esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Principles
### Linear Elasticity Theory  
Linear elasticity theory is a branch of continuum mechanics [34] that deals with the description of stress and strain on solid materials subjected to small deformations. It is a mathematical theory that builds a relationship between the mechanical properties of a material and its response to an applied loading. Linear elasticity theory models the response of a material to an applied load using a series of equations. These equations are employed to calculate the stress, displacement, and strain of the material. The theory is based on the assumption that the material behaves linearly and that the stress and strain levels remain within the linear elastic range. This means that the material does not deform to a significant extent, and its response to an applied load is only slightly affected. In linear elastic problems, the material is assumed to behave as a linear elastic material, meaning that the deformation induced in the material is directly proportional to the stress applied to it.  
The governing equations for linear elastic problems are as follows:  
\[\sigma_{ij,j}+f_{i}=0 \tag{1}\]where \(\sigma_{ij}\) denotes the Cauchy stress tensor and \(f_{i}\) denotes the body force per unit of mass. Under the assumption of a small deformation, the stress tensor and the strain tensor can be expressed as follows:  
\[\sigma_{ij}=\lambda\delta_{ij}\epsilon_{kk}+2\mu\epsilon_{ij} \tag{2}\]  
\[\epsilon_{ij}=\frac{1}{2}\big{(}u_{i,j}+u_{j,i}\big{)} \tag{3}\]  
where \(\lambda\) and \(\mu\) are the Lame constants [34], which describe the material's resistance to compression and shear deformation, respectively. \(\delta_{ij}\) is the Kronecker delta function. \(\epsilon_{ij}\) is the strain tensor and \(u_{i}\) represents the displacement. The boundary conditions can be expressed as follows:  
\[u_{i}=\overline{u}_{i} \tag{4}\]  
\[\sigma_{ij}u_{j}=\overline{f}_{i} \tag{5}\]  
where \(\overline{u}_{i}\) and \(\overline{f}_{i}\) represent the displacement and force of the corresponding boundaries, respectively, and \(\mathbf{n}\) is the unit's outward normal vector on the corresponding boundaries.
# Methods of Atmospheric Coherence Length Measurement
## 2 Retrieval of \(r_{0}\) Based on \(C_{n}^{2}\)
### Radar Measurement Method  
As far as the radar measurement method is concerned, there are two strategies. One involves the backscattering cross section, while the other focuses on the emitted power. For the former, the radar signal carrier can be either acoustic or electromagnetic waves. Under the assumptions of isotropy and statistical homogeneity, the backscattering cross section for the case of acoustic waves can be expressed as [34]:  
\[\rho_{s}(\theta)=\frac{\pi}{2}k^{4}\cos^{2}\theta[\frac{\Phi_{T}[2k\sin(\theta/ 2)]}{T^{2}}+\cos^{2}(\theta/2)\frac{V[2k\sin(\theta/2)]}{\pi c_{a}[2k\sin( \theta/2)]^{2}}], \tag{8}\]  
where \(\theta\) is the scattering angle; \(k\) is the wave number; \(T\) is the atmospheric temperature, \(c_{a}\) is the acoustic velocity, \(\Phi_{T}[]\) is the temperature spectral function; and \(V[]\) is the velocity spectral function. The expression for the backscattering cross section for the case of the electromagnetic wave is given by  
\[\rho_{s}(r^{\prime})=\frac{\pi}{2}k^{4}\Phi_{T}[2k\sin(\theta/2)]\sin^{2}i(r^{ \prime}), \tag{9}\]  
where \(i(r^{\prime})\) is the angle between the electric field vector and the line from the scatterer to observation point, with \(r^{\prime}\) denoting the vector from the transmitter towards the scatterer. The temperature structure constant \(C_{T}^{2}\) can be derived in light of the backscattering cross section \(\rho_{s}\) above [35],  
\[C_{T}^{2}=\frac{\rho_{s}\times T^{2}}{4\times 10^{-3}\times\lambda^{-1/3}}. \tag{10}\]  
Similar to the preceding temperature structure measurement method, since \(C_{T}^{2}\) can be known, so can \(C_{n}^{2}\). As such, a retrieval of the atmospheric coherence length \(r_{0}\) is performed.  
