esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Security Analysis and Numerical Simulation  
We mainly analyzed the security of the discrete-modulated underwater CV-QKD protocol under collective attack in asymptotic case and finite-size regime. The performance of the underwater communication systems based on the four-state protocol and the eight-state protocol were analyzed by numerical simulation, and the parameters were optimized to improve the system performance.
## 3 Results
### Experimental Results
#### 3.1.2 Shear Strength  
The shear strength of the sand can depend on multiple factors related to the specimen, such as the particle shape, size, and gradation of the sand particles. Upon comparing the poorly graded fine, medium, and coarse sand, it was found that the coarse sand exhibited a higher shear strength compared to the others, as shown in Figure 10.  
Furthermore, by examining the impact of gradation, the well-graded sand exhibits a higher density and shear strength when compared to fine and medium poorly graded sand, as shown in Figure 11. The well-graded sand had a higher shear strength value, though not as high as the coarse, poorly graded sand. This can be related to the particle shape, size, and surface roughness. Coarse sand particles, particularly those that are angular, can achieve higher shear strength due to particle interlocking. Furthermore, particles with high surface roughness can induce even greater shear strength due to the interlocking of asperities between the particles.  
Figure 11: The particle size and gradation impact on the shear strength at different normal stresses (25, 50, 100, and 200) at different densities: (**a**) loose state, and (**b**) dense state.  
Figure 10: Shear strength versus mean particle size for B-Sand in (**a**) a loose density state and (**b**) a dense density state.
Both studies used weighted factors to integrate the **ShFs** and moments into the optimization process, enabling a balanced consideration of these dynamic parameters through a weighted combination. Optimizing the balance of **ShF** and \(ShM\) through multi-objective optimization algorithms can handle the problem as a set of objectives that must be minimized simultaneously. This approach eliminates the need to weigh the objectives and offers a more comprehensive solution to the balancing challenge. Notable examples of such research include the application of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II [23]), an enhanced version of the original NSGA [24], and the Multi-Objective Genetic Algorithm (MOGA) to optimize the force and moment balance of a four-bar linkage, as demonstrated by [PERSON] et al. [25]. In subsequent studies, Multi-Objective Particle Swarm Optimization (MOPSO [26; 27]) and NSGA-II to were employed to achieve similar objectives [28]. Additionally, [PERSON] et al. [29] proposed a method utilizing the NSGA-II algorithm to perform robust balancing of **ShF**, \(ShM\), and torque in robotic manipulators. While numerous studies have demonstrated the efficacy of various optimization algorithms in mechanism balancing, comparative analyses remain relatively scarce. This paper seeks to address this gap by conducting a comprehensive evaluation of diverse multi-objective optimization algorithms, including NSGA-II, NSGA-III, R-NSGA-II, R-NSGA-III, UNSGA-III, MOEA/D, AGE-MOEA, C-TAEA, SMS-EMOA, and RVEA. Specifically, the study minimizes the **ShF** and moment in a six-bar Watt linkage. The governing equations for the optimization are derived using a mathematical method based on \(FCC\), ensuring a straightforward yet robust and precise formulation of the dynamic problem. In addition, this document proposes a methodology to analyze the sensitivity of counterweights to determine if it is possible to reduce them. The rest of this manuscript is organized as follows. Section 2 describes the mechanical analysis and how the mathematical equations are calculated. The optimization foundations and algorithms are introduced in Section 3. Section 4 shows the experiments and results, including a comparison of algorithms and a sensitivity analysis. Section 5 describes the balancing optimization results. Finally, Section 6 concludes this document.
## 3 Field-Circuit Coupling Analysis Method  
### Field-Circuit Coupling Analysis Model  
In an offshore wind power MMC-HVDC transmission system, the overcurrent levels of the valves are typically high, and the current rises rapidly after a fault occurs [18]. As illustrated in Figure 2, the variation in fault current is primarily determined by the current loop formed after the fault.  
Figure 5: Impact factor under frequencies ranging from 1 to 1000 th order (**a**) and frequencies ranging from 1000 th to 20,000 th order (**b**).  
According to Figure 4, the transient parameter variations in nonlinear components, stray parameters of the converter valve, and the electric field distribution can all influence the fault loop during transient conditions.  
A field-circuit coupled analysis model for overcurrent calculation is established, as shown in Figure 6.  
As shown in Figure 6, the field-circuit coupled model (CF model) incorporates the electric field characteristics of the MMC valve, the stray parameters of the MMC valve, and the nonlinear model of the surge arrester within the MMC-HVDC system.
