esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Greedy Strategy Based on Edge Importance
When there is only one element in the set of important nodes, its time complexity is \(\overline{k}\), and \(\overline{k}\) is the average degree of the network; when there are two elements, its time complexity is \(2\overline{k}\), and so on. The time complexity of this part can be written as \(\overline{k}+2\overline{k}+3\overline{k}+\ldots+n\overline{k}\), which equals to \(O\left(n^{2}\overline{k}\right)\). One can also notice that the previously calculated node importance scores are fully reusable when new nodes are added to the set of important nodes. In this case, each time an important node is added, only the neighboring node scores of the newly added node need to be calculated. Based on above analysis, one can conclude that the time complexity actually depends on the number and average degree of nodes in the network, thus the overall time complexity would be \(O\left(n\overline{k}\right)\). The following section will use the network shown in Figure 1 to illustrate the computation process of the edge greedy strategy. Step 1, number the nodes in Figure 1 sequentially are 1, 2, 3, 4, 5, 6 and 7. Let node 1 be a known important node, and add node 1 to set \(C\). Calculate the edge scores first according to the known important node. The edge scores here are (1,2):2.33, (1,3):1.65 and (1,4):2.0. Node 2, which corresponds to edge (1,2), has the largest value of 2.33, and it is added to set \(C\). Step 2, calculate the edge importance score for nodes 1 and 2 in set \(C\), and one obtains (1,3):1.65, (2,4):1.75, (2,5):1.125, and (2,6):1.0625. Node 4, which corresponds to edge (2,4), has the largest value and is added to set \(C\). Step 3, calculate the edge importance score for nodes 1, 2 and 4 in set \(C\), and one obtains (1,3):1.65, (2,5):1.125, (2,6):1.0625 and (4,7):1.083. Node 3 has the largest value and is added to set \(C\). Step 4, calculate the edge importance score for nodes 1, 2, 4 and 3 in set \(C\) and obtain (2,5):1.125, (2,6):1.0625 and (4,7):1.083. Node 5 is selected and added to set \(C\). Step 5, calculate the edge importance score for nodes 1, 2, 4, 3 and 5 in set \(C\) and obtain (2,6):1.0625 and (4,7):1.083, and node 7 is added to set \(C\). Step 6, calculate the edge importance score for nodes 1, 2, 4, 3, 5 and 7 in set \(C\). The edge importance score is (2,6):1.0625 and node 6 is added to set \(C\). At this point, all nodes are added and the loop ends. For node 1, the order of relatively important nodes possibilities would be 2, 4, 3, 5, 7 and 6. Figure 1: Example network of the edge importance greedy strategy.
## 5 Simulation Results
### SE Bounds Achieved by Type I Codebook
However, the computational complexity is higher in UPA because a DFT precoder is computed in both dimensions. Then, the Kronecker product is applied in (9) to obtain the final wideband precoding matrix. On the contrary, these expressions are simplified in ULA since only a DFT precoder in one dimension is required. Although the complexity and overhead are higher in UPA, its performance is superior since the beams can be steered in horizontal and vertical directions. It must be noted that the highest SE in Figure 8 is obtained with the UPA configuration because the number of antennas in both dimensions becomes higher and hence, the spatial properties of the channel are better exploited.
## 7 Optimization Techniques for Demand-Side Resources in Active Distribution Networks
### Heuristic/Non-Deterministic Optimization Methods  
Heuristic and non-deterministic optimization methods have gained prominence in optimizing demand-side resources (DSRs) within active distribution networks. These methods offer valuable tools for solving complex, non-linear, and computationally intensive DSR optimization problems [146]. Heuristic and non-deterministic optimization methods are characterized by their ability to handle complex and computationally challenging problems. Unlike classical optimization methods, they do not guarantee globally optimal solutions but provide near-optimal solutions within a reasonable computation time. These methods are particularly well-suited for DSR optimization in dynamic and uncertain environments [147].  
Metaheuristic algorithms, such as genetic algorithms and particle swarm optimization, are widely used in DSR optimization [148]. Genetic algorithms mimic the process of natural selection to evolve potential solutions to optimization problems [149]. They are effective in optimizing DSRs by exploring a diverse solution space and iteratively improving solutions. Genetic Algorithms (GAS) have been applied to DSR management problems, such as load scheduling and demand response planning, often yielding near-optimal solutions in complex scenarios. Particle Swarm Optimization (PSO) is inspired by the social behavior of birds flocking or fish schooling. It optimizes DSRs by iteratively adjusting potential solutions based on both individual and group performance. PSO has been applied in DSR optimization to tackle problems involving distributed resources, such as distributed energy generation and load control [150].
