esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Having this in mind, and on the basis of remote-sensing techniques, this study quantifies the severity of wildfires in the municipality of Macao and the consequent changes in gross primary production (GPP). According to [PERSON] [31], GPP can be defined as \"the total amount of carbon dioxide 'fixed' by land plants per unit time through the photosynthetic reduction of CO\({}_{2}\) into organic compounds\". Techniques based on remote sensing, GPP (MODIS product), and indices such as CO\({}_{2}\) flux were applied to some studies to monitor the deforestation of the Maionbe forest in Cabinda (Angola) [32], to update the boundaries between the Amazonia and Cerrado biomes in Brazil caused \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Date of Wildfire** & **Burnt Area [ha]** & **Municipality (DISTRICT)** & **Source of Data** \\ \hline August, 2005 & \textgreater{}13,000 & Coimbra, Lousá, Miranda do Corvo, Penacova, Penela, Vila & [18] \\  & & Nova de Poiares (COIMBRA) & \\ August, 2005 & \textgreater{}12,000 & Abrantes, Chamusca, Ourem (SANTAREM) & [18] \\ August, 2005 & \textgreater{}11,000 & Caminha, Paredes de Coura, Ponte de Lima, Viana do Castelo, & [18] \\  & & Vila Nova de Cerveira (VIANA DO CASTELO) & \\ July, 2012 & 24,843 & Tavira (FARO) & [18] \\ July, 2013 & 13,706 & Alfandega de Fe (BRAGANCA) & [23] \\ August, 2016 & 21,910 & Arouca (AVEIRO) & [24] \\ June, 2017 & 27,364 & Pedrograo Grande (LEIRIA) & [19] \\ June, 2017 & 17,521 & Gös (COIMBRA) & [19] \\ July, 2017 & 29,758 & Serta (CASTELO BRANCO) & [19] \\ October, 2017 & 16,610 & Figueira da Foz, Lousá (COIMBRA) & [19] \\ October, 2017 & 35,806 & Lousá (COIMBRA) & [19] \\ October, 2017 & 30,142 & Serta (CASTELO BRANCO) & [19] \\ October, 2017 & 57,534 & Sea (GUARDA) & [19] \\ October, 2017 & 15,687 & Alcobaca (LEIRIA) & [19] \\ August, 2018 & 26,763 & Monchique (FARO) & [25] \\ July, 2019 & 9,249 & Vila de Rei (CASTELO BRANCO); Macão (SANTAREM) & [22] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._by anthropic actions [33], and to evaluate the postwildfire recovery process in Gangwon province (South Korea) [8]. The period under analysis ranges from 1991 to 2019, i.e., comprising the major forest fires that affected Macao. In the last three decades, there has been an increase in burnt area and fire severity. The lack of proper land management creates conditions for major forest fires to occur every 10 years, as it corresponds to the recovery time of the vegetation and its ground accumulation. This study contributes to better forecasting future fires and to helping stakeholders to manage the territory, preventing and mitigating the harmful effects of fires to the inhabitants and environment of the region of Macao. This is of particular significance for a region that has lost population, and struggles to become more capable of attracting investment through forestry and tourism.
## 3 Results and Analysis
### Low Multipath Single Point and Differential Positioning  
A lower multipath test was conducted in the middle of a flat playground with no obstruction in a radius of 100 m, a section of which is shown in Figure 12. The two GPSMap 66 and Huawei units being tested were mounted on a vertical wooden pole with the vertical separations shown in the figure. The data collection occurred over a period of 75 min during which four to five Galileo SVs were available with a PDOP of 3 to 7. The corresponding values for GPS were 10 to 11 SVs and a PDOP better than 2. A Trimble R10 was installed at the base of the pole and operating in differential mode with a second R10 unit at a reference station 5 km away at the same point as used previously to provide an accurate position of the test point to evaluate the other units.  
The C/N\({}_{0}\) measurements of the R10, GPSMap 66 green and P10 units are shown in Figure 13. The GPSMap 66 blue unit has similar values. The R10 values are better than those of the GPSMap  
Figure 11: P10 measured C/N\({}_{0}\) values and epoch-by-epoch GPS coordinate variations with respect with ITRF2014 coordinates under high multipath using external antenna.  
