esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Fundamentals of RF Energy Harvesting
### Components of an RF Energy-Harvesting System
#### 3.2.1 Antenna
Table 3 presents a detailed comparison of representative antenna structures used in RFEH systems, emphasizing not only their efficiency but also critical design attributes such as physical size, gain, and sensitivity. These parameters play a vital role in the performance and practicality of RF energy harvesters. Antenna size directly influences both gain and sensitivity. Larger antennas, such as logarithmic spiral or tapered slot designs, typically provide higher gain, improving energy capture from weak signals. However, these designs are less suitable for space-constrained or wearable applications due to their footprint. In contrast, compact antennas, such as those based on meander or fractal structures, offer miniaturization benefits but often exhibit lower gain, requiring optimized matching circuits or rectifiers to maintain acceptable sensitivity [28]. Sensitivity, expressed as the minimum input power level (in dBm) at which the rectenna begins effective energy conversion, is a critical metric for evaluating antenna suitability in low-RF environments. Systems designed for ambient RF environments, particularly in rural or shielded settings, benefit from antennas with higher sensitivity and low activation thresholds. There is an inherent trade-off between size and efficiency. While miniaturized designs are ideal for embedded or implantable applications, they often suffer from reduced gain and narrower bandwidth [29]. This necessitates advanced rectifier topologies and power management strategies to compensate for the lower energy input. Moreover, antennas developed using flexible or wearable materials (e.g., printed or textile-based dipoles) offer exciting prospects for biomedical and consumer electronics but may compromise performance  
Figure 3: Antenna designs [21]. under mechanical stress. Hence, material properties should be considered in conjunction with electrical characteristics for real-world deployments.
## 7 Conclusions  
The following conclusions can be drawn from this review paper:  
* Detecting defects in materials is crucial for monitoring their structural health and ensuring their integrity. There are two main kinds of methods for detecting defects: destructive testing methods and nondestructive testing methods.  
While destructive testing methods guarantee definitive results, NDT approaches are still preferred, owing to their cost-effectiveness, safety, and reliability.
* NDT methods can be categorized into standard and MNDT techniques. Although standard NDT techniques are frequently employed in industry, MNDT offers exceptional advantages such as greater penetration depths in nonmetallic materials and safer inspection due to using low-power non-ionizing electromagnetic signals.
* MNDT techniques are highly effective in noninvasively detecting minute surface defects in materials as well as concealed imperfections in non-conductors, even at large distances from the specimen under test.
* Numerous microwave probes have been devised and refined for a wide range of inspection scenarios, each offering unique advantages and limitations.
* The resulting integration of MNDT techniques in different applications offers promising solutions for defect detection, making it vital for ensuring the structural integrity of components.
* In hardware, microwave microscopy, active microwave thermography, and chipless radio frequency identification have emerged to enhance testing precision and sensitivity.
- In algorithms, the incorporation of AI, and specifically ML, has been shown to improve the data analysis process, making MNDT methods more robust and reliable.  
By staying abreast of the latest advancements in this field, engineers can leverage innovative microwave testing techniques to address the evolving challenges in NDT and boost the overall reliability of composite materials and structures. Collaboration with industry stakeholders, continuous education, and investment in research and development also serve as a pivotal way to position NDT techniques effectively within the industry. Engaging with standardization bodies and promoting knowledge exchange through workshops and seminars can help achieve consistency, reliability, and broader acceptance of these techniques in various industrial applications.  
Investigation, A.G. and R.A.-S; data curation, A.G. and R.A.-S; writing--original draft, A.G.; writing--review and editing, R.A.-S., A.Z., and N.Q.; visualization, A.G. and A.Z.; supervision, A.Z. and N.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the American University of Sharjah (AUS) Faculty Research Grant FRG23-C-E02, the Open Access Program (OAP), and the Master of Science in Electrical Engineering (MSEE) program at AUS. This paper represents the opinions of the authors and does not mean to represent the position or opinions of the American University of Sharjah.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.
