esa-sceva/satcom-chunk-collection huggingface dataset:
# Online Detection and Fuzzy Clustering of Anomalies in Non-Stationary Time Series +
## 5 Offline Anomaly Clustering
### Known Cluster Numbers
It is worth noting that hierarchical clustering is sensitive to noise and outliers. Meanwhile, for handling clusters with different sizes, hierarchical clustering tends to split large clusters and merge small clusters. Meanwhile, the results in Table 4 show that, compared to the histogram, an ECDF can directly reflect the structures of time series segments without binning bias and be more robust to the outliers. Therefore, the ECDF appears to be a more suitable distribution summary for clustering collective anomalies. Compared to the other methods, the fuzzy c-means clustering with ECDF has achieved the best results for identifying all 16 types of anomalies with the least error rates. We summarise the clustering results using ECDF with confusion matrices in Figure 5(a)-e, which allows for a more detailed comparison of different clustering methods. Figure 6: Clustering results by method presented as contingency matrices over 16 collective anomaly types. (**a**) K-means clustering with ECDF for 16 types of collective anomalies. (**b**) Hierarchical clustering with ECDF for 16 types of collective anomalies. (**c**) Fuzzy c-means clustering with ECDF for 16 types of collective anomalies. (**d**) EPmeans for 16 types of collective anomalies. (**e**) HMCluster for 16 types of collective anomalies. Fuzzy c-means clustering with ECDF has outperformed other clustering methods, especially for differentiating shifts with small intensity from the interference (seeing Figure 6c). EPmeans has achieved the second-best accuracy, only having some issues mixing the interference and the splits as in Figure 6d. As shown in Figure 6a, conventional k-means clustering shows reasonable clustering results, successfully dividing segments with different structures but still failing to differentiate the anomalies with varying intensity. Hierarchical-based clustering methods give the worst outcome as they could neither differentiate based on structure nor intensity of anomalies, as seen in Figure 6b,e.
# Climate-Quality Calibration for Low Earth-Orbit Microwave Radiometry +
## 1 Introduction  
Accuracy and long-term consistency in space-based measurements of the earth require comparisons of satellite instruments to one another and to high-accuracy reference instruments [1]. For these calibration intercomparisons to be useful for climate studies, they must be traceable to standards that are stable over decades, in order to exclude the possibility of measurement drifts that would obscure long-term atmospheric trends. The proposed CLARREO mission [2, 3] would provide reference-standard measurements at infrared and visible wavelengths. In the microwave part of the spectrum, improvements have been made with each new generation of weather-satellite instruments, but as yet, no single instrument combines all of the features of a calibration system that would constitute a state-of-the-art calibration standard.  
Although microwave sounding instruments on weather satellites have been used to monitor global trends in atmospheric temperature (see the discussion in [4]), they were designed to observe weather phenomena. The requirements for the two types of measurement are different. Weather observations benefit from good spatial resolution (i.e., narrow antenna beams) and radiometric resolution (i.e., low noise) because individual measurements are assimilated into a numerical prediction model. For monitoring of climate, the measurements are spatially averaged and temporally smoothed, so calibration-related issues predominate in the error budget. For example, any non-linearity in radiometer response introduces scene-dependent errors with adverse consequences for the determination of long-term trends [5]. Therefore, a microwave radiometer designed as a reference instrument in a space-based climate observing system would differ in several respects from weather-satellite instruments. The antenna should have extremely low sidelobe levels; there is no trade-off with respect to a narrow beamwidth requirement as in the case of weather satellites.  
The radiometer should have excellent linearity and stability in both frequency and calibration accuracy, with on-orbit traceability in SI units.  
In this paper we focus on orbital radiometric calibration in the 50-60 GHz frequency band for long-term temperature monitoring. However, most of the features discussed in Section 2 would facilitate inter-satellite transfer-calibration at frequencies used by microwave imagers as well as sounders. Section 3 discusses occultation of Global Navigation Satellite System (GNSS) signals as a comparison reference to provide traceability of radiometer stability to frequency standards. Other sources for comparisons are radiosondes, ground-based radiometers, and vicarious calibration scenes [6; 7; 8]. Section 4 considers some factors relevant to the choice of an orbit for a reference satellite.  
Pre-launch calibration of target emissivity and temperature sensors, although an essential part of an instrument calibration plan, is not within the scope of this paper. However, a recent development in that area at the U.S. National Institute of Standards and Technology is described in [9].
