esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America
## 1 Introduction
While the MTBS program already announced a plan to begin integrating Sentinel imagery upon availability of a harmonized Landsat and Sentinel dataset [24], to date, no formal analysis of Sentinel's performance for burn severity mapping has been conducted in the United States or Canada. Regional studies in other parts of the world generally show the suitability of Sentinel for burn severity mapping, including Australia [37; 38], Bolivia [39], Greece [25], India [40], Italy [41; 42], Mexico [43], Portugal [44], Spain [45; 46; 47; 48], and Siberia [49]. However, the methods used to map and assess burn severity estimates are highly variable, with a limited number of studies directly comparing Landsat and Sentinel using field-validated data. Thus, there is still a need for a broad assessment of Sentinel's burn severity mapping ability across multiple fires with consistent, field-based measures of burn severity. Our main objective was to assess the relative accuracy of Sentinel and Landsat for burn severity mapping in western North America using a dataset spanning the largest number of fires and field validation plots of any study to date. Using multiple mapping approaches and burn severity indices, we compared the performance of Landsat and Sentinel by modeling the relationships between remotely sensed estimates of burn severity to a field-based measure, the CBI. As a secondary objective, we also explored the implications of mapping burn severity at a higher spatial resolution with Sentinel, first by comparing the proportion of each fire mapped at a given severity class, and second by estimating the proportion of high-severity patch interiors within each fire. Using a dataset of 912 CBI plots collected from 26 wildfires across western North America, we compared the performance of burn severity estimates derived from Sentinel and Landsat imagery using several common spectral indices. We created burn severity maps for each fire using two methods: (1) a conventional paired scene approach in which a single scene from before and after a fire are differenced, and (2) a recently developed approach termed \"compositing\", in which per-pixel values are computed across a stacked time-series of imagery [50]. In the compositing approach, all clear pixels (i.e., free of clouds and cloud shadows) acquired during a date range of interest are stacked, after which a summary statistic (e.g., mean and median) is computed for each pixel, creating composite images from before and after the fire, which are then differenced. The composite approach is being increasingly used recently [51; 52; 53], as it eliminates the need for careful a priori scene selection, while retaining similar or better performance to the paired scene approach [50].
## 2 Path Loss Profile Additionally, Modeling for UAV Swarm
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Description** \\ \hline \(P_{n}\) & Cluster power \\ \(E_{tx,i}^{H},E_{Tx,j}^{H}\) & Horizontal polarization of antenna \\ \(E_{rx,i}^{V},E_{Tx,j}^{V}\) & Vertical polarization of antenna \\ VV,HH & Received co-polarized signal \\ VH,HV & Received cross-polarized signal \\ \(\Phi_{n,m}^{VV}\Phi_{n,m}^{VH}\) & Initial phases of a ray \(m\) in cluster \(n\) \\ \(\Phi_{n,m}^{HV}\Phi_{n,m}^{HH}\) & Initial phases of a ray \(m\) in cluster \(n\) \\ \(\overline{\Psi}_{n,m},\overline{\Psi}_{n,m}\) & Tx and Rx antenna elements unit vectors \\ \(\overline{r}_{s_{r}}\overline{r}_{nt}\) & Spatial Coordinate of Tx and Rx MIMO \\ \(\lambda_{0}\) & wavelength of the carrier frequency \\ \(\kappa\) & Cross-polarization power ratio \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Variables used in the channel matrix equation. Figure 3: Channel characteristics of UAV at \(h=500\) m.  
presented in Figures 4 and 5. As a result, the regression score of RF algorithm is observed around 99%.
