esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Geoeffectiveness and Arrival Time Prediction of CMEs by a Unified Deep Learning Framework
## 2 Methods
### Geoeffectiveness Prediction of CMEs
#### 2.1.1 Deep Residual Network Embedded with the Attention Mechanism
Based on Equation (1), the dimensions of \(x\) are changed to the dimensions of \(y\) through \(W_{s}\). The attention mechanism has achieved great success in computer vision tasks [39; 40; 41; 42; 43; 44; 45], it can enhance the core information that is strongly related to the target task, while suppressing other information that is weakly related to the target task. Here, our network combines the channel attention module with the spatial attention module, and the realization of the attention mechanism is the same as CBAM [39]. The channel attention module explores the inter-channel relationship of the feature map and pays more attention to key channels, the computation process is as follows. First, the input feature map \(F\in\mathbb{R}^{H\times W\times C}\) using average pooling and max pooling on the spatial dimensions to aggregate the spatial information in the feature map and obtain two spatial context descriptions, and the shape of each descriptions is all \(\mathbb{R}^{1\times 1\times C}\). Next, both two spatial descriptions are forwarded to two shared full connection layers, and merged by element-wise summation. Then, sigmoid is performed to scale the attention weights to \([0,1]\) and the shape of generating channel attention weights is \(\mathbb{R}^{1\times 1\times C}\). Finally, the attention weights are multiplied with the input feature map as the output feature map. The spatial attention module is complementary to the channel attention mechanism, and its main purpose is to explore the inter-spatial relationship of the feature map. Through the spatial attention module, the network can pay more attention to key spatial regions, and the computation process is as follows. First, the input feature map \(F\in\mathbb{R}^{H\times W\times C}\) using average pooling and max pooling on the channel dimension to aggregate the channel information of the feature map, and generate two channel context descriptors \(\mathbb{R}^{H\times W\times 1}\). Next, the two channel context descriptors are connected in the channel dimension and fuse the concat channel context descriptors through the convolution operation with the filter size of \(7\times 7\). Then, perform the sigmoid to scale the attention weights to \([0,1]\), and obtain  
Figure 2: The illustration of the residual block with embedded attention. It combines residual learning with the attention mechanism. the spatial attention weights \(\mathbb{R}^{H\times W\times 1}\). Finally, the spatial attention weights are multiplied with the input feature map as the output feature map.
# Temperature Fluctuation Attenuation of Circulating Cooling Water Using Dynamic Thermal Filtering
## 5 Conclusions  
This paper proposes a dynamic thermal filtering method, which can significantly reduce the temperature fluctuation of CCW and achieve the quick response of CCW temperature control. A DTFM based attenuator is achieved with a tube-tank heat exchanger. Theoretical analysis has been carried out with the designed attenuator, and the performance of the attenuator is verified by experiments. In summary, conclusions are as follows:  
1. Theoretical results indicate that the attenuator based on DTFM has low-pass characteristics for temperature fluctuation. Though there is only a few of dB beyond 0.005 Hz, the temperature fluctuation can be eliminated by the closed loop feedback control of the temperature control system.  
Figure 8: Experimental results of temperature fluctuation attenuation ratio (Compared with theoretical results in Figure 3).  
The attenuation ratio of tens of dB (\(-3.47\) dB, \(-6.91\) dB, \(-10.97\) dB and \(-15.28\) dB, corresponding to temperature fluctuation frequencies of \(0.01\) Hz, \(0.025\) Hz, \(0.053\) Hz and \(0.105\) Hz, respectively) is achieved, while the thermal capacity medium updating time is \(82\) s, which means that the designed DTM attenuator can remarkably improve the temperature stability of CCW with the response time of CCW temperature control less than one minute.
3. The conventional methods can provide quite large attenuation ratio of temperature fluctuation, but cannot satisfy the demand of temperature fluctuation attenuation from dynamic temperature control system. Compared with the conventional methods, the proposed method can be applied for the applications demanding the dynamic temperature control of CCW, since the updatable thermal capacity medium is introduced.
