esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Morphology and Element Analysis  
This study characterized PCU samples processed through different molding methods using SEM images and investigated the elemental differences in PCU processed by various methods using EDS spectra, aiming to analyze the impact of different molding methods on the material properties of PCU. The SEM images (magnified 10,000 times) of PCU samples processed by IM, CM, and 3D are shown in Figure 2. It could be observed that the surfaces of the samples produced by all three molding methods exhibited a dense morphology. As the hardness of the samples increased, the CM samples showed fewer material adhesion marks caused by demolding. The SEM examination focused on potential defects on the sample surfaces, and regardless of whether the samples were produced by IM, CM, or 3D printing, the microscopic surface structures were free from defects such as incompletely melted lumps and tear-like defects during demolding, as previously reported [24]. The IM samples exhibited a good surface finish with a smooth texture. The surface of the 3D samples contained a small amount of particulate protrusions, and the CM samples had tiny flocculent tear marks on their surface. In terms of overall effect, the aforementioned SEM  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & **75A** & **85A** & **95A** & **55D** \\ \hline IM & 70.8 \(\pm\) 0.56 & 79.9 \(\pm\) 0.15 & 89.1 \(\pm\) 0.35 & 95.5 \(\pm\) 0.70 \\ CM & 70.0 \(\pm\) 0.49 & 79.5 \(\pm\) 0.61 & 87.8 \(\pm\) 0.53 & 94.7 \(\pm\) 0.40 \\
3D & 69.6 \(\pm\) 0.50 & 79.3 \(\pm\) 0.56 & 87.5 \(\pm\) 1.35 & 94.5 \(\pm\) 0.66 \\ Reference & 70 & 80 & 90 & 94 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of PCU hardness obtained by different molding methods with the reference hardness (unit: Shore hardness A).  
Figure 1: Histogram of PCU hardness distribution across different molding methods.  
images demonstrated that the processing temperature ranges ultimately employed for the three methods were all feasible.  
Figure 3 presents the EDS energy-dispersive spectroscopy analysis charts of PCU samples produced by IM, CM, and 3D. Samples with a hardness of 85A were selected as representatives for EDS analysis. It could be observed that the surfaces of the samples prepared by the three different molding methods showed no introduction of other impurity elements. The surface element contents of the three basic elements, C, N, and O, were similar, and there were no significant differences among them.
# A Non-Smooth Numerical Optimization Approach to the Three-Point Dubins Problem (3 PDP)
## 1 Introduction
### Paper Contribution and Layout  
Our research presents an efficient and compact solution to the Three-Point Dubins Problem (3 PDP), addressing the non-smooth minimization challenge. Our key contributions are as follows:  
* _Analytical Derivative Computation:_ We formulated a compact, closed-form solution for the Markov-Dubins problem. With this formulation, we significantly reduce computational overhead and improve the efficiency. This is instrumental to finding the analytical expressions for the derivatives of the Dubins path length with respect to the initial and final angles. These expressions allow for a more precise and faster computation of path lengths and gradients, enabling a better optimization performance. Additionally, we established bounds for the existence regions of each of the six possible solutions. With this analysis, we can isolate discontinuities and provide a more accurate and efficient solution to the 3 PDP.
* _Optimal Path Length and Derivative Analysis:_ We computed the length and derivatives of the optimal 3 PDP path, precisely framing it as a non-smooth minimization problem.
* A pattern search algorithm, which can be further enhanced with Algorithm 748 [22].
- A trichotomy algorithm, similarly augmentable with Algorithm 748. Algorithm 748 can be used to perform the root-finding of the first derivatives of the path length, improving the efficiency and accuracy of the solution up to machine precision.
* _Implementation and Accessibility:_ We provided a comprehensive implementation of our algorithms in a C++ library with a Matlab interface, named _Clothoids_[23; 24]. The library, along with clear descriptions and pseudocode of the algorithms, is freely available and open-source, facilitating further research and practical applications, bridging the gap between theoretical research and practitioners, ensuring that our work can be readily used and extended by others.  
Figure 1: An example and scheme of an instance of the 3 PDP. This path is an example of the LSLRS type. The three arcs of each Dubins are represented by colors red, green, and blue  
This paper is structured as follows: Section 2 gives insight into the 3 PDP and presents state-of-the-art methods on the subject. Section 3 develops the solution to the standard Dubins problem and, in particular, the length of the solution as a function of the givens of the problem. Our novel derivation allows us to compute the derivatives of the lengths of the various cases, as shown in Section 4. In Section 5, we detail our solution of the 3 PDP, comprising all cases. The corresponding algorithms are discussed in Section 6 and tested in Section 7. Finally, we present our conclusions in Section 8.
