esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Materials and Methods
### Validation of Data Integration and Processing  
The ability to handle heterogeneous data sources and formats was assessed, focusing on scalability, transformation capabilities, and visualization. The prototype successfully integrated multiple database systems, including Microsoft SQL-Server, InfluxDB, and PostgreSQL. Tests conducted under controlled conditions demonstrated high compatibility, although integration into distributed environments may require additional network and security configurations [20].  
Data transformation and preprocessing capabilities were validated using large datasets, where the system's ability to normalize, clean, and structure raw data was tested. The ibaAnalyzer proved to be an effective tool for transformation, ensuring high data quality and structured formatting for subsequent analysis. However, hardware constraints such as CPU performance and network bandwidth were identified as critical factors influencing data transformation efficiency [79].
# Large-Area Coverage Path Planning Method Based on Vehicle-UAV Collaboration
## 4 Simulation and Discussion
However, in the coverage problem of the large area test case, the classic zigzag coverage path generated a total path length of up to 1,127,467 m, while the total path length generated by the proposed method based on vehicle-UAV collaboration was only820,678 m. The proposed method can achieve up to a 27.21% path saving advantage. The vehicle-UAV collaboration-based coverage path method proposed in this paper has significant advantages in reducing the round-trip path length of a UAV. It can be obviously concluded that the proposed method excels at large-scale coverage path planning scenarios, and achieves a significant improvement in coverage efficiency. It is noted that the mixed paths are described using two-dimensional coordinates, while the actual collaborative trajectory of drones and vehicles is three-dimensional. The height information of vertices in the 2D path is missing, but this can be calculated through interpolation of the digital elevation model of the coverage area. Additionally, the generated trajectory can be saved as a KML (Keynote markup language) file. As KML is a universal file format, the planned coverage trajectory can be used for data exchange in almost any ground control station, which is beneficial for improving the compatibility of the proposed vehicle-UAV collaboration method.
# Modeling of Distorted Degradation Data Based on Oil Analysis
## 1 Introduction
As shown in Figure 1 (Vehicle 1 dataset), the observed data exclusively contain Cu (copper) concentration measurements with the corresponding sampling time but lack documentation of oil top-up times and quantities. Detailed experimental information is provided in Section 4. It is evident that the acquired Cu concentration exhibits non-monotonic behavior despite the inherent monotonicity of mechanical wear processes. This discrepancy stems from dilution effects caused by fresh oil additions. The contributions of this paper can be summarized as follows:  
* A statistical correction model is proposed to process degradation data that are disturbed by non-periodic oil top-ups. The proposed model can not only automatically identify the locations of oil top-up points but also globally correct the observed degradation data. * A three-step explosion search algorithm is developed, enabling robust parameter estimation through both parametric and non-parametric approaches. * The corrected degradation data can reflect the actual physical degradation; thus, these corrected data can be used to assess system conditions and predict a failure. Notably, while the methodology is developed through wear degradation analysis, its data-driven architecture ensures generalizability to various degradation monitoring scenarios requiring measurement distortion correction. The rest of the paper is organized as follows: The proposed methodology is presented in detail in Section 2. In Section 3, we perform some numerical simulations. Case studies are shown in Section 4. Section 5 provides discussion and conclusions.
