esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Evaluations  
In this section, we evaluate our proposed method by a simulation program developed in C using the random walk (RWK) mobility model for \(N\) nodes. In our mobility model, nodes are distributed in a uniformly random manner at initial setup and move in one randomly selected direction for a certain fixed time (0.1 s). In addition, we introduced a bouncing rule when nodes encounter the area boundary. The time until the convergence of the potential is considered as the transient phase, after which simulation results start to be monitored. Unlike in [2], evaluations are performed to study the detailed behavior of our proposed method, and hence, parameter values are used that cover a wide range from optimistic to pessimistic scenarios. Basic simulation parameters are listed in Table 1, where default values are shown in bold. Each presented result is obtained as the average of 1000 simulation runs, so we omit showing confidence intervals. If not mentioned otherwise, the default number of nodes is 4000, the mobile speed is 100 m/s, the firework hop limit is set to 5 hops, the firework temperature threshold is 0.5 and the simulation time is 100 s. Note that the high vehicular speed only represents the relative difference between the traveled distance of a node and the transmission range, and we assumed a rather large value of 100 m/s in this paper in order to cause sufficiently large changes in connectivity, although it may appear unrealistic to actually occur in reality. Moreover, from the viewpoint of simplicity, an ideal wireless transmission channel is assumed in our simulation, and therefore, the success or failure of packet transmissions depends on the wireless range and the mutual distance of nodes. The bandwidth of the wireless channel is set at 2 Mbps, representing the practical bandwidth of WiFi.
## 4 Case Study  
In this section, the presented EMAC formulation for complex modes is applied to a case study involving the mode pairing between numerical and experimental modes of a BBN tourist-class passenger railway vehicle. The BBN vehicle (Figure 1) is a 25.9 m and 55-ton car with two motor bogies and capacity for 62 passengers. This vehicle is part of the CPA 4000 series (\"Alfa Pendular\") train which operates in the line connecting the cities of Porto and Lisbon in Portugal.
## 3 Formulation of the Satellite Module Layout Problems  
### Geometry of the Containers and the Components  
The satellite module corresponds to two bearing plates defined by their radius \(R_{out}\) and their thickness \(H_{p}\). They are disposed at the heights \(H_{1}\) and \(H_{2}\) in a cylindrical module of height \(H_{3}\). Thus, four surfaces \(S_{k}\), \(k\in\{1,2,3,4\}\) are laid out. A central column of radius \(R_{in}\) results in circular exclusion zones centered at the \(z\) axis on each surface. The whole module is characterized by its mass \(m_{sat}\) and its geometrical inertia along each axis--\(I_{x,sat}\), \(I_{y,sat}\) and \(I_{z,sat}\)--resulting in a global inertia defined as \(I_{sat}=I_{x,sat}+I_{y,sat}+I_{z,sat}\). The center of gravity of the empty module is positioned at \((x_{sat},y_{sat},z_{sat})\). Figure 2 illustrates the multi-container configuration.  
\(N=60\) components are to be positioned, divided into \(N_{cub}\) cuboids and \(N_{cyl}\) cylinders, as proposed in [20]. They are listed in Table 1 in Appendix A alongside the numerical values of the dimensions and dynamical features of the container.
## 5 Results and Discussion
### Cost-Benefit Analysis and Scalability of Hybrid RF-PSC Energy Harvesting Systems  
The financial viability of energy harvesting technologies is crucial for widespread adoption, particularly in cost-sensitive applications such as large-scale IoT networks. Hybrid RF-PSC systems offer a promising alternative to conventional battery-powered and standalone solar solutions by enhancing energy efficiency and reducing long-term operational costs. The cost of materials, including perovskite solar cells and RF antennas, has been compared against conventional battery-powered and standalone solar solutions. Installation and maintenance costs have been assessed, focusing on deployment scalability, PSC longevity encapsulation methods, and RF antenna recalibration requirements. The analysis also highlights the economic benefits of reducing battery dependency, particularly in large-scale IoT networks. The cost analysis is provided in Table 2.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Technology** & **Power Density** & **Efficiency** & **Scalability** & **Reference** \\ \hline RF-PSC Hybrid & 11 \(\mu\)W–110 \(\mu\)W/cm\({}^{2}\)–Combining RF & \(>\)90\% & Excellent & This paper \\  & and Perovskite & & & & \\ \hline Thermoelectric & 40 \(\mu\)W–10 mW/cm\({}^{3}\)–Industrial & 10–50\% & Good & [62; 63; 64] \\ (TEG) & 1 \(\mu\)W–60 \(\mu\)W/cm\({}^{3}\)—Wearable & & & \\ \hline Piezolectric & 0.33 mW–30 mW/cm\({}^{3}\) & 25–50\% & Good & [65; 66; 67; 62] \\ Electromagnetic & 4 \(\mu\)W–306 \(\mu\)W/cm\({}^{3}\) & 60–70\% & Moderate & [62; 63] \\ Perovskite-PV & 10 \(\mu\)W–100 \(\mu\)W/cm\({}^{2}\) & 20–38\% & Excellent & [45; 55; 62; 68] \\ Solar (PV) Outdoor & 15 mW/cm\({}^{3}\)—Sunny day & 10–25\% & Excellent & [63; 69; 33; 62] \\  & 0.15 mW/cm\({}^{3}\)—Cloudy day & & & \\ \hline RF Energy & 0.08 nW–6.3 \(\mu\)W/cm\({}^{2}\) & 30–88\% & Good & [69; 70; 71] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of energy harvesting technologies.  