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 2 Background on Concepts and Requirements
### Dimensions of Privacy and Privacy Protection  
Scholars and experts, as well as countries, define the concept of privacy from different perspectives. As a fundamental human right, privacy includes the right to be free from interference and intrusion. It supports many other rights essential to preserving personal freedom and dignity, protecting identities, managing interactions, and dealing with surroundings. Furthermore, privacy allows us to build solid boundaries to control who can access our personal spaces, belongings, communications, and data. Privacy takes different shapes across our lives, including physical, informational, and communication privacy [7] Figure 1.  
* Physical privacy: The capability of people to set boundaries regarding their belongings and actions and control access to their physical space.
* Informational privacy: The capability of people to control access to their sensitive information, including financial and medical records.
* Communication privacy: The capability of individuals to control access to their private and personal communications, such as phone calls, emails, and messages.  
Privacy in the healthcare sector includes the condition that patients' information, like their medical history, health conditions, or even treatment details, should be protected from non-authorized access. Due to the transition towards digitization in healthcare, data must be exchanged with multiple networks [8], including insurance and financial organizations; therefore, ensuring the privacy of the data is paramount. IoT technologies pose exceptional challenges to traditional privacy guarantees. Due to the variable nature of these systems, which involve continuous data flows, communication between different devices, and variable access control, they need careful strategies to be implemented to ensure secure access. In the era of IoT technology, the complexity of protecting patient data has increased, necessitating the development of new standards to address traditional issues related to privacy and dealing with information-rich environments.  
Privacy in healthcare presents in many ways [8], including the following:  
* Electronic privacy: Focuses on protecting electronic health records and any other health data so that they remain safe and protected from any unauthorized access or leakage. These records hold detailed information, including the patient's medical history, diagnoses, determined treatments, vaccination dates, allergies, radiological images, and medical test results [9].
* Digital health-related data privacy: This term covers a broader range of digital health information, extending beyond traditional electronic health records. It includes data gathered from wearable devices, remote monitoring devices, genetic testing results, and a wealth of other digital health information that, while not always included in the electronic health record, plays an important role in facilitating a fuller picture of a patient's health status.
## 3 Results and Analyses
### Macroscale Behaviors  
3.1.1. Analysis of Macroscopic Mechanical Behaviors  
The triaxial test simulations of S-RM specimens with varying gravel contents and consolidation states, from which stress-strain relationship curves (\(q=\sigma_{1}-\sigma_{3}=\sigma_{1}^{\prime}-\sigma_{3}^{\prime}\)) were derived, are illustrated in Figure 6. The results presented in Figure 6 indicate that, for S-RMs with identical gravel contents, the peak stress progressively increases as the confining pressure rises. This observation aligns with typical laboratory findings [2; 63], as depicted in Figure 6d,e. A detailed comparison of Figure 6a-c reveals that, under the same consolidation pressure, when the gravel content exceeds 20% (GC > 20%), the peak stress of the specimen gradually increases with increasing gravel content; conversely, when the gravel content is below 20% (GC < 20%), the peak stress decreases with increasing gravel content. These observations suggest that there is a threshold value for the influence of gravel content on the shear strength of S-RMs: beyond this threshold, an increase in gravel content enhances the shear strength. Large-scale triaxial tests conducted by researchers [64; 65] further support these findings, as illustrated in Figure 6f,g. [PERSON]'s experimental results [64] show minimal differences in shear strength between specimens containing 50% and 30% gravel, while a significant discrepancy is observed for specimens with 70% gravel content. [PERSON]'s triaxial test results [65] also corroborate this finding: the shear strength of the pure sand specimens is only slightly higher than that of S-RMs with 30% gravel, although the difference is not noticeable. It should be noted that these conclusions are qualitative rather than quantitative due to the inherent limitations of numerical simulations.  
Figure 5: Comparison of results from laboratory large-scale triaxial tests and numerical simulations for S-RM.
## 2 Theory and Simulation
### Straddling Oil and Gas Pipeline Model and Parameters  
The prototype used in this paper is a real oil and gas pipeline, constructed as a single-tower and double-cable-plane cable-stayed pipe bridge. A cable oil and gas pipe bridge system was established, and the three-dimensional finite element model of the entire bridge was created using ANSYS Workbench. The model represents a single-tower cable-stayed bridge with a vase-shaped structure, featuring a tower height of 72 m. The truss beam has a width of 0.284 m, consisting of vertical rods, chord rods, and oblique belly rods. Each truss beam measures 2 m in length, with every two trusses supported by an arrangement. For oil and gas transmission purposes, two pipes were installed in parallel on the support of each truss beam. The oil and gas transmission adopted two pipes, set up on the support of the truss beam in parallel. The pipe specification is: \(\Phi\) 711 mm \(\times\) 12.7 mm, the total length is 284 m, and the bridge span arrangement is 142 m + 142 m. The cable-stayed cables were arranged in a fan shape, and 6 \(\times\) 37 galvanized steel wire rope was used. The basic cable distance is 10 m and the cable distance on the bridge tower is 1.5 m. The specifications are: \(\Phi\)25.5, \(\Phi\)36, \(\Phi\)42, and \(\Phi\)45. The span structure model of the cable-stayed pipe bridge is shown in Figure 1. The model has 77,582 nodes and 14,414 units.  
