esa-sceva/satcom-chunk-collection huggingface dataset:
# Origin and Composition of Exosomes as Crucial Factors in Designing Drug Delivery Systems
## 5 Exosomes Natural Cargo and Structure
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**miRNA Level** & **Source of** & **Disease** & **Source** \\
**Change** & **Exosomes** & **Disease** & **Source** \\ \hline \(\uparrow\) miR-21 & \multirow{2}{*}{Serum} & Esophageal squamous cell cancer & [75] \\ \(\downarrow\) miR-21 & & Benign diseases & \\ \hline \(\uparrow\) miR-21 & \multirow{2}{*}{Serum} & Hepatocellular carcinoma & [76] \\ \(\downarrow\) miR-21 & & Chronic hepatitis B or healthy volunteers as well & \\ \hline \(\uparrow\) miR-1246 & \multirow{2}{*}{Saliva} & Pancreatobiliary tract cancer & [77] \\ \(\uparrow\) miR-4644 & & & \\ \hline \(\uparrow\) beta-miR-142-5p & \multirow{2}{*}{Milk} & Detection of early mammary gland bacterial infection & [78] \\ \(\uparrow\) beta-miR-223 & & & \\ \hline \(\uparrow\)miR-1290 & \multirow{2}{*}{Plasma} & Poor overall survival of castration-resistant prostate cancer patients & [79] \\ \(\uparrow\) miR-375 & & & \\ \hline \(\uparrow\) miR-146a & \multirow{2}{*}{Urine} & Lupus nephritis patients. Also distinguishing between active and remission stages of the disease. & [80] \\ \end{tabular}
\end{table} 
Table 2: Examples of miRNA-level changes proposed for diagnosing diseases. Figure 2: Exosomes structure and cargo. Stimulation of human cells by mouse exosomes leads to the synthesis of mouse proteins in human cells. In addition, the mRNA profile of exosomes and recipient cells was different, suggesting that mRNA is subjected to active sorting into MVB. This can be crucial in terms of therapeutic mRNA delivery by exosomes [74]. To summarize, exosomal cargo can further improve its therapeutic properties when properly designed. This can be used for the fabrication of double-action exosomes, containing both therapeutic substances with the additional effect enhancement by incorporating proper nucleic acids, lipids, and proteins into exosomes. To this date, only a few concepts of this simultaneous strategy were proposed [8; 84].
## 5 Computational Efficiency of Complex Variable Averaging Method and Numerical Method  
In the previous section, the correctness and accuracy of the complex variable averaging method were demonstrated by comparing the results of the complex variable averaging method with those of the traditional numerical method. Under the premise that the computational results are guaranteed, the computational speed became an issue to be considered.  
In practical engineering applications, the value added by the efficiency improvement to the overall project is undoubtedly huge. The complex variable averaging method was used to generate the frequency response curves of the primary structure, while the Runge-Kutta numerical method was used for comparison. The time required to generate the frequency response curves for both methods with the same parameter settings was recorded for 100 sets, and the first 10 sets of data recorded in Table 2 are shown below.  
From Table 2, the average time taken to generate the frequency response curve by the complex variable averaging method was 1.240 s, while the average time required by the numerical method under the same conditions was 85.595 s. The complex variable averaging method was significantly more efficient than the numerical method, saving about 98.55% of the time. The statistical results showed that the complex variable averaging method generates frequency response curves nearly 69 times faster than the basic numerical method, and it is able to obtain the steady-state solution quickly, saving a lot of time for the engineers designing the system.  
Figure 16: Comparison of the time course curve of the primary structure of linear optimal design TMD and nonlinear optimal design NTMD.
## 5 Numerical Simulation
### Comparison of FOSMC with Internal Model Control (IMC).  
Here, the same simulation parameters as above and \(\beta=0.9\) were used. The FOSMC has been compared with an IMC in the simulation of the photobioreactor subjected to the same model parameter disturbances described above. The IMC is a controller that has been thoroughly studied in the literature. It has proven to be robust to certain types of disturbances when applied to bioprocess control. The FOSMC used the mean integration order constant 0.5 and the IMC used the filter time constant \(\varepsilon=0.1\) h (tuned by trial and error).  
