esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### STAMP/STPA-Based Framework for UAS Integration in Public Safety  
The proposed framework is based on STAMP/STPA. As shown in Figure 2, STAMP/STPA specifies the guidelines and outcomes that need to be achieved, but it does not specify the tools and models to be used in the detailed steps [40]. This flexibility allows for highly adaptable application, enabling the development of the proposed framework to achieve the research objectives.  
Figure 2 presents a framework based on the existing STAMP/STPA methodology, with the addition of STEP 0 and STEP 4 to the conventional STPA steps. STEP 0 considers the early stage of the system and defines the necessary aspects for analysis based on the existing literature and case studies. Furthermore, two approaches were employed to enhance cost-effectiveness, which is one of the objectives of this research. In a typical STPA, stakeholders are identified to define losses. However, because of the nature of the system under consideration, which involves a wide range of stakeholders, the framework aims to maintain traceability by linking stakeholders with losses, preventing redundancy in resource allocation and roles. The other approach is to prioritize risk migration using a risk matrix, enabling cost-effective operations.  
A risk matrix is a tool used to assess and prioritize risks based on their likelihood and potential consequences. It provides a visual representation of risk levels, allowing decision-makers to focus on the most critical risks. The matrix consists of a grid with likelihood on one axis and severity on the other. Each cell in the matrix represents a risk  
Figure 2: The steps of the proposed STAMP/STPA-based frameworks.  
level determined by the combination of likelihood and severity ratings. The risk matrix enables a semi-quantitative prioritization of risks, helping organizations allocate resources effectively to mitigate the most significant threats. Risk matrices are frequently used in the aviation industry, including EASA and FAA, and the risk is calculated as \"Risk = probability \(\times\) severity\", which is then converted into a matrix to determine the risk level. However, STPA does not generate a probability number related to the hazard. In fact, it discourages the use of probability in the analysis because of the uncertainty in predictions and false beliefs in complex systems [21; 22]. Therefore, in this framework, the risk matrix is used to derive cost priorities for risk mitigation [41]. Moreover, to avoid relying on probability predictions, a statistical approach based on UAS accident data was employed.
# Dynamic Impact Factor and Resonance Analysis of Curved Intercity Railway Viaduct
###### Abstract
[25] analyzed the resonance and vibration elimination mechanism of railway bridges under moving loads by an analytical method. In the research findings of [PERSON] et al. [26], a VTBS that considers system uncertainty was established, and the bridge resonance and parameter sensitivity were analyzed. [PERSON] et al. [27] investigated the condition of resonance for a high-speed railway bridge under vehicle load considering longitudinal track slab constraints based on an analytical method. For curved bridges, due to the centripetal force, the transverse load they bear is much larger than that of straight bridges. Therefore, in the analysis of bridge resonance conditions, in addition to the vertical vehicle load, it is also imperative to consider the radial vehicle load [20; 28; 29]. In terms of the dynamic response of the curved railway bridge, [PERSON] et al. [30] established the maglev vehicle curve bridge model and compared the dynamic system response under the influence of different parameters. In the studies mentioned earlier, the research focus has been either on the high-speed railway bridges or the maglev railway bridges. Studies on the dynamic response of intercity railway curved sections have remained a neglected research area. An intercity railway is very different from a high-speed railway because of its specifications, span, and vehicle characteristics. The curve radius of a high-speed railway bridge is large; therefore, in some design or analysis, the curve bridge of a high-speed railway can be approximately considered as the straight-line bridge, while the curve radius of an intercity railway bridge is small, and considering it as the straight-line bridge may cause some calculation errors. In this paper, the vertical and radial dynamic impact factors and conditions of resonance of the bridge are analyzed based on the planned curved section of the intercity rail transit viaduct. The optimization range of the natural vibration characteristics of the curved bridge under different design speeds is also determined by parameter analysis.
