esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Design and Control of MMC STATCOM for Improving Power Quality Indicators
## 6 Proposed Optimization Methods
### Atom Search Optimization (ASO)
\begin{array}{ll}a_{i}^{d}(t)&=\frac{F_{i}^{d}(t)}{m_{i}^{d}(t)}+\frac{G_{i} ^{d}(t)}{m_{i}^{d}(t)}\\ &=\alpha\Big{(}1-\frac{t-1}{2}\Big{)}^{3}e^{-\frac{2\alpha}{T}}\\ &\times\sum\limits_{j\in\mathcal{K}best}\frac{\text{mmd}_{j}\big{[}2\big{(}h_{ ij}(t)\big{)}^{-13}-\big{(}h_{ij}(t)\big{)}^{-2}\big{]}}{m_{i}(t)}\\ &\cdot\frac{\big{(}x_{ij}^{d}(t)-x_{i}^{d}(t)\big{)}}{\|\overrightarrow{x_{i} }(t),\overrightarrow{x_{j}}(t)\|_{2}}+\beta e^{-\frac{2\alpha}{T}}\frac{(x_{ \text{hard}}^{d}(t)-x_{i}^{d}(t))}{m_{i}(t)}\end{array}  \tag{48}\]  
where \(m_{i}^{d}(t)\) denotes the mass of the atom number \(i\) in the dimension \(d\) of iteration \(t\), and is determined from the value of its fitness function as described below:  
\[M_{i}(t)=e^{-\frac{F_{i}(t)-F_{\text{hard}}(t)}{F_{\text{hard}}(t)-F_{\text{ hard}}(t)}} \tag{49}\]  
\[m_{i}(t)=\frac{M_{i}(t)}{\sum_{j=1}^{N}M_{j}(t)} \tag{50}\]  
where  
\[Fit_{best}(t)=\min\limits_{i\in[1,2,\ldots,N]}Fit_{i}(t) \tag{51}\]  
\[Fit_{\text{worst}}(t)=\max\limits_{i\in[1,2,\ldots,N]}Fit_{i}(t) \tag{52}\]  
Finally, the velocity of the \(i\)th atom in the search space and the new position acquired at the next iteration (\(t\)+1) can be described by the following formulas:  
\[v_{i}^{d}(t+1)=rand_{i}^{d}v_{i}^{d}(t)+a_{i}^{d}(t) \tag{53}\]  
\[x_{i}^{d}(t+1)=x_{i}^{d}(t)+v_{i}^{d}(t+1) \tag{54}\]  
For a deeper explanation of the atom search optimization technique, the procedure is discussed in detail in [34; 35].
## 2 Problem Statement  
In implementing DMPC methodologies, local decisions must follow a global control objective to maintain the performance and stability of the system. In this paper, we propose reinforcement learning (RL) in the upper-level negotiation layer of a DMPC control system in search of consensus to achieve global decisions. It requires an upper-level agent negotiator to provide a final solution for each DMPC agent of the lower level in which several control actions are available.  
A PG-DMPC (policy gradient-DMPC) algorithm is proposed to carry out negotiation. A deep neural network trained by the policy gradient [29] algorithm has as inputs local decisions of a multi-agent FL-DMPC (fuzzy logic DMPC) [30]. Hence, it provides negotiation coefficients applied to the consensus of the global decisions through its pairwise local decisions to achieve the global goal and stability requirements of the FL-DMPC multi-agent control in the following control architecture (Figure 1).  
At each time instant \(t\), a local decision is the control sequence defined as follows:  
\[U(t)=\big{[}u(t),\ldots,u(t+1),\ldots,u\big{(}t+H_{p}-1\big{)}\big{]}^{\intercal},\]  
where \(U(t)\) is the instant control action vector, and \(H_{p}\) is the prediction horizon of the local MPC controllers in the lower layer. The agent \(i\) of the MPC with \(i\in\{1,2,\ldots,N\}\) of \(N\) agents have local decisions \(U_{i}=\bigg{[}U_{i}^{f_{1}}(t),U_{i}^{f_{2}}(t),\ldots,U_{i}^{f_{M_{i}}(t)} \bigg{]}\) of \(M\) pairwise negotiations in the FL-DMPC lower layer. To achieve a global decision \(U_{i}^{f}\), it is proposed to agree as follows:  
\[U_{i}^{f}(t)=\sum_{m=1}^{M_{i}}U_{i}^{f_{m}}(t)\cdot\alpha_{i}^{m}. \tag{1}\]  
where \(\alpha_{i}^{m}\) is the output of the deep neural network considered, like the negotiation coefficients weighing local decisions \(U_{i}^{f_{M_{i}}}\), to achieve the control objective.
