esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Semi-Supervised Classification Based on Low Rank Representation
The last term \(\|f\|_{H}^{2}\) of Equation (7) is used to control the complexity of \(f(\mathbf{x})\), it is computed as follows:  
\[\|f\|_{H}^{2}=tr(\mathbf{W}^{T}\mathbf{W}) \tag{10}\]  
Based on Equations (8)-(10), \(J(\mathbf{W},\mathbf{b})\) can be reformulated as:  
\[J(\mathbf{W},\mathbf{b})=tr(\frac{1}{l}(\mathbf{W}^{T}\mathbf{X}+\mathbf{b} \mathbf{1}^{T}-\mathbf{Y}^{T})\mathbf{H}(\mathbf{W}^{T}\mathbf{X}+\mathbf{b} \mathbf{1}^{T}-\mathbf{Y}^{T})^{T}+\frac{\alpha}{N^{2}}\mathbf{W}^{T}\mathbf{ X}\mathbf{L}\mathbf{X}^{T}\mathbf{W}+\frac{\beta}{D^{2}}\mathbf{W}^{T} \mathbf{W}) \tag{11}\]  
Equation (11) can be solved by taking partial derivative of \(J(\mathbf{W},\mathbf{b})\) with respect to \(\mathbf{W}\) and \(\mathbf{b}\) as below:  
\[\frac{\partial J}{\partial\mathbf{W}}=\frac{2}{l}\mathbf{X}\mathbf{H}(\mathbf{ X}^{T}\mathbf{W}+\mathbf{1}\mathbf{b}^{T}-\mathbf{Y}^{T})+\frac{2\alpha}{N^{2}} \mathbf{X}^{T}\mathbf{L}\mathbf{X}\mathbf{W}+\frac{2\beta}{D^{2}}\mathbf{W} \tag{12}\]  
\[J(\mathbf{W},\mathbf{b})=\arg\min_{\mathbf{W},\mathbf{b}}\frac{1}{l}\sum_{i=1 }^{l}\Psi(\mathbf{x_{i}},\mathbf{Y_{i}},f)+\frac{\alpha}{N^{2}}\|f\|_{I}^{2}+ \frac{\beta}{D^{2}}\|f\|_{H}^{2} \tag{7}\]0  
Let \(\frac{\partial J}{\partial\mathbf{W}}=0\) and \(\frac{\partial J}{\partial\mathbf{b}}=0\), we can obtain:  
\[J(\mathbf{W},\mathbf{b})=\arg\min_{\mathbf{W},\mathbf{b}}\frac{1}{l}\sum_{i=1 }^{l}\Psi(\mathbf{x_{i}},\mathbf{Y_{i}},f)+\frac{\alpha}{N^{2}}\|f\|_{I}^{2}+ \frac{\beta}{D^{2}}\|f\|_{H}^{2} \tag{7}\]1\[J(\mathbf{W},\mathbf{b})=\arg\min_{\mathbf{W},\mathbf{b}}\frac{1}{l}\sum_{i=1 }^{l}\Psi(\mathbf{x_{i}},\mathbf{Y_{i}},f)+\frac{\alpha}{N^{2}}\|f\|_{I}^{2}+ \frac{\beta}{D^{2}}\|f\|_{H}^{2} \tag{7}\]2  
where \(\mathbf{U}\) is:  
\[J(\mathbf{W},\mathbf{b})=\arg\min_{\mathbf{W},\mathbf{b}}\frac{1}{l}\sum_{i=1 }^{l}\Psi(\mathbf{x_{i}},\mathbf{Y_{i}},f)+\frac{\alpha}{N^{2}}\|f\|_{I}^{2}+ \frac{\beta}{D^{2}}\|f\|_{H}^{2} \tag{7}\]3  
Given \(\mathbf{X}\) and \(\mathbf{Y}\) are already known, \(\mathbf{L}=\mathbf{D}-\mathbf{S}\), \(f(\mathbf{x})\) is mainly determined by \(\mathbf{S}\). Here, \(\mathbf{S}\) is the weighted adjacent matrix of a graph constructed by low rank representation coefficient matrix \(\mathbf{Z}\) (see Equation (4)). In contrast to the transductive classifier, our proposed SSC-LRR directly uses \(\mathbf{W}\) and \(\mathbf{b}\) to predict the likelihood of \(\mathbf{x}\) with respect to \(C\) classes. The predicted label of \(\mathbf{x}\) is:  
\[J(\mathbf{W},\mathbf{b})=\arg\min_{\mathbf{W},\mathbf{b}}\frac{1}{l}\sum_{i=1 }^{l}\Psi(\mathbf{x_{i}},\mathbf{Y_{i}},f)+\frac{\alpha}{N^{2}}\|f\|_{I}^{2}+ \frac{\beta}{D^{2}}\|f\|_{H}^{2} \tag{7}\]4  
where \(f(\mathbf{x},c)\) indicates the \(c\)-th entry of \(f(\mathbf{x})\in\mathbb{R}^{C}\), and \(l(\mathbf{x})\) is the predicted label of \(\mathbf{x}\). Figure 1 briefly lists the process of SSC-LRR algorithm.
