esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability Study on FRP Composites Exposed to Wet-Dry Cycles
## 4 Discussion
### The Safety Factors for FRP
This not only considers the degradation of FRP materials, but also considers the degradation of the performance of concrete structures. According to European Fib Bulletin 14 [33], the design tensile strength \(f_{\text{fd,fib}}\) was calculated by:  
\[f_{\text{fd,fib}}=\frac{f_{\text{fk,fib}}}{\gamma_{\text{f}}}\frac{\epsilon_{ \text{fue}}}{\epsilon_{\text{fum}}} \tag{18}\]where \(f_{\rm{ft},fib}\) is the characteristic strength and corresponds to the 5% fractile of the tensile strength. \(\gamma_{\rm{f}}\) is the FRP material safety factor and suggested to be 1.35 and 1.50 for wet lay-up CFRP systems and GFRP systems, respectively. The ratio \(\epsilon_{\rm{fuc}}/\epsilon_{\rm{fum}}\) normally equals 1, as the effective ultimate FRP strain \(\epsilon_{\rm{fum}}\) expected in situ will not significantly differ from the mean strain \(\epsilon_{\rm{fum}}\) obtained through uniaxial tensile testing, and as small variations are accounted for in the FRP material safety factor \(\gamma_{\rm{f}}\). The design tensile strength in Italian CNR guidelines [34] can be expressed as follows:  
\[X_{\rm{d}}=\eta\cdot\frac{X_{\rm{k}}}{\gamma_{\rm{m}}} \tag{19}\]  
where \(\eta\) is a conversion factor accounting for environmental factors, and the values for GFRP and CFRP are 0.50 and 0.85, respectively. \(\gamma_{\rm{m}}\) is the partial factor of FRP materials and is equal to 1.10. The design values (\(f_{13}\)) of tensile strength of FRP materials used in this study according to the four representative specifications were calculated and listed in Table 4. It is found that the GFRP design values in the four specifications are basically the same and range from 700 MPa to 1189 MPa, and the range of CFRP design values is relatively large with 1660 MPa to 2609 MPa.
## 3 Results  
### Depth of the Vaporization and Coagulation Zone at a Laser Power of 20 W  
At a motion speed of 5 mm/s and contact of the tip of the bare fiber to the lung surface, we measured a depth of the vaporization zone of \(756.4\pm 1.2\) mm and of the coagulation zone of \(221.8\pm 2.9\) mm. When the distance of the tip of the bare fiber was increased to 5 mm, there was no more vaporization effect, and the coagulation depth was \(143.9\pm 1.7\) mm. At a distance of one centimetre, the coagulation depth decreased to \(72.6\pm 1.4\) mm. This was equivalent to a mean decrease in coagulation depth of 67.2%. When the motion speed was doubled and there was contact with the bare fiber, the mean depth of the vaporization zone was \(436.0\pm 0.8\) mm. This was equivalent to a mean decrease of 42.3%. The coagulation zone decreased to \(145.0\pm 3.3\) mm. This was equivalent to a mean decrease of 34.6%. At a greater distance of 5 mm or 10 mm of the tip of the bare fiber, the depth of the coagulation zone was reduced to \(68.6\pm 2.0\) mm and \(28.08\pm 1.1\) mm, respectively. When the motion speed was set to 20 mm/s, with contact of the bare fiber we measured a vaporization zone of \(246.3\pm 1.4\) mm and a coagulation zone of \(67.45\pm 1.5\) mm. This was equivalent to a decrease of 67.4% and 69.6%, respectively, compared to the tests conducted at a motion speed of 5 mm/s. At a distance of the tip of the bare fiber to the lung surface of 5 mm and 10 mm, mean depths of the coagulation zones of \(38.4\pm 1.2\) mm and \(13.73\pm 0.4\) mm were measured, respectively (see Table 1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Motion Speed (mm/s)/Distance of the** & **Vaporization Zone (μm)** & **Coagulation Zone (μm)** \\
**Bare Fiber to the Lung Surface (mm)** & **Vaporization Zone (μm)** & **Coagulation Zone (μm)** \\ \hline
**5/0** & \(756.4\pm 1.2\) & \(221.8\pm 2.9\) \\
**5/5** & \(0\) & \(143.9\pm 1.7\) \\
**5/10** & \(0\) & \(72.6\pm 1.4\) \\
**10/0** & \(436.0\pm 0.8\) & \(145.0\pm 3.3\) \\
**10/5** & \(0\) & \(68.6\pm 2.0\) \\
**10/10** & \(0\) & \(28.08\pm 1.1\) \\
**20/0** & \(246.3\pm 1.4\) & \(67.45\pm 1.5\) \\
**20/5** & \(0\) & \(38.4\pm 1.2\) \\
**20/10** & \(0\) & \(13.73\pm 0.43\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Depth of the vaporization and coagulation zone (μm) at a laser power of 20 W of the 600 μm bare fiber.  
