esa-sceva/satcom-chunk-collection huggingface dataset:
# CMOS Fixed Pattern Noise Removal Based on Low Rank Sparse Variational Method
## 3 Motivation for Presenting this Method  
Most scene-based correction methods start directly from the observation image to estimate the clear image, and lack some structure information about the strip itself. Then, the structural information of the strip is the key to improve the correction quality. This paper proposes an FPN correction method, based on the details of the image and the stripe itself. By analyzing the structure of CIS, an observed image can be roughly decomposed into three parts (Figure 7U,S,N). Figure 7Y is from the rice grain image in the quiet area of the sun taken by the high-resolution imaging terminal of the one-meter infrared solar tower of Yunnan Observatory, Chinese Academy of Sciences.  
\[\text{Y}=\text{U}+\text{S}+\text{N}. \tag{2}\]  
Y is a noise image, U is a clear image to be estimated, N is the comprehensive image composed of PFPN, dark current, Poisson, and other random noises, and S is a CFPN. We need to estimate the values of S and U from Equation (2). Observations show that the equation is an underdetermined equation with more unknown quantities than the given data, but it can be converted into a well-defined equation with reasonable prior constraint information added. How to find constraint information, model, and solve the equation will be the key to this article. By analyzing the noise image, the following clues can be found:  
1. The specific directional structure of CFPN.  
The vertical gradient histogram of noise image is very similar to that of clear image. From Figure 8 (histogram probability distribution of Figure 7Y,U), it can be found that the vertical gradient histogram distribution of the two images is very similar. The vertical gradient similarity between the two can be guaranteed by using the sparse constraint term of \(\|\frac{\partial\Upsilon}{\partial y}-\frac{\partial U}{\partial y}\|_{1}\).  
Figure 7: Noise image composition. (\(\Upsilon\)) noise image; (**U**) clear image; (**S**) column fixed pattern noise (CFPN); (**N**) random noise.  
2. The Striped noise has the characteristic of low rank.  
Through the analysis of the CMOS output structure, it can be found that each column shares an output amplifier. Therefore, low rank can be used to describe the characteristics of CFPN.
3. Gaussian distribution of CFPN  
\(\Delta V_{A_{out}}\) has the characteristic of random Gaussian distribution [36], so we can add \(\|S\|_{2}^{2}\) to express this random characteristic.
4. Structural similarity before and after noise removal  
In order to maintain the structural similarity before and after noise removal, the constraint of 2 norm fidelity term \(\|Y-U-S\|_{2}^{2}\) can be added.
5. Minimum variation of clear image  
According to the correlation theory of total variation denoising, we know that the clear image has the characteristics of low variation value [37]. The regular term constraint of \(\alpha_{2}\|\frac{\partial U}{\partial x}\|_{1}+\alpha_{3}\|\frac{\partial U }{\partial y}\|_{1}\) can be added.
# Increasing Ecosystem Fluxes Observed from Eddy Covariance and Solar-Induced Fluorescence Data
## 3 Results  
### Relationships Between SIF and Ecosystem Fluxes  
We examined the relationships between 8-day SIF and GPP, as well as between SIF and ET, at global FLUXNET sites (Figure S2 and Table 1). All SIF products showed positive correlations with the observed GPP, with correlation coefficients (r) of 0.78, 0.7, and 0.69 (\(p\) < 0.05) for CSIF, GOSIF, and RTSIF, respectively. The slopes (g C m\({}^{2}\) day\({}^{-1}\)/m W m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\)) were 21.68 (23.02), 21.82 (26.63), and 12.35 (14.86) with (without) intercepts for the three SIF products (Figure S2a-c), respectively. Among various PFTs, the SIF-GPP relationships showed positive correlations from 0.59 to 0.91 for CSIF, 0.21 to 0.9 for GOSIF, and 0.19 to 0.9 for RTSIF products (Table 1), with the highest correlations observed in mixed forest (MF) and the lowest in evergreen broadleaf forest (EBF). For forests, excluding EBF due to its weaker relevance [28], the SIF-GPP correlations were higher (0.73 to 0.90) compared to non-forest ecosystems (0.61 to 0.79) for all SIF products. Additionally, the SIF-GPP slopes were higher for forests, ranging from 19.59 to 21.07 g C m\({}^{2}\) day\({}^{-1}\) per unit SIF, indicating the stronger reabsorption of SIF by more complex canopy structures [30; 65]. In comparison, the SIF-ET relationships showed lower correlations, with an r of 0.59-0.67 (\(p\) < 0.05) across the three SIF products (Figure S2d-f). The linear slopes of SIF-ET (mm hr\({}^{-1}\)/m W m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\)) were 0.26, 0.27, and 0.15 for CSIF, GOSIF, and RTSIF, respectively. Among the various PFTs, positive SIF-ET correlations were found, ranging from 0.51 to 0.75 for CSIF, 0.41 to 0.77 for GOSIF, and 0.4 to 0.75 for RTSIF. The lowest correlations were found for EBF and wetland (WET) sites (Table 1). The linear slopes of SIF-ET ranged from 0.21 to 0.51 for CSIF, 0.19 to 0.52 for GOSIF, and 0.12-0.25 for RTSIF
