esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Antenna Sensor Simulation and Parameter Optimization
### Influence of the Dielectric Layer Thickness on the Antenna Parameters  
Not only does the dielectric layer of the antenna play a physical supporting role for the antenna, but its parameters also have a great influence on the performance of the antenna. This study conducts a simulation to analyze the influence of the dielectric layer thickness on the antenna. In the simulation, the feed point is selected on point 5 of \(X\)-axis, as shown in Figure 6. Dielectric layer thicknesses of 1, 2, 3, 4, and 5 mm are selected, and the simulation results are shown in Table 2.  
The results in Table 2 indicate that the increase of the dielectric layer thickness has no influence on the number of resonant points. The lowest resonant frequency have a small change, and it is approximately 450 MHz. The resonant frequencies of the second and third resonant points show an increasing trend as the dielectric layer thickness increases. By contrast, the resonant frequency of the fourth resonant point decreases with the increase of the dielectric layer thickness. However, the increase or decrease in the resonant frequency is not obvious.  
The VSWR values displayed in Table 2 show that the dielectric layer thickness changes have a significant influence on the VSWR. The VSWR of the fourth resonant point decreases, whereas the VSWR of the other resonant points present an evident increasing trend with the increase of the dielectric layer thickness. The relevant equations mentioned in the Section 2 can be used to explain the results qualitatively. Based on Equation (2), the inductive reactance \(X_{L}\), the quality factor \(Q_{T}\) and the resonant resistance \(R\) will change accordingly as the dielectric layer thickness changes. These changes of parameters above lead to the changes of the input impedance \(Z_{int}\), which eventually results in the VSWR change of the antenna. Moreover, Equation (4) also manifests that the resonant frequency is unaffected by the dielectric layer thickness.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feed Point** & **Resonant Frequency (MHz)** & **Minimum VSWR** \\ \hline
1 & 935 & 2.876 (935 MHz) \\
2 & 446, 658, 928 & 1.411 (928 MHz) \\
3 & 444, 656, 734, 919 & 1.235 (656 MHz) \\
4 & 446, 657, 921 & 1.552 (657 MHz) \\
5 & 454, 674, 728, 922 & 1.308 (922 MHz) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Resonant points and minimum standing wave ratio at different feed position.  
Figure 7: Current distribution of the antenna.  
Based on the simulation analysis of the antenna performance with different dielectric layer thicknesses, we come to the conclusion that thickness has an insignificant effect on the resonant frequency, but a serious effect on the impedance matching degree. For these reasons, optimizing the dielectric layer thickness is very important to improve the performance of the antenna sensor.
# Distributed Fault Detection and Isolation Approach for Oil Pipelines
## 3 Model-Based Distributed FDI  
This section proposes a method to design a set of distributed local diagnosers that together guarantee the same diagnosability as a centralized system. To achieve this, each local diagnoser must detect and isolate the set of faults corresponding to its subsystems. To solve this problem, an approach is proposed that builds a select set of FMSO sets for each subsystem that guarantees maximum diagnosability and the least exchange of sensor information between subsystems.  
Initially, FMSO sets are built only with local information, and, if it is not enough to detect and isolate all the faults of that subsystem, sensors from neighboring subsystems are added until the possible detectability and isolability are reached. In each iteration of building FMSO sets, a Binary Integer Linear Programming (BILP) optimization problem is solved to select the most appropriate FMSO sets.  
The general idea is to incorporate information of additional sensors to minimize the coupling between FMSO sets; this is achieved by adding equations that minimize the number of connections between FMSO sets.  
**Definition 11** (Global FMSO set).: _A global FMSO set is an FMSO set of \(\Sigma(z,x,f)\). The set of global FMSO sets is denoted by \(\Phi\)._  
In order to develop a methodology to build and select an efficient set of FMSO sets, it is desirable to build only a useful part of all Global FMSO sets; for this, it is proposed to focus the building process in a reduced subgraph related to a subset of interest faults.  
To compute FMSO sets that require information from other subsystems, a classification of neighboring subsystems is performed according to their proximity: if they are directly connected neighbors, they are classified as level 1 neighbors; if they are connected neighbors through 1 subsystem, they are classified as level 2 neighbors and so forth.  