## 2 RIS-Assisted System and Channel Model
### Design of Precoder  
Dirty paper coding (DPC), successive interference cancellation (SIC), maximum ratio transmission (MRT), zero-forcing (ZF), and minimal mean squared error (MMSE) are examples of linear and non-linear precoders that can be taken into consideration in a MIMO system. Although non-linear precoders exhibit the best performance, implementation is impractical because of the enormous computational complexity. Liner precoders can be implemented in the MIMO system due to the low complexity with sub-optimal performance. In multi-user systems, the linear precoder of ZF performs IUI removal more efficiently than the linear precoder of MRT. Additionally, the ZF precoder performs similarly to the MMSE precoder when the noise value is taken into account [50]. Hence, ZF precoder \(\mathbf{Z}=[\mathbf{z}_{1},...,\mathbf{z}_{l}]\in\mathbb{C}^{M\times l}\) is implemented in this study. For total matrix channel \(\mathbf{H}_{all}=[\mathbf{H}_{1},...,\mathbf{H}_{l}]^{T}\in\mathbb{C}^{l\times M}\), the precoder \(Z\) can be written as follows:  
\[\mathbf{Z}=\overline{\mathbf{Z}}\sqrt{\mathbf{P}}, \tag{11}\]  
where the ZF precoder matrix stands for \(\overline{\mathbf{Z}}\), and the power allocation matrix is represented by \(\mathbf{P}=\text{diag}(\delta_{i},...,\delta_{l})\). The \(i\)-th UE's transmit power at the BS is represented by the \(\delta_{i}\) symbol.
## 2 Fifth-Generation Telecommunications Technology and Architecture
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Generation** & **Security Concerns and Measures** & **Impact on Security** & **Key Technological Advances** \\ \hline \multirow{2}{*}{1G} & Limited security priorities; primarily focused on basic voice communication. & Minimal impact; minimal data vulnerabilities & - Analog voice calls \\ \hline \multirow{2}{*}{2G} & Digital data vulnerability; introduced encryption methods like A5/1 & Increased security with encryption; vulnerabilities like A5/1 attacks & - A5/1 encryption \\ \hline \multirow{2}{*}{3G} & Broadened threat landscape; robust encryption and authentication required & Improved security with robust encryption; counteracted malware and phishing & - IP-based services - Robust encryption - User-network authentication \\ \hline \multirow{2}{*}{4G} & Increased vulnerabilities; advanced security measures adopted & Enhanced security with advanced encryption; focus on all-IP networks & - Advanced encryption - Secure IP-based protocols - All-IP architecture \\ \hline \multirow{2}{*}{5G} & New security challenges; dynamic security solutions needed; ML and DL adoption & Heightened security challenges; dynamic security solutions; ML and DL usage & - Network Function Virtualization - Software-Defined Networking - Mobile edge computing - Flexible architecture - Spectrum flexibility \\ \hline \multirow{2}{*}{6G} & Potential security challenges; reliance on AI; terahertz frequencies & Expected security enhancements with AI; potential risks with AI manipulation & - Test-network integration - Quantum encryption - Collaborative AI-driven defenses \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evolution of mobile network security concerns and key technological for each generation [16; 17]. Table 1 seeks to delineate this evolution by mapping each generation of mobile networks to its associated security concerns, the impact these concerns have on security, and the key technological advances introduced in each era. As the table elucidates, security measures have evolved from minimal concerns in 1G, primarily focused on voice communications, to multifaceted strategies in 5G that leverage ML and DL techniques for dynamic security solutions. The anticipated 6G technology is poised to bring its own set of challenges and solutions, potentially hinging on artificial intelligence (AI) and quantum encryption. The 5G security challenges are shown in Figure 1:
