esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Description of IDMMFO-GM Design
### Non-Linear Decreasing Strategy of Weight Coefficient  
To achieve a more effective balance between global and local searching, a non-linear decreasing approach for the weight coefficient associated with the logarithmic helical term is introduced in this study. This approach aims to address the inherent limitation of the moth-flame optimization, which is prone to converging prematurely to local optima. The specific mapping expression for the logarithmic helical location adjusting behavior of the moth individual is as follows:  
\[T:\left\{\begin{array}{l}M_{i}=\omega\cdot D_{ij}\cdot e^{\pi t}\cdot\cos(2 \pi t)+F_{j}\\ D_{ij}=|M_{i}-F_{j}|\end{array}\right. \tag{20}\]  
When the weight coefficient \(\omega\) is higher, the algorithm exhibits significant global searching performance, which is particularly appropriate for the preliminary phase of the optimization procedure. Conversely, when the weight coefficient \(\omega\) is lower, the algorithm demonstrates enhanced local searching. In this study, we propose a non-linear decreasing approach about the flexible weight coefficient \(\omega\) and propose it relying on the cosine structure.  
The specific non-linear decreasing function about the weight coefficient \(\omega\) of iteration progress \(t_{r}\) is presented in Figure 5.  
The specific formula for calculating the weight coefficient \(\omega\) is presented as follows:  
\[\omega=\omega_{\text{min}}+\omega_{d}\cdot\cos\left((1-t_{r})\cdot\frac{\pi}{2} \right)^{\beta} \tag{21}\]  
where \(\omega_{d}\) represents the weight coefficient decrement, \(\omega_{d}=\omega_{\text{max}}-\omega_{\text{min}}\), and \(\omega_{\text{min}}\) and \(\omega_{\text{max}}\) denote the lower and upper limits of the weight coefficients, respectively. \(t_{r}\) signifies the progression of the iteration, \(t_{r}=t\cdot{t_{\text{max}}}^{-1}\), and \(\beta\) is the optimization factor for the non-linear decreasing about the weight coefficient \(\omega\).  
As illustrated in Figure 5, the cosine structure non-linear decreasing strategy exhibits significant adaptability and flexibility. Specifically, this strategy is employed to optimize and adjust the weight coefficient by selecting the most appropriate optimization parameter; hence, it enables real-time adjustment of the weight coefficient decreasing rate during the iterative process, which is conducive to the improvement in the balance effect between global exploration and local development, to improve the overall optimization ability of the algorithm [23].
## 6 Cramer-Rao Lower Bound (CRLB)  
In this section, a performance evaluation of a parameter estimation technique was conducted by considering a set of performance bounds [25]. These bounds were employed to evaluate the possible performance of the technique. Therefore, the Cramer-Rao lower bound (CRLB), which provides a lower bound on the estimation variance of an unbiased estimator for the parameter \(\theta\), is denoted as \(\sigma_{\text{CR}}(\theta)\). The expression for CRLB is as follows:  
\[\sigma_{\text{CR}}(\theta)=\sqrt{\frac{1}{\xi}\Big{[}\frac{1}{(N\gamma)}+ \frac{1}{(N\gamma)^{2}}\Big{]}\frac{6}{(N^{2}-1)}}, \tag{24}\]  
Thus, Equation (24) expresses \(\sigma_{\text{CR}}(\theta)\) for DoA estimation as a function of \(N\), \(\xi\), and \(\gamma\), thereby simplifying its derivation. However, the model for \(\sigma_{\text{CR}}(\theta)\) is only applicable to cases where AWGN, or thermal noise, is present. The calculation of this model does not account for the random variable of the channel phase.
## 5 Attributes and Characteristics of FL
### Best Practices and Standards  
Below, we summarize the best practices and standards and give guidelines to ensure privacy in FL deployment.  
* Local Data Storage: At its core, FL ensures that data remain on the user's device (like edge devices or nodes). This practice should be maintained, ensuring that raw data are never transmitted, reducing the potential for data exposure.