**Definition 12** (Neighbors of \(\Sigma_{i}\)).: _Given two subsystems \(\Sigma_{i}\) and \(\Sigma_{j}\) of \(\Sigma\), \(\Sigma_{i}\
eq\Sigma_{j}\). \(\Sigma_{i}\) is a first level neighbor of \(\Sigma_{j}\) if \(z_{i}\cap z_{j}\
eq\emptyset\), which is denoted by \(\Sigma_{i}^{1,j}\) and \(\Sigma_{i}^{1,i}\). The set of all neighbors of the first level of \(\Sigma_{i}\) is denoted by \(\Sigma^{1,i}\)_  
Classification of the neighbors is illustrated with another example in Figure 3 where the subsystems \(\Sigma_{10}\) and \(\Sigma_{13}\) are taken as examples. For the subsystem \(\Sigma_{10}\), the first level neighbors are \(\Sigma_{11}^{1,10}\), \(\Sigma_{3}^{1,10}\) and \(\Sigma_{9}^{1,10}\). Then, \(\Sigma^{1,10}\) and \(\Sigma^{2,10}\) are the sets of first and second level neighbors with respect to \(\Sigma_{10}\).
## 2 Morphological Reconstruction and Multi-feature Analysis for TIR Ship Images
### Characters of Small Ship Targets and Sea Clutter in TIR Image
\begin{table}
\begin{tabular}{c c c c} \hline
**Radiations** & **Wavelength (\(\upmu\)m)** & **Temperatures of Blackbody (K)** & **Temperatures of Blackbody (\({}^{\circ}\)C)** \\ \hline VIS & 0.38\(\sim\)0.75 \(\upmu\)m & 3865.07\(\sim\)7628.42 & 3591.92\(\sim\)7355.27 \\ NIR & 0.75–3 \(\upmu\)m & 966.27\(\sim\)3865.07 & 693.12–3591.92 \\ TIR & 3–15 \(\upmu\)m & 193.25–966.27 & \(-\)79.9–693.12 \\ \hline \end{tabular}
\end{table} 
Table 1: The temperatures of blackbody of visible (VIS), near-infrared (NIR), and TIR. Figure 2 shows three representative TIR ship images with complex sea background clutter, in which it is difficult to detect all small ship targets because of low signal-to-clutter ratio (SCR), fewer pixels, heavy sea clutter, and lack of textural and structural information. The small ship targets are enlarged as corresponding three-dimensional (3-D) mesh plots by red boxes. Nevertheless, according to above character analysis, as the ship targets can be viewed as local bright or dark uniform abnormal regions under the sea clutter in TIR images due to long imaging distance, the small ship targets still have some solid features. Firstly, because the small ship targets (as a manmade object) and sea clutter have different thermal radiation characteristics, the ship targets have a certain pixel intensity feature against background sea clutter. Secondly, as depicted by the enlarged 3-D mesh plots in three TIR images, as small ship target will destroy the local self-similarity characteristics of sea background clutter, the ship targets have obvious local contrast and contour features compared with sea clutter. Thirdly, as shown in Figure 2a,b, as the small ship targets are almost uniform regions in sea clutter, they have obvious shape features. Therefore, based on these TIR imaging features of small ship targets and sea clutter, we propose an effective TIR small ship target detection scheme by reasonably integrating these features. As a primary step, a pre-processing procedure based on gray-level morphological reconstruction is introduced to remove noise and smooth sea clutter. Next, according to the intensity and local contrast features of ship targets, the IFSM and BCSM are presented and fused to well enhance potential ship targets and suppress heavy sea clutter. Then, motivated by the contour feature of target region, the STAEM-based contour descriptor is proposed to characterize candidate ship targets and eliminate residual clutter simultaneously. After that, based on the statistics and observation of shape parameters of TIR ship targets selected from VAIS dataset, the statistical shape knowledge is obtained and utilized to further distinguish true ship targets from non-ship targets. Finally, the whole proposed small ship target detection scheme is presented.
