esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we proposed an infrared and visible image adversarial network based on cross-scale attention mechanism, termed DAGANFuse. Different from the existing end-to-end fusion methods, we designed a cross-scale differential features attention module to let fused images contain more complementary information of infrared and visible images on the basis of combining the intensity and texture information of the source images. A parallel path of differential features and fusion features is adopted to calculate the attention weights, and parallel spatial and channel attention weight calculations are carried out on the two paths. Moreover, a cross-scale decoder framework is constructed to interact with feature information of different scales and continuously optimize the activity level in an iterative manner. In terms of the discriminator, a dual discriminator is used to establish a more balanced adversarial game with the generator. In addition, the effectiveness of the key parts of DAGANFuse is verified through ablation experiments, and comparisons with seven state-of-the-art methods are made on the public datasets TNO and Roadscene. Qualitative and quantitative analyses verify that DAGANFuse has significant advantages in fusion performance.  
**Author Contributions**: Conceptualization, Y.W.; methodology, Y.W.; software, Y.W.: validation, Y.W.; formal analysis, W.L.: investigation, Y.W.: resources, W.L.: data curation, Y.W.: writing--original draft preparation, Y.W.: writing--review and editing, Y.W.: visualization, Y.W.: supervision, W.L.: project administration, Y.W. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This research received no external funding.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **EN** & **SD** & **AG** & **MI** & **VIF** & **SF** \\ \hline FusionGAN & 7.0087 & 37.1567 & 2.6824 & 1.8554 & 0.2914 & 5.8114 \\ DDcGAN & 7.5950 & **56.6306** & 3.5391 & 1.4670 & 0.1763 & 7.0693 \\ MDLatLRR & 7.3976 & 54.5890 & 5.3283 & 1.2881 & 0.3524 & 10.2970 \\ DenseFuse & 7.2343 & 42.0498 & 3.3448 & 1.7809 & 0.3531 & 6.8627 \\ MFEIF & 7.0695 & 38.8528 & 2.9874 & 2.1377 & 0.3787 & 6.3558 \\ Res2 Fusion & 7.3308 & 46.6483 & 3.3744 & 2.3449 & 0.4073 & 7.1569 \\ SDDGAN & 7.5453 & 56.1106 & 3.2748 & 2.0076 & 0.3529 & 6.7498 \\ DAGANFuse & **7.8632** & 47.3390 & 6.4322 & **2.4970** & 0.4505 & **12.3760** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The average metrics values obtained by the existing fusion methods and the proposed network on Roadscene.  
Data Availability Statement: The data that support the findings of this study are available from the authors upon reasonable request.
# The Preparation of Active Support-Based Sealing Material and Sealing Effect Analysis
## 3 Analysis of Mechanical Characteristics of Sealing Material
### Analysis of Active Support Force of Sealing Material  
In order to study the active support force of sealing materials with different expansion rates, an active support force testing device is designed, which is mainly composed of an upper baseplate, lower baseplate, bolts and cylinder, as shown in Figure 9. The test mechanism of this device is as follows: The mixed grout is poured into a cylinder with an inner diameter of 50 mm. After a hydration reaction, volume expansion occurs in the grout, and the resulting expansive force is applied to the inner wall of the cylinder, resulting in the deformation of the inner wall. Four strain gauges are uniformly pasted around the cylinder wall to obtain the deformation. The expansive force of different sealing materials can be obtained by formula conversion, and the active support force calculation formula of sealing materials is [32] expressed as follows:  
\[p=\frac{E\varepsilon\Big{[}(r+\delta)^{2}-r^{2}\Big{]}}{2(1-\mu^{2})r^{2}}, \tag{2}\]  
where \(p\) denotes the expansive force, MPa; \(E\) represents the elastic modulus of the cylinder, MPa; \(\mu\) is Poisson's ratio; \(r\) denotes the inner diameter of the cylinder, mm; and \(\delta\) refers to the thickness of the cylinder wall, mm.  