## 4 Conclusions  
In this study, the characteristic volatile fingerprint of 20 foxtail millet varieties were successfully constructed by using E-nose, HS-GC-IMS, and HS-SPME/GC-MS. A total of 43 volatile compounds were identified in 20 foxtail millet samples: 34 and 18 by HS-GC-IMS and HS-SPME/GC-MS. From these, 39 volatile compounds were found in all varieties and the main volatile compounds in the foxtail millet were hexanol, hexanal, 1-pentanol, ethyl acetate, acetone, heptanal, 2-heptanone, and nonanal. Different varieties of foxtail millet usually exhibited different volatile compounds concentrations, although at similar compositions. Six volatile compounds, hexanal, heptanal, 1-pentanol, acetophenone,2-heptanone, and nonanal, contributed significantly to the volatile differences observed among the different varieties of foxtail millet. The results of this study indicate that the combination of E-nose, HS-GC-IMS, and HS-SPME/GC-MS could better retain the overall information of the volatile compounds of 20 different foxtail millet, and the application of multivariate statistical analysis made it able to distinguish foxtail millet in different varieties.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/foods12234273/s1](https://www.mdpi.com/article/10.3390/foods12234273/s1)]([https://www.mdpi.com/article/10.3390/foods12234273/s1](https://www.mdpi.com/article/10.3390/foods12234273/s1)), Figure S1: Chromatograms of the 20 foxtail millet varieties by E-Nose analysis, Table S1: The signal intensities of volatile compounds in 20 varieties of foxtail millet analyzed by HS-GC-IMS, Table S2: Contents (mean \(\pm\) standard deviation) of the volatile compounds tentatively identified in 20 varieties of foxtail millet by HS-SPME/GC-MS.  
Conceptualization, Y.G. and X.W.; methodology, Y.G.; software, Z.R.; validation, Z.R., W.M. and Y.L.; formal analysis, M.K.; investigation, Z.R.; resources, X.W.; data curation, M.K.; writing--original draft preparation, M.K.; writing--review and editing, M.K. and Y.G.; visualization, K.Z.; supervision, Y.G. and X.W.; project administration, Y.G. and X.W. All authors have read and agreed to the published version of the manuscript.  
The study was supported by PhD Research Startup Foundation of Shanxi Agricultural University, China (Grant No. 2021 BQ30) and Fundamental Research Program of Shanxi Province (Grant No. 20210302123402).  
Not applicable.  
Data Availability Statement: Data are contained within the article or Supplementary Material.  
The authors would like to thank [PERSON] from Shanxi Institute for Functional Food, Shanxi Agricultural University for providing E-nose and all those who contributed directly or indirectly to the project.  
The authors declare that they have no known competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.
# A Critical Review of SCWG in the Context of Available Gasification Technologies for Plastic Waste
## 2 Gasification Process
### Variables Affecting the Gasification Process
In this gasifier, gas flows upward through a bed of free-flowing granular material at a gas velocity sufficient to agitate the material into a churning emulsion of levitated particles and gas bubbles. The fluidized bed itself resembles a boiling liquid and has many of the same physical properties as a fluid. Typical bed materials used in this kind of gasifier are sand, olivine, limestone, dolomite, or alumina. Beds can be fluidized with the gasification agent, typically air, oxygen, and/or steam [34]. * Other types of reactors are used in the gasification of plastic material, such as fixed beds ([88; 89; 90]) and spouted beds reactors ([67; 91]). Fixed bed reactors represent the oldest and most proven technology for gasification. They are chosen for their simple design and low cost, but they are characterized by a poor heat transfer rate and a limited gas-solid contact. Fixed bed reactors are commonly used on small scale units and have many different designs. Spouted beds are gas-solid Contactors in which the gas is introduced through a single orifice from the center of a flat [92]. They are characterized by their high heat and mass transfer rates, good solid mixing, and suitable gas-solid contact. A disadvantage for their application deals with the short residence of the volatiles, which hinders tar cracking reactions [93].
