esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Determination of Parameters of the SMIKE Model Used for the Dynamic Analyses
#### 3.3.2 Determination of the Space Scale Parameter for Conditional Random Field  
Simulation of Ground Motions Based on the USCB Regression Function  
Determining the spatial correlation function (see Equation (6)) between two field points requires an assumption of the space-scale parameter, which, according to the author  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Wave Component Frequency [Hz]**} & \multicolumn{3}{c}{**Differences [\%] in the Wave Velocities Obtained with**} \\ \cline{2-4}  & \multicolumn{3}{c}{**Different Methods for Support Pairs**} \\ \cline{2-4}  & **P1–P2** & **P2–P3** & **P1–P3** \\ \hline
10 & 3.7 & 2.6 & 2.8 \\
12 & 1.4 & 6.0 & 4.2 \\
14 & 2.7 & 2.3 & 2.0 \\
16 & 2.1 & 3.3 & 2.0 \\ Mean apparent velocity & 1.1 & 3.6 & 2.9 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The differences in the wave velocities obtained from the cross-correlation and the phase angle method for particular support pairs.  
Figure 13: The function of phase angle in unwrapped form for pairs: (**a**) P1–P2, (**b**) P1–P3, (**c**) P2–P3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Wave Component Frequency [Hz]**} & \multicolumn{3}{c}{**Wave Velocity Determined for Support Pairs [m/s]**} \\ \cline{2-4}  & **P1–P2** & **P2–P3** & **P1–P3** \\ \hline
10 & 130 & 335 & 247 \\
12 & 140 & 280 & 229 \\
14 & 144 & 297 & 240 \\
16 & 151 & 297 & 245 \\ Mean apparent velocity & 141.0 & 302.0 & 239.9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The mean value of the wave velocity between supports determined by the unwrapped phase angle function.  
of the method, should be obtained experimentally for each analyzed field (see Section 2.1.2). For the purpose of this study, the space field parameter \(\alpha\) is determined based on the empirical amplitude regression curve employed in the USCB region (see Section 2.1.4). It is assumed that the curve describing the decay of maximal amplitude with the epicentral distance also characterizes the loss of wave coherency, and the correlation Function (6) decreases according to the same formula.  
Since the USCB regression function, defined based on tremors registered in the coal mines, describes the bedrock (hard rock) acceleration amplitudes, the wave velocity was assumed as \(v=1000\) m/s, which is the typical wave velocity in hard rock.  
Fitting the distance (\(D_{ij}\))-dependent part of the correlation Function (6) \(\mathrm{e}^{\frac{-\alpha_{i}D_{\mathrm{i}}}{2\pi\alpha\mathrm{s}}}\)) with an assumed velocity of \(v=1000\) m/s for the shock with dominant angular frequency \(\omega_{d}=3.516\cdot 2\cdot\pi=22.1\) rad/s into the empirical decay function (14) gives the value of space-scale parameter \(\alpha=7.08\approx\,\sim 7\) for the USCB region.
## 3 Results and Discussion
### Dynamic Changes during Processing Analyzed via GC-MS
#### 3.3.4 Overview of Related Mechanisms of Key Aroma-Active Compounds in SFABT
Specifically, 2-methylpropanal, 2-methylbutanal, and 3-methylbutanal, categorized as Strecker aldehydes, were derived from their corresponding precursors: valine, isoleucine, and leucine, respectively [6]. Additionally, 2-methylpropanal, 3-methylbutanal, and 2-methylbutanal were reported to contribute to the malty flavors. Dimethyl sulfide was recognized for its cooked corn-like aroma. The potential formation mechanism suggests that amino acid of \(S\)-methylmethionine undergoes Strecker degradation to form dimethyl sulfide [31]. It has been demonstrated that linalool and \(\beta\)-ocimene, renowned for imparting floral and fruity scents in black tea, are derived from the primary geranyl pyrophosphate (geranyl-PP) substrate activated by linalool synthase and ocimene synthase, respectively [23]. Notably, linalool is subsequently dehydrated to form \(\beta\)-myrcene. Citral and \(\beta\)-citral are enantiomers of each other, primarily derived from geranyl-PP catalyzed by geraniol synthase. Methyl salicylate, known for its unity, fresh, and sweet fragrances, is liberated from \(\beta\)-primerevoside. The 1-octen-3-ol and 3,5-octadecine-2-one are typical FADVs. In cucumber, earth, fatty, and mushroom-like aromas, 1-octen-3-ol exerts a pivotal role, and has been documented to originate from linoleic acid, which is oxygenated by lipoxygenase (LOX) to form lipid hydroperoxides [30]. The 3,5-ocadiene-2-one is associated with creamy and fruity flavors and is generated from \(\alpha\)-linolenic acid. In the future, the formation pathway and mechanism will likely be traced in depth based on stable-isotope-labeling technology. The results will provide theoretical support for the directional regulation of SFABT.