## 3 Results and Discussion
### Impact of TMAdaOH/Si  
By optimizing the TMAdaOH/Si ratio, the crystallinity (ordered framework), membrane layer thickness, and intergrowth crystals can be improved to achieve a membrane with higher performance and lower defect density [21]. To investigate, different TMAdaOH/Si ratios were used as listed in Table S6. After calcination, membranes with TMAdaOH/Si ratios of 0.1, 0.15, 0.2, and 0.5 were selected for gas permeation measurement. As displayed in Figure 2g, with increasing TMAdaOH ratio, the permeance of gas molecules increased (especially for large molecules e.g., SF\({}_{6}\) and CH\({}_{4}\)), and the CO\({}_{2}\)/CH\({}_{4}\) permselectivity reduced from 104 to 4. In general, the crystal size increases with increasing TMAdaOH content. However, a high TMAdaOH/Si ratio in the gel composition is associated with increased defect formation during the template removal step. Furthermore, the increase in alkalinity caused by OSDA can lead to the dissolution of zeolite layers and afterwards reduce crystallite intergrowth [6]. The literature on SSZ-13 membranes commonly reports high TMAdOH/Si ratios, ranging from 0.19 [29], 0.2 [20; 35; 48], 0.5 [38; 39], and finally 0.6 [21; 49]. In contrast, this study achieved high CO\({}_{2}\)/CH\({}_{4}\) permselectivity with reduced SF\({}_{6}\) permeance using a significantly lower TMAdOH/Si ratio of \(\sim\)0.1 (minimizing OSDA consumption and associated costs while mitigating environmental impact).
# Combination Pattern Method Using Deep Learning for Pill Classification
## 3 The Proposed Pill-Classification System
### Feature Detection  
In the proposed MCPL-based pill-classification system, feature detection plays a crucial role in enhancing the accuracy and reliability of pill identification. The feature-detection process is designed to capture and analyze the overt and subtle characteristics of pills that are significant for their classification.  
First, we detect color and texture as specific features of the pill. These are essential for distinguishing pills with similar shapes but different therapeutic uses. The system identifies subtle variations in color gradients and texture patterns characteristic of different pill coatings and materials. We also detect the shape and size of the pill, as contour analysis techniques detect the geometric features of pills, including round, oval, rectangular, and other irregular shapes. Size measurements help differentiate between dosage forms.  
For feature-extraction techniques, we first perform initial image processing, then utilize advanced algorithms for feature detection, and finally apply deep learning models. High-resolution images are pre-processed using techniques such as denoising, normalization, and color correction to enhance the quality of the input data for feature extraction. Next, the proposed method employs a combination of edge detection, corner detection, and blob-detection algorithms to identify and outline critical features. Algorithms like the Shi-Tomasi and Harris corner detectors identify key points on the pill images that are invariant to rotation and scale. CNNs are used for more complex feature detection, such as identifying textural patterns and classifying pills based on learned image features from extensive datasets. This approach enhances the pill-classification system's ability to handle variability in pill appearances and ensures high identification accuracy.
## 3 Results
### Chl Concentration and Landsat 8 VIs  
One Landsat 8 image acquired in January 2014 was used to derive broad band VIs for comparison with CC at the ESU level. Using the coordinates recorded in the field, each ESU plot was located on the Landsat 8 image. The average CC per ESU was plotted against its corresponding pixel on the Landsat 8 NDVI green and the coefficient of determination was computed. The correlation analyses demonstrated that Landsat 8 NDVI green is the broad band VI most sensitive to CC at the ESU level (\(R^{2}\) = 0.805), (Figure 7). The linear model that produced this large correlation, described by Equation 1, was used to construct a Chl map.  
\[\mathrm{y=} - 54.545+149.396x \tag{1}\]  
where \(x\) = pixel value of the Landsat 8 NDVI green image.  
Figure 6: Correlations between VIs and CC (\(\mathrm{\mu g\cdot cm^{-2}}\)) at ESU level.  
