esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study gathers and examines information about the flow state's emergence during tests and its factors using an electroencephalogram (EEG) to establish a method and reveal an individual student's flow construct. Through a single-case experimental design and 766 test items, multiple measurements were performed on a 14-year-old junior high school science-gifted student. During the test, self-efficacy, item difficulty, cognitive load, and test performance (long-term test performance [LT-tp] and short-term test performance [ST-tp]) were examined to establish the construct of EEG-detected, real-time flow states (EEC-Fs). Based on the chi-square test of independence results, the EEG-F had a significant correlation with the student's cognitive load, self-efficacy, LT-tp, and item difficulty. Furthermore, a J48 decision tree analysis and logistic regression revealed four inhibiting and two inducing conditions affecting the emergence of EEC-Fs. The two inducing conditions included (1) high self-efficacy with a low cognitive load (odds ratio (OR) = 3.7) and (2) high cognitive load when combined with high self-efficacy and LT-tp for low-difficulty items (OR = 3.5). The established method and findings may help teaching designers or automated teaching applications detect the individual student's flow construct to select appropriate test tasks accordingly, resulting in an optimal experience and better achievements.  
the flow state. As such, this exploratory study intended to adopt a continuous detection method that uses EEGs to examine an individual student's flow states [15; 16] and cognitive load [17; 18; 19] and explores interactions between the flow state and its associated factors, namely, cognitive load, self-efficacy, LT-tp, ST-tp, and item difficulty. This exploration can establish the construct of an individual student's EEG-detected, real-time flow states (EEG-Fs) concerning these particular factors, addressing this research area's gaps. In contrast to previous experimental research group approaches, this study adopted a single-case experimental design research approach [20] to develop a method that uses EEGs to analyze an idiographic student's flow construct during school tests and answer the following research questions (RQs).  
RQ1: Based on the detecting method, do cognitive load, self-efficacy, LT-tp, ST-tp, and item difficulty significantly affect a student's EEG-Fs?  
RQ2: Based on the detecting method, how and to what extent do cognitive load, self-efficacy, LT-tp, ST-tp, and item difficulty contribute to the construct of a student's EEG-Fs?
## 3 Numerical Results
### Linear Landau Damping
The UGKS shows a great advantage on correctly representation for the Landau damping with a much lower computational cost than particle-based methods, which makes it a promising method in plasma simulation. Figure 1: Density contour of the linear Landau damping at different time steps under non dimensional axis.