# Blockchain-Based Secured Access Control in an IoT System
## 5 Proposed Solution
### Blockchain Managers (BCMs)  
BCMs are the core of our proposed framework and they manage all communications in four layers: Local Blockchain Manager (LBCM), Fog Blockchain Manager (FBCM), Core Fog Blockchain Manager (CFBCM) and Cloud Blockchain Manager (CBCM). Furthermore, BCMs are responsible for setting the access control policy for each device/node in each layer. Each block in the blockchain comprises two headers: a block header and a policy header. The MAC policy is inserted into the block structure of each blockchain transaction. The BCMs have a policy header that allows them to control access permission for all transactions. Although all blocks in a blockchain have a policy header, the most updated one at the head of the block header in BCMs is used for checking and changing policies. The miner in the proposed solution is the BCM in each layer, which is the central security device responsible for authenticating, authorizing and auditing transactions. Moreover, when a block is added, BCMs create a pointer to the prior block, copy the policy in the prior block header to the new block and attach the block to the blockchain.
# Metal-Organic Frameworks Characterization via Inverse Pulse Gas Chromatography
## 3 Applications of IGC for the Characterization of MOFs
### Isoreticular Metal-Organic Frameworks (IRMOF)
Most of the data points outside of the nonpolar line were related to the strong alkali and chlorinated molecules. From this fact and the observation that all the aromatics tended to lie on the nonpolar line, it was concluded that the basicity component of adsorption of the hydrogen bond prevailed for IRMOF-1. Similarly, the influence of the \(\pi\)-bond polarizability seems to be less significant [67]. In another study, [PERSON] et al. [68] evaluated the interaction of some volatile organic compounds (VOCs) with three IRMOFs (IRMOF-1, IRMOF-8, and IRMOF-10) using IGC in order to assess the adsorption and link it to the morphological structure. The organic linkers of the three MOFs are terephthalatic acid (IRMOF-1); 2,6-naphalene dicarboxylic acid (IRMOF-8); and 4,4-diphenyl dicarboxylic acid (IRMOF-10). Thus, they vary only in pore size. Overall, the strength of adsorption was enhanced in the presence of lattice defects and increasing numbers of carbon atoms in the organic linkers; accordingly, IRMOF-10 had the strongest interaction with the VOCs, followed by IRMOF-8 and finally IRMOF-1. [PERSON] et al. deduced that the dispersive component of the surface free energy and the thermodynamic compensation effect of the n-alkanes exhibited the same centers of adsorption on all three MOFs. Furthermore, they witnessed that the existence of \(\pi\) electron-rich aromatic rings or double bonds tended to increase the specific interaction element through a preferred interaction with the aromatic rings. Consequently, the specificity of the interactions was connected to the chemistry of the organic ligands rather than to the structure.
## 2 Experimental Analysis of the Bearing Capacity of the Horizontal Bar
### Eccentric Tensile Properties of the Socket on the Horizontal Bar
#### 2.1.3 Experimental Results and Analysis
To accurately simulate the sockets' eccentric tension, an opposite tensile force was imposed on the connection of the sockets and the connecting wheel. The load adopted the step-by-step force control loading method, and the normal construction load was applied; no damage occurred, only the horizontal direction displacement is set for this model, and there is no corner deformation. The translational degrees of freedom must be restricted except for the loading direction, and the rotational degrees of freedom in the loading direction should be restricted. According to the analysis of the numerical simulation results, the stress distribution diagrams were obtained for the sockets at both ends under the normal working load (25 kN), as shown in Figure 6.  
The stress distribution of the horizontal bar socket under eccentric tension in the normal construction state was obtained from the \(FEA\). According to the results of calculations with ABAQUS, the following conclusions were drawn:  
1. According to the finite element analysis, the deformation and displacement of the sockets on the horizontal bar under normal working conditions were obtained, and the specific values are shown in Table 2.  
Figure 5: Socket model on the horizontal bar. Figure 6: Tensile failure mode of the socket on the horizontal bar. The results of the \(FEA\) were compared with the average experimental values for the five groups of specimens, as shown in Figure 7. The trends of the simulated and experimental values were basically the same, and the data did not differ significantly. The agreement between the simulation and experimental results in the figure verified the correctness and accuracy of the model. 2. Regarding stress distribution, according to the simulation analysis and experimental results, when the socket on the horizontal bar was pulled eccentrically, the stress concentration area was located at the connection between the socket and the horizontal bar. The results of the simulation analysis were in good agreement with the experimental results. 3. Regarding failure morphology, according to the simulation analysis and experimental results for the eccentric tension of the socket on the horizontal bar, stress concentrations led to plastic deformation at the connection. The plastic deformation increased continuously and eventually led to the loss of the bearing capacity of the specimen. The results showed that under the normal working state of the formwork support, the connection between the socket and the horizontal bar was prone to damage; it was the weak point of the bearing capacity of the horizontal bar of the formwork support.
