esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiple Extended Target Tracking Algorithm Based on Spatio-Temporal Correlation
###### Abstract  
In the clutter environment, the measurement of a set of multiple extended targets, with an unknown number of targets, poses challenges in partitioning, and the computational cost is high. In particular, the multiple extended target tracking method, based on distance partition, has obvious potential estimation errors when the extended targets intersect. This paper proposes a partition algorithm, based on spatio-temporal correlation, which considers the correlation between adjacent moments of the extended target and uses this prior information to divide the measurement set into a survival target measurement set and a born target measurement set for the first time. Then, the survival target measurement set is clustered by the K-means++ algorithm, and the extended target tracking is transformed into point target tracking. The born target measurement undergoes preprocessing by the DBSCAN clustering algorithm, and then uses the directed graph with shared nearest neighbors (SNN) dividing the measurement set. The method proposed in this paper significantly reduces the number of partitions and the computational time. The effectiveness of the algorithm is demonstrated through experimental simulations.  
multi-extended target tracking; spatio-temporal correlation; measurement set partition; probability hypothesis density filter +
Footnote †: journal: Applied sciences
# Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios
## 5 Conclusions  
The presented tool proposes a methodology to create synthetic thermal infrared images, enabling the simulation of the infrared camera output given the camera position and attitude. It is demonstrated how the tool creates realistic thermal images in both spacecraft and asteroid proximity scenarios and both in single and multi-material cases, proving to be suited for multiple mission cases. Indeed, the tool has already been used in a real space mission design phase B, proving to be an essential tool for testing navigation algorithm pipelines. Furthermore, two camera models are presented, and they can be used as starting points for the production of realistic instrument output. The presented work is a step in the existing literature, as no methodologies to produce synthetic infrared images from thermal simulations have ever been presented, up to the authors' knowledge. The approach can be repeated and implemented by any other research team since all the software used are open source and free of license, thus making the method available to anyone interested in the research.  
Figure 28: Earth thermal image (**left**) and TANGO spacecraft with Earth in background plus noise model (**right**) [17].  
Figure 29: Region of interest extraction through weak gradient elimination and adaptive thresholding. Credits to [PERSON] for the image.  
**Author Contributions:** Conceptualization, M.Q.; Methodology, M.Q.; Software, M.Q.; Writing--original draft, M.Q.; Visualization, M.Q.; Supervision, M.R.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The authors thank [PERSON] and [PERSON] from the ASTRA research group of the Department of Aerospace Science and Technology of Politecnico di Milano for the use in this article of the fused images in the visible spectrum of TANGO, and [PERSON] again for the use of the VESPA images in the visible spectrum. The authors thank [PERSON], again from ASTRA Department of Aerospace Science and Technology of Politecnico di Milano, for the use of the infrared images of the Earth and TANGO with Earth in the background. The authors thank [PERSON], [PERSON], and [PERSON] from the Department of Energy of Politecnico di Milano for checking and reviewing the infrared heat flux modeling.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Radiation Analysis
### Radiation Simulations Results
#### 4.2.1 GCR Effective Dose Equivalent on the Moon
To simulate the radiation shielding of a lava tube, 1.6 g/cm\({}^{3}\) highlands regolith was tested at 300 and 1000 g/cm\({}^{2}\) corresponding to 1.88 m and 6.25 m respectively, where the effective dose equivalent drops significantly with depth, as shown in Table 2. Real lava tubes may extend far further and it is safe to assume that the deep basalt regolith is denser, which increases the areal density for a given thickness in metres. Therefore, the dose is a safe overestimation of the experienced dose. It is evident that lava tubes provide strong competition for artificial passive shields, and just a few metres of soil reduce the effective dose equivalent more than 20 times. The 6.25 m tube can be a habitat with a comfortably low GCR-induced effective dose equivalent. Furthermore, the capacity of lava tubes to withstand meteoroid impacts increases significantly with their depths. However, with depth come new challenges in terms of providing all elements of life support under the surface and one cannot imagine that the explorer astronauts will either spend all their time or perform all scientific research underground.
