esa-sceva/satcom-chunk-collection huggingface dataset:
# End-to-End Service Availability in Heterogeneous Multi-Tier Cloud-Fog-Edge Networks
## 1 Introduction
### Evolution and Significance of Multi-Level Communication Networks  
From its inception, the internet's expansive web has grown in complexity and importance. Currently, with the emergence of IoT and the nascent fields of edge and fog computing, we are on the cusp of more comprehensive integration. The merger of space-air-ground-ocean networks into this fold foretells an even more nuanced future for the internet's structure.  
As our technological era progresses, a surge in devices connecting to the internet is evident, ranging from domestic sensors in smart homes to industrial robots. These multi-level communication networks, now an integral part of modern connectivity, are distinguished by their multiple tiers. Each layer, whether it be cloud, fog, or edge, comes with unique computing, storage, and communication capacities. Central to this evolution is the cloud-fog-edge computing dynamic, which is decentralizing intelligence and transforming service delivery. At the core of this system, robust centralized cloud data centers stand tall, processing and storing colossal amounts of data from myriad endpoints.
## 1 Introduction  
The hypersonic glide vehicle (HGV) [1] is a rapid strike weapon, which can strike any place in the world within two hours. HGVs can make a long glide in near space by aerodynamic force with a Mach number bigger than five. Usually the first step of defending the HGV is to find and track HGVs using radars. We will miss lots of important information if we track an HGV using kinematic models [2, 3], such as the constant acceleration (CA) model, the constant turning (CT) [4] model and the Singer model [5]. To develop the HGV tracking algorithm using as much information as possible, we established the process equation based on the aerodynamic model.  
There are some papers about HGV tracking using kinematic models [2, 3] and using aerodynamic models [6, 7]. However, the problems of the uncertain initial state and big errors for HGV tracking have not been studied in any published paper, and they are important for improving the tracking performance.  
When tracking an HGV using radar, the velocity and position can be directly measured roughly but the two control variables, the bank angle and the angle of attack (AOA), cannot be directly measured. The initial states of the bank angle and AOA are unknown by defenders. This problem of choosing the initial states of the two control variables for HGV tracking will be studied in this paper.  
We proposed that we conduct HGV tracking with a multiple model algorithm in the initial tracking stage and with a single model algorithm in the stable tracking stage, and they are divided by a proposed parameter. The multiple model algorithm can avoid divergence and reduce the errors caused by the uncertain initial state. The single model algorithm can track the HGV at higher accuracy. The proposed hybrid model algorithm will be compared with the single model algorithm and multiple model algorithm in computational cost and tracking accuracy.  
We will establish the HGV tracking algorithm using the Cubature Kalman filter (CKF). The CKF [8] is proposed by [PERSON] based on the Cubature rules with rigorous mathematical derivation. CKF is more accurate than the extended Kalman filter (EKF) [9] in a nonlinear filter and more accurate than the unscented Kalman filter (UKF) [10] in a high-degree nonlinear system filter [11]. Furthermore, the system of tracking an HGV is a high-degree nonlinear system. According to the above considerations, every model filter in the algorithms in this paper can be constructed using CKF.
# Ten Years of TerraSAR-X Operations
## 5 Radar Experiments with TerraSAR-X  
The high flexibility provided by TerraSAR-X allowed for research in and demonstration of new SAR modes. The Staring Spotlight mode and the wide-ScanSAR mode were demonstrated and were further developed to fully operational TerraSAR-X modes. Other examples are TOPSAR, BiDiSAR, and Wrapped Staring Spotlight, which were demonstrated by TerraSAR-X.
# Parametric Analysis for 3D Modeling of Consolidation-Induced Solute Transport Using OpenFOAM
## 4 Results and Discussion
### Effects of the Degree of Saturation on Consolidation  
The analysis of pressure variation shows that (Figure 18), under high saturation, the initial pressure peak at the center is higher due to the difficulty of expelling water, leading to pore water pressure accumulation. In contrast, lower saturation results in lower initial pressure as the water drains more easily. At the edge, the pressure pattern is similar but with lower values. As depth increases, pressure drops at the center under high saturation, while pressure variation remains smaller under low saturation.  
For vertical displacement (Figure 19), higher saturation leads to smaller displacement at the center, whereas lower saturation increases soil deformation, resulting in greater vertical displacement due to air voids that enhance compressibility. At the edge, the overall displacement is smaller but follows the same trend. Deformation at depth is limited under high saturation but more pronounced under low saturation.  
