esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Software and Methodology
### Bacterial Chemotaxis Model  
To explore the feasibility of applying chemotactic bacteria in the industrial plant area, a numerical simulation was conducted, incorporating a bacterial concentration field to simulate bacterial chemotaxis. The free movement of bacteria is represented by an effective diffusion coefficient, while convection is determined by the flow velocity obtained from the flow field model. An additional chemotactic term is introduced to account for motion towards the gradient of the attractant concentration [26]. The term of bacterial chemotaxis is coupled to pollutant diffusion processes by customizing equations in COMSOL.  
\[v_{Clx}=\frac{2}{3}v_{b}\tanh\left(\frac{\chi_{0}}{2v_{b}}\frac{K_{c}}{(K_{c}+ a)^{2}}\frac{\partial a}{\partial x}\right), \tag{8}\]  
\[v_{Chy}=\frac{2}{3}v_{b}\tanh\left(\frac{\chi_{0}}{2v_{b}}\frac{K_{c}}{(K_{c}+ a)^{2}}\frac{\partial a}{\partial y}\right), \tag{9}\]  
\[v_{Chz}=\frac{2}{3}v_{b}\tanh\left(\frac{\chi_{0}}{2v_{b}}\frac{K_{c}}{(K_{c}+ a)^{2}}\frac{\partial a}{\partial z}\right), \tag{10}\]  
In this context:  
* \"\(v_{b}\)\" represents the bacterial free-swimming speed.
* \"\(a\)\" represents the attractant concentration.
* \"\(\chi_{0}\)\" stands for the chemotaxis sensitivity coefficient, indicating the degree of response to the gradient of attractant concentration.
* \"\(K_{c}\)\" is the chemoreceptor's kinetic constant, related to the receptor saturation concentration.
* \(\partial a/\partial x\), \(\partial a/\partial y\), \(\partial a/\partial z\) represent the gradients of the attractant concentration in the x, y, and z directions, respectively.  
Bacterial chemotaxis velocity is directly proportional to the local gradient of attractant concentration and is also influenced by the bacterial chemotactic parameters.
## Approach based on non-overlapping community
### Graphical Representation Learning  
In recent years, due to its powerful performance and wide application range, the research on graph representation learning [16] has received more and more attention, and it has been widely used in social network analysis such as community detection, node classification, link prediction, and behavior analysis. There is significant related work in this field, and researchers divide these into dimensionality reduction-based, random walk-based, graph decomposition-based, and neural network-based methods according to different graph embedding methods. The methods based on dimensionality reduction include PCA [17], LDA [18], MDS [19], etc. Representative works based on random walk include DeepWalk [20] and node2 vec [21]. The method based on graph decomposition realizes graph embedding by decomposing the adjacency matrix of the graph; representative works include graph Laplacian eigenmaps [22], GraRep [23], and so on. Recently, inspired by RNNs and CNNs, researchers have begun to generalize these to graphs, resulting in a new class of neural network-based methods. This class of methods integrates semi-supervised information into graph representation learning and has strong performance; representative works include GCN [24], GraphSAGE [25], GAT [26], etc.  
Before graph representation learning, there were also many studies applying traditional machine learning methods, such as logistic regression, SVM, etc., to social network analysis. Due to the high degree of fit between the graph data structure of social networks and graph neural networks, some researchers have begun to try to apply graph representation learning to social network influence recognition, such as with Deepinf [27], RCNN [28], and InfGCN [29]. These research works realize the embedding process of the network by extracting the relevant attributes of the node network topology structure; further, graph convolution is used to realize the aggregation of node features and generate a low-dimensional representation of the node. The trained and tuned graph neural network model can finally predict the influential nodes in the network. Experiments show that although transforming the network influence prediction task into a classification problem [28] produces more severe application scenarios, this method is better than some other methods in terms of efficiency and accuracy.
