esa-sceva/satcom-chunk-collection huggingface dataset:
### Model Description and Notations  
#### State Description  
Table 1 describes the different potential transient states of the system. States labeled \(S_{0},S_{1},S_{2},S_{3},S_{4},S_{5}\), and \(S_{6}\) represent active states, while \(S_{7},S_{8},S_{9}\), and \(S_{10}\) denote inactive states within the system. In Figure 2's transition diagram, \(S_{0}\) represents the initial fully functional state. Subsequent states, such as \(S_{1},S_{3},S_{4},S_{5}\), and \(S_{6}\), indicate varying degrees of partial failure or degradation within the system. States \(S_{7},S_{8},S_{9}\), and \(S_{10}\) represent complete failure states. When a unit within subsystem 1, 2, or 3 fails, the system transitions to partially failed states \(S_{1},S_{3}\), or \(S_{5}\) respectively. If two units fail within any subsystem, the system progresses to major partially failed states, namely \(S_{2},S_{4}\), or \(S_{6}\). Complete failure states \(S_{7},S_{8}\), and \(S_{9}\) occur when all three units within their respective subsystems fail simultaneously. State \(S_{10}\) represents complete failure specifically caused by a ZooKeeper failure.  
\begin{table}
\begin{tabular}{c l} \hline
**State** & **Description** \\ \hline \(S_{0}\) & At this stage, it is expected that all components of the subsystem are operating correctly and without any issues. \\ \hline \(S_{1}\), \(S_{3}\), & The mentioned state has worsened and is considered to be a minor failure within a system. However, despite the failure of any single unit within subsystems 1, 2, and 3, the overall system remains operational as two units are still functioning properly. Efforts are underway to restore the system through comprehensive repairs. \\ \hline \(S_{2}\), \(S_{4}\), & After experiencing the malfunction of at least two units within subsystems 1, 2, and 3, the system has reached \\ \(S_{6}\) & a significantly impaired operational state, requiring ongoing repairs to restore full functionality. \\ \hline \(S_{7}\), \(S_{8}\), & These states have experienced significant breakdowns due to the failure of more than two units within subsystems \\ \(S_{9}\) & 1, 2, and 3. To address this issue, the Gumbel–Houagard copula distribution is being employed to rectify the system. \\ \hline \(S_{10}\) & Due to a malfunction in the ZooKeeper unit, subsystem 2 has completely stopped working. The issue was resolved using copula distribution. \\ \hline \end{tabular}
\end{table}
Table 1: Explain the present state of the system.  
Figure 1: Block diagram of Apache Kafka system.
# Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
## 4 Discussion
### Background Reflectance Affects the Accuracy of Model  
A forest has a complex hierarchical structure in the vertical direction. Temperate forests have two vertical layers: a forest canopy composed mainly of trees and understory vegetation composed mainly of shrubs, herbs and mosses [43]. The reflected signals received by the sensor come not only from the forest canopy, but also from the background features under the forest canopy [44]. The results show that forest background reflectance has significant spatiotemporal variation, and the spatiotemporal variation in background reflectance is usually ignored in forest parameter inversion studies based on remote sensing data, resulting in significant error of the inversion model, and the error of the inversion model is larger when the forest canopy is low [45; 46]. As shown in Figure 8, the reason for the deviation between the simulated forest stand age and the reference forest stand age of young forest and old forest may be directly related to the background reflectance. The canopy of young forest is small, the coverage is insufficient and the pixel reflectance mainly comes from the forest background [47]. As a result, the model overestimates young forest stand age. From the perspective of the inversion of forest canopy parameters, the impact of forest background reflectance cannot be ignored. In a sparse forest area, the difference in forest background reflectance leads to a great difference in canopy reflectance [48]. It was observed that the total reflectance of the same forest area when the background reflectance is high is much larger than that when the background reflectance is low under the same forest period in the direction of substellar points in the red wavelength band [49].  
Therefore, the inversion of forest parameters by ignoring forest background reflectance leads to significant errors in the model. \(SR\) is the ratio of light scattered in the near-infrared to light absorbed in the red band, which reduces the influence of atmosphere and topography [27; 28]. So, background reflectivity is one of the key parameters for establishing a remote sensing estimation model of forest parameters.
## 3 History of Neuromylitis Optica (NMO)  
The term 'neuromyelitis optica' was first used by [PERSON] and [PERSON] in 1894 while working with patients who suffered bilateral ON and myelitis. NMO shares some clinical features with MS; therefore, NMO was long considered another clinical form of MS [11]. The term 'neuromyelitis optica acuta', translated from the French term 'neuromyelite optique aigue,' was first used by [PERSON] in 1894. Hence, NMO is also known as Devic syndrome [11].  