66 for a few SVs, although the averages are similar. However, no cyclic variations are present in the R10 values, demonstrating the high multipath rejection capability of its antenna. Although the test environment was considered to be low multipath, cyclic variations likely due to multipath caused by buildings in a radius of 200 m are still present in the GPSMap 66 unit results. Helix antennas, although compact and well suited for hand held units, are not as effective for multipath rejection as high performance but larger geodetic antennas. The P10 C/N\({}_{0}\) values are much lower, with an average of 8 to 9 dB lower than those of the R10 and GPSMap 66 units. The P10 provided carrier phase for only very short periods, which is not surprising given the low C/N\({}_{0}\) values. Another test with the GPSMap 66 on a 2200 m mountain summit with the unit on the ground resulted in a C/N\({}_{0}\) value average of 1 dB-Hz higher than in the current case, due to the absence of any natural or person-made objects below the 360\({}^{\circ}\) horizon.  
Figure 12: GPSMap 66 and P10 unit testing in low multipath environment. Trimble R10 added to provide accurate reference positions (Coach Hill, 12 Sep18).  
Figure 13: C/N\({}_{0}\) measurements of R10, GPSMap 66 and P10 during test shown in Figure 12.
# Comparison of Dual-Combustion Ramjet and Scramjet Performances Considering Combustion Efficiency
## 3 Results and Discussion
### Comparison of Overall Performances of the Scramjet and DCR
#### 3.3.3 Comparison of the Overall Performances  
To compare the overall performance of DCR and scramjet engines, the combustion efficiency, total pressure recovery coefficient, thrust, specific thrust, and specific impulse at the outlet are summarized as evaluation parameters, as shown in Table 3. The combustion efficiency of DCR 1 is 35.6% higher than that of scramjet 1, and the combustion efficiency of DCR 2 is 25.4% higher than that of scramjet 2. This indicates that the enhanced combustion advantage of DCR decreases with the increase in the Mach number. When the Mach number increases from 6 to 7, the combustion efficiency of DCR decreases because the static temperature increases greatly after the free flow stagnates to the subsonic velocity, and the static temperature will further increase to more than 3200 K due to chemical reaction exothermic heat. With increasing temperature, the dissociation rate of CO\({}_{2}\) into CO increases, which ultimately leads to a decrease in combustion efficiency.  
The total pressure recovery coefficient of scramjet is 0.9% lower than that of DCR under the Ma6 condition, but 5.6% higher than that of DCR under the Ma7 condition.  
Figure 15: Curves for (**a**) combustion efficiency and (**b**) stream thrust function.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(\eta_{c}\) & \(K_{p}\) & \(F\), N & \(S_{p}\), m/s & \(I\), m/s \\ \hline Scramjet 1 & 44.4 & 0.371 & 1019 & 274 & 5009 \\ Scramjet 2 & 44.6 & 0.298 & 799 & 292 & 5338 \\ DCR 1 & 80 & 0.38 & 2719 & 731 & 13,364 \\ DCR 2 & 70 & 0.242 & 1339 & 442 & 8087 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overall engine performance for DCR and scramjet.  
Therefore, as the Mach number increases, the total pressure recovery performance of the scramjet is higher than that of DCR.  
In terms of specific impulse, under Ma6 and Ma7 conditions, the specific impulse of DCR is 2.67 and 1.51 times that of scramjet, respectively, indicating that the performance of DCR is better than that of scramjet under these two conditions. However, with the increase of Mach number, this advantage gradually decreases. The reason is that with the increasing Mach number, the specific impulse of scramjet increases slightly, but the specific impulse of DCR decreases by 30%. The specific thrust is similar and will not be repeated.  
Compared to scramjet, the subsonic combustor of DCR provides a low-speed, high-temperature, and high-pressure environment, which is not only conducive to uniform fuel mixing, but also can promote ignition and improve combustion efficiency, and ultimately improve the overall performance of the engine.