Figure 1: (**A**) Experimental design (illustration); (**B**) Image of the experimental design.
## 3 Materials and Methods
### Using a Variational Autoencoder for Noise Suppression
#### 3.2.2 Autoencoders  
These two-part models first use a multilayer encoder network to represent high-dimensional structures in a low-dimensional space. Then, there is a decoder network to convert the data from this space into high-dimensional structures with some relation to the first one [30]. This architecture works as follows. The input data go through the different convolutional layers of the encoder, obtaining a small piece of data with the main features. These data are distributed in the bottleneck, creating a representation called latent space. Finally, the feature representation goes through the decoder to obtain an output like the input data.
# A Hierarchical Mapping System for Flat Identifier to Locator Resolution Based on Active Degree
## 6 Conclusions  
This paper presents an identifier resolving architecture called _HMAA_ under the ID/Locator separation mechanism. The identifier mapping information (mapping item) is stored at different levels according to their degree of activity, and dynamically flows up and down through different levels. According to different mobility characteristics of an endpoint, a different policy report is executed to reduce the cost of mapping item updating. The top layer CHORD is constructed by the Markov Decision Process, which can keep the consistency between the physical topology and the logical topology. The simulation results show that HMAA can satisfy the scalability and efficiency requirements of the Identifier (ID)/Locator separation network. Of course, the simulation of QFR is relatively simple. Next, we will further study the impact of relevant factors on QFR.  
Figure 8: Query failure rate (QFR) compared to the rate of AMS local cache size to remote AID numbers (L/R).  
**Author Contributions:** Conceptualization: J.L. and S.H.; Data curation: S.H.; Methodology: J.L. and Y.W.; Writing--original draft: J.L.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China (Grant No. 61672471), the key scientific research project of colleges and universities in Henan province plan (Grant No. 19A630033, Grant No. 19A510025 and Grant No. 19A510024), by the China Postdoctoral Science Foundation (Grant No. 2018M633733), and the Scientific and Technological Key Project of Henan Province (Grant No. 182102210449).  
**Acknowledgments:** The authors acknowledge all the support from the above programs. They would also like to thank the anonymous reviewers and the associate editor for their useful comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Test Results  
### Capillary Section Time  
The CST values of the tested slurries are presented in Figure 5. The untreated slurry exhibited a CST value of 597 s, indicating poor filtration performance. Flocculation treatment significantly improved the filtration performance of the slurries. Under single-dosing conditions, 0.1% APAM demonstrated the best performance, reducing the CST value to 343 s, followed by 2% lime, which achieved a CST value of 384 s. Under dual-dosing conditions, the dual use of 0.8% PAC and 0.1% APAM exhibited the most effective filtration performance, achieving a CST value of 77 s. This enhancement is attributed to the synergistic effect of PAC and APAM, where the electrostatic neutralization of PAC and the adsorption bridging of APAM jointly promote soil-water separation. Based on these results, 2% lime, 0.1% APAM, and dual use of 0.8% PAC and 0.1% APAM were selected for the follow-up tests.  
Figure 5: CST test results.