## 2 Materials and Methods
### Characterization of Membranes  
The fiber micromorphology of the PET fiber membrane, recrystallized porous PET fiber membrane, CuBTC powder, and CuBTC@PET composite fiber membrane was observed and analyzed using a JSM-7610F Plus field emission scanning electron microscope (SEM) from JEOL, Tokyo, Japan.  
The distribution of elements such as C, O, Cu, Si, and F on the surface of the CuBTC@PET composite fiber membrane and superhydrophobic CuBTC@PET composite fiber membrane was quantitatively and qualitatively examined by using an EDS spectrometer of 7021-H from HORIBA, Kyoto, Japan.  
A Bruker D8 Advance X-ray diffractometer (USA) was used to slowly sweep the samples at 2\({}^{\circ}\)/min, and the measurement angles were 20 = 5-50\({}^{\circ}\). CuBTC@PET composite nanofibers with different cycles, CuBTC powder, and CuBTC@PET water stability before and after hydrophobic modification were tested.  
The elemental full spectra of PET nanofibers, 5-cycle CuBTC@PET composite fiber membranes, and superhydrophobic CuBTC@PET composite fibers were analyzed by an X-ray photoelectron spectroscopy (XPS) instrument (ESCALAB 250 Xi) from Thermo Fisher, Waltham, MA, USA, in the scanning range of 50 to 1500 eV, and the elemental narrow spectral scans of C, O, Cu, Si, and F were performed after the split-peak fitting.  
Figure 1: Schematic route of CuBTC@PET preparation.  
The functional groups of PET-dissolved porous fiber membrane, CuBTC powder, CuBTC@PET composite fiber membrane, and superhydrophobic CuBTC@PET composite fiber membrane were analyzed at 400-2300 cm\({}^{-1}\) by a Nicolet iS10 Fourier transform infrared spectrometer (FTIR) from Thermo Fisher, Waltham, MA, USA, with a signal resolution of 2 cm\({}^{-1}\).  
The contact angle of the fiber membrane was experimented with using the DCAT21 dynamic contact angle measuring instrument from Beijing Huahai Henghui Science and Technology Co., Ltd., Beijing, China. The droplet method was chosen for the contact angle test, and the volume of water droplets was 5 \(\upmu\)L. The contact angle of each sample was obtained at different moments by timing the droplets' contact with the samples until the end of the recordings after the droplets were stabilized on the surface of the samples.  
Changes in thermal properties of CuBTC@PET composite fibers before and after hydrophobicity modification were analyzed using an STA7300 thermogravimetric-differential thermal analyzer from Hitachi, Japan, with a set heating rate of 15 \({}^{\circ}\)C/min and a measurement temperature interval from 20 \({}^{\circ}\)C to 800 \({}^{\circ}\)C.