## 5 Algorithm Performance Evaluation and Comparison
### Simulation Result Analysis
From Figure 6, it can be observed that as the number of MEC servers increases, the overall system cost gradually decreases. This is mainly because, with an increased number of MEC servers, IoT devices can offload more tasks to MEC servers simultaneously, significantly enhancing the system performance. From an algorithmic perspective, the total cost generated by the local offloading strategy is the highest. The random offloading strategy results in a large variation in the system costs due to the different choices made by the strategy. The D3 QN algorithm, which uses  
Figure 5: Comparison of total system costs with different numbers of M-IoT devices. neural networks for training, has achieved a relatively optimal model; however, since it operates with only a single agent, it can easily fall into a local optimum, failing to find a global optimal solution. In contrast, the MADDPG algorithm performs better than the D3 QN algorithm, yielding an optimized offloading strategy that demonstrates superior optimization results and plays a significant role in resource allocation and load balancing for the servers. Therefore, setting an appropriate number of MEC servers suitable for the current network conditions is crucial, as it can greatly improve the system performance. Figure 7 shows the changes in the total system cost under different computing capacities of MEC servers. As the computing capacity of the MEC servers increases, the total system cost shows a downward trend for all the algorithms except the All-local algorithm. This is mainly because the UAV can allocate more computing resources to the maritime IoT devices, thereby reducing the devices' computational energy consumption and latency, achieving the goal of lowering the total system cost. The total system cost of the All-local algorithm remains constant because the delay and energy consumption of the devices executing tasks locally are unrelated to the increase in the computing capacity of the MEC servers. Figure 6: Comparison of total system costs with different numbers of MEC servers. Figure 7: Comparison of total system costs with different MEC server computing capacities. For the MADDPG algorithm, the system cost continues to decrease and is consistently lower than the system costs of the All-local, All-offload, Random, and D3 QN algorithms. However, when the MEC computing capacity increases to a certain level, limited communication resources constrain the reduction in the total system cost, and the downward trend of the MADDPG algorithm gradually slows down. In summary, the MADDPG algorithm proposed in this section effectively allocates computing resources and learns better offloading strategies through cooperative learning among multiple agents, thereby reducing the total system cost and achieving better system performance. Finally, the contributions of this paper are summarized in Table 2.
## 4 Discussion
### Microorganisms as the Agents of Biodeterioration
#### 4.1.1 Biophysical and Biochemical Alterations  
Stone substrates represent oligotrophic environments, since the nutrient availability is scarce, the ultraviolet exposure is high, and the moisture content is usually low [66], yet the formation of microbial communities occurs, starting with the occurrence of autotrophic microorganisms, such as cyanobacteria and algae, which can grow in the complete absence of organic matter [67]. The sulfur-oxidizing bacteria (SOB) and nitrifying bacteria involved in limestone weathering are autotrophic as well [57]. Fungi are also considered to be common limestone inhabitants, mainly due to the transporatition of their spores by air, atmospheric water, insects, and other biotic vectors [19] and because they possess the ability to excrete different types of acids, such as the following: citric, formic, fumaric, gluconic, lactic, malic, succininic, oxalic, and tartaric acids, which act as metal chelators or molecules involved in the dissolution of limestone [21; 51]. Oxalic acid excreted from fungal hyphae has long been known for its ability to dissolve calcium carbonate and form secondary minerals such as calcium oxalates (whewellite and weddellite) on limestone cultural heritage assets, which causes the formation of a white patina on them [68; 69]. Hyphae are not the only calcium oxalate that excrete fungal structure, as the crystalline can be excreted from fungal strands, cords and rhizomorphs, fruiting bodies, and lichen thalli as well [69]; however, they are the only fungal structures that are capable of enforcing mechanical pressure onto limestone by drilling into the substrate, due to the high mechanical resistance caused by the turgor pressure exerted by the protoplast on the inner side of the rigid fungal cell wall [70].
