esa-sceva/satcom-chunk-collection huggingface dataset:
# Resilience Analysis of Traffic Network under Emergencies: A Case Study of Bus Transit Network
## 4 Case Analysis
### Experimental Process and Result
Next, the resilience values of the 152 bus routes are calculated based on the bus card swipe passenger flow data and normalized. The normalized weights of the routes are shown in Figure 21.  
Then, the weights of the routes are input into the network topology to form a weighted scale-free network. The average resilience values of the nodes and edges are calculated separately, and the arithmetic mean of these values is obtained as \(R_{total}\). Here, the weighted network resilience value \(R_{total}\) is compared with the resilience value R' of the unweighted network. \(R\prime\) is calculated by taking the average of the normalized node closeness centrality and the normalized edge betweenness centrality. The difference between the two is that \(R_{total}\) takes into account the resilience weights of the bus routes, while R' only considers the centrality of nodes and edges. The resilience values of the network are calculated separately for comparison, and the results are shown in Table 12.  
The top five nodes and edges with the highest resilience values in the calculation of \(R_{total}\) are shown in Table 13.  
The five nodes and five edges in Table 13 have higher resilience, indicating that they are less affected by unexpected events. There are 49 bus routes passing through these 5 nodes, and it can be observed that these nodes are located near interchange hubs such as overpasses. Moreover, the bus routes passing through these edges mostly cover other important transportation hubs. The experimental results reflect the effectiveness of the resilience calculation model proposed in this study. \begin{table}
\begin{tabular}{c c} \hline \hline
**Statistical Characteristics** & **Value** \\ \hline Network diameter & 81 \\ \hline Network average distance & 17.1582 \\ Network local efficiency & 0.0830 \\ Global network efficiency & 0.0692 \\ Network average clustering coefficient & 0.0776 \\ Global clustering coefficient of the network & 0.1186 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Public transit network statistics. Figure 21: Bus network route weight distribution (\(x\)-axis: resilience value, \(y\)-axis: number of routes.). Figure 20: Four centrality indexes of public transit network topology (\(x\)-axis: index value, \(y\)-axis: number of indexes. ).
# On-Orbit Polarization Calibration for Multichannel Polarimetric Camera
## 1 Introduction
A built-in rotatable linear polarizer (herein, called the linear analyzer) is used to analyze the polarizing effects of the camera. Additionally, the built-in linear analyzer can be very small, as it is placed after the optical system. Here, we used a micro precision rotation stage to control the linear analyzer to rotate precisely. When the polarimetric camera is taking on-orbit solar radiometric calibration, the linear analyzer rotates continuously. The Stokes parameters were calculated by the received radiation intensity. A special model is established for polarization calibration of the polarimetric camera. The proposed method can meet the need of on-orbit polarization calibration with a higher accuracy. This paper is organized as follows. In Section 2, the main polarization parameters of the camera are analyzed, based on the principle of a multichannel polarimetric camera. In Section 3, the new polarization calibration method is proposed, including the calibration model, on-orbit measurement scheme and the factors acting on the calibration accuracy. Section 4 describes a verification experiment which is conducted to verify the accuracy of the polarization calibration method that this paper proposed. In Section 5, we summarize and conclude the paper.
## 2 Methods  
### Architecture Overview  
To address CNN's limitations in capturing long-range dependencies and the constraints of columnar Transformers for semantic segmentation, we propose a parallel encoder-decoder structure as shown in Figure 1. Both the encoder and decoder adopt a stepped design instead of the traditional columnar approach, decoupling model performance and computational cost from input resolution and reducing computational cost. It progressively extracts features by reducing spatial dimensions while increasing feature depth, allowing multi-scale contextual information capture.  
Inside the encoder, the multi-head attention mechanism in TEMs alleviates gradient correlation issues from stacked CNN layers, effectively capturing global context. Features learned by CEMs are passed to subsequent stages to strengthen the Transformer's inductive bias, thereby enhancing learning and generalization capabilities. To retain maximum feature information and enrich the model's feature representation for subsequent extraction, tensor concatenation fuses the outputs of the encoders. Notably, the CNN and Transformer modules learn the image features in parallel rather than sequentially. This approach avoids the potential for irreversible defects in feature maps caused by previous stages of a sequential process. Parallel learning preserves the CNN's strength in capturing local features while utilizing the Transformer's capability to understand global context.  