Figure 10 shows the active support force-time curve of sealing materials with different expansion rates. As can be seen, the active support force of the sealing materials has a significant time effect. It increases rapidly at first, and then its growth rate slows down and tends toward a certain value. For the sealing materials with different expansion rates, the greater the expansion rate is, the greater active support force the material generates. When reacting for 24 h, the material with an expansion rate of 5% can provide an active support force of 1.44 MPa, and that of 3% and 1% can provide 0.68 MPa and 0.46 MPa, respectively. The maximum active support force that the material with an expansion rate of 5% can provide reaches 2.63 MPa, and that of 3% and 1% reaches 2.1 MPa and 1.4 MPa, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Expansion Rate** & **0\%** & **1\%** & **3\%** & **5\%** \\ \hline Cohesion C/MPa & 1.81 & 1.63 & 1.20 & 0.58 \\ Internal friction Angle \(\varphi/^{\circ}\) & 25 & 23 & 16 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Internal friction angle and cohesion of specimens with different expansion rates.  
Figure 9: The active support force testing device.
# Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study
## 4 Results and Discussion
### Photointerpretation with Orthophoto Results
In Figure 16, the result of the flooding map obtained with the RADAR image is compared with the layers with different return periods (10, 50, 100, 500 years) for a selected zone of the study area of SNCZI. On one hand, all flooded areas derived from Sentinel-1 SAR images are included inside the boundary for flood risk areas to 50, 100, and 500 years return period of SNCZI (Figure 16c-e. While, on the other hand, there are flooded areas that are outside the limits of the 10-year flood risk areas (Figure 16b). Therefore, we conclude that the results of flood area mapping derived from Sentinel-1 images are consistent with those of SNCZI, and correspond to a flood between 10 and 50 years of return. In view of the results obtained, the usefulness of Sentinel-1 images as baseline data for the improvement of the methodological guide is appreciated, and should be used as a new source of input, calibration, and validation for hydrological models to improve the accuracy of flood risk maps. Flood maps obtained from Sentinel-1 SAR images complement and validate the information obtained by other means and help to understand the spatial and temporal evolution of the flood. Our results advise using flood maps derived from Sentinel-1 SAR images for the calibration and evaluation of hydrological models and as a data source that can complement and be compared with analysis obtained from other methodologies. \begin{table}
\begin{tabular}{c c} \hline \hline
**Flood area RADAR 13 April 2018** & 2640.61 ha \\ \hline
**Flood area Orthophotography 14 April 2018** & 4653.13 ha \\ \hline
**Common flood area** & 1767.88 ha \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Flooded surface values in the Ribera Alta. Figure 16: Comparative maps between (**a**) flood area RADAR image (13 April 2018) and (**b**) flood area high probability (10-year return period), (**c**) frequent flood zone (50-year return period), (**d**) flood zone of medium or occasional probability (100-year return period), and (**e**) flood zone of low or exceptional probability (500-year return period) (Source: MITECO, 2017, [66]).
## 3 Results
### Distribution Uniformity Results via Catch Can Method  
To control the irrigation rate and check the distribution uniformity at 3.8 bar (\(\cong\)55 psi) operating pressure, the applied plastic cups different dimensions were measured. The can bottom radius of 2.5 cm and 19.6 square centimeter bottom area were sized with capacity of two hundred milliliters. During 4 runs of sample collection (45 min for each run), in average 15.1 milliliters of water was dispersed in each can while the low quarter average was 11.9 milliliters. In order to calculate the irrigation rate during the measurement time, these volumes of water were divided by the can bottom area at 10.3 mm per hour. Low or high amounts of water around anyone of the sprinklers (which sometimes can be confirmed via observations too) means that the area is under or over irrigated and hence it must be exchanged. In order to compute the DU, the low quarter's mean of the recorded data for 45 out of 178 samples were calculated at 6.08 mm and then divided by the entire 178 samples average for 7.72 mm.  
Some relative research have achieved a distribution uniformity of 74% for a linear system and 90% for center-pivot sprinkler irrigation systems [36]. [PERSON] and [PERSON] achieved 62% up to 84% uniformity for sprinkler irrigation [37; 38], while others obtained 57% for a semi-permanent system [39]. In this work average of 79% DU\({}_{\text{LQ}}\) was attained as a relatively high uniformity coefficient. This clearly shows that the new layout can efficiently fulfil the required water depth in every spot in the farm. The following graph (Figure 8) presents the water distribution pattern of CPL sprinkler coverage for one of the sprinklers. As can be seen, the graph is slightly imbalanced for the irrigated circle in which the amount of received water (in average) was 12 percent higher in one side.  