## 3 Delay Propagation Model for Route Networks
### Set the Parameters of the Route Delay Propagation Model  
The model equation of infectious disease transmission dynamics of delay in the airline network is shown in Equation (10). Solving this model yields the number of delayed nodes within the network, while the model applies to the propagation process throughout the network nodes.  
\[\left\{\begin{array}{c}\frac{dS}{dt}=-\frac{(\beta_{1}E+\beta_{2}I)S}{N}\\ \frac{dE}{dt}=\frac{(\beta_{1}E+\beta_{2})S}{N}-\alpha E\\ \frac{dt}{dt}=\alpha E-\gamma I\\ \frac{dR}{dt}=\gamma I\end{array}\right. \tag{10}\]  
In the above equation, the rate of change in the number of susceptible persons over time is proportional to the number of infected persons and the rate of exposure; the rate of change in the number of exposed persons over time is affected by the rate of newly infected susceptible persons and the rate at which exposed persons convert to infected persons; the rate of change in the number of infected persons over time is affected by the rate at which exposed persons convert to infected persons and the rate at which infected persons recover or die; the rate of change in the number of recovered persons over time is proportional to the rate at which infected persons recover.  
To aid the GA in deriving state values, which are treated as Markov chains in this study, we posit that the state of one day is solely dependent on the state of the preceding day. Consequently, we introduce the following iterative equation:  
\[\left\{\begin{array}{c}S_{n}=S_{n-1}-\frac{(\beta_{1}E_{n-1}+\beta_{2}I_{n- 1})S_{n-1}}{N}\\ E_{n}=S_{n-1}-\frac{(\beta_{1}E_{n-1}+\beta_{2}I_{n-1})S_{n-1}}{N}-\alpha E_{n -1}\\ I_{n}=I_{n-1}+\alpha E_{n-1}-\gamma I_{n-1}\\ R_{n}=R_{n-1}+\gamma I_{n-1}\end{array}\right. \tag{11}\]  
To further clarify the propagation dynamics formula mentioned above, the significance of the variables within the formula is detailed in Table 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Significance** & **Symbol** & **Significance** \\ \hline S & Predisposing nodes & \(\alpha\) &
\begin{tabular}{c} Probability that a delayed node becomes a \\ delayed propagation node \\ probability that a susceptible node becomes a \\ delayed node \\ probability that a susceptible node becomes a \\ delayed node \\ probability that a susceptible node becomes a \\ delayed propagation node \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 6: Alphabetical meaning summary.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**State** & **Reason** \\ \hline S & The susceptible node refers to the node with no route network delay when the air traffic network is delayed. \\ E & Delay node refers to the node that has experienced route network delay but does not have the transmission ability. \\ I & Delayed transmission node refers to the node that has experienced route network delay and has transmission ability. \\ R & The recovery node is the node where the route network delay has dissipated. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Node status fact sheet.
## 3 Results and Discussion  
### Characterization of STxB Purified with a Novel Purification System  
The construction of the new STxB purification system has been modified. Briefly, 6 \(\times\) His-MBP tag with strong solubilization capacity was combined with PET expression vectors. B subunit was expressed in _E. Coli_ BL21 (DE3) strain and cytoplasmic extracts were purified and subjected to Tricine-SDS-PAGE (shown in Figure S1). Confirmation of STxB production was supported by Western blot analysis of the identical Tricine-SDS-PAGE gel (shown in Figure S1) with commercially available anti-STxB antibody (Sigma, Cat# SAB4200774). To test the binding specificity between STxB and Gb3, the giant unilamellar vesicles (GUVs) were chosen as a model membrane system, and reconstituted the surface with Gb3. Cy3-labeled STxB (red) bound to GUVs (spiked with BodipyFL-C5-HPC, green, shown in Figure 1, which proved the physiological activity of STxB purified with the novel expression system. This affinity purification strategy was practical due to the property of 6 \(\times\) His-MBP tag. Such tag can be fully excised using ULP1 enzyme without any excess amino acids. The advantage of this method was cytoplasmic expression of toxin protein, which could obtain mature protein of interest without signal peptide in large amounts.
# Remote-Controlled Method with Force and Visual Assists Based on Time to Collision for Mobile Robot
## 3 Proposed Method
### Force and Visual Assists  
In this subsection, the proposed remote-controlled method with visual and force assists is shown. The visual assist is applied to improve the operability of the driver, and the force assist is used to obtain safe performance. When the time to collision is less than \(T_{th}\), the force assist is applied to the control device to ensure safe performance and collision avoidance.  