## 3 Existing Works  
This section presents nascent research on network coverage and deployment in WSNs, mainly based on CSO and its improved versions. We also discuss the hybrid versions of CSO along with other metaheuristic algorithms and their limitations. The deployment of sensor nodes with limited and uniform sensing radius can lead to optimal coverage in WSNs. The coverage area is classified into active coverage and blind spots. Active coverage is the sensing area covered by at least one sensor node and able to monitor events. The blind spots are the areas where no sensor nodes can monitor the events. The coverage accuracy will be high if the sensing area is covered by multiple sensor nodes. Most of the metaheuristic algorithms use the exploration and exploitation strategy to enhance network coverage through the optimal placement of sensor nodes. CSO has various applications in enhancing the performance of WSNs in terms of locating sensor nodes accurately, cost, energy consumption, and load distribution [7, 8]. Table 1 describes various network coverage and deployment techniques in WSNs based on CSO and integrated with other metaheuristic algorithms. Cauchy distribution-based CSO [9] enables improvement in the exploration ability and minimizes the risk of falling into local optima. It allows the algorithm to explore the entire solution space addresses. It provides better coverage and lifetime optimization in WSNs.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Ref.** & **Authors** & **Objective** & **Methodology** & **Limitations** \\ \hline \multirow{3}{*}{[9]} & \multirow{3}{*}{Yang and Xia (2024)} & Design routing and coverage & Coverage optimization is & \multirow{3}{*}{
\begin{tabular}{l} The algorithm’s minimum \\ run time is 1.47 min, with an \\ energy loss rate of 0.84\%. \\ \end{tabular} } \\  & & optimization in WSNs using & addressed through & \\ \cline{1-1}  & & improved CSO and & Cauchy distribution. & \\ \cline{1-1}  & & non-uniform clustering. & The average coverage is 96\%. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Related works on self-localization and coverage techniques in WSNs using metaheuristic approaches.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Ref.** & **Authors** & **Objective** & **Methodology** & **Limitations** \\ \hline  & & & & Though the coverage rate of the proposed algorithm is enhanced by 18.36\% & \\
[10] & [PERSON] et al. (2023) & Maximize network coverage using a hybrid algorithm that combines CSO and PSO. & Convert the regional monitoring space into point monitoring space by using discretization methods. & compared with CSO, it enters an early stagnation state and is trapped in a local optimal solution. &  
CSO [25] in combination with Levy flights was introduced to solve structural engineering optimization problems. It has been used in WSN applications to enhance network performance in terms of energy consumption, CH selection, lifetime, coverage, and connectivity [26,27].
## 4 Examples
### Implementation on Gpu  
In recent years, the programmable Graphic Processor Unit (GPU) has evolved into a highly parallel, multi-threaded processor with tremendous computational horsepower and very high memory bandwidth. More specifically, the GPU is especially well-suited to address problems that can be expressed as data-parallel computations, that is, the same program is executed on many data elements in parallel. Due to fine-grained parallelism, the proposed approach is accelerated by GPU  
Figure 8: (**a**) Initial triangular mesh with 4914 inverted elements. (**b**) Optimized mesh using the Size untangler. (**c**) Optimized mesh using the Shape-Size untangler. (**d**) Optimized mesh using the proposed approach.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Time(s)** & \(\mathbf{q}_{ave}\) & \(\mathbf{q}_{min}\) & **Number of Inverted Elements** \\ \hline Initial mesh & \(-\) & 0.2566 & 0 & 4914 \\ Size & 4.927 & 0.2043 & 0 & 1825 \\ Shape-Size & 32.472 & 0.1729 & 0 & 2636 \\ EEGT & 37.488 & 0.9130 & 0.6461 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mesh quality statistics and the number of inverted elements for triangular mesh example. The mean ratio quality metric is used to measure the mesh quality.  
to demonstrate its potential. The GPU used in this test is NVIDIA Quadro K2200, and its detailed parameters are listed in Table 4. Again, mesh smoothing process is terminated if the average quality numbers of two consecutive meshes deviate less than 0.001. Optimization results on CPU and GPU are given in Table 5. More than 10 times speedup is achieved by GPU implementation in the test platform, which shows the great potential of the proposed approach in solving large-scale mesh optimization problems. With the increase of the number of mesh elements.
## 3 The GAGAN Framework: Evolutionary Optimization in GAN Architectures
### Genetic Operators in GAGAN
#### 3.2.3 Mutation Process  
In the GAGAN framework, mutation introduces random changes to the weight vectors of the discriminator network, promoting genetic diversity and preventing premature convergence to local optima. The mutation operator is implemented using a Gaussian mutation strategy, where Gaussian noise is added to each weight in the network. This process alters the weights slightly, enabling the algorithm to explore different regions of the solution space, fostering the discovery of improved solutions.  
The mutation rate in GAGAN indicates the probability of mutating each individual weight, and is set at 0.2. This rate strikes a balance between exploration (introducing new variations) and exploitation (fine-tuning existing solutions). The Gaussian mutation strategy ensures diversity within the population, thereby addressing the risk of the algorithm becoming trapped in suboptimal solutions and enhancing the overall optimization process.  
In summary, the genetic operators--selection, crossover, and mutation--work together in GAGAN to optimize the weights of the discriminator. By employing tournament selection, two-point crossover, and Gaussian mutation, GAGAN efficiently navigates the solution space, improving the GAN's performance and enabling the generation of high-quality images.
