esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Moreover, the suggested dynamic adjustment of transmission range in [8] also introduces additional hardware that adds extra cost to the deployment. As opposed to the study in [8], we consider a common setup of UWSNs where the sensor nodes equip with only basic functions and thus the minimum connectivity that requires distance information cannot be obtained. In this case, the transmission range of sensor nodes is predetermined, and a tradeoff between energy efficiency and network connectivity arises. Shorter transmission ranges may offer higher energy efficiency in packet transmissions but risk losing network connectivity. Conversely, longer transmission ranges may maintain network connectivity but reduce energy efficiency in packet transmissions. In this paper, we seek analytical modeling to investigate this tradeoff for an UWSN where sensor nodes are randomly deployed. We first present the relationships among the transmission range, the average energy consumption and the connectivity. We show that to achieve operation at the optimal transmission range with a targeted network connectivity, a certain network density is required. We further illustrate that the use of multiple sink setup can significantly reduce the need for high network density while maintaining the targeted network connectivity. We would like to point out that there have been a number of studies focusing on the optimal transmission range in the literature for both terrestrial networks and underwater networks. Most of the studies primarily focus either on how to enhance throughput by adjusting transmission range [8] (or interchangeably transmission power [9]), or on the optimal deployment patterns [4] (including the optimal ratio of the transmission range to the sensing range [10], and the minimum number of sensors [7]) by which full coverage and full connectivity over a given region can be realized. They usually assume sensors can be manually placed anywhere, which is not the case, particularly in underwater environments. To the best of our knowledge, jointly considering the energy efficiency and the network connectivity for the selection of the transmission range in such a random network scenario has not been studied before. The major contribution of this paper lies in an analytical framework to model the relationships between the transmission range, the average energy consumption and the connectivity. Such an analytical framework is important in the sense that it provides a means for network designer to appropriately design the deployment of an UWSN for joint energy-efficiency and network-connectivity considerations. The remainder of this paper is organized as follows. In Section 2, we describe our considered UWSN model, followed by the derivations of the energy efficiency and the network connectivity in Section 3. In Section 4, we present numerical and simulation results and illustrate the optimal transmission range and the tradeoff between energy efficiency and network connectivity. Finally, we conclude this paper in Section 5.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 3 Methodology
### Step 2: Develop and Down-Select Nanogrid Architectures
#### 3.2.1 Step 2.1: Evaluate Potential Nanogrid Architectures  
As discussed above, there are a variety of nanogrid architectures available. In this step, evaluation of potential nanogrid architectures to serve critical loads at a specific military base is performed. We suggest using a Pugh matrix approach although a variety of other Analysis of Alternatives (AoA) methods are available to the practitioner. The Pugh matrix criteria map to the requirements developed in Step 1.1. The Pugh matrix is a type of tool that uses a scoring system of plus and minus to determine if the base energy manager's intuition of a baseline grid structure is the best decision. An example Pugh matrix is provided in Section 4.2.
## 1 Introduction  
### Background and Motivation  
With the development of the Internet of Things (IoTs) and massive machine type communications (mMTCs), more and more wireless devices require connections [1]. The surge in the number of wireless devices has led to a shortage of wireless spectrum resources. In recent years, people have found that reusing the functions of wireless communication networks has many advantages, such as improving spectrum efficiency and reducing hardware equipment costs [2]. Traditional radar positioning networks typically include two types of nodes: anchors with known positions and radars with unknown positions. The positions of radars are determined through measurements from other active radars and anchors, including time of arrival (TOA) [3, 4], time difference of arrival (TDOA) [5, 6], angle of arrival (AOA) [7, 8], received signal strength (RSS) [9, 10], and so on. In addition to active positioning, this paper also conducts research on passive sensing, where positions of the targets can be determined through time sum of arrival (TSOA) [11, 12]. The realization of wireless localization and sensing relies on accurately extracting relevant parameters such as signal amplitude, propagation delay, and Doppler shift from the received signals, and both can be achieved with high accuracy through UWB waveforms [13]. The similarity between wireless localization and target sensing provides a prerequisite for the integration of localization and sensing networks.  
