esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In this paper, we investigate the probability of interference-optimality by adjusting the transmission range. First, we prove that the interference issues can be abstracted as the issues of node's coverage. The relationships between the coverage area and the distance between two nodes are approximately linear and quadratic in homogeneous network and heterogeneous network, respectively. The probability of interference-optimality by adjusting the transmission range relates to the node density \(\rho\) and the original transmission range \(r\), which can be found in Figures 22 and 24. Based on the probability functions that we proposed, we find the optimal transmission range which has the highest probability of interference-optimality. The optimal transmission ranges in homogeneous networks and heterogeneous networks are 0.85\(r\) and 0.84\(r\), respectively. In addition, we also prove that when the network is energy-efficient, the probability that the network is also interference-optimal is approximately 1. This conclusion means that the energy-efficient network is also the interference-optimal network at the same time.  
The conclusions of this paper can be used in the probability analysis of WSNs in the future.  
**Acknowledgments: The research leading to the presented results has been undertaken with in the SWARMs European project (Smart and Networking Underwater Robots in Cooperation Meshes), under Grant Agreement n. 662107-SWARMs-ECSEL-2014-1, partially supported by the ECSEL JU (Electronic Components and Systems for European Leadership Joint Undertaking) and the Spanish Ministry of Economy and Competitiveness (Ref: PCIN-2014-022-C02-02). Also the China Scholarship Council (CSC), which has partially supported the first author's research described in this paper.**  
**Author Contributions: All the authors contributed to the conception, design and performance of the experiment, the analysis of the data and the writing of the paper. [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON], [PERSON] contributed analysis tools; [PERSON] and [PERSON] wrote the paper.**  
**Conflicts of Interest: The authors declare no conflict of interest.**  
Figure 26: The probability that the network cannot meet the requirements of interference-optimality and energy-efficiency.
## 3 Single Channel Modulated Wideband converter
### Power Spectrum Sensing
Through solving the linear equations \(\mathbf{V}=\mathbf{\Phi}\ \mathbf{U}\) for a unique sparsest solution, we will get the the support which is the same as the support \(S_{x}\) of Equation (18). In compressed sensing theory, there are many ways to solve this, such as Basis pursuit [18] and Matching pursuit [19] and so on. Here we use a relatively faster algorithm Orthogonal Match Pursuit (OMP). So far, through the analysis above, we have shown the way to detect the support \(S_{x}\).
## 1 Introduction
On the other hand, chain-based topology arranges nodes to form and construct chains, where each chain has a chain leader and sensor nodes, as shown in Figure 4[9]. In WSN, a chain can be built either by the nodes themselves, using a greedy strategy, or by the BS, which will calculate and broadcast the chain to all nodes. Single chains or multi-chains can be constructed in chain-based routing. Chain-based routing is simpler to configure and maintain than cluster-based routing [13; 18]. Furthermore, local communication conserves energy compared to cluster-based routing, since nodes only transmit data to nearby nodes (i.e., their next nearest neighbor nodes) [12; 18; 24]. There are various protocols and algorithms that can be implemented based on chain routing, such as power efficient gathering in sensor information systems (PEGASIS), concentric clustering scheme (CCS), balanced chain-based routing protocol (BCBRP), and rotation PEGASIS-based (RPB) [2; 13; 18]. However, the most popular protocol is PEGASIS, where a chain is constructed from the farthest node from the BS, followed by the closest not-connected neighbor node, and so on [13]. The chain head is responsible for data aggregation and data processing. Therefore, a new chain head can be selected randomly in the event that the current chain head fails. In this paper, we focus on hierarchical routing protocols that are categorized according to the network structure, particularly chain-based routing protocols. There are a number of applications that benefit from chain-based topology, including monitoring coal mine gas, periodic monitoring of building conditions, wind power, parking management systems, IoT-WSN-based smart agricultural environments, and underwater smart things. Furthermore,  
Figure 4: Chain-based WSN (the color yellow is used to represent sensor nodes, whereas the color red is used to represent the chain leader). Figure 3: Tree-based WSN (distinct colors are used to represent each tree level). a chain-based topology can be applied as part of a fog-supported WSN, along with other topologies [9; 25]. This work proposes various chain configurations and evaluates the system reliability of different proposed chain-based routing models, based on the proposed RBD in WSN. The power consumption for each node is the main factor that is considered to evaluate the system's reliability. The main contributions of this work are summarized below:  
* Various chain-based routing models are considered and discussed. * Chain head selection approaches that are based on weight \(Q\), or distance \(D\), are investigated and evaluated. * We propose a new RBD-based methodology, to evaluate the reliability of different chain-based routing models in cases of fixed and dynamic chain head settings, where the node reliability is evaluated based on energy. The work in this paper is presented as follows: Section 2 presents the related work. The background of the reliability block diagram is illustrated in Section 3. The proposed model is presented in Section 4. Section 5 discusses the experimental setup and results, and Section 6 gives the conclusions of our proposed work.
