esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Finally, another work worth mentioning is proposed by [PERSON] et al. [29], which examines eight wildfires affecting New South Wales (eastern Australia). It runs several tests using the Random Forest approach, trained on several sets of spectral indices (dNBR, RdNBR, dFCB, dNDVI, dBSI, etc. ), highlighting the contribution of each one to the final prediction of the CBI index value. Also, in this case, the features are computed from pre- and post-wildfire acquisitions and the proposed approach has been validated on a single event. Overall, the state-of-art techniques leverage on dNBR and other indices derived from spectral bands acquired from satellites (i.e., RdNBR, NBR, and dNDVI), either computing thresholds or applying state of the art machine learning approaches. Generally, they are based on the comparison of pre- and post-wildfire satellite imagery, which is not always possible, and on region-dependent empirical thresholds (i.e., on non-generalized approaches). To address the automation of the severity damage estimation problem, we propose a generalized and an automatic deep learning-based system that, given a set of training grading maps related to past wildfires, provides a severity map for a new area by analyzing Sentinel-2 satellite data acquired after a new wildfire. The automatically returned grading map uses the same severity scale used by the EMS experts. Therefore, our system can be used to automatize the EMS grading map generation after a wildfire. The main contributions of this paper are as follows. * An automatic system, called Double-Step U-Net, for the estimation of wildfire damage severity by means of deep-learning models inferred from Sentinel-2 satellite images. * An automatic solution based only on post-wildfire imagery. * A land-type-independent approach that can be applied to European regions. The paper is organized as follows. Section 2 introduces (i) the data sources used to fetch data and annotations (Section 2.1); (ii) data acquisition, processing, and analysis (Section 2.2); (iii) the problem statement (Section 2.3), (iv) the proposed methodology (Section 2.4); (v) the framework and tools adopted to accomplish this paper (Section 2.5); and (vi) the experimental setup, the hyperparameter adopted for the used machine learning algorithms, and the evaluation metrics used to compare the performances (Section 2.6). Section 3 reports and analyses the results, discussing the pros and cons of the proposed method with respect to current approaches. Finally, Section 4 draws conclusions and describe future work.
# Optimization of Effective Throughput in NOMA-Based Cognitive UAV Short-Packet Communication
## 2 System Model and Problem Formulation  
As shown in Figure 1a, we consider a NOMA-based cognitive UAV short-packet communication system consisting of a GBS, a UAV, and several secondary users, where the UAV is equipped with onboard sensors and transmits measurement information (e.g., meteorological, imagery, and electromagnetic information) to the secondary users. We assume that the UAV flies in a circular path around the GBS, where. its radius is \(R\) and the altitude is \(H\). The GBS has priority to access the licensed spectrum with bandwidth \(B\). Spectrum sensing is required to determine whether the GBS is idle or busy before UAV communication. When the GBS is idle, the UAV is allowed to access the licensed spectrum and provide communication to the secondary users around the flight path by utilizing NOMA. The secondary users are divided into multiple groups and the UAV provides communication services to one of the secondary user groups in one frame. In this paper, we focus on the effect of the system parameters on the effective throughput under the proposed model. Considering the high complexity of multi-user SIC in NOMA, we assume that each group has two secondary users. The horizontal distance from the UAV to the secondary user \(u_{i}\) is \(r_{i}\), \(i\in\{1,2\}\). In addition, since a GBS frame duration is usually very short, the distance between the UAV and \(u_{1}\),\(u_{2}\) can be seen as unchanged for the duration of one frame.  
Figure 1: (**a**) Illustration of a NOMA-based cognitive UAV short-packet communication system. (**b**) GBS and UAV frame structures.  
The channel power gain from the UAV to the secondary user \(u_{i}\) is denoted as \(h_{i}\). When the UAV is located above a certain altitude, the free-space channel model can be adopted [33]. Hence, \(h_{i}\) can be represented as \(h_{i}=\beta_{0}(H^{2}+r_{i}^{2})^{-1}\), where \(\beta_{0}\) is the channel power gain at a distance of one meter. We assume that \(r_{1}<r_{2}\) and the distance between \(u_{1}\) and the UAV is smaller than the distance between \(u_{2}\) and the UAV. Then, it is obtained that \(h_{1}>h_{2}\). Next, we provide the sensing model, the transmission model, and the optimization problem formulation.
