esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Furthermore, condition monitoring of wheel treads has two notable features: (a) there exists multiple uncertainty sources including failure dependence, inherent temporal uncertainty, unit-to-unit heterogeneity due to materials and measurement errors and (b) the variation degree of vibration sensors is closely related to degradation velocity. Currently, there lacks a systematic prognostic framework that can effectively capture both features of wheel treads. To address the above-mentioned challenges, this paper proposes a parameter-related Wiener-process-based prognostic framework. Unlike existing RUL prediction models, the proposed model relates degradation variation to degradation rate quantitatively. To the best of our knowledge, this is the first attempt to formulate such correlation, which better reflects evolution pattern of degradation. Furthermore, multiple uncertainties including unit-to-unit uncertainty, failure mode uncertainty, measurement uncertainty as well as temporal uncertainty can be integrated into our framework, which better characterizes the health status of treads under non-stationary operation environment. Based on this framework, a recursive updating algorithm based on Kalman filter (KF) and expectation maximization (EM) technologies is proposed to realize dynamical prediction of RUL. The main contributions of this paper are threefold. 1. A novel parameter-related Wiener process model with consideration of multiple uncertainties is proposed for accurately characterizing the real degradation process of wheel treads. 2. A recursive algorithm, which integrates KF and EM algorithm, is established to update model parameters and RUL distribution with the updating of online monitoring data. 3. An investigation of real-world wheel tread signals is used to demonstrate the superiority of the proposed prognostic framework in accuracy improvement. 4. The rest of the paper is organized as follows. Section 2 introduces the proposed RUL prediction methodology. Section 3 conducts experiment using real monitoring data of wheel treads of high-speed train. Section 4 draws the main conclusions.
## 3 Results and Discussion  
As shown in Equation (22), the effects of rotational kinetic energy depend on the densities of the feed and draw solutions, which vary with concentration due to phenomena such as ICP, CECP, and DECP. By calculating these densities numerically or using mathematical models that account for these effects, it would be possible to couple the system equations and simulate the impact of rotational kinetic energy on these phenomena. Figure 3 illustrates the concentration variations across the bulk and membrane regions in both the active and support layers. The effective osmotic pressure, considering the impact of ICP, is expressed as \(\Delta\Pi=C_{2}-C_{3}\) and will be simulated using Equation (23) from [17].  
\[C_{3}=\frac{C_{2}\Big{[}B(e^{(I_{\text{pr}},K)}-1\Big{)}+I_{\text{pr}}\frac{C_ {3}}{C_{2}}e^{(I_{\text{pr}},K)}\Big{]}}{B\big{(}e^{(I_{\text{pr}},K)}-1\big{)}+ I_{\text{pr}}} \tag{23}\]  
To verify the analytical model behavior under typical PRO conditions, Equations (22) and (23), with the parameters in Table 1, are used to simulate the PRO system with rotational kinetic energy. The concentrations of the feed and draw solutions are used with a library developed by [18] for Engineering Equation solver (EES) software, version 10.834-3D, to calculate the values for the osmotic pressure and the densities.  
Figure 3: Schematic of the different concentrations related to a membrane in osmotic processes. Modified from [17].
## 11 Responses for the Incremental-N
### Noise  
In this section, the system will be subjected to noise in both \(Q_{s}\) and \(T_{s}\) in order to analyze its behavior.  
Figure 27 shows the responses of incremental-0 to step-type references for \(Q_{s}\) and \(T_{s}\), along with output noise of \(N\) (\(0,10^{-3}\)) and \(N\) (\(0,10^{-2}\)), respectively, where the first term is the mean of the distribution and the second is the variance. Also, Figure 27 illustrates the system's behavior for incremental-1 under the same inputs and noise.  
Figure 26: Responses of \(Q_{s}\) and \(T_{s}\) to variations in the parameter \(T_{1}\) using Incremental-2.  