# Challenges and Implications of Virtual Reality in History Education: A Systematic Review
## 3 Results and Discussion
### Psychological and Social Impact  
From a psychological and social perspective, several challenges, including anxiety, frustration, and even fear, have been identified in certain usage contexts, particularly when virtual environments are not designed to be accessible or user-friendly [7; 43]. Moreover, concerns have been raised about the potential for younger students to confuse reality with fiction, which raises ethical questions regarding the design and implementation of these technologies [43].
# Effect of Fermentation Humidity on Quality of Congou Black Tea
## 3 Results and Discussion
### Effects of Fermentation Humidity on Congou Black Tea bioactivity
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Samples** & **DPPH** & **\(\alpha\)-Amylase** & **\(\alpha\)-Glucosidase** \\  & **IC\({}_{50}\) (mg/mL)** & **IC\({}_{50}\) (mg/mL)** & **IC\({}_{50}\) (mg/mL)** \\ \hline RH55 & 397.27 \(\pm\) 6.53 c & 37 \(\pm\) 1.17 c & 28.63 \(\pm\) 1.7 c \\ RH65 & 404.33 \(\pm\) 9.87 bc & 36.73 \(\pm\) 1.11 c & 27.38 \(\pm\) 1.79 c \\ RH75 & 413.03 \(\pm\) 3.17 b & 47.91 \(\pm\) 1.55 b & 33.65 \(\pm\) 2.38 b \\ RH85 & 431.6 \(\pm\) 11.25 a & 55.19 \(\pm\) 5.3 a & 33.54 \(\pm\) 1.85 b \\ RH95 & 437.07 \(\pm\) 8.01 a & 57.81 \(\pm\) 5.1 a & 38.36 \(\pm\) 2.17 a \\ \hline \hline \end{tabular} Note: Significant differences at \(p<0.05\) are indicated by different lowercase letters in the same column.  
\end{table} 
Table 3: DPPH scavenging and inhibition of \(\alpha\)-amylase and \(\alpha\)-glucosidase activities by congou black tea under different fermentation humidity treatments. and streptomyces, and often consist of disulfide bonds and a variety of amino acids [50]. Glutamic acid, arginine and leucine from microalgae have good antioxidant and enzyme activity inhibiting properties due to their small molecular weight, hydrophobic nature or hydroxyl group content [51]. [PERSON] et al. also mentioned that the functional groups of amino acids, such as aromatic and sulfur-containing amino acid residues, were associated with their inhibitory effects [52]. These reports demonstrated a positive correlation of amino acid content with the antioxidant activity and enzyme activity inhibitory effects of tea samples. The above results indicated that tea samples fermented under low humidity conditions (RH55, RH65) have stronger antioxidant activity and enzyme activity inhibition. However, sensory review results showed that tea fermented at low humidity has a heavier astringent taste and green scent, so its quality is not as good as tea fermented at high humidity. Based on the results of the tea sensory quality review and the bioactivity analysis, an appropriate low humidity fermentation without affecting tea sensory quality is beneficial to enhance the bioactive function of black tea.
# Quantum Private Comparison Based on Four-Particle Cluster State
## 4 Security Analysis  
When transmitting the quantum sequence among the participating entities, external eavesdroppers may intercept the quantum sequence and employ various quantum-based attacks to extract information regarding the encoded inputs of the participants. Additionally, the participants themselves may exhibit curiosity, potentially utilizing the received immediate sequences to infer the private inputs of others.  
To mitigate these risks, the quantum sequence incorporates decoy states inserted at random positions, enabling the detection of any tampering or eavesdropping through an analysis of the consistency between the measurement results of the decoy states and the prepared decoy photons. Furthermore, a QKD protocol is employed for sharing a secret key, which is utilized to encrypt the private information, thereby preventing attackers from extracting meaningful information without knowledge of the secret key. As a result, our protocol effectively shows that both external attacks and insider threats are unable to compromise the confidentiality of private data, owing to the decoy state method for preventing eavesdropping and the QKD techniques for encrypting secrets.