## 4 Example Analysis
### Analysis of Results  
The multi-objective optimization mathematical model was solved by the ISSA, and gray correlation analysis was used for the scheme preference. Finally, the optimal construction scheme of the steel pipe pile cofferdam was determined to be A2. A comparison of the durations, costs, and quality levels of the A2 scheme and the pre-optimization scheme is shown in Table 5.  
As seen in Table 5, the construction schedule was able to be advanced by 18 days, reducing the total duration to 69 days by optimizing the critical work. This reduction in duration has led to a decrease in overhead costs and a total cost savings of CNY 220,000. Despite the shortened duration, the quality has been maintained at a high level of 0.90 or above. By optimizing the project duration while ensuring a certain level of quality, the construction cost has been reduced, leading to a scientifically and reasonably determined construction scheme for the steel pipe pile cofferdam based on the balanced optimization of duration, cost, and quality.
## 1 Introduction
### Study Objectives and Outline  
The aim of this work is to investigate the magnitude and spectral location of optical water-leaving signals resulting from phytoplankton assemblage changes; to determine how these signals respond to changes in biomass and functional type; to evaluate their optical ambiguity in the context of the optical effects of other in-water constituents; and to assess their robustness against measurement uncertainties in satellite radiometry. This work does not present a PFT detection method, but instead aims to identify the reasonable limits of PFT detection from satellite, inferred from appropriate illuminative case studies and sensitivity analyses demonstrating the source and magnitude of PFT signals in terms of both cell size (assemblage \(D_{eff}\)) and accessory pigments.  
To give context to the discussion on the case studies, an analysis is first made of the contribution of the phytoplankton-driven signal to the bulk \(R_{rs}\), and how this relates to the proportional contribution of phytoplankton to the IOP budget. A Southern Ocean based case study then demonstrates the optical impact on the \(R_{rs}\) of transitioning assemblages in terms of both biomass and \(D_{eff}\) changes. This discussion is developed further with a Benguela-like example more representative of productive upwelling systems investigating the relative magnitude of pigment-driven PFT changes. A sensitivity analysis then shows the spectral position and magnitude of the accessible phytoplankton optical signal in \(R_{rs}\) as biomass and \(D_{eff}\) vary. The source of these signals is traced back to phytoplankton backscatter and its relationship with biomass, and ambiguity associated with non algal variability is evaluated.  
It is clear that the case studies reflect simplified representative examples of much wider pigment- and size-related variability in nature, but the described dependence of absorption-driven pigment signals versus scattering-driven cell size signals on biomass holds across assemblage types. Optical PFT effects are most easily identified in relatively high biomass environments (Chl \(a>1\) mg\(\cdot\)m\({}^{3}\)) [36; 37; 38], and where the IOP budget is dominated by phytoplankton [37; 39], and so the case studies deal with these water types. However, as the sensitivity analysis shows, together with the contextual discussion around ambiguity and uncertainty in satellite \(R_{rs}\), the conclusions of this study have implications for the identification of PFT changes from satellite \(R_{rs}\) across all water types.
# Incorporating Spatial Autocorrelation into GPP Estimation Using Eigenvector Spatial Filtering
## 3 Methodology  
The steps of this study are as follows: (1) assessing the Pearson correlation coefficients and feature importance of the initial variables and screening out the variables closely related to GPP as the model variables; (2) confirming the spatial autocorrelation effect on GPP estimation at the study area sites, constructing the spatial weights matrix, extracting the eigenvectors, and incorporating spatial factors into the input variables; (3) constructing the GPP estimation model founded on a spatial autocorrelation light gradient boosting machine model (SA-LGBM) and validating its accuracy; (4) comparing the performance of various GPP products at different temporal scales, in addition to the applicability of models across different spatial and temporal scales (Figure 2).