# A Full-Spectrum Registration Method for Zhuhai-1 Satellite Hyperspectral Imagery
## 3 Methods
### Relative Registration
#### 3.1.2 Matching Point Adjustment  
1. Homography matrix estimation  
The matching point pairs of two images are defined as \(p_{1}(x_{1},y_{1})\) and \(p_{2}(x_{2},y_{2})\), and their homography matrix H is given as:  
\[\left[\begin{array}{c}x_{2}\\ y_{2}\\ 1\end{array}\right]=\left[\begin{array}{ccc}H_{11}&H_{12}&H_{13}\\ H_{21}&H_{22}&H_{23}\\ H_{31}&H_{32}&H_{33}\end{array}\right]\left[\begin{array}{c}x_{1}\\ y_{1}\\ 1\end{array}\right]\Leftrightarrow p_{2}=Hp_{1}, \tag{2}\]  
The matrix can then be expanded as:  
\[\left\{\begin{array}{l}x_{2}=H_{11}x_{1}+H_{12}y_{1}+H_{13}\\ y_{2}=H_{21}x_{1}+H_{22}y_{1}+H_{23}\\ 1=H_{31}x_{1}+H_{32}y_{1}+H_{33}\end{array}\right., \tag{3}\]  
where:  
\[\left\{\begin{array}{l}x_{2}(H_{31}x_{1}+H_{32}y_{1}+H_{33})=H_{11}x_{1}+H_{ 12}y_{1}+H_{13}\\ y_{2}(H_{31}x_{1}+H_{32}y_{1}+H_{33})=H_{21}x_{1}+H_{22}y_{1}+H_{23}\end{array} \right., \tag{4}\]  
Equation (4) can be further converted to:  
\[\left\{\begin{array}{l}x_{2}(H_{31}x_{1}+H_{32}y_{1}+H_{33})-(H_{11}x_{1}+H_ {12}y_{1}+H_{13})=0\\ y_{2}(H_{31}x_{1}+H_{32}y_{1}+H_{33})-(H_{21}x_{1}+H_{22}y_{1}+H_{23})=0\end{array} \right., \tag{5}\]  
Equation (5) is then rewritten as:  
\[a_{x}^{T}h=0, \tag{6}\]  
\[a_{y}^{T}h=0, \tag{7}\]  
where  
\[h=(H_{11},H_{12},H_{13},H_{21},H_{22},H_{23},H_{31},H_{32},H_{33})^{T}, \tag{8}\]  
\[a_{x}=(-x_{1},-y_{1},-1,0,0,0,x_{2}x_{1},x_{2}y_{1},x_{2})^{T}, \tag{9}\]  
\[a_{y}=where  
\[A=\left(\begin{array}{c}a_{x_{1}}^{T}\\ a_{y_{1}}^{T}\\ \vdots\\ a_{x_{N}}^{T}\\ a_{y_{N}}^{T}\end{array}\right). \tag{12}\]  
2. Matching point adjustment  
The coordinates of each matching point for the reference band can be predicted in the sensed image using the estimated homography matrix H. The difference in the \(x\) and \(y\) directions and the root mean square (RMS) is calculated based on the predicted values and the actual matching point, respectively. Figure 4 displays a schematic diagram of adjustments for the matching points. In Figure 5, P1 and P2 are the matching point pairs and \(P2^{\prime}\) is the predicted position of P1 on the sensed image, calculated using the transformation matrix H. The root mean square error of the matching point pair is calculated using Equation (13), and the matching point pairs with a root mean square error greater than one are eliminated. This process is repeated until the total root-mean-square error of all matching point pairs (e.g., Equation (14)) is less than one pixel.  
\[R_{MSE}(i)=\sqrt{\Delta x^{2}+\Delta y^{2}}, \tag{13}\]  
\[R_{MSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left[R_{MSE}(i)^{2}\right]}, \tag{14}\]
## 4 Mathematical Modeling and Analysis
### Simulation Results
#### 5.2.3 Scenario 3: Different Traffic Loads for CUE and DUE  
In Scenario 3, we consider high, medium, and low traffic loads in CUEs and DUEs. Real-time video streaming services can be a high traffic load service. Web browsing and voice call can be typical examples of medium traffic load and low traffic load services, respectively. According to [28], we can simplify the traffic loads to 242 kbps, 100 kbps, and 12 kbps, respectively. Figure 15 shows CDF of throughputs based on the traffic loads. The proposed and conventional schemes achieve almost the same throughput under a low traffic load. However, the proposed scheme shows higher throughput compared to the conventional FFR under medium and high traffic loads. In the FFR-ABS scheme, interferences from nearby CUEs can be ignored because the CUEs are muted in ABS. Thus, DUE-Rxs have better throughput in the proposed FFR-ABS than in the conventional FFR.  
Figure 14: Channel capacity received at the target DUE-Rx under the same CUE-Tx and DUE-Tx power levels.  