# Fast Coherent Video Style Transfer via Flow Errors Reduction
## 3 Methods
### Loss Functions for Temporal Consistency
#### 3.5.2 Feature-Level Coherent Loss  
In previous methods [27; 28; 30], RGB-level coherent loss was considered to constrain the consistency between pixel values of the warped \(w^{t}\) and generated \(\widehat{x}^{t}\) images. However, it may not be accurate in preserving stylized texture consistency since their methods do not take into account the temporal consistency of feature representations in CNN layers. Hence, we adopt a feature-level Coherent Loss [64] for preserving texture consistency which is capable of constraining the feature consistency in high-level CNN layers. Let \(\psi^{t}(\widehat{x}^{t})\in\mathbb{R}^{R_{l_{1}}\times H_{l}\times W_{l}}\) and \(\psi^{t}(w^{t})\in\mathbb{R}^{N_{l}\times H_{l}\times W_{l}}\) denote the feature representations of generated images \(\widehat{x}^{t}\) and warped images \(w^{t}\) at layer \(l\), respectively, and \(M^{t}\) denote the per-pixel mask, then the _two-frame Feature-level Coherent Loss_ for two frames is defined as the mean squared error between \(\psi^{t}(\widehat{x}^{t})\) and \(\psi^{t}(w^{t})\):\[\mathcal{L}_{tav}^{fca}(\hat{x}^{t},w^{t},M^{t})=\sum_{l\in\mathcal{L}_{coh}^{fca}} \frac{1}{N_{l}}\sum_{i}^{N_{l}}M_{i}^{t}\cdot(\psi_{i}^{l}(\hat{x}^{t})-\psi_{i }^{l}(w^{t}))^{2} \tag{8}\]  
where \(L_{coh}^{fca}\) denotes the set of layers computing Feature-level Coherent Loss and \(N_{l}\) denotes the dimensionality of feature representations \(\psi^{l}(\cdot)\). Similar to RGB-level Coherent Loss, we also consider the feature-level coherent loss between more adjacent frames (same \(\mathcal{T}\) in \(\mathcal{L}_{mul}^{RGB}\)), and propose the _multi-frame Feature-level Coherent Loss_ term:  
\[\mathcal{L}_{mul}^{fca}(\hat{x}^{t},w^{t-T})=\sum_{i\in\mathcal{T}:t>j}\mathcal{ L}_{tav}^{fca}(\hat{x}^{t},w^{t-i},M^{t-i}) \tag{9}\]  
The total multi-frame Coherent Losses are defined as the combination of \(\mathcal{L}_{mul}^{RGB}\) and \(\mathcal{L}_{mul}^{fca}\):  
\[\mathcal{L}_{cohrent}=\lambda_{coh}^{RGB}\mathcal{L}_{mul}^{RGB}+\lambda_{coh }^{fca}\mathcal{L}_{mul}^{fca} \tag{10}\]  
where \(\lambda_{coh}^{RGB}\) and \(\lambda_{coh}^{fca}\) are the weights to corresponding terms. We find that the ratio of \(\lambda_{coh}^{RGB}/\lambda_{coh}^{fca}\) close to 2.5 makes a better temporal consistency preservation.  
**Overall** The total loss term for the optimization process in each time step is defined as:  
\[\mathcal{L}_{total}=\mathcal{L}_{sharpness}+\mathcal{L}_{coherent} \tag{11}\]
# PET/CT Radiomics in Lung Cancer: An Overview
## 3 Applications
### Prediction of Response to Treatment  
Predicting response to treatment--and in particular to chemotherapy, radiotherapy, and immunotherapy--is crucial to maximize the outcome, and, at the same time, minimize the side effects by avoiding the administration of inefficient treatments.  
Several studies have investigated the potential of radiomics to predict response to treatment in lung cancer. In [10; 23], textural features from PET/CT scans at the baseline were found to correlate with local recurrence and disease-specific survival in patients treated with radiotherapy. Radiomic signatures from baseline PET/CT were also predictive of disease-free survival in NSCLC patients undergoing surgery [8], whereas, in [75], CT baseline feature predicted response to chemotherapy in lung adenocarcinoma. Image biomarkers from PET/CT also showed the ability to predict immunotherapy response in advanced NSCLC [76].
# Edge Effect Analysis and Edge Defect Detection of Titanium Alloy Based on Eddy Current Testing
## 2 Edge Effect Evaluation
### Indicator Selection for Edge Effect Evaluation  
In this paper, one single coil was used acting as transmitter and receiver simultaneously. The impedance of the coil was calculated based on Equation (1).  
\[Z_{n}=\sqrt{\left(R-R_{0}\right)^{2}+\omega^{2}\left(L-L_{0}\right)^{2}} \tag{1}\]  
where \(\omega\) is the excitation frequency, \(\omega\)\(L\) is the imaginary part of the complex impedance, and \(R\) is the real part of the complex impedance. \(R_{0}\) and \(L_{0}\) is the resistance and inductance of the coil when it is far from the test piece (air field).  
Based on the modeling setup presented in Section 2.1, the self-inductance of the EM sensor varied along the scanning path as shown in Figure 4.  
Figure 4: The self-inductance of the coil along the scanning path.  
Figure 3: Mesh-energy error curve.  
The coil located exactly above the edge of the test piece at the distance of 10 mm as shown in the \"edge effect zone\" in Figure 4. As shown in Figure 4, when the coil was scanned to a position away from the plate, its self-inductance was basically unchanged, which was recorded as \(L_{1}\) (as shown in \"air zone\" in Figure 4). When the coil was scanned above the plate and was away from the edge, its self-inductance was also almost constant, which was recorded as \(L_{2}\) (as shown in \"test piece zone\" in Figure 4). Between \"test piece zone\" and \"air zone\", the self-inductance of the EM sensor varied dramatically since the eddy current flow was distorted at the edge of the test piece.  