In a large-scale IoT deployment of 1000 devices over 10 years, the hybrid RF-PSC system demonstrates a significantly lower total cost, which is a reduction of approximately 50% compared to conventional solar and battery-powered systems. It benefits from reduced material, installation, and maintenance expenses and minimal battery dependency. This leads to substantial cost savings over time, with a shorter return-on-investment period (3-5 years due to reduced maintenance and battery replacement costs) and enhanced economic viability for large-scale IoT networks [73, 81]. An RF-PSC hybrid system offers linear scalability with minimal additional costs, unlike conventional battery-powered systems, which face exponential scaling challenges due to logistical constraints. Thus, the RF-PSC hybrid system provides a financially feasible and operationally efficient alternative. Its scalability and efficiency make it well-suited for sustainable and cost-effective IoT deployments, ensuring long-term reliability with reduced maintenance and energy costs.
# Alginate Modification and Lectin-Conjugation Approach to Synthesize the Mucoadhesive Matrix
## 2 Alginate Structure and Properties  
### Chemical Structure of Alginates  
Alginates are marine biopolymers, originated from brown seaweed (_Phaeophyceae_e), including _Laminaria hyperborea_, _Laminaria digitata_, _Laminaria japonica_, _Ascophyllum nodosum_, and _Macrocystis pyrifera_[2]. Alginate is also found in bacteria. The main difference between algae and bacterial alginate is the presence of O-acetyl groups at C\({}_{2}\) and/or C\({}_{3}\) in the bacterial alginates. They are linear water-soluble polysaccharides of 1-4 linked \(\alpha\)-L-galuronic acid (G) and \(\beta\)-D-mannuronic acid (M), with free active functional hydroxyl and carbonyl groups (Figure 1). The composition, sequence, and molecular weight of alginates depend on the biopolymer source. Their M/G ratio typically defines them; the distribution of M and G units along the chain with a molecular weights ranges between 32,000 and 400,000 g/mol [11].  
The alginate functional properties depend on the composition (M/G ratio), the internal structure, and the molecular weight, while its solubility relies upon the pH and the carbonyl group's counterions existed on the solution [3]. Sodium alginate can slowly dissolve in water and form a viscous solution, but it is insoluble in alcohol. The aqueous solution of sodium alginate has shear-thickening properties and also presents pH responsive characteristics [12].
## 3 Results
Conversely, the group performing stretching with neural load demonstrated improvements in flexibility after first session (MD = \(-\)5.2; 95%CI = 8.4 to \(-\)2.0; \(p\) < 0.001), which were maintained at two months (MD = \(-\)8.6; 95%CI = \(-\)12.5 to \(-\)4.7; \(p\) < 0.001) and even at the one-month follow-up point (MD = \(-\)6.4; 95%CI = \(-\)10.1 to \(-\)2.8; \(p\) < 0.001). No statistically significant differences were found between the initial evaluation and subsequent assessments for PPT [After 1 st Session (MD = \(-\)0.7; 95%CI = \(-\)1.4 to 0.02; \(p\) = 0.061); Post-Treatment (MD = \(-\)0.3; 95%CI = \(-\)0.7 to 0.2; \(p\) = 0.643); Follow-up (MD = \(-\)0.3; 95%CI = \(-\)0.8 to 0.2; \(p\) = 0.481)] and strength [After 1 st Session (MD = \(-\)0.2; 95%CI = \(-\)0.8 to 0.2; \(p\) = 1.000); Post-Treatment (MD = \(-\)13.1; 95%CI = \(-\)27.0 to 0.7; \(p\) = 0.072); Follow-up (MD = \(-\)7.4; 95%CI = \(-\)19.5 to 4.7; \(p\) = 0.605)] (Table 4). Although both the NGL and CG groups showed similar results for flexibility, the PPT of the tibial nerve, and the maximum isometric hamsrring strength over time, a smaller reduction in flexibility was observed at the follow-up assessment one month after the end of treatment (Figure 2a), along with a greater reduction in PPT after the first session (Figure 2b) and greater improvements in maximum isometric hamsrring strength (Figure 2c) in subjects who performed the stretching program with neural load. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variables** & **F** & \(p\) & \(\eta_{p}\)**2** & **Power** \\ \hline KEA & 1.000 & 0.500 & 0.032 & 0.214 \\ PPT & 0.393 & 0.758 & 0.016 & 0.124 \\ Strength & 0.495 & 0.687 & 0.020 & 0.146 \\ \hline \hline \end{tabular} Abbreviations. \(\eta_{p}\)2: partial eta squared; \(p\): \(p\)-value; KEA: Knee Extension Angle; PPT: pressure pain threshold.  