## 3 Kinematic Analysis  
### Principle of Pose Calculation  
#### 3.1.1 Coordinate System Description and Coordinate Transformation  
The shield machine has six degrees of freedom when tunneling in soil, three for position and three for attitude. Two coordinate frames were defined to describe the pose of the shield machine. The global coordinate frame [_A-xyz_] was attached to the rings and the _x_-axis was in the direction of the ring axis. The origin of the base coordinate system was selected as the distributing center of the gripper shoes of cylinders. The local coordinate frame \(\{B\)-\(x_{B}ygz_{B}\}\) moving with the TBM was attached to the backplane of the middle shield, and origin B is the distributing center of front hinges of cylinders. The \(x_{B}\)-axis is in the direction of the machine axis. In the initial stage, the corresponding axes of the two frames were aligned. The diagram of the shield driving mechanism and layout of coordinate systems are shown in Figure 1.  
In this paper, the homogeneous transformation matrix, characterized by solving the position and attitude simultaneously, was employed to express the transformation relationship between two coordinate frames \(\{A\}\) and \(\{B\}\), and it proved to be convenient for mechanism design and kinematics solution [20; 22]. The matrix \(\frac{A}{B}\mathbf{T}\) describes the position and attitude of the coordinate frame \(\{B\}\) with respect to the coordinate frame \(\{A\}\), from which the pose of the shield machine was also obtained. The \(\tensor{A}{B}\mathbf{T}\) is described in Equation (1) [23]:  
\[\tensor{A}{B}\mathbf{T}=\left[\begin{array}{cc}\tensor{B}{R}_{3\times 3}&\mathbf{P}_{3 \times 1}\\ 0_{1\times 3}&1\end{array}\right] \tag{1}\]  
where \(\tensor{B}{R}_{3\times 3}\) denotes the rotation matrix from the coordinate frame \(\{B\}\) to the coordinate frame \(\{A\}\). \(\mathbf{P}_{3\times 1}=\left[\begin{array}{cc}x&y&z\end{array}\right]^{T}\) denotes translation vector from point \(A\) to point \(B\), and it represents the position of coordinate system \(\{B\}\) located in coordinate system \(\{A\}\).  
In Figure 1, \(L_{BC}\) denotes the distance from the center of the cutter head to point B; \(L\) denotes the length of cylinder sleeve; \(l_{i}\) denotes the stroke of the \(i\)th cylinder; \(R\) denotes the internal radius of shield machine; \(R_{0}\) denotes the radius of the distribution of the cylinders; \(\Delta r_{i}\) denotes the shield tail gap approaching the \(i\)th cylinder. The shield diameter was assumed to be a constant along the longitudinal direction in this study. In this model, the size of spherical joints were so small relative to other parts that it could be neglected in the calculation.  
Convention: the coordinates of parameters in this paper were automatically selected as homogeneous coordinates or ordinary coordinates to satisfy the operation.
## 1 Introduction
These catalysts were employed in the esterification of oleic acid and \(n\)-butanol. In each case, the catalytic activity of the obtained species depended on several factors, such as preparation conditions, type and amount of ATRP initiator intermediate, degree of polymerization, type of active site isomer (ortho-, meta-, or para-isomer), and the product's hydrophilicity-hydrophobicity character [2; 7; 9; 10]. Moreover, the surface area and porosity of the silica-based support and, as a consequence, of the catalyst, are highly important characteristics that significantly influence catalytic performance. The synthesis of solid acids with excellent catalytic properties remains a challenge; however, with many trials already made, whereby the type of silica precursor, the type of organo-silanes, and the fabricating methods used for support modification were made to vary [5; 6; 14]. Mesoporous materials are generally characterized by high surface areas and uniformly sized pores (2-30 nm). The generation of pores with specific target shapes and sizes close to 30 nm can be facilitated employing micelle expanders and structure-directing agents during the synthetic step [10; 15]. Thus far, evidence indicates the ATRP method to be the most powerful to generate uniform layers, control polymeric film thicknesses, and avoid pore blockage by polymers [10; 16; 17]. Importantly, the sulfonic acid active sites supported on mesoporous organosilica displayed the advantage of being effective even in water-sensitive reactions [10; 18]. These advantages were attributed to an increased hydrophobicity near the active sites (i.e., the sulfonic acid moieties) and the promotion of the diffusion of reactants within the hydrophobic environment of mesopores. Notably, balancing between the requirement of a high organic surface area and the risk of pore saturation is one important concern in the design and synthesis of such catalysts. Taking into consideration the demand for efficient catalysts and the reported advantage of using solid acid catalysts based on poly (styrenesulfonic acid)-functionalized SBA-15 in, for instance, the acceleration of the esterification reaction, a novel SBA-15-PSSA species has hereby been prepared implementing a newly developed pathway. The catalyst thus obtained was characterized by Fourier-transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA) and differential thermal analysis (DTA), transmission electron microscopy (TEM), and nitrogen adsorption isotherms; subsequently, its catalytic performance was evaluated using acetic acid and _n_-heptanol as reactants in a model reaction system whereby fairly long-chain esters were expected to be generated. The effects of various factors on catalyst activity, including temperature, catalyst dose, and reaction time were examined, as well as catalyst reusability after two catalytic runs.