# ICRSSD: Identification and Classification for Railway Structured Sensitive Data
## 2 Methods
### Semi-Canonical Data Identification
#### 2.2.2 Sensitive Data Identification Based on Regular Expressions  
A regular expression is a sequence of characters, including both literals and special symbols, used to concisely define a text pattern to be matched [22]. It is typically crafted manually by domain experts with deep knowledge of the specific field.  
When identifying semi-canonical data, we use regular expressions to detect specific types of sensitive information, such as equipment descriptions, railway section descriptions, and similar free-text fields. Each data value is matched against a set of regular expressions to determine whether or not it contains any pattern-compliant information. If a match is found, the data are identified as sensitive; otherwise, they are considered non-sensitive.  
Finally, the sensitivity of an attribute is determined based on the proportion \(\varphi\) of data values identified as sensitive within that attribute. Specifically, the sensitivity is defined as \(1-\varphi\).
## 1 Introduction
We have also formulated an iterative algorithm-Golden Section Search Method to find the optimal time switching and power splitting factor for sum-throughput maximization. * Our proposed system analytical results for TS and PS are validated by simulation results. The developed analysis is corroborated through Monte-Carlo simulations and some representative performance comparisons are presented. Figure 1: Generic RF EH relay communication system. The rest of the paper is organized as follows. In Section 2, we present the system model for the considered scenario. Section 3 deals with the considered system model based on time switching and NOMA protocol along with outage probability, throughput and sum-throughput derivations. Section 4 deals with the considered system model based on power splitting and NOMA protocol along with outage probability, throughput and sum-throughput derivations. In Section 5, we explain the algorithm--Golden Section Search Method to find out the optimal time switching and power splitting factor that maximizes the sum-throughput for our proposed system. Numerical results and discussions are presented in Section 6. Conclusions and future works are drawn in Section 7.
## 4 Proposed Hybrid FVIMDE Algorithm
### Computational Complexity  
The computational complexity of the Hybrid FVIMDE Algorithm can be determined by analyzing the operations performed in each phase of the algorithm. Let us break it down step by step.  
First, consider the initialization phase. The population \(\mathbf{P}\) is initialized by generating \(N\times D\) random values. This step has a time complexity of \(O(N\times D)\), as shown in Equation (11). Additionally, evaluating the fitness of each individual in the population requires \(O(N)\) operations, as indicated in Equation (12).  
\[O(N\times D) \tag{11}\]  
\[O(N) \tag{12}\]  
Next, in the Differential Evolution (DE) phase, the algorithm runs for \(\text{DE\_iter}=\frac{\text{Max\_iter}}{2}\) iterations. During each iteration, mutation, crossover, and selection are performed for each individual. Each of these operations is linear with respect to the dimensionality \(D\). Consequently, the complexity of these steps is \(O(N\times D\times\text{DE\_iter})\), as shown in Equation (13). After generating the trial vector, the fitness of the new vector is evaluated, leading to a complexity of \(O(N\times\text{DE\_iter})\), as indicated in Equation (14).  