Figure 15: Cumulative distribution function (CDF) of throughput based on traffic loads.
## 2 Method for RUL Prediction
### Cart  
To enhance the feature capture ability of the model for battery aging data, this paper adopts CART as the base learner to depict the correlation between battery aging information and the RUL. The CART grows iteratively and can establish a nonlinear mapping on the given data [34]. Moreover, CART employs the principle of minimizing squared error to identify data features and grows into a binary decision tree by recursively splitting nodes based on prominent features in the data [35]. Randomly select the \(k\)-th variable \(x^{(k)}\) of the feature data of the training set as the split variable, and the value \(s\) of this variable is used as the split point. The split point can divide the dataset into two regions \(R_{1}\) and \(R_{2}\):  
\[R_{1}(k,s)=\Big{\{}x\Big{|}x^{(k)}\leq s\Big{\}},R_{2}(k,s)=\Big{\{}x\Big{|}x^ {(k)}\geq s\Big{\}} \tag{5}\]  
The output predictions \(c_{1}\) and \(c_{2}\) for each region are  
\[c_{m}=\frac{1}{N_{m}}{\sum_{x_{i}\in R_{m}(k,s)}c_{im}(m=1,2)} \tag{6}\]  
Among them, \(c_{im}\) is the optimal value of the \(i\)-th group of training data in the region \(m\), and \(N_{m}\) indicates the amount of data in the region. Taking this as a comparison, the sum of squared errors in each area can be minimized by traversing the different values of \(k\) and \(s\). The equation is as follows  
\[L_{CART}=\min_{k,s}\left[\sum_{x_{i}\in R_{1}(k,s)}\left(c_{im}-c_{1}\right)^{ 2}+\sum_{x_{i}\in R_{2}(k,s)}\left(c_{im}-c_{2}\right)^{2}\right] \tag{7}\]  
where \(k\) and \(s\) are the best-split variables and split points. In subsequent splits, the above steps will be repeated until a complete decision tree is formed. The number of repetitions is related to the depth setting of the tree. In this paper, the maximum depth of CART is set to 5 layers, as shown in Figure 1. The resultant tree structure indicates a sequential arrangement of battery aging feature importance within the training dataset, serving as the base learner for RT-TrAdaBoost methodology and playing an essential role in predicting the RUL.  
Figure 1: CART growth process.
# Advanced Medical Image Segmentation Enhancement:
## 4 Results and Discussion
### Segmentation Performance  
Segmentation performance evaluation is a critical aspect of any medical image processing methodology. In this section, we delve into a comprehensive analysis of the particle swarm optimization (PSO) approach's performance when applied to the segmentation of two distinct medical imaging datasets: lung CT scan and chest X-ray. We examine the impact of preprocessing--specifically, histogram equalization (HE)--on the PSO algorithm's performance to investigate its ability to enhance image quality and segmentation accuracy. Furthermore, we compare the outcomes of the PSO segmentation approach with alternative methods such as Otsu, Watershed, and K-means. To achieve a comprehensive evaluation, we consider both quantitative measures and graphical representations, enabling us to gain insights into the method's strengths and limitations when applied to different medical imaging modalities.  
The best-cost values and evaluation metrics such as accuracy, precision, recall, F1-score/Dice, specificity, and Jaccard provide a multifaceted assessment of the segmentation quality. This analysis aims to shed light on the effectiveness of the PSO segmentation approach and the significance of selecting appropriate preprocessing techniques tailored to the specific characteristics of each imaging dataset.
# Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds
## 4 Discussion
### Analysis of Network Extraction Robustness and Efficiency  
From the visualization results in Figures 14 and 15, we can see that the distributions of nodes and edges in the extracted navigation networks are highly consistent with the actual spatial layout for different datasets. In particular, for the stairwell spaces in each scene, our network provides a more detailed representation that captures the staircase direction and layout. Compared to other studies that represent the staircase with a single node, our proposed method for automatically extracting staircase nodes results in a more refined representation that better reflects the geometric structure characteristics of the staircase.  
Table 2 indicates that the runtime of our algorithm is closely related to the input data size and the complexity of the scenes. Larger input files generally require longer processing times. For smaller scenes, the algorithm efficiently extracts the navigation network within a few seconds, whereas for larger and more complex scenes, such as those in the S3 DIS dataset, the runtime increases significantly. Despite the considerable differences in input file sizes, the extracted navigation network files remain lightweight, with sizes in the kilobyte (KB) range. This suggests that the extracted networks are efficiently compact.  
