esa-sceva/satcom-chunk-collection huggingface dataset:
# A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm
## 4 Research Methodology  
In this paper, for the security problems of port information dissemination, a research method named port information security link prediction based on the HWA algorithm is proposed, and the overall flowchart of the method is shown in Figure 2. It is generally divided into three parts. Firstly, the data construction part involves constructing the basic relationships of the ports. By understanding the basic relationships between various entities in the ports, a general graph adjacency matrix is formed. Then, using the hypergraph-based self-attention forest prediction (HWA) algorithm proposed in this paper, the adjacency matrix of the general graph is constructed as a hypergraph association matrix. Next is the data preprocessing part, which uses the hypergraph adjacency correlation matrix as the input and the GCN as the model for training, continuously optimizing the loss function to obtain node feature vectors. Finally, there is the model evaluation section; taking node feature vectors as inputs, the MLP learns through node feature vectors to obtain edge scores for link prediction. Throughout the process, gradient descent and other optimization algorithms are used to update the model parameters to improve the predictive performance of the model. Finally, the model was evaluated using the area under the curve (AUC) and the F1 value as evaluation indicators.
## 4 Results and Discussion
### Performance Evaluation Matrices
#### 4.2.1 Accuracy  
Generally, accuracy gives the recognition rate of the classifier. It is defined as the ratio between the sum of true positive (TP) and true negative (TP) values over the sum of TP, TN, false positive (FP), and false-negative (FN) values. The mathematical expression for accuracy is shown in Equation (3):  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{3}\]  
The tier-I of the proposed model achieves 85.63%, 84.53%, and 81.32% accuracies for training, validation, and test set, respectively. In contrast, tier-II achieved 91.63%, 87.57%, and 86.75% accuracy, respectively. However, Tier-III outperforms Tier-I and Tier-II and achieved 96.85%, 95.28%, and 89.29% accuracy for training, validation, and test set, respectively. Globally, the proposed model achieved 91.37%, 88.85%, and 86.06% accuracy. The confusion matrix of training, validation, and testing accuracies of Module-I classification is given in Figure 9.  
Recall (Sensitivity): Recall is the ratio between true positive values and total actual positive values. Mathematically, it is written as shown in Equation (4):  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{4}\]  
Recall of the proposed model was 93.49%, 95.98%, 95.87%, and 98.12% for Tier-I, Tier-II, Tier-III, and global, respectively, as shown in Table 4.  
Figure 9: Confusion matrix of training, validation, and testing accuracies of Module-I classification.
# Application Characteristics of Bioethanol as an Oxygened Fuel Additive in Diesel Engines
## 3 Results and Discussion
### Emissions
#### 3.3.4 Smoke  
Figure 10 displays the smoke emissions under different engine load conditions. In this study, the smoke emissions from the CRDI diesel engine were evaluated by smoke opacity (%), which is the percentage of light absorbed into the smoke [25]. As shown in Figure 10, with the increase in the engine load from 40 Nm to 80 Nm, the smoke opacity was slightly reduced at first and then increased. The smoke opacity of all fuels at 60 Nm and 80 Nm were reduced by 10.00% on average compared with that at 40 Nm. The specific result is that the smoke opacity of all test fuels was reduced by 1.29% and increased by 30.09% at 60 Nm and 80 Nm on average, respectively, compared with that at 40 Nm. This is because properly increasing the engine load can increase the temperature in the cylinder, which is conducive to the oxidation of smoke particles. However, when an excessive load is operated, a large amount of fuel is injected into the combustion chamber, resulting in a reduction in the air-fuel ratio. The formation of smoke opacity occurs at the extreme air deficiency, and carbon smoke (soot) is produced by the thermal cracking of long-chain molecules under anoxic conditions. When there is a local lack of air or oxygen in the diesel engine, smoke increases with the reduction in the air-fuel ratio [26]. On the other hand, with the addition of ethanol, the smoke opacity of all diesel-ethanol-blended fuels is lower than that of diesel under the same load conditions. The specific result is that, at 40 Nm, the smoke opacities of E5, E10 and E15 were, respectively, reduced by 12.37%, 27.84% and 41.24% compared to that of E0; at 60 Nm, the smoke opacities of E5, E10 and E15 were, respectively, reduced by 6.98%, 9.30% and 29.07% compared to that of E0; at 80 Nm, the smoke opacities of E5, E10 and E15 were, respectively, reduced by 80.00%, 24.80% and 45.60% compared to that of E0. It can be explained by ethanol being an oxygenated fuel, and the oxygen atom carried by ethanol itself improving the problem of local hypoxia. The addition of ethanol promotes the complete combustion of the blended fuel, which is conducive to the oxidation of soot in the combustion process.  