\[O(N\times D\times\text{DE\_iter}) \tag{13}\]  
\[O(N\times\text{DE\_iter}) \tag{14}\]  
In the FVIM phase, which runs for \(\text{FVIM\_iter}=\text{Max\_iter}-\text{DE\_iter}\) iterations, the position of each individual in every dimension is updated. This involves calculating intermediate positions and updating the current position, requiring \(4\times D\) operations per individual. Thus, the complexity of position updates is \(O(N\times D\times\text{FVIM\_iter})\), as shown in Equation (15). Additionally, after updating positions, the fitness of each individual is evaluated, with a complexity of \(O(N\times\text{FVIM\_iter})\), as indicated in Equation (16).  
\[O(N\times D\times\text{FVIM\_iter}) \tag{15}\]  
\[O(N\times\text{FVIM\_iter}) \tag{16}\]  
The computational complexity of FVIMDE Algorithm is the sum of the complexities of the initialization, DE phase, and FVIM phase. This can be expressed as shown in Equation (17).  
\[\begin{split}& O(N\times D)+O(N)+O(N\times D\times\frac{\text{Max\_iter}}{2})\\ &+O(N\times\frac{\text{Max\_iter}}{2})+O(N\times D\times(\text{ Max\_iter}-\frac{\text{Max\_iter}}{2}))\\ &+O(N\times(\text{Max\_iter}-\frac{\text{Max\_iter}}{2}))\end{split} \tag{17}\]  
Simplifying this expression, we obtain the overall computational complexity of the algorithm, as shown in Equation (18):  
\[O(N\times D\times\text{Max\_iter}) \tag{18}\]  
This complexity reflects the FVMDE's dependence on the population size \(N\), the dimensionality of the problem \(D\), and the total number of iterations Max_iter.
## 3 Results and Discussion
### Density and Porosity  
Figure 4 shows the density and porosity values of the pure Mg and Mg/Si\({}_{3}\)N\({}_{4}\) nanocomposites. The theoretical and experimental densities of the nanocomposites increased with Si\({}_{3}\)N\({}_{4}\) vol%. This is due to the significant difference between the densities of the matrix and Si\({}_{3}\)N\({}_{4}\) reinforcement. Among the nanocomposites, the 1.5 vol% Si\({}_{3}\)N\({}_{4}\) nanocomposite has a maximum porosity of 1.3%. The comparatively high vol% of Si\({}_{3}\)N\({}_{4}\)  
Figure 3: XRD analysis results of the samples: (**a**) pure Mg, (**b**) Mg-0.5 Si\({}_{3}\)N\({}_{4}\), (**c**) Mg-1.0 Si\({}_{3}\)N\({}_{4}\), and (**d**) Mg-1.5 Si\({}_{3}\)N\({}_{4}\).  
Figure 2: Representative SEM micrograph and EDX spectrum of the Mg-1.5 Si\({}_{3}\)N\({}_{4}\) nanocomposite. (**a**) SEM micrograph of mapped area, (**b**) EDX spectrum with elemental composition.  
reinforcing particles in the Mg-1.5 Si\({}_{3}\)N\({}_{4}\) nanocomposite leads to greater particle concentration zones, increasing the possibility of voids or pores. Pure Mg and the nanocomposites have porosity values below 1.5%, making them dense materials. Low porosity is due to the appropriate stirring conditions, ultra-sonication, and limited nanoparticle agglomeration.
###### Abstract  
Using a low non-saturable loss Bragg-reflector-based semiconductor saturable mirror, a passively Q-switched Er:Lu\({}_{2}\)O\({}_{3}\) ceramic laser at 2.7 \(\upmu\)m demonstrated short pulse-width and efficient operation, generating stable pulses of 70 ns pulse-width and ~71 kHz repetition rates. Over 692 mW of average output power was measured, corresponding to a pulse energy of ~9.8 \(\upmu\)J. In a modified resonator design of reduced round-trip time, pulses of 32 ns duration were generated. The achieved results suggest that Er:Lu\({}_{2}\)O\({}_{3}\) ceramic could be a promising gain medium for efficient and high power pulsed laser generation at 2.7 \(\upmu\)m. The prospects for further improvement in laser performance at this wavelength are discussed.  