## 3 Polystyrene in TIPS Pentacene Crystallization  
As discussed above, amorphous polymers offer significant advantages over conjugated and semicrystalline polymers in organic semiconductor applications by improving thin film morphology, minimizing misalignment, enhancing crystal orientation, alleviating thermal stress, preventing cracks, and enabling controlled crystallization. Among various amorphous polymers, PS stands out as a representative material for its significant impact on the crystallization and morphology of organic semiconductors. PS originates from styrene monomers extracted from liquid petrochemical substances [95, 96, 97]. This polymer has a long hydrocarbon chain with phenyl groups bonded to alternating carbon atoms [98, 99, 100]. PS is naturally transparent but can be colored by incorporating colorants. It is heat-resistant, lightweight, and exhibits high strength and durability, making it ideal for various applications [101]. These uses include packaging, toys, and household items like computer housings and kitchen gadgets [102].  
As an amorphous polymer, PS is particularly effective in minimizing misalignment and promoting the parallel orientation of organic crystals within the thin film. This enhancement in film morphology is crucial for achieving better charge transport properties, which directly translate to improved performance in OTFTs [103, 104, 105]. PS helps in controlling the crystallization environment by reducing defects such as grain boundaries and promoting larger, more uniform crystals. Additionally, the integration of PS can mitigate thermal stress, preventing thermal cracks, and can induce beneficial phase segregation within the film. These attributes make PS an excellent candidate for enhancing the structural and electronic properties of organic semiconductors, which sets the stage for a detailed examination of its role and benefits as an additive. In this section, we will review the important studies that utilize PS with different Mw to modulate crystallization, morphology and mobility of the organic semiconductors. Our discussion will be based on the benchmark organic semiconductor TIPS pentacene as an example due to its exceptional electronic properties, stability, and widespread usage in organic electronic devices [106, 12, 107].
## 1 Introduction
[39] applied the strain gradient theory and electromagnetic theory, considered the microstructure effects and the action of the Lorentz force, and studied the reflection and transmission problems of elastic waves. A common characteristic of the aforementioned studies Is the focus on the propagation of elastic waves at the macro level, disregarding the influence of the material microstructure on wave propagation and the ability to simultaneously study the influence of an external magnetic field on the dispersion characteristics of elastic waves or to study only the reflection and transmission problems of a single type of wave. However, as the wavelength of the incident wave decreased, or the frequency increased, the microstructural effect becomes increasingly prominent. Consequently, it is necessary to consider the microstructural effects when investigating the propagation of elastic waves. In this paper, both the microstructural effect and the Lorentz force effect were considered to study the reflection of elastic waves. Specifically, the dipole strain gradient theory was utilized to incorporate the microstructural effect of materials, whereas [PERSON]'s electromagnetic theory was used to account for the Lorentz force effect. Through numerical calculations, the amplitude ratios between various reflected and incident waves were derived for the incident P waves and SV waves. Moreover, the detailed effects of microstructural parameters and applied magnetic fields on the propagation of reflected waves are thoroughly discussed.
## 3 Results and Discussion
### LEM/FEM-Based Rapid Drawdown Analysis  
In order to investigate the capability of LEM/FEM procedures to evaluate the safety factors' changes based on rapid drawdown, a comparison of permeability and different drawdown rates on different R values is presented in Figure 8. As presented in Figure 8, which illustrates the FS variations based on R (m/day), the FEM-based modeling provides more conservative results than the LEM-based modeling for estimating the F.S under various drawdown rates for the earther dam. Regarding this figure, the main variations in the F.S follow a close trend, but the LEM results indicated that various harmonic safety factors with different R and FEM models provided lower F.S with wider estimations. A closer look at the LEM/FEM models indicated two stages for probable sliding surfaces on dams, which can be classified as shallow and deep slip surfaces. Figures 9 and 10 provide detailed analyses of both instability statuses of the dams, which lead to the stability charts for these cases. According to these figures, it was stated that by increasing the slip surface's depth, the F.S increases, and by decreasing the hydraulic hydration, the reliability decreases. Also, the sensitivity of F.S for the shallow and deep sliding situations is influenced by the drawdown rates. Paying closer attention to the graphs presented in Figures 9 and 10 indicates that the more superficial the slip surface becomes, the lower the confidence level. It can be clearly seen that the drawdown rate has a significant impact on the stability of the dam and the drawdown rate causes a significant impact on the F.S values.
