esa-sceva/satcom-chunk-collection huggingface dataset:
# A Dynamic Programming Approach to Ecosystem Management
## 2 Materials and Methods  
### Networks  
Just for the benefit of the reader, we now proceed to refresh some basic concepts, bearing in mind that the latter are well-known definitions constituting widespread knowledge in the field. Thus, the next three subsections contain standard material that widely overlaps the current literature and can be easily found even in specific textbooks, see, e.g., [5; 6; 7]. We briefly review the basic terminology in graph theory, before proceeding to the illustration of the algorithm.  
A _network_ (or _graph_) consists of a set of _vertices_ (or _nodes_) \(V\) and a set of _arcs_ (_links, edges_) \(E\subseteq V\times V\), i.e., pairs of vertices. An arc connects two nodes of \(V\) and is labeled by the nodes it connects.  
_Directed arcs_ (_indirected arcs_) are given by ordered, respectively, unordered, pairs of vertices. In particular, directed arcs may represent flows through the network, allowed only in one direction. A network is correspondingly named _directed network_ if it contains only directed arcs; otherwise, it is an _indirected network_. Any network can be written as a directed network, replacing undirected arcs with pairs of oppositely directed arcs.  
A _path_ between two nodes is a sequence of distinct arcs connecting the two nodes. When some of the arcs in the network are directed, we can have _directed paths_ or _indirected paths_. _Adjacent_ vertices are connected by an edge.  
A graph can be represented by an _adjacency matrix_. It is a square matrix \(A=(a_{i,j})\) whose elements indicate adjacent pairs of vertices in the graph. Here, \(a_{i,j}\in\{0,1\}\) and \(a_{i,j}=1\), if and only if \(i\) and \(j\) are adjacent nodes. An indirected network is described by a symmetric matrix. More generally, the nonzero elements of an adjacency matrix can be replaced with numbers representing the weights of the corresponding arcs.
## 4 Experiments and Analysis
### Comparative Analysis of the Results of Different Domain Adaptation Methods
#### 4.2.1 Visual Comparative Analysis of Output Features  
In order to intuitively demonstrate the effectiveness of the proposed method, t-distribution stochastic neighbor embedding (t-SNE [36]) was introduced for the multi-source migration task A-C\(\rightarrow\)B. T-SNE is a nonlinear dimensionality reduction algorithm. Because it can clearly show the relationship between different categories and clusters, it is often used for visualization in the field of fault diagnosis.  
The target domain samples are input into the trained network, and the fault features extracted from the last layer of the network are reduced to a two-dimensional plane. The results are shown in Figure 6, where different colors represent different class labels. It can be seen that there is still more cross-over after extracting features using the four comparison methods (a-d). After feature extraction using the proposed method, the four classes of faulty samples have basically been separated, and the target samples can be better classified. The specific recognition accuracy is shown in Table 4. It can be observed that the recognition accuracy of method (a) is 97.81%, the recognition accuracy of method (b) is 98.53%, the recognition accuracy of method (c) is 98.75%, the recognition accuracy of method (d) is 98.81%, and the recognition accuracy of method (e) is 99.65%, which is higher than the other four methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **State of Health** &
\begin{tabular}{c} **Rotating** \\ **Speed(R/Min)** \\ \end{tabular} & **Label** & **Sample Size** \\ \hline A & Normal & 600 & 0 & 800 \\ A & Inner ring fault & 600 & 1 & 800 \\ A & Outer ring fault & 600 & 2 & 800 \\ A & Rolling element fault & 600 & 3 & 800 \\ B & Normal & 800 & 0 & 800 \\ B & Inner ring fault & 800 & 1 & 800 \\ B & Outer ring fault & 800 & 2 & 800 \\ B & Rolling element fault & 800 & 3 & 800 \\ C & Normal & 1000 & 0 & 800 \\ C & Inner ring fault & 1000 & 1 & 800 \\ C & Outer ring fault & 1000 & 2 & 800 \\ C & Rolling element fault & 1000 & 3 & 800 \\ \hline \hline \end{tabular}
\end{table}
Table 3: JNU bearing dataset settings.