###### Abstract  
With a rising demand for utilizing unmanned aerial vehicles (UAVs) to deliver materials in outdoor environments, particular attention must be given to all the different aspects influencing the deployment of UAVs for such purposes. These aspects include the characteristics of the UAV fleet (e.g., size of fleet, UAV specifications and capabilities), the energy consumption (highly affected by weather conditions and payload) and the characteristics of the network and customer locations. All these aspects must be taken into account when aiming to achieve deliveries to customers in a safe and timely manner. However, at present, there is a lack of decision support tools and methods for mission planners that consider all these influencing aspects together. To bridge this gap, this paper presents a decomposed solution approach, which provides decision support for UAVs' fleet mission planning. The proposed approach assists flight mission planners in aerospace companies to select and evaluate different mission scenarios, for which flight-mission plans are obtained for a given fleet of UAVs, while guaranteeing delivery according to customer requirements in a given time horizon. Mission plans are analyzed from multiple perspectives including different weather conditions (wind speed and direction), payload capacities of UAVs, energy capacities of UAVs, fleet sizes, the number of customers visited by a UAV on a mission and delivery performance. The proposed decision support-driven declarative model supports the selection of the UAV mission planning scenarios subject to variations on all these configurations of the UAV system and variations in the weather conditions. The computer simulation based experimental results, provides evidence of the applicability and relevance of the proposed method. This ultimately contributes as a prototype of a decision support system of UAVs fleet-mission planning, able to determine whether is it possible to find a flight-mission plan for a given fleet of UAVs guaranteeing customer satisfaction under the given conditions. The mission plans are created in such a manner that they are suitable to be sent to Air Traffic Control for flight approval.  
unmanned aerial vehicles; UAV routing and scheduling; UAV fleet mission planning +
Footnote †: journal: Accepted: 18 September 2019; Published: 22 September 2019
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 4 Energy Market
### DR in the Energy Market
#### 4.2.2 Market Integration and Economic Models of DR  
The integration of DR into electricity markets involves sophisticated economic models and digital technologies to optimize operations and incentives. Study [67] introduces a distributed DR market clearing algorithm using cloud computing for computational efficiency, showcasing the ability of digital tools to handle market complexities. It also discusses a data-driven market strategy to coordinate dispatch between virtual power plants (VPPs) and DR aggregators, utilizing digital data processing to boost market operation efficiency. This approach employs a two-stage framework with noisy inverse optimization to accurately estimate customer load response, facilitating economically viable and efficient DR operations [68]. Additionally, Ref. [21] highlights AI's role in managing aggregator clustering problems and optimizing pricing and scheduling via multi-agent systems, which significantly improves economic and operational performance by predicting market trends. Future DR strategies may incorporate advanced ML models and game theory to navigate energy supply and demand challenges, emphasizing the need for addressing data privacy, system integration, and scalability to adapt to evolving energy and market demands.  
This streamlined version focuses on categorizing DR's impact through digital tools and economic models, maintaining fluency and cohesion while highlighting the transformative influence of AI and ML on DR strategies and market integration.
# People Detection Using Artificial Intelligence with Panchromatic Satellite Images
## 2 Materials and Methods
### Validation Process  
After applying the methods, it is necessary to determine how accurate each of the methods are. The schema of validation process is shown in the Figure 6. First, it is necessary to determine the center of gravity for each predicted polygon. This is an approximation of the most probable location of a predicted person (PP). Next, the shortest distances between the PP and the ground truth data (GT) are compared. This is calculated as follows: A Euclidean distance for each pair of GT and PP locations is determined and the nearest distance for each GT is found. Both points are then excluded from the list to allow for a subsequent search, where the next shortest distance between points is found until the shortest distances for all points have been determined. A new script to provide such a function was prepared in the Python language since the available software lacked a suitable function. The results then undergo statistical evaluation to determine the quality of the methods. The precision, recall, and F1 scores are calculated, for which it is necessary to evaluate TPs (true positives), FPs (false positives), TNs (true negatives), and FNs (false negatives). When evaluating the precision, recall, and F1 scores, the distance limits between the ground truth points and the detected points must be established. The following distance limits were set: 15 cm (corresponding to a 1/2 pixel), 30 cm, 45 cm, and 60 cm.