# Comparison of Palliative Care Models in Idiopathic Pulmonary Fibrosis
## 3 Results
### Improved EOL Care
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **All Cohorts** & **EC** & **BC** & **QC** & **_p-Value_** \\ \hline Deceased of total & 40.9\% (122/298) & 47.4\% (45/95) & 60.7\% (51/84) & 21.8\% (26/119) & \textless{}0.001 \\ Palliative care (\%) & 76.2\% (93/122) & 91.1\% (41/45) & 92.1\% (47/51) & 19.2\% (5/26) & \textless{}0.001 \\ Interval between last visit and death (median months, IQR) & 3, IQR 1-6 & 2, IQR 1-5 & 3, IQR 1-5 & 1.25–15.5 & 0.01 \\ Median \#ECU visits (last 6 months), IQR & 1, 0–2 & 1, 1–2 & 1, 1–2 & 0 *, 0–2 & 0.3 \\ Symptom Management & & & & & \\ Pacing or Energy & 72.1\% (88/122) & 91.1\% (41/45) & 92.2\% (47/51) & 0\% (0/26) * & \textless{}0.001 \\ conservation & 66.4\% (81/122) & 82.2\% (37/45) & 76.5\% (39/51) & 19.2\% (5/26) * & 0.04 \\ _Initiated (median months, IQR)_ & 15, IQR 6–27 & 21, IQR 9–31 & 10, IQR 5–21 & NR & \textless{}0.001 \\ Opiates for dyspnea (\%) & 35.2\% (43/122) & 51.1\% (23/45) & 39.2\% (20/51) & 15.4\% (4/26) * & 0.01 \\ _Initiated (median months, IQR)_ & 5, IQR 2–9 & 5, IQR 2–12 & 4, IQR 2–8 & N/A & 0.1 \\ ACP (A-D) & & & & & \\ \hline A. Documentation of patient & & & & & \\ understanding/goals/ & 80.3\% (98/122) & 93.3\% (42/45) & 100\% (51/51) & 19.2\% (5/26) * & \textless{}0.001 \\ values/CG (any) & & & & & \\ B. Patient & 68.8\% (84/122) & 93.3\% (42/45) & 72.5\% (37/51) & 19.2\% (5/26) * & 0.001 \\ preferences documented & & & & & \\ Preferred type of Care & 63.9\% (78/122) & 80\% (36/45) & 72.5\% (37/51) & 19.2\% (5/26) * & \textless{}0.001 \\ (DNR/DNI/Others) & & Home 50\% (61/122) & Home 60\% (27/45) & Home 66.6\% (34/51) & \\ Preferred location of death & Hospice 0.001\% (4/122) & Hospice 8.8\% (4/45) & Hospice 0 & NR & \\ Hospital 5.7\% (7/122) & Hospital 13.3\% (6/45) & Hospice 152 (1/51) & & \\ C. GOC form & 61.4\% (57/122) & 60\% (27/45) & 86\% (44/51) & 15.4\% (4/26) * & \textless{}0.001\textless{}0.001 \\ D. EOL disc & 54.1\% (66/122) & 73.3\% (33/45) & 62.7\% (32/51) & 3.8\% (1/26) * & 0.001 \\ Palliative assessments (\%Y) & & & & & \\ PC Specialist Consultation & 13.9\% (17/122) & 6.6\% (3/45) & 17.6\% (9/51) & 19.2\% (5/26) * & 0.2 \\ Palliative & & & & & \\ Multidisciplinary Discussion & N/A & N/A & 49.0\% (25/51) & N/A & \\ \hline \hline \end{tabular}  
* QC data is an underestimate; data located in paper records was not included. Some patients would have received PC in the community but that data is not available. GOC = goals of care.  
\end{table} 
Table 3: Clinic-based end of life palliative interventions (decedents)—last 12 months before death.
# Diagnosing Phosphorus Limitation in Subtropical Forests in China under Climate Warming
## 5 Conclusions  
We diagnose P limitation in subtropical forests in China under climate warming based on model-simulated NPP. The forest NPP in subtropical China increased significantly in recent decades, and the P limitation has been strengthened due to climate warming. The AAPD in 1981-2010 are 1.67 and 1.8 times that in the former 30 years for EBF and ENF, respectively. The higher AAPD sporadically distributed in the western subtropical China for the period of 1951-1980, but covered large areas of central and eastern subtropical China for the period of 1981-2010.  
The observed current ASP density in forest sites varied from 812 to 3432 mg\(\cdot\)m\({}^{-2}\) in EBF and 950 to 1297 mg\(\cdot\)m\({}^{-2}\) in ENF, and the AAPD/ASP for EBF were greater than those for ENF. The AAPD for S\({}_{1}\) is about 10 times that for S\({}_{2}\). The AAPD/ASP in the four sites varied between 6.7% and 25.5% for EBF, and between 0.86% and 12.7% for ENF for the period of 1951-2010 for S\({}_{1}\).  
Future climate change will enhance P limitation in the four sites. The increasing trends of forest NPP will be doubled, and the AAPD/ASP are estimated to be in the range of 32.2-68.3% and 3.2-6.9% for S\({}_{1}\) and S\({}_{2}\), respectively, under RCP8.5 at the end of this century.  