## 4 Results and Discussion  
### Results  
The main criterion used to evaluate the performance of each algorithm is accuracy, which we define based on how close each predicted (retrieved) value is to its corresponding synthetic model value. To determine accuracy, we use the average percent difference (APD), the difference between our predicted value and the model value in comparison to the model value (relative error), and the mean absolute error (MAE), based on the difference between the predicted value and the synthetic model value. A summary of the performance of the four snow parameter retrieval algorithms is shown in Table 2. Overall, all of the algorithms performed well for retrievals of the albedo and snow grain size. The SBG algorithm offers improvements over the other algorithms for retrieval of the snow impurity concentration, having the best \(R^{2}\) score and the lowest average percent difference (5.6% for SBG, versus 14%, 9.4%, and 7.8% for MODIS, SGLI, and Max-Min) and mean absolute error. The SGLI, MODIS, and Max-Min algorithms struggled to reliably estimate snow impurities in the \(10^{-9}\) to \(10^{-8}\) ranges, leading to a worse performance than the SBG algorithm in this range. Evaluation of the performance of SBG, MODIS, SGLI, and Max-Min prediction algorithms against synthetic data is shown in Figures 8-11.  
Figure 8: Performance of SBG snow parameter retrieval algorithm predictions versus model synthetic data (N = 5000).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SBG Algorithm** & \(R^{2}\)**Score** & **APD** & **MAE** \\ \hline Albedo VIS & 0.999 & 0.660 \% & 0.004 \\ Albedo NIR & 0.999 & 0.673 \% & 0.001 \\ Albedo VSWIR & 0.999 & 0.670 \% & 0.002 \\ Snow grain size & 0.999 & 1.135 \% & 10.33 um \\ Snow impurity & 0.998 & 5.586 \% & \(4.754\times 10^{-8}\) \\ \hline
**MODIS algorithm** & \(R^{2}\)**Score** & **APD** & **MAE** \\ \hline Albedo VIS & 0.999 & 1.025 \% & 0.006 \\ Albedo NIR & 0.999 & 0.914 \% & 0.001 \\ Albedo VSWIR & 0.999 & 0.943 \% & 0.002 \\ Snow grain size & 0.998 & 1.602 \% & 14.78 μm \\ Snow impurity & 0.997 & 13.99 \% & \(8.907\times 10^{-8}\) \\ \hline
**SGLI algorithm** & \(R^{2}\)**Score** & **APD** & **MAE** \\ \hline Albedo VIS & 0.999 & 0.699 \% & 0.004 \\ Albedo NIR & 0.999 & 0.809 \% & 0.001 \\ Albedo VSWIR & 0.999 & 0.744 \% & 0.002 \\ Snow grain size & 0.998 & 1.610 \% & \(14.52\) μm \\ Snow impurity & 0.997 & 9.34 \% & \(6.148\times 10^{-8}\) \\ \hline
**Max-Min algorithm** & \(R^{2}\)**Score** & **APD** & **MAE** \\ \hline Albedo VIS & 0.999 & 0.618 \% & 0.004 \\ Albedo NIR & 0.999 & 0.688 \% & 0.001 \\ Albedo VSWIR & 0.999 & 0.641 \% & 0.002 \\ Snow grain size & 0.998 & 1.545 \% & 14.02 μm \\ Snow impurity & 0.998 & 7.829 \% & \(5.336\times 10^{-8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: A summary of the performance of the three snow parameter retrieval algorithms based on \(R^{2}\) score, APD, and MAE.  
Figure 9: Performance of MODIS snow parameter retrieval algorithm predictions versus model synthetic data (N = 5000).