## 2 Background and Related Work
### Explainable Artificial Intelligence (XAI) in AI-Based Systems  
Explainable Artificial Intelligence (XAI) aims to enhance the transparency, interpretability, and trustworthiness of artificial intelligence models, addressing critical concerns related to fairness, accountability, and societal impact [89]. As AI systems grow in complexity, particularly with the widespread adoption of deep learning and reinforcement learning [90], the opacity of model decision-making increases, making it difficult for stakeholders to understand, validate, or contest outcomes [91].  
The lack of interpretability presents substantial challenges, especially in high-stakes domains such as healthcare, finance, and autonomous systems, where explainability is essential for regulatory compliance, ethical accountability, and public trust [92]. XAI seeks to bridge this gap by developing techniques that provide human-understandable explanations without compromising model performance.  
XAI methods are broadly categorized into two groups [93]:  
* Intrinsic Interpretability: Designing models that are inherently understandable, such as decision trees, linear models, and rule-based systems. These models trade some performance for enhanced transparency.
* Post Hoc Explainability: Applying interpretability techniques after model training, using methods such as feature attribution (e.g., SHAP, LIME), surrogate models, counterfactual explanations, and visualization tools to provide insights into model behavior.  
Within reinforcement learning (RL), the subfield of Explainable Reinforcement Learning (XRL) has emerged to clarify how agents learn policies and make decisions in dynamic, often adversarial environments [94]. Still, a fundamental trade-off persists, as enhancing model interpretability can come at the cost of reduced predictive performance or increased system complexity.  
Ultimately, XAI contributes not only to enhancing user trust and regulatory compliance but also plays a crucial role in reducing technical debt. Systems that lack transparency are harder to validate, debug, maintain, and audit, thereby increasing long-term maintenance burdens. Embedding explainability principles early in AI system design thus represents a proactive strategy for sustainable AI development [91; 92; 95].
###### Abstract  
The temperate evergreen needleleaf forest (ENF), primarily composed of Mon-golian Scots pine (_Pinus sylvestris_ var. _mongolica_), plays a pivotal role in the \"The Great Green Wall\" Shelterbelt Project in northern China as a major species for windbreak and sand fixation. Solar-induced chlorophyll fluorescence (SIF) has emerged as a revolutionary remote sensing signal for quantifying photosynthetic activity and gross primary production (GPP) at the ecosystem scale. Meanwhile, eddy covariance (EC) technology has been widely employed to obtain in situ GPP estimates. Although a linear relationship between SIF and GPP has been reported in various ecosystems, it is mainly derived from satellite SIF products and flux-tower GPP observations, which are often difficult to align due to mismatches in spatial and temporal resolution. In this study, we analyzed synchronous high-frequency SIF and EC-derived GPP measurements from a Mongolian Scots pine plantation during the seasonal transition (August-December). The results revealed the following. (1) The ENF acted as a net carbon sink during the observation period, with a total carbon uptake of 100.875 gC-m\({}^{-2}\). The diurnal dynamics of net ecosystem exchange (NEE) exhibited a \"U\"-shaped pattern, with peak carbon uptake occurring around midday. As the growing season progressed toward dormancy, the timing of CO\({}_{2}\) uptake and release gradually shifted. (2) Both GPP and SIF peaked in September and declined thereafter. A strong linear relationship between SIF and GPP (\(R^{2}\) = 0.678) was observed, consistent across both diurnal and sub-daily scales. SIF demonstrated higher sensitivity to light and environmental changes, particularly during the autumn-winter transition. Cloudy and rainy conditions significantly affect the relationship between SIF and GPP. These findings highlight the potential of canopy SIF observations to capture seasonal photosynthesis dynamics accurately and provide a methodological foundation for regional GPP estimation using remote sensing. This work also contributes scientific insights toward achieving China's carbon neutrality goals.  
t
# Detecting Plasma Detachment in the Wendelstein 7-X Stellarator Using Machine Learning
## 2 Methods
### Models  
During our work we looked at three classical supervised machine learning models, using the implementations found in the Sklearn Python library [16]. We presumed that the classical machine learning models need much less data and time of training to achieve a sufficient level of accuracy then advanced ones like neural networks, thus these algorithms are the primary focus of this paper.  
The first and simplest classical classification algorithm is the Logistic Regression [17]. Despite its name it is a linear classification method. Using this model we fit the logistic function to the data to get the probabilities for a sample belonging to one of two classes. Sklearn's implementation uses an L2 regularized version of this model as default, for further information we refer to [18].  
The Support Vector Machine (SVM) [17] fits a separating hyperplane to the data, in such way that the model tries to find the hyperplane which has the maximal distance to the nearest data point, thus achieving good generalization ability.  