Furthermore, the presence of the force assist is an important factor in the operability of the proposed method. This is because an excessive force assist obstructs the operation of the remote-controlled robot. The visual assist exhibits the same phenomenon. An excessive visual assist distracts the operator. Therefore, an appropriate amount of force and visual assists is necessary for improving operability.  
The color gradation is set to enhance the visual assist by considering the presence of the force assist. However, it is necessary to avoid distracting the operator by using a strong color or solid line [34]. The green color is set to indicate a normal trajectory without the force assist. The yellow color is used to express attention to the force assist. The color of the dots is chosen as gradations, keeping in mind the need to indicate the degree of possibility of collision and to avoid distractions for the operator. A green color means \"go\" and \"safety\" whereas a yellow color expresses \"danger\" and \"caution\" [41]. A gradation is applied to connect the two colors. If the possibility of collision depending on the TTC becomes high-risk, the green color gradually changes to a yellow color, as shown in Figure 9. Each color is indicated by red, green, and blue (RGB) intensities for generating the gradation. In addition, color gradation is generated by \(l_{k}\). The RGB intensities of the gradation are expressed as follows:  
\[C_{k}^{g} = 255 \tag{31}\] \[C_{k}^{b} = \left\{\begin{array}{ll}255\cdot(1-(\frac{l_{k}}{L_{\text{max} }}))\cdot G_{rs}&if\ \ \frac{L_{\text{max}}}{K_{\text{res}}}\leq l_{k}\\ 255\cdot(\frac{l_{k}}{L_{\text{max}}})&otherwise\end{array}\right.\] (32) \[C_{k}^{r} = \left\{\begin{array}{ll}255\cdot(1-(\frac{l_{k}}{L_{\text{max} }}))\cdot G_{rs}&if\ \ \frac{L_{\text{max}}}{K_{\text{res}}}\leq l_{k}\\ 255\cdot(1-(\frac{l_{k}}{L_{\text{max}}}))\cdot G_{r}&otherwise\end{array}\right. \tag{33}\]  
where \(C_{k}^{g}\), \(C_{k}^{b}\), and \(C_{k}^{r}\) are the intensities of green, blue, and red at the \(k\)th dot of the predicted trajectory, respectively. \(G_{rs}\) and \(G_{r}\) are the color gains of gradation that decrease the intensity value and increase the intensity value. \(K_{\text{res}}\) is the resolution gain of the gradation.
# Learning from Imbalanced Datasets: The Bike-Sharing Inventory Problem Using Sparse Information +
## 2 Related Research
### Learning from Imbalanced Datasets  
_Data Imbalance_ means that an uneven distribution of classes exists within the data, and it is a serious threat for classification problems, as standard classifiers assume the data to be well-balanced. In general, class imbalance is one of the greatest challenges in machine learning and data mining research [29] and can appear in two main forms: _rare classes_ or _rare cases_[30]. The problem of rare classes refers to datasets that contain different proportions of observations (or instances) per class. The concept of rare cases refers instead to the sparse distribution of examples in the feature space [30]. The two problems are closely related, as they both result in an uneven distribution of observations. However, rare cases refer to data that is sparse by nature, while a rare class might simply depend on the sampling procedure.  
Different approaches have been proposed in the literature to deal with imbalanced datasets, and they can be broadly divided into two: methods working at a learner level and methods working at a sampling level [31]. Methods working at a learner level modify an existing algorithm to increase the precision of the minority class. The most common approach is to use cost-sensitive approaches, in which the learner associates the rare class with some weights to compensate for data imbalance [32]. The main limitation of these methods is that they are designed for specific learners and are hard to generalize. Methods working at a sampling level are considered more general [33] as they use resampling to artificially rebalance the dataset. Over-sampling and undersampling are the most common resampling techniques [34]; this approach consists in creating a balanced dataset by artificially generating new observations for the rare class. In the case of over-sampling, the algorithm creates new artificial data points for the minority class. The SMOTE (synthetic minority over-sampling technique) approach is one of the most common techniques [35]. The other main option is to use undersampling procedures, which consists of using a subsample of the majority class [36]. One particularly advanced model is balance cascading [37]: in this algorithm, the model iteratively drops observations from the majority class that are correctly classified. The argument is that these observations are redundant and might negatively affect the quality of the classifier, making it biased toward the majority class.