The other method is to directly measure the atmospheric refractive index structure parameter \(C_{n}^{2}\) by employing the wind profile radar to evaluate its echo power \(P_{s}\)[36; 37; 38]:  
\[C_{n}^{2}=\frac{P_{s}r^{2}}{7.3\times 10^{-4}\times P_{t}G(c\tau/2)E^{2} \lambda^{5/3}}, \tag{11}\]  
where \(G\) is the gain; \(r\) is the radar-scatterer distance; \(E\) is the antenna loss; \(c\) is the speed of light; \(\tau\) is the signal pulse width; \(P_{t}\) indicates the transmitted power. For a specific wind profile radar, the parameters \(G\), \(r\), \(E\), \(c\), \(\tau\), \(P_{t}\) are given. The return signal power, \(P_{s}\) is estimated directly from the original signal-to-noise ratio of the return signal obtained from  
Figure 2: Schematic sketch of the traditional scintillator.  
the wind profile radar by probing the atmosphere. Once \(C_{n}^{2}\) has been calculated, one could perform the retrieval of \(r_{0}\).
## 6 Improved DPF with Frequency Regulation of DFIG Integrated through VSC-HVDC  
### Improved DPF with DFIG Integrated through VSC-HVDC Considering Governor Delay of SG  
With the DFIG participating in frequency regulation through the VSC-HVDC, the inertial response of the DC capacitor and the DFIG is represented by the equivalent inertia constants. The virtual inertia control to the DC capacitor is applied, and its equivalent inertia constant is quantified based on Equations (7)-(10). As for the DFIG, its inertia is quantified by the stored kinetic energy in the rotor that can be released during the frequency regulation, as quantified by Equations (16)-(19). So, as for the VSC-HVDC integrated DFIG, the inertial response is added to the combined inertia of the conventional SGs, as shown in Equation (20), to include both the traditional SGs and the enabled inertial support from the DC capacitor and the DFIG.  
\[\sum H^{\prime}=\sum H_{\text{G}}+H_{\text{DC}}+H_{\text{DFIG}} \tag{20}\]  
Considering the governor delay, the active power constraint of bus \(i\) is corrected,  
\[\Delta P_{i}=\left(P_{\text{C,M0}}-\sum_{m=1}^{n}K_{\text{C,M}}\Delta f_{m} \left(1-e^{-\Delta t\left(n+1-m\right)/T}\right)\right)-\left(R_{\text{L0}}+K_ {\text{L}}\Delta f\right)-\frac{H_{\text{G}i}}{\sum H^{\prime}}P_{\text{acc}}- P_{i}=0 \tag{21}\]  
The drop of \(U_{\text{DC}}\) shows the drop of \(f\). It is introduced to the droop control of the DFIG. The output of the DFIG is corrected by (15). The accelerating power is decided by \(H_{\text{DC}}\) and \(H_{\text{DFIG}}\). The active power constraint of the public coupling point is given by,  
\[\Delta P_{i}=P_{\text{wt,der}}-K_{\text{DFIG}}K_{\text{f_{wt}}/\
u_{\text{DC} }}\Delta U_{\text{DC}}-\frac{H_{\text{DC}}+H_{\text{DFIG}}}{\sum H^{\prime}} P_{\text{acc}}-P_{i}=0 \tag{22}\]  
Finally, the improved DPF model with the frequency regulation by the DFIG integrated through VSC-HVDC and the SG of governor delay is shown in Figure 5. Compared with the existing DPF model, the governor delay is considered in the output of the SGs, and the virtual inertia of the VSC-HVDC is set based on the estimation to the frequency nadir and constraint of the DC voltage. The inertia of the DFIG is corrected according to the changing kinetic energy for a more accurate frequency response.  
Figure 5: Improved DPF model with DFIG integrated through VSC-HVDC.