## 5 Discussion
### The Applicability of Titis Method in Other Passive Seismic Datasets  
Due to differences in collection environment and conditions, there are often great differences between different passive seismic datasets. Therefore, in addition to testing the passive seismic dataset in Sichuan, we also attempted to apply this method to a passive seismic dataset acquired in Inner Mongolia [37] to enhance the surface waves but did not achieve very obvious results. As the survey line arranged in the research area of Inner Mongolia was located near a village, and it was the busy farming season, when farmers used agricultural machinery to harvest corn in the field, the human activities were complex and the interference was serious, so this method might not be able to completely eliminate these complex noises. However, the study area in this paper is far away from the village, with little human interference, and only the mining activities about 4 km northwest of the survey line are prominent. Therefore, although this method had a good effect in processing the Sichuan dataset, it may not have obvious effects on the datasets collected in different environments (especially in areas where human activities are prominent).
## 3 Results and Discussion
### Characterization and Distribution of Metal(loid)s in Soil
However, Igeo values were higher than 0 in the Cd 47.83%, Hg 33.48%, Cu 4.35%, Pb 3.04%, Zn 2.17%, and As 0.87% samples, and the sample numbers of Cd and Hg far exceed the other six metal(loid)s.  
The box plots in Figure 2 indicate that the study area soils are contaminated (Igeo \(>\) 0) by Cd, Hg, and especially Cd, which probably originated from anthropogenic sources. Moreover, the distribution shows a large high-value area of Cd and Hg (Figure 3). The high Cd values are mainly concentrated in the BD, SJZ, and CZ areas (Figure 1). [PERSON] et al. (2011) showed that the metal(loid)s in this area primarily originated from a chemical fiber factory that had industrial wastewater rich in Cd, which polluted the soil [52]. The high Hg values are mainly concentrated in the BD, SJZ, XT, and HD areas (Figure 1). China's use of phosphate fertilizer ranks first in the world, accounting for about 25% of the world's phosphate fertilizer consumption. The application of phosphate fertilizer is a main source of Hg and As in farmlands within the study area [53]. Additionally, emissions from coal burning also increased soil Hg levels in the study area [54]. Cu, Pb, and Zn distribution patterns point to source pollution. Figure 3 shows that areas with high Zn, Cu, and Pb levels are mainly concentrated southeast of Baoding city and in the southern part of Shijiazhuang City. Researchers have confirmed that the metal(loid) pollution was due to toxic substances (e.g., dust, sewage) from lead-zinc ore smelters, paper mills, and chemical plants. The metal(loid)s entered and polluted farmland soils via atmospheric deposition and sewage [42; 55]. However, Ni and Cr levels were not large enough to be considered soil contaminants in the study area and were likely from natural origins. Figure 2: Box plots of metal(loid) Igeo values.
## 3 Results and Discussion
### Study of Adaptive Properties of Fungi
#### 3.3.3 Extracellular Polysaccharides Production  
The third strategy of adaptation toward the pollutant contamination of the environment can be extracellular polymeric substances such as exopolysaccharideacharides (EPS) production. EPS are produced by both bacteria and fungi. EPS are compounds consisting of a large number of monosaccharides linked glycosidically and can be homo- or heteropolymers of monosaccharide residues and can be linear or branched [82]. For a long time, it has been known that lacases and other lignin-modifying enzymes are embedded within EPS sheets around the hyphae of wood-rotting basidiomycetes, and such structures have been proposed to be involved in recognition mechanisms in fungal cell-wood surface interactions and to play specific roles in the biodeterioration of wood [83]. However, direct effects of fungal EPS on the mode of action of lignin-modifying enzymes have not yet been described in the literature.  
In our experiments, EPS concentrations varied from 0.15 to 1.5 \(\upmu\)g/mL for all studied fungi. _G. candidium_, which produced about 3.3 \(\upmu\)g/mL of EPS in control treatment (without pollutants), was one exclusion. The decrease in EPS concentration in the presence of all studied pollutants was found for all studied fungi. Its concentration decreased significantly, and in some cases was nearly zero. Simultaneous decrease in EPS concentration and increase of emulsifying activity of cultivation medium in the presence of pollutants should be noted. Perhaps any emulsifying compounds, which fungi produce as a response to the presence of pollutants, can decrease the surface tension of the cultivation medium. This can result in disruptions of the production/synthesis EPS or affect their migration from cell surface to environment. This phenomenon will be studied further.
## 6 Discussion
### Limitations and Challenges
In order to tackle the challenges faced by SVAD systems in real-world scenarios, researchers need to come up with innovative methods that can enhance their accuracy and effectiveness. However, it is worth noting that there may be certain challenges and trade-offs associated with overcoming these challenges. For instance, [PERSON] et al. [121] highlighted that collecting more data or performing further fine-tuning may be necessary to achieve better performance. Similarly, [PERSON] et al. [122] identified the primary challenge of spatially and temporally localizing anomalies in SVAD systems. These studies underscore the need for ongoing research and development to address the complex challenges involved in building effective anomaly detection systems.
