esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Observability of Uncertain Nonlinear Systems
: _If \(\text{rank}\left(Q(x)\right)=n\) holds for one \(x\in D_{x}\), the system_  
\[\dot{x} = f(x(t)),\quad x(0)\ =\ x_{0}\] \[y = h(x(t))\]  
_is locally observable in this point \(x\)[1; 19]._  
Definition 3 only accounts for a single point \(x\in D_{x}\). A more general statement is stated in the following definition. **Definition 4** (Local observability of autonomous systems). : _If \(\text{rank}\left(Q(x)\right)=n\) holds for all \(x\in D_{x}\), the system_  
\[\dot{x} = f(x(t)),\quad x(0)\ =\ x_{0}\] \[y = h(x(t))\]  
_is locally observable [1; 19]._  
To verify the condition of Definition 4, intervals are used. Thereby, the observability matrix turns into an interval matrix  
\[q(x_{a})\
eq q(x_{b}) \tag{9}\]0  
The nonlinear system is locally observable, if \(Q^{I}\left(x\right)\) is full rank, such that  
\[q(x_{a})\
eq q(x_{b}) \tag{9}\]1  
The verification of (12) is explained further in Section 2.3. The examined interval \(x^{I}\) in (12) is the hull \(\tilde{x}^{I}\) of the currently considered interval \(\tilde{x}^{I}\) with all intervals that are indistinguishable according to condition (10). By verifying condition (12) for \(\tilde{x}^{I}\), it can be proven that \(\tilde{x}^{I}\) is locally observable in the area \(\tilde{x}^{I}\). Since \(\tilde{x}^{I}\) is locally observable in the area \(\tilde{x}^{I}\) and distinguishable to all other intervals in \(D_{x}\), all initial states \(x_{0}\in\tilde{x}^{I}\) are globally observable in \(D_{x}\). For all other intervals \(x^{I}\) inside \(\tilde{x}^{I}\), global observability cannot be proven. The reason is that \(\tilde{x}^{I}\) only contains the indistinguishable intervals of \(\tilde{x}^{I}\). For any \(x^{I}\subset\tilde{x}^{I}\) there could exist indistinguishable intervals outside \(\tilde{x}^{I}\). Hence, to prove global observability for all intervals in \(D_{x}\), it is necessary to determine all indistinguishable intervals for every interval in \(D_{x}\), build the respective hull and check condition (12) in each case. Note that condition (12) can only be met if \(\tilde{x}^{I}\) is small enough to minimize the overestimation. If global observability for all intervals in \(D_{x}\) can be proven by the outlined procedure, the nonlinear system is globally observable in \(D_{x}\). Until now, it is assumed that the system is indeed globally observable. If only a subset \(\mathcal{B}\subset D_{x}\) happens to be observable, the conditions (10) and (12) can be used to determine \(\mathcal{B}\). The observable area satisfies both conditions, whereas the other areas violate at least one of them. Additionally, the method provides all indistinguishable states. This information could be beneficial for the design of a controller. It could also be used to receive an idea about how to adapt the measurement equation to achieve global observability of the system. Further work in the field of sensor replacement can be found in [20, 21].
## 3 Ground Motion Selection Techniques  
Non-linear phenomena, such as plastic hinge deformation and soil-structure interactions, contribute to intricate building responses. To capture these complexities, non-linear dynamic analysis of buildings is necessary. A linear elastic dynamic analysis, however, is useful for multiple-supported infrastructures, such as bridges, because of dominant vibration modes [53]. Linear analysis uses a response spectrum as input, while non-linear analysis requires a complete earthquake ground motion time history as input. As computing capabilities have advanced, seismic building codes recommend using non-linear dynamic analysis. However, near-field ground motions themselves are very complicated due to intricacies of the earthquake rupture process, seismic wave propagation in 3D heterogeneous Earth, and local site effects [54]. Ground motion selection is thus key to determining whether a dynamic analysis provides a building response that is \"relevant\" for the site. Because of the subtleties involved in the selection process, this section provides an overview on available selection techniques.  
