esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Hydrogen Sensors for ECLSS: Fundamentals, Recent Advances, and Challenges
## 7 Summary and Prospect
However, it is difficult for WO\({}_{3}\) monolithic materials to have the characteristics of fast response, low detection limit, and wide detection domain at the same time, and it is necessary to prepare composite materials via physical mixing, loading, and doping, thus enhancing their hydrogen-sensingperformance. The future goal can be to develop hydrogen-sensitive materials with fast response, long-term stability, and good selectivity. Currently, Pd alloys are used to reduce the response time and increase the stability of hydrogen sensors. In addition, magnesium-titanium (Mg-Ti) and magnesium-titanium-nickel (Mg-Ti-Ni) alloy composite films are made into sensors with good repeatability and stability. However, little research was previously performed regarding the selectivity of hydrogen sensors. Moreover, regardless of the measurement method used, the response time is still quite long (tens of seconds and, in some cases, even several minutes or hours); therefore, further research on new sensitive materials is needed. 3. Research on the applicability of hydrogen sensors at high temperatures is required. The current commercially available several hydrogen sensors have narrow operating temperature ranges, can only be measured at room temperature, and require higher accuracy and lifetimes in high-temperature environments compared to room temperature; thus, the optimization of signal processing and packaging technology should be one of the directions of future research. For example, the performance of fiber-optic hydrogen sensors at high temperatures can be improved by adjusting the internal structure and external packaging of the fiber. 4. Studying the signal detection and demodulation technology of fiber-optic sensors is also required. The excellent performance of fiber-optic hydrogen sensors facilitates temperature compensation and distributed multi-parameter measurement, which is conducive to the research and development of intelligent sensing systems in the context of the Internet of Things, while it also poses new challenges to the signal detection and demodulation technology of fiber-optic sensors. Currently, the price of a single-channel grating demodulator is over 10,000, and the price of a multi-channel grating demodulator and spectrum analyzer is even higher, which limits the commercialization of fiber-optic hydrogen sensors. Therefore, the development and optimization of software and hardware systems for signal detection and demodulation will be the focus of future research. Conceptualization, L.Z. and Y.M. ; methodology, G.H. ; formal analysis, L.L. ; investigation, W.W.; writing--original draft preparation, C.J. ; writing--review and editing, Y.Y. ; All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Data Availability Statement: Data will be made available on request. The authors declare no conflict of interest.
## 3 Results and Discussion
### Free Amino Acid Composition of FBP Hydrolysates  
A heatmap of the FAAs' composition in FBP hydrolysates at different hydrolysis times is shown in Figure 4. Most of the FAA contents increased with the increase in hydrolysis time. When hydrolyzed for 3 h, the amounts of total FAAs increased dramatically, from 211.48 and 145.20 mg/100 mL to 3586.24 and 3224.32 mg/100 mL, for the SFBP and HFBP hydrolysates, respectively. Before the enzymatic hydrolysis, the contents of most FAAs of SFBP were higher than those of HFBP, except for phenylalanine, tryptophan, and proline. The dominant FAAs of all the hydrolysates were leucine, phenylalanine, and arginine, followed by glycine and lysine for the SFBP hydrolysates, while the top five FAAs in the HFBP hydrolysates were leucine, phenylalanine, arginine, tyrosine, and lysine. Compared with HFBP, more glycine and alanine residues were released from SFBP after the hydrolysis by Flavourzyme. This indicates that more cleavage sites were generated after SE pretreatment compared with the HPE process.  
The composition and content of FAAs have a major impact on the flavor properties, directly or indirectly [45]. Flavourzyme displays a great advantage in improving the flavor of protein hydrolysate, by releasing monosodium glutamate-like amino acids [46]. The contents of umami amino acids (glutamic acid and aspartic acid) in the SFBP and HFBP hydrolysates increased by 6.24 and 8.30 times, respectively, after Flavourzyme hydrolysis for 3 h. Remarkably, the SFBP hydrolysates had a significantly (\(p<0.05\)) higher level of umami and sweet amino acids than the HFBP hydrolysates did, during the whole hydrolysis.  