# Attention-Based Background/Foreground Monocular Depth Prediction Model Using Image Segmentation
## 5 Conclusions  
This paper proposed an architecture that exploits foreground and background region information to individually predict foreground and background depth maps using attention mechanisms. As depth variations can be wide in outdoor scenes, monocular depth estimation is easily disturbed. Therefore, an image segmentation-based monocular depth estimation model with attention mechanisms was developed to segment images into foreground and background regions and use foreground and background depth prediction models to predict individual depth maps. To enhance depth prediction, we also adopted attention mechanisms to extract meaningful features from salient regions and use them as clues to predict depth in complex scenes. Subsequently, the foreground and background depth maps were stitched together to create the final prediction results.  
Several experiments were conducted on the KITTI dataset [37] to demonstrate the improved performance of the proposed method over other methods for depth map estimation. The experimental results showed that our model could more clearly contour the shapes of foreground objects. This showed that individually predicting foreground and background depth maps using different attention modules is beneficial for depth map prediction. The critical methods utilized by our approach included a multi-scale depth decoding scheme and the utilization of different attention mechanisms. Although our approach performed well on the KITTI dataset, there is room for improvement in indoor scenes. Future work will investigate model reduction strategies and improvements in the depth map prediction of indoor scenes.  
Methodology, T.-H.C.; Software, T.-H.C. and M.-H.C.; Validation, T.-H.C. and M.-H.C.; Writing--original draft, T.-H.C.; Writing--review & editing, T.-H.C., M.-H.T. and C.-C.C. All authors have read and agreed to the published version of the manuscript.  
This research is co-sponsored by 110-2634-F-A49-004- National Science and Technology Council (NSTC) and 110-2221-E-035-032-MY3 NSTC.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Proposed Methodology
### Incorporation of H-TEEN  
In H-TEEN, after selecting the CHs, CHs forwards the following parameters:  
1. Attributes (A): This physical set of parameters helps the user to collect the data.
2. Thresholds: This is composed of hard and soft thresholds denoted by HT and ST, respectively. HT is the particular value that triggers the node to broadcast the data. ST is a little alteration in the significance that triggers the node to rebroadcast the information.
3. Schedule: It is scheduled by TDMA that allows the slot to each node.
4. TimeCount (TC): This is the utmost duration of the pair consecutive reports forwarded by a node. This is the numerous length of the schedule that accounts for the practical component. In a WSN, closer nodes form a cluster that senses analogous data and forwards them concurrently, which leads to collisions. TDMA schedule is introduced so that every cluster member is assigned a slot for transmission.
## 2 Theory and Experimental Method
### MR Fluid Power Distribution System
#### 2.1.2 MR Fluid Multi-Plate Clutch Design  
Friction clutches create power transmission through engagement between the disc and the plate that connect the power and the load. There are two clutch types, including dry clutches and wet clutches.  
Dry clutches have the disadvantages of poor durability, noise, vibration, and overheating due to the shocks that occur during engagement; however, they are small, light, and have excellent power transmission efficiency. In contrast, wet clutches are large, heavy, and have low transmission efficiency owing to the oil in their interiors. However, they experience smaller shocks than dry clutches during engagement; therefore, they have less noise and vibration, and they also have excellent durability [20].  
Applying MR fluid between rotating discs can effectively dampen the vibrations of the rotor. Therefore, applying MR fluid to a multi-plate clutch will effectively reduce vibrations. The MR (magnetroheological) fluid multi-disc clutch, as illustrated in Figure 1, is designed to combine the characteristics of both dry and wet clutches, thus enabling continuous and smooth engagement.  