Figures 8 and 9 show that the FOSMC is superior to the IMC when model parameters are subjected to disturbances. Even though model parameter \((a_{1}(t))\) changes at times 100 and 200 h, deviations from the biomass optimal path started to appear at time 200 h, reaching a maximum deviation at time 340 h. In turn, the FMSC perfectly tracked the biomass optimal path since it kept the controlled system on the sliding surface (see to Figures 10 and 11). Several control quality indices were computed to compare both controllers: the integral of the time-weighted absolute error (ITAE), the integral of the absolute error (IAE), the integral of the square error (ISE), the integral of the square control signal input (ISI), and the integral of the absolute control signal input (IAI).  
These indices also show that the FOSMC performed much better than the IMC. Both control signals are equally smooth since indices ISI and IAI are practically the same, as shown in Table 4. Hence, highly nonlinear and complex systems like photobioreactors require robust nonlinear control techniques, such as the proposed FOSMC, to achieve acceptable performance.
## 5 Results
### Reaction Time Analysis
#### 5.2.2 Comparison of LKA and ACC Functions  
In this section, we compared the response times of LKA and ACC. Both functions are activated via buttons located on the shifter module. However, LKA was trained through video instruction, whereas ACC was taught using a text-based user manual. This divergence in training methods set the groundwork for examining their potential impact on user responsiveness.  
The descriptive analysis reveals that, the LKA function demonstrated a median response time of 4.04 s, with a range from 2.82 to 7.88 s and a standard deviation of 1.22. The ACC function, in comparison, showed a higher median response time of 5.32 s, extending over a range from 2.93 to 8.34 s, with a greater standard deviation of 1.59. As illustrated in Figure 7, responses to the LKA function were generally faster and more consistent among participants compared to the ACC function.  
To validate the significance of the observed differences in response times between LKA and ACC, we conducted a Mann-Whitney U test. This analysis resulted in a \(p\)-value of 0.02, indicating that the variations in response times are statistically significant. This finding provides concrete evidence supporting the hypothesis that different training methods can significantly affect user interaction efficacy with ADAS functions.  
The significance of these results is manifold. Firstly, the observable difference in median response times between LKA and ACC highlights the importance of investigating the most effective training modalities for each specific ADAS function. Secondly, the variance in standard deviations suggests that the training method may influence the speed and the uniformity of responses among participants.  
Figure 6: AP-On and CA reaction time comparison between the video vs. user manual training methods. “+” shows the outlines.
# Self-Adaptive Alternating Direction Method of Multipliers for Image Denoising
## 3 Preliminaries and Problem Statement
### Traditional Alternating Direction Method of Multipliers  
The Alternating Direction Method of Multipliers (ADMM) is an iterative algorithm used for solving convex optimization problems. ADMM decomposes the original problem into easier-to-handle subproblems and iteratively solves these subproblems alternately, combined with updating the multipliers to progressively approach the optimal solution of the original problem. This method excels in dealing with problems that have structural constraints and finds wide applications in distributed optimization and machine learning fields. Introducing the augmented Lagrangian function for problem (4):  
\[L(\mathbf{Z},\mathbf{X},\mathbf{Y},\mathbf{u}) =||\mathbf{Z}||_{*}+\lambda||\mathbf{X}||_{1,1}+<\mathbf{Y}, \mathbf{D}-\mathbf{X}-\mathbf{Z}> \tag{5}\] \[+\tfrac{7}{2}||\mathbf{D}-\mathbf{Z}-\mathbf{X}||_{F}^{2},\]  
where \(\mathbf{Y}\) is the Lagrange multiplier and \(\gamma>0\) is the given penalty parameter. Using the Exact Augmented Lagrangian Method (EALM), alternatively iterate matrices \(\mathbf{Z}\) and \(\mathbf{X}\),  
\[\mathbf{Z}_{k+1}^{j+1} =\arg\min_{\mathbf{Z}}L(\mathbf{Z},\mathbf{X}_{k+1}^{j},\mathbf{ Y}_{k},u_{k}), \tag{6}\] \[\mathbf{X}_{k+1}^{j+1} =\arg\min_{\mathbf{X}}L(\mathbf{Z}_{k+1}^{j+1},\mathbf{X}, \mathbf{Y}_{k},u_{k}). \tag{7}\]  
If \(\mathbf{Z}_{k+1}^{*}\) and \(\mathbf{X}_{k+1}^{*}\) represent the precise values of \(\mathbf{Z}_{k+1}^{j+1}\) and \(\mathbf{X}_{k+1}^{j+1}\), the formula for updating matrix \(\mathbf{Y}\) is as follows:  
\[\mathbf{Y}_{k+1}=\mathbf{Y}_{k}+u_{k}(\mathbf{D}-\mathbf{Z}_{k+1}^{*}-\mathbf{ X}_{k+1}^{*}), \tag{8}\]  
where the updating method for parameter \(u_{k}\) can be found in reference [15].