Figure 25: Responses of \(Q_{s}\) and \(T_{s}\) to variations in the parameter (\(\alpha\)) using Incremental-1.  
It is clearly observed that Incremental-1 fails under the same noise conditions. This is because, as more incrementals are added, the noise is amplified due to the difference between the current and previous samples, which accumulates and becomes larger for the next incremental. On the other hand, if the output noise is \(N\) (\(0,10^{-4}\)) for \(Q_{s}\) and \(N\) (\(0,10^{-3}\)) for \(T_{s}\) (10 times lower than in the previous case), a better response is obtained (see Figure 28).  
Figure 29 shows the response of incremental-2 to ramp-type references for \(Q_{s}\) and \(T_{s}\), as well as output noise of \(N\) (\(0,10^{-5}\)) and \(N\) (\(0,10^{-4}\)), respectively. Also, Figure 29 shows the response of incremental-3 to second-order references for \(Q_{s}\) and \(T_{s}\), along with output noise of \(N\) (\(0,2\times 10^{-7}\)) and \(N\) (\(0,2\times 10^{-6}\)), respectively.  
Meanwhile, Figure 30 shows the response of incremental-4 to successive third-order references for \(Q_{s}\) and \(T_{s}\), along with output noise of \(N\) (\(0,9\times 10^{-10}\)) and \(N\) (\(0,9\times 10^{-9}\)), respectively. Also, Figure 30 presents the response of incremental-5 to successive fourth-order inputs for \(Q_{s}\) and \(T_{s}\), along with output noise of \(N\) (\(0,3\times 10^{-12}\)) and \(N\) (\(0,3\times 10^{-11}\)), respectively.  
It can be observed that as the number of incrementals increases, the noise variance must be reduced to prevent the system from failing drastically. Additionally, the noise has a greater effect on the output temperature.
## 3 Results
### Research Performance of Institutions  
The network is comprised of 489 nodes and 1186 links, exhibiting a modularity measure of 0.763 and mean silhouette coefficient of 0.920. However, a couple of the nodes involved numerous connections, indicating that those institutions had good communication and collaboration in this research area (Figure 4a). The findings also showed  
Figure 3: Showing (**a**) mapping cooperative metrics in climate change and forest WUE research; (**b**) 20 leading countries with the highest research frequency, and (**c**) highest centrality score, in this research domain. (In (**a**), circles represent nodes that are connected to each other by links (lines). Nodes with a pink border indicate centrality; the thicker the border, the higher the centrality; the color of the links represents the timeline of collaboration, ranging from dark blue to light yellow, indicating the period from 2000 to 2024.).  
that the Chinese Academy of Sciences is a leading research institution globally, and produced 260 articles on climate change and forest WUE (Table S3). Among the entire list of 20 leading institutions, 7 are Asian, 6 are from North American and Europe, and 1 is from Oceania. The majority of these prolific organizations (>90%) are public organizations as displayed in Table S3; this emphasizes the importance of studying climate change and forest WUE is regarded as a top priority at the level of government. The results also specified one Oceanian organization, namely Western Sydney University, which ranked among 20 leading higher research productivity organizations globally, while not a single institution from Africa, Central America, or South America was represented.  
The peak centrality score of 0.40 was documented for the Max Planck Institute for Biogeochemistry, Germany, which secured the premier position in the 20 leading institutions based on higher value of cooperative centrality (Table S4). The distribution of the top 20 institutions reveals 10 in North America, 7 in Europe, and 1 country in Asia, 1 from Oceania, and 1 from South America, indicating a notable deficiency of international collaboration research teams in African and Central American institutions (Table S4). This shows that these top 20 high-centrality institutions are playing the vital role in the enhancement of academic development of climate change and WUE research.  