The material types and mechanical properties of each component in the cable-stayed tube bridge span structure are shown in Table 1.
## 6 Removal of Pharmaceutical Pollutants in Wastewater by Nanosorbents
### Removal of Non-Steroidal Anti-Inflammatory Drugs
The sorption kinetics appeared to follow the pseudo-second order, indicating that there is possible development of a chemical bond. The Langmuir adsorption efficiency of biochar-based adsorbents for ibuprofen was \(\sim\)39.9 mg/g, and the mechanism was identified as a chemical process, viz. stacking-based interactions, hydrogen bond formation, and dipole-dipole linkages aiding in monolayer adsorptive retrieval. The magnetic coating provides additional adsorption sites, allowing for the quick removal of up to 90% of ibuprofen during the first few minutes of contact. With simple methanol and magnetic-based treatment, desorption efficiency was maintained for five desorption-adsorption cycles [103]. Adsorptive retrieval of significant NSAIDs such as ibuprofen, ketoprofen, and diclofenac has also been accomplished using poly(amidoamine)/silica nanohybrids [92]. At pH 9.0 and room temperature, 1 mg/L of the functionalized sorbent exhibited adsorption ability towards 100 g/L of NSAIDs within 25 min. For NSAIDs, the greatest sorption capacity was in the range of 112-134 mg/g, and the adsorption process was found to follow the Langmuir isotherm, with a separation factor less than one, indicating favorable adsorption [92]. [PERSON] et al. [143] developed monodispersed hierarchical layered double hydroxide on silica microspheres for diclofenac adsorption. In this study, 758 mg of diclofenac was adsorbed on 1 g of the adsorbent within 30 min, and the adsorbent was reused four times [143]. In another investigation, silica-alumina was found to be an efficient adsorbent for metformin elimination. At an optimum pH of 9.0, high metformin removal (46 mg/L) was achieved, and the adsorbent retained 95% pollutant removal after three cycles [144]. Ref. [115] produced and utilized mesoporous silica SBA-15 as an adsorbent for pharmaceutical extraction. At acidic pH, the adsorbent demonstrated high adsorption of carbamazepine (85.2%), diclofenac (88.3%), ibuprofen (93.0%), ketoprofen (94.3%), and clofibric acid (49.0%) (pH 3.0). The pseudo-second-order equation and the Freundlich isotherm model best suit the kinetics and equilibrium of adsorption, respectively [115].
# A Precise Modeling Method for PMSM Servo System of Spacecraft
## 4 Experimental Testing and Validation  
### Nonlinear Identification  
The picture of the experiment setup is shown in Figure 8. In Section 4.1, the nonlinear factors of friction, clearance and stiffness of the PMSM servo system for spacecraft are identified and combined into a complete nonlinear model.
# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 6 AI-Based Malware Detection
### Network Behaviors  
Wireless Multimedia System (WMS) can be utilized to maintain a constant monitoring of data and manage the condition of distant apparatuses. Wireless multimedia devices typically feature multiple sensors, which facilitate the transmission of data to adjacent nodes according to established routing protocols. The decentralised architecture of WMS facilitates the propagation of malicious software, thus posing a risk to other nodes, wireless routers, and terminals through data exchange. The acquisition of network behavioursis imperative for the identification of WMS malware. A malware detection scheme was devised which enabled the acquisition of network behavior in WMS using the data sniffer (DroidSniffer) in conjunction with Support Vector Machines (SVM) and Backpropagation Neural Networks (BPNN) for the detection and elimination of malicious codes. The experiment yielded an infection rate of 22.17%, demonstrating that malware can be identified even at a comparatively low incidence [55].
# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
###### Abstract  
Structural health monitoring (SHM) is an important aspect of the assessment of various structures and infrastructure, which involves inspection, monitoring, and maintenance to support economics, quality of life and sustainability in civil engineering. Currently, research has been conducted in order to develop non-destructive techniques for SHM to extend the lifespan of monitored structures. This paper will review and summarize the recent advancements in non-destructive testing techniques, namely, sweep frequency approach, ground penetrating radar, infrared technique, fiber optics sensors, camera-based methods, laser scanner techniques, acoustic emission and ultrasonic techniques. Although some of the techniques are widely and successfully utilized in civil engineering, there are still challenges that researchers are addressing. One of the common challenges within the techniques is interpretation, analysis and automation of obtained data, which requires highly skilled and specialized experts. Therefore, researchers are investigating and applying artificial intelligence, namely machine learning algorithms to address the challenges. In addition, researchers have combined multiple techniques in order to improve accuracy and acquire additional parameters to enhance the measurement processes. This study mainly focuses on the scope and recent advancements of the Non-destructive Testing (NDT) application for SHM of concrete, masonry, timber and steel structures.  