mid-infrared laser materials; transparent ceramic; passively Q-switched lasers Article
## 4 Biomethane Production via Anaerobic Digestion (AD)
### Biochemistry of Anaerobic Digestion
Different ways also have been explored by researchers to achieve the highest output as possible, such as re-used DF effluent to increase methane production [102] and hydrogen production [103], recirculation of digested sludge to shorten HRT and reactor stability [104], finding optimum process parameters [18] and studied the role of microorganisms during the digestion process [105]. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**Organic**} & & & & COD & \\
**Types of Waste** & **Inoculum** & **Integrated System** & **Logation Rate** & **Temperature** & & & **HPR** & **MPR** & **Removal** \\  & **System** & **Applied** & **(OLR; g L-1)** & **(°C)** & **(L H2)** & **(L CH\({}_{4}\))** & **(\%)** & **References** \\  & & **Applied** & **d-1)** & **H2** & **L-1 d-1)** & **L-1 d-1)** & **H2** & & \\  & & & **CH\({}_{4}\)** & & & & **CH\({}_{4}\)** & & \\ \hline \multirow{3}{*}{Organic Fraction of Municipal Solid Waste} & Waste & \multicolumn{4}{c}{16 kg TVS m3} & & & \\  & & & DF-AD & \({}^{-1}\) d\({}^{-1}\) & 55 & 0.43 \(\pm\) & 0.60 \(\pm\) & 43 & \\  & & & (CSTR-CSTR) & 3 kg TVS m3 & 55 & 0.04 & 0.09 & 52) & [105] \\  & & & & \({}^{-1}\) d\({}^{-1}\) & & & & \\ \hline \multirow{3}{*}{Garbage slurry and shredded office papers} & Seed & \multicolumn{4}{c}{DF-AD} & 97,000 & 60 & \multirow{3}{*}{5400} & \multirow{3}{*}{6100} & \multirow{3}{*}{-} & \multirow{3}{*}{[106]} \\  & & & (CSTR-Packed Bed Reactor) & 15,700 & 55 & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & & & \\ \hline \multirow{3}{*}{Food waste from organic fraction municipal solid wastes (OFMSW)} & Anaerobic & DF-AD & 39 & 55 & 11.1 & 47.4 & 90 & \\  & & & & & & & 85 & [107] \\ \cline{1-1} \cline{5-5}  & & & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & & & \\ \hline \multirow{3}{*}{Sugarcane syrup} & Brewery & \multirow{3}{*}{UASB granules} & DF-AD & 2167 & 35 & 7.53 & 75.6 & 69 & [108] \\  & & & (CSTR-ABR*) & 10,773 & 35 & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & & & \\ \hline \multirow{3}{*}{Coffee drink manufacturing wastewater (CDMW)} & Anaerobic & DF-AD & 80 & 55 & 101.76 & 2.06 & 50 & [109] \\ \cline{1-1} \cline{5-5}  & & & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & & & \\ \hline \hline \end{tabular}  
* ABR—Anaerobic baffle reactor  
\end{table} 
Table 7: Different organic wastes used for biohydrogen and biomethane production using integrated systems.
# Knowledge Graph Completion Based on Entity Descriptions in Hyperbolic Space
## 4 Experiment and Analysis
### Experiments
#### 4.4.4 DMuRP vs. MuRP  
According to the experimental results in Table 4, compared with the models of Euclidean space, the performance of DMuRP is more prominent. In order to prove that the effect of containing entity descriptions is better than that without containing entity descriptions in hyperbolic space, the results of DMuRP and MuRP are compared. As shown in Table 6. DMuRP contains entity descriptions, and MuRP does not contain entity descriptions.  
According to the experimental results shown in Table 6, the experimental results of DMuRP are obviously better than those of MuRP on both datasets. It can be seen that  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**FB15k**} \\ \cline{2-3}  & **MR** & **Hit@1** \\ \hline DKRL [22] & 2.03 & 90.8 \\ SSP [36] & 1.47 & 90.9 \\ EHP [24] & **1.18** & 91.1 \\
**DMuRP** & 1.29 & **94.3** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Relation prediction results on FB15k.  
Figure 7: Rank distribution of FB15k.  
the results of Hit@10 have an average increase of 2.35%, and the results of MR rose by 15.5 ranks on average. By comparing DMuRP with the model of fusion entity descriptions in Euclidean Space in Section 4.4.2, and DMuRP and MuRP in hyperbolic space in this section, it shows that the method proposed is effective and feasible.  