## 4 Redundant Transmission Control Clustering Approach
### Network Architecture and Methodology  
Our designed framework is application specific for the purpose of gas or oil fields monitoring, and hence sensor nodes are installed in the whole sensing field to collect the information periodically. The proposed model is very robust and has a very good delivery ratio due to the continuous field tours and good data forwarding management of the MS. It saves energy by avoiding the redundant data and repeated transmission over the link, minimizing the control packets, and sleep-awake awareness of the FNs. The SS and MS are enriched with high bandwidths and unlimited power resources. The depth of deployed FNs is considered different with the control of surface buoys [33; 35; 36; 37]. These FNs can freely move in the horizontal direction, but the movements in the vertical direction are negligible [17]. In this way, FNs set themselves into layers from the bottom to the ocean surface. Transmission range of FNs is kept \(150m\) by considering the average ocean depth as defined in [6; 40]. In some special cases, this range can be increased, however, it is not necessary to increase this range for normal cases. We supposed an UAN which can be looked as a directional graph \(G=(N,L)\), where \(N\) belongs to a set of deployed nodes such as \(|N|=n\), while \(n\) is the number of FNs, and \(L\) represents the set of links between the FNs. Furthermore, a three-dimensional rectangular cuboid area with dimensions (\(500\) m) \(\times\) (\(500\) m) \(\times\) (\(500\) m) is taken in which the FNs are divided into four regions and each region is further divided into sub-regions as shown in Figure 2. The reason for dividing the region into cubes is that, we are not engaging all the nodes every time for sensing. So, we select some nodes from each cube for sensing and to cover the entire sensing field. The communication inside each of the region is controlled by a RH, while the CH organizes and manages the FNs entering or leaving from its cluster due to the frequent horizontal movements.  
UANs communication is not similar to the terrestrial wireless communication in many aspects like low communication bandwidth due to the effect of ocean current. The designed scheme pursues the following steps:  
Figure 2: FNs and region heads deployment strategy of designed framework.
# Leveraging Digital Twin Technology for Sustainable and Efficient Public Transportation
## 2 Review of Different Aspects of Digital Twins
### Current Applications of Digital Twins in Public Bus Transportation
#### 2.3.3 Passenger Experience  
Intelligent demand prediction and rerouting models, as well as facility design factors to increase passenger comfort, have been proposed in multiple studies to improve patron satisfaction [23; 53; 87; 88; 91]. More specifically, in public transportation, the passenger experience and comfort quality is a significant metric used to determine the standard of public transportation. In smart city concepts, the simulation of not only routes and schedules that reduce wait times but also of different policy scenarios can enhance the passenger experience. The cohesiveness of different transportation modes in smart cities can reduce inconvenience for passengers if they must switch between different modes. Provision of real-time information in terms of schedules, probable delays, and alternative routes allows passengers to operate on dynamic individual preferences (personalized), ultimately improving transportation management [54; 69].  
A study focused on incorporating passenger feedback and participation when developing more user-centered DT models, promoting occupant interaction [70]. In one study conducted in Gujarat, India, passenger estimation at each stop was found to be very accurate (predicted value almost equal to the actual value for two routes on weekdays and weekends). This was highlighted by a low Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) [23]. In an experimental study conducted by collecting real-time five-level Likert-scale ratings by bus patrons, and evaluating external factors and indicators like whole-body vibration due to acceleration and jerk, ride comfort was assessed using ANN algorithms. The ANN model generated an average Mean Squared Error (MSE) of 0.03, indicating a low level of error in predictions. The model also demonstrated a strong correlation between the independent variables and the dependent variable (level of comfort as rated by the passengers), with an R-value of 0.83 [93].
# Metallurgical Aspects in the Welding of Clad Pipelines--A Global Outlook
## 3 Microstructure Evolution
### Heat-Affected Zone
#### 3.4.3 HAZ of Ni-Based Superalloy Clad  
Ni-based superalloys are divided into solid-solution-strengthened (substitutional alloying elements: Cr, Fe, W, Cu, Mo, Co, Ta, and Re; among them are 59, 617, 625, 690, 800, 825, and 902 alloys [65]) and precipitation-strengthened (precipitation formation elements: Ti, Al, and Nb; among them are 300, 263, 713, 718, 725, 751, 925, 945, and Rene 41 alloys [65]) categories, the latter of which are not frequently used for cladding. Therefore, further metallurgical aspects for solid-solution-strengthened alloys only are discussed.  