Figure 3: Distribution of molecular weights of SFBP-H and HFBP-H. **(a)** The molecular weight distribution of bone protein and bone hydrolysates. **(b)** Fractions of the chromatogram based on molecular weights of SFBP-H and HFBP-H. SFBP: fish bone protein prepared through steam explosion-assisted extraction; HFBP: fish bone protein prepared through hot-pressure extraction; SFBP-H: SFBP hydrolysates; HFBP-H: HFBP hydrolysates.  
For both the FBP hydrolysates, bitter amino acids were the most abundant, followed by sweet and umami amino acids. Bitter amino acids occupied 43.54% of the total FAAs in the SFBP hydrolyses, while the total amount of umami and sweet amino acids accounted for 29.44%. Similarly, for the HFBP hydrolysates, bitter amino acids account for 53.53%, while the total of umami and sweet amino acids account for 22.47%. Furthermore, the ratio of bitter/umami components in the SFBP hydrolysates at 3 h was 10.31, whereas that in the HFBP hydrolyses was 16.31. These results indicate that SE pretreatment and Flavourzyme hydrolysis can produce a collagen hydrolysate with less bitterness and umami taste. In a previous study, protein hydrolyses with low bitterness could be obtained from Alaska pollock frame-treated by a hot-pressure process, followed by mixed enzymes for animal proteolysis, Protamex or Flavourzyme [47]. In the present study, further sensory evaluation was needed to confirm the phenotype flavor of the bone hydrolysates.
## 2 Related Works
### Image Segmentation with Change Detection Method  
Change detection is a key technique that quantifies differences between temporally distinct image pairs, making it highly suitable for extracting disaster-induced infrastructure damage, as demonstrated by [PERSON] and [PERSON] [24] and [PERSON] et al. [43].  
[PERSON] et al. [44] introduced the Fully Convolutional Siamese Network (FC-Siamese), which internally resolves image alignment issues across pre- and post-disaster imagery, offering significant improvements in computational efficiency. [PERSON] and [PERSON] [45] utilizeda Triplet U-Net architecture to fuse spatial and spectral features for high-resolution remote sensing imagery. [PERSON] et al. [46] improved the Siamese U-Net by incorporating a flow alignment module, resulting in a gain of 7. 32% in the F1-score. More recently, [PERSON] et al. [47] and [PERSON] and [PERSON] [48] developed lightweight models such as the Siamese Swin-U-Net using a Swin Transformer, achieving a precision of 94.67% with minimal parameters.  
These developments reflect a paradigm shift from object-based detection toward change-focused frameworks. In the context of road infrastructure, which is linear, structured and topologically constrained, difference-based segmentation and Siamese architectures offer promising strategies. Based on this rationale, this study introduces and theoretically contrasts three types of models with differing algorithmic structures. The first is a baseline single-timeframe segmentation model, which performs standard semantic segmentation using only post-disaster imagery. The second is a difference-based model that explicitly computes pixel-wise differences between pre- and post-disaster images before feeding the result into a segmentation network. The third is a Siamese-based model, which processes paired images through dual-branch networks to learn temporal change representations via feature similarity and implicit alignment.  
A detailed comparison of the advantages and limitations of these three approaches is summarized in Table 1.
## 5 Conclusions  
In this paper, we propose an explicit seasonal-trend decomposed transformer, called DESTformer, for long-term forecasting. DESTformer first explicitly extracts seasonal and trend components via high- and low-frequency filtering of the data after frequency transform. To enhance information utilization, a multi-scale attention mechanism in the trend domain and a multi-view attention mechanism in the frequency domain are proposed, capturing complex periodic changes and fine-grained sub-trends under different receptive fields, respectively. Experimental results verify the effectiveness of our method, thus providing a new approach for handling trend and seasonal patterns in long-term time series prediction tasks. Despite the outstanding performance of DESTformer in long-term forecasting tasks, there are still some limitations. First, the effect of the multi-scale attention mechanism is influenced by the sub-trend selection methods, but determining a suitable set of sub-trends adds an extra workload to the model training. Second, we only conducted experiments on datasets with obvious periodicity, and we hope to further test our model on more complex (even non-stationary) tasks in the future.  