4. Our further work will focus on the structure optimization of the attenuator to achieve a larger attenuation ratio with more compact size.  
Conceptualization, Y.L., J.C. and J.T.; Funding acquisition, J.C. and J.T.; Investigation Y.L.; Methodology, J.C. and J.T.; Project administration, J.C. and J.T.; Resources, Y.L.; Visualization, X.B. and Y.Z.; Writing--original draft, Y.L.; Writing--review & editing, Y.L., J.C. and J.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 51675139 and the Fundamental Research Funds for the Central Universities.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Wetland Interpretation Method  
Quantitative and accurate extraction of wetland spatial information is the main prerequisite for wetland research. There are two kinds of methods to extract wetland information from RS data: (i) a computerized automatic classification method; and (ii) a manual interpretation method based on RS images. The former, which includes the unsupervised and supervised classification method, regression tree method and cellular automaton method, is widely applied in analyzing wetland changes based on RS data [25; 26; 27; 28; 29; 30]. The latter not only has rigorous requirements for the selection of RS data sources and accurate data handling, but also needs interpretation staff having an integrated understanding of wetland distribution in the study region. However, the precision and efficiency of wetland information extraction is relatively high [31; 32; 33; 34]. This method is used in the wetland changes study in the TRH region in Qinghai Province.  
In order to build a temporal-spatial distribution data set of wetland, an effective research team was organized to work on remotely sensed data through human-machine interactive interpretation to guarantee classification consistency and accuracy. The workflow of this integration is displayed at Figure 2.  
The outline of wetland changes was delimited by comparison of RS images in different time periods, with the references from the former. Finally, wetland type thematic maps were completed in three time periods, including in the early 1990s, 2004 and 2012. The interpretation of RS images and wetland classifications was validated against 1:100,000 topographic maps in the early 1990s and extensive field surveys in 2004 and 2012. For example, in 2004, we conducted ground truth checking for more than 3500 km of transects across the TRH region and more than 2000 field photos were taken using global positioning system (GPS)-equipped cameras. The results showed that the overall accuracy of the wetland classification is 92.43% in the early 1990s, 94.5% in 2004 and 95.2% in 2012.
# Static Liquefaction Assessment Combining Shear Wave Velocity, Peak Strength and Soil Grading
## 2 Material Properties  
During this research, five materials were studied: (1) Osorio sand, a uniformly graded sand from Brazil, well-documented in past research (e.g., [26; 27]); (2) a silt, artificially produced from grinding Osorio sand; (3) a silty sand (designated as \"mixture\"), artificially produced by mixing soils 1 and 2; (4) Algeria sand and (5) Coimbra sand, two uniformly graded sands, also well-documented in the literature (e.g., [28; 29; 30; 31]).  
The results obtained for these materials were presented and compared with the study previously published by [19] on gold tailings.  
Table 1 summarizes the main gradation characteristics of these materials, including the fines content (_FC_), the coefficients of uniformity and shape, \(C_{U}\) and \(C_{C}\), and the specific gravity. The fines are non-plastic as the fine fraction is silt and not clay. The maximum and minimum void ratios are also presented for the three sands, but not for the finer materials as the standards of these tests (for instance, [32]) are not applicable to soils with high fines content.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Material** & \(G_{\bullet}\) & \(D_{\text{bg}}\) & \(D_{\text{bg}}\) & \(C_{U}\) & \(C_{C}\) & _FC (\%)_ & \(\epsilon_{\text{max}}\) & \(\epsilon_{\text{min}}\) & **ASTM Classification** \\ \hline Gold tailings\({}^{\ast}\) & 2.94 & 0.044 & 0.841 & 10.0 & 1.35 & 70 & (1) & (1) & silt with sand \\ Osorio sand & 2.65 & 0.190 & 0.420 & 1.9 & 1.00 & 4 & 0.85 & 0.57 & non-plastic uniform \\ Silt & 2.65 & 0.017 & 0.106 & 9.6 & 1.47 & 99.85 & (1) & (1) & fine sand \\ Mixture & 2.65 & 0.110 & 0.425 & 32.4 & 2.60 & 40 & (1) & (1) & well-graded silty sand \\ Algeria sand & 2.69 & 0.310 & 0.850 & 1.76 & 0.97 & 0 & 0.89 & 0.551 & poorly graded sand \\ Coimbra sand & 2.66 & 0.360 & 1.000 & 2.13 & 1.37 & 0 & 0.81 & 0.48 & poorly graded sand \\ \hline \hline \end{tabular} \({}^{\ast}\) results from [PERSON] et al. (2013) [19]; (1) no information.  