Figure 3: The regions A, B, C, D and E indicated with blue ellipses in one image (**left**). Two examples for profile A (**right**); one detached (blue) and one non-detached (orange). The confined region in this case is in the direction of the increasing pixel indexes, we can see the maximums of the graphs shifting that way during detachment.  
The last classical supervised algorithm we used is the Random Forest [17]. It is among the most powerful and widely used statistical models. It is an ensemble of hierarchical data structures called decision trees, which are built from bootstrap samples of the training set. Individual decision trees typically exhibit high variance and tend to overfit, but combining their predictions using averaging makes the random forest a robust method.
# Remote Sensing for International Peace and Security: Its Role and Implications
## 2 Methodology
### Conceptual Issues  
A conceptual analysis of the remote sensing approaches for peace and security has been carried out in Reference [36]. The conceptual issues can be mainly classified into two strands viz. definitions and cross-disciplinary interpretations.  
'Peace' can be defined as a state or a period in which there is no war, or a war has ended [37; 38]. However, there are many types of social settings where society does not experience peace despite the absence of war or conflict [39]. For instance, [PERSON] et al. [40] have argued that poverty is one of society's major threats. Countries with a high level of poverty, prohibition, intimidation, repression, terror, and other deterrents cannot be categorized as peaceful countries. Based on these observations, analysts working in peace and conflict management have varying definitions for peace from different perspectives. 'Security' is a state of ensuring protection from the direct/indirect notions and actions threatening an individual or, collectively, a group [37; 38]. The international and regional organizations like the United Nations (UN), European Union, and other multi-lateral groups strive for attaining the standards of safety and security of individuals, countries, and regions.  
Contextualizing the disciplinary connections becomes germane to broaden the understanding of the applications of remote sensing technology for peace and security. [PERSON] [35] has identified the issues of measurement validity and selection bias with approaches in GIS technologies to the domains of peace and security in the studies of international relations. The measurement validity issues arise when the institutional structures are built over novel propositions with behavioral and pragmatic approaches that are not sufficiently absorbed by technical operational parameters (raster, vector, and other files) of the GIS interfaces [35]. During the analysis stage, the selection bias leads the discrepancies and prejudices between spatial and non-spatial information to creep into the study/system [35].  
The successful application of remote sensing for peace and security depends on different kinds of datasets and how it is coded and analyzed within the framework of measurement validity and selection bias [35]. For instance, the large datasets about ethnic conflicts have the potential to overshadow the reasons contributing to their origins. The  
Figure 2: Flowchart of literature review.  
quantitative study of ethnic conflicts with a focus on micro-level studies has elucidated the essence of individuality during a conflict [41].
## 4 System Performance Analysis
### The Analysis of the HDT Protocol
#### 4.1.2 System Performance Analysis of the HDT Protocol Scheme  
Because the transmission time of each sensor node in the HDT protocol is independent of one another, the CDF of \(T_{max}^{(v)}\) and \(T_{min}^{(v)}\) can be expressed as follows:  
\[F_{T_{max}^{(v)}}(t) =\Pr\bigg{\{}\max_{k\in\{1,2,\ldots,K\}}\left\{T_{k}^{(v)}\right\} <t\bigg{\}} \tag{23}\] \[=\prod_{k=1}^{K}F_{T_{k}^{(v)}}(t),\]  
\[F_{T_{min}^{(v)}(t)} =\Pr\bigg{\{}\min_{k\in\{1,2,\ldots,K\}}\left\{T_{k}^{(v)}\right\} <t\bigg{\}} \tag{24}\] \[=1-\prod_{k=1}^{K}\bigg{(}1-F_{T_{k}^{(v)}}(t)\bigg{)}.\]  
The system reliability has been defined in (14), and combining (23), we can re-deduce the definition of the system reliability of the HDT protocol as follows:  
\[\mathcal{B}^{(v)}=F_{T_{max}^{(v)}}(t_{out})=\prod_{k=1}^{K}F_{T_{k}^{(v)}}(t _{out}). \tag{25}\]  
Through formula (15), we can easily derive the expression of the outage probability of the HDT protocol as follows:  
\[\mathcal{O}_{\text{gsg}}^{(v)} =1-F_{T_{min}^{(v)}}(t_{out}) \tag{26}\] \[=\prod_{k=1}^{K}\bigg{(}1-F_{T_{k}^{(v)}}(t_{out})\bigg{)},\quad v \in\{MRC,SC\}.\]