# Superior Control of Spacecraft Re-Entry Trajectory
## 2 Optimization Mathematical Model for Re-Entry Trajectory and Superior Control Algorithms
### Mathematical Model for Re-Entry Trajectory Design  
The mathematical model for re-entry trajectory design can be found in papers [11; 12; 13; 14; 15; 16]. This article considers the six degree of freedom variables during the re-entry flight of spacecraft, models its re-entry process, and obtains differential equations:  
\[\dot{V}=\frac{P\cos\alpha\cos\beta-c_{x}\phi S}{m}-g\sin\gamma+ \tag{10}\] \[\omega_{d}^{2}r\cos\phi(\sin\gamma\cos\phi-\cos\gamma\sin\psi \sin\phi)+a_{Wx}\ \,\]  
\[\dot{\gamma}=\frac{P(\sin\alpha\cos\sigma+\cos\alpha\sin\beta\sin\sigma)}{ mV}+\frac{c_{x}\phi S\cos\sigma}{mV}-\] (11) \[\frac{c_{  
\[\begin{array}{c}\dot{\psi}=\frac{P(\sin\alpha\cos\sigma+\cos\sin\beta\cos\sigma)}{ mV}-\frac{qS}{mV\cos\gamma}(C_{y}\sin\sigma+\\ C_{z}\cos\sigma)+2\omega_{d}(tg\gamma\sin\psi\cos\phi-\sin\phi)-\\ \frac{\omega_{2f}^{2}}{V\cos\gamma}\cos\psi\sin\phi\cos\phi-\frac{V}{r}\cos \gamma\cos\psi qg\phi+\frac{a_{Wz}}{V\cos\gamma}\\ \\ \dot{h}=V\sin\gamma+V_{Wy},\\ \\ \dot{\theta}=V\cos\gamma\cos\psi+V_{Wx},\\ \\ \dot{\phi}=-V\cos\gamma\sin\psi+V_{Wz},\\ \\ \end{array} \tag{12}\]  
In the above equation, \(V\), \(\gamma\), and \(\psi\), respectively, represent the speed, path angle, and heading angle of the re-entry vehicle; \(\alpha\), \(\beta\), and \(\sigma\) correspond to the angle of attack, sideslip angle, and roll angle, respectively; \(S\) and \(L\), respectively, represent the reference area and reference length; \(C_{x}\), \(C_{y}\), and \(C_{z}\) are the drag coefficient, lift coefficient, and side force coefficient, respectively; \(h\), \(\theta\), and \(\varphi\) are the altitudes at which the spacecraft is located and the distance traveled in the longitude and latitude directions, respectively; \(P\) is the engine thrust; \(V_{W_{y}}\), \(V_{W_{x}}\), and \(V_{W_{z}}\) are the wind speeds in the vertical, longitude, and latitude directions of the spacecraft in the ground coordinate system, respectively; \(a_{W_{y}}\), \(a_{W_{x}}\), and \(a_{W_{z}}\) are the wind shears in the \(y\), \(x\), and \(z\) directions in the ballistic coordinate system; \(R\) is the Earth's radius; and \(g_{0}\) is the gravitational acceleration at sea level.  
The six degrees of freedom equations of motion used in this paper include the wind field term--wind shear--for two reasons: Firstly, with the improvement of re-entry accuracy requirements, more influencing factors should be considered. Secondly, when the spacecraft enters other planets with a high wind field magnitude, the influence of the wind field must be considered  
Resistance of spacecraft during aerodynamic deceleration re-entry into the atmosphere, \(D\), is as follows:  
\[D=\frac{1}{2}\rho V^{2}C_{D}S, \tag{16}\]  
Among them, \(\rho\) is the density of air of the current position; \(C_{D}\) is the drag coefficient.
## 6 Application and Results
### Factors of Change Maps for Precipitation  
The outcomes of many recent climate studies generally call for an increase in precipitation extremes due to warming climate [35], confirming the observational evidence of heavy rainfall intensifications in several regions all around the world in the last six decades [36; 37].  
The observations support the classical [PERSON] thermodynamic law, which states that warmer air has a higher water vapor holding capacity [38]. As stated by this law, the moisture content of the atmosphere increases with a scaling rate of about 6-7% \(\Delta K^{-1}\), being \(\Delta K\) in Kelvin.  
Even if precipitation is not directly considered in the Eurocodes, changes in the frequency and intensity of precipitation extremes may have significant adverse implications on the hydrologic design of water infrastructures, since it can cause an increase of risk of flooding events as well as a reduction of service life of bridges, associated with higher scouring rate of bridge foundations.  
Following the methodology previously described, factors of change for characteristic values (2% probability of exceedance on an annual basis) \(p_{r,k}\), have been evaluated for Italy and Germany, in terms of ratios, according to Equation (5).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Institute\_id** & **RCM Name** & **Driving\_GCM** & **Driving Experiment** & **Driving Ensemble Member** & **Period** \\ \hline \multirow{3}{*}{DMI} & \multirow{3}{*}{HIRHAM5} & \multirow{3}{*}{EC-EARTH} & historical & & 1951–2005 \\  & & & RCP4.5 & r3 ilp1 & 2006–2100 \\  & & & RCP8.5 & & 2006–2100 \\  & & & historical & & 1951–2005 \\ CLMcom & CCLM4-8-17 & CNRM-CM5-LR & RCP4.5 & rilip1 & 2006–2100 \\  & & & RCP8.5 & & 2006–2100 \\ CLMcom & CCLM4-8-17 & EC-EARTH & RCP4.5 & r12 ilp1 & 2006–2100 \\  & & & RCP8.5 & & 2006–2100 \\ KNMI & RACMO22E & EC-EARTH & RCP4.5 & rilip1 & 2006–2100 \\  & & & RCP8.5 & & 2006–2100 \\  & & & historical & & 1951–2005 \\ MPI-CSC & REMO2009 & MPI-ESM-LR & RCP4.5 & rilip1 & 2006–2100 \\  & & & RCP8.5 & & 2006–2100 \\ IPSL-INERIS & WRF331F & IPSL-CM5A-MR & RCP4.5 & rilip1 & 2006–2100 \\  & & & RCP8.5 & & 2006–2100 \\  & & & historical & & 1951–2005 \\ CNRM & ALADIN53 & CNRM-CM5 & RCP4.5 & rilip1 & 2006–2100 \\  & & & RCP8.5 & & 2006–2100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the main characteristics of analyzed climate projections.  