First, it is a friction mode that transmits power using a friction force generated by the contact between the input and output shaft friction surfaces, like regular dry friction clutches. This friction mode can generate maximum torque, but a significant impact also occurs when the plates are in contact with each other. Second, it has a fluid mode. Unlike conventional fluid couplers that deliver power using the viscous force of the fluids in the plates, it transmits power using an MRF whose shear stress varies with the magnetic field intensity. Considering the above, the MR fluid multi-plate clutch was designed, as shown  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(\eta_{0}\) & 0.1 Pa-s \\ \(\eta_{0o}\) & 3.8 Pa-s \\ \(\sigma_{5y}\) & 4.5 T\({}^{-1}\) \\ \(\tau_{y0}\) & 12 Pa \\ \(\tau_{yoo}\) & 40,000 Pa \\ \(\sigma_{5y_{y}}\) & 2.9 T\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The rheological properties of the MRF-132 DG fluid.  
Figure 1: Diagram of the MR fluid multi-plate clutch. (**a**) An exploded CAD model of an MR fluid multi-plate clutch. (**b**) Prototype MR fluid multi-plate clutch.  
in Figure 1. For more details, including the detailed geometry of the MR clutch, see the authors' previous research [19].
## 4 Results and Discussion
### Accuracy Analysis Using Confusion Matrix  
#### 4.1.1 K-Nearest Neighbours (K-NN) Is an Example of this Type of Supervised ML Algorithm  
It is applicable to both classification and regression problems. K-NN classification relies on nearby feature space to classify samples. The K-NN algorithm's default performance is illustrated in Figure 10's confusion matrix. Of the 184 cases tested, the test identified 17 patients and 153 healthy subjects correctly. Therefore, the accuracy of the test was equal to 170 divided by 184 (92.39%).  
1.2 The Support Vector Machine (SVM) The Support Vector Machine (SVM) Works on the Margin Calculation Concept  
It draws margins between the classes. The margins are removed so that the distance between the margin and the types is at a maximum and minimises the classification [42]. As illustrated in Figure 11, of the 184 cases that were tested, the test determined 29 patients and 149 healthy subjects correctly. Therefore, the accuracy of the trial was equal to 96.74%.
# Multiple Instances QoS Routing in RPL: Application to Smart Grids +
## 2 Related Work
### RPL Proposed Metrics and Modifications
Finally, note that \(OFQS\), by integrating the different requirements of the SG applications, is suitable for any other application with these same demands and criticality variations e.g., Smart City applications.
## 2 System Model
### Multi-Agent Reinforcement Learning Algorithm  
Multi-Agent Reinforcement Learning (MARL) represents a significant subfield of reinforcement learning, characterized by the involvement of multiple agents that interact with a shared environment. These agents may exhibit either cooperative or competitive behaviors, implying that the reward obtained by each agent is influenced not only by its individual actions but also by the actions of other agents in the system. This interdependence among agents introduces additional complexity to the learning process, as agents must adapt their strategies based on the dynamic behaviors of their counterparts. Consequently, MARL is widely applied in scenarios requiring distributed decision-making, such as autonomous systems, multi-robot coordination, and complex network optimization.  
In LEO satellite networks, each satellite acts as an agent, capable of independent learning and forwarding state information. Each agent maintains a Q-table, which records the Q-values for forwarding data packets to neighboring nodes. Table 2 shows the Q-table for node \(i\), where \(i\) represents the node number, \(Des_{i}\) represents the target node of the data packet, and \(Q_{i}((i,Des_{1}),N_{1})\) represents the Q-value for forwarding to neighbor node \(N_{1}\). The agent looks up the Q-table based on the destination node of the data packet, selects the node with the highest Q-value, and forwards the packet. Therefore, the update of the Q-value should be influenced by the current Q-value, the Q-value after forwarding, and the reward for the forwarding action. The Q-table update is shown in Equation (1).  
\[Q_{i}(s,a)=(1-\alpha)Q_{i}(s,a)+\alpha\left[r+\gamma\max_{a^{\prime}\in A_{c} }Q_{j}(s^{\prime},a^{\prime})\right] \tag{1}\]  
where \(s\) represents the packet current state, \(s^{\prime}\) represents the packet state after forwarding, \(a\) is the action to be executed, \(\alpha\) is the learning rate determining the degree of impact of action \(a\), \(r\) is the reward for executing action \(a\) in state \(s\), \(\gamma\) is the discount factor, \(i\) represent current node index, \(j\) represent the index of node which node \(i\) forwards packet to, \(A_{c}\) is the action space in state \(s^{\prime}\), and \(\gamma\underset{a^{\prime}\in A_{c}}{max}Q_{j}(s^{\prime},a^{\prime})\) predicts the maximum cumulative future rewards from the next state.