# Toward Vulnerability Detection for Ethereum Smart Contracts Using Graph-Matching Network
## 5 Evaluation
### Performance Evaluation  
In the first experiment, we investigated how well the proposed approaches could detect smart contract vulnerabilities. We compared _ASGVulDetector_ and _BASGVulDetector_ with their competitors in terms of accuracy (**Acc**), recall (**Rec**), precision (**Pre**), and F1-score (**F1**). The performance results of different methods are listed in Tables 3 and 4, respectively, from which we have the following observations. It can be observed from Table 3 that _SmartCheck_ commonly has the lowest **Acc** and **F1** among the four tools, e.g., only 55.7% and 67.91% for reentrancy. This phenomenon can be explained by the XML-based mechanism used in _SmartCheck_, which is utilized not only for intermediate representation but also to define violation patterns. Thus, the performance of vulnerability detection is limited by the expressiveness of XML language, and thus cannot reflect sufficient syntactic and semantic information. In contrast, _Stilher_, as another tool based on intermediate code, provides much better detection performance than _SmartCheck_. This could profit from two intensive designs. One is that _Stilher_ uses a fine-grained intermediate form called static single assignment (SSA) to preserve more semantic information, while the other is that it employs data flow and taint tracking to obtain more understandings about program behaviors. _DR-GCN_ has a relatively low detection performance, even worse than _Stilher_. This can be explained by _DR-GCN_ transforming code fragments of a contract to a graph that contains specific types of nodes and edges, which reduces the ability of representing smart contracts. In the cases of block info dependency and Tx.Origin, the contract code can even hardly be represented in the required form, which reveals that _DR-GCN_ is applicable to a limited set of scenarios. Among the \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Vulnerability** & **Method** & **Acc (\%)** & **Rec (\%)** & **Pre (\%)** & **F1 (\%)** \\ \hline \multirow{4}{*}{Reentrancy} & ContractFuzzer & 37.94 & 67.12 & 31.36 & 42.75 \\  & Oyente & 41.64 & 74.02 & 42.50 & 54.00 \\  & Securify & 53.62 & 77.46 & 54.42 & 63.93 \\  & BASGVulDetector & **80.59** & **92.73** & **80.00** & **85.90** \\ \hline \multirow{4}{*}{Timestamp dependency} & ContractFuzzer & 32.96 & 82.94 & 28.36 & 40.70 \\  & Oyente & 43.24 & 76.62 & 38.45 & 52.55 \\  & Securify & 52.06 & 84.62 & 50.00 & 62.86 \\  & BASGVulDetector & **79.28** & **92.17** & **81.36** & **86.43** \\ \hline \multirow{4}{*}{Block info dependency} & ContractFuzzer & 30.52 & 58.08 & 28.75 & 38.46 \\  & Oyente & - & - & - & - \\ \cline{1-1}  & Oyente & - & - & - & - \\ \cline{1-1}  & Securify & 47.12 & 71.93 & 51.00 & 59.68 \\ \cline{1-1}  & BASGVulDetector & **81.56** & **86.94** & **89.81** & **88.35** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison for _BASGVulDetector (‘-’ denotes not applicable).
# SMM: Leveraging Metadata for Contextually Salient Multi-Variate Motif Discovery
## 5 Experimental Evaluation
### Results for the Mocap and Energy Datasets
#### 5.5.2 Efficiency  
We report the execution times for SMM-s and MPC algorithms, both implemented in Matlab 2015.b on Intel(R) Core(TM), i7-7700 HQ CPU at 2.80 GHz, 4 cores, with 16 GB RAM. We report the execution times for the default configuration:  
* SMM-s took 6.67 s for one time series (of length 2500) to locate motifs. Roughly 97% of this (6.48 s) was spent on locating salient multi-variate subsequences (SMSs) and the rest (0.19 s) was spent on identifying frequently recurring patterns.
* In contrast, MPC took 68.2 s for the same series. Roughly 57.7% of this (39.3 s) was spent on locating matching sub-sequence pairs and the rest (28.8 s) was spent on identifying frequent patterns.  