Overall, our algorithm demonstrates good robustness and efficiency across the different indoor datasets used in this study, but there is potential for further refinement. Specifically, we have refined the extraction of staircase nodes within our self-collected dataset. As shown in Figure 15, when extracting the indoor navigation networks for stairwells and elevator lobbies, the edges connecting door nodes, elevator nodes, and staircase nodes accurately represent the topological relationships among these elements. However, due to the influence of the actual spatial structure, there are still some overlaps with the actual geometric layout. This indicates that the indoor geometric layout can still affect the accuracy of our final navigation network extraction. In practical applications, additional post-processing may be required to further adjust and optimize the extracted network to achieve a more precise indoor navigation network.
# Prospects for the Development of High Energy Density Dielectric Capacitors
## 2 A Review of the Literature on Energy Storage Using Dielectric Materials
### Breakdown Strength Characteristics of Polymer Composites and Films  
The energy stored in a dielectric storage device is given by  
\[\mathrm{Wh/L}=1/2\ \varepsilon_{0}\ \varepsilon_{\mathrm{r}}\ (V/\delta)^{2}\]  
where \(V/\delta\) is the effective breakdown strength(bds) of the device. The unit of bds is \(\mathrm{MV/m}\) or \(V/\upmu\). Bds depends on materials in the device and the thicknesses of the dielectric material layers. Bds ranges from 100-400 \(V/\upmu\) or even higher for high quality polymer materials, but bds is usually much lower for composite polymers [42; 43; 44; 45]. Developing composite polymers with high dielectric constant \(\varepsilon\) and bds values relatively close to that of uniform polymers is key to the use of dielectrics in high energy storage devices. The effect of the bds on the dielectric constant required for 300 V and 800 V cells having energy densities of 100 and 200 Wh/L is shown in Table 1. The cells are film capacitors requiring very thin films, but the dielectric constant of the films seems to be in a reasonable range using the dielectric materials discussed in this paper.  
Figure 4: Dependences of (**a**) dielectric constants, (**b**) ac conductivities and (**c**) loss tangent on frequency for the graphene/PVDF composites at room temperature [38].
# Path Loss Determination Using Linear and Cubic Regression Inside a Classic Tomato Greenhouse
## 2 Materials and Methods
### Propagation Model  
Propagation models to predict the excess attenuation produced by vegetation were developed to model the excess attenuation found in a forest beyond what is predicted by free space or two-ray propagation [58]. These models can be theoretical and empirical. Theorists or analysts require a large database of environmental characteristics, requiring knowledge of parameters such as electromagnetic, soil moisture and leaves, geometrical characteristics, etc., which may be impractical and therefore we do not use it in our investigation. On the other hand, the empirical path loss models are based on actual radio frequency (RF) measurements of wireless channels [19]. Their main advantages over theoretical path loss models is the simplicity of the mathematical expressions applied that facilitate their direct application, their ease of implementation and their ability to include a greater environment-related factors that affect the propagation of radio waves. However, they do not take into account the geometry of the site [25; 45; 58; 59; 60]. Within the models of empirical propagation, there is the model of exponential decay proposed by [PERSON] (1982), the model COST235 (1996) that considered the situation of trees with leaves and without it. Additionally to Recommendation ITU-R (CCIR 1986), [PERSON] and [PERSON] (1998) proposed the FITU-R model. Each of the models used in the development of the research is summarized in Table 1[61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72].  
The Weissberger model expresses the excess attenuation (dB) of an obstacle in the propagation path. It is applicable in situations in which propagation is likely to occur through a grove and not by diffraction on the tree crown [37; 66; 72]. The basic model MED (Modified Exponential Decay) is described as \(Att_{MED}=Xf^{y}d^{z}\), where \(f\) is the frequency in megahertz (MHz), \(d\) is the depth of the vegetation in meters and \(X\), \(Y\) and \(Z\) are parameters adjusted by techniques of regression [59]. In that sense, the intensity of the received signal (RSSI) for wireless systems in vegetation media is largely based on empirical models that are relatively easy to use [73].  
Figure 1: Possible mechanisms of propagation in the presence of vegetation.