Figure 10: Smoke emissions.  
Figure 9: NOx emissions.
###### Abstract
By comparing with the results obtained using the DMAS, CF-DAS, and CF-DMAS algorithms on the same data, it is demonstrated that the MICF-DMAS algorithm performs better in removing image clutter, with the SCR improved by at least 4 dB and the contrast improved at least six-fold. The images obtained using the MICF-DMAS algorithm have the least amount of clutter, and MICF-DMAS can clearly distinguish the location of the tumor by reducing the influence of clutter on tumor determination. The rest of the paper is structured as follows: The proposed MICF-DMAS algorithm is introduced in Section 2. Section 3 describes the imaging system and the experimental setup, and the proposed method is evaluated based on the experimental results. Finally, the concluding remarks are presented in Section 4.
## 7 Conclusions and Future Work  
In this paper, we designed a multi-hop relay selection strategy based on wireless ad hoc networks to assist T2T communication. The optimization goal of our proposed algorithm is to reduce the T2T transmission delay and increase the throughput of the entire link in a congested network. Since the channel status changes in real time, an MADDQN approach was proposed to better solve the problem. Simulation results showed that our proposed algorithm could effectively avoid congested nodes and reduce the number of hops for the whole-link transmission, thereby better achieving the optimization goal compared with existing routing algorithms. In future work, the energy consumption of the nodes and the problem of retransmission after packet loss should be considered. Moreover, some secure and energy-efficient technologies, such as reconfigurable intelligent surfaces (RIS), will be applied to the CBTC system to better assist in signal transmission.  
Figure 12: Relationship of optimization goal and number of nodes under different schemes.  
Figure 13: Relationship of optimization goal and buffer size under different schemes.  
**Author Contributions:** Conceptualization, S.M. and M.L.; methodology, S.M. and R.Y.; software, S.M.; validation, S.M., R.Y., and Y.S.; formal analysis, S.M. and M.L.; investigation, S.M. and Z.W.; resources, S.M., Z.W., and P.S.; data curation, S.M. and M.L.; writing--original draft preparation, S.M.; writing--review and editing, M.L. and P.S.; visualization, Y.S. and Z.W.; supervision, M.L. and P.S.; project administration, M.L., R.Y., and Z.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partially supported through the Beijing Natural Science Foundation under Grants L211002, 4222002, and L202016, and the Foundation of the Beijing Municipal Commission of Education under Grants KM 202010005017 and KM202110005021.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Fast Spectral Approximation of Structured Graphs with Applications to Graph Filtering
## 1 Introduction
However, their construction relies on the assumption that Givens rotations, despite not being dense in the space of general orthogonal matrices, approximate well the matrix \(\mathbf{U}\). In addition, the main aim of these works is to obtain a fast GFT and not a fast graph spectral decomposition. That is, their focus is not on finding a basis for the transform efficiently, but to perform the action of the basis as fast as possible. Furthermore, no structure, as for example an excluded minor [9], is leveraged in those works. So, despite that these approaches address the problem, they fail to make use of particular structures (combinatorial in nature) that might improve the analysis and performance for restricted (but practical) families of graphs. Therefore, in this work, we aim to provide _a topological approach to devise a fast graph spectral decomposition_, i.e., a fast estimation of both \(\mathbf{\Lambda}\) and \(\mathbf{U}\), as a first step towards a fast GFT. Here, we argue that for some families of graphs there is a structure that can be leveraged to obtain divide-and-conquer algorithms for fast computation. In addition, we provide asymptotic theoretical bounds for the approximation of the spectrum of a graph up to any recursion level. Although the main aim of this work is to provide an approximate method for computing the spectrum and the modes of a graph, we provide theoretical bounds on the quality of this decomposition for other tasks such as graph filtering. These results open a venue, and describe a possible landscape, for further research on graph partitioning for graph filtering and hierarchical graph filtering.
