esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Data Exchange between WebAssembly Modules
## 4 Karmem Approach
### Communication
#### 4.3.2 Interface and Patterns  
Similarly to WASM, Karmem allows import and export functions through interfaces. Such interfaces are also dynamic and mutable, allowing for the addition of new functions. Furthermore, Karmem was inspired by RabbitMQ applications.  
RabbitMQ defines some communication patterns such as \"Fanout\", \"Direct\", or \"Topic\" message exchanges [56]. Additionally, initialization types such as \"Singleton\" and \"Multion\" were considered, along with considerations for parallelism, similar to forking processes. While not exhaustive, communication patterns can be categorized in multiple ways. Notably, the well-known \"Gangs of Four\" design patterns [57] are sometimes critiqued as compensating for a lack of such features in the language itself [33; 58; 59]. Although Karmem is not a programming language, its ability to define communication, initialization, and parallelization patterns within an interface may simplify usage and enhance performance by reducing unnecessary inter-module calls.  
The direct mode is the most common, in which a WASM module communicates with another WASM module, even if multiple modules export the same interface. Figure 16 illustrates this scenario. The selection of a module can be decided by the host and the importing module itself.  
The fanout mode aims to communicate with multiple different modules that implement the same interface. Figure 17 demonstrates how this feature works. In this mode, whenever a new WASM module exports an interface that is imported by the module, it will automatically bind and be called along with all the others. The Singleton mode is an initialization method that has only one instance, meaning that all imports are directed to the same module. This does not prevent multiple modules from exporting the same interface. In essence, it is the opposite of fanout, where all importers use the same exporter. Figure 18 illustrates its operation, assuming there is only one exported module. The key difference here is that when the module does not use pure functions, the altered state is persistent between \"Importer 1\" and \"Importer 2\". This method can cause performance issues when frequently called, as there is only one instance for handling all calls from all importing modules.
## 5 Deployment of PLC for Smart Metering and Smart Grid Systems
### Grid Description
#### 5.1.1.1 MV  
MV topologies can be classified in three groups:  
* Radial topology. Radial lines are used to connect the primary substations with the SSs, and the SSs among them by means of radial lines. These MV lines or \"feeders\" can be used exclusively for one SS, or can be used to reach several of them. Radial systems keep central control of all the SSs. These radial topologies show a tree-shaped configuration when they grow in complexity. Radial topologies are easier and cheaper to develop, operate and maintain, than other structures.
* Ring topology. Ring topology is a fault tolerant topology to overcome the weakness of radial topology when there is a disconnection of one element of the MV line that interrupts electricity service (outage) in the rest of the connected substations. A ring topology is an improved evolution of the radial topology connecting to other MV lines to create of redundancy. Independently of the physical configuration, the grid is always operated radially, but on the event of a failure in a feeder, other elements are maneuvered to reconfigure the grid in such a way that out-of-order lines are restored most of the times.
* Networked topology. Networked topology consists of primary and secondary substations connected through multiple MV lines. Thus, the reconfiguration options to overcome faults are multiple, and in the event of failure, alternative solutions may be found to reroute electricity.
## 3 Results and Discussion  
### Thermoanalysis of Composite PIL/IL/Zn\({}^{2+}\) Materials  
Figure 2 depicts the decomposition curves of the composite PIL-based materials in comparison to the reference material P[DADMA][Tf\({}_{2}\)N], referred to as **P\({}_{9}\)IL\({}_{0}\)Zn\({}_{0}\)**[16]. PIL/IL/Zn\({}^{2+}\) composite materials demonstrate worsening thermal stability with increasing addition of Zn[Tf\({}_{2}\)N] salt. The initial mass loss fluctuates in the range between 300 \({}^{\circ}\)C for **P\({}_{9}\)IL\({}_{6}\)Zn\({}_{9}\)** and 350 \({}^{\circ}\)C for **P\({}_{9}\)IL\({}_{0}\)Zn\({}_{0}\)**, **P\({}_{9}\)IL\({}_{6}\)Zn\({}_{0}\)** and **P\({}_{9}\)IL\({}_{6}\)Zn\({}_{1}\)**.  