Conceptualization, R.K.; Methodology, Y.W. and J.Z.; Project administration, Y.W. and R.K.; Software, J.Z.; Supervision, R.K.; Validation, Y.W.; Visualization, J.Z.; Writing--original draft, Y.W. and J.Z.; Writing--review and editing, R.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Defense Basic Scientific Research Program of China (No. JCKY2020212B003).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.  
Figure 4: Visualizations of seasonal (red) and trend (blue) representations on ETTh1 dataset.
## 2 Materials and Methods
### Overexpression, Purification, and Kinetic Characterization of Mutant PbAcEs  
To investigate substrate specificity and kinetic parameters, mutant PbAcEs were prepared as described previously [14]. The pET28a vectors containing PbAcE mutants were transformed into _E. coli_ BL21(DE3) and grown in 100 mL of LB media. Protein expression was induced using 0.1 mM IPTG. The cell pellets were resuspended in buffer A (50 mM sodium phosphate, pH 8.0, 300 mM NaCl, 5 mM imidazole) with 0.2 mg/mL lysozyme, and lysed by sonication on ice for 5 min (5 s pulse and 10 s pause). The cell lysate was clarified by centrifugation at 12,000 rpm for 30 min at 4 \({}^{\circ}\)C. The supernatant was incubated with Ni\({}^{2+}\) affinity resin (Qiagen, Hilden, Germany) for 30 min with gentle agitation. The protein-bound resin solution was loaded onto the column and settled. The resin was then washed three times with buffer A containing 20 mM imidazole. The protein was eluted in buffer A containing 250 mM imidazole. The fraction was visualized using SDS-PAGE. The fractions containing PbAcEs were concentrated and exchanged with buffer B (50 mM Tris-HCl, pH 8.0, and 150 mM NaCl) using Amicon ultracentrifuge filters (Ultracel-3K; Millipore, Darmstadt, Germany). Protein concentration was determined using the Bradford assay with bovine serum albumin (BSA) as a protein standard [28]. The activity of wild-type and mutant PbAcEs was measured in 10 mM Tris-HCl, pH 8.0 in the presence of 0.04 mg/mL phenol-red using a microplate reader (FilterMax F5, Molecular Devices LLC, Sunnyvale, CA, USA) as previously described [14]. Briefly, 200 \(\upmu\)L reaction mixture containing 10 mM substrate and 5 \(\upmu\)g enzyme was incubated for 80 min at 25 \({}^{\circ}\)C in a 96-well microplate. Linalyl and \(\upalpha\)-terpinyl acetates were used as substrates. Proton release owing to enzymatic cleavage of esters of substrates led to a reduction in the absorbance of phenol red at 550 nm, which was measured using a photometer. The enzyme activity was determined by converting \(\Delta\)A\({}_{\text{550 nm}}\cdot\)min\({}^{-1}\) using the extinction coefficient of phenol red at 550 nm (8450 M\({}^{-1}\) cm\({}^{-1}\)). One unit (U) of enzyme activity was defined as the amount of enzyme required to transform 1 \(\upmu\)mol of substrate in 1 min under the assay conditions. The activity of wild-type PbAcE against each substrate was defined as 100%. All tests were conducted in triplicate. The results were expressed as the mean \(\pm\) standard deviation (SD) with a sample size of three (n = 3).  
The kinetic parameters of mutants were investigated with \(p\)NP-C\({}_{2}\) as a substrate. The reaction mixture contained 5 \(\upmu\)g of wild-type or mutant PbAcEs and varying concentrations of \(p\)NP-C\({}_{2}\) in 1 mL of buffer B at 25 \({}^{\circ}\)C. The absorbance was recorded at 405 nm for 10 min, and the initial velocity was calculated from the initial linear slope of the measurement. The k\({}_{cat}\) and K\({}_{M}\) were determined using the Michalis-Menten equation.