* Data Minimization: Only the essential data required for model training should be processed. This reduces the volume of data at risk and lessens the potential harm in the event of a breach.
* Regular Model Audits: Frequent validation and evaluation of the FL models help in identifying and addressing any potential privacy vulnerabilities.
* Differential Privacy: One should ensure the implementation of techniques like differential privacy to introduce calibrated noise into the data, ensuring that individual contributions are masked during aggregation.
* Encryption: One should always encrypt data during transmission. Techniques like homomorphic encryption, which allows computation on encrypted data without needing decryption, can be pivotal.  
Emerging standards and protocols for data privacy in FL:
* ISO/IEC Standards: Various standards, particularly those of the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), address the privacy aspects of cloud computing and distributed systems, which are relevant to FL.
* NIST Guidelines: The National Institute of Standards and Technology (NIST) offers guidelines and practices on securing data during transmission and storage [78].
* Community-Driven Initiatives: Open-source communities and academic consortia are collaborating to define standards and protocols specific to FL, given its growing significance [79].
# A Transfer Reinforcement Learning Approach for Capacity Sharing in Beyond 5G Networks
## 3 Transfer Reinforcement Learning Approach  
TL techniques leverage the knowledge gained for a source task (i.e., the origin task that has already been previously learned) to accelerate the training process of a target task (i.e., the task to be learned). While TL has been extensively studied for supervised learning, its application to RL is an emerging trend that leads to a subtype of TL techniques known as TRL [24]. TRL deals with the application of TL to RL, in which an RL agent iteratively interacts with an environment to learn an optimal decision-making policy. The knowledge to be transferred in RL can take different forms, depending on the RL component that is transferred, e.g., policies, ANN weights, experiences from the RL agent interaction, etc.  
This section proposes using TRL to accelerate the re-training process of the DQN-MARL solution when extending the number of cells in a RAN infrastructure. The proposed TRL approach, which is depicted in Figure 1, considers as a source task the policy \(\pi\) for capacity sharing in a scenario with \(N\) cells and as a target task the policy \(\pi^{\prime}\) for the case of \(N^{\prime}=N+\Delta N\) cells, where both policies \(\pi\) and \(\pi^{\prime}\) rely on the DQN-MARL capacity sharing solution in [11]. In the source task, a DQN agent iteratively interacts with the RAN environment of \(N\) cells to learn the policy \(\pi\). In each step of this training process, the DQN agent obtains the _state_ of the environment, which is provided by the performance monitoring module. The agent then selects the most appropriate _action_ according to the current policy \(\pi\) available at the DQN agent, and this action is applied to the environment. In the following step, the DQN agent receives a _reward_ from the performance monitoring module. This reward assesses the suitability of the previous action and is used by the DQN agent to update the policy \(\pi\). This process is repeated until a certain termination condition is reached. In the case of the target task, the training process of policy \(\pi^{\prime}\) is sped up by leveraging the previous knowledge gained during the training process of policy \(\pi\). In this case, the knowledge from policy \(\pi\) is first transferred to policy \(\pi^{\prime}\), and then policy \(\pi^{\prime}\) is refined through the interaction of the DQN agent with the new environment consisting of \(N^{\prime}\) cells, as described for the source task. This knowledge transfer occurs according to the state, action, and reward definitions of the RL agents in the source and target tasks, which are formulated in Section 3.1, and follows a specific TRL technique referred to as inter-task mapping, which is described in Section 3.2.  
Figure 1: TRL approach for relearning a new policy after increasing the number of cells.
## 6 Conclusions  
In this study, the AE system incorporated with an improved DIC technology, which can automatically revise the discontinuity error, is utilized to investigate the failure process of jointed rock samples containing a hole in the center. The results help to enhance the understanding of jointed rock's mechanical characteristics and failure behavior. The following conclusions can be drawn.  
(1) At the material level, the jointed rock-like specimens containing holes were fabricated with plaster and defoamer water and then tested under uniaxial compression. The results show that the joint inclination significantly influences the mechanical characteristics and failure behavior of jointed samples containing holes;  
(2) For an optimized measurement of cracks through the DIC, parametric studies are conducted with a suggested criterion to deal with the mesh installment for DIC. The mesh density (spacing) should be controlled within twice the pattern size. The OPFPM algorithm is suggested to reach the required deformation pattern while with a moderate computational load.  