Note that, in this configuration, **SMM-s is 10\(\times\) faster than MPC**, because it limits the motif search to salient sub-sequences and it creates descriptors that are efficient to compare.
# Autonomous Controller-Aware Scheduling of Intra-Platoon V2V Communications
## 5 Heuristic Approach  
As mentioned in the previous section, the optimal solution to (7) cannot, in general, be easily found. Thus, we propose a heuristic solution based on the constraint (8) and based on relaxing the integer problem by finding the solution \(\{\Delta t_{Tx}{}^{sel},\,\tau^{sel}\}\) from the available values in the finite sets \(T_{d}\) and \(T_{\tau}\), respectively, that maximizes the time period until the violation of the minimum distance \(d_{Th}\). We assume that each transmitting vehicle performs such a heuristic search individually, having knowledge on the information transmitted by the leader and on the motion parameters of the following vehicle. The selection process, described in Algorithm 1, relies on the prediction of the distance error \(e_{i}(k)\) evolution for a given pair of vehicles over a predefined time span \(t_{Th}\). The distance between vehicles \(i-1\) and \(i\) is calculated, assuming the update of information used in the acceleration controller for the selected values \(\Delta t_{l}\in T_{d}\) and \(\tau_{m}\in T_{\tau}\). For each prediction step \(\Delta t_{l}\), the predicted position, velocity, and acceleration of the considered vehicles are updated according to the accelerated motion law, and the distance error is calculated. The prediction is performed as long as the distance between vehicles is greater than the emergency braking threshold \(d_{Th}\). If at any prediction point such a distance constraint is violated, then the resulting time span value \(t\) is stored for the evaluated set \(\{\Delta t_{l},\,\tau_{m}\}\). Otherwise, an infinite time span is assumed when at any point, the acceleration and velocity of the following vehicle exceeds the acceleration and velocity of the preceding vehicle, or when \(t_{Th}\) is used if the distance remains above \(d_{Th}\) for the whole prediction window. Finally, the solution is chosen as the pair maximizing the predicted time span. Figure 4: Distance error between leader and the first follower for multiple random disturbances. ```
1:procedureFindTxperiod(\(v_{0}\), \(a_{0}\), \(x_{i-1}\), \(v_{i-1}\), \(a_{i-1}\), \(x_{i}\), \(v_{i}\), \(a_{i}\), \(d_{d}\), \(T_{d}\), \(T_{\tau}\), \(t_{Th}\), \(f(. )\))
2:\(\triangleright\)\(x_{i}\)--long. position of vehicle \(i\), \(v_{i}\)--velocity of vehicle \(i\), \(a_{i}\)--acceleration of vehicle \(i\), \(d_{d}\)--target inter-vehicle distance, \(d_{Th}\)--minimum distance constraint, \(T_{d}\)--set of possible transmission periods, \(T_{\tau}\)--set of possible initial time offsets, \(t_{Th}\)--maximum prediction time span, \(f(.
## 4 Results and Discussion
### Optimization Results  
The optimization results of energy conservation and emission reduction in the iron and steel plants obtained by the NSGA-II are shown in Table 4. For the iron and steel plant with 3 Mt/a capacity, the average value of energy conservation, SO\({}_{2}\) reduction, NOx reduction, PM reduction, and economic benefit of the optimization scheme are 588.98 ktce, 45.86 t, 61.53 t, 47.03 t, and CNY 3.74 billion, respectively. For the iron and steel plant with 9 Mt/a capacity, the five optimized indicators are 1786.95 ktce, 133.02 t, 147.90 t, 145.50 t, and CNY 12.33 billion, respectively. These results showed that the polygeneration system can achieve good energy, environmental, and economic benefits, and there is a synergistic relationship between energy conservation and emission reduction on the whole in the polygeneration system.  