## 3 Results
### Response Time after Irradiation  
Since the maximum yield of Total Polyphenols Content and DPPH scavenging effect of methanolic extract was obtained after 24 h, it is plausible that a shorter delay may reveal an even greater increase of these parameters, provided that optimal living conditions are ensured. For those parameters, the maximum response slowly quenches in time, with a difference of 1.53 fold after 48 h and 1.18 fold after 72 h, above the untreated control for DPPH scavenging, and a difference of 1.33 fold above the untreated control after 48 h and no difference after 72 h for total polyphenols.  
Figure 2: The effect of gamma irradiation and response-time after treatment on the total content of polyphenols in methanolic extract of _Fomes fonentarius_ mycelium. Results are expressed in gallic acid equivalent as average irradiated/untreated control ratio (\(n\) = 120 per time). Bars are one standard deviation (SD).  
Our results also show that the antioxidant capacity (DPPH free radical scavenging) and total polyphenols level increase over time, regardless of the irradiation dose, due to their extracellular nature. Instead of being diluted by the nutritive medium while in culture, secondary metabolites concentrate as a jelly matrix in the mycelial mass, outside the hyphae, partially consisting of exopolysaccharide (EPS), which were shown to present free radical scavenging activity [24]. Flavonoids instead, being accumulated in-cell, are consumed and in constant decline over time, regardless of the irradiation dose. The difference between doses however, maintains in flavonoids, in contrast to the total polyphenols and antioxidant activity by DPPH assay, which tends to fade after 72 h.  
Response in terms of flavonoids synthesis showed more stable over time at all doses, with very small variation between maxima (namely increase of 1.66-fold after 24 h, 1.68- fold after 48 h, and 1.60-fold after 72 h, respectively, at 300 Gy). Flavonoids have great potential to counter the detrimental effects of the massive generation of reactive oxygen species (ROS). The notion that large alterations in the cell redox state trigger the biosynthesis of flavonoids [25; 26], led [PERSON] et al., (2020) [27] to hypothesize that flavonoids may complement the functions of primary antioxidants when their ROS detoxifying capacity declines in plants suffering from severe photo-oxidative stress. [PERSON], who provided a series of definitions of \"antioxidants\", has always been critical of the effective ability of flavonoids to \"delay, prevent or remove oxidative damage to a target molecule\", a widely accepted definition of antioxidant given in the paper written by [PERSON] and [PERSON] [27; 28].  
A general growth trend of all parameters with time was noticed, which was to be expected due to concentration of secreted secondary metabolites outside cells.
## 4 SDP Solution Method for the FFP-IM
### Conversion of the FFP-IM Index  
The FFP is precisely defined as the integral of the joint PDF across the membership level \(\alpha_{i}\) within the failure domain. This definition can alternatively be expressed as the mathematical expectation of the index function \(I_{F}\) corresponding to the failure domain.  
\[\begin{array}{ll}P_{IM}&=\int\cdots\int_{M(\alpha)\leq 0}f_{\alpha}(\alpha_{1}, \alpha_{2},\cdots,\alpha_{n})\mathrm{d}\alpha_{1}\mathrm{d}\alpha_{2}\cdots \mathrm{d}\alpha_{n}\\ &=\int\cdots\int_{R^{n}}I_{F}f_{\alpha}(\alpha_{1},\alpha_{2},\cdots,\alpha_{n })\mathrm{d}\alpha_{1}\mathrm{d}\alpha_{2}\cdots\mathrm{d}\alpha_{n}\\ &=E[I_{F}]\end{array} \tag{12}\]  
\[\begin{array}{ll}\text{where }I_{F}=\left\{\begin{array}{ll}1\ M(\mathbf{\alpha})\leq 0 \\ 0\ M(\mathbf{\alpha})>0\end{array}\right.\text{ is the index function for the failure domain of the LSF }M,\text{ and }\\ R^{n}\text{ is the space of }n\text{ dimensional variables.}\\ \text{ Just as with the unconditional failure probability, the conditional failure probability associated with the fuzzy input variable, given a specific membership level \(\alpha_{i}\), can be formulated as:  