# Intelligent Office Lighting Control Using Natural Light and a GA-BP Neural Network-Based System
## 2 Overall Design
### Genetic Algorithm Design  
This section, outlining the design principles of the Genetic Algorithm-Back Propagation Neural Network (GA\(-\)BP), is analyzed, and evaluates the feasibility of using the solar altitude angle (\(\alpha\)) and azimuth angle (\(\gamma\)) as input parameters for adjusting the angle of slats. By improving the adjustment precision of the control system's adjustments, the deviation between the predicted and actual values is minimized, resulting in a prediction model of practical significance.  
The initial population setup defines the parameters for the genetic algorithm. First of all, parameters that need to be optimized need to be converted into another form of data to meet the principle of the calculation of the algorithm: a binary encoding is used. The neural network is structured as a three-layer model with a configuration of 2-11-1. Once the network structure is determined, the weights and thresholds for each layer are calculated, as shown in Table 2. Based on these weights and thresholds, the coding length for each individual totals 45.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Weights of the** & & **The Weights of the** & \\
**Input Layers** & **Hidden Layer** & **Hidden Layers** & **Output Layer** \\
**Connection to the** & **Thresholds** & **Connected to the** & **Thresholds** \\
**Hidden Layers** & & **Output Layers** & \\ \hline
22 & \(11\) & \(11\) & \(1\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of weights and thresholds.  
Figure 4: (**a**) Distribution of lighting systems in office and (**b**) diffuse reflection diagram of ceiling and work plane.  
Subsequently, the population size and individual selection processes are established. The quality of the algorithm's performance is determined by four parameters: the population size, maximum evolution generations, crossover probability, and mutation probability. For this study, the population size is set to 40, the maximum evolution generation is 50, the crossover probability is 0.7, and the mutation probability is 0.01.
## 4 Numerical Examples  
### A Central Straight Crack Plate  
In order to demonstrate the validation of the present approach, the first benchmark example is a central straight crack plate subjected to a uniform uniaxial tension \(\sigma=3\,\mathrm{MPa}\) on the upper and bottom edges shown in Figure 2. For this example, the length of the plate is \(2L\) (\(L=2\,\mathrm{m}\)), the width is \(2W\) (\(W=1\,\mathrm{m}\)), the ratio of crack length to width \(a/W=0.1\), \(0.2\), \(0.3\), \(0.4\), \(0.5\), \(0.6\) and \(0.7\) are considered, and the plane stress condition is assumed. The material parameters are elastic modulus \(E=1000\,\mathrm{MPa}\), [PERSON]'s ratio \(\
u=0.3\). The analytical solution of the SIF \(K_{I}\) for this model is given in [36] as follows:  
\[K_{I}=\sigma\sqrt{\pi a}\sqrt{\sec\frac{\pi a}{2W}}\left[1-0.025\Big{(}\frac{ a}{W}\Big{)}^{2}+0.06\Big{(}\frac{a}{W}\Big{)}^{4}\right] \tag{15}\]  
For this simulation, 252 elements are used for discretization (as shown in Figure 2b). The computation results of the SIF \(K_{I}\) are summarized in Tables 1 and 2 for the case \(a/W=0.1\), \(0.2\), \(0.3\), \(0.4\), \(0.5\), \(0.6\) and \(0.7\). The errors of energy norm of the P-FEM solutions and relative errors of the SIFs \(K_{I}\) for the example are listed in Table 1; here we only list the results for the case \(a/W=0.4\), the results for other cases are similar. It can be seen from Table 2, compared with the high-order NMM [37], that an obvious improvement in accuracy is observed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(p\) & **DOF** & **Error of Energy Norm (\%)** & \(K_{\mathbf{I}}(\mathbf{MPa\cdot m^{1/2}})\) &
\begin{tabular}{c} **Normalized \(K_{\mathbf{I}}\)** \\ (\(K_{\mathbf{I}}\)/\(\sigma\sqrt{\pi a}\))** \\ \end{tabular} & **Relative Error (\%)** \\ \hline
1 & 548 & 11.92 & 2.999 & 0.892 & \(-\)19.60 \\
2 & 1602 & 3.28 & 3.535 & 1.051 & \(-\)5.23 \\
3 & 2688 & 2.17 & 3.685 & 1.096 & \(-\)1.21 \\
4 & 4278 & 1.45 & 3.724 & 1.107 & \(-\)0.16 \\
5 & 6372 & 1.13 & 3.732 & 1.110 & 0.05 \\
6 & 8970 & 0.94 & 3.733 & 1.110 & 0.08 \\
7 & 12,072 & 0.81 & 3.733 & 1.110 & 0.08 \\
8 & 15,678 & 0.71 & 3.733 & 1.110 & 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Errors of energy norm of the P-FEM solutions and relative errors of the SIFs \(K_{I}\) (\(a\)/\(W\) = 0.4, DOF: degree of freedom).  