Inside the decoder, LCM leverages the continuous distribution and shapes of roads, gathering long-distance information from multiple directions to enhance segmentation accuracy. Each decoder layer directly corresponds to an encoder layer, preserving local details through skip-connections and preventing information loss during upsampling due to feature sparsity and context reduction. This skip-connected trapezoidal structure  
Figure 1: The overall architecture comprises a multi-level encoder with parallel CEM and TEM and LCM connected to the corresponding encoder levels via skip connections.  
achieves efficient multi-scale feature fusion, utilizing both low-level and high-level feature information to improve road edge and detail segmentation. Additionally, it enhances noise resilience and robustness.  
At each stage, features are processed in parallel through both the CEM and TEM then fused and passed to the next stage for further learning. At lower scales, local details such as edges, textures, and fine features are captured; at medium scales, broader shapes, road contours, and environmental context are represented; and at higher scales, global features including road networks and major highways are captured, providing long-range dependencies. The parallel approach generates multi-scale feature maps that effectively capture both global context and fine-grained details.
## 3 Technical Details of the Experiments  
Figure 1 depicts the experimental setup for determining the NO pressure broadening coefficients, which is similar to the setup used in other studies [23,26]. To measure the transition lines in the NO 1\(\leftarrow\)0 fundamental band, a 5.2 \(\upmu\)m interband cascade laser (ICL) (Nanoplus GmbH) was used. To capture the targeted transition lines using the wavelength scanned TDLAS technique, the NO laser current was scanned by a 140 Hz linear saw-tooth-shaped signal generated by a function generator (Agilent, 33220B). A low-noise laser current driver (Thorlabs, LDC8002) controls the laser current, while a Peltier-element controlled by a temperature controller stabilizes the laser temperature (Thorlabs TEC8020). In this study, a 7 cm long stainless-steel single-pass gas cell with two CaF2 windows was used. The laser beam was focused onto a photodetector after passing through the gas cell (VIGO, PVI-4 TE-5). A 16-bit 625 kSps DAQ card was used to record the detector output signal (National Instruments, PXI-6363). An absolute capacitance manometer (AA02A Baratron by MKS, range 1000 mbar) was used to measure gas pressure. A Pt100 sensor was used to measure the temperature of the gas cell. Both temperature and pressure sensors were calibrated and are traceable to the SI using PTB's national pressure and temperature standards.  
The optical set-up is depicted in detail in Figure 1a, showing the alignment of the laser beam. The absorbing beam shown in red was coupled into the gas cell via two mirrors and projected onto the detector surface by another concave mirror. Before each experiment, a  
Figure 1: (**a**) The spectrometer’s experimental configuration for determining the pressure broadening coefficients, where PS: pressure sensor; PD: photodetector; FG: function generator; DAQ: data acquisition card; (**b**) Measured typical laser intensity with two NO absorption peaks.  
Germanium crystal etalon (Q-MACS, Length 76.25 mm) was placed in the beam path to generate the interference signal that was used to calculate the optical frequency domain of the scanned frequency range (i.e., to convert the \(x\)-axis domain to wavenumber).
## 4 The Proposed Framework Approach
### Implementation of the Proposed DRL Framework  
This section focuses on maneuvering a telepresence robot in an unfamiliar healthcare environment. This article implements the model of DRL to control the telepresence robot. It initiates with a common neural network with the cyclic neural network of the GRU unit. Furthermore, it merged the DDQN [50] model with the real neural network. The real neural network has enhanced the study by using the GRU [51] unit's memory ability. The discount factor function, reward value function, and model loss function are more efficient for controlling the telepresence robot. DDQN algorithm is explained in Algorithm 1.  