Figure 17: Concentration contour plots in the \(x\)-\(y\) plane at \(z=1.5\) m under varying shear modulus over 10 years.  
Figure 18: _Cont._Figure 19: _Cont._  
At the top (Figure 20a,b), high saturation produces higher pressure peaks, especially in the loading area, while lower saturation allows for smoother drainage and lower pressure. Over time, pressure stabilizes but remains higher under high saturation. The middle layers (Figure 20c,d) show a similar pattern: higher saturation leads to higher pressure and lower saturation results in lower pressure.  
The horizontal displacement (Figure 21a-e) increases slightly under high saturation, but the overall magnitude remains small. Initially, the differences between the saturation levels are clear, but over time, these differences diminish as the soil reaches equilibrium.  
In summary, saturation significantly affects soil behavior. High saturation leads to higher initial pressure and limited vertical displacement, whereas low saturation allows for easier drainage, less pressure build-up, and greater deformation. Although horizontal displacement starts differently, it stabilizes over time, showing that saturation impacts both short-term and long-term soil response.  
Figure 20: Distributions of pressures along (**a,b**) the top horizontal cut line at \(z=3\) m; (**c,d**) the middle horizontal cut line at \(z=1.5\) m for varying degree of saturation. Solid lines represent **Case 1**; squares represent **Case 6**; triangles represent **Case 7**.
## 4 Results and Discussions
### Comparing Integration Frameworks
#### 4.1.1 Comparing Integrated Models with Single Models (OBIA and CNN)  
Two models were used to assess the integration feasibility in this research, including pixel ANN and patch CNN. Figure 1 shows how these models compare based on three accuracy matrices, i.e., OA, Kappa index, and F1-score, for the training and test areas. The results indicate that the patch CNN outperformed the pixel ANN on any accuracy metric and for both the training and test areas. For the training area, the patch CNN achieved 0.897 OA, 0.860 Kappa, and 0.900 F1-score, while the pixel ANN achieved 0.849 OA, 0.818 Kappa, and 0.850 F1-score. The accuracies of the patch CNN for the test area were 0.898 OA, 0.876 Kappa, and 0.890 F1-score. The pixel ANN for the test area achieved accuracies of 0.858 OA, 0.829 Kappa, and 0.860 F1-score.  
In addition, Table 5 presents a detailed assessment of the four integration frameworks studied in this research. In general, the results indicate that patch filtering achieved the best classification results for both the training and test areas. Based on OA and Kappa index, the patch filtering achieved the best results for both the training (0.919 OA, 0.868 Kappa, and 0.920 F1-score) and the test areas (0.917 OA, 0.872 Kappa, and 0.910 F1-score). OBIA ANN performed slightly worse than patch CNN and pixel ANN. Using the test dataset, it obtained 0.842 OA, 0.784 Kappa, and 0.830 F1-score, whereas decision fusion and feature fusion achieved 0.862 OA and 0.860 OA, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Selected Value** \\ Patch size & \(5\times 5\) \\ Pool size & \(2\times 2\) \\ Size of kernel filters & \(3\times 3\) \\ Dropout rate & 0.5 \\ Hidden layer activation & ReLU \\ Loss function & Categorical cross-entropy \\ Early stopping patience & 15 epochs \\ Classification layer & Softmax \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameters used in the base models.
# Data and Knowledge-Driven Bridge Digital Twin Modeling for Smart Operation and Maintenance
## 5 Bridge Digital Twin Modeling Using Multi-Source Heterogeneous Data
### Temporal Alignment of Bridge Digital Twin  
The temporal alignment of 2D image data, 3D point cloud data, and sensor data is crucial for temporal modeling. By achieving geometric and temporal alignment of multidimensional data, critical data support can be provided for the DTM of bridges. In the modeling of multi-source heterogeneous data fusion, the resampling method is used to align data in time. Since the sampling times and frequencies of 2D image data, 3D point cloud data, and sensor data vary, resampling involves selecting an appropriate sampling frequency to achieve time alignment.  
