esa-sceva/satcom-chunk-collection huggingface dataset:
# Fire Detection and Geo-Localization Using UAV's Aerial Images and Yolo-Based Models
## 6 Geo-Localization
### Depth Estimation  
Our focus was primarily on fire detection, using it as a case study within our methodology. In our approach, we leveraged the previously discussed trained network to execute the fire detection task, a process that can be succinctly summarized as follows.  
The process included the visual representation of the detected object through bounding boxes, as exemplified in Figure 7 below. The process of depth extraction entailed the following sequential steps.  
* Image undistortion: To enhance the accuracy of the depth estimation, the initial image was subjected to undistortion. This rectified the image deformations resulting from the camera's lenses. The outcome was the undistorted image, which was then employed for fire detection, as depicted in Figure 7.
* Center coordinates computation: The center coordinates (X, Y) of each bounding box were calculated.  
Figure 6: Re-projection error.  
This procedure ensured the extraction of the depth information in a structured manner. As depicted in Figure 8 the depth measurement outcome was recorded as 0.56 m. In comparison, the actual measured distance stood at 0.65 m, resulting in an error of 9 cm. This deviation was within an acceptable range, attributable to factors like the camera resolution, potential calibration discrepancies, and camera orientation.
## 2 Controller Design  
### System Description for a General Robot Manipulator  
The governing equation for a general second-order dynamic of the \(j\)-th link of a robot manipulator with n-degree-of-freedom (DOF) is described by the following differential equation:  
\[\begin{split}&\ddot{x}_{j}=f_{j}(\mathbf{x})+\Delta f_{j}(\mathbf{x})+ \sum\limits_{i=1}^{n}[(b_{ji}(\mathbf{x})+\Delta b_{ji}(\mathbf{x}))u_{i}]+d_{j}(t)\\ & j=1,\ldots,n.\end{split} \tag{1}\]  
where \(\mathbf{x}\overset{\Delta}{=}\left[\mathbf{X}_{1}\ldots\mathbf{X}_{n}\right]^{T}\) is the state vector and \(\mathbf{X}_{j}\overset{\Delta}{=}\left[x_{j},\dot{x}_{j}\right]^{T}\). In practice, \(x_{j}\) is the real trajectory for the \(j\)-th link. The terms \(f_{j}(\mathbf{x})\) correspond to the nonlinear driving terms, while \(\Delta f_{j}(\mathbf{x})\) corresponds to their uncertainties. The components \(b_{ji}\) and \(\Delta b_{ji}\) represent the elements of the control gain matrix and their uncertainties, while \(d_{j}\) is the external disturbance and \(u_{j}\) is the control input. The terms \(f_{j}\) and \(b_{ji}\) are assumed as well known continuous functions of state in literature [21]. Perturbation \(\Psi_{j}(\mathbf{x},t)\) is defined as the combination of all the uncertainties in Equation (1): the physical meaning of perturbation includes the unidentified dynamic terms, the dynamic modeling error and the external disturbances of perturbation  
\[\Psi_{j}(\mathbf{x},t)=\Delta f_{j}(\mathbf{x})+\sum\limits_{i=1}^{n}[\Delta b_{ji}( \mathbf{x})u_{i}]+d_{j}(t). \tag{2}\]  
In practice, the main component of the perturbation \(\Psi_{j}(\mathbf{x},t)\) is the external disturbance from the environment for a general motor driven robot. However, for a hydraulic system, the dynamic uncertainties also have a big impact. The control goal is to drive state \(\mathbf{x}\) toward a desired state \(\mathbf{x}_{d}\overset{\Delta}{=}\left[\mathbf{X}_{1d}\ldots\mathbf{X}_{nd}\right]^{T}\) despite these perturbations.  
**Assumption 1**.: _The perturbations have an upper bound by a known continuous function of the state:_  
\[\Gamma_{j}(\mathbf{x},t)=F_{j}(\mathbf{x})+\sum\limits_{i=1}^{n}\bigl{|}\Phi_{ji}(\bm {x})u_{i}\bigr{|}+D_{j}(t)>\bigl{|}\Psi_{j}(t)\bigr{|}. \tag{3}\]  
_where \(F_{j}>\bigl{|}\Delta f_{j}\bigr{|}\), \(\Phi_{ji}>\bigl{|}\Delta b_{ji}\bigr{|}\) and \(D_{j}>\bigl{|}d_{j}\bigr{|}\) represent the expected upper bounds of the uncertainties._The combination of a controller (TSMC) and an observer (SPO) results in a new form called terminal sliding mode control with sliding perturbation observer (TSMCSPO). The flow chart of TSMCSPO is shown in Figure 1.