\[\begin{array}{ll}P_{IM|Z_{i}}&=\int\cdots\int_{M(\alpha_{1},\alpha_{2}, \cdots,\alpha_{1}^{*},\cdots,\alpha_{n})\leq 0}f_{\alpha}(\alpha_{1},\alpha_{2}, \cdots,\alpha_{i}^{*},\cdots,\alpha_{n})\mathrm{d}\alpha_{1}\mathrm{d}\alpha_ {2},\cdots,\mathrm{d}\alpha_{i-1},\mathrm{d}\alpha_{i+1},\cdots,\mathrm{d} \alpha_{n}\\ &=\int\cdots\int_{R^{n}}I_{F}f_{\alpha}(\alpha_{1},\alpha_{2},\cdots,\alpha_{ i}^{*},\cdots,\alpha_{n})\mathrm{d}\alpha_{1}\mathrm{d}\alpha_{2},\cdots, \mathrm{d}\alpha_{i-1},\mathrm{d}\alpha_{i+1},\cdots,\mathrm{d}\alpha_{n}\\ &=E[I_{F}|Z_{i}]\end{array} \tag{13}\]  
\[\begin{array}{ll}\text{where }I_{F}\bigg{|}Z_{i}=\left\{\begin{array}{ll}1\ M(\mathbf{ \alpha}|\alpha_{i})\leq 0\\ 0\ M(\mathbf{\alpha}|\alpha_{i})>0\end{array}\right.\text{ is the failure domain index function of the conditional limit state definition, and }\ M(\mathbf{\alpha}|\alpha_{i})\text{ is the conditional LSF of the fuzzy variable }Z_{i}\text{ given the membership level }\alpha_{i}.\\ \text{ Substituting the unconditional Equation (\ref{eq:LSF}) and conditional failure probability Equation (\ref{eq:LSF}) into Equation (\ref{eq:LSF}), respectively, the relationship between the FFP-IM index and the corresponding variance-based IM of the failure domain index function can be obtained as follows:  
\[\tau_{i}=E_{Z_{i}}\Big{\{}[E(I_{F})-E(I_{F}\,|\,Z_{i})]^{2}\Big{\}}=Var[E(I_{F} \Big{|}Z_{i})] \tag{14}\]  
As demonstrated by Equation (14), the solution algorithm designed for the variance-based IM applies to the presented FFP-IM index. Owing to its superior efficiency and precision, the SDP method will be utilized to develop an advanced algorithm for computing the FFP-IM index, which is crucial for variance-based IM calculations.  
Given the superior efficiency and precision of the SDP method in addressing variance-based IM calculations, it will be employed to develop an efficient algorithm for the FFP-IM index.
# An Automated Framework for Streamlined CFD-Based Design and Optimization of Fixed-Wing UAV Wings
## 3 Framework Breakdown
### Mesh Module
#### 3.2.1. Meshing for CFD  
Selecting the appropriate meshing criteria depends on the geometric complexity, flow conditions, and the desired level of simulation fidelity. Different mesh types--categorized as structured, unstructured, or hybrid--each offer distinct benefits and challenges. Structured grids, characterized by their uniform and ordered connectivity, are highly efficient and accurate for simple geometries and flow-aligned features [43]. However, their rigid structure makes them challenging to apply to complex or multi-element configurations. Unstructured grids, on the other hand, provide flexibility to adapt to highly irregular geometries, covering intricate features like sharp edges or highly curved surfaces. Hybrid grids combine the advantages of both, employing structured grids in regions where precision is important, and unstructured grids in less critical areas.  
In this work, the hybrid mesh approach is selected. A structured curvilinear mesh (inflation layer) is employed over the boundary layer region to capture critical near-wall phenomena, while for the remainder of the domain, an unstructured grid is chosen to flexibly adapt to the overall geometry. As for discretization elements, hexahedrals are predominantly used for both boundary layers and far-field regions, as they offer superior numerical accuracy and alignment with flow gradients [41].  
Considering the practical implementation, the mesh of any arbitrary wing within the framework is generated via custom meshing scenarios which are passed through the ANSA _batch mesh_ tool. These scenarios are tailored by parametrizing meshing options with the wing's geometric characteristics, as observed in Table 4. The meshing module operates autonomously, requiring no additional user inputs. The _batch mesh_ tool for CFD  
Figure 6: _Pre_processor class developed for the meshing module along with its sub-methods.  
applications usually requires a surface meshing scenario, an inflation layer scenario, and a volume-meshing scenario. Each meshing scenario consists of multiple meshing sessions, addressing different aspects of the geometry. Specifics considering the meshing scenarios, the meshing parameters, and special treatments are discussed in Appendix A.3.