As shown in Figure 5, there are synergistic and conflicting relationships among energy, environmental, and economic goals in both scales of iron and steel plants, which are also closely related to the scale of iron and steel plants. More specifically, for the iron and steel plant with 3 Mt/a capacity, the goals of economic benefit, PM reduction, and energy conservation are synergistic, while the energy conservation goal is in conflict with the SO\({}_{2}\) and NOx reduction goals. This is mainly because the energy consumption and cost of some SO\({}_{2}\) and NOx reduction technologies are high in the middle-scale iron and steel plants. For the iron and steel plant with 9 Mt/a capacity, the goals of energy conservation, SO\({}_{2}\) and NOx reduction are synergistic, which means compared with the middle-scale iron and steel plant, the large-scale iron and steel plant may have more advantages in energy conservation, SO\({}_{2}\) and NOx reduction. However, the energy conservation goal is in conflict  
Figure 3: Spacing metrics in different generations. (**a**) For the iron and steel plant with 3 Mt/a capacity and (**b**) for the iron and steel plant with 9 Mt/a.  
with the goals of economic benefit and PM reduction in this case. In short, it is necessary to make technology selection to avoid conflicts between different objectives when establishing the polygeneration system, and the scale of iron and steel plants should also be considered in this process.  
Figure 4: \(Cont.\)  
Figure 4: _Cont._  
Figure 4: Mean values of the solution set. (**a**–**c**) For energy conservation, pollutant emission reduction equivalent and economic benefit of the iron and steel plant with 3 Mt/a capacity, respectively; (**d**–**f**) for energy conservation, pollutant emission reduction equivalent and economic benefit of the iron and steel plant with 9 Mt/a capacity, respectively.  
Figure 5: Spearman analysis results of target correlation. (**a**) For the iron and steel plant with 3 Mt/a capacity; (**b**) for the iron and steel plant with 9 Mt/a capacity. EC = Energy conservation goal; EB = Economic benefit goal; SO\({}_{2}\) = SO\({}_{2}\) reduction goal; NOx = NOx reduction goal; PM = PM reduction goal.
## 2 Materials and Methods
### Numerical Simulations  
The shape of the fluorescence image of an object within a scattering medium registered by a CCD camera is determined by both the lateral size of the fluorescent volume, and its embedding depth. The full width at half maximum (FWHM) of the fluorescence map potentially allows evaluation of these characteristics. Our previous Monte Carlo study [19] considering the spherical shape of a model tumor labeled with nanoconstructs demonstrated that from the FWHM of the fluorescence response, one can estimate the tumor size using the information about tumor localization depth obtained from OA measurements. In this study, previously developed MATLAB-based Monte Carlo code was modified to simulate fluorescence images of the scattering phantom containing cylindrical tubes filled with fluorescence markers.  
The Monte Carlo procedure for simulation of a fluorescence image consists of two steps. At the first step, the Monte Carlo technique is employed to produce a map of the probing radiation partial absorption by the fluorophore. A three-dimensional map of the fluorophore distribution in the medium is predefined. In the voxels containing the fluorophore, the local optical properties (absorption and scattering coefficients) are calculated as the sums of the partial optical properties of the bulk medium (phantom) and the fluorophore. At the second step, the fluorophore partial absorption map serves as a distributed source for Monte Carlo simulations of the fluorescence emission at the corresponding wavelength. Emission of fluorescence photons is considered to be spherically isotropic in direction. Distribution of fluorescence intensity at the medium surface represents a fluorescence image recorded by a CCD camera. Typical number of probing radiation photons employed in simulation amounts to \(10^{8}\).  