For the pure PIL and PIL/IL composite, the major loss occurs at 430 \({}^{\circ}\)C with a rate of 2.5 % min\({}^{-1}\), corresponding to de-polymerization and consequent decomposition of separate IL moieties, starting from approximately 405 \({}^{\circ}\)C, as reported elsewhere for [Pyrr\({}_{14}\)][Tf\({}_{2}\)N] [62]. The PIL/IL/Zn\({}^{2+}\) composite's decomposition curve decreases by 20 \({}^{\circ}\)C to 410 \({}^{\circ}\)C with **P\({}_{9}\)IL\({}_{6}\)Zn\({}_{1}\)**, showing more thermal stability than **P\({}_{9}\)IL\({}_{6}\)Zn\({}_{9}\)**. These results align well with the composite materials consisting of similar building blocks; namely, ILs and Zn[Tf\({}_{2}\)N]\({}_{2}\)[63].  
DSC analysis shows extremely weak thermal effects in all samples. None of the samples have any evident primary transitions (melting point) in the temperature range from \(-40\) to \(180\,\mathrm{\SIUnitSymbolCelsius}\) (ESI Figure S1). The extremely broad apparent peak in the first heating run (ESI Figure S1a) is probably associated with different responses of the membranes related to their manufacturing, i.e., enthalpy relaxation, and to evaporation of traces of humidity or residual solvent [64,65]. The neat \(\mathbf{P_{9}IL_{0}Zn_{0}}\) and \(\mathbf{P_{9}IL_{6}Zn_{0}}\) show a faintly visible glass transition temperature near \(80\,\mathrm{\SIUnitSymbolCelsius}\) and \(20\,\mathrm{\SIUnitSymbolCelsius}\), in close agreement with the literature data for the same polymer [13]. This further descends to ca. \(5\,\mathrm{\SIUnitSymbolCelsius}\) for \(\mathbf{P_{9}IL_{6}Zn_{9}}\). Although the DSC curve of pure [Pyrr\({}_{14}\)][Tf\({}_{2}\)N] was not measured experimentally, the available literature reports its \(T_{g}\) at \(-81\,\mathrm{\SIUnitSymbolCelsius}\)[66, 67, 68]. The \(T_{g}\) of the PIL/IL blends, \(T_{g,blend}\), which can be estimated for example by the simple Fox-Equation (Equation (1)), is expected to decrease rapidly with increasing IL content.  
\[\frac{1}{T_{g,blend}}=\frac{w_{PIL}}{T_{g,PIL}}+\frac{w_{IL}}{T_{g,IL}} \tag{1}\]  
where \(w_{PIL}\) and \(w_{IL}\) are the weight fractions of the PIL and the IL, respectively, and \(T_{g,PIL}\) and \(T_{g,IL}\) are the glass transition temperatures of the pure components. The latter supports well the qualitatively more flexible behavior of the sample, confirming the plasticizing effect of the low molar mass IL.
# Coordinated Control after Grasping the Space Targets Using Controllable Damping Mechanism
## 1 Introduction
The modeling and control problems of the spacecraft-manipulator system with flexible-link flexible-joint (FLFJ) introduced joint friction is studied based on the single direction recursive construction method and velocity variation principle in [26]. The effect of friction, gear stiffness, and damping of the flexible joint is studied in [27; 28; 29]. [PERSON] et al. [30] developed an autonomous mobile manipulator endowed with designed tandem elastic brake, and proposed a novel integral Lyapunov function to stabilize the robotic system under model uncertainty and external disturbance. The paper [31] considered the unmodeled dynamics and dead zone problem in the stabilization control of the flexible-joint manipulator. In [32], a new dynamic modeling of the special flexible robot consisting of prismatic joints is proposed, and the inverse dynamic is analyzed by a novel bisection-method-based algorithm. Other modeling methods for flexible joints are given in [33; 34; 35; 36; 37]. Once an accurate model of the flexible joint introduced with soft-docking mechanism is obtained, the coordinated control strategy that integrates the flexible force output by the soft-docking mechanism and the active force of space robot system is possible to design in order to optimize the momentum distribution and impact absorption. In summary, the main contributions of this paper are as follows. (1) A full-dimensional controllable damping mechanism (FDCDM) with gyroscopic structure is introduced into the joint of the space robot; the six-dimensional damping force outputs by the FDCDM is equivalent to the actuator outputs in the end joint. (2) The whole-body dynamic model of SMS with full-dimensional damping mechanism is established using the Kane method. Furthermore, a contact force model combining the modified Hertz model and the LuGre friction model is used to estimate the contact force between the robot end-effector and the target without additional sensors. (3) A backstepping integral sliding mode controller is proposed to optimize the momentum distribution and impact absorption. Additionally, the global asymptotic stability of the control system is guaranteed using the backstepping algorithm and Lyapunov theory. (4) The results of numerical simulation experiments in ADAMS and MATLAB demonstrate the superior performance of the proposed approach.