## 3 Results and Discussion
### Corrosion Resistance Evaluation
#### 3.2.1 The Equivalent Circuit R(QR(LR))
Figure 3: Equivalent circuit (**a**), Nyquist diagram (**b**) and Bode diagrams (**c**) for the C45/BSW system after one hour of immersion. The Bode diagram shows the existence of a single maximum on the curve \(\theta=\text{f}(\
u)\) (the phase angle depending on the frequency of the applied signal), which implies the existence of a single relaxation time constant. In Black Sea water (BSW), due to the aggressiveness of the chlorine ions, the steel, especially the iron, is rapidly attacked, yet a continuous film of corrosion reaction products isn't formed on the surface. In this case, the reaction products (oxides or oxy-hydroxides insoluble) take the form of granules being only partially adsorbed to the surface. Consequently, a negative loop in the low-frequency region appears on the Nyquist diagram, and a solenoid and R\({}_{\text{L}}\) resistor must be introduced into the equivalent circuit. Under these conditions, the polarization resistance is R\({}_{\text{P}}=1503\)\(\Omega\cdot\)cm\({}^{2}\).
## 3 Methodology
### Feature Extraction and Selection
#### 3.2.11 Statistical Features
\[\sqrt{\frac{1}{N-1}\sum_{n=1}^{N}(x_{n}-\mu)^{2}} \tag{15}\]  
Here, \(N\) represents the total number of observations; \(\mu\) represents the mean of the given data; and \(x_{n}\) is the value of each amplitude point considered. Skewness  
The primary purpose of using skewness is to determine whether the sample under process is normally distributed or not. A normally distributed sample will have zero skewness, whereas if it is left- or right-distributed, then the sample is asymmetrical and points out to be an abnormal PCG. \[\frac{\sum_{i=0}^{N}(X_{i}-\mu)^{3}}{(N-1).\sigma^{3}} \tag{16}\]  
Quantile25  
The 25 th percentile, often referred to as the first quartile or Quantile25, is a statistical measure that provides insights into the distribution of data in a dataset. It represents the value below which 25.00% of the observations can be found. In other words, it is the value at which one-quarter of the data lie below it, serving as a marker that separates the lowest 25.00% of the data from the rest. To compute the Quantile25 for a given dataset, we have the following steps:  
1. First, the data points are sorted in non-decreasing order. 2. Then, the position \(p\) is calculated using the formula \[p=\frac{n+1}{4}\] (17) where \(n\) is the total number of data points in the dataset. 3. If \(p\) is an integer, then the data point at position \(p\) is the 25 th percentile. 4. If \(p\) is not an integer, then the 25 th percentile is typically computed by linear interpolation between the data points at positions \(\lfloor p\rfloor\) (the largest integer less than or equal to \(p\)) and \(\lceil p\rceil\) (the smallest integer greater than or equal to \(p\)). Thus, Quantile25 serves as a threshold, distinguishing the lower quarter of data points from the upper three-quarters in terms of magnitude. Quantile75  
The 75 th percentile, often referred to as the third quartile or Quantile75, is a statistical measure that delineates the distribution of data in a dataset. It represents the value below which 75% of the observations fall. In essence, it is the value at which three-quarters of the data lie below it, serving as a boundary that separates the lowest 75% of the data from the highest 25%. To compute the Quantile75 for a given dataset, we have the following steps:  
1. Initially, the data points are sorted in non-decreasing order. 2. Subsequently, the position \(p\) is determined using the formula \[p=\frac{3(n+1)}{4}\] (18)where \(n\) denotes the total number of data points in the dataset. * If \(p\) is an integer, then the data point at position \(p\) is the 75 th percentile. * If \(p\) is not an integer, then the 75 th percentile is typically derived by linear interpolation between the data points at positions \(\lfloor p\rfloor\) (the largest integer less than or equal to \(p\)) and \(\lceil p\rceil\) (the smallest integer greater than or equal to \(p\)). Thus, Quantile75 acts as a demarcation, distinguishing the lower three-quarters of data points from the uppermost quarter in terms of magnitude.