Two indicators, \(D_{half}\) and \(D_{30\%}\), were selected to quantitatively evaluate the edge effect. \(L_{half}\) means the half amplitude of the self-inductance as marked \"P1\" in Figure 4:  
\[L_{half}=\left(L_{1}+L_{2}\right)/2 \tag{2}\]  
The distance between P1 and the edge (\(D_{half}\)) reveals the intensity of edge effect.  
Similarly, 30% height point P2 can be defined, whose self-inductance is:  
\[L_{30\%}=L_{2}+0.3*\left(L_{1}-L_{2}\right) \tag{3}\]  
Additionally, define \(D_{30\%}\) as the distance between P2 and the edge. Compared with \(D_{half}\), \(D_{30\%}\) is larger, so \(D_{30\%}\) suffers less by scanning step size. To describe the edge effect comprehensively, both \(D_{half}\) and \(D_{30\%}\) were taken as the edge effect indicators.
# Advances in Finite Element Modeling of Fatigue Crack Propagation
## 3 Advanced Techniques in Finite Element Modeling for Fatigue Crack Propagation
### Cohesive Zone Modeling (CZM)  
CZM is a technique commonly used to simulate crack growth in brittle or quasi-brittle materials. CZM represents the crack as a discontinuity or a thin layer with cohesive properties. It introduces cohesive elements along the crack path to capture the separation and slip behavior at the crack surfaces. The cohesive elements possess traction-separation laws that govern the interaction between the crack surfaces. These laws determine the cohesive forces required to initiate and propagate the crack, as well as the energy dissipation during crack growth. CZM allows for the simulation of crack initiation, crack growth, and crack branching in materials where crack propagation is primarily governed by fracture mechanics. Cohesive Zone Modeling approaches have grown in popularity in fracture mechanics due to their ability to capture the intricate details of crack initiation and propagation [121]. One of the primary benefits of Cohesive Zone Modeling is the ability to mimic crack initiation in materials with intricate behavior, such as composites or delaminated materials [122]. Researchers can simulate the behavior of cracks propagating through a material by creating a cohesive zone with certain attributes, such as cohesive strength and fracture energy. Furthermore, CZM can provide insights on phenomena such as crack bridging [123], crack deflection [124], and crack interactions with other defects in the material. This degree of detail is critical for understanding how cracks evolve under different stress circumstances, and it may help engineers build more reliable and durable structures. CZM is well-suited for complex geometries where traditional linear elastic fracture mechanics (LEFM) may not be applicable [125]. Unlike traditional fracture mechanics methods, CZM is less sensitive to the mesh configuration, making it easier to implement in finite element models [126]. CZM can still be affected by the choice of mesh size and element type. Inappropriate mesh settings can lead to inaccurate results [127].
# Transferring Sentiment Cross-Lingually within and across Same-Family Languages
## 8 Results and Discussion
### Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Target Language** & **Source Languages** & **5-Class Accuracy** & **5-Class F1** & **3-Class Accuracy** & **3-Class F1** \\ \hline Bulgarian & Bulgarian English & **53.37** (0.0123) & **54.60 * (0.0097) & 72.73 (0.0142) & **74.22 * (0.7422) \\ Bulgarian & Bulgarian Czech & 52.18 (0.0070) & 53.14 (0.0106) & **72.79** (0.0098) & 74.11 * (0.0081) \\ Croatian & Croatian English & 54.12 * (0.0186) & **53.80 * (0.0163)** & 74.07 (0.0121) & 74.12 (0.0097) \\ Croatian & Croatian Czech & 50.88 (0.0094) & 50.12 (0.0251) & 74.69 (0.0107) & **75.82 * (0.0106)** \\ Czech & Czech Croatian & & 82.29 (0.0035) & 82.24 (0.0036) \\ English & Czech English & 56.22 (0.0099) & 55.36 (0.0123) & **69.09 (0.0035)** & **69.06 * (0.0043)** \\ English & Bulgarian (Latin) English & 56.91 (0.0031) & 56.78 (0.0042) & 68.36 (0.0086) & 68.05 (0.0103) \\ Polish & Bulgarian (Latin) Polish & 52.34 (0.0017) & 52.28 (0.0012) & 87.05 (0.0028) & 87.15 * (0.0016) \\ Polish & Russian (Latin) Polish & 52.19 (0.0010) & 52.15 (0.0005) & 86.92 (0.0016) & 87.00 * (0.0007) \\ Russian & Bulgarian Russian & & & 71.84 (0.0035) & 71.31 (0.0022) \\ Slovak & Slovak English & **68.87** (0.0351) & **68.03 * (0.016)** & 83.51 (0.0182) & 82.14 (0.0076) \\ Slovak & Slovak Croatian Slovene & 64.47 (0.0135) & 58.71 (0.0441) & **85.36 (0.0046)** & **83.44 * (0.0064)** \\ Slovene & Slovene English & & & **69.52 * (0.0203)** & 68.97 * (0.0154) \\ Slovene & Slovene Czech & & & 68.24 * (0.0084) & **69.56 * (0.0078)** \\ Bulgarian (Latin) & Bulgarian (Latin) English & 50.73 (0.0094) & 51.76 (0.0075) & 70.30 (0.0093) & 72.01 (0.0071) \\ Russian (Latin) & Russian (Latin) English & & & 88.14 * (0.0299) & **87.95 * (0.0290)** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification scores are averaged over 5-fold runs. The standard deviation is presented in brackets to the right. Language pairs with Bulgarian (Latin) and Russian (Latin). The highest scores for a specific target language are indicated by numbers in bold. The statistically significant scores are indicated by the asterisk (*) (_p_ < 0.05).