The results revealed a triangular shape (or bell form) of distributed water around the sprinklers, which means the pressure was satisfactory. In case of having under or over pressure sprinkler/sprayers, an imbalance distribution pattern will form that is donut shape with larger droplets (for low pressure) or having over irrigation close to the sprinkler (for high pressure) respectively [19]. Outcomes indicates that the area between the two radial legs was efficiently irrigated with an acceptable sprinklers coverage. Taking into consideration this novel layout coverage pattern, any irrigated circle in it had an overlap of 55 percent from the front side and 45 percent from the back sprinkler.  
Figure 8: Depth of irrigated water in mm & its uniformity around a sprinkler in CPL (6.5 m radius).
## 3 Methodology
### Route Discovery Using MGOA
#### 3.3.3 Derivation of Fitness Function  
In this research, fitness function is formulated using routing metric, number of hops, and intra cluster distance between the nodes for selecting the dynamic routing in WAVLCN. A new routing metric is used to find how suitable a communication link to route information between the nodes. The routing metric considers communication link quality on the basis of received optical power \(p\), [36], which is mathematically defined in Equation (10).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Description** & **Value** \\ \hline \(D\) & Population size & 22 \\ \(Lb\) & Lower bound value & 0 \\ \(Ub\) & Upper bound value & 11 \\ \(Cmin\) & Minimum value of \(c\) & 0.00002 \\ \(Cmax\) & Maximum value of \(c\) & 1 \\ \(Wf\) & Control number of nodes & 6 \\  & selected & \\ \(M\) & Problem dimension & 20 \\ \hline \hline \end{tabular} The pseudocode of MGOA is represented in the following Algorithm 1.  
\begin{tabular}{c} \hline \hline Initialize \(X(t)\), _Maxitr_, _cmin_ and _cmax_ \\ Analyze every solution by using random forest classifier \\ Set \(T\) as the better solution \\
**While** (\(t<\)_Maxitr_) \\ Update \(c\) using Equation (9) \\
**For** every solution \\ Normalize the separation of grasshoppers using Equation (5) \\ Update the current solution location using Equation (8) \\ Change the current solution, if the search space boundary violates \\
**End for** \\ If there is a better solution in the population, update \(T\) \\ \(t=t+1\) \\
**End while** \\
**Return**\(T\) \\
**End** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter setting of MGOA.  
Here, the multi-objective function is converted into single objective using the weighted values \(\gamma_{1},\ \gamma_{2}\), and \(\gamma_{3}\), as shown in the Equation (10).  
\[p_{r}=\gamma_{1}p_{t}H(0)+\gamma_{2}NH+\gamma_{3}dist \tag{10}\]  
where \(p_{t}\) denotes transmit optical power of nodes, \(H(0)\) represents channel gain, \(NH\) denotes the number of hops for each node, and \(dist\) denotes the distance between the nodes. The weighted values such as \(\gamma_{1},\ \gamma_{2}\), and \(\gamma_{3}\) are equal to the 0.5, 0.3, and 0.2 (i.e., \(\sum\limits_{i=1}^{3}\gamma_{i}=1\)). Based on fitness function mentioned in Equation (10), an optimal data transmission path is selected in each iteration of the MOOA. Next, the determined best path (i.e., Target position \(Td\)) is used to update the positions of the grasshopper for the remaining iterations, as shown in the Equation (8). In order to perform effective data transmission, the nodes require a smaller number of hops. The nodes with a smaller number of hops is a more preferable choice for data transmission [37]. In addition, the third objective function is inter-cluster distance between the nodes. The nodes consume less energy to process the data if the transmission distance is at a minimum. In this scenario, the Euclidean distance measure is used to find the distance between the nodes. The experimental analysis of the proposed MOOA-WAVLCN is briefly explained in Section 4.
# Detecting the Structural Hole for Social Communities Based on Conductance-Degree
## 2 Related Work
### Definitions and Notations  
It is necessary for us to introduce several fundamental notations and background regarding social networks before we formally explain our model. Conductance and degree are often used to develop communities or cluster in social networks. These parameters explain the influence and importance of nodes. The conductance describes the topology structure of nodes in the network.  