# Energy-Efficient User Pairing for Downlink NOMA in Massive MIMO Networks
###### Abstract  
The motivations for deploying energy and spectral-efficient network architectures are the high energy consumption and the need for more spectral resources in modern cellular networks. The key method to solve the energy efficiency (EE) maximization problem of the downlink non-orthogonal multiple access (NOMA)-based massive MIMO system is to decouple it into user pairing and efficient power allocation problems. This work studies the performance of three main pairing methods in NOMA-based networks: Hungarian, Gale-Shapley, and correlation-based approaches. Firstly, we provide a mathematical analysis for EE of downlink NOMA in a massive MIMO system for the non-line of sight (NLoS) channel model with perfect successive interference cancellation (SIC). Finally, the sequential convex programming (SCP) approach is used to tackle the power allocation problem. Simulation results show that the Hungarian algorithm for pairing plus SCP for power allocation (Hungarian algorithm-SCP) achieves the highest energy efficiency among all the three pairing algorithms with an identical performance to joint user and resource block association with power allocation (joint user-RB PA) algorithm but with much lower computational complexity and outperforms the NOMA SCP greedy algorithm (NOMA-SCP-GA).  
user pairing; NOMA; massive MIMO; Hungarian; Gale-Shapley; 5G +
Footnote †: journal: Applied  
Article  
Energy-Efficient User Pairing for Downlink NOMA in Massive MIMO Networks  
[PERSON], [PERSON]; [PERSON] Energy-Efficient User Pairing for Downlink NOMA in Massive MIMO Networks. _Appl. Sci._**2022**, 12, 5421. [[https://doi.org/10.3390/appl2115421](https://doi.org/10.3390/appl2115421)]([https://doi.org/10.3390/appl2115421](https://doi.org/10.3390/appl2115421))
# A New Intra-Cluster Scheduling Scheme for Real-Time Flows in Wireless Sensor Networks
## 3 Performance Evaluation
### IntraSend Time Slots Utilization  
To further analyzed the intra-cluster time slots. We compare the previous scheme and proposed scheme based on the number of IntraSend slots which did not utilized by the IntraRecv scheduling in the previous scheme whereas the proposed scheme IntraRecv scheduling utilized these empty IntraSend time slots.  
Figure 8: Simulation results for w.r.t IntraRecv time slots.  
Figure 7: Simulation results for w.r.t IntraSend time slots.  
As shown in Figure 9 the proposed scheme shows higher utilization of IntraSend time slots as compared to previous scheme. The reason is that in the IntraRecv scheduling the clusters that have only incoming flows and there are no outgoing flows then the IntraRecv scheduling in the previous scheduling will only utilized the IntraRecv time slots and will not utilized the empty time slots of IntraSend time slots. While in the proposed scheme for the clusters that have only outgoing flows then that cluster head can utilize the time slots of IntraRecv scheduling as well as the empty time slots of IntraSend time slots. Hence the proposed scheme efficiently utilized the IntraSend and IntraRecv time slots.
## 4 Experiments
### Ablation Experiments
#### 4.5.3 Impact of the MFE  
The model in this paper used a modified RFB module, which we referred to as an MFE. According to [30], the MFE has a stronger feature representation, and the model is more robust compared to the original RFB [24] and RFB-s [24]. For this reason, the related ablation experimental results rendered on the LEVIR-CD dataset are provided in Table 10. The setup of the specific ablation experiments was that the MFE module in the decoder was replaced by an RFB and RFB-s, respectively, which are also used for extracting multiscale contextual features. The metrics of the MFE module were remarkably higher than those of RFB and RFB-s, except for the recall, which was slightly lower than those of RFB and RFB-s.