\end{table} 
Table 2: Repeated-measures analysis of variance to analyze treatments differences over time.
## 5 Results and Discussion
### The Peak Strain  
The peak strains of the pipeline at mid-span derived from the FEA were compared with the measured results from the test and theoretical computation, as summarized in Table 6, where the theoretical results were theoretically derived calculated based on the mathematical Equation (21), and the peak strain (\(\varepsilon_{\text{max, EXP}}\)) in the test was calculated based on the peak longitudinal and transverse strains (\(\varepsilon_{\text{L}}\) and \(\varepsilon_{\text{T}}\)). It can be found from Table 6 that the standard deviations among \(\varepsilon_{\text{max,EXP}}\) and \(\varepsilon_{\text{max,THE}}\), and \(\varepsilon_{\text{max,EXP}}\) and \(\varepsilon_{\text{max,FEA}}\) were 6.85% and 5.12%, respectively, according to the calculation of the relative deviation between theory and FEA; compared with some references [49; 50], the standard deviation of theory and FEA was more accurate. The results show that the peak strain from the FEA results basically matched with the experimental and theoretical results, which verifies the rationality of the mathematical formula and the feasibility of the FEA model. However, some differences still existed. The main reason may be explained by there being some differences in the values of the deformation lag factor and the base coefficient in the theoretical formula, as well as there being some differences in the setting of soil parameters in the FEA.
## 3 Scope and Methodology
### Methodology  
In order to develop an available and efficient management system for steel structures, it is necessary to optimize the system's functions and performances. The overall technical route of this study can be divided into three parts as follows: information extraction and storage, redundancy removal of component instances, and dynamic LOD model loading. The first part mainly deals with the original text information of the IFC format on the server side. The last two parts perform off-line data processing on the server side, as well as optimization and lightweight display on the browser side. The technical route is shown in Figure 1. The left part of this figure shows the BIM model containing IFC instances, which is the data source of the system, and the right part is the main work of this study. After being extracted from the IFC files, the BIM data falls into two parts as follows: semantic data, which can be stored in a database, and geometric data, which is used for optimized visualization.  
Information extraction and storage: Current BIM applications mainly focus on the complete extraction and lossless storage of building information from IFC files. The storage structure is relatively complex, and it is not suitable for network transmission because of the coupled IFC Tree. In this study, the geometric and semantic information of components based on IFC files were extracted, decoupled, and stored in the database. In the next step, the spatial relationship and geometric information of components were stored in the form of files and cached on the client side, reducing the overhead of database storage and network transmission.  
Removal of redundant instances: Since steel structures are highly industrialized, there are many redundant instances in the BIM model. The existing research [57] pointed out that the removal of redundant instances can significantly improve display efficiency and network transmission efficiency. To achieve efficient rendering for standardized components, this study presented an alignment and visualization technology for redundant instances.  
Dynamic LOD model loading: In most of the existing studies, BIM models are loaded dynamically according to the IFC instance type by spatial index. However, in the steel construction model, since there is no wall or slab to block the view, the spatial index cannot decide which components to display based on the viewpoint location. Therefore, in this study, LOD models of the whole building were established according to the specific sizes of the components rather than the spatial index, and the display parameters were determined by the camera distances and the sizes of the instances. Based on this mechanism, the dynamic LOD model loading is effectively supported, and the display efficiency is greatly improved.