# Effective Selfish Mining Defense Strategies to Improve Bitcoin Dependability
## 4 Acceptance Limitation Policy (ALP)  
The traditional bitcoin system adopts the proof-of-work (POW) protocol as its consensus mechanism. The longest chain broadcast is accepted as the valid version of the bitcoin main chain. The selfish mining attacker takes advantage of this mechanism by intentionally withholding his/her mined blocks and publishing his/her longer private chain. The ALP intends to set a limitation to the acceptance ratio when multiple blocks are broadcast at the same time. Let \(\gamma\) be the limitation ratio. This process is equal to reducing the transition rate \(\lambda_{34}\) using Equation (3):  
\[\lambda_{34}\_new=\lambda_{34}\ \times\ \gamma\ \text{where}\ 0<\gamma\leq 1 \tag{3}\]  
To investigate the effectiveness of the ALP, we apply Equation (3) to generate the parameter set \(a^{\prime}\) based on set \(a\), and both are presented in Table 5.  
Figure 6 shows bitcoin dependability for five different values of \(\gamma\), where \(\gamma\) = 1 corresponds to the case before the adjustment. It is observed that as the value of \(\gamma\) decreases, the improvement in bitcoin dependability becomes more significant.  
To select the optimal value for the key parameter \(\gamma\) (i.e., the value that provides the largest degree of dependability improvement), we run a series of tests using different values of \(\gamma\) and compute the \(p\)-value. Among the values of \(\gamma\) tested, the lowest \(p\)-value 0.0033 is obtained at \(\gamma\) = 0.8, as shown in Table 6 and Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(\gamma\) & **0.6** & **0.7** & **0.8** & **0.9** \\ \hline \(p\)-value & 0.0049 & 0.0044 & 0.0033 & 0.0057 \\ \hline \end{tabular}
\end{table}
Table 6: The results of the \(p\)-value under the ALP.  
Figure 6: Bitcoin dependability before and after the application of the ALP.  
It can be observed from the above results that the optimal \(p\)-value under the ALP is 0.0033, which is lower than the optimal \(p\)-value of 0.0145 obtained under the DDAA (Section 3.2). Therefore, in terms of the degree of improving dependability, the ALP performs better than the DDAA in defending selfish mining attacks. However, in terms of the absolute improvement in dependability, the DDAA is more effective than the ALP since the DDAA works on \(\lambda_{01}\), \(\lambda_{01^{\prime}}\), \(\lambda_{12}\), \(\lambda_{23}\), and \(\lambda_{34}\), while the ALP only reduces one of those transition rates \(\lambda_{34}\).
# The SXFEL Upgrade: From Test Facility to User Facility
## 2 Main Parts of the SXFEL User Facility  
### Schematic and Parameters  
The SXFEL user facility comprises one linac, two undulator lines, two beamlines and five end-stations.  
The linac can provide a high-quality electron beam with energy of 1.5 GeV, charge of 0.5 nC, peak current of about 700 A and normalized project emittance of about 1.5 mm\(\cdot\)mrad. A beam distribution switchyard lies downstream of the transport line after the linac, distributing the beam to two downstream undulator lines while maintaining the quality of the electron beam. Two undulator lines, a SASE line and a Seeding line, will produce FEL radiation with the shortest wavelength of about 2 nm and 3 nm, respectively. Two helical undulators located at the end of the seeding FEL line aim to provide polarization-controllable, fully coherent radiation pulses.  
Two beamlines, located downstream of the undulator lines, measure the pulse energy and the spectral properties of the radiation pulse online, and transport the radiation pulses to the end-stations. Five experimental end-stations, downstream of the whole facility, hold the ability to carry out various experiments including coherent diffraction imaging [30], ultra-fast physics [31], AMO [32], pump-probe [33], etc. The schematic layout of the SXFEL-UF and the SXFEL-TF are given in Figure 1, showing the differences between the test facility and the user facility. The main parameters of the electron beam and the radiation at SXFEL-UF are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Values** & **Unit** \\ \hline  & linac & \\ \hline Electron energy & 1.5 & GeV \\ Project energy spread (rms) & \(\leq\)0.1 & \% \\ Normalized project emittance (rms) & \(\leq\)1.5 & mm\(\cdot\)mrad \\ Bunch charge & 0.5 & nC \\ Peak current & \(\geq\)700 & A \\ \hline  & Undulator & \\ \hline  & SASE line & \\ \hline Central wavelength & \(\sim\)2 & nm \\ Output peak power & \(\geq\)100 & MW \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of the SXFEL-UF.  