# Dynamic Corrosion Test Using LiNO3 Containing Molten Salt for CSP Applications
###### Abstract  
Low melting point thermal energy storage (TES) materials have been proposed in the last years to reduce the storage cost in concentrating solar power (CSP) technology. One of the most interesting additive due to the enhancement in thermal properties is lithium nitrate. However, there is a lack of dynamic corrosion tests to simulate real operation conditions in CSP plants. In this work, we present a dynamic reactor set up where a mixture of 30 wt.% LiNO3 + 57 wt.% KNO3 + 13 wt.% NaNO3 is moved through a mechanical stirrer obtaining a lineal speed of 0.30 m/s. A commercial carbon steel A516 was tested as container material at 390 \({}^{\circ}\)C during 1000 h. Fe2O3 and Fe3O4 were obtained as the main corrosion products by scanning electron microscopy (SEM) and x-ray diffraction (XRD) with a metallographic corrosion rate of 0.015 mm/year.  
dynamic corrosion; thermal energy storage; concentrated solar power; lithium nitrate salts +
Footnote †: journal: Applied Sciences
## 3 Mathematical Formulation
### Generation of the Connection Network
Next, we add the edge \((sd^{3},j^{(d)})\) and the edges connecting \(j^{(d)}\) to all its compatible trips to the graph, such as the trip pairs \((sd^{3},3^{(10)})\) and \((3^{(10)},6^{(10)})\) shown in Figure 2. Following this, the currently explored node \(sd^{3}\) is removed, the derived nodes are added to \(W^{b}\), and \(W^{b}\) is reordered. Let \(\hat{\mathbf{V}}_{t}^{b}=\{j^{(d)}\}\) denote the trip copies and \(\hat{\mathbf{E}}_{t}^{b}=\{(sd^{3},j^{(d)})\}\cup\{(j^{(d)},i)|\varphi(j^{(d)},i)=1,i\in\mathbf{V}^{b}\}\) indicate the edges connecting the trip copies, as shown by the dashed nodes and edges in Figure 2, respectively. By repeating the above process until list \(W^{b}\) is empty, the complete connection network of bus \(b\) is updated with \(\hat{G}^{b}=(\hat{\mathbf{V}}^{b},\hat{\mathbf{E}}^{b})\), where \(\hat{\mathbf{V}}^{b}=\mathbf{V}^{b}\cup\hat{\mathbf{V}}_{t}^{b}\), \(\hat{\mathbf{E}}^{b}=\mathbf{E}^{b}\cup\hat{\mathbf{E}}_{t}^{b}\). While exploring, only the compatible copy with minimal delays is considered to avoid producing additional delay times. Then, the connection network for each bus can be constructed in a consistent manner, resulting in many identical trip nodes being shared among them. To facilitate better bus coordination, these networks are merged into a single unified network \(G=(\mathbf{V},\mathbf{E})=\bigcup_{b}\hat{G}^{b}\), where \(\mathbf{V}=\bigcup_{b}\hat{\mathbf{V}}^{b}\) and \(\mathbf{E}=\bigcup_{b}\hat{\mathbf{E}}^{b}\).