(3) For rocks with different jointed angles, localized deformation emerges after the material comes into the plasticity. Significant localization occurs after the failure with cracks surrounding the center hole and pre-existing joints;  
(4) Results show that perpendicular-jointed condition (0\({}^{\circ}\) joints) is the most dangerous situation for its comparatively lower strength and brittle failure mode with a shift energy release. It is instructive to predict jointed conditions based on AE signals for onsite situations.  
Conceptualization, X.Y.; Investigation, Y.L. and Y.J.; Data curation, Y.J.; Writing--original draft, S.Y.; Writing--review & editing, M.C. All authors have read and agreed to the published version of the manuscript.  
The research was supported by the National Natural Science Foundation of China (52374128), The Natural Science Foundation of the Jiangsu Higher Education Institutions of China (21 KJB580004), the Natural Science Foundation of Jiangsu Province (BK20220234), General Funded Project of China Postdoctoral Science Foundation (2023M733760).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
Authors [PERSON] and [PERSON] were employed by the company Shanxi Coal Transportation and Sales Group. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 5 Simulation Results and Discussion
### Simulation Model  
To evaluate the performance of the proposed algorithms, we run several simulations that compare LEACH, CH-active, and CH-partially-active. We used MATLAB (R2015a) as the simulation tool. The same set of sensor nodes was used to track network behavior and energy preservation in identical conditions. Simulation saves all of the parameters and sensor states in each simulation step in a five-dimensional matrix. Then, the history data, covering the complete lifetime and every parameter of all sensors, are used to generate precise figures of performance. Table 1 shows the simulation parameters.  
We randomly placed 100 nodes in two areas. In both areas, the BS is at (0, \(-\)100). The data packet size is 2000 bytes and the control signal packet size is 100 bytes. We used the energy model described in the \"System model\" section. Each node has an initial energy of 2 J. The percentage of the distance that we used in the CH-partially-active algorithm is 25%.  
Two important metrics that reflect the performance of our algorithms are: (1) the number of active nodes related to network lifespan (rounds), and (2) total network energy.
# Achievable Rate Maximization for Multi-Relay AF Cooperative SWIPT Systems with a Nonlinear EH Model
## 2 System Model
### TS Scheme  
Let \(\gamma^{\text{TS}}=\sum_{l=1}^{L}\gamma^{\text{TS}}_{srl}\). To maximize the achievable information rate \(R^{\text{TS}}\) of the TS scheme, the optimal problem is then formulated as:  
\[\begin{array}{ll}\underset{\alpha}{\max}&R^{\text{TS}}=\frac{1-\alpha}{2} \text{log}_{2}\big{(}1+\gamma^{\text{TS}}\big{)}\\ s.t.&0\leq\alpha\leq 1\end{array}. \tag{25}\]  
It can be proven that the objective function in (25) is concave (see Appendix A for detailed proof) so that the problem (25) is convex. By solving (25) using standard convex tools, e.g., the interior point method [38], the optimal EH time ratio \(\alpha\) for the relays with TS scheme, \(\alpha^{*}\) can be obtained.
###### Abstract  
Recently, in the solar energy society, several key technologies have been reported to meet a grid parity, such as cost-efficient materials, simple processes, and designs. Among them, the assistive plasmonic of metal nanoparticles (MNPs) integrating with the downshifting on luminescent materials attracts much attention. Hereby, Si-based Schottky junction solar cells are fabricated and examined to enhance the performance. CdSe/ZnS quantum dots (QDs) with different gold nanoparticles (Au NPs) sizes were incorporated on a Si light absorbing layer. Due to the light scattering effect from plasmonic resonance, the sole Au NPs layer results in the overall enhancement of Si solar cell's efficiency in the visible spectrum. However, the back-scattering and high reflectance of Au NPs lead to efficiency loss in the UV region. Therefore, the QDs layer acting as a luminescent downshifter is deployed for further efficiency enhancement. The QDs layer absorbs high-energy photons and re-emits lower energy photons in 528 nm of wavelength. Such a downshift layer can enhance the overall efficiency of Si solar cells due to poor intrinsic spectral response in the UV region. The optical properties of Au NPs and CdSe QDs, along with the electrical properties of solar cells in combination with Au/QD layers, are studied in depth. Moreover, the influence of Au NPs size on the solar cell performance has been investigated. Upon decreasing the diameters of Au NPs, the blueshift of absorbance has been observed, cooperating with QDs, which leads to the improvement of the quantum efficiency in the broadband of the solar spectrum.  