To simulate the OA image of the BPD-containing tube inside the phantom, the Monte Carlo derived absorption map was used for calculating the acoustic response on the basis of K-wave toolbox [27] for MATLAB. In the OA numerical experiment, acoustic signals from BPD serving as an absorber were recorded by the arc detector with a focal distance \(R=6.7\) mm and numerical aperture \(NA=0.6\) focused at the object surface. The scanning step \(\Delta x=50\)\(\upmu\)m was chosen in accordance with the geometry of the OA phantom experiment. The simulations were performed on the spatial-temporal grid \(\Delta x=\Delta z=50\)\(\upmu\)m (full area \(12\) mm \(\times\)\(12.7\) mm) and \(\Delta t=5\) ns (full time range is \(8\)\(\upmu\)s), corresponding to the maximum supported frequency, \(15\) MHz.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{4}{c}{**Murine Brain**} & \multicolumn{2}{c}{**Phantom**} \\ \hline \(\lambda\)**, nm** & \(\mu_{\text{a}}\) (mm\({}^{-1}\)) & \(\mu_{\text{s}}\)\({}^{\prime}\)(mm\({}^{-1}\)) & \(\mu_{\text{a}}\) (mm\({}^{-1}\)) & \(\mu_{\text{s}}\)\({}^{\prime}\)(mm\({}^{-1}\)) \\ \hline
690 & 0.09 & 1.96 & 0.06 & 3.6 \\ \hline
725 & 0.071 & 1.89 & 0.06 & 3.25 \\ \hline
785 & 0.06 & 1.78 & 0.05 & 3 \\ \hline
835 & 0
## 2 Materials and Methods
### Automatic Compensation Implementation  
The complete compensation process is shown in Figure 8. We used the C# program we developed to import the on-machine measurement results. Because we used the same work coordinate system created by G54 command for cutting tool path and the on-machine measurement, the on-machine measurement data can be registered with the CAD model used for generating the G code for machining. Then, the mirror compensation method can be applied to find the locations of the compensation points. It follows that the curve fitting is performed on the compensation points, and the free-form surface can then be constructed by using the Through Curve Mesh fitting function in NX. Finally, the fitted surfaces will replace the original surfaces in the CAD model so that the model is reconstructed for generating the finishing tool path. The program we developed can automatically execute the entire process. For a model like the one shown in Figure 8, it took only about 30 s to complete the processes on a regular PC. It may take a much longer time to complete the same task by manual operation.  
The complete machining process, including error compensation, is shown in Figure 9. First, the roughing machining and semi-finishing machining are performed. Then the on-machine measurement is performed, and the measurement data are recorded inside the CNC controller. Next, the automatic compensation program is used to perform the procedure, as illustrated in Figure 8. After obtaining the finishing tool path, we can perform the finishing machining to complete the machining with error compensation.  
Figure 7: The locations of the points used for the mirror compensation method.  
Figure 8: Automatic compensation flow for free-form surfaces.  
Figure 9: Complete machining processes with error compensation.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 3 Classification of Routing Protocols for WBAN
### Posture-Based Routing
#### 3.1.1 A Novel Mobility Handling Routing Protocol (MHRP)  
[PERSON] et al. [18] proposed a novel mobile processing routing protocol MHRP. The protocol is designed for heart rate acquisition. Seven nodes are located on the left and right sides of the human body. It is a set of two symmetrical nodes, each set has one sink node, two relay nodes and one common acquisition node. A new type of fault-tolerant system is composed of two identical and independent sets of nodes. The purpose of the design is to change another group, when the current working set energy is scarce, or the topology is interrupted, so as to reduce errors and improve the reliability of the data transmission.  
The MHRP protocol makes network topology analysis for four kinds of elbow actions, and finds the best path for the four kinds of actions. As shown in Figure 4, although the relative position of the nodes will change with the elbow actions, there are only four paths at all times:  
\[\begin{split}(\Delta 1)&=\text{D1}+\text{D2}+\text{D3} \\ (\Delta 2)&=\text{D1}+\text{D4}\\ (\Delta 3)&=\text{D5}+\text{D3}\\ (\Delta 4)&=\text{D6}\\ \end{split} \tag{1}\]  
In path planning, the smallest one in \(\Delta\)1,\(\Delta\)2,\(\Delta\)3,\(\Delta\)4 is the current path, in which the temperature and residual energy of nodes are taken into account. The protocol divides data into general and emergency. When it is an emergency data, it uses \(\Delta\)4 path to transmit directly, and when it is general data, it chooses relay transmission. Finally, the simulation results show that the protocol has a great improvement in the end-to-end delay, error rate, and energy consumption. However, the possible disadvantage is that the comfort of human body cannot be fully considered. Although the form of substitution set can achieve a stable performance, it increases the burden on the human body.