concrete structures; non-destructive testing; sensors; steel structures; structural engineering; structural health monitoring (SHM)
# A New SEPIC-Based DC-DC Converter with Coupled Inductors Suitable for High Step-Up Applications
## 5 Simulations and Experimental Results
### Experimental Results  
A prototype of the proposed SEPIC converter was built in order to prove its functionality. The design parameters were the same as the ones in the design example and simulation. The semiconductors used were: \(S\) = STMicroelectronics STW70N60M2 and diodes \(D_{1,2,4}\) = Silicon Carbide Diodes from STMicroelectronics STPSC4H065-type. The coupled inductors \(L_{1}\) and \(L_{2}\) were built on the same E71/33/32-3C94 FERROXCUBE core, and the turns numbers were manually adjusted in order to obtain the desired inductor values. For inductor \(L_{3}\), a separated core of the same type was used. The experimental waveforms were obtained with a fixed input voltage of \(V_{g}\) = 30 V, a fixed switching frequency of \(f_{s}\) = 100 kHz and a load of \(R\) = 360 \(\Omega\). In Figure 19, the proposed SEPIC converter, together with the equipment used, can be observed.  
Figure 17: Voltage and current corresponding to the diode \(D_{2}\).  
Figure 18: Voltage and current corresponding to the diode \(D_{4}\).  
The reference signal that is present in all waveforms on the oscilloscope is the drain-to-source transistor voltage. In Figure 20, the second waveform is the voltage across inductor \(L_{1}\) (in red) and the third waveform is the inductor \(L_{1}\) current (shown in cyan). In Figure 21, the first waveform is the drain-to-source voltage, (blue), the second one is the voltage across winding \(L_{2}\) (red), followed by the current through inductor \(L_{2}\) (cyan), with the last waveform being the output voltage (purple). The waveforms for inductor \(L_{3}\) are presented in Figure 22. After the reference signal in blue, the voltage across inductor \(L_{3}\) (in green) and the current through inductor \(L_{3}\) (light blue) can be observed, while the last waveform is the output voltage (purple). It is confirmed that the acquired waveforms are in good accordance to those from the simulation.  
Figure 19: View of the experimental setup.  
Figure 20: Oscilloscope waveforms: drain to source voltage (dark blue—\(v_{DS}\)); voltage across \(L_{1}\) (red—\(v_{L1}\)); current through \(L_{1}\) (cyan—\(i_{1}\)).  
Figure 21: Oscilloscope waveforms: drain-to-source voltage (dark blue—\(v_{DS}\)); voltage across \(L_{2}\) (red—\(v_{L2}\)); current through \(L_{2}\) (cyan—\(i_{L2}\)) and output voltage (purple—\(V_{out}\)).  
Figure 22: Oscilloscope waveforms: drain-to source-voltage (dark blue—\(v_{DS}\)); voltage across \(L_{3}\) (green—\(v_{L3}\)); current through \(L_{3}\) (cyan—\(i_{L2}\)) and output voltage (purple-\(V_{out}\)).  
In Figure 23, the dependency of the efficiency against the output power at a constant output voltage of 120 V is presented.
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 1 Introduction
### Review Motivation  
With its cutting-edge connections between patients and healthcare professionals, smart healthcare is revolutionizing the industry. The core of the system is efficient communications. However, delivering QoS is challenging. There are not many publications that explore how to improve QoS in smart healthcare at various layers of the underlying network and services. Delay and energy efficiency concerns are of utmost importance for dependable communications within healthcare networks.  
QoS is a set of technologies and techniques that are used to manage network resources and ensure that network traffic is delivered effectively. It enables you to prioritize different types of traffic so that important traffic gets through first and helps to prevent congestion on the network. QoS optimization is the process of configuring and fine-tuning these QoS settings to improve the overall performance and reliability of the network. This can involve setting priorities for different types of traffic, setting bandwidth limits, and using techniques such as traffic shaping and packet scheduling to manage the flow of traffic. QoS optimization is important in networks that carry a mix of different types of traffic, such as voice, video, and data, to ensure that the network performs optimally for all types of traffic.  
QoS is the overall performance of the network service in terms of transmission delay, throughput, bit error rate, jitter, packet loss, energy drain, etc., from source to destination [7]. Currently, miniature size IoT enabled devices have greatly facilitated healthcare systems, but at the same time, their computationally hungry functions require more battery power and pose several challenges in edge AI-IoT-enabled healthcare systems. Our focus is on the QoS requirements of the healthcare system (as a whole) and as well as on the individual devices present at different levels of smart healthcare infrastructure, as shown in Figure 1.