Over the next decades, different criteria were discussed with regard to the clinical feature of NMO, and the term NMO was changed over the years. NMO has also been referred to as \"autoimmune AQP4 channelopathy\" or \"autoimmune AQP4 disease\" [12], as well as AQP4-ab associated encephalomyelitisitis [11]. Initially, NMO was associated with the NMO-IgG antibody; later, NMO-IgG became AQP4-IgG, which binds with AQP4 and is identified as the target antigen. AQP4-IgG is recognized as a potential biomarker of NMO pathogenesis [13; 14]. Identifying AQP4 seronegative cases with NMO pathogenesis led to the discovery of another antigen called MOG [15]. Alternatively, the AQP4 seronegative NMO phenotype harbors the MOG-IgG antibodies and exhibits different disorders called MOG-associated disorders (MOGAD) [16]. Patients with AQP4-IgG seronegative showed the clinical phenotype of MOGAD with ON, encephalitis with brain lesions, or myelitis, cumulatively termed as MOG-IgG-associated ON, Encephalitis, and Myelitis [15].  
[PERSON] and his team have described how the presence of NMO-IgG differentiates the neuromylitis optica from multiple sclerosis. The discovery of a new class of NMO-IgG and its pathogenicity determines knowledge about neuromyelitis optica pathogenesis in treatment trials [12]. Later, the international panel for NMO diagnosis, which was assembled in 2015, revised the diagnostic criteria for NMO. The panel described the core clinical characteristics required for NMO patients with or without AQP4-IgG. In addition to serological testing, the panel developed additional clinical criteria with neuroimaging techniques to diagnose NMO [17].  
Figure 1: Schematic representation of the PRISMA chart explaining the selection of studies.
# Reconstruction of Rainfall Field Using Earth-Space Links Network: A Compressed Sensing Approach
###### Abstract  
High-precision rainfall information is of great importance for the improvement of the accuracy of numerical weather prediction and the monitoring of floods and mudslides that affect human life. With the rapid development of satellite constellation networks, there is great potential for reconstructing high-precision rainfall fields in large areas by using widely distributed Earth-space link (ESL) networks. In this paper, we have carried out research on reconstructing high-precision rainfall fields using an ESL network with the compressed sensing (CS) method in the case of a sparse distribution of the ESLs. Firstly, ESL networks with different densities are designed using the K-means clustering algorithm. The real rainfall fields are then reconstructed using the designed ESL networks with CS, and the reconstructed results are compared with that of the inverse distance weighting (IDW) algorithm. The results show that the root mean square error (RMSE) and correlation coefficient (CC) of the reconstructed rainfall fields using the ESL network with CS are lower than 0.15 mm/h and higher than 0.999, respectively, when the density is 0.05 links per square kilometer, indicating that the ESL network with CS is capable of reconstructing the high-precision rainfall fields under sparse sampling. Additionally, the performance of reconstructing the rainfall fields using the ESL networks with CS is superior compared to the reconstructed results of the IDW algorithm.  
Earth-space links (ESL) networks; compressed sensing (CS); rainfall fields; high-precision; performance
# A Survey on MLLMs in Education: Application and Future Directions
## 3 Applications of MLLMs in Education
### Adaptive Learning Platforms
#### 3.1.4 Case Study: Integration of MLLMs in Duolingo  
Duolingo, a widely used language learning platform, provides an illustrative case of integrating MLLMs into adaptive learning. Aiming to make language learning accessible and engaging through gamified lessons and personalized learning paths [44, 46], the platform adapts to learners' proficiency levels, focusing on areas that need improvement.  
Duolingo incorporates MLLMs to enhance its adaptive capabilities. It uses speech recognition and synthesis technologies, employing models such as WaveNet [47] for high-quality speech synthesis and integrating speech recognition to assess pronunciation and fluency. Using MLLM, Duolingo generates and interprets textual, auditory, and visual content, providing a comprehensive learning experience [43]. It uses data from the students across the modalities to tailor the difficulty of the lesson, the types of content, and the feedback [48].  
For example, MLLMs enable Duolingo to create interactive exercises that involve listening, speaking, reading, and writing, catering to different learning styles. By processing speech and written input, the platform provides immediate, personalized feedback, helping learners correct errors in real time. Analyzing learner performance allows the platform to adjust the sequence of lessons and introduce new vocabulary or grammar concepts when appropriate.  