# Grating-Assisted Fiber to Chip Coupling for SOI Photonic Circuits
## 5 Lowering Footprint
### Linear Grating Based Compact Tapers
The length \(z\) varies from 0 to 1, where 0 is the beginning of the taper (LHS or \(W_{min}\)) corresponding to the width of the initial waveguide and 1 is the end (RHS or \(W_{max}\)) corresponding to the width of the final waveguide. The final width profile, \(X=f\) (\(z\), \(a\), \(b\), \(c\)) is a superposition of a parabolic baseline and the square of a sine. The coefficient \(a\) controls the fraction of the sinusoidal as well as the parabolic component. For case (i) \(a=0\), the sinusoids are at maximum amplitude, and the entirety of the taper is determined by the sinusoidal part of the function. For case (ii) \(a=1\), the sinusoidal component of the tapers is eliminated, and the taper follows a simple quadratic (or linear) change in width. The parameter \(b\) controls the parabolic curvature of the baseline: case (i) \(b=1\), a convex parabola is obtained, case (ii) \(b=-1\), a concave parabola is obtained, and case (iii) \(b=0\), a simple linear taper is obtained. Finally, \(c\) controls the number of full oscillations of the sinusoidal component part of the taper. The restriction of \(c\) (odd integers) is due to the boundary conditions that must be met at both ends of the taper's interpolation formula. All the three design parameters allow one to design an appropriate taper profile for maximum transmission between the waveguides. Thus, to summarize, \(a\) controls the amplitude of oscillations and thereby is the most critical parameter; \(c\) dictates the oscillations but is limited to 2-3 discrete values. \(b\) which controls the parabolic baseline needs to be tweaked for fine-tuning the optimal response. Figure 12a illustrates the effect of the three design parameters \(a\), \(b\) and \(c\) on the taper profile and Figure 12b shows the optimized compact taper's optical intensity profile and the evolution of modes along the length (L) of the compact taper (14.5 \(\upmu\)m) for SOI Platform [46, 47]. The transmission with variation in waveguide width is shown in Figure 13a,b for C, L and O band. Figure 13c shows the total taper width variation for various values of \(b\) variable on SOI ridge configuration. Figure 13d,e shows the experimental results of the compact taper performance and evidently, they yield the same coupling efficiency as the adiabatic taper. The insertion loss per coupler is -5.45 dB and -6.1 dB for grating coupler with compact taper and adiabatic taper respectively. Figure 13f compares the alignment tolerance of the focusing and linear grating coupler where one of the fibers is aligned from the optimum position and the change in efficiency is measured.
# Security Assessment of Agriculture IoT (AIoT) Applications
## 1 Introduction
### Security Standards Overview  
The following overview of well-known safety and security standards shows that the main initiatives to create security standards come from the industrial automation and mobility (vehicles, railway, avionics) domains.  
Security standards for agriculture mainly handle the area of food and nutrition security, while no dedicated standards are defined for the agriculture IT/OT security domains at the present. But in this case, today's well-established industrial control security standards are a perfectly good source for agriculture applications too.  
The following overview lists some cybersecurity standards from the IT/OT domain:  
* ISO/IEC 15408 establishes the general principles of IT security evaluation;
* ISO/IEC 27000 overviews the Information Security Management Systems;
* ISO/IEC 27001 specifies a management system for information security;
* ISO/IEC 27002 describes guidelines for organizational information security and information security management practices;
* ISO/IEC 27005 brings guidelines for information security risk management;
* ISO/IEC 62443 Industrial Communication Networks--Network and System Security series.  
Food Security concerns the availability and sufficient access to food. Food security is ensured by using new agricultural technologies and by establishing good product quality.  
The focus of safety standards for agriculture are in safety regulations: to prevent illnesses and injuries from agriculture work by pesticide handling and to guarantee a safe use of heavy machines and safe work with animals.  
Food Safety concerns the production of wholesome food products. A lot of safety standards and legal regulations for food safety are defined.  
Animal welfare regulations ensure animal-friendly treatment of livestock.  
To provide cybersecurity requirements, a successful approach could be the adaptation of the cybersecurity standard IEC 62443 as a basis to assess the security vulnerabilities in the agriculture domain. The standard describes a security analysis flow and recommendations for the system development and the assessment of existing systems. Adapting a cybersecurity standard for Industrial Automation Control System (IACS) makes sense because the hardware and software architecture is similar in these two domains and the vulnerabilities are of the same types, except for the extensive differences at the field level. Both system architectures have a similar structure: field area, edge processing area (mid-dleware), and administration and planning area (farm management, data repository). On the industrial control side, in most cases, the field elements are enclosed in industrial control buildings and are, by this circumstance, easy to protect and to supervise. In the agriculture domain, the field elements may be installed and located far away from the farm buildings and these elements are more likely to be endangered by manipulations, destruction, and risk of theft.  
There are many reasons why cybersecurity attacks will be performed. The following must be considered for a cybersecurity analysis.