plasmonic effect; downshifting; Schottky junction; Si-solar cell; Au NPs; CdSe/ZnS QDs +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### High-Frequency Dielectric Properties of the Composites  
For the substrates applied in the high frequency, the dielectric properties, including the dielectric constant and dielectric loss, are of great concern [40; 41; 42; 43]. Figure S6 shows that the dielectric constant of the composites remains stable across frequencies from 5 to 40 GHz, indicating a highly symmetric internal structure. Meanwhile, the dielectric loss increases with frequency, which is attributed to enhanced ionic conductivity at high frequencies. As shown in Figure 5, the dielectric constant of the composites decreases with the increase in FGi content, which is due to the decrease in crystallinity of the PTFE matrix [36; 44]. The dielectric loss of the composites first decreases and reaches a minimum of 0.0021 at PTFE-0.5, and then increases. The decrease in dielectric loss is initially caused by the reduced crystallinity of the composites. However, in PTFE-0.75, the aggregation of FGi sheets forms pores within the composite, which results in an increase in dielectric loss (Figure S7). The PTFE-0.5 exhibits a lower dielectric constant and dielectric loss (2.06, 0.0021@40 GHz) than pure PTFE (2.07, 0.0029@40 GHz), demonstrating its excellent dielectric properties.  
In general, due to the low contents of FGi and its effects on PTFE molecular orientation and crystallinity, the PTFE-based composites retain excellent dielectric properties. These comprehensive properties significantly surpass those reported in previous studies (as shown in Tables 1 and S1), rendering the material highly suitable for applications in high-power, high-frequency, and high-speed electronic systems.  
Figure 4: Stress–strain curves (**a**), tensile stress and modulus (**b**), breaking elongation and toughness (**c**) of FGi/PTFE composites.
# Practical Performance Analysis of Interference in DSS System
## 5 Field Test and Performance Analysis  
### Test Environment  
The data to be analyzed in this chapter are processed from field measurements with large sample sizes. The test analysis introduced in this chapter is mainly to test the DSS cell that is interfered by neighbor cells.  
This performance test was conducted in a commercial LTE/NR network. The test area includes residential buildings, small commercial streets, and schools, which are typical test scenarios. The test area is a multi-cell networking scenario with small station spacing and coverage overlap between the test cell and neighbor cells. We respectively verified the effect DSS neighbor cells have on LTE and NR of DSS test cells for no-load and 50% loading cases.  
The modulation in this test is 256 QAM, the antenna height is 35 m, the bandwidth is 40 MHz, the subcarrier interval is 15 kHz, and the frame structure is in FDD mode.  
The test environment is shown in Figures 12 and 13.  
The distances of the six test points from the test cell are shown in Table 1.  
The on and off of the test base station are shown in Table 2.  
The specific data analysis of the test is performed below.
# EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms
## 2 Materials and Methods  
Figure 1 shows the system architecture for implementing the proposed method. The PPG signal is recorded using a wrist device and then undergoes the preprocessing steps. First, the data were segmented and filtered. Afterwards, a number of PPG waveform-based features were extracted and the EMD was also performed to obtain intrinsic mode functions (IMF). Both PPG -waveform-based and IMFs-based features were provided as input to the regression algorithm, and after feature selection, 50 features were selected to estimate the blood glucose levels. Results were evaluated on 15% test sample data and validated using reference glucometer data.