# Studying the Cable Loss Effect on the Seismic Behavior of Cable-Stayed Bridge
## 3 The Effect of Incremental Cable Loss on the Seismic Behavior  
### Cable Loss Scenario  
The structure behaves differently depending on the location of the lost cable. Additionally, the loss of the cable that is furthest from the pylon to the center-span direction, the longest cable, has the greatest impact on the behavior of the bridge structure. Therefore, the cable loss scenario was classified into three categories as follows.  
CASE1 is a scenario where the cable is gradually lost from the longest cable of the center span to the pylon 1 direction; CASE2 is a scenario where cable loss is spread from the longest cable of the center span to both the pylon 1 and 2 directions; and CASE3 is a scenario where the cable is gradually lost from the longest cable of the side-span to the pylon 2 direction. For comparison, CASE0 is set as a state where the cable is not lost. Additionally, in the event of cable loss on the target bridge, it is more likely that only one side of the cable will be lost rather than both sides being damaged simultaneously. Therefore, the damage scenario was set up so that only the left-side cable was lost.  
The cables were set to be lost in sequence, up to a maximum of six, using the *CONTROL_STAGED_CONSTRUCTION function. The time interval between cable failures was set to 2 s so that the next cable is lost 2 s after the previous one, considering the dynamic effects due to successive cable losses. The damage scenario is shown in Figure 6 and Table 4.  
Figure 6: Cable loss damage scenario: (**a**) CASE1; (**b**) CASE2; and (**c**) CASE3. (Red arrows indicate cable lost direction.)  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Mode Shape**} & **Reference** & **FE Model** & **Error** \\  & **Eigenvalue** [14] & **Eigenvalue** & **(\%)** \\  & **(Hz)** & **(Hz)** & **(\%)** \\ \hline
1 st Vertical & 0.2557 & 0.2611 & 2.07 \\
2 nd Vertical & 0.3256 & 0.3313 & 1.72 \\
1 st Horizontal & 0.3725 & 0.3227 & \(-\)13.67 \\
1 st Torsional & 0.4555 & 0.4206 & \(-\)8.29 \\ \hline \end{tabular}
\end{table}
Table 3: Eigenvalue comparison.  
So that the cable breakage occurs after the bridge reaches its initial equilibrium state, the first cable was intentionally severed 20 s after the dead load and cable pre-tension forces were applied. In order to evaluate the structural performance after cable loss, the resulting deflection and cable tension were compared. The limit value for deflection was determined based on the Korean cable-stayed bridge design standards [18; 19], and the cable tension was based on the yield strength.  
In the Korean highway bridge design code (LSD)--Cable Bridge Edition, deflection due to continuous loading is limited to 1/250 of the span length (L). Therefore, in the case of the Seohae Grand Bridge with a central span length of 470 m and side span length of 200 m, the deflection of the central and side spans is limited to 1.88 m (=470 m/250) and 0.80 m (=200 m/250), respectively.
# A Real Model of a Micro-Grid to Improve Network Stability
## 3 Description of the DC Micro-Grid
### Micro-Grid Storage  
The central aim is to utilize the maximum renewable energy available at any moment. Ideally, this would be a logical and the most economical principle to follow, but in practice it denies the physical fundamentals of electric energy, considering the consumer requirements and respecting the possibilities of not only the electrical distribution but also the information and control systems. Moreover, the actual electricity storage is a difficult problem involving most of the methods currently applied for the given purpose. In the presented micro-grid, however, only two types of storage are used, and these will be discussed in the following sections.