Due to the high alloy level of Inconel 625 (see Table 1), additional metallurgical reactions may take place when it is used as a clad. In the HAZ, these may consist of the dissolution and reprecipitation of NbC, grain boundary segregation, and liquation phenomena [65]. A segregation mechanism of some elements (S, P, Pb, and B) has the most profound effect on liquation in the HAZ, and the principal mechanisms are illustrated in Figure 18. The same mechanisms are valid for stainless steels.  
Grain growth in welding of these alloys does not present any problems, since the initial grain size is coarse (~20-40 um [85]). Accordingly, the driving force for grain growth is very low and similar to stainless steels. A secondary factor is the interaction of secondary phases (e.g., borides, sulfides, and carbides such as TiC and NbC) with grain boundaries by a constitutional liquation [86]; this is called penetration mechanism [87]. During welding (rapid heating), secondary phases do not dissolve within the matrix, and upon heating, they react with the matrix by forming interfacial  
Figure 18: Solute segregation and localized melting along grain boundaries in HAZ during different temperature phases in welding [65]; 1, 2, and 3 define progression phases of liquation cracking.  
liquid films [65]. However, small-scale M\({}_{23}\)C\({}_{6}\) particles dissolve at high temperatures during GTAW welding [88]. NbC and TiC can also be responsible for hardness and strength increases, based on experimental observations made by GTAW of 2 mm Inconel 625 [89]. As a result, a high-heat-input process must be avoided.  
The liquation cracking may occur in the partially melted zone (PMZ) in the HAZ adjacent to the FL. The crack length has been observed to increase with a decrease in heat input [66]. Preheating suppressed the crack length, possibly by reducing the cooling rate. Therefore, the cooling rate must be adjusted to avoid the liquation cracking.  
Another factor contributing to crack forming is stresses built up during solidification and further cooling [65].
# A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke
## 9 Challenges in the Hybrid Deep Learning Model for Brain Stroke Detection and Prediction
### Data Imbalance  
Stroke datasets frequently suffer from class imbalance, with non-stroke cases greatly outnumbering stroke cases. This might cause the model to be biased toward the majority class, resulting in low sensitivity in recognizing stroke patients. Oversampling, undersampling, and synthetic data synthesis (such as SMOTE) are necessary to address this problem. However, artificially balancing the data may create noise or impair the model's ability to generalize to real-world situations. Furthermore, class imbalance affects performance parameters like precision, recall, and F1-score, necessitating a thorough study in addition to accuracy.
## 4 Conclusions  
An SAS model was developed on the basis of a previously developed elliptic blending-based RANS model. The new SAS model was tested for three cases with boundary layer separation from curved surfaces: 3D diffuser flow, 2D periodic hills flow, and 2D U-turn duct flow. Comparisons of the model predictions with the experimental, DNS, and LES results from the literature, and with the results obtained using the RANS model (SST-\(k\)-\(\omega\)-\(\varphi\)-\(\alpha\)) and another SAS model (SST-SAS) were provided. The following conclusions can be obtained:  
(1) In the 3D diffuser flow, the results predicted by the proposed SST-\(k\)-\(\omega\)-\(\varphi\)-\(\alpha\)-SAS model were the best. The SST-\(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model failed to accurately predict the separation of the flow, leading to incorrect velocity contours and poor pressure distribution. The results predicted by the SST-SAS model were better than those from the SST-\(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model, but they were quite different from the DNS data.  