Though climate warming is one of the major factors in increasing the intensity of P limitation in subtropical forest in China, human disturbance is also another important factor. The S\({}_{1}\) corresponds to low recycling of P by plants, which is more likely to correspond to forests with human disturbance. Therefore, future P limitation in subtropical China forest to some degree depends on human activities, and intense human disturbance will strengthen P limitation. In addition, the amount of current ASP is another factor that affects the intensity of P limitation in subtropical China, and understanding the current ASP distribution is essential for understanding the intensity of P limitation in subtropical China.  
**Author Contributions:** Conceptualization, F.G., H.Y. and S.W.; Data curation, Z.W., X.S., W.X., F.Y. and G.C.; Formal analysis, Z.W.; Investigation, X.S., W.X., F.Y. and G.C.; Methodology, M.H. and F.G.; Project administration, M.H., H.Y. and S.W.; Resources, M.H. and H.H.; Software, N.W.; Supervision, Z.W.; Validation, X.S., W.X., F.Y. and G.C.; Visualization, N.W.; Writing-original draft, N.W.; Writing-review & editing, M.H.  
**Funding:** This study is supported by the Chinese National Natural Science Fund (No. 41671101), the Strategic Priority Research Program (XDA20010202) for the Chinese Academy of Sciences, the National Key Research and Development Program of China (2017 YFC0503905) and the State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, CAS (SKLLQG1835).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Capacity Optimal Design Method  
### Response Surface Method  
As a statistical method, the RSM can establish a predictive model via multiple nonlinear regression. The RSM is widely used as an alternative model in parameter sensitivity analysis and design optimization to improve the computational efficiency. The RSM assumes that the response of the structure can be approximated as  
\[Z=\sum_{n}^{i=1}k_{i}\varphi_{i}(x), \tag{1}\]  
where \(Z\) is the structural response, \(\varphi_{i}(x)\) is the basis function, \(n\) is the number of basic functions, and \(k_{i}\) is the response function coefficient.  
According to the least-squares method, the response surface function coefficient is determined as  
\[K=\left(\Phi^{T}\Phi\right)^{-1}\left(\Phi^{T}Z\right), \tag{2}\]  
where \(K\) is the response surface function coefficient matrix, \(K=[k_{0},k_{1},\ldots,k_{n}]\). Moreover, \(\Phi\) is the matrix formed by the sample points and is expressed as follows:  
\[\Phi=\begin{bmatrix}\varphi_{1}\left(x^{(1)}\right)&\cdots&\varphi_{n}\left( x^{(1)}\right)\\ \vdots&\vdots&\vdots\\ \varphi_{1}\left(x^{(i)}\right)&\cdots&\varphi_{n}\left(x^{(i)}\right)\\ \vdots&\vdots&\vdots\\ \varphi_{1}\left(x^{(m)}\right)&\cdots&\varphi_{n}\left(x^{(m)}\right)\\ \end{bmatrix}. \tag{3}\]  
The response surface function \(Z\) can be obtained by solving the matrix equation of the random variables of the response surface to find the response surface function coefficient. For most engineering problems, \(Z\) is represented as a second-order polynomial function with cross-terms to take into account the linearity, interaction, and curvature linear terms.  
\[Z=a_{0}+\sum_{c}^{i=1}a_{i}x_{i}+\sum_{c}^{i=1}a_{ii}x_{i}^{2}+\sum_{c}^{i=1} \sum_{c}^{i=1}a_{ij}x_{i}x_{j}, \tag{4}\]  
where \(a_{0}\) is the constant coefficient of the response surface, \(a_{i}\) is the coefficient of the linear term of the response surface, \(a_{ii}\) is the coefficient of the quadratic term of the response surface, \(a_{ij}\) is the coefficient of the interaction term of the response surface, and \(c\) is the number of design parameters.