In the figures, maps in the first row refer to medium RCP scenario (RCP4.5), and maps in the second row refer to the highest RCP scenario (RCP8.5).  
The maps confirm that significant changes can occur. Strong increases are awaited for \(p_{r,k}\) in large parts of the Italian and German territories, especially when the highest emission scenario RCP8.5 is taken into account, thus further validating the conclusions of previous studies about the influence of global warming on precipitation extremes [39; 40].
# UVsq-Sat NG, a New CubeSat Pathfinder for Monitoring Earth Outgoing Energy and Greenhouse Gases
## 2 Scientific Objectives
### Earth's Radiation Budget  
Several space missions have been focused on the measurement of the Earth's Radiation Budget (ERB). Satellites' measurements from both the earlier Earth Radiation Budget Experiment (ERBE) sensors and the current-generation Clouds and the Earth's Radiant Energy System (CERES) sensors [11] form the foundation of a multi-decadal record of the ERB at the TOA. Among these, the CERES sensors are currently considered the most dependable and consistent in providing accurate measurements of the radiative components at the top of the atmosphere. The Geostationary Earth Radiation Budget (GERB) aboard EUMETSAT's Meteosat Second Generation geostationary satellites is another satellite instrument that provides valuable data on the ERB component at the TOA. From 2028, the future Libera NASA mission will continuously measure the energy leaving Earth's atmosphere on a daily basis, extending the ERB record. Libera will acquire spectrally integrated radiance over the CERES heritage broad spectral bands in the shortwave (0.3-5.0 \(\upmu\)m), longwave (5.0-50.0 \(\upmu\)m), and total band (0.3-beyond 100.0 \(\upmu\)m) and will add a split-shortwave band (0.7-5.0 \(\upmu\)m) to provide deeper insight into shortwave energy deposition [12]. This will help us to maintain a data record spanning several decades, taking advantage of the CERES instruments. There are also ERB observations from other satellites which are obviously of interest and serve as points of comparison to consolidate the observations and trends. One of the objectives of the Uvsq-Sat NG mission is to measure the ERB components, including OLR and OSR, with increasing accuracy with respect to the previous LATMOS CubeSat missions (Uvsq-Sat and Inspire-Sat7). The scientific value of ERB observations obviously depends on their accuracies [8; 9], which are critical in the derivation of the EEI. Table 1 provides the current ERB requirements for the different missions (Uvsq-Sat, Inspire-Sat7, Uvsq-Sat NG, Terra-F constellation) developed by LATMOS. The ERB measurements requirements for the hypothetical satellite constellation Terra-F presented in Table 1 represent the best-case scenario. So far, existing satellite instruments have not been able to measure the solar and terrestrial radiative fluxes accurately enough to directly determine the absolute value of the EEI, though there is more confidence in the derivation of the EEI's relative changes. Currently, satellite-based determinations of the EEI are usually anchored to ocean heat content data, which provide a better indicator of the absolute magnitude of the EEI [13].
## 4 Discussion
### Applicability to Other Vegetation Zones  
In this study we focused on the pioneer zone of a salt marsh. Hence, it is possible that in the middle and high marsh other drivers become relatively more important, especially where differences in flood duration become relatively small. In our study, we focused on a single species (_Sparitna_), the pioneer zone was dominated by this species. At higher elevations, other species become more important. Previous research showed that even small differences in flood duration can already cause large differences in vegetation zonation, i.e., yielding different vegetation species or communities [26]. This indicates that this flood duration has an effect even in the middle and high marsh. Additionally, the effect of wave forcing is also most likely to be strongest in the pioneer zone, and competition is known to be an important process throughout salt marshes [2; 38; 39]. Therefore, it seems unlikely that the large influence of flood duration is only limited to the pioneer zone. However, further study will have to establish its importance in other salt marsh regions, and with species other than _Sparitna_. Another consequence of limiting ourselves to the pioneer zone is that we reduce the effects of competition, as competition is likely to have strong effects near the border between vegetation types. These borders were excluded as they presented mixed pixels that could not be inverted by the model. We therefore recommend future studies into modeling all salt marsh vegetation types in a single model, in order to expand research beyond the pioneer zone and establish the most important spatial drivers for the entire salt marsh.  