## 3 Case Study
### Cracking Risk Analysis of the Core Wall
#### 3.3.3 Analysis of the Cracking Risk  
Since the densities of concrete, the foundation, fractures, and disturbed zones vary little, 30 random parameters were identified, including the reservoir water level, Duncan-Chang E-B model parameters for the core wall, rockfill, and overburden materials, as well as cohesion, the friction angle, and the elastic moduli of the cutoff wall, galleries, and curtain materials. Based on Equation (11), the response surface equation for the performance function related to core wall cracking was established as follows:  
\[G(X)=\sum_{i=1}^{n}b_{i}X_{i}^{2}+\sum_{i=1}^{n}c_{i}X_{i}+a_{0}=\mathbf{AX}_{ 1}^{\text{T}} \tag{16}\]  
\[\mathbf{X}_{1}=[1,x_{1},x_{2},\ldots,x_{29},x_{30},x_{1}^{2},x_{2}^{2},\ldots x _{29}^{2},x_{30}^{2}] \tag{17}\]  
where \(\mathbf{X}_{1}\) is the random variable matrix, and \(x_{1}\), \(x_{2}\), \(\ldots\), and \(x_{30}\) are the random variables for analyzing the cracking risk of the core wall.  
Using the response surface equation for the performance function related to core wall cracking in the PB dam, Monte Carlo simulation, the proposed stochastic characteristics, and a joint distribution model for multi-component material parameters of high core rockfill dams were employed. The number of sampling iterations significantly affects the risk probability results. Since the risk probability \(P_{f}\) of the dam failure in Equation (13) is usually very small (e.g., less than 0.001), it is recommended that the number of samples \(N\) satisfies \(N\geq 100/P_{f}\)[35]. Consequently, the cracking risk of the core wall was determined to be \(2.14\times 10^{-7}\) using the Monte Carlo simulation.
## 2 Related Work
### Research Gaps and Potential Contributions  
A critical analysis of the existing literature reveals several significant research gaps in railway network resilience studies. Despite advances in resilience theory and complex network analysis, their integration for railway systems remains inadequate in several aspects.  
Most studies examine railway networks through either physical infrastructure or service performance perspectives, rarely integrating both dimensions. The methodological approaches often lack comprehensiveness, focusing on singular aspects like connectivity, vulnerability, or fault propagation rather than the full spectrum of resilience characteristics. Additionally, simulation approaches typically address limited disruption scenarios, with insufficient research on recovery strategies and their comparative effectiveness. A comparison with existing studies is shown in Table 1.  
This research seeks to address these gaps by developing an integrated framework for railway network resilience that combines physical and service network perspectives, employs multiple complementary resilience metrics, evaluates performance under diverse disruption scenarios, and assesses the effectiveness of different recovery strategies. By adopting this comprehensive approach, the study aims to advance both theoretical understanding and practical applications in railway network resilience, ultimately contributing to more robust and adaptable railway systems.  
\begin{table}
\begin{tabular}{c c c} \hline
**Research Aspect** & **Existing Studies** & **Our Research** \\ \hline Network Scale & Primarily urban transit or regional networks [8; 11] & National railway network at station level \\ Network Layers & Single-layer analysis focusing on either physical or service aspects [16; 19] & Dual-layer analysis integrating physical infrastructure and service performance \\ Resilience Metrics & Limited metrics focusing on single aspects like connectivity or efficiency [7; 18] & Comprehensive metrics covering accessibility, invulnerability, and restorability \\ Disruption Types & Usually limited to one or two disruption scenarios [9; 20] & Four distinct disruption types: external attacks, natural disasters, technical failures, and flow fluctuations \\ Recovery Strategy & Rarely addressed or simplified recovery models [17] & Multiple recovery strategies evaluated: random, degree-based, and betweenness-based \\ Simulation Approach & Static or simplified dynamic models [4; 15] & Advanced cascade failure model with load redistribution mechanisms \\ Application Focus & Theoretical analysis with limited practical guidance [5; 6] & Clear implications for railway planning and emergency management \\ \hline \end{tabular}
\end{table}
Table 1: Comparison between this research and existing studies.  