According to the experimental results shown in Figures 8 and 9, DMuRP outperforms MuRP on both datasets. Specifically, DMuRP is suitable for hierarchical data WN18, but due to the low quality of entity descriptions of WN18 and the small number of relations in triples, in Figure 8, it is shown that the improvement of WN18 is not high. Contrary to WN18, FB15k has a higher quality of entity descriptions and a moderate number of
## 3 Results and Discussion
### Major Sources of Aerosols
Both regions have different dynamics with respect to agriculture fires and industrial activities. The Upper Indus plains host most of the populated regions with extensive vehicular and industrial emissions. Various studies have shown increasing trends in NO\({}_{2}\)[12], SO\({}_{2}\)[75] and formaldehyde [59], mainly caused by vehicular, industrial and biomass burning activities. In contrast, the southern plains host few industries, less population but extensive agriculture activities and fires activities (Figure 5). Correlation between AOD and aerosol precursor gases over both regions is presented in Table 1 during the winter periods of 2012-2015. It can be clearly identified that AOD in the UIP region is contributed to by NO\({}_{2}\) and SO\({}_{2}\) emission sources (precursor of nitrates and sulfate aerosols) while the LIP region is dominated by CO emissions--a good tracer of fire activities and for the presence of both organic and elemental carbon. Our findings about major aerosol sources are in agreement with source apportionment of the particulatematter presented in previous studies [37; 38; 39; 40; 41]. High aerosol levels are influencing climate and the hydrological cycle due to dimming and atmospheric warming [76; 77]. During winter, 50-70 WM\({}^{-2}\) of atmospheric heating is observed because of a high level of black carbon [64]. Many recent studies confirm that the accelerated snow melt in the Himalayas is due to deposition of BC over snow sheets and has caused a reduction in surface albedo during the pre-monsoon [78; 79; 80]. Therefore, abatement of agriculture fires and open solid waste burning can result in a substantial decrease in dense fog formation in the study region. Similarly, the outcome of this study can be effectively used to raise awareness to stop the usage of dirty fuel (old rubber tires, solid waste, low standard furnace oil _etc._) in factories and brick kilns in Pakistan. These sources trigger the aerosol load which results in the frequent occurrence of dense fog/smog and consequently results in casualties (road accidents) and economic losses in Pakistan, besides increasing health impacts.
## 3 Illustrative Examples
### Example I: A Two-Layered Cohesive Slope
#### 3.1.1 Effects of ACF types on Slope Reliability Analysis
This shows that the lower layer might dominate the stability of the slope, although the spatial variability for the lower layer is varied. Overall, the assumption of the same ACF for different soil layers might produce conservative (case 1 vs. cases 2-5) or unconservative (case 1 vs. cases 6-9) results. Statistical analysis of FS was then performed for various cases to further investigate the effects of ACFs on FS and \(P_{f}\) based on the MCS results. Figure 4 presents the histograms and PDFs of FS for the nine cases. It can be seen that the FS is well fitted by the lognormal distribution for all cases and there is little difference between the nine cases in terms of the mean, standard deviation, and PDF of FS. However, compared with case 1, the standard deviation for case 2 and 6, where the spatial variability of the two soil layers are described by the SEF and SQEF, is the smallest, showing the large difference between the SEF and SQEF. This finding was also observed by Li [8],_ENREF_36 where the same ACF was applied to different soil layers. Besides, Figure 5 compares the cumulative distribution functions (CDFs) of FS for all cases. As can be seen from the figure, the ACF has a minor effect on CDF. To gain more insights into the relative relationships between different ACFs, the locally enlarged images in Figure 5 reveals similar results as Figure 4, where the difference between SEF and SQEF is relatively larger than other combinations of ACFs. The result implies that the \(P_{f}\) acquired from the other four ACFs may be overestimated compared with the baseline case, except for case 6. Figure 2: Number of Monte Carlo simulation (MCS) samples versus \(P_{f}\) and \(COV_{P_{f}}\). Figure 4: Histograms and fitted probability distribution functions (PDFs) of factor of safety (FS) for different cases. Figure 3: Typical realizations of random fields underlying \(S_{u_{1}}\) and \(S_{u_{2}}\) associated slope stability results (color online). Figure 6 compares the \(P_{f}\) obtained for different cases. As seen from the figure, different combinations of ACFs have different effects on the \(P_{f}\). Generally, the change of ACF in the lower stratum has a greater influence on the \(P_{f}\) than that for the upper stratum. Within each group, the results using CEF are relatively larger than those characterized by the other four ACFs. By contrast, the result using SEF is the minimum among the five ACFs. This phenomenon indicates that the \(P_{f}\) obtained by the CEF is comparatively more conservative while the SEF may lead to an unconservative estimation. The COV of the \(P_{f}\), \(COV_{P_{f}}\), shows the opposite trend to the value of \(P_{f}\), which means that smaller \(P_{f}\) leads to a greater coefficient of variation with the same MCS size.