Figure 4: Evidence showing (**a**) a collaboration network of research institutions, and (**b**) a collaboration network of authors in the domain of climate change and forest WUE. (The color of the links represents the timeline of collaboration, ranging from dark blue to light yellow in (**a**), indicating the period from 2000 to 2024, and in (**b**) the cluster classification is represented from the 0 th to the 9 th cluster, using colors that range from light yellow to dark orange.).
## 3 The PPBR Key Management Scheme
### The Initialization Phase  
The server generates polynomial pool \(F\) including \(Sp\)\(t\)-degree bivariate polynomials, the polynomial can be expressed as Equation (2):  
\[f_{k}(x,y)=\sum_{i,j=0}^{t}a_{ij}x^{i}y^{j} \tag{2}\]  
where \(k=1,2,\cdots,Sp\), and it satisfies \(f(x,y)=f(y,x)\).  
Each polynomial contains a unique identifier \(ID_{Pk}\) (\(k=1,2,\)\(\ldots\)\(S_{p}\)), and it creates the key pool \(K\) which contains \(S_{k}\) keys. Each key has a unique identifier \(ID_{Ki}\) (\(k=1,2,\)\(\ldots\)\(S_{k}\)) as well. Before deployment, the MS node needs to pre-store the following key information: the identifier \(ID_{MS}\), the polynomial ring which consists of \(M\) polynomials selected from the polynomials pool \(F\), and the key ring which consists of \(m\) keys selected from the keys pool \(K\) randomly by the server. On the other hand, sensor nodes need to pre-load the identifier \(ID_{i}\), the polynomial ring which consists of \(N\) polynomials selected from the polynomials pool \(F\), and the key ring which consists of \(n\) keys randomly selected from the keys pool \(K\) by the server. The MS and sensor nodes both need to store the hash function \(H\).
## 4 Conclusions  
In this study, the capabilities of state-of-the-art QC lasers for free-space communications operated in an MWIR spectral range are defined. The performed simulations show that weather conditions have a significant impact on data link performances. For \"clear\" atmosphere, a better link range can be obtained with 1.5 \(\upmu\)m wavelength (SWIR) because of lower attenuation by water vapor. If visibility decreases and air turbulence increases, the longer range is provided by longer wavelengths (MWIR). According to the parameters of the designed FSO laboratory models with pulsed QC laser (\(\sim\)3 W peak power, \(\sim\)4.5 \(\upmu\)m wavelength) and with cw QC laser (\(\sim\)20 mW average power, \(\sim\)4.8 \(\upmu\)m wavelength), link budgets were compared. A longer link distance was determined for the pulsed laser resulting from its 150-times higher radiation power. However, this difference is negligible in the case of strong scintillations. A preliminary test of these lasers indicated that the pulse duty cycle was more critical for a pulsed laser. It was also noticed that the cw laser could be operated with a much higher peak power (10 times), but with lower duty cycle, and therefore a compromise between data rate and link distance of the FSO system should be confirmed. Finally, the eye diagram tests of the FSO systems with two lasers were performed. The results indicated that both QC lasers can be used in FSO systems providing a modulation rate of 6 MHz (for the pulsed laser) and 100 MHz (for the cw laser).  
This research received no external funding.  
Not applicable.  
I would like to thank my colleagues [PERSON] and [PERSON] for their support. The research was carried out in the laboratory of the Institute of Optoelectronics MUT and supported by the Narodowe Centrum Badari i Rozwoju, grant no. MAZOWSE/0196/19-00. The assistance of the Siec Badavcza Lukasiewicz--Instytut Technologii Elektronowej ([PERSON]) by providing support with the quantum cascade laser technology is gratefully acknowledged.  
The author declares no conflict of interest.
## 4 Discussion  
Nafion and PSSA have been regarded as the most advanced membrane materials for CPC so far. Unfortunately, Nafion is not cost-effective for large-scale applications, and PSSA shows undesirable mechanical properties. Hence, we prepared a series of PVDF-g-SSS  
Figure 11: Selectivity of water/DMMP and water/CEPS for M1–M6.  