```
1: Initialize: online network \(Q_{\theta}\) and replay buffer \(\mathcal{R}\), target network \(Q_{\theta}\), with weights \(\theta^{\prime}\leftarrow\theta\)
2:for each episode, do
3:   observation of the current state \(s_{t}\)
4:   for each step in the environment, do
5:   select action \(a_{t}\sim\pi(Q_{\theta}(s_{t}))\) with respect to the policy \(\pi\)
6:   implement action \(a_{t}\)
7:   observation of the next state \(s_{t+1}\) and reward \(r_{t}=R(s_{t},a_{t})\)
8:   store \((s_{t},a_{t},s_{t+1},r_{t}\) in replay buffer \(\mathcal{R}\)
9:   adaptation of the current state \(s_{t}\gets s_{t+1}\)
10:   end for
11:   for each update step, do
12:   sample N of experiences with \(e_{i}=(s_{i},a_{i},s_{i+1},r_{i})\) from replay buffer \(\mathcal{R}\)
13:   calculate expected Q values; \[Q^{*}(s_{i},a_{i})\approx r_{i}+YQ_{\theta^{\prime}}\bigg{(}s_{i+1},\underset{ a^{\prime}}{argmax}Q_{\theta}(s_{i+1},a^{\prime})\bigg{)}\]
14:   calculate loss \(L=1/\kappa\sum_{i}\big{(}Q^{*}(s_{i},a_{i})-Q_{\theta}(s_{i},a_{i})\big{)}^{2}\)
15:   calculate the stochastic gradient step on L
16:   updatation of target network parameters: \(\theta^{\prime}\leftarrow\tau\theta+(1-\tau)\theta^{\prime}\)
17:   end for
18: end for
```  
**Algorithm 1**Double Deep Q-Networks Learning (DDQN)  
Figure 10: Proposed approach to control telepresence robot in assistance with human teleoperator.  
Primarily, we need to generate a replay buffer \(\mathcal{R}\) and initialize it online along with target artificial neural networks (ANNs). A duplicate of the online network with identical weights is found in the target network. The agent's interaction with the environment is represented in the first for-loop, as mentioned in lines 4-10. The learning procedure enabled by the experience replay concept is described in the second for-loop, as mentioned in lines 11-17. The memory storage stores all transactions between the environment and the agent in the form of tuples (\(s_{t}\), \(a_{t}\), \(s_{t+1}\), \(r_{t}\)) is served by the replay buffer \(\mathcal{R}\), which allows the agent to reuse accumulated experience for better data efficiency.
# An EMD-SARIMA-Based Modeling Approach for Air Traffic Forecasting
## 5 Result Analysis
### International Passenger  
The forecasting results and comparisons with other models for international passenger flow are presented in Table 4 and Figure 8. From the results, it can be concluded that:  
1. Consistent with the domestic passenger cases, the EMD-SARIMA model forecasts with the best accuracy, followed by the SARIMA model, the Holt-Winters model, and the naive model, especially for the local maximum and minimum values;
2. Due to greater variability of the international passenger data, the performances of the forecasting models generate larger prediction errors compared with the domestic passenger series;
3. Again, the EMD-SARIMA model forecasts with better accuracy consistently regardless of whether it is single- or multi-step forecasting.  
Authors should discuss the results and how they can be interpreted in perspective of previous studies and of the working hypotheses. The findings and their implications should be discussed in the broadest context possible. Future research directions may also be highlighted.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Six-Month Horizon Forecast**} \\ \hline  & EMD-SARIMA & SARIMA & Holt–Winters & Naive \\ MAE & 0.15 & 0.25 & 0.31 & 0.23 \\ MAPE(\%) & 5.45 & 9.10 & 11.33 & 8.09 \\ \hline  & \multicolumn{4}{c}{**Twelve-Month Horizon Forecast**} \\ \hline  & EMD-SARIMA & SARIMA & Holt–Winters & Naive \\ MAE & 0.15 & 0.19 & 0.21 & 0.18 \\ MAPE(\%) & 6.01 & 7.46 & 8.06 & 6.96 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different model performances on six- and twelve-month horizons for international passengers.  
Figure 7: Forecasting results of the international cargo series.
## 3 Types of Supplementary Dampers and Their Characteristics
### Viscous Dampers
#### 3.1.6 Applications  
Viscous dampers find applications in a wide range of structures due to their versatility and effectiveness (Figure 7). In building structures, they are commonly used in high-rise buildings, hospitals, and data centers, where vibration control is critical for functionality and occupant comfort. Bridge structures, particularly long-span bridges and cable-stayed bridges, also benefit from viscous dampers to mitigate wind-induced and seismic vibrations. Industrial facilities such as nuclear power plants and offshore platforms utilize these dampers to enhance safety and operational reliability under various loading conditions.