A study examined how Duolingo leverages AI and MLLM to improve language learning [43]. The findings indicated improved engagement: the integration of multimodal exercises increased user engagement and time spent on the platform. The learners showed enhanced proficiency gains due to personalized feedback and adaptive content. MLLMs allowed Duolingo to generate content across multiple languages efficiently, supporting a diverse user base.  
However, challenges such as data privacy emerged, as handling multimodal learner data raised concerns about privacy and data security. In addition, resource intensity was a concern, as training and deployment of MLLMs require significant computational resources.
## 5 Conclusions  
This paper proposed a new numerical approach solving the multi-domain physical problem of the direct operating solenoid valve. A precise model of the valve was developed to investigate the performance of the direct operating solenoid valve. The accuracy of the simulation results was verified by the experimental data in the test rig. Effect of various forces, including the magnetic force, the spring force, the viscous force, the feedback force on the pressure response of the direct operating solenoid valve were investigated. Furthermore, the influences on the dynamic characteristics of the valve were analyzed based on the model. The leakage flow in different clearance has been compared. As a consequence, the conclusions can be drawn:  
1. The simulation results of this study agreed with the experimental results. Thus, the mathematical model developed in this study was effective and accurate.
2. Both the magnetic force and the viscous force had significantly influence on the pressure response. The pressure response time would be shortened if the magnetic force responded faster or the viscous force was reduced.
3. To improve the response time of the solenoid valve, the clearance height should be reduced. The resultant force on the spool in 10 \(\upmu\)m would reach the equilibrium point 0.1 s earlier than that in 30 \(\upmu\)m.
4. The clearance height was proved to have great influence on the leakage of the solenoid valve. The leakage increases with the growing clearance height, which showed the leakage in 30 \(\upmu\)m was triple the amount of that in 20 \(\upmu\)m.
5. The leakage of the shifting control system employing the direct acting solenoid valve can be reduced by 60% compared to the conventional two-stage pilot valve in our previous product.  
This paper could benefit the researchers dealing with the direct operating solenoid valve design for pressure smooth control and energy conservation in the shifting control system of an automatic transmission.  
This work was supported by the National Natural Science Foundation of China (grant number 51405010).  
[PERSON] and [PERSON] designed and conducted the experiments and wrote the paper. [PERSON] is the research advisor, who proposed the structure of the paper. [PERSON] and [PERSON] were responsible in improving of the quality of this article.  
The authors declare no conflict of interest.
# A High Efficiency Multistage Coder for Lossless Audio Compression using OLS+ and CDCCR Method
## 7 Adaptive Golomb Code and Context Adaptive Binary Arithmetic Coder
### The Way of Determining the Number of Contexts  
Binary streams representing \(u_{G}\) and \(v_{G}\) are coded using separate arithmetic coders, with separate ways to determine the context number. The \(ctx_{u}\) context number used to encode the \(u_{G}\) series with zeros terminated with one is calculated for each consecutively coded bit as follows,  
\[ctx_{u}=2^{3}\cdot(18\cdot b_{\text{Golomb}}+2\cdot b_{\text{medium}}+b_{ \text{ultra}})+b_{\text{unary}} \tag{33}\]  
The \(b_{\text{unary}}\) value is a three-bit number (in the range from 0 to 7) specifying the number (counting from the most to the least significant) of the currently coded unary bit of the \(u_{G}\) number. When the bit number is greater than 7 (which takes less than 1% of cases) then \(b_{\text{unary}}=7\). We therefore obtain \(2^{3}\cdot 18\cdot 40=5760\) contexts \(ctx_{u}\).  
In the case of the second coder used to encode the \(v_{G}\), number, there are slightly fewer contexts, because \(2^{4}\cdot 5\cdot 40=3200\). The \(ctx_{v}\) number is calculated from the dependency  
\[ctx_{v}=2^{4}\cdot(5\cdot b_{\text{Golomb}}+b_{\text{phased-in}})+2^{3}\cdot b _{\text{binary2}}+2^{2}\cdot b_{\text{binary1}}+b_{\text{unary2}} \tag{34}\]  
where \(b_{\text{unary2}}\) is equal \(b_{\text{unary2}}=\min\{b_{\text{unary}},\,3\}\). The \(b_{\text{binary1}}\) is the oldest (first in coded order) bit of value \(v_{G}\). The \(b_{\text{binary2}}\) is the second oldest (second coded) bit of value \(v_{G}\). Although \(b_{\text{phased-in}}\) is a number (in the range from 0 to 4) specifying the number (counting from the more-significant to less-significant bits) of currently coded bits of value \(v_{G}\), if the bit number is greater than 4, then \(b_{\text{phased-in}}=4\). Furthermore, when \(b_{\text{phased-in}}=0\), then \(b_{\text{binary1}}=b_{\text{binary2}}=0\) is set, and if \(b_{\text{phased-in}}=1\), then \(b_{\text{binary2}}=0\).