Figure 10: Vapor permeation of water, DMMP, and CEPS through M1–M6.  
membranes with different contents of GO-SSS (M1-M6) using a one-pot process. FT-IR, SEM, and XRD clearly proved the success of SSS grafting onto GO.  
The SEM image of GO and GO-SSS clearly shows that through in situ free radical polymerization, the polymer chain has been successfully grafted onto the GO sheet. The morphology of the membrane is uniform, dense, and non-porous. Generally, there are two mechanisms to control the penetration of substances in separation membrane materials. One is to screen substances of different sizes through their own pore structure, such as the microfiltration membrane and the nanofiltration membrane. The other is to screen substances with different physical and chemical properties through their own dissolution and diffusion properties, such as the water-oil separation membrane [24]. The SEM image of the composite membrane indicates that the selectivity of the membrane is not due to the pore size screening mechanism, but due to the dissolution-diffusion mechanism.  
The IEC measurement shows that the conversion rate of the SSS monomer is very high (>70%). It was found that WU and LSR decrease nonlinearly with the decrease in IEC. Contact angle measurement showed that doping with different contents of GO-SSS can significantly reduce the hydrophilicity of the membrane.  
The WVTR test shows that with the increase in the GO-SSS ratio, the WVTR of the composite membrane decreases, but the WVTR of the M1-M6 composite membranes exceeds 2000 g m\({}^{-2}\) days\({}^{-1}\), which can meet the requirements of CPC applications. The steam permeation test results show that when the GO-SSS addition ratio is 0.5%, the composite membrane can obtain a water/DMMP selectivity greater than 120 and a water/CEPS selectivity greater than 2200. The excellent selectivity of high-IEC membranes is due to the formation of interconnected SSS phases that allow water transmission while preventing the transmission of DMMP. Vapor penetration occurs through two steps: vapor adsorption and vapor diffusion [25]. The SSS group can form hydrated regions, namely water channels, which can improve the absorption of water. Simultaneously, hydrophobic CWA simulants are not favored by the water channels, leading to low absorption. On the other hand, water diffuses mainly through the hydrophilic phase, while DMMP diffuses mainly through the hydrophobic phase [9]. The greater the proportion of GO-SSS, the better the barrier properties of the composite film. However, as the doping amount of GO-SSS increases, the VP of water decreases, which causes the selectivity of the membrane to increase first and then decrease, so the optimal doping ratio of GO-SSS is 0.5%.
# Interplanetary Student Nanospacecraft: Development of the LEO Demonstrator ESTCube-2
## 2 ESTCube-2 Mission
### Payload Summary
#### 2.2.3 Corrosion Testing in Space (CTS)  
The CTS experiment is an advanced and more robust version of the formerly proposed CRE payload, which was supposed to study the radiation-shielding efficiency of multilayered smart materials and the corrosion behavior of coating materials [7].  
The CTS experiment will study the corrosion behavior of materials in LEO, where they are exposed to atomic oxygen. For this purpose, a compact satellite module has been developed according to patented corrosion testing systems technology [39], which simultaneously allows testing up to 15 materials. Exposure to atomic oxygen is expected to degrade these materials in space and trigger a detectable signal once the materials have suffered severe damage. Knowing the thickness of the materials and exposure time in orbit, it will be possible to calculate the corrosion rate for the tested materials.  
The module will study the corrosion behavior of a novel nanostructured coating [40; 41], graphene, and thin films of various metals such as Ag, Al, Au, Pt, and Ru in LEO (see Figure 6).