The expression \(G=(V,E)\) represents an undirected connected graph, where \(V\) is a set of vertices and \(E\) contains the edges representing the relationships between those vertices, given two sets of vertices \(S,T\), with no common part between them. \(E(S,T)\) is a set that represents the edges between the two groups and \(\text{cut}(S,T)\) represents the cut of the two sets, that is, the number of the edges between \(S\) and \(T\). The conductance of a cluster is defined as the probability that a one-step random walk begins in one cluster and finally leaves that cluster. S_bar is the complement of S. The conductance of the set S and S_bar, denoted \(\phi(S)\), is as follows:  
\[\phi(S)=\frac{\text{cut}(S)}{\min(d\_sum(S),d\_sum(S\_bar))} \tag{1}\]  
There is \(\phi(S)\in[0,1]\) and \(\phi(S)=\phi(S\_bar)\). \(\text{cut}(S)\) represents the cut of S and S_bar. \(d\_sum(S)\) represents the sum of degrees of the vertices in S. If given \(\text{degrees}(S)\) is twice the number of edges among vertices in S, we have the following:  
\[\text{edges}(S)=d\_sum(S)-\text{cut}(S) \tag{2}\]Let us define a single vertex \(v\)'s neighborhoods as \(N(v)=\{w[d(w,v)=1]\), where \(d(w,v)\) represents the length of the shortest path between \(w\) and \(v\). Now, put \(v\) and \(N(v)\) into a group as a neighbor community of \(v\). If the conductance of the neighbor community of vertex \(v\) is smaller than the conductance of the neighbor community of any neighbor vertex \(w\), the neighbor community of \(v\) is an LMC. Additionally, to the notation of conductance, the lower the conductance, the fewer the cut(S). That indicates fewer communications with others and more information exchange within the group; it is more likely to be a community, so it is appropriate to consider an LMC as an original community. The LMC can be explained as follows:  
\[\forall w_{i}\in N(v),\phi(N(v))\leq\phi(N(w_{i})) \tag{3}\]  
where \(N(v)\) represents the neighbor community of \(v\) and \(N(w_{i})\) represents the neighbor community of \(w\).  
Conversely, the more the conductance, the more the cut(S) is, which indicates that the neighbors of the node have more communications with others than with the node's other neighbors, as shown in Figure 2. There are few relations between the node in dark color and its neighbors, whereas there are more relations both in the left and right groups. In this paper, we consider both conductance and degree to detect structural holes.
# Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control
## 3 Methodology  
### Study Location  
This study was conducted at a signalized traffic intersection located in Balgat, Ankara, Turkey, specifically at the intersection of Kazlirrmak, Ufuk Unv. Cd No:18, 06520 Cankaya/Ankara, as shown in Figure 1. This intersection was chosen based on its representation of typical urban traffic conditions and its suitability for evaluating the impact of AVs on traffic dynamics. The specific location details, including traffic volume, signal timings, and geometric characteristics, were considered to ensure the simulation's accuracy and relevance to real-world scenarios.
# On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks
## 4 UWSNs: Communication Models  
As discussed in the introduction section, the characteristics of acoustic wave propagation are different from the characteristics of radio wave propagation. Thus, precise models are needed.
# Advances in the Neural Network Quantization: A Comprehensive Review
## 1 Introduction
It is necessary to study the feedback of the deployment model on the hardware side of the accuracy as well as the efficiency loss through both the neural model accuracy loss assessment and speed assessment method, optimizing the bit width selection process through the accuracy and efficiency loss of the deployment model. Most of the existing methods tend to be computationally demanding, and some rely on manual configuration to optimize the initial session, posing challenges for different model and device setups, and there is a need to build a fast and initialization-free bit-width search technique to address these issues. For neural network model quantization parameter generation technology, the current quantization methods are usually only for single application scenarios. Most of them adopt the unified symmetric uniform quantization mapping strategy, but their robustness is poor, and they cannot adapt to different neural network model structures, hardware resource limitations, or computational task requirements, especially for computational tasks with high accuracy requirements or difficult computational tasks, such as small target detection and identification tasks. Existing quantization methods have difficulty meeting accuracy requirements, resulting in a huge loss of performance. Current comprehensive research on quantization methods includes how to improve the accuracy of the quantized neural network model. This is a key problem to be solved, and it is necessary to study how to automatically select the appropriate quantization threshold according to the distribution of the data, how to combine the quantized features with the quantization error to be considered in the training, how to solve the problem of mismatch between quantized forward propagation and back propagation, and how to make the neural network model more friendly to quantization. In this paper, we compare and analyze existing neural network model quantization methods, give the deployment accuracy results of the existing advanced methods in the field, and summarize the technical difficulties and development trends of future neural network quantization technology, providing practical guidelines. The rest of the paper is organized as follows: Section 2 describes the quantization fundamentals. Section 3 describes several key techniques of quantification. Section 4 provides details of the experimental results and analysis. Section 5 concludes our work and discusses the future challenges and trends.