\end{table}
Table 1: Gradation characteristics of the materials.
# Complex Color Space Segmentation to Classify Objects in Urban Environments
## 3 Image Segmentation Approach
### Image in the Complex Space  
As said earlier, planes \(a\) and \(b\) on the CIELab space known as \(intA\) and \(imB\), are combined to generate complex image \(I\). Figure 2 shows chromatic planes \(imA\) and \(imB\) to form complex image \(I\) for a specific color image. Each pixel on image \(I\) is a complex number \(z=a+ib\), processed using algebra for complex numbers. In this case, basic operations such as division, modulus, and argument have been used [27], but the division is the main operation used. Each pixel \(I\) is divided by a reference point \(P_{(r,c)}\); the resulting image is known as the division image and is referred to as \(D\). Image \(D\) shows values such as the threshold ones indicated by reference point \(P_{(r,c)}\) within boundary \(e\). Thus, the same values as the unit or those close to it point to similar areas as those of the threshold value \(P_{(r,c)}\). Equation (1) defines division image \(D\), which is the resulting complex image \(I\) size \(u\times v\) divided by reference point \(P_{(r,c)}\). Values close to the unity in \(D\) represent similar pixels as those of \(P_{(r,c)}\). Therefore, image \(D\) shows the relevance of point \(P_{(r,c)}\) on the color image. However, as \(D\) is in the complex space, searching for values close to 1 cannot be direct. Using module \(D\), the image of module \(|D|\) can generate positive real values.  
\[D_{[i\times v]}=\frac{I_{[i\times v]}}{P_{(r,c)}} \tag{1}\]  
In Equation (2), unitary values in \(|D|\) (around an \(e\) value) are chosen to obtain the thresholded image \(F\) and to highlight areas with a color such as \(P_{(r,c)}\). Figure 3 represents the Division image \(D\) and the corresponding module \(|D|\). \(|D|\) shows in white color the areas whose values are similar to \(P_{(r,c)}\).  
Figure 1: Proposed segmentation method to identify objects.  
Figure 2: Generating complex image \(I\) using the chromatic images \(imA\) and \(imB\).  
To obtain a final segmented image \(F\), first, representative \(P_{(r,\epsilon)}\) thresholds must be found. Each threshold requires the division process. A _chromatic map_\(\mathbb{AB}\) makes it possible to obtain several thresholds for the image automatically.
# An Overview of Distributed Firewalls and Controllers Intended for Mobile Cloud Computing
## 3 SDN, NFV, and CASB Technologies
### Network Function Virtualisation (NFV)
#### 3.2.1 Pillars of NFV  
Figure 3 shows the structure of NFV in MCC. It illustrates how NFV decouples network functions from dedicated hardware, enabling flexible and scalable network infrastructures.  
Virtualisation: NFV uses virtualisation technology to detach network operations from dedicated hardware, allowing them to operate on regular server hardware.  
Automation and Orchestration: In NFV, orchestration coordinates and manages the lifetime of VNFs.  
Isolation: NFV separates network operations from the underlying hardware, allowing for more agnostic and flexible service deployment and management.