## 2 Criticality Indexes
### Water Quality Index  
Various water quality issues exist in WDNs, such as disinfection by-product formation, microbial regrowth, sediment deposition, and odor/taste of chlorine [21]. However, these issues are difficult to simulate and it is hard to develop a quantification index for them. Meanwhile, water age is relatively simple to simulate, thus can be used as an indicator for measuring water quality in WDNs. Water age of a node is defined as the time for water to travel from a source to the particular node. Here, the water age is calculated using a chemical option in EPANET. A chemical with zero decay is put at the source and elapsed time is recorded for individual nodes when a node firstly detects the arrival of the chemical. Here, the water age growth due to pipe isolation is measured using a water age degradation (_WAD_) index. The nodal demand is considered as a weighting factor so that a node with higher demand has a greater impact on the index. Equation (5) expresses the calculation of the _WAD_ index.  
\[WAD_{i}=\frac{\sum_{j=1}^{N}\left(1-\frac{T_{n,j}}{T_{e,j}}\right)\times Q_{n, j}}{\sum_{j=1}^{N}Q_{n,j}}, \tag{5}\]  
Here, \(WAD_{i}\) is the water age degradation index with the isolation of pipe \(i\); \(T_{e,j}\) is the water age at node \(j\) after the event; and \(T_{n,j}\) is the normal water age at node \(j\).
# Thermodynamics and Magnetism of SmFe\({}_{12}\) Compound Doped with Co and Ni: An Ab Initio Study
## 4 Magnetic Properties of the Sm(FeCo-Ni)\({}_{12}\) Alloys
The differences between \(a\) and \(c\) for the iron-rich compound SmNi\({}_{4}\)(Fe\({}_{1-x}\)Co\({}_{x}\))\({}_{8}\) (here \(x=0\), 0.1, and 0.2) are actually very small, while for \(x=1\) the total atomic volume decreases by about 4%. We present the calculated equilibrium atomic volume (\(\Omega_{0}\)) and \(\mu_{0}H_{a}\) (with \(H_{a}=2K_{1}/M_{\rm s}\)) [17] in Table 5.  
As was mentioned in [17; 180], the relation (interchange) between the magnetic anisotropy and saturation magnetization is the key criterion for a magnet to be fabricated in any possible shape with a good chance of resisting self-demagnetization. This criterion may be formulated in terms of the magnetic hardness parameter of the materials, \(\kappa=\sqrt{\left(\frac{K_{1}}{\mu_{0}H_{a}^{2}}\right)}\), where \(\kappa>1\) is an empirically required rule to fabricate a good permanent magnet. As  
Figure 7: The samarium magnetic moments for various theoretical models (see main text) of the SmNi\({}_{4}\)(Fe\({}_{1-x}\)Co\({}_{x}\))\({}_{8}\) alloys, \(x=0.0\), 0.1, 0.2, 1.0.  
reported in Table 5, all calculated magnetic hardness parameters of the SmNi\({}_{4}\)(Fe\({}_{1-x}\)Co\({}_{x}\))\({}_{8}\) magnets meet the manufacturability criteria (i.e., \(x>1\)). Finally, the compositional dependence of the anisotropy field (\(\mu_{0}H_{\rm a}\)) of the SmNi\({}_{4}\)(Fe\({}_{1-x}\)Co\({}_{x}\))\({}_{8}\) alloys is shown in Figure 8. We find that on the iron-rich side of the compound, adding a small amount of cobalt increases \(\mu_{0}H_{\rm a}\) in a linear fashion.
# Multi-Agent Reinforcement Learning: A Review of Challenges and Applications
## 2 Background
### Multi-Agent Framework
#### 2.1.1 Markov Decision Process  
In the single-agent RL, the environment is usually modeled as a Markov decision process (MDP).  
Formally, a Markov decision process is defined by a tuple \((S,A,P,R,\gamma)\), where  
* \(S\) is the state space;
* \(A\) is the action space;
* \(P:S\times A\rightarrow\delta(S)\) is the transition probability from state \(s\in S\) to \(s^{\prime}\in S\) given the action \(a\in A\);
* \(R:S\times A\times S\rightarrow\mathbb{R}\) is the reward function, whose value is the reward received by the agent for a transition from the state-action pair \((s,a)\) to the state \(s^{\prime}\);
* \(\gamma\in[0,1]\) is the discount factor and is a parameter used to compensate for the effect of instantaneous and future rewards.