## 2 Stochastic Subspace Identification Method
We can further solve the eigenproblem of the system matrix \([A]\) to obtain the dynamic characteristics of the system, and the characteristic equation can be written as:  
\[[A][\Psi]=[\Psi][\Lambda] \tag{10}\]where \([\Psi]\) consists of eigenvectors, i.e., mode shapes, and \([\Lambda]\) contains eigenvalues \(\lambda_{i}\). The relationship between discrete-time matrix \([A]\) and continuous-time matrix \([A_{S}]\) can be expressed as:  
\[[A]=e^{[A_{S}]\Delta t}\in R^{2n\times 2n} \tag{11}\]  
Denote the eigenvalues of \([A]\) and \([A_{S}]\) as \(\lambda_{i}\) and \(\lambda_{si}\), respectively. The relationship between \(\lambda_{i}\) and \(\lambda_{si}\) can then be expressed as  
\[\lambda_{i}=e^{\lambda_{si}\Delta t} \tag{12}\]  
Through the eigenvalue analysis associated with the continuous-time system matrix, \([A_{S}]\), the eigenvalues \(\lambda_{si}\) can be obtained as:  
\[\lambda_{si}=-\omega_{i}\xi_{i}\pm j\omega_{i}\sqrt{1-\xi_{i}^{2}} \tag{13}\]  
Set the eigenvalues \(\lambda_{si}\) of continuous-time system matrix \([A_{S}]\)  
\[\lambda_{si}=a_{i}+jb_{i} \tag{14}\]  
The natural frequencies \(\omega_{i}\) and damping ratios \(\xi_{i}\) of the structural system can be obtained as:  
\[ \begin{array}{l}\omega_{i}=\sqrt{a_{i}{}^{2}+b_{i}{}^{2}}\\ \xi_{i}=-\frac{a_{i}}{\sqrt{a_{i}{}^{2}+b_{i}{}^{2}}}\end{array}  \tag{15}\]  
Consequently, the parametric estimation of structures can be implemented through the eigenvalue analysis associated with the system matrix, \([A_{S}]\), once the system matrix \([A_{S}]\) is obtained through the least-squares estimate from measured response data.
# An Approach to Designing Critical Railway Voice Communication
## 8 Discussion and Limitations  
One of this paper's contributions is the identification of user data management and policy management functionalities as essential components of security-oriented user requirements. Rather than implementing these functionalities repeatedly in each FRMCS application, which would require evaluating predefined rules and accessing data specific to different users, functional identities, and applications, we extracted and synthesized these functionalities as microservices that can be used by other FRMCS services and applications.  
Based on the user requirements specified in [1], FRMCS applications that are used by other FRMCS applications, namely role management and presence, location, invite-a-user messaging, assured voice communications, and multiuser talker control, are also defined as separate services and each one may run its process and manage its storage.  
The proposed approach offers the main advantage of decoupling from existing proprietary solutions. It features the well-known advantages of service-based architecture such as scalability, fault isolation, technology agnostics, easy deployment, improved data security, and flexibility. The synthesized services are easy to virtualize and cloudify, allowing them to evolve, operate, and be deployed independently.  
Along with the advantages, there are a few disadvantages that need to be considered. Deploying and running cloud services on virtualized infrastructure requires costs for security and maintenance support. Any changes to a service API will result in changes to applications that use the API. As each service has its own log files, troubleshooting may be a challenge. Finally, integrating services with different levels of internal complexity may also be difficult.
## 3 Comparison between Circular and Slab Diffusion Models
### Chloride Concentration Estimation
Figure 8 shows that the coefficent \(b\) varies slightly with \(R\) and is simplified as a constant value of 1. Thus, the final form of Equation (13) for calculating \(C_{\text{cir}}/C_{\text{slab}}\) is expressed as follows:  
\[\frac{C_{\text{cir}}}{C_{\text{slab}}}(R,x)=1.8R^{-1.3}x+1 \tag{14}\]  
According to Equation (14), the new diffusion model for circular section based on modifying slab diffusion model is expressed as:  
\[C_{\text{cir}}(x,t)=K_{\text{s}}C_{0}\Bigg{[}1-erf\Bigg{\{}x/\Bigg{(}2\sqrt{ \frac{1}{1-m}D_{\text{i}}t^{1-m}}\Bigg{)}\Bigg{\}}\Bigg{]} \tag{15}\]  
where \(K_{\text{s}}\) represents the shape influence coefficient of circular section, \(K_{\text{s}}=1.8\)\(R^{-1.3}\)\(x+1\). The error \(\eta\) for using the slab diffusion model in estimating \(C\) for the RC circular column is expressed as:  
\[\eta_{C}=1-\frac{C_{\text{cir}}}{C_{\text{slab}}}(R,x)=1.8R^{-1.3}x \tag{16}\]  
\(\eta_{c}\) corresponding to the concrete cover thickness of 4, 5, and 6 cm estimated by using Equation (16), is displayed in Figure 10. Figure 10 shows that the maximum error is over 20%. And for errors within 5%, the modified slab diffusion model (Equation (15)) is preferable for a circular column radius below 60 cm. Figure 8: Variation of the coefficient \(k\) as a function of radius. Figure 9: Plot of the concrete \(b\) versus radius.