###### Abstract  
The object of this paper was the application of machine learning to a clinical dataset that was anonymized using the Mondrian algorithm. (1) Background: The preservation of patient privacy is a necessity rising from the increasing digitization of health data; however, the effect of data anonymization on the performance of machine learning models remains to be explored. (2) Methods: The original EHR derived dataset was subjected to anonymization by applying the Mondrian algorithm for various k values and quasi identifier (QI) set attributes. The logistic regression, decision trees, k-nearest neighbors, Gaussian naive Bayes and support vector machine models were applied to the different dataset versions. (3) Results: The classifiers demonstrated different degrees of resilience to the anonymization, with the decision tree and the KNN models showing remarkably stable performance, as opposed to the Gaussian naive Bayes model. The choice of the QI set attributes and the generalized information loss value played a more important role than the size of the QI set or the k value. (4) Conclusions: Data anonymization can reduce the performance of certain machine learning models, although the appropriate selection of classifier and parameter values can mitigate this effect.  
machine learning; anonymization; Mondrian
# Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks +
## 6 Experimental Validation
### Testbed Setup
The concept of CHaChA mandates the presence of two WLAN interfaces per node to be able to communicate simultaneously on the base and cluster channels after initial cluster formation. In general, it would be possible to equip each Galileo board with a USB WLAN dongle in addition to its mPCIe WLAN card. However, no USB products are available that match the chipset of the mPCIe cards (Atheros AR9280) while supporting external antennas for attaching attenuators. We deliberately avoid a mixed hardware configuration of WLAN interfaces with different range and performance characteristics and instead only use the mPCIe card as primary interface in each test case. It is configured to the fixed WLAN channel 149 (center frequency \(f_{c}=5745\,\mathrm{MHz}\)), serving as base channel. For the investigation of the initial cluster formation (see Section 6.2), a second interface is not necessary, as all CHaChA control messages are sent on the base channel, and there is the possibility to select the cluster channel only virtually without actually performing the configuration of the secondary interface. Accordingly, when testing dynamic cluster adaptation (see Section 6.3), only topology changes on the base channel are examined.
## 2 The DM Source Preparation via ZPC Operations
### The CVQKD System with the DM Source Preparation  
For the prepare-and-measure (PM) version of CVQKD that can be used for practical implementations, [PERSON] randomly draws \(n\) quaternary or octal variables, each corresponding to a coherent state of \(\left|\hat{a}_{k}^{N}\right\rangle=\left|\alpha e^{i\hat{k}\pi/N}\right\rangle, \forall k\in\{1,2,\cdots,N\}\), where \(\alpha\) is a real positive number related to the modulation variance of coherent state given by \(V_{A}=2\alpha^{2}\)[16; 17; 18; 19; 20]. For \(N=4\) and \(N=8\), we have the four-state DM source and the eight-state DM source, as shown in Figure 1[28; 29].  
[PERSON] generates \(n\) coherent states, which are sent through quantum channel characterized by transmission \(T\) and excess noise \(\varepsilon\). Then, for the received state, [PERSON] measures one of quadratures (i.e., \(\hat{x}\) or \(\hat{p}\)) by homodyne detector.  