## 3 Delay and Energy Consumption Analysis
### Energy Consumption Analysis  
Based on the delay models for each FSA protocol variant presented in the previous subsection, we can now derive the energy consumption of the network coordinator and the end-devices by determining their operation states in each slot of each frame in the contention process.  
In general, these devices have four different operation states (transmitting a packet, receiving a packet, idle-listening or sleeping) that is associated with a power consumption (i.e., \(\rho_{tx}\), \(\rho_{rx}\), \(\rho_{\sigma}\) and \(\rho_{sleep}\), respectively). Notice that in this model we assume that the energy required to switch between sleep and active modes (i.e., transmitting, receiving and idle-listening) is negligible. Hence, in the following subsections we derive the energy consumption of the network coordinator and the end-devices.
## 3 Results
### Validation of VIs and SIF Maps from HyPlant  
The validation of airborne derived VIs and SIF was based on the comparison of HyPlant-derived VIs and SIF from DUAL and FLUO modules with simultaneously taken ground-based observations. HyPlant-derived vegetation products showed generally good agreement with the ground-based measurements (Figure 5a-d), with R\({}^{2}\) values of 0.89, 0.91, 0.90, 0.84 for SR, NDVI, EVI, and PRI, respectively. However, HyPlant-derived SR and EVI values were underestimated at the upper value range (i.e., above 6.0 and 0.4 for SR and EVI, respectively) and overestimated in the lower one. Large agreement between airborne and ground observation was found for NDVI, whereas PRI values were slightly underestimated in the whole range of the index values.  
The HyPlant FLUO module derived TOC SIF values showed a strong correlation with ground-based measurements (Figure 5e,f). SIF showed a strong correlation in the red and far-red regions observed by the R\({}^{2}\) of 0.92 and 0.87 for SIF\({}_{760}\) and SIF\({}_{687}\), respectively. Higher values of SIF\({}_{760}\) and SIF\({}_{687}\) were observed for the _Carex gracilis_- and _Carex lasicoarpa_-dominated parts of the peatland (points V1-V2) at the outer zones, and then SIF signals become weaker towards the middle of the peatland and decreased with decreasing biomass of vascular plants, as indicated by lower Leaf Area Index (LAI) (see Figure 6  
Figure 4: Airborne maps of the different vegetation indices from the experimental site, derived from DUAL module (370–2500 nm) of the HyPlant with a spatial resolution of 1 m \(\times\) 1 m per pixel. The data was recorded on 11 July 2015, and was acquired during the afternoon overpasses of HyPlant. (**a**) Simple Ratio (SR), (**b**) Normalized Difference Vegetation Index (NDVI), (**c**) Enhanced Vegetation Index (EVI), (**d**) Photochemical Reflectance Index (PRI), (**e**) SIF map for O\({}_{2}\)A (760 nm), and (**f**) SIF map for O\({}_{2}\)B (687 nm). The associated range of each of the vegetation indices and SIF maps is represented in color stretch on the left.  
and Table 2). The lowest SIF values were observed at the _Sphagnum_-dominated parts of the peatland (V8-V9) where the LAI of vascular plants was the lowest (Table 2).  
Figure 5: Validation of airborne HyPlant-derived VIs and SIF with ground observations on corresponding plots; (**a**) SR, (**b**) NDVI, (**c**) EVI, (**d**) PRI, (**e**) SIF at 760 nm, and (**f**) SIF at 687 nm. Error bars represent the spatial variability of the index values within the selected plots during HyPlant overpasses. Note: HyPlant SIF values were retrieved from the rescaled data (for details see Section 2.6.  
Figure 6: Relationship between HyPlant-derived SIF and LAI and fAPAR at the ground validation plots. Note: HyPlant SIF values were retrieved from the rescaled data (for details see Section 2.6).