Results of seismic hazard analysis (SHA), either deterministic (DSHA) or probabilistic (PSHA), are required to aid in the selection process. Using a deterministic approach, the magnitude of the maximum credible earthquake (MCE) for the site of interest is identifiedbased on the tectonic setting and past data [55; 56]. As a result, records are selected based on the magnitude-distance combinations that are likely to generate the highest ground motion amplitude. A probabilistic approach involves integrating the expected seismicity over a period and a chosen magnitude range (i.e., all earthquakes larger than moment magnitude \(M_{w}\) 4) to estimate strong motion parameters (such as peak ground acceleration, \(PGA\), or spectral values \(Sa(T)\)), including their uncertainties [57; 58; 59; 60; 61]. Subsequently, hazard curves are computed for specific strong ground motion parameters. A uniform hazard spectrum (UHS) can then be derived and used for the time history selection process. In the next few sections, we review different ground motion selection approaches described in the literature.
## 2 Materials and Methods
### Evaluation of the Accuracy of the Reconstruction  
To evaluate the accuracy of the images created by interpolation, they were compared with actual images. The color of each pixel obtained was checked and transformed to red if it differed from the real color by more than 2% in any of the three channels (R, G, or B).
# Current Biomedical Applications of 3D Printing and Additive Manufacturing
## 4 Healthcare Applications
### Drug Delivery
#### 4.2.3 Drug-Releasing Implants  
Implants play a wide variety of functions in medicine to restore anatomical integrity, conduct procedures and aid normal physiological functions. Long-term implants such as plates, joints, screws, stents or cement require the use of materials that do not interfere with normal functioning of the surrounding tissue. Temporary implants, such as urinary or vascular access catheters, require antibacterial coatings to prevent catheter-associated infections [149]. Aside from the great level of control AM provides in fabricating complex implants, it also allows the incorporation of therapeutics within an implant's structure [150]. The use of composite biocompatible materials within implants can allow for controlling the rate of release of drugs from these implants [150]. Studies have shown that 3D-printed antibiotic-infused PLA constructs are superior in prevention of bacterial biofilm formation compared to constructs that are only coated with antibiotics [151]. In vitro studies on extrusion 3D-printed scaffolds in our lab have also shown equal efficacy in drug release as compared to direct treatment with drugs, highlighting the potential for the use of 3D-printed scaffolds for sustained local drug delivery within tissue [6; 7].
# Rethinking the Operation Pattern for Anomaly Detection in Industrial Cyber-Physical Systems
## 7 Discussion
### Proprietary Protocols  
This work was conducted on the Modbus TCP protocol; however, we would like to clarify that the proposed model can be rapidly extended to other protocols. In the ICPS, most master and slave devices support open industrial control protocols (such as Modbus, opc u [53]). However, manufacturers developed private protocols (e.g., Schneider's unified messaging application services (UMAS), Siemens' S7 Communication(S7 comm)/S7 comm Plus) and prefer to use these protocols in internal systems. These private protocols limited the proposed detection method because it needs to decode the ICPS traffic into plain text messages to extract operation features. Applying the proposed method to private protocols requires reverse engineering or the manufacturers' own efforts to decode the traffic to plain text.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Detection Method** & **Precision** & **Recall** & **F1-Score** \\ \hline Proposed & **1.00 \({}^{*}\)** & **1.00** & **1.00** \\ HMM-ANN [24] & 0.82 & 0.89 & 0.85 \\ DAE-XGBoost [52] & 0.93 & 0.93 & 0.90 \\ \(k\)-means & 0.91 & 0.92 & 0.90 \\ PCA & 0.92 & 0.92 & 0.89 \\ \hline \end{tabular} \({}^{*}\) Bold means the best result.  
\end{table}
Table 7: Detection performance of the proposed model with four comparative approaches.
## 4 Network Approaches to Optimize 360-Degree Video Streaming
### Mobile Edge Computing (MEC)  
Mobile Edge Compute (MEC) is one of the important elements of 5G [69] specifically beneficial for VR streaming. VR applications require high computing power and data processing power, the CPU and storage capacity of mobile nodes is insufficient for the rendering and computational tasks in VR. In this context, the MEC server may assist by calculating the necessary blocks as target tasks and then delivering the complete task to the mobile VR device [14]. Next, to reduce data related to virtual reality content directly sent to mobile VR devices, the cloud server will first pre-render the VR material, followed by secondary rendering on the mobile VR device.  