# Optimization Strategy for Electric Vehicle Routing under Traffic Impedance Guidance
## 2 Establishment of the Electric-Power-Traffic Network Model
### Dynamic Traffic Network Model  
In the road network topology model described in Section 2.1, the factors influencing vehicle travel are only the node positions and road segment lengths, which constitute a static road network model. However, EVs are often affected by various factors, such as road traffic volume and travel time throughout the day, and the restrictions they encounter during congested and uncongested periods are different. In a static road network, the road lengths do not change with the operating conditions, and considering only the distance of road segments in EV travel paths without considering the real-time traffic conditions can increase user costs and exacerbate traffic congestion. In this paper, we combine real-time traffic operation information of the road network and use graph theory methods to establish a dynamic traffic network model [22], as shown in Equation (3):  
\[\left\{\begin{array}{l}G^{\rm T}=\left(V,E,T,W^{\rm T}\right)\\ V=\left\{v_{i}|i=1,2,\cdots n\right\}\\ E=\left\{v_{ij}|v_{i}\in V,v_{j}\in V,i\
eq j\right\}\\ T=\left\{t|t=1,2,\cdots k\right\}\\ W^{\rm T}=\left\{w_{ij}^{t}|v_{ij}\in E,t\in T\right\}\end{array}\right. \tag{3}\]  
In the equation, G\({}^{\rm T}\) represents the dynamic traffic network, which consists of the following basic elements: V, E, and W\({}^{\rm T}\). V represents the set of all nodes in the road network, i.e., road intersections. E represents the set of all road segments in the road network. W\({}^{\rm T}\) represents the set of weights for each road segment, which quantifies the attributes of the road or the factors restricting vehicle travel. The dynamic road network weights can be quantitatively studied using variables such as varying traffic speeds, travel times, and toll fees. T represents the k time intervals partitioned within a day, with each interval corresponding to different road network operating conditions.  
In comparison to the static road network model, the dynamic road network model takes into account the actual operating conditions of the roads at all times of the day. For  
Figure 2: Topological structure of the traffic road network.  
example, it can use traffic volume at different times of the day and real-time road travel time as road network weights, which can better reflect the factors limiting vehicle travel in actuality compared to just considering the length of the road segments. This allows for a more reasonable guidance of user travel behavior.
###### Abstract  
To date, physical, numerical or data-driven models have been used to forecast water surface elevation in rivers for specific times or locations in the literature. Recently, the trend of forecasting water surface elevation changed from physical and numerical models to data-driven models with the help of the development of big data processing technology and fast simulating time of data-driven models. In this study, a data-driven model with Long Short-Term Memory (LSTM) was developed using TensorFlow, one of the famous deep learning frameworks and forecasting of water surface elevation affected tidal river was performed in Hangang River, Korea. From many types of field measurements, the hourly hydrological data, precipitation, outlet discharge of dam upstream and tidal levels were selected as the input dataset through a \(t\)-test and a \(p\)-value. In particular, the hybrid activation function was proposed to alleviate the vanishing gradient and dying neuron problems generally issued in the application of the activation function. The model showed that the root mean square error (RMSE) and peak error (PE) decreased by 0.22-0.25 m and 0.11-0.21 m, respectively, and the Nash-Sutcliffe efficiency (NSE) increased up to 79.3%-97.0% compared with the single activation functions. For \(w_{1}=0.6\) and \(w_{2}=0.4\) in the hybrid activation function, the improvement of accuracy and the enhancement of the application range of the leading time interval were obtained through a sensitivity analysis. Moreover, the hybrid activation function showed a good performance. The forecasting results provided by this model can be used as reference data for the establishment of the emergency action plan (EAP).  
Keywords:flood forecasting; Long Short-Term Memory (LSTM); hybrid activation function; Hangang River +
Footnote †: journal: Applied Sciences
## 2 Experimental Setup
### Mock Circulation Loop and Flow Visualization Setup
The camera control, the data collection, and parameter settings were performed via the PFV software (Photron FASTCAM Viewer software, Photron, Tokyo, Japan). To process images from a high-speed camera, the PIV lab toolbox [14] developed for MATLAB (MathWorks, Natick, MA, USA) was used. A direct Fourier transform correlation with multiple passes and deforming windows was used for the data analysis. In most cases, this algorithm gives more accurate results. The algorithm analyzes the data in several stages: the first pass uses relatively large interrogation areas to calculate the displacement of image data reliably. The larger the interrogation areas, the better the signal-to-noise ratio, and the more robust the cross correlation. However, large interrogation areas will only give a very low vector resolution (\"vectors per frame\"). Therefore, in the following passes a smaller window size was used. The displacement information of the first pass is used to offset the interrogation areas in the second pass and so on (Figure 5). This procedure yields a high vector resolution, a high signal-to-noise ratio, and a high dynamic velocity range. With the high-speed camera resolution and the height of the camera above the model of 200 mm, 1 pixel is equal to 60 microns of the real distance. Particles have a size of 1-2 pixels. The estimated maximum particle velocity can reach 2 m/s or 9 pixels per frame. Thus, the minimum window of the first pass should be at least 18 pixels. For the faster algorithm operation, a window of 32 pixels was selected (Figure 5). Figure 4: (**A**) Photo of the experimental bench and (**B**) optical scheme of the experimental bench, where BS is beam splitters, M—mirrors, L—cylindrical lens.