## 3 Methodology
High-confidence tracks are stored in a dedicated database for ReID, ensuring robust tracking continuity across diverse scenarios. Figure 3: The proposed MOT flow diagram depicting the integration of object detection, feature extraction, visual language modeling, track management, confident track storage, and re-id modules to achieve robust and accurate tracking of humans in real-world scenarios. The flow diagram addresses key challenges such as IDSws, occlusions, and the need for robust feature utilization, resulting in a more reliable and scalable MOT solution. ```
1:Video frames \(F=\{f_{1},f_{2},\ldots,f_{T}\}\)
2:Object tracks \(T=\{t_{1},t_{2},\ldots,t_{N}\}\)
3:Initialize: \(T_{0}\leftarrow\emptyset\)\(\triangleright\) Initial tracks
4:CTS \(\leftarrow\emptyset\)\(\triangleright\) Confident Track Storage
5:for each frame \(f_{t}\in F\)do
6:Object Detection:
7:Apply YOLOv8 detector to \(f_{t}\). 8: Extract bounding boxes \(\text{bbxes}_{t}=\{b_{1},b_{2},\ldots,b_{M}\}\). 9:for each bounding box \(b_{i}\)do
10: Extract keypoints and descriptors using XFeat. 11: Compute overlap ratios and confidence scores. 12: Construct embeddings \(e_{i}=\{b_{i},k_{i},d_{i},o_{i},c_{i}\}\). 13:endfor
14:Track Prediction:
15: Predict positions of existing tracks \(T_{t-1}\) using a Kalman filter. 16: Generate predicted states \(P=\{p_{1},p_{2},\ldots,p_{N}\}\). 17:Association:
18: Compute association scores between \(\text{bbxes}_{t}\) and \(P\) using:
19: (a) IoU for spatial alignment. 20: (b) Appearance similarity from VLM embeddings. 21: Solve the association problem using the Hungarian algorithm. 22:Track Update:
23: Update \(T_{t}\) based on associations:
24: - Matched bounding boxes are assigned to tracks. 25: - Unmatched bounding boxes initialize new tracks. 26: - Unmatched tracks are updated with predicted states. 27:Zero-Shot ReID:
28: Query \(CTS\) for unmatched tracks:
29: - Compare embeddings of unmatched tracks with \(CTS\). 30: - Re-identify tracks based on similarity scores. 31: - Update track identities if a match is found. 32: **Confident Track Storage Update:**
33: Add high-confidence tracks \(t\) to \(CTS\):
34: - Tracks with stable identities over multiple frames are stored. 35:endfor
36:Post-Processing:
37: Remove fragmented or low-confidence tracks. 38: Smooth track trajectories for visualization. 39:Output Results:
40: Save tracks \(T\) in MOT-compliant format for evaluation. ```  
**Algorithm 1** The algorithm for the complete ReTrackVLM framework.
# Random Walker Coverage Analysis for Information Dissemination in Wireless Sensor Networks
###### Abstract  
The increasing technological progress in electronics provides network nodes with new and enhanced capabilities that allow the revisit of the traditional information dissemination (and collection) problem. The probabilistic nature of information dissemination using random walkers is exploited here to deal with challenges imposed by unconventional modern environments. In such systems, node operation is not deterministic (e.g., does not depend only on network nodes' battery), but it rather depends on the particulars of the ambient environment (e.g., in the case of energy harvesting: sunshine, wind). The mechanism of information dissemination using one random walker is studied and analyzed in this paper under a different and novel perspective. In particular, it takes into account the stochastic nature of random walks, enabling further understanding of network coverage. A novel and original analysis is presented, which reveals the evolution network coverage by a random walker with respect to time. The derived analytical results reveal certain additional interesting aspects regarding network coverage, thus shedding more light on the random walker mechanism. Further analytical results, regarding the walker's spatial movement and its associated neighborhood, are also confirmed through experimentation. Finally, simulation results considering random geometric graph topologies, which are suitable for modeling mobile environments, support and confirm the analytical findings.  
information dissemination; random walkers; coverage; energy harvesting +
Footnote †: journal: _technologies_
# A Thermodynamic Model for Cryogenic Liquid Hydrogen Fuel Tanks
###### Abstract  
Hydrogen is used as a fuel in various fields, such as aviation, space, and automobiles, due to its high specific energy. Hydrogen can be stored as a compressed gas at high pressure and as a liquid at cryogenic temperatures. In order to keep liquid hydrogen at a cryogenic temperature, the tanks for storing liquid hydrogen are required to have insulation to prevent heat leakage. When liquid hydrogen is vaporized by heat inflow, a large pressure is generated inside the tank. Therefore, a technology capable of predicting the tank pressure is required for cryogenic liquid hydrogen tanks. In this study, a thermodynamic model was developed to predict the maximum internal pressure and pressure behavior of cryogenic liquid hydrogen fuel tanks. The developed model considers the heat inflow of the tank due to heat transfer, the phase change from liquid to gas hydrogen, and the fuel consumption rate. To verify the accuracy of the proposed model, it was compared with the analyses and experimental results in the referenced literature, and the model presented good results. A cryogenic liquid hydrogen fuel tank was simulated using the proposed model, and it was confirmed that the storage time, along with conditions such as the fuel filling ratio of liquid hydrogen and the fuel consumption rate, should be considered when designing the fuel tanks. Finally, it was confirmed that the proposed thermodynamic model can be used to sufficiently predict the internal pressure and the pressure behavior of cryogenic liquid hydrogen fuel tanks.  