Due to the fact that both wireless localization and target sensing are parameter estimation issues, before designing a parameter estimation algorithm for wireless localization or target sensing, we need to first obtain the theoretical estimation error lower bound to benchmark the accuracy of the designed algorithm. The authors in [14] show that inter-node measurements and intra-node measurements can bring spatial and temporal cooperation gains to the accuracy of localization systems, respectively. In addition, due to the fact that practical networks are often resource-constrained, in order to improve the accuracy of localization and sensing as much as possible, it is necessary to optimize the allocation of resources in the networks.
## 4 The Virtual Astronaut for Santa Maria
### Merging Images from Multi-Instruments  
Navcam image mosaics acquired by Opportunity provided nearly full coverage of the Santa Maria study area. Navcam images also have 3 times less resolution than Pancam data. The Pancam coverage  
Figure 5: Pancam false color mosaic shows the interior of Santa Maria Crater. The floor of the crater is covered by sand ripples.  
was focused on the crater interior and the locations around the two rock targets, Luis De Torres and Ruiz Garcia. In order to generate a mosaic of the entire study area with the best resolution and color where available, the higher-resolution Pancam color image mosaics were resampled to match the resolution of Navcam mosaics. In addition, the tiny MI focal section merged images of the two rock targets were registered to Pancam images covering the rocks to provide context of the MI coverage relative to the surrounding parts of the rocks. HiRISE orthoimage had a complete coverage of the crater and was selected as the base control. The rover-based data were georeferenced to the HiRISE orthoimage by a number of registrations. The images were taken under different lighting conditions, so the contrast was adjusted slightly among images to make the color look consistent across image seams. Figure 6 shows the result of all the image mosaics registered in the same coordinate frame.  
This mosaic combined with the integrated DEM discussed in section 4.2 was used for visualization in the 3D virtual environment. Figure 6b displays a MI focal section merge image taken at target Luis De Torres merged with a Pancam color mosaic. The circle in the MI image is a 5 cm \(\times\) 5 cm hole ground into the rock by the Rock Abrasion Tool (RAT) mounted on the rover's robotic arm. Two views of the Santa Maria Crater area derived from Mars in Google Earth are shown in Figure 7 for comparison with the detail gained by combining orbital and ground-based images (Figure 6).  
Figure 6: Image mosaics generated at Santa Maria Crater for the Virtual Astronaut. (**a**) A mosaic generated using HiRISE orthoimage, Pancam mosaics and Navcam mosaics provides the textural information for the VA; (**b**) A close up view of Luis De Torres shows a MI focal section merge image overlaid on a false color Pancam image. The area shown corresponds to the red box shown in (**a**).
## 4 Discussion
### Mangrove LAI and GPP  
The measurement of leaf area and biomass of mangrove at different scales (local, regional, and global) is very scarce. Traditional destructive methods require a high demand of manpower, cost, and time, and the allometric equations generated for biomass calculation are usually site specific [75]. The majority of the scientific data collection based on flux tower has only built on inland forests, and this kind of data is less available for wetland forests [76]. Therefore, we propose the initiative to relate MODIS LAI and GPP to show their capability of detecting leaf area depletion and GPP rate changes of a selected area with active mangrove loss and fragmentation.  
The LAI and GPP values extracted from MODIS products showed reliable values when compared to previous studies. [PERSON] et al. [77] reported the LAI of the main mangrove species (_Rhizophora apiculata_) in Malaysia to be in the range of 3.10-5.10, which is within the range reported in IM (mean LAI from All PW pixels, 3.18-3.46, respectively in 2000 and 2019). In another study, [PERSON] et al. [78] showed MODIS GPP to have a good match with the modelled GPP of a mangrove area in India, which can be useful for carbon assimilation estimation. The mean GPP of mangroves in the IM region is 5.49 g C m\({}^{-2}\) (based on 2019 data), and this value is close to the GPP value of the tropical forests in Asia and the Americas, which are in the range of 8.22 and 10.96 g C m\({}^{-2}\). Nevertheless, it should be noted that the sparse resolution of MODIS products (500 m) could have a mixing pixel issue, where the contribution of oil palm plantation can affect the accuracy of the LAI and GPP. Thus, data with higher spatial resolution can improve future analysis.  