# A Novel Noncontact Ultrasonic Levitating Bearing Excited by Piezoelectric Ceramics
## 3 Working Frequency and Output Vibrating Amplitude of the Transducer  
Using a commercially available FEM package (ANSYS12.0, ANSYS Inc., Canonsburg, PA, USA), the resonance frequency of the transducer is determined by modal analysis. The material of the horn and radiator is titanium alloy with a mass density of 4430 kg/m\({}^{3}\), Young modulus of 1.15 \(\times\) 10\({}^{11}\) N/m\({}^{2}\) and Poisson ratio of 0.32. The material of the back mass is stainless steel with mass density of 7930 kg/m\({}^{3}\), Young modulus of 2.09 \(\times\) 10\({}^{11}\) N/m\({}^{2}\) and Poisson ratio of 0.3. The material of the ceramic is PZT-8. The relative dielectric permittivity matrix [\(\varepsilon\)] at constant strain, piezoelectric stress matrix [\(\varepsilon\)] and elastic coefficient matrix [\(\varepsilon\)] are:  
\[[\varepsilon]=\left[\begin{array}{cccc}919&0&0\\ 0&919&0\\ 0&0&826\end{array}\right]\times 10^{-11}\,\mathrm{F/m}\quad[\varepsilon]= \left[\begin{array}{cccc}0&0&-4.1\\ 0&0&-4.1\\ 0&0&14.0\\ 0&0&0\\ 0&10.3&0\\ 10.3&0&0\end{array}\right]\mathrm{C/m^{2}}\]  
\[[c]=\left[\begin{array}{cccccc}14.9&8.11&8.11&0&0&0\\ 0&14.9&8.11&0&0&0\\ 0&0&13.2&0&0&0\\ 0&0&0&3.4&0&0\\ 0&0&0&0&3.13&0\\ 0&0&0&0&0&3.13\end{array}\right]\times 10^{10}\,\mathrm{N/m^{2}}\]  
The block Lanczos method recommended by ANSYS instructions is chosen to calculate transducers' modes. The first-order longitudinal mode is an available mode, as shown in Figure 2. The calculated resonant frequency of first-order longitudinal mode is 16.241 kHz.  
In order to obtain the amplitude on vibrating surface of radiator, harmonic analysis is conducted in a frequency domain which covers the first-order longitudinal vibrating frequency. According to the modal analysis result, the frequency domain is set between 16 kHz and 16.5 kHz. Given the energy loss when transducer is driven by an ideal voltage generator, the transducer's structural damping ratio (0.3%) is taken into consideration. Figure 3 shows the exciting method and structural constraint in harmonic analysis. The input peak voltage is 150 V during  
Figure 1: Structure of the proposed noncontact bearing: (**a**) three-dimensional model of the ultrasonic bearing and transducer’s constitution; (**b**) working principle of ultrasonic levitating bearing.  
The results of harmonic analysis are shown in Figure 4. The nodes numbered 45693 and 5537, which are near the center of the concave surface and in the thinnest area of the lateral surface, respectively, are chosen to be calculated. It is clearly seen that the maximum amplitude of Node 1 is approximately 12 \(\upmu\)m at the resonant frequency, while that of Node 2 is about 3.5 \(\upmu\)m.  
Figure 4: Nodes chosen to be calculated and their simulation results: (**a**) nodes on concave surface and lateral surface; (**b**) vibrating amplitude of Node 1 in \(x\)-direction and Vibrating amplitude of Node 2 in \(z\)-direction.  
Figure 3: Exciting method and structural constraint for harmonic analysis.  
Figure 2: The first-order longitudinal mode of the transducer at 16.241 kHz.
## 3 Optimization Agent Model and Sensitivity Analysis
### Accuracy Analysis of the Agent Model  
According to the initial design requirements of the flow control system of the unloader, the value range of the reset spring stiffness was 46,000-80,000 N/m, the value range of the oil inlet pressure was 60-80 bar, and the value range of the oil return pressure was 0-12 bar. According to the practical application of the flow regulation system, when the regulating load was less than 30%, the lubrication effect of the mechanical parts of the compressor decreased, and the gas reflux loss increased, resulting in a decline in the compressor performance. Therefore, 30 samples were obtained using the optimal Latin hypercubic sampling method (Opt-LHD), and the objective function values were calculated under 90-30% load conditions. The agent model was fitted with the experimental data of the design of experiments (DOE) under different loads using the optimization agent model function, and the precision of the agent model was verified by sampling data points randomly. The \(R^{2}\) and Adjusted \(R^{2}\) of different proxy models in Table 2 indicate that both values were above 0.9, which indicates that the proxy model had high calculation accuracy and could be used satisfactorily.  