Resilience Measurement of Railway Physical Network and Service Network Based on Complex Structure Analysis
## 4 Methodology
### Model Training
Then, the training data can be input into the feature extraction network for feature extraction. Finally, the extracted features and corresponding category information are substituted into Formula (8) to obtain the fitness of the individual vector. Figure 6: The flow of the two-stage training method. Step 3: Mutation. In each round of evolution, a mutation operation is performed on all individuals \(X_{i,g}\) in the current generation. If the mutated individual vector is \(V_{i,g}\), the mutation operation is as shown in Formula (11). \[V_{i,g}=X_{a,g}+F\cdot\left(X_{b,g}-X_{c,g}\right) \tag{11}\]  
In (11), \(X_{a,g}\), \(X_{b,g}\), and \(X_{c,g}\) are three different random individual vectors in the current generation \(g\), where \(a\
eq b\
eq c\
eq i\), and \(F\) is a constant between 0 and 1 that represents the scaling factor. Step 4: Crossover. Some components of each individual vector \(X_{i,g}\) are swapped with the corresponding mutation vector \(V_{i,g}\) to obtain the crossover vector \(U_{i,g}\). For the \(j\)-th element \(u_{i,g}^{j}\) of \(U_{i,g}\), the crossover operation is as shown in Formula (12). \[u_{i,g}^{j}=\left\{ \begin{array}{l}v_{i,g}^{j},rand(0,1)<CR\text{ or }j=j_{rand}\\ x_{i,g}^{j}\end{array} \right. \tag{12}\]  
In (12), \(CR\) represents the crossover probability (which is a real number between 0 and 1), \(D\) is the dimension of the individual vector, and \(j_{rand}\) is a random integer between 0 and 1. Step 5: Selection. All individual vectors \(X_{i,g}\) and their corresponding cross variables \(U_{i,g}\) are substituted into the fitness function \(l(\cdot)\) to obtain the individual vectors \(U_{i,g+1}\) in the next generation. The selection operation is as shown in Formula (13). \[X_{i,g+1}=\left\{ \begin{array}{l}U_{i,g},l\left(U_{i,g}\right)<l\left(X_{i, g}\right)\\ X_{i,g},\text{else}\end{array} \right. \tag{13}\]  
Steps 2 to 5 are repeated until the number of iterations reaches the preset maximum value, and then the optimal individual is used as the weight of the distance calculation layer. Using the above two-stage training method, we obtained a neural network that can extract features from the detection dataset of LTE-R cells with degraded communication performance. Then, simply using traditional classification methods, such as SVM, to classify the data after feature extraction can achieve high detection accuracy.
## 3 Results and Discussions
### Thermal Analysis  
The thermal gravimetric (TG) and differential thermal analysis (DTA) curves, obtained for the powder \(\alpha\)-MnO\({}_{2}\) synthesized using the hydrothermal method, are presented in Figure 8 and were recorded in the 50 \(\div\) 1000 \({}^{\circ}\)C range, with a heating rate of 10 \({}^{\circ}\)C/min, using alumina crucibles.  
Figure 6: (**a**) The total density of states for ferromagnetic (FM) and antiferromagnetic (AFM) cases with HSE, and PBE.. The zero-energy is set to the valence band maximum. (**b**) PBE + U DOS with projections for Mn d (red) and O p (blue) states (AFM).  
Figure 7: SEM micrographs of nanowires were obtained on the brown \(\alpha\)-MnO\({}_{2}\) (**a**) and the black \(\alpha\)-MnO\({}_{2}\) (**b**).  
The thermogram curve and its derivative show three main stages of weight loss. Under inert conditions, the DTA curve of the sample shows three endothermic peaks. These peaks, located at 260 \({}^{\circ}\)C, 615 \({}^{\circ}\)C, 795 \({}^{\circ}\)C, and 942 \({}^{\circ}\)C, indicate the phase transformations occurring in the sample.  
The first step of thermal decomposition occurs between 200 \({}^{\circ}\)C and 260 \({}^{\circ}\)C with a small mass loss of \(\sim\)0.35%, which is due to the loss of loosely bound water and indicates the sample is hygroscopic [35]. The second mass loss of 3.7%, in the temperature range of 500 to 735 \({}^{\circ}\)C, is attributed to chemical transformations such as the reduction in Mn(IV) and probably the formation of Mn\({}_{2}\)O\({}_{3}\). The endothermic peak located at 795 \({}^{\circ}\)C appears in the TG curve with a further mass loss of about 2.75% in the 735 \(\div\) 830 \({}^{\circ}\)C range and indicates the second thermal decomposition of MnO\({}_{2}\), from the remaining Mn\({}_{2}\)O\({}_{3}\) to Mn\({}_{3}\)O\({}_{4}\)[60].