###### Abstract  
Nano-titanium dioxide ceramic coatings exhibit excellent wear resistance, corrosion resistance, and self-cleaning properties, showing great potential as multifunctional protective materials. This study proposes a synergistic reinforcement strategy by encapsulating micron-sized Al\({}_{2}\)O\({}_{3}\) particles with nano-TiO\({}_{2}\). A core-shell structured nanocomposite coating composed of 65 wt% nano-TiO\({}_{2}\) encapsulating 30 wt% micron-Al\({}_{2}\)O\({}_{3}\) was precisely designed and fabricated via a slurry dip-coating method on Q235 steel substrates. The microstructure and surface morphology of the coatings were characterized using scanning electron microscopy (SEM) and X-ray diffraction (XRD). Comprehensive performance evaluations including densification, adhesion strength, wear resistance, and thermal shock resistance were conducted. Optimal coating properties were achieved under the conditions of a binder-to-solvent ratio of 1:15 (g/mL), a heating rate of 2 \({}^{\circ}\)C/min, and a sintering temperature of 400 \({}^{\circ}\)C. XRD analysis confirmed the formation of multiple crystalline phases during the 400 \({}^{\circ}\)C curing process, including titanium pyrophosphate (TiP\({}_{2}\)O\({}_{7}\)), aluminum phosphate (AlPO\({}_{4}\)), copper aluminate (Cu(AlO\({}_{2}\))\({}_{2}\)), and a unique titanium phosphate phase (Ti\({}_{3}\)(PO\({}_{4}\))\({}_{4}\)) exclusive to the 2 \({}^{\circ}\)C/min heating rate. Adhesion strength tests revealed that the coating sintered at 2 \({}^{\circ}\)C/min exhibited superior interfacial bonding strength and outstanding performance in wear resistance, hardness, and thermal shock resistance. The incorporation of nano-TiO\({}_{2}\) into the 30 wt% Al\({}_{2}\)O\({}_{3}\) matrix significantly enhanced the mechanical properties of the composite coating. Mechanistic studies indicated that the bonding between the nanocomposite coating and the metal substrate is primarily achieved through mechanical interlocking, forming a robust physical interface. These findings provide theoretical guidance for optimizing the fabrication process of metal-based ceramic coatings and expanding their engineering applications in various industries.  
titanium dioxide; nanocomposite coating; functionality; binding mechanism +
Footnote †: journal: Applied Sciences
# Unsupervised Learning-Based Spectrum Sensing Algorithm with Defending Adversarial Attacks
## 4 Implementation of USDAA Defense Algorithm  
The framework of the USDAA algorithm is shown in Figure 1. The USDAA algorithm consists of two stages: adversarial pre-training and fine-tuning.  
In the adversarial pre-training stage, two encoders extract the features of clean samples and adversarial samples, respectively, and the two decoders reconstruct the clean samples and adversarial samples according to the potential representation of the features \(Z_{i}(i=1,2)\), respectively. After the adversarial pre-training, the parameters of the encoder module of the feature extractor are saved as \(\xi\). In the fine-tuning stage, the feature dimension is reduced to the classification dimension, a linear layer is added to the encoder module obtained by pre-training, the model parameters are trained with only a small number of adversarial samples, and the features and labels of the samples are matched. The fine-tuned model is the final defense model R\({}_{\theta}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**References** & **Attacks** & **Types** \\ \hline
[16; 17; 18; 19] & PUEA & - \\
[20; 21; 22; 23] & SSDF & - \\
[25; 26; 27] & PUAA & Black Box \\
[12] & PUAA & white Box \\ \hline \hline \end{tabular}
\end{table}
Table 1: References to defend different attacks.
## 3 Method
### Parameter Estimation Based on PSO Algorithm  
When running a PSO algorithm, the setting of parameters such as learning factors and inertia weights has a very important impact on the optimal solution of the algorithm for a particular problem [36; 37]. The combination of different parameters of the PSO algorithm causes the particle population to form different trajectories and affects the searchabilityof the particles. For the specific search problem, it is necessary to choose the appropriate combination of algorithm parameters, and in this paper, it is necessary to solve the problem of solving the non-closed expressions for the generalized Pareto distribution with positive 0.5 th/1 st-order moment estimation, positive 0.25 th-order logarithmic moment estimation, and maximum likelihood estimation. Therefore, for the above discussion, this paper will use the appropriate combination of parameters to complete the solution of the corresponding target problem.  