# Efficacy and Tolerability of a Microneedling Device Plus Exosomes for Treating Melasma
## 1 Introduction
### Available Treatments  
Melasma is challenging to treat, and there is currently no known cure. Several treatment options are available, such as chemical peels, lasers, lights, and systemic and topical approaches such as tranexamic acid (TXA), corticosteroids, tretinoin, and hydroquinone, combined with broad-spectrum sunscreen protecting against UVB, long-wave UVA and high-energy VL [8]. Kligman's Trio (KT), initially introduced by [PERSON] in 1975, is acknowledged as the foremost treatment for melasma. This cream incorporates a blend of 5% hydroquinone, tretinoin, and corticosteroid. Nevertheless, this topical solution is linked to discomforting side effects, such as erythema, desquamation, and a burning sensation. Recently, the use of a new triple combination cream containing isobutylamido thiazolyl resorcinol, retinoic acid, and corticosteroid has been introduced as a well-tolerated alternative to KT [9]. Chemical peels are an effective procedure in treating melasma as they exfoliate superficial skin layers, reducing hyperpigmentation. Various acid substances like salicylic acid, glycolic acid, and trichloroacetic acid, among others, are used at different concentrations to achieve improvement of the disease. They are often used in combination with other treatments, such as laser therapy or topical therapies, to optimize results. The use of deep and medium-depth peels in subjects with darker skin phototypes is not recommended due to the risk of hyperpigmentation [10]. Various types of lasers have become available in melasma treatment, particularly nonablative lasers, being widely utilized due to their lower incidence of post-inflammatory hyperpigmentation. The effectiveness of laser therapy is significantly augmented when combined with complementary topical treatments [11; 12].
# Aerial Remote Sensing of Aquatic Microplastic Pollution:
## 3 Literature Survey
### Remote Sensing of Indirect Proxies for Aquatic MPs  
While the remote sensing approach detailed in the preceding section relies on a proxy (surfactants) that directly results from the presence of microplastics, other studies have revealed or investigated other indirect proxies for MPs. In most cases [27; 34; 35; 36], these were various suspended water constituents (e.g., chlorophyll-a, particulate matter, sediment, dissolved organic matter) that are detectable via satellite-borne optical sensors and may be associated with MPs, specifically in river and estuary systems; although [PERSON] et al. [35] found that in situ-sampled MP concentrations were correlated with such water constituents in only one of three study sites, underscoring that such proxies must be used with caution. [PERSON] et al. [37] found \(\sim\)10 times higher in situ-sampled MP concentrations in anticyclonic ocean eddies than cyclonic eddies, the fine-scale spatial structure of which can be mapped using satellite altimetry, providing another potential means of remotely sensing areas of the ocean with likely high MP concentrations. Finally, [PERSON] et al. [38] found significant correlations between remotely sensed algal blooms and in situ-sampled MP concentrations in Chaohu Lake, China.
## 4 Results and Discussions
### SEUT Versus PAUT Methods
#### 4.1.1 Focusing Depth Comparison
Figure 6: Back wall reflection signals for GFRP: (**a**) SEUT method and (**b**) PAUT method. (Freq. = 1.5 MHz, Thickness = 12 mm).
## 3 Results
### Effect of MLE on Plant Osmolytes Accumulation
#### 3.5.2 Phenolics Content of Shoot and Root  
Figure 5C shows the effect of MLE application on phenolics shoot content of wheat facing Cd stress. Results revealed that cadmium treatments of 500 uM and 1000 uM (under light) were significantly increased (_p_ < 0.05) the phenolics content of shoot by 10.9% and 25.56%, while on the other hand, application of 3% MLE along with combinations of 500 uM and 1000 uM cadmium solutions also increased the phenolics content by 22% and 26% compared to control.  
Figure 5D shows the effect of MLE application on phenolics content of root wheat facing Cd stress. Results revealed that cadmium treatments of 500 uM and 1000 uM (under light) were significantly increased the phenolics content of root by 13.8% and 36.9%, while, on the other hand, application of 3% MLE along with combinations of 500 uM and 1000 uM cadmium solutions also increased the phenolics content by 29.21% and 47.6% compared to control.  