## 2 Materials and Methods
### Statistical Analysis of the SIF-GPP Relationship  
According to the LUE theory proposed by [PERSON] [66], the _GPP_ can be obtained by  
\[GPP=APAR\times LUE_{\rm P} \tag{4}\]  
where _APAR_ represents the absorbed photosynthetically active radiation and \(LUE_{\rm P}\) refers to the efficiency in which _APAR_ is used in photosynthesis.  
As a by-product of photosynthesis, _SIF_ can be similarly expressed as  
\[SIF=APAR\times LUE_{\rm F}\times f_{\rm esc} \tag{5}\]  
where \(LUE_{\rm F}\) is the light-use efficiency for _SIF_, which represents the fraction of _APAR_ that is re-emitted from the canopy as _SIF_ photons and \(f_{\rm esc}\) is the fraction of SIF photons escaping the canopy [24].  
By combining Equations (4) and (5), SIF can be theoretically linked with _GPP_ as  
\[GPP=SIF\times\frac{LUE_{\rm P}}{LUE_{\rm F}\times f_{\rm esc}}\approx SIF \times\frac{LUE_{\rm P}}{LUE_{\rm F}} \tag{6}\]  
where \(f_{\rm esc}\approx 1\) can be assumed to be due to the low absorptance of leaves in the far-red wavelengths and the relatively simple plant structure and high leaf area index of crops [24; 67].  
As shown in Equation (6), if the \(LUE_{\rm P}/LUE_{\rm F}\) ratio is constant, the SIF would be a good constraint used to determine GPP regardless of changes in the APAR or stresses. Previous studies have demonstrated that these two terms (\(LUE_{\rm P}\) and \(LUE_{\rm F}\)) covary in most cases, and the ratio of one to the other tends to be constant under high light conditions, which makes it possible to establish a linear statistical model between SIF and GPP [23; 68; 25]. In this study, we generated a statistical model to assess the ability of SIF to estimate GPP.
## 3 Results
### The Results Based on the Sprint Time of the Groups  
Repeated measures ANOVA showed that time had a statistically significant effect on RAST performance in the control group (\(F_{5,85}=96.31\), \(p=0.01\), \(\eta^{2}=0.66\)). Although the results indicated a violation of sphericity, similar results emerged after the GG correction (\(F_{2,56,43.5}=93.39\), \(p=0.01\)). Pairwise post hoc analyses showed that linear sprint performance decreased gradually from Sprint 1 to Sprint 5 (\(t=-4.06\) to \(-11.80\), \(df=17\), \(p=0.01\) to \(0.04\)). No statistically significant differences were found between Sprints 5 and 6 (\(t=-1.10\), \(df=17\), \(p=1.00\)). Details of the control group's repeated measures ANOVA and post hoc analysis are presented in Figure 4c.  
On the other hand, the time factor had a statistically significant effect on RAST performance in the BS85% group (\(F_{5,85}=54.23\), \(p=0.01\), \(\eta^{2}=0.49\)). [PERSON]'s test showed that the assumption of sphericity was violated (W = 0.032, \(p=0.01\)), while after the GG correction, the time factor was found to be significant (\(F_{2.61,44.44}=54.23\), \(p=0.01\), \(\eta^{2}=0.50\)). Post hoc analyses also determined that sprint performance significantly decreased over time for the BS85% group (\(p<0.05\)). However, there were no statistically significant differences (\(p>0.05\)) between consecutive sprints (sprint 1 vs. 2, sprint 2 vs. 3, sprint 3 vs. 4, sprint 4 vs. 5, and sprint 5 vs. 6). Details of the BS85% group's repeated measures ANOVA and post hoc analysis are presented in Figure 4b.  