Selecting the appropriate sampling frequency is crucial during the resampling process. Sensor data typically have a higher sampling frequency than 2D image data and 3D point cloud data, which are captured at lower frequencies. Therefore, the sensor data sampling rate must be reduced to synchronize it with the acquisition frequency of the 2D image and 3D point cloud data. By establishing an appropriate sampling frequency, the timestamps of the sensor data can be aligned with those of the other data sources, ensuring consistent temporal synchronization across datasets.  
Sensor time series data often contain a significant amount of high-frequency information, which can accumulate in large datasets during long-term bridge health monitoring.  
Resampling not only retains the essential physical information but also reduces unnecessary data accumulation, thereby accelerating the data processing speed of the DTM.
## 3 Results
### Wear Track Morphology  
Figures 3-5 show the micrographs of the ASF sample, the steel static partner and the wear track map after the dry and lubricated wear tests, for the ASF samples based on the Small, Medium and Large ASW powders, respectively. All samples show relatively even wear tracks and similar wear progression behaviours as seen by the track surfaces. The wear initiates in the soft Al matrix and gradually progresses to the hard ASW particles. The ceramic particles are brittle and tend to be chipped off the surface. As the samples are porous, the ceramic fragments fall into the pores in the ASW particles or the Al matrix in the ASF sample. As this occurs, the surface becomes worn more evenly. The regions of the worn surfaces where the sliding direction is not evident, as indicated by red arrows in Figure 5, reflect this phenomenon. This behaviour is more evident in the ASF samples based on the Large ASW powder than those based on the Small and Medium ASW powders.  
Figure 3: Micrographs of worn surfaces (**a1,a4,a7,b1,b4,b7**), micrographs of static partners (**a2,a5,a8,b2,b5,b8**) and wear track maps (**a3,a6,a9,b3,b6,b9**) for S-ASF (**a1–a3,b1–b3**), SH-ASF (**a4–a8,b4–b6**) and S-ASF-T6 (**a7–a9,b7–b9**) samples under dry (**a1–a9**) and lubricated (**b1–b9**) sliding wear conditions.  
Some darker patches, as indicated by blue arrows in Figure 5, are observed on the worn tracks of all samples. They are characteristic of oxidative wear, which occurs when heating due to friction during sliding causes surface oxidation, leading to wear through the removal of oxide fragments [31]. Fatigue-related wear or delamination, as circled on Figure 5, is also observed. It occurs because repeated sliding induces subsurface cracks that gradually grow and eventually shear to the surface, forming long thin wear sheets [31,32].  
The static partners generally display even wear profiles. The static partners for the L-ASF samples have even wear surfaces with a clear direction of wear and a symmetrical wear profile. They have no ceramic or ASF material on them, indicating that the wear is predominantly adhesive in L-ASF. The static partners for  
partner for the M-ASF sample also displays uneven wear. It seems the surface roughness of the sample may have played a role.
# GNSS/INS Integration Based on Machine Learning LightGBM Model for Vehicle Navigation
## 3 Experimental Analysis and Method
### Model Predictions  
The model is ready for use once it has been trained. When there is a GNSS outage, the IMU can continue to work regardless of the external environment. The velocity (\(V_{\text{INS}}\)) and azimuth (\(\theta_{\text{INS}}\)) provided by INS as well as the vehicle's three-axis acceleration were employed in the model to obtain the model's prediction of the differences in longitude (\(\delta E\)) and latitude (\(\delta N\)) between the two epochs. The longitude and latitude information provided before the GNSS outage were added to the prediction model's longitude difference and latitude difference outputs, respectively, to obtain the vehicle's latitude and longitude location information.  
In this study, the performance of the GNSS outage position prediction model was evaluated by two metrics: the root mean square error (\(RMSE\)) and the coefficient of determination (\(R^{2}\)), which are both commonly used in regression tasks. The equations are shown in Equations (1) and (2).  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}} \tag{1}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}}{\sum_{i=1}^{n}(y_{i}-\overline {y_{i}})^{2}} \tag{2}\]  
where \(n\) is the total number of data points, \(y_{i}\) is the actual value, \(\hat{y_{i}}\) is the predicted value, \(\overline{y_{i}}\) is the mean of the observed data, \(RMSE\) is the root mean square error, and \(R^{2}\) is the coefficient of determination. The smaller the \(RMSE\) value, the higher the model prediction accuracy; the closer the \(R^{2}\) value is to 1, the better the model fits the data.  