## 2 Results and Discussion
### Volatile Composition
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{**RI**} & \multirow{2}{*}{**Threshold (mg/L)**} & \multirow{2}{*}{**Sensory properties**} & \multicolumn{2}{c}{**Concentration (ug/L)**} \\ \cline{4-6}  & & & & \multicolumn{1}{c}{**F-Land**} & \multicolumn{1}{c}{**LS-Land**} & \multicolumn{1}{c}{**HS-Land**} \\ \hline Alcohols & & & & & & \\ \hline
1-Propanol & 1057 & 306 [55] & Fresh, alcohol & 16659.0 \(\pm\) 880.5\({}^{\circ}\) & 30857.9 \(\pm\) 1609.7\({}^{\circ}\) a & 21572.9 \(\pm\) 503.0 b \\ Isobutyl alcohol & 1111 & 40 [5] & Fusel, alcohol & 10060.8 \(\pm\) 72.4 c & 14970.7 \(\pm\) 144.7 b & 19902.2 \(\pm\) 112.6 a \\
1-Butanol & 1149 & 150 [35] & Medicinal, alcohol & 1332.0 \(\pm\) 140.3 b & 1487.7 \(\pm\) 136.7 a & 1207.7 \(\pm\) 18.5 c \\ Isoamyl alcohol & 1209 & 30 [5] & Cheese & 51052.5 \(\pm\) 36.8 c & 55280.9 \(\pm\) 1131.0 b & 67458.6 \(\pm\) 759.6 a \\
1-Pentanol & 1268 & 80 [36] & Fruily, balamic & 6.0 \(\pm\) 0.5 & 134.4 \(\pm\) 1.3 \\
4-Methyl-1-pentanol & 1309 & 50 [37] & NA & nd & 132.7 \(\pm\) 4.8 & nd \\
1-Hexanol & 1348 & 8 [5] & Green, grass & 13042.1 \(\pm\) 13.2 c & 2851.6 \(\pm\) 49.3 a & 1559.3 \(\pm\) 160.9 b \\ (E)-3-hexen-1-ol & 1354 & \(4\times 10^{-1}\) [5] & Green, floral & 16.5 \(\pm\) 0.6 c & 55.1 \(\pm\) 2.3 a & 45.3 \(\pm\) 5.5 b \\ (E)-3-hexen-1-ol & 1378 & \(4\times 10^{-1}\) [5] & Green & 3.54 \(\pm\) 2.5 c & 580.2 \(\pm\) 10.1 a & 107.1 \(\pm\) 7.8 b \\
2-Octanol & 1417 & \(1.3\times 10^{-1}\) [36] & NA & tr & nd & nd \\
1-Octanol-3d & 1445 & NA & NA & 382.2 \(\pm\) 2.4 & nd & nd \\
1-Hephenol & 1448 & 1 [36] & Grape, sweet & 71.9 \(\pm\) 0.9 c & 191.9 \(\pm\) 9.6 a & 88.9 \(\pm\) 5.0 b \\ _lev_-23-Butanedio & 1542 & 120 [38] & Butter, creamy & 1832.2 \(\pm\) 10.4 b & 805.8 \(\pm\) 3.5 a & 791.2 \(\pm\) 45.4 a \\
1-Octanol & 1554 & \(1.3\times 10^{-1}\) [36] & Intense times, rosses & 176.7 \(\pm\) 0.7 b & 39.8 \(\pm\) 1.2 a & 3.3 \(\pm\) 0.3 c \\
3-Methylthio-1-propanol & 1726 & \(5\times 10^{-1}\) [5] & Boloid potato, rubber & 2014.1 \(\pm\) 14.3 b & 2796.6 \(\pm\) 3.2 a & 2900.4 \(\pm\) 66.9 a \\
1-Decanol & 1781 & \(4\times 10^{-1}\) [5] & Orange flowery, special fatty & 10.9 \(\pm\) 0.7 & 12.8 \(\pm\) 0.3 & nd \\ Benzyl alcohol & 1894 & 200 [39] & Citrus, sweet & 352.3 \(\pm\) 2.7 b & 604.8 \(\pm\) 4.5 a & 622.7 \(\pm\) 14.6 a \\
2-Phenylethanol & 1928 & 10 [5] & Flowery, pollen, perfumed & 11434.8 \(\pm\) 608.3 b & 31433.8 \(\pm\) 1228.5 a & 30600.5 \(\pm\) 50.7 a \\ Citronell & 1767 & \(1\times 10^{-1}\) [5] & Green lemon & 2.6 \(\pm\) 0.1 c & 14.0 \(\pm\) 0.3 a & 5.1 \(\pm\) 0.1 b \\ Subtotal (ug/L) & & & 94619.3 & 142116.3 & 146878.6 \\ Proportion (\%) & & & 41.1 & 41.4 & 40.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: GC-MS analytical results of aroma components in Cabernet Franc wines from the different terrains.