Mangrove forests serve multiple functions that are significant in terms of ecology and economy, and these functions will be lost or reduced due to mangrove fragmentation. The unique forms and rich biodiversity of mangrove forests can provide tourism and recreational activities, including wildlife-watching and fishing [79]. Mangrove forests can be transformed as habitats and nursery grounds for a variety of flora and fauna [57,80], while mangrove trees are also functional as a source of food, fuel, and building materials [81]. Mangrove forests are also effective at protecting shorelines, thus preventing soil erosion, shoreline depletion, and floods [82]. Recent studies have revealed the potential of mangrove ecosystems in sequestrating CO\({}_{2}\) from the atmosphere, thereby reducing the impact of global warming and climate change [21,83]. The complex structure of mangroves with trunk, branches, leaves, various types of roots, and soil enable the ecosystem to store a high volume of carbon (450 Mg ha\({}^{-1}\)) [83]. Mangrove forests have an equal global averaged biomass (247 t ha\({}^{-1}\)) to humid evergreen tropical forests [57,84]. Significant deforestation and degradation of mangrove forests also leads to the loss of a relatively large carbon pool [9,85].
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 3 Method
### Dynamic Motion Model  
The proposed Dynamic Motion Model (DMM) advances the traditional Kalman filter framework [25] by providing robust predictions for non-linear trajectories and abrupt scene transitions. The module's core innovation resides in its expanded state space representation and adaptive parameter recalibration mechanism, collectively enabling a sophisticated prediction system that dynamically adapts to complex environmental variations.
# Simulation and Design of Three 5G Antennas
## 4 Metal Frame Mobile Terminal Dual-Element \"Loop-Groove\" MIMO Antenna Design
### The Dual-Element \"Loop-Groove\" MIMO Antenna Design Process
#### 4.2.2 Design and Simulation of the Slot Antenna  
In this section, a slot antenna operating at 3.5 GHz is designed. Similar to the previous section's loop antenna, a rectangular slot is created on the ground plane, and an L-shaped  
Figure 28: Schematic diagram of the loop antenna structure.  
Figure 29: Comparison chart of loop antenna S parameters with varying gap widths in the metal frame.  
feeding line consisting of a 50 \(\Omega\) microstrip line and a feeding branch is designed to achieve impedance matching for the slot antenna. Figure 30 illustrates the schematic diagram of the slot antenna structure.  
In this design, a slot antenna with closed ends at the gap is selected, exciting the 0.5\(\lambda\) resonant mode. Therefore, the slot antenna's gap length is chosen to be half a wavelength, L = 0.5\(\lambda\), which is approximately 41.6 mm, and the width is set to W = 1 mm = 0.012\(\lambda_{3.5\text{GHz}}\). It is evident that the dimensions of the designed slot antenna, both in terms of width and length, are relatively close to those of the loop antenna, laying the foundation for subsequent design.  
Next, we shall discuss the impact of varying the width, W1, of the feeding branch on the performance of the slot antenna. The comparative changes in the slot antenna's S parameters when the width of the feeding branch varies from 0.3 mm to 0.5 mm are illustrated in Figure 31.  
The feed branch appears in Figure 30, where S is isolation of the loop antenna. From Figure 31, it is evident that, as the feed branch width (W1) increases, the S parameter curves of the antenna shift to the right, indicating improved impedance matching and a wider bandwidth for the slotted antenna. Additionally, when the feed branch width is W1 = 0.5 mm, the resonant center frequency of the slotted antenna is around 3.5 GHz, signifying the optimal performance of the designed antenna.  
Figure 31: The impact of variations in feed branch width (W1) on the S parameters of slotted antennas.  
Figure 30: Schematic diagram of the slot antenna structure. (**a**) Slot antenna front view and (**b**) slot antenna back view.