Theoretical analysis of CVQKD is considered through using its equivalent entanglement-based (EB) version. The transmitter [PERSON] starts with a pure bipartite state \(\left|\Psi\right\rangle\) with variance \(V=V_{A}+1\)Subsequently, she performs a projective measurement on the first half of this state, whereas the second half is sent to the receiver [PERSON]. For the DM-CVQKD protocol, to apply the security analysis technique as the GM-CVQKD protocol [11; 12; 13; 14; 15], we therefore want to find a purification \(|\Psi\rangle\) with a covariance matrix \(\gamma\). This covariance matrix has the form given by [26; 27]  
\[\gamma_{N}=\begin{pmatrix}(V_{A}+1)\mathbb{I}&Z_{N}\sigma_{z}\\ Z_{N}\sigma_{z}&(V_{A}+1)\mathbb{I}\end{pmatrix}, \tag{1}\]  
where \(\mathbb{I}=\text{diag}(1,1)\) and \(\sigma_{z}=\text{diag}(1,-1)\). For example, taking \(N=4\) into account, we have the four-state scheme given by  
\[Z_{4}=V_{A}\sum_{k=0}^{3}\frac{\lambda_{k-1}^{3/2}}{\sqrt{\lambda_{k}}}, \tag{2}\]  
with the parameters \(\lambda_{0,2}\) and \(\lambda_{1,3}\) given by \(\lambda_{0,2}=\frac{1}{2}e^{-\alpha^{2}}(\cosh(\alpha^{2})\pm\cos(\alpha^{2}))\) and \(\lambda_{1,3}=\frac{1}{2}e^{-\alpha^{2}}(\sinh(\alpha^{2})\pm\sin(\alpha^{2}))\)[24].  
We note that the DM source has a positive effect on CVQKD. For example, we have \(Z_{4}\leq Z_{8}\leq Z_{G}\) for \(N=4\) and \(N=8\) in the DM-CVQKD protocol, where \(Z_{G}=\sqrt{V_{A}^{2}+2V_{A}}\) represents the GM-CVQKD protocol. However, it is difficult to distinguish \(Z_{4}\), \(Z_{8}\) and \(Z_{G}\) for \(V_{A}\leq 0.5\), as shown in Figure 2. Therefore, we attempt to derive the security bounds of DM-CVQKD referring to that of GM-CVQKD.
# A Survey on Diabetic Retinopathy Lesion Detection and Segmentation
## 4 Discussion  
Several DL architectures were used to effectively perform DR segmentation and lesion detection tasks in the studies reviewed in the previous section. Some examples of these architectures/models are ResNet, Inception, EfficientNet, U-Net, and Encoder-Decoder. Apart from these, most studies used different pre-processing techniques for gaining better performance and for image enhancement. This can include different steps like intensity conversion, image variation attenuation, contrast enhancement, and denoising [103]. Since there will be a huge disparity in the retinal color of different patients, attenuation of fundus images may have to be done. Additionally, to make the features in an image distinctly visible, intensity conversion can be used. Another pre-processing step is the denoising of fundus images. This is necessary, since throughout the image acquisition procedure plenty of noise may have been introduced in these images. Lastly, since there will be highest contrast at the image centre for fundus images, which steadily reduces when moving away from the centre, contrast enhancement may have to be performed. Additional pre-processing steps consist of image resizing, grayscale conversion, gaussian filtering, applying CLAHE [74], and doing several image augmentations using techniques like rotation, flip, and zoom. Some morphological operations like erosion, dilation, top-hat transformation, and bottom-hat transformation were accomplished on the green channel images of the fundus images as pre-processing in [90]. [PERSON] et al. [92] resized and standardized the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **No. of Images** & **Image Size** \\ \hline DIARETDB0 & 130 & \(1500\times 1152\) \\ \hline DIARETDB1 & 89 & \(1500\times 1152\) \\ \hline STARE & 400 & \(700\times 605\) \\ \hline DRIVE & 40 & \(768\times 584\) \\ \hline IDRiD & 516 & \(4288\times 2848\) \\ \hline E-Ophtha & 463 & Different sizes \\ \hline MESSIDOR & 1200 & Different sizes \\ \hline HRF & 45 & \(3504\times 2336\) \\ \hline REVIEW & 16 & \(1360\times 1024\) \\ \hline DRIONS & 110 & \(600\times 400\) \\ \hline CHASE DB1 & 28 & \(999\times 960\) \\ \hline DDR & 13,673 & Different sizes \\ \hline ARIA & 143 & \(768\times 576\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Datasets used for Diabetic Retinopathy Segmentation/Lesion Detection.