## 5 Working Operations of the Proposed Architecture and Discussion
### Challenge, Limitations, and Issues Emerged in the Current System
#### 5.1.3 Streamlined Process Automation of Attestation Traceability with Digital Contracts  
A blockchain is a decentralized private digital ledger where universities exchange critical information such as educational credentials, previous candidate information, recent records, and complete certification in a chain of blocks [27; 45]. The stakeholders record these core ledgers electronically for further employment or admission to a higher education certificate and trace accordingly. These education-related data records were provided on the blockchain hyperledger fabric enabled platform that allows access to this sensitive private information. The challenging problem is to restrict several repetitive activities that consume high time, increase execution cost, and utilize more computation power. However, manual insertion is tedious, difficult, leads to apathy and is considered a timewasting strategy in the current distributed environment. The tasks are identified in a way that is robust to automate the processes of higher education certificate attestation traceability, which is still a platform implementation challenge with blockchain Hyperledger-enabled technology. The major problem is to design customized digital contracts and certificate authority verification policies between stakeholders and automate (smart contract) them along with the digital signature.
## 7 New Technologies and Future Perspectives
### Late-Phase CEUS  
In ultrasound imaging, the application of late-phase CEUS has been proposed as a technique to directly detect inflammatory elements, and it is performed by imaging the artery minutes after the injection of microbubbles. These microbubbles are small gas-filled bubbles that are injected intravenously and act as ultrasound contrast agents. The microbubbles are designed to remain in the bloodstream and not cross the blood-brain barrier, making them safe for carotid imaging [160]. The analysis of late-phase CEUS images involves the use of semi-quantitative and qualitative techniques. The semi-quantitative technique involves measuring the degree and duration of contrast enhancement within the plaque using a region of interest (ROI) analysis. The qualitative technique involves visual assessment of the contrast enhancement pattern within the plaque, such as the presence of neovascularization or intraplaque hemorrhage [160].  
The primary aim of the study of late-CEUS is to give time for its clearance and detect the remaining signal in the lumen, which is suggestive of contrast tissue retention probably due to two factors: (i) the phagocytosis of microbubbles by monocytes [161] and (ii) the adhesion of microbubbles to the damaged surface of the endothelium [162; 163]. Nanotechnology and nanomedicine have recently been identified as suitable medical imaging techniques using nanoparticles. These extremely small particles, ranging in size from 1 to 100 nanometers, can bind to specific biomolecules found in carotid plaque, such as matrix metalloproteinases (MMPs) or oxidized low-density lipoproteins (LDLs) [164]. Nanoparticles can be designed to emit a fluorescent signal when they bind to their target, allowing the non-invasive imaging of the plaque. Researchers have also developed contrast agents based on nanoparticles, such as iron oxide nanoparticles, that can be targeted to specific biomolecules in carotid plaque. These contrast agents can be visualized using magnetic resonance imaging (MRI), allowing high-resolution images of the plaque. Thebiomolecules can be precisely controlled in terms of their properties and structure and can then serve as imaging contrast agents because of the possibility to label them in accordance to the used technique: Gd\({}^{3+}\) and Fe\({}^{3+}\) for MRI; Au for X-ray and CT imaging; \({}^{64}\)Cu for PET and \({}^{111}\)In for SPECT imaging; and fluorophores and quantum dots for optical imaging or surfaces that can be modified with antibodies or ligands which the target receptors over overexpressed on the surface/inside of atherosclerotic plaques [165; 166].