(2) In the 2D periodic hills flow, the velocity profiles predicted by the proposed SAS and SST-SAS models were in good agreement with LES, but the results of the SST-\(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model were poor. The length of the recirculation zone predicted by the proposed SAS model was in good agreement with the LES results and was better than that predicted by the SST-SAS model. The SST-\(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model significantly overpredicted the recirculation length.  
(3) In the 2D U-turn duct flow, the two hybrid RANS-LES models had a positive effect compared to the RANS model. The velocity profiles and skin friction were significantly improved.  
In summary, the SST-\(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model could not capture the critical features for all three flows. The SST-SAS model could predict reasonable results. The proposed SAS model was capable of resolving more portions of turbulence structures, and it yielded the best results in all the cases.  
The results indicated that the underlying model in the SAS method is very important. Therefore, the development of an advanced RANS model is significant.  
Conceptualization, X.Y. and L.Y.; methodology, X.Y. and L.Y.; software, X.Y.; validation, L.Y.; writing--original draft preparation, X.Y.; writing--review and editing, L.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (Grant No. 2018 YFB2100901) and the Natural Science Foundation of Shenzhen, China (Grant No. 20200814 105851001).  
Not applicable.  
The authors declare no conflict of interest.
# Interface Shear Performance between Porous Polyurethane Mixture and Asphalt Sublayer
## 4 Test Method
### Inclined Shear Test  
Several testing methods have been used to characterize the interface bonding performance. The modes in these testing methods can be mainly divided into shear, tension, and torsion, in which shearing is the most widely used one. Two types of shear test (direct shear test and inclined shear test) have been used to measure interface shear strength. In the direct shear test, the load direction is parallel to the interface and the confining stress is applied separately [29]. As for the inclined shear test, the load is applied in both parallel and vertical directions with respect to the interface at the same time using an inclined frame [30]. Although the setup of inclined shear test is relatively simple, the ratio of confining pressure to shear stress is defined by the inclined angle and fixed during the test. In the direct shear test, the confining pressure can be applied with varying magnitudes. Further work can be conducted to evaluate interface shear failure using different testing procedures, such as Texas overlay test.  
Figure 2 presents the setup of inclined shear test in the laboratory used in this study. The inclination angle between interface axis and horizontal direction was set at 45\({}^{\circ}\) so that the interface was loaded with equivalent shear force and normal force. The load was applied at 5 mm/min until the layer interface failed. The shear bonding strength of interface was calculated using Equation (1).  
\[\tau=\frac{F\cdot\sin 45^{\mathrm{o}}}{S} \tag{1}\]  
where, \(\tau\) denotes shear bonding strength of interface; \(F\) denotes the maximal vertical load; and \(S\) presents the interface contact area between two layers.  
Figure 1: Preparation of composite specimen: (**a**) Schematic illustration and (**b**) picture of real specimen.  
Figure 2: Illustration of inclined shear test: (**a**) Force on the specimen; (**b**) shear clamps; and (**c**) test setup with specimen.
## 1 Introduction
UWB offers excellent transmission performance in complex indoor environments due to its superior multi-path resolution and signal penetration capabilities [10; 11]. It is characterized by extremely low power consumption, wide bandwidth, high transmission speed, and precise timing [12; 13]. UWB transmits very short RF pulses, enabling accurate time delay estimation and high-precision positioning [14; 15]. Among the various UWB-based techniques, those utilizing signal transmission time--such as time-of-arrival (TOA) and time-difference-of-arrival (TDOA)--are widely adopted in indoor positioning systems [16; 17; 18]. These methods can achieve accuracy levels as precise as 10 cm, making UWB especially suitable for indoor environments. Practical applications of indoor positioning systems are numerous. In hospital settings, especially in emergency departments, chaotic conditions may prevent timely location of patients. Equipping patients with smart bracelets that include positioning chips and physiological monitoring capabilities can allow nursing staff to track both location and health data via a centralized system. Similarly, shopping malls can benefit from floor-based indoor positioning systems connected to cloud services. Customers can use these systems to navigate the space, and they can also be leveraged to resolve issues such as missing children. This study focuses on implementing an indoor positioning system using a microprocessor-controlled UWB chip. The system applies the TOA-based triangulation method combined with the intersection midpoint technique. Initial tests evaluated the accuracy of a single tag relative to a single base station. Subsequently, three base stations were placed at the corners of the ceiling in the experimental area. The TOA method was used to measure distances, and these results were fed into a program that simulated real-world conditions to analyze the overall positioning accuracy.