## 2 Materials and Method
### Model Calibration Method  
Remote sensing based carbon flux models usually contain many parameters. When the models are used to simulate carbon flux at a specific site, the parameters should be calibrated. Bayesian theory based parameter estimation method, Markov Chain Monte Carlo (MCMC), is very popular in recent years, because it not only solves the value of parameters but also predicts the posterior probability distribution function of parameters. The posterior probability distribution function can help us to know the characteristics of model parameters and how the parameters will impact on models' output. The MCMC method contains two repeated steps: a proposal step and a movement step [31,32].  
In the proposal step, a newly proposed parameter is generated from the previous accepted parameters with a random walk \(\Delta c\). \(\Delta c\) is calculated using a random number r between 0 and 1, the parameter range (_cmax_ - _cmin_) and a step length factor s. s equals to 5, according to a previous study [31].  
\[c^{i+1}=c^{i}+\Delta c=c^{i}+\frac{(r-0.5)}{s}(c_{\text{max}}-c_{\text{min}}) \tag{9}\]where \(c^{i+I}\) and \(c^{i}\) are the proposed parameter vector and previously accepted parameters vector, respectively.  
In the movement step, \(c^{i+I}\) is tested using the Metropolis rule to determine if the parameter should be accepted. The accepted probability (\(p(c^{i},c^{i+J})\) ) of the proposed parameters is derived from the likelihood function (_L(c)_) using the parameters accepted previously. The mathematical formula is as follows:  
\[L(c)= \prod_{i=1}^{m}\ \prod_{t=1}^{n}\frac{1}{\sqrt{2\pi\delta}}\,\mathrm{e}^{ \frac{-(G\textit{flux}_{c}^{i}-G\textit{flux}_{c}^{i})^{2}}{2\delta^{2}}} \tag{10}\] \[p(c^{i},c^{i+I})= \min\left\{\mathrm{l},L(c^{i+I})\,/\,L(c^{i})\right\} \tag{11}\]  
where _Cflux\({}^{o}\)_ and _Cflux\({}^{i}\)_ are the _in-situ_ and RS-CFLUX model predicted carbon flux, respectively. \(\delta\) is the standard deviation of the data-model difference. m is the number of datasets used in the cost function, and \(n\) is the number of observations. Then, the acceptance probability is compared with a uniform random number U [0, 1]. Only when the acceptance probability is greater than U, will \(c^{i+I}\) be accepted.  
In this study, five parallel MCMC chains with 50,000 iterations each were run in the calibration process. The first 10,000 iterations of each chain were burn-in iterations and were deleted. The remaining iterations of the parameters were used to generate posterior distributions. G-R diagnostic method [33] was used to evaluate the chain convergence. The MCMC algorithm was programmed in Matlab code.
# E2-VINS: An Event-Enhanced Visual-Inertial SLAM Scheme for Dynamic Environments
## 3 Methodology
### Event-Based Dynamicity-Adaptive Bundle Adjustment
For the quadratic of \(w_{ev}^{l}\), the optimal \(w_{ev}^{l}\)\({}^{*}\) under state estimation is derived as follows:  
\[w_{ev}^{l}\ {}^{*}=\frac{\lambda_{w}+\lambda_{m}n_{l}^{2}\overline{w}_{ev}^{l}}{ \mathbf{r}_{\mathcal{C}}^{l}+\lambda_{w}+\lambda_{m}n_{l}^{2}}\in[0,1]. \tag{13}\]  
As expected, the optimal weight value is negatively correlated with visual residuals. During optimization, the weights of outlier features decrease, and their losses become flatter. As a result, the losses of outlier features are close to a zero gradient and do not affect BA. Before optimizing the state vector \(\mathcal{X}\), the dynamicity weight \(w\) for each feature on the current frame is integrated from the event-based dynamicity metric \(\widehat{w}_{ev}\) and the optimized theoretical optimal value \(w_{ev}^{*}\):  
\[ \begin{split}& a_{w}=|\widehat{w}_{ev}-w_{ev}^{*}|,\\ & w=\begin{cases}w_{ev}^{*},a_{w}>\lambda_{e},\\ (1-a_{w})\widehat{w}_{ev}+a_{w}w_{ev}^{*},a_{w}\leq\lambda_{e}.\end{cases}\end{split}  \tag{14}\]  
where \(\lambda_{e}\in[0,1]\) is a constant parameter used to adjust the confidence of event-based dynamicity metrics and optimized weight values. In this design, the event-based dynamicity metrics \(\widehat{w}_{ev}\) implicitly affect the value \(w_{ev}^{*}\) of the current optimized weight by acting on the previous weight \(\widehat{w}_{ev}\).