###### Abstract  
In this paper, we present a study investigating the impact of jamming in a Dual-Hop free-space optical (FSO) communication system assisted by reconfigurable intelligent surfaces (RIS) in the presence of a malicious jammer. We analyze the combined effects of atmospheric turbulence (AT), pointing error (PE), and angle of arrival (AoA) fluctuation of unmanned aerial vehicles (UAVs). Closed-form expressions for the overall average bit error rate (ABER) are derived while considering these impairments. To mitigate the jamming effect, we explore a Single-Input Multiple-Output (SIMO) FSO system and derive the end-to-end Average Bit Error Rate (ABER) under various jamming scenarios. Additionally, we conduct a comprehensive study by examining different placements of the malicious UAV jammer and RIS, drawing insightful conclusions on system performance. The analytically derived expressions are validated through Monte Carlo simulations.  
ABER; jamming; SIMO; RIS; FSO; UAVs Article
# Community Detection Framework Using Deep Learning in Social Media Analysis
## 3 Proposed Community Detection Framework
### Feature Matrix Computation
Node2 Vec is then utilized because of its capacity to efficiently encode node proximity within graph structures, preserving both local and global network properties. This embedding technique is vital for understanding and leveraging the structural nuances of complex social networks. Algorithm 1 illustrates the strategy of generating node features in the community detection model, where the Node2 VecWalk and LearnFeatures functions are from Node2 Vec [7]. ```
0:n: nodes in the user topology graph e: edges in user topology graph w: weights in user topology graph G: user topology graph l: list of communication content
0: User feature matrix user_feature
1: traversal_sequences = node2 vecWalk(G)
2:user_vector = LearnFeatures(traversal_sequences)
3:Initialize user_feature
4:for each ts do
5:\(u_{i}\) = the \(i_{th}\) user in traversal_sequences
6: content = communication content between \(u_{i}\) and \(u_{i+1}\) in l
7: content_vector = Word2 Vec(content)
8: user_feature(\(u_{i}\)) = normalization(user_vector(\(u_{i}\))+ content_vector)
9:endfor
10:return user_feature
```  
**Algorithm 1** Node feature computation algorithm  
The algorithm's inputs are the user topology map and the textual information exchanged between a pair of users. The algorithm starts with computing the node features according to Node2 Vec (lines 1-2). The obtained user vectors are stored in the matrix user_vector. In this case, the vectors only include topology information without any text features. Then, according to the obtained traversal sequence, the communication content feature between each pair of users are calculated (lines 4-7). Lines 8-9 combine user vector and content feature to generate the user feature of each user. The algorithm ends by returning a list of user features.
## 7 Comparative Discussion on Various Energy-Efficient Navigation Systems  
MRs need energy-efficient navigation systems to extend their operating range and decrease their reliance on external energy resources. It is essential to remember that the selection of the navigational system is determined by several parameters, including the intended use of the MR, the environment where it works, the accessible computing power, and the required degree of energy efficiency. For the greatest outcomes in certain situations, an integration of multiple approaches might prove appropriate. Energy-conscious planning of paths techniques provides an explicit emphasis on conserving energy consumption while navigating. Algorithms consider factors including battery lifespan, motor effectiveness, topographical features, and energy expenses related to various MR actions. MRs can choose routes with minimal energy consumption by considering these aspects while developing routes.  
Under the constraints of time and energy, a mobile robot can only manage to execute a portion of the operation from the predefined whole tasks, but it can also actively re-plan its travel route within a complex environment by being conscious of its surroundings and completing its predefined work. PSO technique-based path planning strategies are easy and energy efficient when compared with other techniques. For better conservation of energy, any one of the navigational strategies listed below can be integrated with PSO technique-based path planning.
# Study on the Properties of Cement-Based Cementitious Materials Modified by Nano-CaCO\({}_{3}\)
## 4 Results and Discussion  
### Effect of Nano-CaCO\({}_{3}\) on the Mechanical Properties of Mortar  
#### 4.1.1 Effect of Nano-CaCO\({}_{3}\) on the Compressive Strength of Mortar  
Figure 5 demonstrates how the compressive strength of mortar varies with increasing amounts of added Nano-CaCO\({}_{3}\). Similar in the case of flexural strength, when the content of Nano-CaCO\({}_{3}\) increases from 0 to 2.0%, the compressive strength of cement-based materials shows an increased positive correlation with the amount of Nano-CaCO\({}_{3}\), peaking at 2.0% Nano-CaCO\({}_{3}\). After 3 d, 7 d and 28 d of curing, compressive strength increased by 8.5%, 11.4 and 19.1%, respectively, compared with the reference group. When the content exceeded 2.0%, the compressive strength began to decrease.  