# Spectroscopic Techniques Application for Wine and Wine Byproduct Authentication
## 3 Statistical Techniques in Spectroscopy
### Data Modeling  
Data modeling is a critical process in spectroscopy-based wine analysis. The complexity and multidimensionality of spectral data require the application of machine learning and statistical techniques that enable the pattern recognition, classification, and prediction of key parameters related to wine composition, origin, and quality. The choice of statistical methodology depends on the specific data type and the objectives of the analysis. Some commonly used statistical methods for modeling spectroscopic data include the following:  
Regression techniques, such as multiple linear regression (MLR), partial least squares regression (PLSR), and principal component regression (PCR), are used to predict spectral data quantitatively [125; 127; 134; 135]. PLSR is widely applied to handle highly collinear spectral data by extracting latent variables from the spectra, which are then used to predict outcomes. PCR is a procedure that uses the first principal components as predictors in the regression model.  
Classification techniques such as linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) are based on probability distributions. Support vector machines (SVMs) are used to find the optimal decision boundary. At the same time, Random Forest (RF) is an ensemble learning method that builds multiple decision trees and aggregates their predictions for classification [21; 136; 125]. Techniques such as K-means Clustering, K-nearest neighbors, or Hierarchical Clustering are used to group spectra with similar properties based on spectral features [60; 130]. Multivariate analyses, such as FA, Canonical Correlation Analysis (CCA), and PLS, are applied to identify patterns and relationships between datasets. More recently, advanced classification techniques, such as Artificial Neural Networks (ANNs), have gained popularity in analyzing complex spectroscopic data due to their ability to model nonlinear relationships and handle large datasets. In wine analysis, Artificial Neural Networks can be used, after being trained on spectral datasets, to predict changes in the chemical composition of wine over time, assess wine quality, and quantify various chemical components, among other applications [137; 138; 139; 4, 140].
## 2 Materials and Methods
### Urban Growth
#### 2.1.2 Sentinel-2 Classification  
Cloud-free Sentinel-2 imagery from 10 June 2016, 8 June 2017, 23 June 2018, 13 June 2019, 14 June 2020, and 27 June 2021 were classified into Corine 2018 land-cover classes using a pretrained U-net deep learning model with a reported overall accuracy of 82% [48] (Figure 2b). The model used all 13 Sentinel-2 bands at 10 m resolution (resampled where required) [48]. The 'Urban fabric' and 'Industrial commercial and transport units' classes were merged to form a 'built-up' area class.
## 2 Materials and Methods
### Feature Normalization  
A normalization process is required for the data to have an equal contribution to the proposed model. The aim here is to bring the data to a common standard among the datasets so that they can be compared and to put the significant differences between the data into a single order. The most commonly used methods for data standardization are min-max normalization [26] and z-score normalization [27].  
In min-max normalization [26], the original data are converted by linear conversion to the new data range, which is typically 0-1. The equation is given as follows:  
\[y=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{1}\]  
where \(y\) is the normalization value, \(x\) represents the value of data, and \(x_{min}\) and \(x_{max}\) denote the minimum and maximum value of the data.  
In z-score normalization, any \(y\)-value of the variable is normalized by the z-score, known as the mean and standard deviation of the variable [27]. The equations for mean, standard deviation, and z-score are as follows:  
\[\mu=\frac{1}{N}\sum_{i=1}^{N}(x_{i}) \tag{2}\]  
\[\sigma=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(x_{i}-\mu)^{2}} \tag{3}\]  
\[z=\frac{x-\mu}{\sigma} \tag{4}\]  
where \(z\) is the normalization value, and \(\mu\) and \(\sigma\) denote the mean and standard deviation.