# Non-Rotationally Symmetric Field Mapping for Back-Scanned Step/Stare Imaging System
###### Abstract  
Step/stare imaging with focal plane arrays (FPAs) has become the main approach to achieve wide area coverage and high resolution imaging for long range targets. A fast steering mirror (FSM) is usually utilized to provide back-scanned motion to compensate for the image motion. However, the traditional optical design can just hold one field point relatively stable, typically the central or on-axis field point, on the FPA during back-scanning; all other field points may wander during the exposure due to imaging distortion characteristics of the optical system, which reduces the signal to noise ratio (SNR) of the target. Aiming toward this problem, this paper proposes a non-rotationally symmetric field mapping method for the back-scanned step/stare imaging system, which can make all field points stable on the FPA during back-scanning. First of all, the mathematical model of non-rotationally symmetric field mapping between object space and image space is established. Then, a back-scanned step/stare imaging system based on the model is designed, in which this non-rotationally symmetric mapping can be implemented with an afocal telescope including freeform lenses. Freeform lenses can produce an anamorphic aberration to adjust distortion characteristics of the optical system to control image wander on an FPA. Furthermore, the simulations verify the effectiveness of the method.  
infrared search and track; infrared imaging system; step/stare; fast steering mirror +
Footnote †: journal: Applied Physics Letters  
1
## 2 Results and Discussion  
### Characterization of the PPy@MIL-101(Cr) Nanocomposite Sorbent  
To characterize the PPy@MIL-101(Cr) structure, several batches of the nanocomposite were prepared, and their FT-IR spectra were recorded. As seen in Figure 1a, the absorption peak of 3404 cm\({}^{-1}\) is attributed to -OH vibration in the water molecules that have been adsorbed or coordinated in the MIL-101 structure. The peaks appeared at 1400-1600 cm\({}^{-1}\) are corresponded to the C=C stretching vibrations in the aromatic rings. The peaks in the range of 1500-1640 cm\({}^{-1}\) reflects C=O stretching vibration, and 580 cm\({}^{-1}\) is related to Cr-O vibration. The absorption bands at 1650 and 1620 cm\({}^{-1}\) are attributed to O-C-O asymmetric vibrations. From Figure 1b, bands at 1220, 1160, 1085, and 1042 can be attributed to C-C-C and C-N-H bending, as well as C-N stretch and N-H vibration, in aromatic ring of polypyrrole. The bands at 1500-1650 cm\({}^{-1}\), corresponding to the C=C vibration of the benzene ring, are like those of the MIL-101(Cr) structure. It must be noted that there are remarkable red shifts in the absorption bands in the PPy@MIL-101(Cr) structure. This fact demonstrates that the PPy@MIL-101(Cr) nanocomposite has been formed in the doping mode. Appearing stretched vibrations of C-N at 1462 cm\({}^{-1}\) is another confirmation for doping PPy in the MIL structure.  
The sorbent morphology was studied by using SEM, as is shown in Figure 2. The apparent uniformity and smoothness of the coating is evident in Figure 2a, while it was resistant to scratching and damage. The remarkable porosity of the nanocomposite sorbent can be seen in Figure 2b,c.  
Figure 1: FT-IR spectra of (**a**) MIL-101(Cr) and (**b**) PPy@MIL-101(Cr) nanocomposite sorbent.  
The octahedral configuration of the MIL-101(Cr) molecules, grafted inside the PPy layers, is clearly obvious in Figure 2d. Additionally, penetration of the PPy particles into the MIL-101(Cr) cavities is visible on the SEM images. Furthermore, the porosity, large surface area, durability, and physicochemical stability of the nanocomposite was demonstrated during the extraction and desorption (at high temperature) experiments. The positive effect of the roughness of the fiber substrate on the durability and physical strength of the coating was also proved. Its effectiveness was comparable with the platinization, while it is a simpler, faster, lower-cost process [23]. Homogeneous distribution of the nanocomposite elemental constituents was ascertained by using the EDX mapping experiments (the results are not shown).