It can be inferred that 2.0% is the optimal content of Nano-CaCO\({}_{3}\) in cement-based materials for the improvement of flexural and compressive strength.
## 4 Medical Information System Development  
The development and implementation of a medical informatics system, specifically an EHR with an integrated module for automatic pathology detection, involve several crucial stages to ensure a successful implementation of the solution. Firstly, in the needs identification and planning phase, specific goals are identified for the medical informatics system, including integration objectives for the pathology detection module in the EHR and its limitations. This phase also includes the development of a comprehensive project plan outlining scope, timeline, resources, and budget, as well as the definition of pathology types to be detected and identification of target user groups and their requirements.  
In the system design phase, the EHR system architecture is meticulously designed with considerations for scalability, databases, data security, and potential interoperability with other healthcare systems. Data flow and integration points between the EHR and the pathology detection module are established, and decisions are made regarding technologies, programming languages, and frameworks for development.  
Subsequently, the data integration phase focuses on setting up mechanisms to seamlessly transfer patient data, medical images, and relevant clinical information between the EHR and the pathology detection module. The development of the information system involves creating functionalities and specifications based on the system design, implementing data structures, algorithms, and logic, designing an intuitive interface for healthcareproviders, and creating visualizations and interactive tools to display detected pathologies and clinical data.  
The development of the machine learning model is a critical step, involving the development and selection of suitable machine learning algorithms and models for automatic pathology detection. This includes the collection and preparation of a comprehensive dataset for training and validation and the training and optimization of the machine learning model to achieve precise detection results.  
The integration and testing phase focus on seamlessly integrating the machine learning model and pathology detection module into the EHR system. Tests are conducted to ensure the accurate functioning of the automatic detection module, including integration tests, system tests, and user acceptance testing (UAT). Global tests are performed to validate data flow, integration, and user interface functionality.  
Ensuring data confidentiality and security is a key aspect, involving the implementation of measures to protect patient information and medical images, along with compliance with relevant data protection regulations such as HIPAA or GDPR.  
Finally, the system implementation phase involves the full implementation of the integrated EHR system with the automatic pathology detection module within the healthcare organization. This includes the configuration of databases and infrastructure components, insertion and modification of data in the system, and ongoing monitoring of system performance.
# Simplified Vibration PSD Synthesis Method for MIL-STD-810
## 3 Vibration PSD Synthesis  
### Vibration PSD Synthesis in MIL-STD-810  
MIL-STD-810 states that since fatigue damage is based on a cumulative effect of various environments or conditions, a cumulative fatigue damage index can be calculated as the sum of the fatigue damage spectra for individual environments [1]. Thus  
\[DP_{total}(f_{n})=DP_{1}+DP_{2}+\ldots+DP_{N}=\sum_{i=1}^{N}DP_{i}(f_{n}) \tag{10}\]  
The individual damage indexes, \(DP_{i}\), obtained by Equation (9) are cumulated by Equation (10) to generate the total damage index \(DP_{total}\).  
Then the PSD is derived by Equation (11) which is the inverse form of Equation (9).  
\[G_{test}(f_{n})=f_{n}\zeta\bigg{[}\frac{DP_{total}(f_{n})}{f_{n}T_{test}}\bigg{]} ^{2/b} \tag{11}\]  
Equation (11) reads that in order to apply damage, DPtotal, in Test seconds to the virtual specimen, the PSD must be \(G_{test}\).  
In addition to Equation (9), the damage index can be calculated by various methods as discussed in Section 2. The important thing is that the formula calculating FDS and the inverse formula calculating PSD must be consistent. If the Equation (7) is used instead to calculate FDS, the PSD for testing shall be obtained using the following formula. The 'const' is the same as used in Equation (8).  
\[G_{test}(f_{n})=\frac{8\pi f_{n}\zeta}{k^{2}}\bigg{[}\frac{D_{total}(f_{n}) \cdot\zeta}{f_{n}T_{test}\cdot\Gamma[1+b/2]}\bigg{]}^{b/2}=f_{n}\zeta\bigg{[} \frac{D(f_{n})}{const\cdot f_{n}T}\bigg{]}^{2/b} \tag{12}\]  
In order to create a test specification, in summary, by synthesizing vibration environment conditions according to the MIL-STD-810, calculate each damage index with the Equation (9) and add them to calculate the cumulative damage index, and then substitute it into the Equation (11) along with the test time T. T and G will be adjusted to prevent the G from becoming too large or the test time from being unrealistically extended.