MEC architectures are also successful in improving network responsiveness and latency, as well as reducing communication resources by leveraging the caching and computational capacity of VR devices for mobile use [70; 71; 72; 73]. [PERSON], [PERSON] [74] claimed that the MEC architecture can solve insufficient computing power issues that existed on most mobile VR devices. However, the rate of growth of mobile VR data outpaces the rate of growth of wireless network capacity, resulting in a large communication load when sending VR content with the present MEC architecture. Although the impact is minimal, the combination of edge computing with mmWave in mobile VR has been investigated [75; 76]. [PERSON], [PERSON] [75] researched a user clustering method to increase user field-of-view frame requests, whereas [PERSON], [PERSON] [76] investigated an adaptive computation and caching method on the interactive VR video frames to reduce VR game traffic. However, these approaches are primitive, and they only consider 360-degree video lower quality and lower resolution (4K), which affected the delivered experience significantly.  
A few recent works display the fusion of MEC and tiling approaches that maximize the advantages of each other. [PERSON], [PERSON] [77] proposed a tiled 360\({}^{\circ}\) caching solution based on Mobile Edge Computing (MEC) with Long-Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), where the LSTM model predicts the future viewport based on popularity and the CNN model identifies the most engaging tiles based on the video's saliency map. This effectively improves the cache hit rate by at least 10% and decreases backhaul utilization by at least 35% while reducing end-to-end latency by at least 35%. [PERSON], [PERSON] [78] store all versions of 2D and 3D tiles at the MEC server and process the projection of 2D into 3D at the MEC side. In [78], the weighted-sum technique is used to solve a 360\({}^{\circ}\) video caching optimization issue, which is a sequential decision-making problem with the combinatorial multi-armed bandit (CMAB) theory and an upgraded combinatorial UCB (ICUCB). [PERSON] and [PERSON] [79] suggested an Unmanned aerial vehicle (UAV)-assisted MEC network and formulated a combined UAV deployment, MEC and radio resource allocation, and 360-degree video content layer assignment (UAV-MV) to enhance QoE for all mobile VR users.
# A New Windage Loss Model for S-CO\({}_{2}\) Turbomachinery Design
## 2 Description of S-CO\({}_{2}\) Windage Loss Experiment
### Experimental Design of S-CO\({}_{2}\) Windage Loss Measurement
\[L_{ext}=W_{disk}+W_{windage}+W_{bearing}\ \sim\ W_{windage} \tag{2}\]  
\[C_{m}=0.146*Re_{r}^{-\frac{1}{5}} \tag{3}\]  
\[W_{disk}=C_{m}*\frac{1}{2}\rho\omega^{2}r_{i}^{5} \tag{4}\]  
\[W_{bearing}=0.0156*\rho^{0.8}\omega^{28}r_{o}^{-0.4}\left(r_{o}^{5}-r_{i}^{5} \right)/\mu^{-0.2} \tag{5}\]  
Since the relation between \(c_{p}\) and \(c_{v}\) can be described with Equation (6) for real gas, the heat capacity ratio \(\gamma\) can be used to consider the dramatic property changes of S-CO\({}_{2}\) from viscous heating due to windage loss. \(\gamma\) is defined in Equation (7). Therefore, the test range is determined to cover the heat capacity ratio \(\gamma\) of S-CO\({}_{2}\) compressor conditions shown in Ref. [11]. Figure 3.0  
Figure 3.1  
The compressor outlet conditions in the reference are a pressure of 20 MPa, temperature of 142 \({}^{\circ}\)C and \(\gamma\) of 1.83. While satisfying the reference of \(\gamma\) value, the test conditions are limited so as not to exceed the maximum pressure of 100 bar due to the limitation of the facility. As a result, the inlet density range is determined from 100 to 250 kg/m\({}^{3}\), and the inlet S-CO\({}_{2}\) temperature was kept under 55 \({}^{\circ}\)C to maintain similitude in the specific heat ratio. It is noted that the test range includes supercritical and gas phases so that the windage loss model can be used in various conditions. For instance, even though the reference design condition is at a supercritical state, the gas phase can occur during the operation since depressurization of a cavity using a scavenging pump can change the characteristics. Since existing empirical models handle only single phase, the test data are classified with the phase in which the data are measured.