## 4 Discussion
### The Influence of Loading Rate on the Mechanical Properties of Coal Fracture  
The fracture toughness of coal gradually increases with the loading rate, exhibiting significant ductile failure characteristics. [PERSON] et al. [53] reported that as the loading rate increases, the scale of coal debris ejected from the samples becomes larger, the ejection distance increases, the integrity of the coal deteriorates, and the intensity of energy release increases. Additionally, [PERSON] et al. [54] discovered that higher loading rates cause coal failure to occur earlier. At the critical loading rate, coal failure transitions from a brittle to a ductile state. In contrast, [PERSON] et al. [55] found that as the loading rate increases, the ultimate stress and strain of coal samples first decrease before increasing. Although the loading rate significantly impacts the mechanical strength, elastic modulus, fracture properties, and failure characteristics of coal, the variation patterns of these mechanical performance parameters are not entirely consistent, which is influenced by the water saturation state of the coal.  
The interaction between pore water pressure and crack propagation rate leads to complex mechanical and damage characteristics in saturated coal samples subjected to varying loading rates. Although the coal used in this experiment contains natural micropores and fractures, its overall structure remains weak. Micro-defects, such as micropores and microcracks within the coal, require sufficient time to expand, evolve, and penetrate. At low loading rates, internal cracks and pores in the coal are gradually activated and evolve under applied force, resulting in increased damage and enhanced connectivity among pores and cracks. Particularly, as the degree of damage increases at the tip of a crack, the crack becomes more susceptible to instability and expansion, ultimately facilitating the penetration of rock microcracks and reducing resistance to fracture. Conversely, at high loading rates, primary pores and cracks within the coal do not have adequate time to respond, resulting in a failure to accumulate damage effectively. This condition necessitates that the coal skeleton quickly and directly withstand the applied load, thereby increasing the destructive load required and resulting in relatively higher absorbed energy [56]. The load at failure is positively correlated with fracture toughness, which increases with the loading rate.
# Physarum-Inspired Bicycle Lane Network Design in a Congested Megacity
## 3 Physarum-Inspired Bicycle Lane Network Design
### Problem Statement: BLN Design Considering Feasible Routes  
Roods in the congested areas of any megacity are categorized as either wider main roads or narrower link roads. Main roads connect zones and serve as the backbone for inter-city communication, with high importance placed on maintaining their flows. Link roads mainly interconnect different residential blocks within a zone and allow mainly small vehicles to pass. Both main and link roads have different issues to consider in BLN design. Main roads usually carry massive traffic, with oversaturated flows in the peak hours, and therefore allocating space for a bicycle lane on such roads is impossible. The footpaths alongside these roads are also unsuitable for bicycles, for several reasons, e.g., it is either narrow or mostly occupied by pedestrians and/or street hawkers. On the other hand, some link roads are very narrow and often end up disconnected from other routes, i.e., exhibit lack of connectivity; such roads are not suitable for inclusion in a BLN. However, some link roads provide easy connectivity between the residential blocks and the main roads.  
A suitable network optimization problem needs to be formulated to design a BLN with the constraints mentioned above. Most importantly, the desired BLN might not always be the shortest path connecting individual zones, but be considered suitable for bicycle use. Furthermore, the BLN should bypass main roads as much as possible to avoid disrupting major traffic in congested megacities.
## 3 Experimental Setup  
### Finite Element Model of Prototype Transmission Tower  
The full-scale transmission tower, i.e., the prototype structure, has a height of 43.6 m, and \(7.2\times 7.2\) m base. It is recognized that the lower the fundamental frequency is, the more significant the resonant dynamic response of a lattice tower will be [2], and the control effectiveness of ECTMD can be expected to be better. Consequently, with a relatively high fundamental frequency of 1.88 Hz, the selected prototype structure is appropriate to examine the lower bound of the ECTMD's effectiveness.  
A finite element model of the prototype structure is established by using ANSYS software. The first six natural frequencies are listed in Table 2, with corresponding mode shapes shown in Figure 7.