# Trapezoidal Cantilevers with Irregular Cross-Sections for Energy Harvesting Systems
## 5 Conclusions  
The numerical and experimental investigation of piezoelectric trapezoidal cantilevers with an irregular cross-section area was performed. The numerical investigation revealed that the strain and electrical characteristics of the piezoelectric cantilevers depend on the cross-section design. Strain variation along the length of the trapezoidal cantilever is nonlinear because of the irregular cross-section design.  
The differences between the natural frequency obtained during numerical modeling and experimental measurement is up to 17.69%. The errors mainly come because of the simplified numerical model and high vibration damping in the plastic structure used for prototype clamping. The numerical investigation showed that the generated electric power of the modified piezoelectric cantilevers is more than 11.5-times greater than the power obtained from the conventional cantilever.  
The experimental investigation in principle validated the results of the numerical investigation. Measurement of the electrical characteristics showed that the output voltage density has a 9.9% higher value compared with the conventional cantilever. The output current density was increased 6.9%, and the power density has more than a three-times higher value compared with the conventional cantilever. The results of the numerical and experimental investigations showed that the energy  
Figure 21: Power density: (**a**) conventional cantilever; (**b**) cantilever with cylindrical gaps; (**c**) cantilever with trapezoidal gaps.  
Figure 20: Summary of the maximum values of output current density: (**a**) conventional cantilever; (**b**) with cylindrical gaps; (**c**) with trapezoidal gaps.  
conversion coefficient of cantilevers can be improved by employing an irregular design of the cantilever cross-section area.  
**Author Contributions:** [PERSON] designed and fabricated the prototypes, performed the numerical and experimental investigation and contributed to the manuscript preparation. [PERSON] conceived of the idea, supervised the experiments, analyzed the data and contributed to the manuscript preparation. [PERSON] conducted the measurements of the prototypes.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Optimal User Association Strategy for Large-Scale IoT Sensor Networks with Mobility on Cloud RANs
## 4 Evaluation
### Load-Balancing Performance
The fairness performance overview of the five algorithms with respect to different \(v\) is provided in Figure 5.  
As expected from the previous results in Figure 4, the proposed schemes always outperform both SDF and RND, as shown in Figure 5. Although ALGO-1 outperforms ALGO-2/3 in some cases and vice versa, the difference is not significant. When \(v\) is small, i.e., from 1 to 3, performance of the five algorithms are quite close to each other. However, as \(v\) gets larger, the difference in the fairness performance becomes significant. The clustered mobility pattern shown at \(v=60\) also resulted in a low fairness performance for all five algorithms. It is worth mentioning that both SDF and RND show very similar fairness performance especially when \(v\) is large, and it can be explained by the same reasoning as what we have provided: the uniformity of the IoT devices' locations. It should be noted that different values of \(\alpha\) do not cause any significant difference in the performance of ALGO-2/3. We also conducted the same measurement with different number of IoT devices when \(v=20\) m/s or, equivalently, 72 km/s. As it can be seen in Figure 6, the relative performance remains the same. ALGO-1/2/3 outperform both SDF and RND in terms of both the maximum number of packets to process by individual RRHs and the fairness index. Overall, the fairness performance of all five algorithms have been improved. Since the increased number of IoT devices randomly moves in the network, they tend to spread out evenly improving the fairness performance of both SDF and RND as well. However, the proposed schemes still achieve the highest fairness performance. In fact, for some cases, for example, such as when there are 100 and 200 devices in the network, the proposed schemes achieve close-to-perfect fairness. The increasing trend in Figure 5(a) is natural and expected, due to the increased number of IoT devices associated with each RRH on average. Figure 5: Average load balance performance measure by the Jain’s fairness index with respect to different upper bounds on the speed of 50 mobile IoT devices. (**a**) When IoT devices are moving at a slow speed; (**b**) When IoT devices are moving at a fast speed.