To address the problem of solving non-closed expressions in the parameter estimation methods of generalized Pareto distribution, the whole thought process of the PSO algorithm introduced to solve this problem is introduced in detail, including the construction of the fitness function and the setting of algorithm parameters. According to the derivation in Section 2, it is known that the positive 0.5 th/1 st-order moment estimation, positive 0.25 th-order logarithmic moment estimation, and maximum likelihood estimation have non-closed expressions, so the fitness functions of the three types of estimation methods are constructed as follows:  
\[\left\{\begin{array}{l}f_{1}(a)=\dfrac{\Gamma(a-0.5)\Gamma(a)}{\Gamma^{2}(a- 0.25)}-\dfrac{\Gamma^{2}(1.25)m_{1}}{\Gamma(1.5)m_{0.5}^{2}}\\ f_{2}(a)=\dfrac{\kappa_{0.25}}{m_{0.5}}-\kappa_{0}-\psi(1.25)+\psi(1)-\psi(a)+ \psi(a-r)\\ f_{3}(b)=\dfrac{1}{n}\underset{i=1}{\overset{n}{\sum}}\ln\bigl{(}1+bx_{i}^{2 }\bigr{)}\left(1-\dfrac{1}{n}\underset{i=1}{\overset{n}{\sum}}\dfrac{bx_{i}^{ 2}}{1+bx_{i}^{2}}\right)-\dfrac{1}{n}\underset{i=1}{\overset{n}{\sum}}\dfrac{bx _{i}^{2}}{1+bx_{i}^{2}}\end{array}\right. \tag{19}\]  
In this case, the three fitness function constructions correspond to one-dimensional nonlinear function extremum search problems, and the final parameter estimates can be obtained as long as the particle population is made to find the location in the feasible solution space where the minimal value point is located.
###### Abstract
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Modeling Approach** & **Advantages** & **Shortcomings** & **Main Characteristics** & \begin{tabular}{c} **Solar** \\ **Inradance** \\ \end{tabular} & **References** \\ \hline Beer–Lambert’s law & Simple and efficient & \begin{tabular}{c} Inaccurate, unable to analyze \\ the delay spread effect \\ \end{tabular} & _Path loss (PL)_ & N & [11; 12] \\ \hline Experimental modeling & \begin{tabular}{c} Real measured \\ validation data \\ \end{tabular} & \
\begin{tabular}{c} High cost, difficult to deduce the \\ impact of transceiver configurations \\ \end{tabular} & _PL, h(t)_ & N & [13] \\ \hline Analytical RTE & Analytical results & Very difficult derivation & _PL_ & N & [8; 9; 10] \\ \hline Numerical RTE & Easy Programming & Low efficiency in case of error & _PL, h(t)_, t\({}_{DS}\) & N & [14; 18] \\ \hline Chlorophyll Monte-Carlo &
\begin{tabular}{c} Accurate in simulation \\ environment \\ \end{tabular} & Less efficient for errors & _PL, h(t)_, h(f)_, & N & [15; 16] \\ \hline MC-Curve Fitting & Accurate results & Time consuming & _H(0)_, h(t)_, t\({}_{DS}\) & N & [17; 19] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of UOWC (underwater optical wireless communication) channel modeling approaches of previous works.
## 6 Results Analysis and Discussion
### Performance of RTT across Varying AQM Algorithms  
**Scenario 1 (Bandwidth = 10 Mbps, Delay = 20 ms)**: We evaluated the performance of FQ-CoDel, FQ-PIE, and L4S in two cases: Case 1 with enabled ECN and Case 2 with ECN disabled for comparison. Figures 32 and 33 present the results for the two cases.  
In Figure 32a, where ECN is disabled, FQ-CoDel exhibits high RTT but demonstrates higher data transfer due to higher retransmissions, as shown in Table 2. This is mainly because FQ-CoDel does not drop packets from its buffer when congestion is too high, unlike FQ-PIE and L4S. Upon disabling ECN, FQ-CoDel has a very similar RT to CoDel, with the lowest consistent RTT among its competitors, as depicted in Figure 33a.  
L4S demonstrates a distinct reduction in RTT when ECN is enabled, highlighting the efficacy of its prioritization strategies. This contrasts with FQ-PIE, which exhibits higher RTT and occasional fairness concerns, particularly around the 60-second mark when ECN is enabled. When ECN is disabled, both L4S and FQ-PIE display comparable RTT characterized by slightly higher RTT. Figure 33b,c depict their RTT behavior without ECN, whereas Figures 32b and 34c illustrate the impact of ECN on their performance metrics.  