In practical application, the model is used to predict the position and trajectory of a vehicle when there is a GNSS outage, and it is compared with the actual vehicle trajectory. Then, it gives the average error of the predicted trajectory compared to the real trajectory. Model training time is critical in practice because the data acquisition system can capture new data that are generated while the vehicle moves with a strong GNSS signal. A subset of this new data can be used to augment the dataset. The shorter the training time for each model, the less time it takes to retrain the LightGBM model for vehicle position prediction during a GNSS outage. This means new models can be deployed faster and adapt to more driving conditions. As a result, the training time of the model was also used as the final evaluation metric.
## Nomenclature  
\begin{tabular}{l l}
**Notation** & **Description** \\ \(\left[\begin{array}{cc}x_{i}&y_{i}\\ x_{di}&y_{di}\end{array}\right]\) & Real trajectories \\ \(\left[\begin{array}{cc}x_{ri}&y_{ri}\\ x_{ai}&y_{ai}\end{array}\right]\) & Reference trajectories \\ \(\left[\begin{array}{cc}x_{ai}&y_{ai}\\ \end{array}\right]\) & Adjusted trajectories \\ s & Path parameter \\ \(\eta\) & Switch function \\ \(\theta_{i}\) & Real angle \\ \(\theta_{di}\) & Desired angle \\ \(\theta_{ri}\) & Reference angle \\ \(\theta_{ai}\) & Adjusted angle \\ \(\Gamma_{i}\) & Potential function \\ \(V\) & Formation error \\ \end{tabular}
# W-Band 4 th Order Waveguide Filter Based on Double Layer SU8 Microfabrication
## 3 Experimental Results  
### Fabrication of the Designed Filter  
The designed filter was fabricated by the SU-8 micromachining technology. The technology mainly includes the steps of spin coating, pre-baking, exposing, post-baking, developing, removing, and sputtering. The high-precision machining process is described in detail as follows: (a) About 2.5 g of SU-8100 photoresist was spun coated on a 4-inch silicon wafer and baked it on a high-level hotplate. To more easily ensure the accuracy of the overall uniformity of the SU-8 layer, the 500 um thick SU-8 layer is divided into two times spin coating. The SU-8 layer thickness is controlled by the weight of the photoresist spread on the silicon wafer. (b) The SU-8 photoresist was exposed to UV light at an appropriate dose. At this time, attention should be paid to the close contact between the silicon wafer and the mask to reduce diffraction and avoid processing imperfections. (c) The SU-8 layer was developed with a SU-8 developer within a suitable time. The length of development time is determined by the high aspect ratio and (or) the SU-8 layer thickness. (d) The SU-8 layer was removed from the wafer by dipping it into a 40% KOH solution. (e) All sidewalls of the SU-8 layer are deposited with 700 nm thick silver. The coated silver is at least three times the skin depth of the designed filter at 90 GHz. This will reduce the surface roughness of the device as much as possible during the metallization process, thereby reducing the insertion loss. The processing schematic diagram of the filter is shown in Figure 4. It is worth noting that during the sputtering process, if the processed SU8 chip is relatively thin (the thickness is about <400 um), the phenomenon of SU8 chip bending will occur. Due to the large shear stress between the sputtered silver metal and the contact surface of the SU8 chip, the SU8 chip is deformed. There are two main methods to solve this problem: First, during sputtering, reduce the sputtering power in the equipment parameters so that  
Figure 3: Magnetic field distribution map of the designed structure. (**a**) Front view of the designed filter (**b**) Left side view of the designed filter.  
the deposition rate of metal is slowed down, thereby reducing the possibility of device deformation due to excessive stress. Second, before sputtering, it can be fixed with tape around the edge of the SU8 chip, which can also greatly reduce the deformation of the SU8 chip.