## 4 Experimental Results and Analysis
### Analysis of Different Tampering Test Results  
In this section, the edge detection and filtering operation performances are tested. For comparison, the differences among the four methods are as follows: the method in Reference [8] uses only chaotic mapping; the method in Reference [9] uses variable watermarks extracted from roughness information; the method in Reference [7] extracts texture information and uses neighborhood adjustment and the proposed method extracts texture and edge information and also adds the final filtering operation. Due to the diversity of images and tampering operations, the tampered regions have different characteristics. Some mainly contain smooth information, while some are full of texture details. To analyze the adaptability and performance of the methods under different conditions, different images are selected and displayed in the following subsections.
# A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
## 3 Advanced Infrastructure for DC Fast Charging for Electric Vehicles
### Charging Modes Control
#### 3.2.2 Constant Voltage Charging  
As the battery's SoC reaches a predefined level, the charging station transitions to constant voltage charging. In this control mode, the charging station gradually reduces the charging current while sustaining a consistent voltage. Constant voltage charging is employed during the final stage to prevent overcharging the battery and ensure its longevity. Throughout this stage, the charging system continuously monitors the battery voltage and adjusts the current to prevent it from surpassing the designated level.
# Distributed Average Consensus Algorithms in d-Regular Bipartite Graphs: Comparative Study
## 3 Theoretical Background
### Applied Mathematical Model of Multi-Agent Systems  
In this paper, we model MASs as simple finite undirected unweighted graphs determined by the vertex set \(\mathbf{V}\) and the edge set \(\mathbf{E}\) (i.e., \(G=(\mathbf{V}\),\(\mathbf{E})\)) [71; 72]. We assume that both sets are of finite size and time-invariant in our experiments. The first set (\(\mathbf{V}\)) contains all the graph vertices (each with a unique index), which represent agents in MASs (i.e., \(\mathbf{V}=\{v_{1},v_{2},...,v_{n}\}\)). The cardinality of \(\mathbf{V}\) (\(|\mathbf{V}|\)) is determined by the graph order \(n\) or, in other words, by the size of MAS. The edge set \(\mathbf{E}\) consists of all the graph edges, which are direct connections between two vertices (i.e., the distance between two vertices is one hop, and these two nodes are referred to as neighbors). An edge linking \(v_{i}\) and \(v_{j}\) is unique and labeled as \(e_{ij}\) later in this paper. The parameter \(d_{i}\) represents the degree of \(v_{i}\), which is the number of all the edges incident to \(v_{i}\). The maximum degree of \(G\) is labeled as [73]:  
\[\Delta=\underset{i}{max}\{d_{i}\} \tag{1}\]  
In the spectral graph theory, there are many useful tools to describe graph connectivity, as seen in [74; 75]. One of the most common is the Laplacian matrix \(\mathbf{L}:\) a diagonally symmetric squared matrix with \(n^{2}\) entries that is defined as follows [76]:  
\[[L]_{ij}=\begin{cases}-1,&\text{if }e_{ij}\in E\\ d_{i},&\text{if }i=j\\ 0,&\text{otherwise}\end{cases} \tag{2}\]  
As shown in [77], many relevant graph properties can be easily studied via the Laplacian spectrum (i.e., via the Laplacian eigenvalues and eigenvectors). The Laplacian spectrum is formed by a set of \(n\) eigenvalues sorted in descending order as follows:  
\[SPEC(\mathbf{L})=\{\lambda_{1}(\mathbf{L}),\lambda_{2}(\mathbf{L}),...,\lambda _{n-1}(\mathbf{L}),\lambda_{n}(\mathbf{L})\} \tag{3}\]  
Here, \(\lambda_{1}(\mathbf{L})\) is the largest Laplacian eigenvalue, while, \(\lambda_{n-1}(\mathbf{L})\) represents the second smallest eigenvalue of the Laplacian spectrum and is known as the algebraic connectivity of \(G\).  
As mentioned earlier, we analyzed the chosen algorithms in \(d\)-regular bipartite graphs--see Figure 5 for an example of a 3-regular bipartite graph and Definition 1 for their definition.