# Bridging the Gap: Enhancing Maritime Vessel Cyber Resilience through Security Operation Centers
## 1 Introduction  
Critical infrastructure sectors have witnessed an increased deployment of Internet of Things (IoT) ecosystems involving sensors and actuators to communicate with physical systems. These IoT ecosystems help optimize and improve real-time operations [1].  
The maritime industry, identified as a critical infrastructure sector by the Network and Information Security (NIS-2) directive [2], is a key element in world trade and the global economy, accounting for the carriage of nearly 90% of the world's goods. Much of this has historically been undertaken on vessels with limited connectivity due to the constraints of operating via satellite networks, whose shortcomings include low bandwidth, high propagation delays, high packet error rates, high initial costs to set up, and finally a high frequency of link breakages [3]. However, the connectivity landscape is quickly changing with the introduction of low-orbit, large-scale communication satellite constellations. The result is rapid digitalization and automation of the maritime domain.
## 3 The Optimized Kinematic Modeling Method
### Limitations of Kinematic Modeling
#### 3.2.2 Limitations of Kinematic Modeling of the Mobile Platform  
To determine the position and posture of the mobile platform during the movement, we need to simplify the mobile platform into two translation joints and one rotation joint. After simplifying the mobile platform, we still need to establish the corresponding coordinate system on the center of gravity of the mobile platform, which can be seen in Figure 3a. However, three different coordinate systems were established at the same location according to the SDH method, which inevitably increased the difficulty of system analysis and reduced the speed of the solution.  
Figure 2: Some coordinate frames established for the kinematic modeling of the manipulator subsystem. (**a**) Kinematic modeling based on the classical method; (**b**) Kinematic modeling based on the optimized method.
# Robust and Accurate Wi-Fi Fingerprint Location Recognition Method Based on Deep Neural Network
## 3 Theoretical Frameworks
### DNN-Based Wi-Fi Location Recognition  
#### 3.2.1 Data Preprocessing  
When every AP is observed, a pair information \(<\)MAC \(RSS\)\(>\) is recorded, in which MAC represents its unique ID and \(RSS\) its received signal strength. All AP information that can be observed in each scan is recorded as Wi-Fi training fingerprint or test data.  
The preprocessing mechanism runs during both the offline and online stages. The first 15 APs with the largest \(RSS\) in every scan are retained to make the \(RSS\) set input to the depth model consistent, \(\mathsf{o}=\{rss_{1},rss_{2},\ \ldots,rss_{15}\}\), where each entry represents observation information in the test area. But it should be noted that there are only 15 currently observed elements in each recorded sample. Therefore, for the entire training set, all the elements in the sample are arranged uniformly in the AP list in the fingerprint database, and the \(RSS\) of the unobserved AP in each input is set at \(-\)100 dB.  
We standardized the observed raw \(RSS\), which ranges from \(-\)100 dB to 0 dB, to adjust its value to between 0 and 1 in order to reduce the distribution of \(RSS\) and facilitate the subsequent training. Formula (1) shows the standardization process. Formula (2) shows the fingerprint database built after standardizing the \(RSS\) of all fingerprint points.  
\[RSS_{i}{}^{\prime}=\frac{RSS_{i}+100}{\max(RSS)+100} \tag{1}\]  
\[\textit{Fingerprint Database}\rightarrow\left[\begin{array}{ccccc}RSS_{1}^{1}& RSS_{1}^{2}&RSS_{1}^{3}&\cdots&RSS_{1}^{n}\\ RSS_{2}^{1}&RSS_{2}^{2}&RSS_{2}^{3}&\cdots&RSS_{2}^{n}\\ \cdots&\cdots&\cdots&\ddots&\cdots\\ RSS_{2-1}^{1}&RSS_{2-1}^{2}&RSS_{2}^{3}&\ddots&RSS_{2-1}^{n}\\ RSS_{2}^{1}&RSS_{2}^{2}&RSS_{2}^{3}&\cdots&RSS_{2}^{n}\\ \end{array}\right] \tag{2}\]  
where \(RSS_{i}{}^{\prime}\) represents the standardized value, \(\max(RSS)\) is the maximum in the fingerprint database, \(n\) the number of APs observed in offline training, \(z\) the number of fingerprint points. Every row is an observation sample, and the \(RSS\) of the unobserved AP is set to \(-\)100 dB.