# Finger-Vein Verification Based on LSTM Recurrent Neural Networks
## 2 The Proposed Approach
### Label Vein Sequences
#### 2.1.2 Labeling Vein Sequences  
In this section, the training sequences are produced based on labeled pixel (as shown in Figure 2j) for SCNN-LSTM training. Firstly, we select a labeled pixel as a current point \(c_{0}\) and determine its \(K-1\) adjacent pixels along a given orientation \(\theta\). This results in a set of \(K\) pixels \(\{c_{-(K-1)/2,\theta},...,c_{0},...c_{(K-1)/2,\theta}\}\) for orientation \(\theta\), where \(0\leq\theta<\pi\) and \(K\) is the odd number to enforce symmetry. Then, we create \(K\) patches of \(s\times s\) centered on \(c_{0}\) and its \(K-1\) adjacent pixels from image in training, and the resulting patches construct a sequence \(S_{\theta}=\{P_{-(K-1)/2,\theta},...,P_{0},...P_{(K-1)/2,\theta}\}\). Similarly, the labels of \(K\) pixels create a labeled sequence \(L_{\theta}=\{l_{-(K-1)/2,\theta},...,l_{0},...l_{(K-1)/2,\theta}\}\) for \(S_{\theta}\). Here, we quantize all the possible vein orientations \(\theta\) into a set of \(C\) values by  
\[\theta_{i}=\frac{i\pi}{C} \tag{1}\]  
where \(i=1,2,...,C\) and \(C\) is heuristically set as 4. Namely, \(\theta\in\{0^{\circ},45^{\circ},90^{\circ},135^{\circ}\}\), as shown in Figure 3a. Therefore, there are four sequences for each labeled pixel \(c_{0}\). Figure 3 shows a sequence \(S_{0^{\circ}}=\{P_{-(K-1)/2,0^{\circ}},...,P_{0},...P_{(K-1)/2,0^{\circ}}\}\) of current pixel \(c_{0}\) along the \(0^{\circ}\) orientation.  
Figure 1: The framework of the proposed approach.  
Figure 2: Segmented results. (**a**) Original finger-vein image; (**b**) Gabor filter; (**c**) Difference curvature; (**d**) Maximum curvature point; (**e**) Mean curvature; (**f**) Region growth; (**g**) Repeated line tracking; (**h**) Wide line detector; (**i**) Probability map; (**j**) Labeled pixels (wight region and black region denote vein and background, respectively).
## 2 Theoretical Analysis of CBIS
### Government Equations of the SDOF Structure of a CBIS  
As is shown in Figure 4, an SDOF structure, of which the mass, stiffness, damping coefficient are \(m\), \(k\), and \(c\), respectively, is equipped with a CBIS. In Figure 4, a pair of dashed boxes with the connotation \(\beta\) indicate the cable-bracing system, of which the displacement transition ratio equals \(\beta\).  
According to the principle of virtual work, the relationship between the control force applied to the structure, and that of the inerter system is:  
\[F_{\text{control}}\text{-}1=F_{\text{is}}\text{-}\beta. \tag{7}\]  
where \(F_{\text{control}}\) and \(F_{\text{is}}\) are the control force applied to the structure and the force of the inerter system, respectively.  
Thus, the governing motion equations of the SDOF structure with CBIS underground motion excitations are:  
\[\left\{\begin{array}{l}m\ddot{u}+ci\dot{u}+ku+F_{\text{control}}=-m\ddot{u}_{ 0}\\ F_{\text{control}}=\beta F_{\text{is}}=\beta k_{\text{s}}(\beta u-u_{\text{d}}) =\beta\big{(}m_{\text{in}}\ddot{u}_{\text{d}}+c_{\text{in}}\dot{u}_{\text{d}} \big{)}\end{array}\right. \tag{8}\]  
where \(u_{0}\) and \(u\) are the ground motion displacement, and the displacement of the structure relative to the ground, respectively.  
Figure 4: Schematic of the single-degree-of-freedom (SDOF) structure with a cable-bracing inerter system (CBIS).  