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 5 Adversarial Ant Colony Optimization Algorithm for Solving Non-TT Routing Problem
### Convergence of AACO  
AACO is also based on a heuristic algorithm and the average time complexity of its convergence is complex and can be influenced by various factors, including the number of non-TT flows, number of ants, number of iterations, and randomness. Thus, it was difficult to produce an accurate expression. For its convergence, we could only estimate its time complexity as \(O(m\times Z\times n\times I_{max})\).
## 7 Conclusions  
This paper addresses the problem of time-dependent observation scheduling for the AEOS constellation, where the transition time required between two consecutive observations is determined by angular change over time. This time-dependent characteristic increases the complexity of the scheduling problem.  
To facilitate our understanding, we propose a mathematical model of the studied problem and a new fitness function that maximizes the profit sum while also considering system load balancing. We also compute non-conflict VTWs for each non-scheduled target to ensure that each AEOS can observe one target at a time. Using the basic idea of a distributed PI algorithm, we develop the PIDSM to solve the scheduling problem. The PIDSM iterates between two phases: the first selects the optimal task for each AEOS, while the second achieves consensus over all AEOSs and removes targets that may decrease the overall fitness. Each AEOS can run the PIDSM concurrently, and useful information is exchanged between AEOSs via ISLs during the PIDSM procedure. The convergence of the PIDSM is naturally guaranteed since the global fitness is increased whenever the schedule solution is changed at the end of each iteration. Compared with the existing distribution algorithms, CBBA [30], CNP [36], and ECNP [32], the PIDSM performs better for almost all experimental instances. Sensitivity analyses further validate the effectiveness of the PIDSM. In future work, we intend to reduce the communication overhead of the PI-based distributed scheduling approach.  
[PERSON] and [PERSON] conceived the study and algorithm. [PERSON] completed the implementation of the algorithm and the writing of the paper. [PERSON] and [PERSON] supported the writing-- review and editing. [PERSON] and [PERSON] completed certain preliminary simulations and undertook the preliminary research and summary. [PERSON] provided theoretical guidance and suggestions for the revision of the paper. Y.F. provided funding support and necessary assistance for writing. All authors have read and agreed to the published version of the manuscript.  
Figure 11: The results of running time.  
**Funding:** This research was funded by the Science and Technology Innovation 2030-Key Project of \"New Generation Artificial Intelligence\" under Grant 2020 AAA0108203 and the National Natural Science Foundation of P.R. China under Grants 62003258, 62003265, and 62103062.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Discussion
The next decade of operational coastal mapping and monitoring will see improved image datasets (i.e., higher signal-to-noise ratios and more spectral bands in the visible wavelengths) and increased efficiencies [78]. The growing SuperDove constellation will provide access to higher and more stable radiometric quality imagery that will overcome the aforementioned technological limitations, providing progress towards a more accurate and near real-time monitoring system for inland/coastal waters. When cloud cover and sea state conditions permit, systems will be developed to enable rapid mapping of benthic changes on a daily to weekly basis. The improvement in fidelity and spectral sensitivity (eight multispectral bands) will enhance our ability to map and discriminate between features. With improved mapping ability, additional benthic habitat classes can be identified. For example, the coral/algae class can be further divided into different reef classes based on species composition, rugosity, or geomorphic zones (e.g., _Acropora_ dominated reef crests, patch reef and fringing reef composition, gorgonian/_Orbicella_, and mixed assemblages). Distinction between reef types could guide and improve management decisions. Key challenges remain, including the development of more accurate satellite-derived bathymetry (SDB) models and the ability to sense at greater depths, dealing with turbid water, improving the accuracy of training and validation data integration, and making change detection systems more stable and accurate [22; 27; 33; 36]. Techniques for mapping benthic habitats will also continue to improve with more powerful machine learning classifiers, such as Support Vector Machines and Convolution Neural Networks (CNN) [79; 80]. Recent advances that will enhance benthic mapping in the future include the development of a spectral database for corals [20] and the deployment of large-scale operational mapping of live coral [66]. New products, such as _Reef Cover_, will improve our ability to more accurately represent benthic types, blending what can be mapped remotely with the geo-ecologicalunderstanding of reef formation, growth, and functioning [49]. Future methods will more readily integrate field data and real-time ocean conditions, which will increase accuracy and improve feature discrimination between different types (e.g., shallow coral reef, coral rubble cover, seagrass beds) and more reliable bathymetric estimates.