Our study site is relatively sheltered. In a more exposed site, the balance between wave forcing and flood duration might shift. It seems unlikely that this would affect the relative importance of the other considered drivers (i.e., competition and creeks). In the sheltered pioneer zone, which is relatively important with regard to the ecosystem service provision [62], it was found that flood duration is the most important driver behind spatial patterns.
# Nonlinear Vibration and Post-Buckling Behaviors of Metal and FGM Pipes Transporting Heavy Crude Oil
## 6 Numerical Results  
### Modeling Assumptions and Limitations  
In the present study, the fluid flow is modeled as one-dimensional and steady, without incorporating detailed fluid-structure interaction (FSI) phenomena such as boundary layer development, viscous shear forces, or transient pressure pulsations. This simplification allows a more tractable analysis of the nonlinear structural dynamics, though it may underrepresent dynamic coupling effects that become significant in highly unsteady flow conditions or near resonance frequencies. Furthermore, while temperature-dependent material properties are considered using a predefined empirical model, the thermal field within the pipeline is assumed to be uniform. This assumption neglects the possibility of radial temperature gradients across the pipe wall, which may arise in practical applications due to internal convective heat transfer or localized thermal inputs. Such gradients can induce thermal stresses and bending moments, particularly in functionally graded materials, as highlighted in prior studies. To address this, key literature has been cited that elaborates on the role of thermal moments and radial temperature variations in FGM structures [35]. Although this approach provides initial insights into the thermal-mechanical behavior of FGMs, we acknowledge that the uniform temperature assumption is a limitation.  
In future works or studies to enhance the current model, it can be extended to incorporate radial and axial heat conduction, realistic convective boundary conditions, and comprehensive FSI coupling to improve physical fidelity and predictive capability. Furthermore, the analysis may be expanded to include various boundary condition configurations, and tabulated results can be provided to enhance the clarity, reproducibility, and practical applicability of the findings.
## 3 Materials and Methods  
### System Architecture  
The weather station system architecture comprises four layers: application, middleware, communication, and sensor. The application layer uses mobile and web-based apps, with a chatbot as the user interface. The middleware layer handles data storage and web API, storing data in NoSQL and implementing a simple web API. Depending on the area, the communication layer uses wireless network communication, such as 3/4/5G, LoraWAN, or NB-IoT. This paper focuses on the sensor layer, with an IoT station attached to the weather sensor in the crisis area, collecting and sending data to the middleware layer or IoT cloud.  
The weather station consists of 4 modules: a weather sensor, a microcontroller, a network, and a power source. This weather station is designed to monitor environmental crises. Thus, the power consumption needs to be monitored. Moreover, the limitation of network bandwidth is a factor that affects the system. The microcontroller and weather sensor are the supporters of power consumption minimization.  
The power source is the vital module of the IoT station. This system selects the solar power system as the primary source. In a normal situation, the power source works with full performance. On the other hand, the power source is the system's decision. Thus, the capacity of solar cells and batteries needs to be calculated.  
The solar cells convert the solar energy into electrical energy stored in the batteries. The system employs the Maximum Power Point Tracking (MPPT) algorithm to optimize the solar power system. The battery capacity calculation also considers the worst-case scenario of 3 days without sunlight. The battery is also the module that the system can control to reduce power consumption in crises. Therefore, the battery capacity is selected based on the available energy balance equation. The following make up the software architecture:  
* The network module is the main power consumption module. Each network module consumes a different level of power that results in the total level of throughput. On the other hand, it represents the main objective of the monitoring system. If the network fails, the analysis module in the big data system cannot process continuously.
* The microcontroller is the system's computation, memory, and storage module. Power-aware programming needs to be a priority in resource-constrained devices. Power constraints need to be considered, especially because they affect the lifetime and stability of the system.
* Weather sensors are the collection of the sensor modules that collect sensing data. The type of sensor affects the accuracy of sensing data and power consumption, too.  
Our design proposes power management at the software level. The specific sensor, microcontroller, and network devices have yet to be determined. We plan to schedule the sensing and sending periods to reduce power consumption. Figure 2 illustrates our approach where the microcontroller turns the network module's power on/off to ensure the network module is active during the sending time and is turned off during inactive time. The solid line represents the power line.