# Quantifying the Effect of Catalysts on the Lifetime of Transformer Oil
## Materials and Methods  
### Tested Oil and Thermo-Oxidative Aging  
The tested oil was Nytro Lyra X oil manufactured by Nynas AB (Sweden). Nytro Lyra X is a high-grade naphthenic oil, inhibited using the use of DBPC with amounts less than 0.4 wt%, with a typical density of 0.870 kg\(\cdot\)dm\({}^{-3}\) (at 20 \({}^{\circ}\)C), a viscosity of 9.3 mm\({}^{2}\cdot\)s\({}^{-1}\) (at 40 \({}^{\circ}\)C) and 926 mm\({}^{2}\cdot\)s\({}^{-1}\) (at \(-\)30 \({}^{\circ}\)C), and a pour point at \(-\)48 \({}^{\circ}\)C [27]. The oxidation stability of the tested oil is declared to be 500 h at 120 \({}^{\circ}\)C.  
The tested oil was aged in an accelerated manner in a Venticell laboratory oven (BMT Medical Technology, Brno, Czech Republic) with limited access to air. The oil was placed in 50 mL amber laboratory flasks with wide-mouth glass stoppers which remained closed during thermal aging. Based on our previous experience [25] and the IEC 60,216 standard [14], three testing temperatures (110, 120, and 130 \({}^{\circ}\)C) were chosen, and the exposure time varied from 24 to 2424 h for 110 \({}^{\circ}\)C, from 24 to 1656 h for 120 \({}^{\circ}\)C, and from 24 to 600 h for 130 \({}^{\circ}\)C.  
Copper and Kraft paper in the form of a small piece of insulated transformer copper winding were used as a catalyst (see Figure 1) to simulate (as much as possible) a real environment in a transformer oil tank under laboratory conditions. The weight of the catalyst used was chosen to match the weight of the oil it was immersed in, i.e., the weight ratio between catalyst (copper and Kraft paper) and oil was chosen to be 1:1. The same ratio can be found, for example, in 110 kV distribution transformers.  
Samples of oil, as well as copper and Kraft paper, were separately conditioned for 12 h at 110 \({}^{\circ}\)C. After this conditioning, copper and Kraft paper samples were placed as quickly as possible in the laboratory flasks, and the experiment started. All properties were then measured on oil samples in the delivered state (thermally untreated) and for each level of thermal aging on both the samples with and without the catalysts.
# Radiomics Analysis in Ovarian Cancer: A Narrative Review
## 3 Ovarian Cancer  
Ovarian cancer (OC) ranks as the seventh most diagnosed cancer among women worldwide and the second most common gynecological malignancy. It was responsible for about 14,000 deaths in 2020 in the US [31].  
About 90% of cancers of the ovary are epithelial ovarian cancer (EOC) types. OC has multiple cellular origins. The term tubo-ovarian cancer is often used because OC may manifest as an ovarian or fallopian tube mass or as a primary peritoneal cancer [32].  
Type I cancers (low-grade serous, mucinous, endometrioid, and clear cell) arising from the ovaries are typically less aggressive and very likely to be diagnosed promptly, as they usually grow slowly. Type II malignancies (high-grade serous carcinomas (HGSC), undifferentiated carcinomas, and carcinomas) may develop from the tubal and/or ovarian surface epithelium and are usually more aggressive [33; 34].  
The absence of proper screening and diagnostic programs capable of detecting OC at the early stage and the fast spread of disease through the surface lining of the peritoneum represent the main factors causing OC lethality [35]. There is still a lack of an accurate protocol to identify high-risk patients, and the identification of tools for accurate screening, early diagnosis, and prognosis of OC represents a currently unmet clinical need.  
Furthermore, the role of medical imaging (US, RMI, CT, and PET) in OC is fast evolving. Soon, a simple tumor description and its extension may not be sufficient. The use of precision medicine may help to answer questions related to treatment response, best timing for surgery, prognosis, or molecularly targeted drug therapy [36; 37].  
Hence, radiomic technology has been introduced as an emergent tool for post-processing of medical images and the advancement of new quantification metrics linking qualitative and/or quantitative imaging data to clinical results [38; 39; 40; 41].