## 3 Results and Discussion
### The Effect of Volume Flow Rate and Temperature on Conversion
On the other hand, there are discernible differences between the experimental data and the kinetic model at low flow rates in the reactor system with a T-shaped reactor, indicating a lack of mixing under these conditions. In this case, reactor performance depends on both chemical kinetic and mass transport in the reactor, providing higher conversions due to lower volume flow rates. Behavior such as this is called the chemical/diffusional regime [49]. In this case, several additional mixing elements, or a more complex reactor structure, are necessary to give a much faster mixing rate than the reaction rate [14; 19; 49; 50]. The parallel multilamination mixing principle, which is utilized in the interdigital microreactor, provides fast mixing across the entire range of the investigated volume flow rates. In the microreactor system with a chicane microreactor, mixing intensification is mainly excessive. Homogeneous mixing can be achieved after the elements of the chicane channel are first mixed at all volume flow rates. Figure 11: Mixing in a chicanes microreactor at different volume flow rates: (**a**) 0.1 mL/min; (**b**) 5 mL/min. The remaining elements serve as a residence time unit with complex geometry. A similar microreactor with a lower number of chicane mixing elements could be sufficient to carry out such a slow reaction. Similar results for reactor systems based on microfluidic devices were observed in the article by [PERSON] et al. [PERSON] _et al._ (2018). The authors evaluated mixing time lower than one second at high flow rates. Comparing these mixing times with the reaction time-scale needed for the ethyl acetate saponification, it can be concluded that such a fast mixing rate is not necessary for such a slow reaction. Regardless, reaction systems with interdigital and chicane microreactors provide mixing of constant quality. This is especially important at lower volume flow rates because under these conditions, higher conversions can be achieved.
## 4 Prediction of Shanghai's Carbon-Peaking Implementation Path Based on STIRPAT Model  
### Extension of the STIRPAT Model Construction  
The STIRPAT model is capable for capturing the comprehensive impact of social, economic, and technological driving factors on carbon emissions. Its basic expression is as follows:  
\[I=aP^{b}A^{c}T^{d}u \tag{1}\]  
In the equation, \(I\), \(P\), \(A\), and \(T\) represent the carbon emissions, population factor, wealth factor, and technological level, respectively; \(a\) represents the model coefficient; \(b\) represents the population elasticity coefficient; \(c\) represents the wealth elasticity coefficient; \(d\) represents the technological elasticity coefficient; and \(u\) represents the model's error term. To comprehensively consider the influences of the population size, per capita GDP, urbanization rate, industrial structure, energy structure, and energy intensity on carbon  
Figure 1: Research framework.  
emissions in Shanghai, this study introduces these factors to the basic STIRPAT model and constructs a multivariate nonlinear model as follows:  
\[I=aP^{b}A^{c}U^{d}IS^{e}ES^{f}EI^{g}\;u \tag{2}\]  
In the equation, the meanings of the variables are shown in Table 1. The coefficient \(a\) represents the model coefficient, while \(b\), \(c\), \(d\), \(e\), \(f\), and \(g\) represent the elasticities of the corresponding variables.  
Taking the logarithm of both sides of Equation (2) yields the following linear model:  
\[lnI=ln(a+u)+blnP+clnA+dlnU+elnIS+flnES+glnEI \tag{3}\]  
Considering the existence of the environmental Kuznets curve (EKC) [40], this study includes the quadratic term of the per capita GDP in the model. Thus, the STIRPAT model is further extended to incorporate this term as follows:  
\[lnI=ln(a+u)+blnP+c_{1}lnA+c_{2}(lnA)^{2}+dlnU+elnIS+flnES+glnEI \tag{4}\]