## 9 Future Directions and Research Opportunities
### Integration with Smart Port Systems  
The integration of autonomous ships with smart port systems offers one of the most transformative opportunities for maritime logistics. Intelligent ports, enhanced by IoT technologies, big data analytics, and automation, can create a seamless operational continuum between vessel and shore. AI agents can coordinate with port authorities in real time to exchange information about vessel positioning, estimated arrival times, and berth allocations. This synchronization enables dynamic scheduling, reduces port congestion, and accelerates turnaround times (Figure 8).  
Autonomous vessels can also interface directly with automated port equipment, such as robotic cranes and guided vehicles, to streamline loading and unloading operations. Such coordination minimizes human error and enhances safety and efficiency. Additionally, smart ports can leverage AI-driven predictive maintenance systems that monitor the condition of critical infrastructure. By forecasting failures before they occur, ports can proactively address maintenance needs, thereby preventing delays and improving reliability.  
Beyond logistics, the collaboration between AI agents onboard ships and environmental monitoring systems within ports can support the real-time management of emissions, waste, and water quality. These joint sustainability efforts align with regulatory frameworks and demonstrate the value of integrated digital ecosystems in achieving both operational and environmental objectives [124; 125; 126; 127].
## 1 Introduction
The results of the 2014 edition of the Round Robin Test on Explosives (RRTE) indicate significant differences in the results of sensitivity determinations (shown in Table 1) depending on the laboratories carrying out tests and the porcelain components used (sensitivity was determined for identical samples of double-base propellant). Limiting the number of manufacturers of plates and pegs during these studies significantly reduced the discrepancies from the range of 96-288 N to the range of 96-160 N, leaving only room for differences caused by the human factor (Table 2) [9]. Furthermore, another discrepancy in the results of friction sensitivity determinations for penthrite and hexogen (according to the statistical Bruceton's method) is mentioned in STANAG 4487 (edition 2) [10]. The sensitivity of PETN ranges between 56 and 102 N, whereas the range for RDX is 110-185 N. Such differences may be due to possible wetting of the samples (resulting in the phlegmatisation of the material [11]); nevertheless, the influence of the applied porcelain components may also be the reason. The results of comparative tests between Polish laboratories (carried out in 2008) also show differences in the obtained values, with results ranging between 107 and 240 N for crystalline RDX samples tested according to the European standard [12]. [PERSON] highlighted the variation in the roughness of the available porcelain plates. He suggested using an emery paper while testing primary explosives owing to its normalized surface (in comparison with the lack of uniformity of roughness for plates) [13]. Both the plates and pegs for the BAM friction test have standardized dimensions. The grooves are made with a special sponge imprinted before firing the ceramics. The European standard [14] also specifies requirements for the surface roughness of the plates: the arithmetic mean of the plate profile deviations (_Ra_ parameter) should be between 6 and 15 \(\upmu\)m, with individual readings between 5 and 19 \(\upmu\)m. STANAG 4487 (edition 2) does not specify the roughness of plates. Currently, there are no requirements for checking the roughness of plates prior to friction sensitivity testing. However, the discrepancies in the results raise questions as to whether such a procedure should be introduced, especially when the examined explosives are on the edge of admission for civil use. According to European standards secondary explosives should not be sensitive to loads lower than 80 N (EN 13631-1:2005 [15]) whereas in the case of explosives used for detonating cords, the limit is 30 N (EN 13630-1:2003 [16]). This paper presents an attempt to determine the effect of porcelain plate roughness on friction test results. To this end, the profiles of selected types of plates available on the European market were measured and the number of scratches on the plates was visually assessed. The results of the measurements were compared with the results of subsequent sensitivity tests for penthrite and hexogen, according to [PERSON]'s statistical method and the European standard EN 13631-3:2005.