## 3 Methodology
### Extraction of Geophysical DCA Based on Sentinel-1 OCN Data  
The DCA extracted from SAR signals can effectively reflect dynamic processes on the ocean surface, including wind fields, currents, and internal waves. However, DCA information in the Sentinel-1 OCN products is susceptible to interference from non-geophysical factors such as antenna electronic pointing errors, satellite attitude and orbit motion errors, as well as atmospheric effects [13]. To obtain accurate geophysical DCA that reflects ocean dynamic processes, these Doppler shifts caused by error sources must be corrected. The process of extracting the geophysical DCA from Sentinel-1 OCN data is shown in Figure 3[2].  
The DCA of single-antenna SAR satellite can be expressed as follows [25]  
\[f_{DCA}=f_{DC}-f_{DP} \tag{3}\]  
where \(f_{DC}\) is the measured value of the Doppler centroid frequency, defined as the frequency of the radar echo along the centerline of the antenna beam, and \(f_{DP}\) is the predicted value of the Doppler centroid frequency, primarily caused by the relative velocity between the SAR satellite and the Earth, which can be estimated using the satellite's precise orbit and antenna pointing information. \(f_{DCA}\) can be expressed as  
\[f_{DCA}=f_{DCA}^{non\_phys}+f_{DCA}^{phys} \tag{4}\]  
where \(f_{DCA}^{non\_phys}\) is the non-geophysical DCA term, and \(f_{DCA}^{phys}\) is the geophysical DCA term, which contains information about ocean surface wind fields, sea state, and ocean surface currents. This term is primarily influenced by wind [1]. \(f_{DCA}^{non\_phys}\) needs to be removed when inverting physical parameters, which consists of the following parts  
\[f_{DCA}^{non\_phys}=f_{DCA}^{alec}+f_{DCA}^{sca}+f_{\Delta} \tag{5}\]  
where \(f_{DCA}^{alec}\) is the antenna electronic pointing error term, representing the Doppler centroid anomaly prediction error caused by the radar beam pointing angle error, \(f_{DCA}^{sca}\) is the scalloping effect term, and \(f_{\Delta}\) is the residual error caused by imperfect prediction of non-geophysical terms and other unknown biases [26], which can be corrected using land reference. Specific operation steps for this correction are detailed in Section 4.1.  
Figure 3: Geophysical DCA extraction based on Sentinel-1 OCN data.
# An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns
## 4 Discussion
### Limitations of the Model  
Although this study assessed the interpretability of different input features for marine heatwave patterns, it is difficult to discern some complex information using existing expert experiences. Some evidence suggests that LSTM (Long Short-Term Memory) models can effectively interpret results [50], but we believe that directly associating the LSTM network's memory with marine heatwave patterns may not necessarily be reliable. Marine heatwaves are discrete, persistent abnormal warming events occurring in the ocean, and there are certain teleconnections. Current research shows that the occurrence of marine heatwaves is often closely related to large-scale ocean climate modes, especially low-frequency climate modes (such as ENSO). Therefore, different from the concept of how marine heatwaves form, the internal dynamics in LSTM units may have their own principles to reproduce the nonlinear relationships and time dependence in the variables related to marine heatwaves. We hope that future research will apply advanced interpretability and visualization techniques to unveil the mystery of LSTM memory, such as fully understanding the reactions between input features and hidden memories, which might possibly reveal causal relationships we have yet to recognize.