## 3 Results
### Analytical Performance
However, using the regression equation y = \(-\)0.0786x\({}^{3}\) + 0.3805x\({}^{2}\) + 0.0796x, very low concentrations of KCN (below 28 ng mL\({}^{-1}\)) and HCN (less than 12 ng mL\({}^{-1}\)) can be measured. Nevertheless, the Lambert-Beer law cannot be applied to such low concentrations because the relationship between the absorbance values and concentrations is not linear. Separately, we added in each artificial solution of cyanide, as well as in the reference solution without cyanide, a small amount of cyanide solution (1 mL artificial solution + 0.02-0.1 mL of 2.6 \(\mu\)g mL\({}^{-1}\) KCN) so that the calibration curve becomes linear. For the new calibration curve obtained by adding 0.1 mL of 2.6 \(\mu\)g mL\({}^{-1}\) KCN, including the reference solution, a standard deviation s\({}_{\rm x}\) = 0.00593 was calculated. The limit of detection (LOD) being three times higher: LOD = 3-s\({}_{\rm x}\) = 0.177 \(\mu\)g mL\({}^{-1}\) KCN, which means 0.074 \(\mu\)g mL\({}^{-1}\) HCN. The colored product resulting from the cyanide reaction with ninhydrin was stable for more than 60 min. The slope, intercept, and the correlation coefficient evaluated by least squares regression analysis were also considered. Because the HCN concentration in the blood sample of fire victim three (M.L.) was high enough, we used duplicates of 0.5 mL of blood and not 1 mL (Tables 1 and 2). In addition, we determined the total concentration of HCN in blood, including that metabolized to the salts of thiocyanic acid. Therefore, in the outer chamber of the Conway unit, we pipetted 1 mL of water, 0.5 mL of blood, 1 mL of 20% H\({}_{3}\)PO\({}_{4}\), and 1 mL of 7.5 mg mL\({}^{-1}\) KMnO\({}_{4}\). The Conway units containing either 0.5 mL of blood plus the other reagents or 1 mL of 2.6 \(\mu\)g mL\({}^{-1}\) KCN solution (as standard) or 1 mL of water (reference) were kept on the heater at 40 \({}^{\circ}\)C for 30 min. Then, 100 \(\mu\)L of absorbent from each Conway cell were mixed with 1 mL of NR and the kinetics of the reaction was studied at 493 nm for 30 min. Only data collected 20 min after reagent mixing was shown in Tables 1 and 2. Figure 3: Linear and polynomial shape of the calibration curve: (**a**) linear calibration curve in the range of 0.26 \(\mu\)g mL\({}^{-1}\) to 2.6 \(\mu\)g mL\({}^{-1}\), which is of interest for the determination of HCN in the blood; (**b**) better results with a calibration curve of the polynomial form in the concentration range of 0–2.6 \(\mu\)g mL\({}^{-1}\).
## 1 Introduction
[33] has taken the objects proposals generated by SSD and replaces the original visual geometry group 16 (VGG16) [18] with a densely connected network [34] as the backbone network to improve the relevance of contextual information. However, this method increases the computational cost of the network, and the detection accuracy of the method for small-scale targets is poor. Figure 1: Conventional single shot multibox detector (SSD) vs. The proposed context information scene perception (CISP)Net. (**a**) SSD detects ship targets with overlapping boxes and the small aircraft targets that cannot be located. (**b**) Ship and small aircraft targets that can be located by the proposed CISPNet. To address the above problem, especially for small target detection, in this paper, we present a single-stage detector named context information scene perception (CISP)Net for target detection on remote sensing images, which is based on the Single Shot MultiBox Detector (SSD) [27] and apply a context information scene perception (CISP) module to obtain the context information for targets of different scales. Compared with other detection methods such as YOLO [24], SSD [27], DSSD [28], and RSSD [32], our framework is more suitable for target detection in remote sensing images, and has achieved the relatively advanced performance. The contributions of our work can be summarized as follows:  
1. Different from previous detection models, we built a remote sensing image target detection framework based on SSD that can improve the relevance of contextual information and handle different complex scenes. 2. To improve the relevance of contextual information, we propose a context information scene perception (CISP) module to obtain the context information for targets of different scales. 3. We show that CISPNet can implement relatively advanced and reliable performance on remote sensing image dataset and the NWPU VRH-10 dataset and verify the effectiveness of the optimization for its architecture. The rest of this paper is organized as follows. Section 2 introduces the basis of the proposed method. Section 3 introduces the details of the proposed CISPNet framework. Section 4 presents experiments conducted on remote sensing dataset and the NWPU VRH-10 dataset to validate the effectiveness of the proposed framework and discusses the results of the proposed method. Finally, Section 5 concludes this paper.