## 3 Results and Discussion
### LT-SCB Test  
(a) Both the \(G_{f}\) and \(K_{\text{IC}}\) of the asphalt mixture were influenced by its gradation (as shown in Figure 12). These two parameters showed a similar trend and magnitude for different gradations. Furthermore, a strong linear correlation (R\({}^{2}\) = 0.962 and \(p\)-value < 0.01) between the \(G_{f}\) and \(K_{\text{IC}}\) was observed, as shown in Figure 13. The higher the values of \(G_{f}\) and \(K_{\text{IC}}\), the better the performance and crack resistance of the asphalt mixture. Therefore, \(G_{f}\) and \(K_{\text{IC}}\) were recommended as indicators for evaluating the quality of the asphalt mixture.  
Figure 12: LT-SCB results of AR-SAMI.  
Figure 13: Correlation between \(G_{f}\) and \(K_{\text{IC}}\).  
(b) The \(G_{f}\) and \(K_{IC}\) of different gradation mixtures were different. The gradations of 10A-1 and 10B-1 had higher values of \(G_{f}\) and \(K_{IC}\), indicating better resistance to load-induced damage. As shown in Figure 12, the maximum difference in \(G_{f}\) between the gradations was 342 J/m\({}^{2}\), and the maximum difference in \(K_{IC}\) was 0.637 MPa\(\cdot\)m\({}^{0.5}\), with a maximum difference of 56.07%. The gradation of 10B-1 had the highest \(G_{f}\) and \(K_{IC}\), while the gradations of 10C-1 and 10C-3 had the lowest values, suggesting that there might be considerable variability within the same gradation range.  
(c) There were differences in the correlation between the grading parameters and the \(G_{f}\) and \(K_{IC}\). As shown in Figure 14, the asphalt film thickness had the best correlation with \(G_{f}\) and \(K_{IC}\), with R\({}^{2}\) values of 0.6873 and 0.6916, respectively. These values were both greater than 0.6, indicating a strong linear relationship. Therefore, the asphalt film thickness was selected as a parameter to further analyze the effect of gradation changes on the performance of the asphalt mixture.  
(d) The \(G_{f}\) and \(K_{IC}\) of the asphalt mixture decreased as the asphalt film thickness increased (as shown in Figure 15). The reason for this was that when the asphalt film thickness reached a certain value, further increasing the thickness would result in some free asphalt inside the mixture, which made the mixture more susceptible to damage at low temperatures.  
Figure 14: R\({}^{2}\) of gradation parameters fitting curve. (a) \(G_{f}\). (b) \(K_{IC}\).  
Figure 15: Correlation of the asphalt film thickness with \(G_{f}\) and \(K_{IC}\).
# FSOpt_k: Finding the Optimal Anonymization Level for a Social Network Graph
## 4 FSOpt_k Algorithm
### Time Complexity Analysis  
In this section, the proposed algorithm in this paper is examined in terms of time complexity. Since the FSopt_k and UMGA algorithms differ in the first step of the algorithm, in this section, only the time complexity of this step of the two algorithms is compared.  
**Remark 1**.: _Creating a cumulative vector has a time complexity \(O(n)\) such that \(n\) is the number of vertices of a graph [37]._  
**Theorem 1**.: _The time complexity of the computation of the fitness function in the NaFa algorithm is \(O\) (\(1\,AV\) (\(G\)))\(1\), where \(1\,AV\) (\(G\))\(1\) represents the cumulative vector length._  
Proof.: As described in before, the position vector length is \(1\,AV\) (\(G\))\(1\), where AV is an aggregate vector. For the calculation of the fitness function value, each of the aggregate vector elements is examined, and the SSE value is calculated for elements greater than or equal to \(0.5\). In order to calculate SSE for a group, the value  
**Theorem 3**.: _Finding the k value for the best solution found by the NaFa algorithm is O ( 1 AV (G)1)._  
Proof.: To obtain the value of k for a solution, all elements of the position vector must be investigated, and the number of elements in each group must be calculated. Given the number of elements in the position vector is 1 AV (G)1, the time complexity of obtaining k is also O 1 AV (G)1. \(\square\)  
**Theorem 4**.: _The time complexity of the FSopt_k algorithm is O(nlogn)._  
Proof.: In this algorithm, the graph sequence is first sorted, with time complexity O(nlogn). Then, this ordered degree sequence is given to the FSopt_k algorithm as input. This algorithm consists of three steps: creating an aggregate vector, finding the best solution by NaFa, and finding the optimal value of k based on the solution obtained. Therefore, the time complexity of this algorithm consists of the time complexity of these three steps. Therefore, the time complexity of the proposed algorithm in this paper is as follows:  
\[\text{T(n)}=\text{O(nlogn}+\text{n}+\text{1 AV(G)1}+\text{1 AV(G)1})=\text{O(nlogn)}, \tag{24}\]  
where n represents the number of graph nodes. \(\square\)
## 4 Discussion
### Effect of Leaf Structure on WUE\({}_{\rm s}\) of Larch Forest at Different Canopy Heights  
The effect of light limitation at different canopy heights leads to some differences in leaf structure, and leaf structure changes may have a great impact on photosynthetic characteristics of plants, such as light absorption, carbon fixation and water loss [45; 75], which in turn affects WUE\({}_{\rm s}\).  