In this study, the objective was to minimize the residual sum of squares (\(RSS\)), optimize the items of the agent model, remove the key items with relatively small influence that had a greater influence on the retention of items, and simplify the agent model while ensuring the accuracy of the agent model. Using an 80% load as an example, the terms and coefficients of the proxy model are listed in Table 3 (The proxy model for the remaining loads is shown in Appendix A). The \(RSS\) calculation equation is as follows:  
\[RSS=\sum_{i}^{n}{(U_{i}-\widetilde{U}_{i})}^{2}. \tag{17}\]
## 3 Results and Discussion
### Analysis of 6061-T6 Aluminium Vessel  
Aluminium material with a yield strength of 280 MPa generally exhibits higher stress tolerance compared to 304 stainless steel. However, when analysed under pressures ranging from 35 MPa to 70 MPa, with a thickness of 25 mm, the aluminium demonstrated poor performance. The deformation-band equivalent stresses experienced are notably high, and thence they are associated with a low safety factor. Specifically, at 25 mm, the deformation increases with pressure: 2.1268 mm at 35 MPa, 3.0383 mm at 50 MPa, 3.6459 mm at 60 MPa, and 4.2536 mm at 70 MPa. Table 3 shows that that maximum deformation (highlighted in red) occurs in the middle of the vessel and inside the top of the hemisphere. Moreover,  
Figure 4: Safety factor vs. pressure for 304 stainless steel vessel.  
Figure 3: Deformation vs. pressure for 304 stainless steel.  
the safety factor falls below unity for pressures ranging from 35 MPa to 75 MPa, indicating expected failure of the 25 mm wall-thickness aluminium vessel. However, increasing the thickness from 25 mm to 50 mm improves the vessel's performance, particularly under the lower pressure of 35 MPa. With a thickness of 50 mm and pressure of 35 MPa, the aluminium experiences moderate deformation of 1.08 mm, with a stress of 229.94 MPa--lower than the aluminium's yield strength of 280 MPa. Additionally, the safety factor of 1.277 exceeds unity. However, the safety factor at a thickness of 50 mm under pressures of 50, 60, and 70 MPa falls below unity. To ensure the aluminium vessel can withstand higher pressures, such as 50, 60, and 70 MPa, the wall thickness needs to be increased. With an increase in thickness from 50 mm to 100 mm, the aluminium vessel becomes capable of withstanding pressures ranging from 50 MPa to 70 MPa. Although it experiences moderate deformation ranging from 0.8339 mm to 1.1675 mm at pressures of 50, 60, and 70 MPa, respectively, the stresses generated in the wall remain lower than 280 MPa. Additionally, the safety factor exceeds 1 for pressures of 50, 60, and 70 MPa when the thickness is increased to 100 mm, indicating that the aluminium vessel can withstand these pressures. Figures 5 and 6 show the deformation and safety factors for the aluminium vessel under various pressures. Table 14 presents the equivalent stress generated for the aluminium hydrogen vessel.  
Figure 5: Deformation vs. pressure for 6061-T6 aluminium vessel.  
Figure 6: Safety factor vs. pressure for aluminium vessel.
# RSSI and Device Pose Fusion for Fingerprinting-Based Indoor Smartphone Localization Systems
## 4 Results and Analysis
### RSSI-Only vs. RSSI-and-Device-Pose Neural Network Cross-Entropy Results
Additionally, as shown in Figures 10 and 11, the RSSI-and-device-pose network reached an overfitting point at earlier training epochs than the RSSI-only network (and with a lower \(Loss_{\text{CE\_TR}}\)), implying that it reached an optimal training state at earlier epochs. This also points toward a better localization model being learnt more quickly (in epochs) by the RSSI-and-device-pose localization network, resulting from the addition of the device pose information.