Figure 1: Schematic layouts of the SXFEL: from test facility (**upper**) to user facility (**lower**).
###### Abstract  
Cloud computing has experienced rapid growth in recent years and has become a critical computing paradigm. Combining multiple cloud services to satisfy complex user requirements has become a research hotspot in cloud computing. Service composition in multi-cloud environments is characterized by high energy consumption, which brings attention to the importance of energy consumption in cross-cloud service composition. Nonetheless, prior research has mainly focused on finding a service composition that maximizes the quality of service (QoS) and overlooks the energy consumption generated during service invocation. Additionally, the dynamic nature of multi-cloud environments challenges the adaptability and scalability of cloud service composition methods. Therefore, we propose the skyline-enhanced deep reinforcement learning approach (SkyDRL) to address these challenges. Our approach defines an energy consumption model for cloud service composition in multi-cloud environments. The branch and bound skyline algorithm is leveraged to reduce the search space and training time. Additionally, we enhance the basic deep Q-network (DQN) algorithm by incorporating double DQN to address the overestimation problem, incorporating Dueling Network and Prioritized Experience Replay to speed up training and improve stability. We evaluate our proposed method using comparative experiments with existing methods. Our results demonstrate that our approach effectively reduces energy consumption in cloud service composition while maintaining good adaptability and scalability in service composition problems. According to the experimental results, our approach outperforms the existing approaches by demonstrating energy savings ranging from 8% to 35%.  
cloud service composition; multi-cloud; deep reinforcement learning; skyline; energy consumption; QoS aware +
Footnote †: journal: Applied Sciences  
1
## 3 Results  
### Estimation Model for Grassland Production  
The correlation analysis results showed that the grassland production was significantly correlated with the four remote sensing indices and the correlation coefficient was above 0.75, followed by mean annual precipitation and mean annual temperature, with correlation coefficients of 0.645 and \(-\)0.477, respectively (Table 1). The correlation between mean annual temperature and grassland production was significant at the confidence level of 0.05, and the correlation was relatively weak. There were few significant correlations between DEM data and grassland production.  
According to the correlation analysis results documented in Table 1, compared with other factors, the correlation between mean average temperature and grassland production was lower, and there was no significant correlation between DEM and grassland production in the Mongolian plateau where altitude was not very different. We selected mean precipitation data and four types of remote sensing indices that were significantly correlated with grassland production to establish estimation models (Table 2). The regression analysis was performed on the above five independent variables and the grassland production data by SPSS statistical software. The results showed that all the models passed the significance test (\(p<0.01\)), and the determination coefficient (R\({}^{2}\)) of most models was above 0.5. The overall estimation accuracy of the model based on MSAVI and NDVI was better than other models. Although NDVI usually performs well on vegetation change studies in areas with good vegetation coverage, MSAVI is extremely sensitive to changes in soil background. This study area is located in the transition area of vegetation change in Mongolia, which is an arid and semi-arid environment. The comparison showed that the exponential model based on MSAVI had the best fitting effect. The p value of the model was 0, and the determination coefficient was the highest at 0.72, which can explain 72% of the variation of the dependent variable. The model fitting accuracy results showed that the exponential model based on MSAVI had the highest accuracy at 78%, and the RMSE was 279.09 kg/ha.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Element** & **Precipitation** & **Temperature** & **DEM** & **EVI** & **MSAVI** & **NDVI** & **PsnNet** \\ \hline Correlation coefficient & 0.645 ** & \(-\)0.477 * & 0.108 & 0.762 ** & 0.804 ** & 0.798 ** & 0.856 ** \\ \(p\) value & 0.001 & 0.022 & 0.625 & 0.000 & 0.000 & 0.000 & 0.000 \\ \hline \end{tabular}  
* When the confidence is 0.01, the correlation is significant.
* When the confidence is 0.05, the correlation is significant.  
\end{table}
Table 1: Correlation analysis between grassland production and impact factors.  
Although there are still some potential uncertainties in the best-fit model, this accuracy can generally reflect the state of grassland changes in central Mongolia.