# Designing Hydrogel-Based Bone-On-Chips for Personalized Medicine
## 4 Hydrogel-Based Bone-On-Chips
### Cell Interactions
Results showed, for the first time in a microfluidic device, that mechanically stimulated  
Figure 3: Inducing cell differentiation in hydrogel-based bone-on-chips. (**A**) A vascularized human bone marrow-on-chip was developed with optically clear poly(dimethylsiloxane) channels. In the top channel, hematopoietic stem cells (CD34\({}^{*}\)) were seeded, while endothelial cells (EC) created a vascular lumen in the bottom channel. After 2 weeks of in vitro culture, hematopoietic stem cells differentiated in multiple blood cell types (magenta: erythroid lineage; yellow: megakaryocyte lineage; blue: neutrophil and other haematopoietic lineages). Scale bar, 20 μm. Image adapted from [16]. (**B**) Collagen-based bone-on-chip modeling primary human osteoblast differentiation into osteocytes. Cells increased primary protrusion length over time and exhibited dendritic morphology after 21 days of culture. Osteogenic differentiation was confirmed by an increasein alkaline phosphatase activity and synthesis of Dental Matrix Protein 1 (DMP1). \({}^{*}\) represents statistical significance (\(p<0.05\)). Scale bar, 50 μm. Image adapted from [88]. Images were taken with permission from publishers. osteocytes reduced breast cancer extravasation [91]. The use of primary human osteocytes, or fully differentiated HOBs, in hydrogel-based BOCs will be critical for future patient-specific applications. Besides including different cell types involved in the development, growth and remodeling of bones, a more realistic bone microenvironment requires modeling interactions between tissues made of those cells. For example, vascular tissue interacts with bone forming cells and affects both bone pathology and physiology. Indeed one BOC devicewas designed to deposit a layer of Human Umbilical Vein Endothelial Cells (HUVECs) on the external side of the hydrogel recreate the interface between blood vessels and the bone matrix, and to simulate the extravasation process of cancer cells [97; 104] (Figure 4). Indeed, the use of organ-on-chips facilitates the interaction between three to four different cells types to model the interaction between tumors, vascular and bone tissue. A few studies include fibroblasts when mimicking a tumor microenvironment since stromal cells are expected to nurture the tumor microenvironment and influence metastasis of the cancer [99; 102]. Altogether those microengineered devices with endothelial cells contributed to identify the molecular pathways involved in the extravasation of breast cancer cells in bone [97], showed that the bone marrow microenvironment facilitates leukemia cell survival during drug treatment [103], and generated a vascularized network inside cell spheroids that permit to transport nutrients and cells to study metastasis [102].
# Boiling Flow Pattern Identification Using a Self-Organizing Map
## 4 Conclusions  
In this paper, a self-organizing map combined with the recurrence quantification analysis was used to identify flow boiling patterns in a circular horizontal minichannel with a diameter of 1 mm and determine the flow boiling regimes during a single pressure-drop oscillations cycle.  
In the self-organizing map, neurons of similar weight locate themselves close to one another, while dissimilar neurons are far from each other. Therefore, the 2D self-organizing map identifying the differences between system dynamics was created. The input data for the self-organizing map was the results of the quantitative analysis of recurrence plots created from pressure drop oscillations.  
The self-organizing map allows us to distinguish five types of two-phase flow patterns, such as bubble flow, confined bubble flow, wavy annular flow, liquid flow, and slug flow. The flow pattern identification was confirmed by images obtained using a high-speed camera.  
The self-organizing map successfully classified four boiling regimes during a single cycle of the long-period pressure drop oscillations. We can conclude that this technique is promising as an objective classifier of boiling flow patterns.  
The combination of the _RQA_ and the neural network (_SOM_) proposed in this paper shows that the adopted method of pressure fluctuation analysis allows the identification of various flow patterns in non-stationary boiling in minichannels. At the current stage of the research, the method of selecting the correct _RQA_ coefficients has not been solved. Although, the obtained results suggest that the application of more complex neural network algorithms will allow for the automation of boiling regime identification by analyzing all _RQA_ coefficients.  
Conceptualization, I.Z. and R.M.; methodology, I.Z.; software, I.Z.; validation, I.Z. and R.M.; formal analysis, I.Z.; investigation, H.G. and R.M.; resources, I.Z., R.M. and H.G.; data curation, H.G. and R.M.; writing--original draft preparation, I.Z.; writing--review and editing, H.G., R.M. and I.Z.; visualization, I.Z. and H.G.; supervision, R.M.; project administration, I.Z. and R.M.; funding acquisition, H.G. and R.M. All authors have read and agreed to the published version of the manuscript.  
This study was performed within the framework of research project No. UMO-2015/17/N/ST8/03079, funded by the National Science Centre, Poland and project financing through the program of the Minister of Science and Higher Education of Poland named \"Regional Initiative of Excellence\" in 2019-2022, project number 011/RID/2018/19, amount of financing 12,000,000 PLN.  
The authors declare no conflict of interest.