## 3 Fabrication and Integration
### Bonding Materials  
Outgassing is a frequent problem that affects most of bonding materials. There are three major bonding materials that are suitable for CubeSat assembly. Epoxy can be utilized when a hard and lasting bond is required, e.g., to secure fasteners, to encapsulate electronics, and as strain relief for electric wires. Epoxy has a prolonged curing time that typically lasts one hour or more depending on the composition and the catalyst dose. Further, room-temperature vulcanizing (RTV) silicone can be used in CubeSats as a filling, and to consolidate harnessing or any other appendages. After the silicone is cured, it is easy to remove by hand, but it is commonly used as a permanent bond for light components. The curing time takes a few minutes, so it is more suitable as a quick adhesive solution than epoxy. Additionally, Kapton tape can be used to make labels, secure a beam of wires, routing harness, hold components that do not need a strong and permanent bond or even protect surfaces from scratches. The adhesive is very sticky and problematic to handle, but it does not leave any residue when removed from surfaces. In general, it is not convenient to overuse bonding materials, because they can change significantly the inertial properties of the satellite.
## 3 Results and Discussion
If we compare the convergence time (per 60 epochs) for training, the proposed method takes slightly more time (by 1.3%) than VDSR. It is due to the extra time for the optimization of MF values and the associated multiplication and addition operations. Figures 5-7 show the results of super-resolution image reconstruction using VDSR and the proposed method from bicubic interpolated images with the scale factor \(\times\)4. The bars on the right of Figure 5 are low-brightness straight objects arranged side by side at regular intervals. In the image reconstructed using VDSR, linear objects do not maintain their shape, while the proposed method maintains the linear shape well. In Figure 6, the proposed method shows better performance than VDSR in maintaining the pattern of the grid with the correct shape of the ceiling, where grids with high contrast are arranged regularly. Because the linear objects in the building in Figure 7 are very tightly spaced, the VDSR result is blurry and the straight lines are not well preserved, while the proposed method maintains the straight lines relatively well. These results of super-resolution image reconstruction demonstrate that the proposed method yields better subjective quality than the VDSR method.
# High-Speed Vehicles in Low-Pressure Tunnels--Influence of Choked Flows
## 4 Longer Tunnels  
The base case tunnel is shorter than the lengths commonly envisaged in studies of high-speed vehicles in evacuated tunnels. To assess the importance of this, Figure 12 compares the vehicle pressures, temperatures and power requirements in the base case tunnel and in a tunnel that is four times as long. The only other change from the base case is the duration of travel at maximum speed. In the longer tunnel, the vehicle accelerates for 5 km, travels at constant speed for 50 km and then decelerates for 5 km. The additional 45 km at 200 m/s increase the journey time by 225 s to a total of 350 s.  
The conditions in the two tunnels during the early stages of the journey are identical. No difference is possible until reflections begin to arrive at the vehicle from the distant end of the tunnel, namely at approximately 30 s in the case of the 15.4 km tunnel. Likewise, conditions in tunnels longer than 60 km would be identical to those in the 60.4 km tunnel throughout the first two minutes of the journey. After the arrival of the first reflections, the rate of increase in pressure ahead of the vehicle nose increases and, during the period of constant vehicle speed, the rate is greater in the shorter tunnel than in the longer one. Thereafter, the pressure ahead of the vehicle in the longer tunnel continues to increase and the eventual maximum value exceeds that in the shorter tunnel by about 50%.  