For simplicity, we define an equivalent inerter displacement \(u_{\rm e}=\frac{u_{\rm d}}{\beta}\), and rearrange Equation (8):  
\[\left\{\begin{array}{l}m\ddot{u}+c\dot{u}+ku+F_{\rm control}=-m\ddot{u}_{0}\\ F_{\rm control}=k_{\rm e}(u-u_{\rm e})=m_{\rm e}\ddot{u}_{\rm e}+c_{\rm e}\dot{u }_{\rm e}\end{array}\right. \tag{9}\]  
where \(m_{\rm e}=m_{\rm in}\beta^{2}\), \(c_{\rm e}=c_{\rm in}\beta^{2}\), \(k_{\rm e}=k_{\rm s}\beta^{2}\) are the equivalent apparent mass, equivalent damping coefficient and equivalent stiffness, respectively, as is shown in Figure 4.
## 6 Conclusions  
In this study, a numerical investigation of an LT-MZI structure is conducted and the structure is proposed for a CO2 gas sensing application by covering the sensing arm with a functional polymer. The MZI represents a paradigmatic model of precision in sensing realms, relying on light wave interference to discern infinitesimal environmental alterations. By dividing a light beam into two trajectories and subsequently reuniting them, the MZI exhibits exceptional sensitivity in detecting variations in parameters such as the refractive index, temperature, and pressure. The Sagnac loop emerges as a versatile component within optical systems, particularly when integrated with MZIs like the LT-MZI, playing a pivotal role in enhancing both performance and functionality. Within the LT-MZI framework, a primary function of the Sagnac loop is to halve the interferometer's length, thereby facilitating the creation of compact, seamlessly integrated optical devices that optimize spatial resources while upholding performance integrity. As the concentration of the CO2 gas increases, the refractive index of the functional polymer changes, resulting in the shift in the interference pattern. The estimated sensitivity of the proposed device is 7.63 pm/ppm for a sensing arm length of 5 \(\upmu\)m, with potential enhancement to 74.78 pm/ppm as the sensing arm length extends to 15 \(\upmu\)m.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData is contained within the article.  
The author acknowledges the constant support of the Warsaw University of Technology, Poland, in the completion of this work.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Soil Leaching by Inorganic Acids
#### 3.1.4 Effect of Stirring Time  
According to the results of the orthogonal experiment, stirring time also has effect on the remediation efficiency by HCl, but appropriate leaching time could shorten the period of soil remediation as well as reduce costs. Therefore, we had made a stirring time experiment in order to find a suitable stirring time, and the results are shown in Figure 3. When the stirring time was 0.5 h, the remediation efficiency was 66%, and it reached 70.3% at 1 h. When the stirring time exceeded 1 h, the repair efficiency decreased slightly and then stabilized, indicating that HCl reacted with the lead in the soil and reached equilibrium quickly. With further prolonged stirring time, there was no significant improvement in remediation efficiency. Thus, 1 h of stirring is usually enough for the extraction of pollutants.  
Therefore, considering various factors for remediating the heavy Pb-contaminated soil, such as leaching cost, soil property after leaching, remediation period, etc., for the inorganic acid leaching system, HCl performed better in terms of its removal effect of lead, and the optimum concentration was 0.2 mol\(\cdot\)L\({}^{-1}\), the optimal solid-to-liquid ratio was 1:20,  
Figure 3: Lead-contaminated soil is remediated by 0.2 mol\(\cdot\)L\({}^{-1}\) HCl: (**a**) remediation efficiency, (**b**) Pb content in soil after leaching.  
Figure 2: Lead-contaminated soil is remediated by HCl: (**a**) remediation efficiency, (**b**) Pb content in soil after leaching.  
and the optimal stirring time was 1 h. Herein, the remediation efficiency of Pb was 70.3%, and the content of lead in soil after leaching was 2388 mg\(\cdot\)kg\({}^{-1}\); however, the pH value of detoxifying soil was 3.88.
