after processing using the proposed algorithm. The results illustrate that the algorithm effectively concentrates the energy in the corresponding scenarios.  
Scenario 3 Targets of the same acceleration and different initial distances and velocities are considered. The velocity of target E and target F is 10 m/s\({}^{2}\), while the initial ranges and accelerations of target E and target F are [9000 m, 150 m/s] and [14,400 m, 60 m/s]. Figure 10a depicts the trajectories of targets E and F. Figure 10b-d illustrate the integration results on the range-velocity, range-acceleration, and velocity-acceleration planes after processing with the proposed algorithm. All results demonstrate that the energy peak aligns with the corresponding target parameters.  
Figure 8: Simulation results for multiple targets (scenario 1). (**a**) Multiple-target echo distribution, (**b**) integration result on velocity–range plane, (**c**) integration result on range–acceleration plane, (**d**) integration result on velocity–acceleration plane.
## 2 Proposed Communication System
### Overview of Proposed Communication System
The peak value of the pixel intensity (\(I_{peak}\)) in the received image \(I_{ac}\) is computed as per Equation (5):  
\[I_{peak}=max_{Rows}(max_{Cols}(I_{ac})) \tag{5}\]  
From Equations (4) and (5), PSNR can be expressed as:  
\[PSNR((I_{in},I_{ac})=10\log_{10}(I_{peak}^{2}/MSE((I_{in},I_{ac})) \tag{6}\]  
The data throughput (\(D_{t}\)) for the proposed system was evaluated as per Equation (7), where \(N_{LEDs}\) is the number of LEDs on the Tx panel, \(N_{ColorRits}\) is the number of bits required to represent color, \(N_{AnchorLEDs}\) is the number of LEDs used for the anchor pattern, and \(fps\) is the frame rate of the Rx camera. \[D_{t}=(N_{LEDs}*N_{ColorBits}-N_{AnchorLEDs})*\frac{fps}{2} \tag{7}\]  
The percentage success of reception is calculated as per Equation (8), where \(T_{b}\) is the total number of transmitted bits, and \(E_{b}\) is the number of error bits. \[Successofreception(\%)=\frac{T_{b}-E_{b}}{T_{b}}*100 \tag{8}\]