# MCIDN: Deblurring Network for Metal Corrosion Images
## 3 Method
### Spatial Channel Attention Module (SCAM)  
The architecture of the SCAM is illustrated in Figure 3. This module employs a convolutional network to generate weights between different channels, facilitating enhanced feature representation.  
First, adaptive average pooling (AAP) is applied to the input feature map \(I\) to obtain global channel information. Then, a \(1\times 1\) convolution (\(W_{1\times 1}\)) is used to compress or expand the number of channels in the globally pooled features. Next, the convolution output is normalized using LN. Following normalization, the features undergo a nonlinear transformation via the Tanh activation function. Finally, the activated features are processed by the function \(V\) to generate attention weights \(W^{1}\). The process of generating attention weights can be formally expressed as  
\[W^{1}=V_{1}(Tanh(LN(W_{1\times 1}(AAP(I))))) \tag{1}\]  
where \(AAP\), \(W_{1\times 1}\), \(LN\), \(Tanh\), and \(V\) correspond to adaptive average pooling, \(1\times 1\) convolutional layer, layer normalization, Tanh activation function, and dimension transformation, respectively. The use of the \(V\) function for dimension transformation here matches the operation of multiplying it with the feature tensor of another path. The other path first performs a dimensional transformation on the input tensor, followed by a sliding window operation to extract local features. Subsequently, the shape of these features is transformed. The specific process is detailed below:  
\[W^{2}=V_{3}(Unfold(Pad(V_{2}(I)))) \tag{2}\]  
Figure 3: The architecture of proposed SCAM.  
where \(Pad\) and \(Unfold\) represent the reflection padding operation and the sliding window operation. The final output of the SCAM can be formulated as follows:  
\[\widehat{I_{c}}=\beta I+\gamma\text{R}\left(W^{1}W^{2}\right) \tag{3}\]  
where \(R\) denotes the reshape operation, \(\gamma\) and \(\beta\) are two learnable parameters.
# Full-Scale Model Test of Subway Contact Channel under Mechanical Construction +
## 2 Test Scheme  
### Test Object  
The shield section of Beijing Metro Line 19 starts at the south side of Qinghe River and ends at Xiaoying West Road. The mechanical method is proposed for the construction of three connection channels in this project. The soil layers that the connection channels cross are mainly silty clay, clayey silt, sandy silt, sandy soil, and pebble gravel layers. Through geological and hydrogeological investigations, it was found that the existing mechanical method of connection channel construction technology could not be directly applied in this project. The main reason is that the communication channel construction technology is developed for coastal soft-soil areas. The earth pressure and formation resistance are different in different geological conditions, which further affects the construction safety. In this regard, a full-scale model test for connection channel construction with typical geological conditions in Beijing was conducted.  
Given the condition of the test platform, the engineering tunnel structure was simplified as illustrated in Figure 1. The structural system included the segment of the cutting position and the segment of the main influence area of the cutting construction, which had seven rings in total. Each ring segment consisted of a cap block (F), adjacent blocks (L1 and L2), and standard blocks (B1, B2, and B3). The whole-environment, full-scale test model had the same size and assembly method as those of the actual segments, of which the middle three rings (the fourth ring is called the cutting ring; the third and fifth rings are called the half-cutting rings) were assembled with a straight joint with a ring width of 1500 mm. The other four rings (also called standard rings) were assembled with staggered joints with a ring width of 1200 mm. The segment had an outer diameter of 6200 mm, an inner diameter of 5500 mm, and a ring thickness of 350 mm. The two special-position segments of the middle three rings (B1 and B3) were steel-ring, double-lined composite segments composed of steel liners, fiber bars, and concrete.