The results of this study show a significant association of WUE\({}_{\rm s}\) with LMA and SD at different canopy heights, which is similar to the results of [PERSON] et al. [76]. This is because leaf structure changes affect the GS of the leaf to a certain extent, which in turn affects the Pn and Tr of the leaf. As canopy height increases, stomatal resistance and cell boundary layer resistance increase, and the amount of CO\({}_{2}\) diffused into the leaf is greatly reduced [77]. Thus, low concentrations of intracellular CO\({}_{2}\) left leaves to fractionate for \({}^{13}\)C, causing the increase of \(\delta^{13}\)C of the upper leaves [50], eventually affecting the WUE\({}_{\rm s}\) at different canopy heights. The results of this study also show that LMA and SD had the lowest contribution among the factors to WUE\({}_{\rm s}\). This is because of the little variation in short-term leaf structure, although differences in specific leaf weight and stomatal density vary significantly at different canopy heights. Their lowest contribution rate to WUE\({}_{\rm s}\) can also be explained by the fact that LMA and SD had the lowest effect on WUE\({}_{\rm s}\) compared with other affecting factors. It can be concluded that in the cold-temperate zone, different canopy heights' WUE\({}_{\rm s}\) in larch forests are more influenced by meteorological and photosynthetic factors.
# A Blockchain Solution for the Internet of Vehicles with Better Filtering and Adaptive Capabilities
## 7 Simulation Experiments and Analysis of Results
### Throughput  
In this section, we will conduct a throughput experiment. Throughput refers to the number of transactions completed per unit of time. Higher throughput indicates better scalability, concurrency, and data processing capabilities. We set the number of requests at 200 and tested 100 transactions across a range of 0 to 100 nodes in increments of 10, calculating the average as the experimental result to compare the actual performance of the different algorithms. Throughput is calculated as follows:  
\[TPS=\frac{N_{\Delta t}}{\Delta t}, \tag{8}\]  
where \(N_{\Delta t}\) is the number of transactions completed within time \(\Delta t\). The results are shown in Figure 9.  
Analysis of the experimental images and data Table 7 reveals that as the number of nodes increases, the throughput of all four algorithms exhibits a downward trend. However, PBFT and GPBFT show a faster decline rate. Although CBFT demonstrates more stability than the other three algorithms, it suffers from slow startup times. Overall, AE-PBFT maintains a throughput that is approximately 24.7% higher than that of the closest competitor, GPBFT, indicating superior performance. This advantage is primarily due to the reduced delay associated with each transaction. Consequently, our solution can complete  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Nodes** & **PBFT** & **GPBFT** & **CBFT** & **AE-PBFT** \\ \hline
10 & 0.912718 & 0.218563 & 0.019014958 & 0.013005 \\
20 & 4.692819 & 2.744698 & 0.983819 & 0.0393131 \\
30 & 8.134397 & 4.1610796 & 1.7307227 & 0.086635 \\
40 & 28.775688 & 10.2884976 & 6.0453126 & 0.13285766 \\
50 & 81.806661 & 21.5135286 & 12.62448 & 0.210629 \\
60 & 130.896491 & 32.104809 & 17.6887 & 0.31799066 \\
70 & 195.557394 & 38.658515 & 21.898924 & 0.40999 \\
80 & 245.676349667 & 47.2555075 & 24.003403 & 0.55243 \\
90 & 302.76820014 & 56.00594428 & 28.6441059 & 0.729233 \\
100 & 489.4821655 & 65.407161344 & 44.297028 & 0.92298 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Consensus latency of different algorithms.  
Figure 9: Throughput.  
more transactions in a given timeframe, resulting in higher throughput compared to other solutions. Therefore, the approach proposed in this paper offers significant advantages in practical applications.