# Performance Analysis of Dual-Hop AF Relaying with Non-Linear/Linear Energy Harvesting
## 5 Throughput Analysis  
In this section, the throughput of the DH AF relaying system is calculated for the non-linear and linear EH models. The outage probability, defined as the probability that the target rate exceeds the instantaneous achievable rate, can be given as  
\[P_{out}^{i}=\mathcal{P}_{r}\{\frac{1}{3}\log_{2}(1+\gamma)<R\}=\mathcal{P}_{r }\{\gamma<\gamma_{th}\}=F_{\gamma}(\gamma_{th}), \tag{33}\]  
where \(R\) is the target rate, \(\gamma_{th}=2^{3R}-1\) is the threshold SNR, \(\gamma\) is the instantaneous SNR at D and \(F_{\gamma}(\cdot)\) is the CDF of \(\gamma\). The factor \(1/3\) is due to the transmission of one symbol per three time slots. Please note that \(i=\mathrm{NL}\) and \(i=\mathrm{L}\) stand for non-linear and linear EH models, respectively. The throughput of the considered system is calculated as  
\[\tau^{i}=\frac{R}{3}(1-P_{out}^{i}). \tag{34}\]
## 3 Database-Building and Exploratory Analysis
### Data Exploratory Analysis
#### 3.2.1 Numeric Variables  
To visualize the numerical variables and identify the outliers, a box-normal plot was employed. As shown in Figure 7, the skewness coefficient (SK) and kurtosis (BK) were introduced to depict the distribution patterns of these variables. SK provided information about the direction and extent of skewness in the distribution while BK described the steepness of the distribution pattern.  
As shown in Figure 7f, R had a limited effect on the output \(S_{\text{max}}\), where no significant change was observed. The variables B, L\({}_{\text{c}}\), He, and Ht exhibited wider distributions, as shown in Figure 7a-d, and all presented slightly positive skew values (SK > 0). Figure 7e reveals that the variable L was concentrated towards the lower end of the box plot, exhibiting a clear positive skewness. Its kurtosis (BK) was calculated as 6.6581 (after subtracting 3). While some outliers may have held valuable information for training the prediction model, only the extreme outlier of the variable L was removed, as represented by the red triangle in Figure 7e.  
The correlation between variables was assessed using Spearman's correlation coefficient, as illustrated in Figure 8. It could be observed that the variables L\({}_{\text{c}}\) and B exhibited a high correlation of 0.93 (L\({}_{\text{c}}\) = B \(\times\) sin\(\alpha\), where \(\alpha\) is the angle of the tunnel passing through the excavation). However, when variables are highly correlated, it can reduce the interpretability of a model. Therefore, in this case, the variable L\({}_{\text{c}}\) was chosen to train the model with a higher correlation to the output \(S_{\text{max}}\).
## 5 Conclusions  
In this study, we developed a low-cost, nondestructive fruit testing system based on parallel-polarized NIR light sources, which comprises a halogen lamp, a parallel polarizer, several NIR bandpass filters and an NIR photodetector. The probing wavelengths used in this system are a combination of wavelengths whose reflectances correlate strongly with the vitamin C content of Indian jujube--1200, 1400, 1450, 1500 and 1550 nm. The probing light sources were parallel-polarized and designed to be incident on the fruit's flesh at \(\theta_{\text{B}}\), which reduced interference from the fruit's skin and increased the transmittance and backscatter intensity of the incident light. The light reflected by the fruit's flesh was then collected by an NIR photodetector. This approach increased the accuracy of the vitamin C measurement and reduced errors. The \(r_{\text{p}}^{2}\) and RMSEP of the PLS predictions were 0.84 and 1.65 mg/100 g, respectively.  
As compared to current NIR spectroscopy-based nondestructive testing systems, our detection system is small, relatively cheap, consumes little power and has a competitively high \(r_{\text{p}}^{2}\) value while retaining a low RMSEP. The varieties of fruit that can be tested by this detection system could be increased in the future, so that our system can be widely applied in fruit quality inspections.  
Conceptualization, S.-H.H.; methodology, S.-H.H., P.H.; validation, S.-H.H., H.-C.H.; data curation, S.-H.H., H.-C.H.; writing--original draft preparation, S.-H.H.; writing--review and editing, S.-H.H., P.H.; funding [PERSON], P.H.  