# Copernicus Global Land Cover Layers--Collection 2
## 2 Methodological Overview  
### General Overview  
In this section, a broad overview of the CGLS-LC100 production workflow is given. For a more detailed explanation of the whole workflow, including detailed technical descriptions of algorithms, used ancillary data, and produced intermediate products, we refer to the Algorithm Theoretical Basis Document (ATBD) [25].  
It is important to note that the overall workflow used in the Copernicus Global Land Service Land Cover processing workflow is not sensor-specific, and can be applied to any satellite data, irrespective of the sensor or resolution (Figure 1). Where sensors like Sentinel-1, Sentinel-2, and Landsat were up to now mainly used for regional/continental LC mapping applications [26; 27], the CGLS-LC100 Collection 2 product uses PROBA-V sensor data, despite the workflow is already tested with Sentinel-2 and Landsat. The CGLS-LC100 product is generated by combining high-quality external data and several proven individual methodologies for processing Earth Observation (EO) data (Figure 1). Each of the workflow blocks in Figure 1, EO data pre-processing, classification/regression pre-processing,classification/regression and product generation, and validation and comparison are described in the sections below.
# An Automated Framework for Streamlined CFD-Based Design and Optimization of Fixed-Wing UAV Wings
## Appendix A Geometry Module Features  
The following paragraphs dive into the geometry pre-processing methods and treatments.  
Trailing Edge Treatment  
Trailing edge (TE) treatments are essential in CFD modeling, as they influence the progression of the inflation layer and ensures stability in numerical solutions. For subsonic wing simulations, two common TE treatments--rounded and straight cut--are considered (Figure 11), each with unique implications for modeling and meshing.  
The straight cut design is chosen for this framework due to its compatibility with meshing automation, ensuring robust feature recognition by the meshing software. This decision prioritizes automation reliability over marginal quality improvements offered by rounded edges [41]. OpenVSP facilitates the parameterized control of TE thickness via the input of the chord lengths (\(c_{1},\ c_{2},\ c_{3}\)), automatically trimming the trailing edge for each airfoil to 1% of the chord length, which adheres to the common guidelines.
# Influence of Construction Joint and Bridge Geometry on Integral Abument Bridges
## 2 Parameters and Description
### Uncontrollable Parameters
#### 2.2.3 Soil-Structure Interaction  
The soil-structure interaction is one of the main sources of the nonlinear behavior of IABs. Two types of soil-structure interaction should be considered to understand IAB behavior: (1) abutment-backfill interaction and (2) soil-pile interaction. Abutment-backfill interaction significantly influences IAB response to thermal and time-dependent loads [12; 15; 37]. For the purposes of this study, typical backfill soil properties--standard backfill material variations are limited--were adopted in the study and are presented in Table 3.  
Soil-pile interaction is another source of nonlinear behavior of IABs. Soil-pile interaction is usually exerted by the interaction between pile stiffness and sounding soil stiffness. Many previous studies [2; 8; 10; 11; 13; 15; 26; 27; 33; 34; 37; 39] reported that soil-pile interaction is a key variable to determine IAB behavior. However, several boring tests are performed before bridge design, thus properties of surrounding soils have uncertainties. During the pile driving stage, piles are sometimes crooked owing to rocks in geological stratum. Thus, this study assumed properties of the soil-pile interaction based on typical supporting steel piles and soil strata. A total of 13 HP310 \(\times\) 110, \(F_{y}\) = 345 MPa piles were used and oriented to the weak-axis for the parametric study. Soil strata properties from the previous field test [5] were selected for this study and are presented in Figure 3. The values behind the abutment were used to define the abutment-backfill interaction based on [PERSON]'s theory. The values on both the left and right were used to define \(p\)-\(y\) curves for the soil-pile interaction [40].  