## 3 Simulation and Results
### Area-B  
To demonstrate that the proposed approach is comprehensive and can be generalized, another simulation is performed on area-B (Manhattan). The area of this environment is 1 km\({}^{2}\), and the system is developed such that it has 42 cells located in 14 different locations and 148 SNs. The ASE is found to be 3.5 bps/Hz, with 66 SNs receiving NNoS connections, 78 are LoS, and 4 SNs are not connected. Figure 8 shows the relationship between the ASE and UAV altitude for this simulation area. Compared with area-A, a higher altitude is required to achieve the maximum ASE. This is due to the fact that area-B has higher buildings than area-A. It can be observed that increasing the UAV altitude to 145 m results in a maximum ASE of 6.776 bps/Hz, despite the fact that about 90% of the SNs in this case will be served by the UAVs. In addition, it is found that three of the remaining terrestrial APs do not serve any SN and can be relocated to serve another area.  
Figure 9 shows the number of SNs reporting each CQI index, and Figure 10 shows the distribution of SNs based on the DL throughput.  
Table 10 summarizes the results and shows the improvement achieved when UAVs are used in conjunction with terrestrial APs.  
Figure 8: Area spectral efficiency versus UAV altitude, Area-B.  
Figure 9: SNs distribution (ascending) based on CQI index, Area-B.
## 4 Parametric Study
### Variation of Core Parameters on the Blast Performance of the RCS  
It is crucial to examine the effect of the core parameters on the blast behavior of the sandwich structure in order to maximize the protection level of the structure. The geometric parameters covered in  
Figure 11: Energy dissipation for Ribbon-Core Sandwich Panel (RCSP) components with variation of (**a**) front-plate thickness. (**b**) Back-plate thickness.  
Figure 10: Central deformation with the face sheets thicknesses variation.  
this study were core thickness \(t_{c}\), the angle of corrugation \(\Upcap\) (\({}^{\circ}\)), and core height \(h_{c}\). The effect of the core thickness run through varying the thickness of the corrugated ribbon core: 0.3, 0.5, 0.7, 0.9 and 1.1 mm. Figure 12a illustrates the influence of the core thickness on both facing sheet deflections and energy dissipated by the structure component due to the blast loading. It can be noticed that the front-layer deflection decreases upon increasing the core thickness; however, the back-layer deflection slightly increased. Moreover, the energy dissipated by the RCS decreased, which means that a softer core dissipates more energy. The angle of corrugation controls the behavior of the structure by increasing the corrugation angle from 40\({}^{\circ}\) to 60\({}^{\circ}\), and the facing sheets deformations decreased by about 30% and the dissipated energy decreased by an average of 25%, as indicated in Figure 12b. The impact core height variation was also examined on the blast behavior of the TZRC sandwich structure, as shown in Figure 12c. The core height varied between 10, 12, 14, 16, and 18 mm. The results show that the core height had a significant impact on the blast performance of the structure. The deflections of the facing sheets of the sandwich structure increased by an average of 27% by increasing the core height by 40%.
# Machine Learning Applications in Seismology
## 2 Summary of the Published Articles
By incorporating an adaptive attention mechanism into the CNN architecture, the proposed method effectively suppresses noise and restores valid waveform features. The results demonstrate improved signal clarity and continuity, highlighting the method's applicability in passive seismic exploration, particularly in scenarios characterized by uneven source distributions and limited active sources. [PERSON] et al. (Contribution 15) investigate the application of machine learning for detecting earthquake precursors through the analysis of seismic multi-parameter data across twelve tectonic regions in western China. This study employs a sliding extreme value relevancy method to analyze various seismic parameters, including the b value, earthquake frequency, and intensity factors. Their findings indicate that significant anomalies frequently precede target earthquakes, with high anomaly rates correlating with earthquake occurrences. The results emphasize the effectiveness of a comprehensive multi-parameter approach in enhancing earthquake prediction accuracy and spatial risk assessment, providing valuable insights for future evaluations of seismic hazards in the investigated regions.