## 5 Engineering Application  
The infrared Earth sensor (IES) is the core attitude measurement subsystem in a satellite. Its main function is to capture Earth boundary information in the far-infrared wavelength range by optical means and then output processed satellite attitude information relative to the geocentric vector. Accurately assessing the reliability of the IES is vital to ensure the safe operation of the satellite and prolong its service lifespan. This section aims to verify whether the reliability of an IES at the end of the 8-year lifespan meets the index requirement of 0.93 with a 90% confidence level using the original dataset of its key components provided in the literature [31].  
The overall reliability of an IES depends on three key components: the electronic circuit, the rotating device, and the infrared detector. During operation, a failure of any component will lead to the failure of the whole system. Accordingly, the reliability assessment model of the IES can be expressed as a series model of the electronic circuit, the rotating device, and the infrared detector. The system reliability block diagram is shown in Figure 5.  
The reliability mathematical model of the IES is  
\[R_{ies}=R_{ec}\times R_{rd}\times R_{id} \tag{27}\]  
where \(R_{ec}\), \(R_{rd}\), and \(R_{id}\) are the reliabilities of the electronic circuit, rotating device, and infrared detector, respectively. \(R_{ies}\) is the reliability of the IES.
# Data Exchange in Cluster Structure for Longevity of IoT
## 2 Related Work
### Routing Protocols
#### 2.3.1 Data Collection in Tree Structure  
Data collection in a tree structure is the basic approach among hierarchical data collection methods. In a tree structure, several sensor nodes become the parent nodes and collect data from the child nodes, which are deployed in the surrounding working areas. The parent nodes play the role of a bridge such that the data received from other parent nodes are transmitted to the sink node. In order to designate some specific sensor nodes as the parent node, various metrics, such as the geometric location [22], remaining resources of sensor nodes [23], measured data types [24], and cost of data collection [25] have been employed.  
As mentioned above, for data collection with a tree structure, each sensor node is classified into a parent or child node, but the parent node does not manage the child nodes. Furthermore, since data are always requested from the sink node in WSNs, the sink node must be the center of the tree structure. During the sharing of information in the IoT environment, many things can request information from other things frequently. Consequently, the number of tree structures increases significantly. Since all tree structures incur the burden of formation and maintenance, it is difficult to apply the tree structure method to IoT networks.
## 4 Multi-Threaded Control with C/S Architecture Incorporating AES Encryption and TLS Protocol
### Multi-Threaded Control Design for C/S Architecture Combined with Multi-Level Security Protection  
This paper is based on the C/S multithreaded infrastructure, combining the TLS protocol with AES encryption algorithm, and at the same time protects the data security in the transport layer and the application layer, which is able to guard against man-in-the-middle attacks, tampering attacks, replay attacks and so on, and improves the confidentiality, integrity, and reliability of the data. Figure 6 shows the specific flow of this paper's multithreaded optimization design.  
Figure 5: Flowchart of multilevel security protection incorporating AES encryption and TLS protocols.  
Figure 6: Multi-threaded flowchart for C/S architecture incorporating multi-layered security protection.  
On the server side, first initialize the object and then load the SSL certificate and key, and start listening on the specified host and port. When a client connection request arrives, the server accepts the connection and creates a new asynchronous task for each client connection to process. When processing client connections, the server asynchronously receives the encrypted message and decrypts it, then processes the request and generates a response message, and finally encrypts the response message and sends it to the client. On the client side, the connection to the server is first established after initialization, then it sends an encrypted request message, waits for and receives an encrypted response message; the client decrypts the server's response and then processes the data and disconnects when it is finished.