Figure 3: Abutment soil properties.  
\begin{table}
\begin{tabular}{c c} \hline
**Property** & **Values** \\ \hline Density, \(\rho\) & 18.7 kN/m\({}^{3}\) \\ Angle of friction, \(\phi_{f}\) & 34\({}^{\circ}\) \\ Modulus of Subgrade Reaction, Kh & 11,900 kN/m\({}^{3}\) \\ \hline \end{tabular}
\end{table}
Table 3: Backfill properties.
# Application of the KINEROS 2 Model to Natural Basin for Estimation of Erosion
## 3 Erosion Estimation Models Used
### Rusle  
In 1985, experts agreed to revise the USLE, to incorporate the results of new investigations and technologies. This revision was named the Revised Universal Soil Loss Equation (RUSLE). It incorporates important changes for estimating each parameter [20, 21, 22].  
1. New maps of isolines to estimate the \(R\) factor.
2. The \(K\) factor now incorporates aspects related to frost processes.
3. The \(L\) and \(S\) parameters are now estimated according to new formulas because the influence of the slope length \(\lambda\) is increased, considering that increasing the length will increase the formation of streaks [20].
4. The \(C\) parameter includes new subfactors because RUSLE considers that erosion properties of soil change during the year depending on five new parameters: prior land use, canopy cover, surface cover, surface roughness and soil moisture [20].
5. The \(P\) parameter includes new considerations of agricultural practices taking into account crop slope [21].  
While several authors have proposed different ways to calculate the factors of RUSLE, we have relied upon those proposed by [PERSON] et al. (1997) [PERSON] et al. (1997).  
As we can see in the conclusions, the RUSLE method is long and complex but if it is done with caution, better results are obtained from the RUSLE method than USLE method [3].
## 5 Conclusions  
This study presents an MSPSO-based IPTBK model that can analyse the effects of multiple-swarm search strategies under different topology sizes and optimal fitness function settings for the quality of PSO solutions to enhance the reconstruction of the probable paths of DDoS attacks. The experimental results confirm that the proposed method can analyse the possible attack paths of botnets and the attack sources of DDoS threats.  
Although MSPSO techniques have been proposed for the IPTBK problem, there are practical challenges of using the proposed method. For example, in case of a DDoS attack, the proposed method generates unusually high rates of packet loss across a network that may disorder the sequence of packet marking, and therefore may harmfully affect the traceback accuracy of MSPSO. Moreover, it needs to integrate the proposed scheme into the traceback module of intrusion detection system (IDS). The scalability challenge and convergence of large complex network topologies (number of nodes > 1024) in the IPTBK problem will be addressed in a future study. Behavioural analysis with classification rules may be considered for determining the class of new threat in the future to implement rapid countermeasures against cyber-attacks.  
Conceptualization, P.W.; methodology, H.-C.L.; resources, P.W.; formal analysis, H.-C.L.; data curation, H.-C.L.; writing--original draft, H.-C.L. and W.-H.L.; writing-review and editing, P.W.; software, H.-C.L. and Y.-H.H.; validation, H.-C.L. and W.-H.L.; visualization, H.-C.L. and Y.-H.H.; project administration, P.W.; funding acquisition, P.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology of Taiwan under Grant Nos. MOST 108-3116-F-168-001-CC2, and MOST 109-2410-H-168-005.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Topology**} & \multicolumn{1}{c}{**\(n\) = 32 Nodes**} & \(n\) = 64 Nodes** & \(n\) = 128 Nodes \\ \hline A* search algorithm & 94.42\%/0.95 msec & 93.81\%/4.64 msec & 92.13\%/9.98 msec \\ \hline PSO & 95.16\%/47,482.19 msec & 94.79\%/396.17 msec & 94.01\%/211,453.41 msec \\ \hline MSPSO (\(w\) = 1) & 95.83\%/32,125.30 msec & 96.57\%/65,209.73 msec & 96.61\%/141,071.80 msec \\ \hline MSPSO (\(w\) = \(x\)) & 97.50\%/32,202.44 msec & 98.33\%/64,434.63 msec & 98.69\%/132,566.52 msec \\ \hline \end{tabular}
\end{table}
Table 7: Traceback accuracy vs. execution time of DDoS attacks when using the proposed algorithm with two path-search algorithms.  
**Acknowledgments: This work was supported jointly by the Ministry of Science and Technology of Taiwan under Grant Nos. MOST 108-3116-F-168-001-CC2, and MOST 109-2410-H-168-005. Conflicts of Interest: The authors declare no conflict of interest.**