hydrogen energy; liquid hydrogen; cryogenic tanks; thermodynamic model +
Footnote †: journal: Applied  
doi: 10.3390/app14093786
# Numerical Simulation of Seakeeping Performance on the Preliminary Design of a Semi-Planing Craft
## 2 Mathematical Model
### Basic Assumptions and Boundary Conditions  
This study assumed that the vessel sailed at a constant velocity U in deep water with regular waves and that the fluids in the surrounding flow field were ideal non-viscous, incompressible, and non-rotating. Surface tension was not considered, and the hull was assumed to have reached a steady state after experiencing a small-amplitude incident wave. If the incident wave was a, incident  
Figure 1: Schematic of the hull coordinate systems.  
wave angle was \(\mu\), and incident wave frequency was \(\omega\_0\), then the incident wave height h was expressed as follows:  
\[h=a\epsilon^{i(v_{0}xcos\mu+v_{0}ysin\mu-\omega_{0}t)}. \tag{1}\]  
where encounter frequency \(\omega_{e}\) can be expressed as follows:  
\[\omega_{e}=\left|\omega_{0}-Uv_{0}cos\mu\right|. \tag{2}\]  
The incident velocity potential \(\Phi_{I}\) and number of wave cycles \(v_{0}\) are expressed as follows:  
\[\Phi_{I}e^{-i\omega_{0}t}=\frac{-iga}{\omega_{0}}e^{v_{0}z}e^{i(v_{0}xcos\mu+ v_{0}sin\mu)e^{-i\omega_{0}t}} \tag{3}\]  
\[v_{0}=\frac{{\omega_{0}}^{2}}{g}. \tag{4}\]  
where \(a\) is the amplitude of the incident wave and \(g\) is gravitational acceleration.  
According to the [PERSON] linear theory [5], if the incident wave amplitude and motion response created by such a wave are weak, then the unsteady-state synthesized velocity potential \(\mathcal{H}(x\_y,z)\) can be expressed as follows:  
\[\begin{array}{ll}\Phi(x,y,z)&=\Phi_{I}(x,y,z)+\Phi_{D}(x,y,z)+\Phi_{R}(x,y,z )\\ &=\Phi_{I}(x,y,z)+\Phi_{D}(x,y,z)+\sum_{i=1}^{6}\zeta_{i}\beta_{j}(x,y,z).\end{array} \tag{5}\]  
where \(\Phi_{I}\) is the incident velocity potential, \(\Phi_{D}\) is the diffraction velocity potential, \(\Phi_{R}\) is the radiation potential created by the motion of various degrees of freedom, \(\zeta_{i}\) is the motion displacement of each degree of freedom, and i = 1, 2, 3, 4, 5, and 6 are the surging, swaying, heaving, rolling, pitching, and yawing motion, respectively. Surging, swaying, and heaving motion are the translation amount along the \(x,y,\text{ and }z\) axes, respectively, whereas rolling, pitching, and yawing motion are the rotation amount along the \(x,y,\text{ and }z\) axes, respectively.  
The synthesized velocity potential \(\Phi\) must meet the boundary conditions including the Laplace's equation, linear free surface conditions, radiation conditions, deep water bottom conditions, and kinematical boundary conditions on the hull surface. Therefore, when a hull is in harmonic motion and moves at a constant speed \(U\) along still water, its added mass and damping coefficient can be expressed as follows:  
\[M^{\prime\prime}_{ij}=\frac{\rho}{\omega}\int_{L}dx\int_{c(x)}\text{Im}\left[ \left(i\omega+U\frac{\partial}{\partial x}\right)\phi^{(i)}_{R}(x,y,z,t) \right]u^{(i)}ds, \tag{6}\]  
\[N^{\prime\prime}_{ij}=\rho\int_{L}dx\int_{c(x)}\text{Re}\left[\left(i\omega+U \frac{\partial}{\partial x}\right)\phi^{(i)}_{R}(x,y,z,t)\right]. \tag{7}\]  
where \(i,j\) = 1, 2,..., 6.