Figure 2: (**a**) A central straight crack plate; (**b**) Global and local meshes (the total number of elements is 252).  
The stress nephogram of the example is shown in Figure 3. For the sake of simplicity, only the stress nephogram for the first example is plotted, the others are similar.
# A Watermarking Technique for Biomedical Images Using SMQT, Otsu, and Fuzzy C-Means
## 5 Conclusions  
In this paper, an approach to digital watermarking on biomedical images was put forward to ensure the safety and confidentiality of the image. This process is an application of biometrics since it provides unique identification through retention of a watermark after transmission. Additionally, access to the medical images by the authorized users can be validated through cross checking of the distinct watermark. The proposed model utilizes the improved SMQT, Otsu threshold, fuzzy c-means, DWT, and IDWT. Firstly, the model operates on SMQT and Otsu threshold to pre-process the image. Secondly, the enhanced image is segmented using the Otsu threshold and fuzzy c-means. Then, the segmented image is watermarked using DWT and IDWT. After that, the watermark is extracted using the reverse process of embedding. Finally, the performance was measured, which indicates that the suggested scheme shows superior results in terms of efficiency and imperceptibility as well as upholding the robustness against various attacks.  
All authors contributed equally to the conception of the idea, the design of experiments, the analysis and interpretation of results, and the writing and improvement of the manuscript.  
This work was supported by Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government foundation (19 ZS1320, The development of smart context-awareness technique for major industry acceleration).  
The authors declare no conflicts of interest.
# Post-Flood Risk Management and Resilience Building Practices: A Case Study
## 6 Results and Discussions
### Literature Review Results
In Swat and Sukkur high microbial load was observed in collected water samples. Federal level NDMA and Provincial level PDMAs collaborated with all the NGOs to enhance the humanitarian aid work. Around 36 NGOs were registered to help the communities. During the initial three months, NGOs focused on providing clean drinking water, setting up latrines, hygiene awareness, etc., after the emergency response period was over, attention was laid on mid to long-term development interventions. As per the reported activities carried out by the NGO, it was assumed that all field activities have been carried out smoothly; however, the on-ground reality was quite different, and people were not satisfied with the relief response by the government and NGOs. Pakistan's measures on flood forecasting, early warning systems, and evacuation plans are not developed yet. A lot of work needs to be done in this space to develop effective systems for flood management. Lack of disaster preparedness and management leads to huge losses and damage to lives, infrastructure, and livestock [21]. The government needs to improve flood warning and response systems across the country. Implementation of cutting-edge technology and equipment which can assist in decision making, creating awareness among the community, developing flood maps, and evacuation plans are highly necessitated. Satellite imagery and big data analysis will help authorities in flood predictions. Important consideration must be given to wetland restoration, and cleaning river basins will also facilitate the flood retentions [22; 23; 24]. Installation of radar will improve gathering information of real-time data on precipitation, accuracy in forecasting, and improved planning. Other environmental factors such as soil characteristics, infiltration rates, and vegetative cover are important to comprehend catchment response to flood vents. Urban planning and land use also play a vital role tool [25; 26; 27].