This work was supported by the Ministry of Science and Technology (MOST) of Taiwan under contract number MOST 107-2221-E-005-059-MY3 and in part by the National Chung Hsing University, Taiwan.  
The authors declare no conflict of interest.
## 2 Related Work
[16] explore resource allocation in FL systems, contributing to the understanding of QoS under imperfect channel conditions. These insights are vital for optimizing both QoS and QoE in FL systems. Ensuring high QoS and QoE in FL systems, particularly in data-intensive applications like healthcare, involves addressing challenges such as resource heterogeneity and network variability. Adhering to standards like ITU-T Supp. 9 of the E.800 Series [30] ensures consistency and interoperability in different computing scenarios. Optimization of QoS and QoE in FL requires adaptive strategies that consider the dynamic nature of edge computing. Simulation tools like iFogSim and FogNetSim++ are instrumental in modeling network characteristics and resource utilization, facilitating the development of strategies that enhance both QoS and QoE [32]. The integration of QoS and QoE considerations is crucial for the development of efficient, reliable, and user-centric FL systems in edge computing. By focusing on these quality metrics and leveraging advanced simulation tools, FL systems can be optimized to meet diverse user needs and network conditions, enhancing the overall effectiveness of edge computing services. There is a gap in the literature concerning the significance of measuring performance metrics in optimizing edge performance, as well as the appropriate classification of edge resources and the relevant metrics.To the best of our knowledge, previous works on FL resource profiling have not combined both edge static and dynamic resource quality profile attributes with edge reputation information to ensure optimal edge node selection. With this in mind, we proposed a hybrid resource-aware FPSN solution that integrated multi-criteria resource quality evaluations and edge's reputation to decrease the response times and improve the prediction performance.
## 4 Results and Discussion
### Model Calibration and Validation
Using the Nash-Sutcliffe efficiency coefficient (NSE) to evaluate  
Figure 7: The simulated groundwater hydraulic head distributions: (**a**) the distribution of averaged groundwater hydraulic heads in the PPG; (**b**) differences of the averaged hydraulic heads between wet and dry seasons through the three-year simulation period. simulation results shows that the results at the Xing She Da Qiao station have better performance than those at the Li-lin Bridge station. The NSE values are 0.92 and 0.94 for the Li-lin Bridge and Xing She Da Qiao stations, respectively. Besides that, the calculated normalized root-mean-square errors (NRMSE) showed different results for the two selected streamflow stations. In detail these NRMSE's were 0.67 for the Li-lin Bridge and 0.24 for the Xing She Da Qiao stations. The percent bias (Pbias) aims to evaluate the average tendency of the simulated values to be larger or smaller than their observed ones. In the study, the Pbias values for the Li-lin Bridge and Xing She Da Qiao stations are \(-0.24\) and 0.015 for the model validation, indicating that the modeling results at the Li-lin station tend to underestimate the observations. However, the results at the Xing She Da Qiao station slightly overestimate the streamflow. Figure 10 further shows the variations of the discharge rates obtained from the GSFLOW model. The daily averaged outflows from the rivers are 2.5 million m\({}^{3}\)/d for the Kaoping River, 1.3 million m\({}^{3}\)/d for the Tungkang River, and 0.5 million m\({}^{3}\)/d for the Linien River. However, the variations of discharge rates for the Kaoping River in dry and wet seasons are extremely high as compared with those for the Tungkang and Linien Rivers. The flow rates of the Kaoping River in the dry and wet seasons can vary in three orders of magnitude. The relatively large watershed might induce the high variations of flow rates as compared with the watersheds of the other two rivers. Notice that there is no stream  
Figure 8: The comparison of the observed and simulated streamflow at Li-lin Bridge station in the PPGB: **(a)** the variations of stream discharge rates in logarithmic scale, and **(b)** the scatterplot of simulated and observed stream discharge rates and the associated performance statistics for the model. station at the outlets of the rivers. In the study, the results have solely relied on the results obtained from the calibrated GSFLOW model. Figure 10: The volumetric flow rates at outlets of the three main rivers in the PPG. Figure 9: The comparison of the observed and simulated streamflow at Xing She Da Qiao station in the PPG: (**a**) the variations of stream discharge rates in logarithmic scale, and (**b**) the scatterplot of simulated and observed stream discharge rates and the associated performance statistics for the model.