## Materials and Methods
### Test Apparatus  
The experimental setup for the laboratory model test is shown in Figure 1. It is composed of three parts: pore solution collection box, polymethyl methacrylate (PMMA) strip, and PMMA box.  
As cation concentration in soil bulk solution cannot be accurately and conveniently measured, but it can be reflected by the change of that in soil pore solution according to the Boltzmann distribution equation. Therefore, pore solution collection boxes were designed to collect the pore solution of different expansive soil layers in the saturation process for analyzing the migration regulation of cations. It contains a rectangle case, a water outlet pipe, and an intake pipe. These two pipes are respectively connected to both sides of the collection box, the former bottom being connected to the pump water device and the latter being used for air admission. The pump water device with the maximum negative pressure of 100 kPa can ensure that the pore solution in soil layer is extracted into separate container. The size of collection box is 5 cm \(\times\) 5 cm \(\times\) 2 cm, its upper cover has multiple holes of 1 mm diameter, and the whole box body is wrapped with a layer of geotextile.  
The size of PMMA box is 30 cm \(\times\) 30 cm \(\times\) 30 cm, its outer wall is marked with a scale on four sides, and the minimum scale is 1 mm. Four PMMA strips with dimensions 10 cm \(\times\) 2 cm \(\times\) 0.5 cm are placed at the four sides of different soil layers every 30 mm. When expansive soil produces swelling deformation, the PMMA strip will move along with it. This value can be observed by the scale in outer wall of the PMMA box, which is the swelling deformation of expansive soil.  
\begin{table}
\begin{tabular}{c c} \hline
**Property** & **Expansive Soil** \\ \hline Grain size distribution & \\ \hline Sand (\%) & 3.90 \\ Slit (\%) & 31.70 \\ Clay (\%) & 64.40 \\ Specific gravity & 2.70 \\ \hline Atterberg limits (\%) & \\ \hline Liquid limit & 63.00 \\ Plastic limit & 24.94 \\ Unified soil classification & CH \\ \hline Compaction properties (standard Proctor) & \\ \hline Optimum moisture content (\%) & 16.20 \\ Maximum dry density (g/cm\({}^{3}\)) & 1.80 \\ \hline Swell properties & \\ \hline Free swelling ratio (\%) & 65.50 \\ Swelling pressure (KPa) & 182.72 \\ Swelling percent (\%) & 19.00 \\ \hline Main minerals & \\ \hline Montmorillonite (\%) & 23.17 \\ Kaolinite (\%) & 5.93 \\ Quartz (\%) & 59.65 \\ Calcite (\%) & 11.25 \\ \hline Main cations of pore solution (mg/L) & \\ \hline Na\({}^{+}\) & 12.50 \\ K\({}^{+}\) & 10.00 \\ Mg\({}^{2+}\) & 35.00 \\ Ca\({}^{2+}\) & 405.50 \\ \hline \end{tabular}
\end{table}
Table 1: Basic physical and chemical parameters of materials used in experimental work.  
As shown in Figure 1, the depth of expansive soil is 210 mm, and the thickness of each soil layer is 30 mm. The surface expansive soil refers to the soil layer at a depth of 0-30 mm, the middle and bottom expansive soil are the soil layers at depths of 90-120 mm and 180-210 mm, respectively.
## 2 Proposed System Model  
In Figure 1, we consider a cooperative transmission from a source node to a destination node through a relay node, which uses DF relaying to decode and encode the received signal, then retransmits the data to the next destination. The communication link from the source to the relay is PLC, while FSO communication is used for the link from the relay to the destination. Furthermore, it is assumed that the relay node is equipped with the capability to interface between the PLC and FSO link.  
Figure 1: System model.