**Scenario 2 (Bandwidth = 1 Mbps, Delay = 20 ms)**:  
Figure 31: Case 2: (ECN disabled) Throughput Scenario 2 with Bandwidth = 1 Mbps, Delay = 20 ms. (**a**) FQ-CoDel (Throughput); (**b**) FQ-PIE (Throughput); (**c**) L4S (Throughput).  
Figure 33: Case 2: (ECN disabled) Smoothed TCP RTT measured in seconds for Scenario 1 with Bandwidth = 10 Mbps, Delay = 20 ms. (**a**) FQ-CoDel (Smoothed RTT); (**b**) FQ-PIE (Smoothed RTT); (**c**) L4S (Smoothed RTT).  
We evaluated the performance of FQ-CoDel, FQ-PIE, and L4S in two cases: Case 1 with ECN enabled and Case 2 with ECN disabled for comparison. Figures 34 and 35 present the results for these two cases.  
In both low and high-bandwidth environments, FQ-CoDel, FQ-PIE, and L4S demonstrate similar RTT behaviors. FQ-CoDel exhibits significantly high RTT when ECN is enabled (Figure 34a), contrasting sharply with its low RTT when ECN is disabled (Figure 35a), similar to scenario 1. Subsequently, FQ-PIE shows high RTT and unfairness among connections irrespective of ECN status, as depicted in Figures 34b and 35b. Notable, L4S shows significantly reduced RTT and enhanced fairness with ECN enabled due to its prioritization mechanisms. Conversely, with ECN disabled, L4S shows elevated RTT, except for TCP stream two.
## 4 Construction of Core-Shell Nanostructures
### Other Composited Nanostructures  
According to Table 1, we can get that it is difficult to construct noble metal@Ag core-shell nanostructures except gold by epitaxial growth because these nanostructures must accord with three criteria listed as follows [107]: (1) the atomic radius of shell should be smaller than that of core which is easier to grow epitaxially; (2) the shell should have smaller bond dissociation in order to ensure stable growth; (3) the electronegativity of the shell should be smaller than that of core, or displacement reactions will occur. Therefore, a few core-shell nanostructures are prepared by the epitaxial growth method. [PERSON] _et al_. [109] synthesized Ag@Cu alloy by reducing the mixture of AgNO\({}_{3}\) and Cu(OAc)\({}_{2}\) 2H\({}_{2}\)O in EG, then they used Ag@Cu alloy to produce new Ag@Cu alloy Cu shells, which was consistent with the rules. [PERSON] _et al_. [110] proposed thatb ZnO@Ag nanoflowers can be prepared by depositing zinc power on silver layers using a vapor transportation method under an oxygen atmosphere. Moreover, Ag@Pt bimetallic nanoparticles were prepared by [PERSON] _et al_. [111] via a chemical reduction method.  
Figure 11: Schematic crystal structures of the Au cores and Au@Ag nanocrystals prepared in DMF by an oil-bath heating (right side) and in EG by a MW heating (left side) (reprinted with permission from [94]. Copyright (2008) American Chemical Society).
# Forest Volatile Organic Compounds and Their Effects on Human Health: A State-of-the-Art Review
## 1 Introduction
### Forest Volatile Organic Compounds (VOCs): Background and Natural Functions
All plants can potentially synthesize volatile isoprenoids if triggered by abiotic and biotic stress factors, but only some species can do this constitutively [15]. * **Constitutive forest VOCs** are already synthesized in the organism and either stored in specialized structures or constantly emitted without any form of storage [16]. They are emitted at baseline levels and are mainly made of terpenoids, plus shikimate derivatives and polykeptides [5]. * **Inducible forest VOCs** (herbivore-induced plant volatiles; henceforth HIPVs) are compounds whose synthesis is increased or initiated de-novo after herbivore attacks but also after stimulation by abiotic stressors. They have some metabolic costs but they make the plant phenotypically plastic and herbivore adaptation more unlikely to occur [5]. HIPVs comprise isoprenoids, products of the lipoxygenase (LOX) pathway, such as GLVs, some carotenoid derivatives, indoles and phenolics, and phytohormones such as ethylene, jasmonic acid, and others [17,18]. It is hypothesized that forest VOCs are not only involved in some plant-related physiological functions (biogenic stress response, adaptation to climate changes), but they also have a central role in forest ecosystems (interplant communication, antimicrobial and insecticidal activity against parasites, influence on animals' feeding behaviors as well as on underwood environmental conditions) [19]. Thus, forest VOCs can have several functions for plant physiology [18]; the most important of them are summarized in Table 1 and Figure 1.