In both tunnels, the vehicle temperatures vary relatively slowly during the period of constant vehicle speed. They then decrease for a short period before increasing strongly as the vehicle approaches the closed end of the tunnel. Short-term changes in temperature approximately mirror those for pressure, although sustained heat transfers at the tunnel wall gradually counter the changes over long periods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Vehicle**} & \multicolumn{1}{c}{**Length**} & \multicolumn{1}{c}{**Max Drag**} & \multicolumn{1}{c}{**Max Power**} & \multicolumn{1}{c}{**Total Energy**} \\ \cline{2-5}  & **m** & **kN** & **kW** & **MJ** \\ \hline
**1 st of 3** & 50 & 0.592 & 11.8 & 5.25 \\ \hline
**2 nd of 3** & 50 & 0.567 & 11.3 & 5.23 \\ \hline
**3 rd of 3** & 50 & 1.133 & 16.0 & 8.14 \\ \hline
**Single** & 150 & 1.069 & 19.5 & 10.76 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Energy consumption to overcome aerodynamic resistance.  
The aerodynamic drag force and consequential tractive power requirement depend strongly on the difference between pressures ahead of and behind the vehicle. In both tunnels, these values increase slowly, but continually, throughout the period at constant vehicle speed and the maximum value in the longer tunnel is about 35% greater than that in the base case tunnel.
## 5 Emerging and Potential Opportunities for WUE
### Remote Sensing
Key opportunities and advances to watch include future collection of very high resolution (<10 m) data through hyperspectral sensors such as the current commercial IKONOS and Quickbird satellites, rapid data access availability of data from multiple sensors with a wide array of spatial, spectral, and radiometric features and remote sensing multi-data synthesis through streaming technology.
# Synchronous Grouting Analysis of Shield Tunneling through High Water Pressure Fault Fracture Zone
## 2 Fault Fracture Zone Model
### Model Parameter Selection  
Fault zones represent complex geological structures composed of various rocks with many different material characteristics. The geological and mechanical properties of fault rock masses are difficult to describe, as it is difficult to obtain representative samples during on-site investigation, sample preparation, and laboratory testing. According to [PERSON] suggestion for heterogeneous rock masses, the equivalent material properties of fault zones can be reasonably approximated by the \"weighted average\" of the complete strength characteristics of strong and weak materials (such as the uniaxial compressive strength UCS of intact rocks). Therefore, for shear failure of fault zones, the model assumes that the rock and soil mass conform to ideal elastic-plastic materials and adopts the Mohr Coulomb yield criterion. The rock and soil element adopts a tetrahedral linear stress seepage coupling element (C3D4P), and the rock and soil parameters of the fault fracture zone are selected based on the geological survey report, as shown in Table 3.  
In practical engineering, the working state of concrete is mostly linear elasticity, so in this model, the linear elastic constitutive equation is used for the pipe segment. The shield tunnel shell, grouting body, and pipe segments all adopt a linear elastic model,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Rock and Soil Mass** & **C (kPa)** & \(\boldsymbol{\varphi}\) (\({}^{\circ}\)) & \(\boldsymbol{\gamma}\) (kN/m\({}^{3}\)) & **Elastic Modulus (MPa)** & **Poisson’s Ratio** &
\begin{tabular}{c} **Permeability** \\ **Coefficient (cm/s)** \\ \end{tabular} \\ \hline Ordinary & 1000 & 40 & 24 & 6140 & 0.24 & \(1.1\times 10^{-6}\) \\ surrounding rock & 500 & 33 & 22.8 & 3200 & 0.27 & \(6.4\times 10^{-5}\) \\ Induced crack zone & 200 & 25 & 21 & 800 & 0.35 & \(1.3\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Rock and soil parameters.  
and consider the effects of pipe segment joints to reduce the stiffness of the pipe segment concrete, with a reduction coefficient of 80%. During the excavation process, the slurry will gradually harden after synchronous grouting at the shield tail. The hardening process of the slurry is simulated by changing the elastic modulus and Poisson's ratio of the grouting unit. The selection of mechanical parameters for the segment, slurry, and shield machine casing is shown in Table 4.  
The soil, shield machine, pipe segments, and grouting layer in the model are all simulated using solid elements. The segment and shield tunneling adopt an isotropic linear elastic model based on the generalized Hooke's law. Due to the good impermeability of the lining, the seepage effect of the lining is not considered in the model. The segment, shield tunneling machine, and grouting body adopt an eight-node linear reduced integral hexahedral element (C3D8R).