# Estimating Destination of Bus Trips Considering Trip Type Characteristics
## 2 Literature Review  
### Methodology for Estimating Destination Based on Trip Chain  
A trip-chain-based estimation methodology has been used as the most basic method of estimating alighting stops when alighting tag information is missing. Basically, trip-chain-based alighting stop estimation studies assume that the passenger travels many times consecutively and starts the next trip around the destination of the previous trip. Based on this assumption, they estimate alighting stops when there are two or more boarding records per day for the same card ID [1; 2; 3; 4; 5]. Some studies assume that the passenger returns to the resident in the case of the last trip on the analyzed day, while the origin of the first trip on a pertinent day is the resident [7; 8; 9; 10; 11]. In some other cases, studies look at the origin of the first trip on the next day to estimate the alighting stop of the final trip on that day [12; 13]. Studies have also been conducted for a trip-chain-based alighting stop estimation methodology by considering public transport routes (railways and buses) [14; 15].  
Some studies validated the estimation results to improve the success rate and accuracy of alighting stop estimations and analyzed major parameters and sensitivity. Specifically, studies were conducted on the validation of trip-chain-based alighting stop estimation results [16; 17]; sensitivity analysis for \"allowed walking distance\" to determine the continuity between two trips as a major parameter when estimating the alighting stop based on the trip-chain [9; 10]; determining whether the passenger transferred and analyzing the walking journey time in terms of the trip-chain-based estimation [11; 18]. The trip-chain-based alighting stop estimation method is applicable only when the trip-chain can be constructed, and cannot be applied to a single trip.
## 2 Materials and Methods
### Data Analysis  
Kinetic data for the study were obtained using the following pseudo-first-order (PPO, Equation (1)) [33], pseudo-second-order (PSO, Equation (2)) [34], and Elovich (Equation (3)) [35] models:  
\[q_{t}=q_{e}[1-\exp(-k_{1}t)] \tag{1}\]  
\[q_{t}=\frac{k_{2}q_{e}^{2}t}{1+k_{2}q_{e}t} \tag{2}\]  
\[q_{t}=\frac{1}{\beta}\ln(1+\alpha\beta t) \tag{3}\]  
where \(q_{t}\) (mg/g) is the adsorbed amount of heavy metal at time \(t\), \(q_{e}\) (mg/g) is the adsorbed amount of heavy metal at equilibrium, \(k_{1}\) (1/min) is the PFO rate constant, \(k_{2}\) (g/mg\(\cdot\)min) is the PSO rate constant, \(\alpha\) is the initial rate constant (mg/g\(\cdot\)min), and \(\beta\) is the desorption constant (g/mg). Kinetic adsorption data within 180 min were used to analyze PFO, PSO, and Elovich models. The intraparticle diffusion (IPD, Equation (4)) [36] model was also used to analyze kinetic experimental data from 0 to 1440 min:  
\[q_{t}=k_{i}t^{0.5}+C \tag{4}\]  
where \(k_{i}\) is the IPD rate constant (g/mg/h\({}^{0.5}\)), and \(C\) is the intercept that reflects the boundary-layer effect or surface adsorption (mg/g).  
The equilibrium data were analyzed using the [PERSON] isotherm model (Equation (5)) and Freundlich isotherm models (Equation (6)) [37]:  
\[q_{e}=\frac{q_{o}bC_{e}}{1+bC_{e}} \tag{5}\]  
\[q_{e}=K_{F}C_{e}^{1/n} \tag{6}\]  
where \(q_{0}\) (mg/g) is the maximum biosorption capacity, \(b\) (L/mg) is the Langmuir constant, \(C_{e}\) (mg/L) is the metal concentration in solution at equilibrium, \(K_{F}\) ((mg/g)\(\cdot\)(L/mg)\({}^{1/n}\)) is the distribution coefficient, and \(n\) is the Freundlich constant. The variable was determined by fitting the [PERSON] and Freundlich models to the observed data.  
All parameters of the kinetic and equilibrium models were estimated by non-linear regression using the Dynamic Fit Wizard function of Sigma-Plot 10.0 (SPSS INC, Chicago, IL, USA). The goodness of model fit was expressed by the determination coefficient (\(R^{2}\)) and the sum of squared errors (\(SSE\)).