###### Abstract
The current study is based on the background of design optimization for stone columns composite foundation at a liquefiable site in the East Timor Tibar Bay port project (TBPP). The project, located on the Circum-Pacific Belt, has a high seismic fortification intensity according to Chinese standards. The design seismic acceleration (475-year return period) of the wharf area reaches 0.53 g on the basis of the seismic hazard assessment report for TBPP. The site is mainly underlain by a mixed layer of sand and gravel, the gradations of which are spatially varied, and a high proportion of fine sands can be found in local areas. To mitigate the potential liquefaction, granular columns are adopted for the ground improvement and are mainly installed in areas where layers of sand or sand-gravel mixture a very loose to loose or partially dense state spread. The standard penetration test (SPT) values of these layers range from 0 to 18, with a mean value being 7. The design relative density of the site is 50%, and the design permeability is 6.4 \(\times\) 10\({}^{-2}\) cm/s. To investigate the influence of relevant factors on mitigation and further optimize the design, the stone columns are firstly designed using the orthogonal design method. Then, seismic response analysis is conducted through fully coupled FE simulations on OpenSees, which adopt the aforementioned u-p formulation for saturated soils. The constitutive model of soils plays a critical role in this fully coupled numerical approach and decides its prediction capability. The multi-yield surface elastoplastic constitutive PDMY can well describe the cyclic mobility and irreversible shear strain accumulation behavior of liquefiable sand under seismic excitation, which has been verified in many research applications [15; 16; 17; 18; 19; 26; 27]. In addition, with a total of 17 input parameters, the PDMY model allows for a comprehensive multi-factor analysis. Therefore, PDMY was adopted to simulate the liquefiable sand and stone materials. Taking seismic response of ground acceleration and excess pore pressure ratio (EXPPR) as key indexes, the influences of design parameters including columns diameter, replacement ratio, shear stiffness, and permeability ratio on mitigation effect are evaluated. Subsequently, the design scheme of the stone columns is optimized based on the analysis results. Overall, the conducted research provides a means of comparing the significance of relevant factors to the mitigation effect of stone columns, and the insights and conclusions can be helpful for practical engineering design.
## 5 Analysis and Discussion
### Reliability Block Diagram  
The RBD developed from the relationships from the FTA is shown in Figure 4. The events connected by AND-gates will appear as parallel blocks, while those connected by OR-gates will appear as parallel on the RBD.  
Similar to the model in ref. [2], it can be noted that the contributing causes that are connected by OR-gates form a series system. In this case, failure of either of events 6, 7, 8, 9, 10, 11, or 12 will result in the collapse of the whole branch, with remaining reliance on the direct causes branch. Ref. [2] therefore emphasized on the need to treat indirect causes seriously when carrying out analyses of such failures.  
The minimum cut set can be derived via application of the Boolean algebra rules. \"The minimum cut set is a group of fault tree initiators which, if all occur, will cause the TOP event to occur\" [3].  
The logical expression of the TOP event for the LHD fire (HPI) is as follows:  
HPI = (1 + 2). (3 + 2). (4.5). (6 + 7 + 8 + 9 + 10 + 11 + 12) HPI = (1.3 + 1.2 + 2.3 + 2). (4.5). (6 + 7 + 8 + 9 + 10 + 11 + 12) [applying a.a = a] HPI = (1.3 + 1.2 + 2). (4.5). (6 + 7 + 8 + 9 + 10 + 11 + 12) [applying a + a.b = a] HPI = (1.3 + 2). (4.5). (6 + 7 + 8 + 9 + 10 + 11 + 12) [applying a + a.b = a] HPI = (1.3 + 2.4.5). (6 + 7 + 8 + 9 + 10 + 11 + 12)  
Figure 4: The Reliability Block Diagram (RBD) of the PGM LHD fire HP incident (N.B. The numbers in this figure represents the numbers of the basic events in Figure 3).  
HPI = {1.3.4.5.6 + 1.3.4.5.7 + 1.3.4.5.8 + 1.3.4.5.10 + 1.3.4.5.11 + 1.3.4.5.12 + 2.4.5.6 + 2.4.5.7 + 2.4.5.8 + 2.4.5.9 + 2.4.5.10 + 2.4.5.11 + 2.4.5.12}  
Therefore, the cut sets are: 1.3.4.5.6; 1.3.4.5.7; 1.3.4.5.8; 1.3.4.5.9; 1.3.4.5.10; 1.3.4.5.11; 1.3.4.5.12; 2.4.5.6; 2.4.5.7; 2.4.5.8; 2.4.5.9; 2.4.5.10; 2.4.5.11; 2.4.5.12.  
Any of the cut sets will result in the occurrence of the TOP event.  
Based on the minimum cut sets, the importance of the events 2, 4, and 5 can be noted, as their occurrence weakens all the other sets. In other words, the occurrence of poor routing of cables, the inability of the fire suppression system automatically activate and extinguish the fire, and failure to extinguish after manual activation weakens all the other sets.  
Also, to show the benefits of RBD analysis, ref. [3] suggested spending resources on basic events represented by blocks in the RBD that control two or more other blocks. In this case, it will also be recommended to spend resources on 2, 'poor routing of cables', because it controls two blocks (1 and 3). This ultimately means controlling the initiation of the fire by controlling the ignition and fuel source (by referring to the FTA). However, the weakness of the RBD analysis is that it does not show the physical connections between the basic events and component failures.