# Evaluating the Feasibility of Intelligent Blind Road Junction V2I Deployments
## 2 Intelligent Perception System (IPS) Overview
### Measurement Scenarios  
To evaluate the trade-offs between IPS network architectures, they must first be defined and discussed. The locations of the data sources, data processing, and data consumers involved in the system must be considered. This is pertinent as these locations will have the most significant bearing on the topology of the network used to implement the system. The data sources in the case of the IPS are the FSNs and their location will be determined by the position that provides the most appropriate perspective(s) of that region. Similarly, the data consumers in this case are the CIVs that must navigate the blind or occluded junction.  
The location of the computational resources in IPS can have a significant bearing on the feasibility of the system in terms of the performance capabilities and cost, i.e., deployment and maintenance costs. In this work, we evaluate and compare two different configurations or locations for the computational resources of the IPS. Specifically, the trade-offs between configurations are evaluated in terms of network performance, i.e., latency, throughput, and reliability. In the following subsections, the two configurations are presented and discussed to explore their apparent advantages and disadvantages.  
Figure 4: Simplified View of Computing Architectures for V2X Communications, highlighting connections between nodes in the network.
# Real-Time Extensive Livestock Monitoring Using LPWAN Smart Wearable and Infrastructure
## 3 Results
### Field Tests  
For the field test, we deployed two LoRaWAN gateways in Laspuna, north of Spain in the Pyrenees area. They feature the same receiver, but they were fitted with different antennas to prove that gain was not the most important factor with moving targets. The antennas have 4 and 6 dBi gain. Five sensor nodes were set with a movement burst streaming config, reporting position each minute and sampling IMU data at 12.5 Hz. With several devices, we could sample more data and spot any outliers. Sensors were fitted on a 1.8 m person's back using straps as in a cow's neck. The route chosen was a typical cattle path and a nearby ravine where cows usually get lost in.
## 3 Method
### Multi-Scale Spatiotemporal Feature Extraction Framework
#### 3.3.2 Federated Feature Fusion Validation (Figure 2)  
Feature Space Alignment Loss Function:  
\[\mathcal{L}_{\text{align}}=\sum_{i=1}^{N}\parallel f\left(x_{i}^{A}\right)-f \left(x_{i}^{B}\right)\parallel^{2}+\lambda R\]  
where f(.) transforms features to a common space, R represents the regularization term, and the regularization term ensures smooth feature distributions across different sensor modalities. To preserve privacy while maintaining coherence, we employ secure multi-party computation (SMC) protocols that allow nodes to compute similarity metrics without sharing raw features. Under non-IID distributions, we achieve a privacy-utility trade-off by adaptively adjusting the noise scale based on local data heterogeneity:  
\[\sigma_{\text{noise}}=\sigma_{\text{base}}\times\left(1+D_{\text{KL}}\left(p_ {\text{local}}\parallel p_{\text{global}}\right)\right)\]  
This ensures stronger privacy protection for nodes with more unique data distributions while maintaining overall model performance within 5% variance.  
Figure 2: Cross-modal attention dynamics and federated feature convergence. (**a**) Asymmetric attention mechanism heatmap; (**b**) federated manifold alignment with loss surface optimization.  
The 3D manifold projection quantifies inter-client feature discrepancies (Table 1) with Client A's cluster centroid (\(-1.2\), \(-8.5\), \(3.1\)) separated from Client B's (\(2.3\), \(6.7\), \(-4.2\)) by a Mahalanobis distance of D = \(7.8\) (\(p<0.01\)). Post-alignment analysis shows 68% KL divergence reduction (\(12.7\)\(\rightarrow\)\(4.1\)) after 50 federated epochs, with overlapping regions (\(x\)\(\in\)\([-5,5]\)) exhibiting 93.7% feature similarity (cosine score \(>0.85\)). The convex loss surface (curvature \(\kappa\) = 0.023 \(\pm\) 0.004) confirms stable convergence, achieving 99.1% global model accuracy under non-IID data distribution (n = 32 clients). Critical separation zones (x \(>2.0\), y \(<-12\)) correlate with a 0.08% false-positive rate in safety certification tests, meeting SIL-2 certification thresholds.  
This dual-perspective validation framework bridges theoretical attention mechanisms with practical federated learning constraints, providing statistically robust evidence (\(\alpha\) = 0.01) for deploying the system in safety-critical human-machine interfaces under IEC 61508 guidelines.