## 4 Methodology
### Evaluation Metrics  
To evaluate the performance of the ensemble feature importance and the different ensemble strategies, we employ three different evaluation metrics, namely, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and \(R^{2}\).
## 3 Minimum Expected Delay Based Routing Protocol
### Data Transmission Algorithm  
In the MEDR algorithm, routing decision is made based on MED and its valid period. For any one node e.g., node i, let it have Z neighbors at the current time t and \(\sum\) = \(\left\{\Psi_{z}\mid 1\leq z\leq Z\right\}\) be the set consisting of the Z neighbors. Through hello messages, node \(i\) learns the mobility parameters, MED and valid period of each neighbor, and further calculates out the link expiration time between it and every neighbor. When node \(i\) has a message M it needs to forward, the routing decision process is as follows: firstly node \(i\) finds out such node(s) with the minimum MED value in all neighbors satisfying \(T_{\Psi_{z}}^{E}>t\), \(T_{\Psi_{z}}^{M}>t\). If it finds none, then node \(i\) has no proper next hop and the routing algorithm ends. Otherwise, if there is only one such neighbor, then it is denoted as m; or else node \(i\) chooses the neighbor with the maximum value of \(\min\)(\(T_{\Psi_{z}}^{M},T_{\Psi_{z}}^{E}\)) among the findings, denoted as m. Secondly, node \(i\) checks whether \(T_{i}^{E}>t\) and \(T_{i}^{S}\leq MED_{m}+t_{hop}\) both hold. If so, then node m is not the proper next hop and the routing algorithm ends; otherwise node m is just the next hop what node \(i\) is looking for, and thus message M will be forwarded to it. The pseudo-code of the routing algorithm is shown in Figure 3.  
Figure 3: Pseudo-code of the routing algorithm.
# Interference Alignment in Multi-Hop Cognitive Radio Networks under Interference Leakage
## 2 Signal and System Model
### Interference Alignment Approach and End-to-End SNR Analysis
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Definition** \\ \hline \(M\) & The number of transmit and receive antennas \\ \(K\) & Number of hops \\ \(L\) & Number of primary users \\ \(\mathbf{H}\) & Channel coefficient matrix \\ \(\mathbf{V}\) & Precoding matrix \\ \(\mathbf{U}\) & Linear suppression matrix \\ \(\mathbf{Q}\) & QR decomposition matrix \\ \(\mathbf{S}\) & Singular-value decomposition matrix \\ \(\alpha\) & The interference leakage coefficient \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the proposed system. and single-output (SISO) channel if one data stream is sent at each transmitter [15]. Thereby, the instantaneous SNRs between \(S\to R_{1}\), \(R_{i}\to R_{i+1}\), and \(R_{K-1}\to D\) can be expressed as:  
\[\Gamma_{S\to R_{1}}=\mathcal{P}_{S}\frac{|h_{S\to R_{1}}|^{2}}{ \sigma_{N}^{2}},\] \[\Gamma_{R_{i}\to R_{i+1}}=\mathcal{P}_{R_{i}}\frac{|h_{R_{i} \to R_{i+1}}|^{2}}{\sigma_{N}^{2}},\text{ and}\] \[\Gamma_{R_{K-1}\to D}=\mathcal{P}_{R_{K-1}}\frac{|h_{R_{K-1} \to D}|^{2}}{\sigma_{N}^{2}}, \tag{7}\]  
where \(\mathcal{P}_{S}\), \(\mathcal{P}_{R_{i}}\), and \(\mathcal{P}_{R_{K-1}}\) denote the transmit powers of \(S\), \(R_{i}\), and \(R_{K-1}\). \(h_{S\to R_{1}}\), \(h_{R_{i}\to R_{i+1}}\), and \(h_{R_{K-1}\to D}\) denote the channel fading coefficients between \(S\to R_{1}\), \(R_{i}\to R_{i+1}\), and \(R_{K-1}\to D\) hops, respectively, which are modeled as zero mean and unit variance.