The time factor also had a statistically significant effect on RAST performance in the BS90% group (\(F_{5,85}=96.31\), \(p=0.01\), \(\eta^{2}=0.66\)). [PERSON]'s test revealed that the assumption of sphericity was not violated (W = 0.39, \(p=0.46\)). Post hoc analyses reported no statistically significant differences (\(p>0.05\)) for the three sprint pairs in the BS90% group (sprint 1 vs. 2; sprint 3 vs. 4; sprint 5 vs. 6). Details of the repeated measures ANOVA and post hoc analysis of the BS90% group are presented in Figure 4a. Also, the descriptive statistics of all groups for repeated measures ANOVA are provided in Supplementary Materials S3 and S4.  
Figure 3: Comparison of RAST performance based on group and time interaction. _Legend_. ns = \(p>0.05\).
## 2 Materials and Methods
### Electrochemical Tests  
After finishing the sample preparation, electrochemical evaluations were carried out on the four different B4C variants. Potentiostat BioLogic Essential SP-150 (Bio-Logic Science Instruments SAS, Seyssinet-Pariset, France) and an electrochemical cell were used to carry out the different tests. A total of 3 electrochemical tests were performed: direct-current open-circuit potential (OCP) and linear polarization and alternative-current electrochemical impedance spectroscopy (EIS). All tests were conducted in a 3.5% NaCl artificial seawater solution to simulate a marine environment. Test parameters were configured, and data analysis was conducted using EC-Lab(r) v-9.55 software, in accordance with the ASTM G5-94(2004) standard [39]. Each experiment utilized a conventional three-electrode electrochemical setup, where the doped B4C samples served as the working electrodes, a saturated calomel electrode acted as the reference, and a platinum electrode functioned as the counter electrode.
## 2 Materials and Methods
### Zeta Potential Measurements  
The zeta potential values were determined in freshly prepared suspensions with a Zetasizer ZS (Malvern Instruments Ltd., Malvern, UK). Each measurement was performed in sequences of ten runs with autocorrelation functions set at 10 s. The zeta potential of each sample was obtained from the average of three measurements.
## 4 Methodology
### Hierarchical Clustering  
Based on the proposed regional indicators, significant spatial heterogeneity in urban freight activities is observed in Shanghai. To classify different types of freight areas, hierarchical clustering is employed, a method widely used in urban transportation research for its ability to reveal the natural hierarchical structure of data without prior assumptions and its robustness in clustering high-dimensional data. After standardizing the regional indicators, an \(n\)-dimensional vector is constructed for each grid, serving as the input for hierarchical clustering. Initially, each grid is treated as an independent cluster, and clusters are iteratively merged based on the similarity between grids (i.e., distance between vectors).  
This study uses the Ward linkage method to calculate cluster distances, which determines merging strategies by minimizing the increase in within-cluster variance. The Ward linkage method is chosen for three key reasons. First, it is insensitive to outliers, making it suitable for handling the highly uneven distribution of urban freight data. Second, unlike traditional single or complete linkage methods, it avoids the \"chain effect\" and \"dominant cluster\" phenomena, generating balanced clusters that better aligns with the natural distribution of urban freight functional zones. Lastly, it has theoretical compatibility. By minimizing variance increments, it effectively identifies grid groups with similar freight characteristics, highlights functional differences between clusters, and provides a clear classification basis for subsequent spatial functional analyses.  
Following the methodological framework described in Section 4, this section delves into the spatial-temporal behavioral patterns of seven HDT types, integrating empirical results with interpretive analysis to unravel the hierarchical organization of urban freight systems. The subsequent subsections will first characterize each HDT type's functional clustering before dissecting the underlying mechanisms linking vehicle specializations to industrial spatial demands.