# Honey-Based Medicinal Formulations: A Critical Review
## 3 Honey-Based Medicinal Formulations
### Honey-Based Dressings
Medihoney(tm), Hydrogel Colloidal Sheets(tm), Manuka Foam HC(tm), Manuka Fill(tm), and Manuka IG(tm) are indicated for minor wounds (abrasion, lacerations, cuts, scalds, burns) when supplied as OTC products, but they can also be used for serious wounds (leg ulcers, pressure ulcers, first- and second-degree burns, diabetic foot ulcers, surgical wounds, and trauma wounds) under medical supervision. MelMax(tm) and MelDra(tm) dressings are produced by Dermagenics in the USA. They contain buckwheat (_Clifonia monophylla_) honey, which has been found to be rich in phenolic constituents. These constituents have been revealed to be responsible for higher anti-oxidant activity and modulation of pH, triggering a cascade of regulatory processes which bring contaminated and chronic wounds back to suitable stages of healing [40; 41]. MelMax(tm) and MelDra(tm) dressings containing buckwheat honey are indicated for acute (burns, postoperative, and trauma) and chronic (ulcus curris, diabetic, and pressure ulcers) wounds. Honey can also be formulated into a dressing for breastfeeding mothers. Manuka Health manufactures Breast Pads that are hydrogel pads impregnated with sterile medical-grade MGO(tm) 400+ Manuka honey on a flexible cloth backing. These pads are designed to absorb milk leakage and, at the same time, provide a cooling and comforting feeling on contact. Major advantages of this formulation are claimed to be the pliability of the gel which provides comfortable contact with the breast skin, as well as its flexible cloth backing. In addition to commercial honey-based dressing, information is available on in vitro and in vivo studies involving some noncommercial honey-containing dressings, for example, an unusual one containing Malaysia Gelam honey. A study conducted by [PERSON] et al. found that the application of this honey hydrogel dressing significantly enhanced (\(p<0.05\)) wound closure and accelerated the rate of re-epithelialization in Sprague-Dawley rats as compared to a control hydrogel and OpSite film dressing [32]. The dressings consisted of Gelam honey (6%, 8%, 10%, 15%) mixed with 15% polyvinyl pyrrolidone (PVP) (Kollidon 90), 1% protein-free agar solution, and 1% polyethylene glycol (PEG).
## 1 Introduction  
Gathering knowledge directly from data (data-driven knowledge) using intelligent data analyses has garnered increasing interest, especially in medical domains, due to their complexity and the large amounts of data available [1]. The growth of observational data, due to the widespread use of electronic health records (EHRs), generates massive amount of medical data storage. Furthermore, extracting hidden usable knowledge of patients' pathological conditions or diagnoses is essential to supporting the process of clinical decision making and knowledge related to patients' pathological conditions is also critical for research in the medical domain [2; 3; 4; 5; 6; 7; 8]. Association rule mining (ARM) is one of the common research objectives that generally generates a pattern of disease based on patients' pathological conditions that are associated by identifying frequent co-occuring item sets from medical data records, depending on the predefined threshold. It describes logical relations with probabilities in the form of an if... then... rule, which is also known as production rules (PRs).  
In EHR, each patient is represented by a set of pathological conditions which leads to a wide variation of data in medical dataset because most pathological conditions are usually presented in continuous values. However, physicians prefer discrete terms rather than precise values to find knowledge more appropriate by means of rules and easier to be interpreted. Consequently, due to this discrete form, it is very common to find redundancies in a medical dataset, such as many patients having the same attribute values, which further contributes to the frequency of co-occurrence of the corresponding attribute values in ARM (illustration given in Figure 1). In addition, one might also find inconsistency data when the same set of attributes' values from one or more data records, e.g., R2 and R4: {age:middle aged, sbp:prehypertension, bmi:acceptable}, belong to different class labels (e.g., mri:normal or mri:abnormal). This kind of data records might require further investigation to ensure which class label is more appropriate to belong to those pathological conditions. Those kind of redundancies may have a negative impact on the quality of clinical documentation and decision making [9; 10].