## 2 Materials and Methods
### Chlorination Reaction
#### 2.2.3 Chlorination Procedure and Product Isolation
The subfractions EA10.4 (16 mg) was re-chromatographed by HPLC using a reversed-phase Kinetex column of 2.6 \(\upmu\)m 100 A C18 (100 \(\times\) 4.6 mm) and eluting with a gradient of acetic acid:acetonitrile (A, 1:99, _v/v_) and acetic acid:water (B, 1:99, _v/v_), starting with 60% B for 5 min and using a gradient up to 100% A in 20 min and returning to 60% B for 10 min, at a solvent flow rate of 1.0 mL/min. It contained DP16 (12.2 mg). The fraction obtained by extraction with \(n\)-butanol (B, 635 mg) was again fractioned by silica gel CC, using a gradient of ethyl acetate:methanol, to give six fractions. The B5 fraction (12 mg), eluted with ethyl acetate:methanol (50:50, _v/v_), was dried, solubilized in water:ethanol (50:50, _v/v_), and analyzed using a Shimadzu 2010 series GC FID (Shimadzu, Milano, Italy). The gas chromatograph was equipped with Zebron ZB-5 column (30 m \(\times\) 0.32 mm I. D., 1.00 \(\upmu\)m; Phenomenex, Bologna, Italy). The following parameters were setduring the experiments: helium as a carrier gas with constant flow of 1.2 mL/min, 1.0 mL injected into samples with a 20:1 split, and introduced into the injector at 250 \({}^{\circ}\)C using an AOC-20i auto sampler (Shimadzu, Milano, Italy). The initial temperature was 60 \({}^{\circ}\)C, then rose with a 4 \({}^{\circ}\)C/min ramp to 200 \({}^{\circ}\)C with a 5 min hold. The identification of DP18 and DP19 was fulfilled by comparison with available standard compound. The aqueous fraction (W, 711 mg) was dried by lyophilization, re-dissolved in methanol and separated with CC silica gel by running an ethyl acetate:methanol gradient (100:0 to 0:100, _v/v_) to get eight fractions. The fraction W2 (155 mg), eluted with ethyl acetate:methanol (60:40, _v/v_), was filtered on reversed phase silica gel CC with water, methanol, and acetonitrile. The fraction eluted with acetonitrile (6 mg) was analyzed by NMR and contained DP20. The fraction W7 (187 mg), eluted with ethyl acetate:methanol (50:50, _v/v_), was chromatographed on a reversed phase Sep-Pak RP-18 with a gradient of acetic acid 1%:acetonitrile (100:0 to 0:100 _v/v_). The fraction eluted with acetic acid 1% was concentrated by lyophilization (31 mg), analyzed by HPLC using a reversed-phase Luna column of 5 \(\upmu\)m C8(2) 100 A (150 \(\times\) 4.6 mm), and eluted with an isocratic solution of KH\({}_{2}\)PO\({}_{4}\) (pH 2.6, 20 mM):acetonitrile (75:25 _v/v_) at a solvent flow rate of 1 mL/min. It contained DP21, as evidenced by the RT comparison with a standard compound and NMR spectra. The fraction eluted with acetonitrile was analyzed by HPLC using a reversed-phase Luna column of 5 \(\upmu\)m Phenyl-Hexyl 100 A (150 \(\times\) 4.6 mm) and eluting with water (A) and acetonitrile (B), starting with 40% B for 10 min and later obtaining 55% B over 10 min, at a solvent flow rate of 1.2 mL/min. It contained DP22, as evidenced by the RT comparison with a standard compound and NMR spectra.