# Software-Defined Small Cell-Linked Vehicular Networks: Architecture and Evaluation
## 4 Implementation of the V2x Data Dissemination over diSRsVN and Its Results
### Influence of the SDN Technology
When the vehicle density is 80, the E2E delay experienced by distributed small cell RSU controllers is 24.29 ms, which is less than 37 ms when only the central SDN controller is used and 58.87 ms when no SDN is used. Varying the three methods for performance metrics allows evaluating the impact of distributed SDN controllers in the topology discovery of the proposed architecture. We observe that the distributed SDN controllers show better performance, which is because the routing rules are updated to small cell-linked RSUs in the cluster area closer to vehicles. The small cell-linked RSUs deliver packets to vehicles that are closer, which in turn implies lower delay. The use of distributed controller achieves a reduction in E2E delay of about 58 percent compared to the traditional one without an SDN controller and a decrease of about 35 percent in relation to the centralized SDN controller. This decrease is due to the distributed SDN controllers' efforts to keep only valid routes in their flow tables; in  
Figure 4: Impact of intelligence based on SDN controller over the proposed architecture versus vehicle density: (**a**) average throughput; (**b**) E2E delay; (**c**) packet delivery ratio (PDR). other words, it works to remove routes that have become invalid due to changes in the network topology. Although a centralized SDN controller has better PDR performance, the distributed controller solution achieves a higher average throughput.
# Energy-Efficient Wireless Hopping Sensor Relocation Based on Prediction of Terrain Conditions
## 3 The Proposed Relocation Protocol for Rugged Terrains
### Basic Operation of the Proposed Relocation Protocol
**Step 7**: When the neighbor cluster header of the occurred sensing hole receives the REQ message forwarded, it broadcasts an ADV message to determine movable member hopping sensor nodes among the current members in its zone. ADV: = {message type, source address, destination address (broadcasting) }  
**Step 8**: Each member node that receives the ADV message sends an ADV-ACK message containing its current physical information to the cluster header. ADV-ACK:= {message type, source address, destination address, some info.} Here, above'some info.' indicates several type of information for relocation strategy [19], including the current energy state, capability of hopping movement, GPS coordinate information, etc. **Step 9**: The cluster header receives ACK messages and selects the appropriately movable hopping sensor nodes. Then, it sends MOVE message, which includes the location information about the sensing hole, to the hopping sensors selected. ADV: = {message type, source address, destination address (multicasting),  
sensing hole address, sensing hole GPS info.} **Step 10**: The member nodes receiving the MOVE message have to hop to the neighbor zone using the GPS information of the cluster header of the sensing hole. Here, as every hopping member is taking into account the usual transmission radius of the neighbor cluster header, it could calculate the coordinates of where to move properly. Thus, each member hopping sensor node no longer moves when the member node has moved as desired location. **Step 11**: As mentioned in Step 1, the cluster header of the sensing hole broadcasts a HELLO message at periodic times. After a predetermined time, when the relocated member sensor node receives the new HELLO message, the member updates its previous cluster header information as the current one. If the relocated member sensor node receives multiple HELLO messages at this time, it would be changed to a relay node. In addition, each cluster header is able to update the current movement success rate p through the number of HELLO-ACK messages to properly reflect the level of obstacles. All hopping sensors have modules that can perform Steps 1 to 1 independently. Eachmodule of a hopping sensor could be activated depending on whether it is currently playing a role of {cluster header, relay node, member sensor node}. This is summarized as follows. Cluster header: = {Steps 1, 3, 5, 7, 9, 11}  
Relay node: = {Steps 3, 4, 5, 6}  
Member sensor node: = {Steps 2, 8, 10}
# Ultrasound Navigation for Transcatheter Aortic Stent Deployment Using Global and Local Information
## 2 Materials and Methods  
The configuration of our US navigation system is shown in Figure 1. During the surgical intervention, an US device, an optical tracking system and a catheter are used. Firstly, the tracking system collects the pose of US probes (S5-1, Philips, Amsterdam, The Netherlands). IVUS scanning probe (Atlantis SR, Boston Scientific, Marlborough, MA, USA) and stent are inserted into the aortic lesion through a catheter (Atlantis SR, Boston Scientific, Marlborough, MA, USA). Next, the collected intraoperative US images, IVUS images and preoperative 3D MR images are combined during the image processing procedure. This procedure mainly includes two parts: (1) 2D US-3D MR registration for a 3D intuitive global navigation map; (2) combining segmented IVUS images with the trajectory of the catheter from US images to finish local high-resolution navigation information. Finally, the global and local navigation images help surgeons to finish accurate transcatheter aortic stent deployment.