# Real-Time Path Planning of Driverless Mining Trains with Time-Dependent Physical Constraints
## 4 Mathematical Model
### Attributes  
For each \((i^{k},j^{l})\in A\) and a given train \(t\in T\), there exists a cost attribute \(c^{t}_{i^{k}j^{l}}\) as follows:  
\[c^{t}_{i^{k}j^{l}}=\left\{\begin{array}{c}\gamma*(l-k),\forall t\in T,\forall (i^{k},j^{l})\in A-A_{w}-A_{d}-A_{s}\\ \alpha*(|Q|-\lfloor k/(60/g)\rfloor),\forall t\in T,\forall(i^{k},i^{k+1}) \in A_{w}\\ \rho*time_{left},\ if\ dest_{t}=i\ or\ k=Q,\forall t\in T,\forall(i^{k},s_{1}) \in A_{d}\\ M,\ if\ dest_{t}\
eq i,\forall t\in T,\forall(i^{k},s_{1})\in A_{d}\\ \beta*(l-depQ_{t}),\forall t\in T,\forall(i^{k},j^{l})\in A_{s},\end{array}\right.\]  
where \((i^{k},j^{l})\) denotes an arc in the time-space network representing possible movement (of a train) starting from node \(i\) at time \(k\) and terminating at node \(j\) at time \(l\). The cost \(\alpha*(|Q|-\lfloor k/(60/g)\rfloor)\) implies that we give weighted penalties for waiting/dwelling, where \(\alpha\) is the constant parameter. The cost \(\beta*(l-depQ_{t})\) implies the penalty for late departure, where \(\beta\) is a constant parameter. The cost \(\rho*time_{left}\) implies that we try to route trains close to the destination. The cost \(\gamma*(l-k)\) implies that we try to route trains along the shortest-time path.
## 4 Experimental Verification
### Experimental Results
#### 4.2.2 High-Speed Diamond-Shaped Contouring Trajectory  
To validate the performance of the proposed control strategy sufficiently, a diamond-shaped contouring trajectory with a side length of 5 \(\upmu\)m and a frequency of 50 Hz was also tested on the manipulator. For non-smooth trajectory, velocity planning is needed, and the S-shape curve [30] was adopted to plan the motion of each side of the diamond-shaped trajectory. Similarly, the contouring results during one motion period are shown in Figure 7, and the contouring errors over 0.2 s are given in Figure 8 while the corresponding performance indexes are compared in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Controller** & **PI** & **ILC** & **DCARC** & **LODCARC** \\ \hline \(\varepsilon_{RMS}\) (\(\upmu\)m) & 6.048 & 0.223 & 0.139 & 0.088 \\ \(\varepsilon_{M}\) (\(\upmu\)m) & 8.499 & 0.436 & 0.341 & 0.217 \\ \hline \hline \end{tabular} \({}^{1}\) PI: proportional-integral controller; ILC: iterative learning control controller; DCARC: desired compensation adaptive robust control controller; LODCARC: learning-based optimal desired compensation adaptive robust control controller.  
\end{table}
Table 1: Tracking performance indexes of circular trajectory in the \(X\)-axis  
Figure 6: Contouring errors of circular trajectory.  
The results in Figure 7 show that there are significant contouring errors when the PI controller and the ILC controller are adopted; the contouring performance of the PI controller is especially  
Figure 8: Contouring errors of diamond-shaped trajectory.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Controller** & **PI** & **ILC** & **DCARC** & **LODCARC** \\ \hline \(\varepsilon_{\text{RMS}}\) (\(\mu\)m) & 0.143 & 0.069 & 0.021 & 0.007 \\ \(\varepsilon_{M}\) (\(\mu\)m) & 0.349 & 0.157 & 0.054 & 0.021 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Contouring performance indexes of diamond-shaped trajectory.  
Figure 7: Contouring performances of diamond-shaped trajectory using **(a)** PI controller; **(b)** ILC controller; (c) DCARC controller; and **(d)** LODCARC controller.  
poor at the corners of the diamond-shaped trajectory. As shown in Figure 8, the contouring errors of the DCARC controller and the LODCARC controller are very small, while for LODCARC, the reference trajectory optimization compensate the effect of repetitive disturbance and uncertainty, so the contouring performance of LODCARC is greatly improved. It is clearly shown in Table 3 that both the \(\varepsilon_{RMS}\) and \(\varepsilon_{M}\) of the proposed LODCARC are the smallest, the \(\varepsilon_{RMS}\) of the proposed LODCARC controller is 33% of the \(\varepsilon_{RMS}\) of the DCARC controller, and the \(\varepsilon_{M}\) of the proposed LODCARC controller is 39% of the \(\varepsilon_{M}\) of DCARC controller, which also verifies the effectiveness of the proposed LODCARC control strategy.