# Deep Feature Fusion Based Dual Branch Network for X-ray Security Inspection Image Classification
## 2 Related Works
### Deep Feature Fusion  
Deep feature fusion can be divided into early fusion and late fusion according to the sequence of fusion and prediction. Early fusion is to fuse the features of various layers first, and then train the predictor based on the fused features. The main methods of this kind of fusion are concatenating and element-wise sum. In addition, the study conducted in [27] introduced a feature fusion method named discriminant correlation analysis (DCA). DCA maximizes the correlation between corresponding features in the two feature sets, while maximizing the difference between different classes. As a result, the authors of [27] effectively improved the classification performance for recognizing very-high-resolution (VHR) remote sensing images. Late fusion mainly improves performance by fusing the prediction results of different layers. Representative work includes single shot multibox detector (SSD) [28], multi-scale convolutional neural network (Multi Scale-CNN, MS-CNN) [29], etc.  
There are also some works utilizing both early fusion and late fusion. One of the representative works is the feature fusion single shot multibox detector (FSSD) [30]. FSSD adds a feature fusion module so that features of the original SSD architecture can be fused. Then, it constructs pyramid feature generator and sends the output to the multibox detector to predict. In addition, it is worth mentioning that research in [31] indicated that, due to the gap between semantic information and high-resolution detail information, simply fusing high-level and low-level features is not effective. On this basis, two optimization schemes were proposed, including the introduction of more semantic information into low-level features and the embedding of more spatial information into high-level features. Experiments in [31] showed that using these schemes can significantly improve the segmentation performance by 4%.
# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 2 Materials and Methods
### Limitations  
It is deemed important to note certain limitations of the review process:  
* Only articles published in English were included, which may have excluded relevant studies in other languages.
* Despite using multiple databases, some relevant studies may have been missed due to the limitations of the search algorithms or restricted access.
* Given the fast-paced developments in DL and remote sensing technologies, some recent advancements may not be fully captured if they were published after our search had been completed.
* There is a possibility of publication bias, as studies reporting significant or positive results are more likely to be published.
* It would have been ideal to be able to reproduce and test the described methods on publicly available data. This, however, was not done for this review due to the inherent cost of such a thorough analysis.  
These limitations were considered when interpreting the results, and efforts were made to mitigate their impact by cross-referencing citations and including studies from a range of sources and publication years.
# Comparison of Cooling Different Parts in a High Pressure Ratio Centrifugal Compressor
###### Abstract  
Cooling in a centrifugal compressor can improve the performance and reduce the impeller temperature. In a centrifugal compressor, external walls can be cool down, which is known as the shell cooling. This method avoids undesirable effects induced by other cooling methods. Cooling can be applied on different external walls, such as the shroud, diffuser or the back plate. This paper focuses on seeking the most effective cooling place to increase the performance and reduce the impeller temperature. It is found that shroud cooling improves the compressor performance the most. Shroud cooling with 2400 W of cooling power increases the pressure ratio by 4.6% and efficiency by 1.49%. Each 500 W increase in the shroud cooling power, increases the efficiency by 0.3%. Diffuser cooling and back plate cooling have an identical effect on the polytropic efficiency. However, back plate cooling increases the pressure ratio more than diffuser cooling. Furthermore, only back plate cooling reduces the impeller temperature, and with 2400 W of cooling power, the impeller temperature reduces by 45 K.  
integrated cooling; high pressure ratio; centrifugal compressor; performance; impeller temperature +
Footnote †: journal: Applied Sciences
# Smart Grid Fault Mitigation and Cybersecurity with Wide-Area Measurement Systems: A Review
## 1 Introduction
As depicted in Figure 1, microgrids are increasingly susceptible to faults propagated by conventional power systems, underscoring a critical challenge in achieving seamless interoperability between these two frameworks [16; 17]. This issue has impeded the advancement of more resilient power system architectures and off-grid solutions, as the integration of microgrids with traditional grid infrastructure continues to present significant technical and operational hurdles. Faults are at some level inevitable, since key components are exposed to environmental and manmade stresses that affect the daily operation of a power network. WAMS are systems that provide grid managers with operational awareness while conducting key power system functions [10; 14]. WAMS are, therefore, becoming well known as tools for improving fault clearing, thus enhancing reliability in power system architecture. The reliability setup described by [PERSON] et al. consists of monitoring and control components, and their study measured its efficiency to accomplish both responsibilities [11]. In a normal power protection system, units are designed to accomplish these tasks whether through SCADA or conventional relay units [18; 19]. However, these approaches are prone to malfunctions when overwhelmed by the faults they are meant to detect. Thus, in cases of high complexity, they fail to provide an accurate real-time assessment of the power system network situation [3; 20].