Figure 5E shows the effect of MLE application on plant osmolytes accumulation (**A**) flavonoids content of shoot (**B**) flavonoids content of root (**C**) phenolics content of shoot (**D**) phenolics content of root (**E**) total soluble sugar content of shoot and (**F**) total soluble sugar content of root, with and without Cd stress under light and subdue light. Different letters above bars represents significant differences (_p_ < 0.05) at each measured point. Values represent mean \(\pm\) SE of three replicates of each treatment.  
Figure 5D shows the effect of MLE application on phenolics content of root wheat facing Cd stress. Results revealed that cadmium treatments of 500 uM and 1000 uM (under light) were significantly increased the phenolics content of root by 13.8% and 36.9%, while, on the other hand, application of 3% MLE along with combinations of 500 uM and 1000 uM cadmium solutions also increased the phenolics content by 29.21% and 47.6% compared to control.  
Interestingly, both Cd treatments with 3% MLE showed significant increase in root and shoot phenolics content compared to MLE 3% treatment under both light and shade conditions.
## 5 Machine Learning Model Designs
### Hyperparamter Optimization Algorithms  
The goal of hyperparameter tuning methods is to optimize the performance of a machine learning model by determining the optimal values for the model's hyperparameters. The most widely used hyperparameter tuning algorithms include:  
1. Grid Search: An exhaustive search technique that evaluates all possible combinations of hyperparameter values specified in a predefined grid [50].
2. Random Search: A simple yet effective method that samples random combinations of hyperparameter values from specified distributions, often more efficient than grid search [50].
3. Bayesian Optimization: A model-based optimization technique that uses surrogate models, typically Gaussian processes or tree-structured Parzen estimators, to guide the search for optimal hyperparameters [51].
4. Gaussian Processes: A Bayesian optimization variant that employs Gaussian processes as the surrogate model to capture the underlying function of hyperparameters and the objective [52].
5. Tree-structured Parzen Estimators (TPE): A Bayesian optimization variant that uses TPE to model the probability distribution of hyperparameters conditioned on the objective function values [53].
6. HyperBand: A bandit-based approach to hyperparameter optimization that adaptively allocates resources to different configurations and performs early stopping to improve efficiency [54].
7. Successive Halving: Successive Halving is a hyperparameter optimization approach used to identify the optimal hyperparameter combination for a particular machine learning model. The technique seeks to determine the optimal choice of hyperparameters given a constrained budget, which is often the total number of times the model can be trained and evaluated. The primary goal of successive halving is to more efficiently allocate resources by swiftly removing configurations with subpar hyperparameters. This is accomplished by running the model in parallel with various hyperparameter values and lowering the number of analyzed configurations iteratively [55].
## 7 Datasets
### Industrial Control Systems  
In the field of Industrial Control Systems (ICS), time series datasets are commonly used for monitoring and analyzing the operational status of industrial equipment. This facilitates the timely detection of equipment failures, prediction of maintenance needs, optimization of production processes, and ensuring production safety. The WADI (Water Distribution) dataset [93], collected and open-sourced by the iTrust institute of the Singapore University of Technology and Design (SUTD), is used to simulate water treatment and distribution networks.This dataset includes sensor data and network data, covering multiple stages of the water treatment process, such as raw water intake, chemical treatment, filtration, dechlorination, and reverse osmosis. The SWaT (Secure Water Treatment) [91] is also provided by the iTrust institute of SUTD and serves as a test platform for research in the field of ISC for water treatment processes. The SWaT dataset contains sensor data from various stages of the water treatment process.  
Industrial Control Systems (ICS) are widely used and are crucial to both industry and society. Failures in these systems can have severe impacts on the economy and human life. As a result, these systems have become attractive targets for both physical and cyberattacks. [PERSON] et al. [118] proposed an efficient method for detecting network attacks in ICS, which combines lightweight neural networks with Principal Component Analysis (PCA). This approach aims to reduce computational resource consumption while maintaining high detection accuracy, which is particularly important in resource-constrained ICS environments. Anomaly detection is critical for preventing cybersecurity intrusions and system attacks. Many AI-based anomaly detection methods have been proposed with high detection performance, but they remain a \"black box\" that is difficult to interpret. [PERSON] et al. [119] used explainable artificial intelligence (XAI) to enhance the perspective and reliability of an LSTM-based Autoencoder-OCSVM learning model for anomaly detection in ICS. This model is capable of providing detailed information about detected anomalies, helping security teams make informed decisions.