# Protocol for Energy-Efficiency in Networked Control Systems Based on WSN
## Preliminaries
### Markovian Model
#### 3.2.1 \(\mathcal{H}_{\infty}\) Norm Computation for MJLS  
The \(\mathcal{H}_{\infty}\) norm for MJLS is defined as a ratio between the expected values of the exogenous input \(w(k)\) and the output \(z(k)\) for the worst case scenario of the signal \(w(k)\in\mathcal{L}_{2}\). One of the possible definitions of the \(\mathcal{H}_{\infty}\) norm of an MJLS is presented in [39], and reproduced below,  
\[\|\mathcal{G}\|_{\infty}^{2}=\sup_{0\
eq w\in\mathcal{C}^{2},\ \theta_{0}\in\mathbf{K}}\frac{\|z(k)\|_{2}^{2}}{\|w(k)\|_{2}^{2}}. \tag{2}\]  
The \(\mathcal{H}_{\infty}\) norm, that is finite only if the MJLS (1) is MSS, can be calculated by solving a convex optimization problem in the form of Linear Matrix Inequalities (LMIs) [40; 41] as presented next.  
**Lemma 1**.: _MJLS (1) in open-loop (\(u(k)=0\)) is MSS and satisfies \(\|\mathcal{G}\|_{\infty}<\gamma\) if and only if there exist symmetric positive definite matrices \(P_{i}\), such that the LMIs_  
\[\begin{bmatrix}A_{i}&E_{i}\\ C_{zi}&E_{zi}\end{bmatrix}^{\prime}\begin{bmatrix}P_{pi}&0\\ 0&I\end{bmatrix}\begin{bmatrix}A_{i}&E_{i}\\ C_{zi}&E_{zi}\end{bmatrix}-\begin{bmatrix}P_{i}&0\\ 0&\gamma^{2}I\end{bmatrix}<0 \tag{3}\]  
_hold for each \(i\in\mathbf{K}\) with \(P_{pi}=\sum_{j=1}^{\sigma}p_{ij}P_{j}\)._
# Future Perspectives for Gamma-ray Burst Detection from Space
## 4 Gamow  
### Mission Overview  
The Gamow Explorer mission [31] is optimized to: (1) probe the high-redshift universe (z \(>\) 6) when the first stars were born, galaxies formed, and Hydrogen was re-ionized and (2) enable multi-messenger astrophysics (MMA) by rapidly identifying electro-magnetic (EM) counterparts to gravitational wave (GW) events. GRBs have been detected out to z\(\sim\)9 (see, e.g., [32; 33] and the references therein), and their afterglows are bright beacons lasting a few days, which can be used to observe the spectral fingerprints of the host galaxy and intergalactic medium. Gamow is designed to detect and rapidly identify high-redshift events. Rapid follow-up spectroscopy with the James Webb Space Telescope (JWST) and \(>\)6 m ground-based telescopes provide NIR R\(\sim\) 2500 spectra to determine the IGM neutral fraction versus redshift using the damping wing of the Lyman-\(\alpha\) absorption line (see Figure 5 from [34]).  
GRB afterglows are particularly advantageous for this measurement versus, e.g., using QSOs. GRBs have a featureless power-law spectrum, ideal for fitting the Ly-\(\alpha\) absorption profile, are hosted by low-mass galaxies directly tracing the typical ionization state of the IGM, and can be seen out to redshifts of 20, whereas the abundance of QSOs drops steeply with redshift. The same spectra will be used to determine metallicities from absorption lines to trace the early chemical enrichment and measure the escape fraction of ionizing photons that escape the galaxies to ionize atoms in the IGM. The co-moving GRB rate generally follows the star-formation rate (SFR). At higher redshifts, the GRB rate exceeds the SFR derived by other means. The greatly improved measurements of the high-redshift GRB rate by [PERSON] will provide crucial information to probe potential changes of the initial mass function of massive stars and the star-formation rate at high redshift and constrain GRB progenitors and their properties (e.g., luminosity distributions, progenitor binary fractions, etc.) in the high-redshift universe [33].  
Figure 4: The large FoV of the eXTP/WFM (red line) overlapped on a background map of the high-energy sky, which has been provided as a courtesy by the MAXI team ([PERSON], RIKEN, JAXA). The red line corresponds to the total FoV covered by the WFM in the configuration used for the eXTP, as illustrated by [PERSON]n’t [PERSON] et al. [30].