Figure 7: Correlations between Landsat 8 VIs and CC at ESU level.
# Full-Scale Measurements of Wind Characteristics on a High- Rise Building during Typhoon Sarika
## 3 Wind Characteristics of Typhoon Sarika  
Firstly, in order to prevent high-frequency signal interference in the process of typhoon wind field data acquisition, it is necessary to carry out band-pass filtering on the measured wind field data before analyzing the data. Because the frequency domain distribution of wind power spectrum is concentrated in the range of 0-12 Hz, the band-pass filtering range used in this paper is 0-12 Hz. Secondly, the measured process is often accompanied by power frequency, frequency doubling, and irregular random interference noise signals, resulting in obvious burr points in the measured data. A large peak data occasionally appears in the time history data. If it is not removed in time, it will have a great impact on the mean value and mean square deviation of the data. Therefore, after band-pass filtering the measured wind field data, the data with burr points are interpolated and replaced by the five-point cubic method to eliminate the error caused by burr points.
## 5 Recent Developments in RF Energy Harvesting  
Recent advancements in RFEH have focused on improving efficiency, scalability, and adaptability in diverse applications. The field has witnessed significant breakthroughs in rectenna design, impedance matching circuits, metamaterial integration, AI-based optimization, and SWIPT systems. These developments have driven the expansion of RF energy-harvesting technology for applications such as the IoT, smart cities, and wireless sensor networks. These developments are particularly crucial in addressing the limitations of traditional passive and active systems, as discussed in Section 4.3. For instance, AI-optimized rectenna designs can dynamically adjust to weak ambient RF environments, enhancing performance without relying entirely on external power sources.
## 3 Results
### MnAl-C/PLA Composites  
Hysteresis loops of three MnAl-C0/PLA composites in Figure 8 differ only slightly, consistent with the comparable magnetic properties of MnAl-C0 powders annealed at 450, 500, and 550 \({}^{\circ}\)C. Both magnetization and coercivity inevitably deteriorate as the powders are consolidated in the PLA matrix. The remanent magnetizations are 3.9-4.3 emu/g in MnAl-C0 powders, reduced to 2.3-2.6 emu/g in MnAl-C0/PLA composites because of the increase in nonmagnetic PLA mass. Interestingly, the coercivity is only reduced from 835.5 to 814.1 Oe in the case of powder annealed at 450 \({}^{\circ}\)C. The coercivity of magnetic polymer composites is reduced by the interactions of magnetic clusters in the polymer matrix, in the case of higher loadings or hard magnetic powders [29]. In addition to distribution of magnetic clusters, the experiment demonstrated varying values of coercivity when cobalt ferrite powders were incorporated into different polymers by different methods with varying mechanical stresses [30].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Photograph** & **SEM Micrograph** & **Elemental Composition Averaged from EDS Spectra** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between magnetic and nonmagnetic powders separated by magnetic field. MnAl–C0 was annealed at 550 \({}^{\circ}\)C for 20 min under argon atmosphere.  
The coercivity around a half of the value previously reported in MnAl-C/PE [5], and the modest magnetization, would rather be raised in the MnAl-C powder preparation. By doping MnAl with 2%-3% C, the substantial coercivity could be obtained as a result of domain wall pinning by the AlMn\({}_{3}\)C phase. The subsequent annealing is still useful for increasing magnetizations, as shown in Figures 4 and 5. The magnetizations of MnAl-C2 and MnAl-C3 samples are maximized after the annealing at the highest temperature of 550 \({}^{\circ}\)C.
## 2 Materials and Methods  
### Literature Search Strategy  
The aim of the literature review was the evaluation of the effectiveness of virtual reality rehabilitation among elderly patients in the prevention of fall risk. The methodology was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and conducted following the PRISMA checklist [22]. A literature review was carried out in six databases: Medline, PubMed, Scopus, Embase, Web of Sciences, and PEDro.  
The tool used to create the research objective and search strategy was PICOT(T) [23].  
* Population--healthy elderly
* Intervention--virtual reality, exergames rehabilitation
* Comparison--any intervention (standard rehabilitation, physical exercises, daily living activities) or no intervention
* Outcomes--fall risk and prevention, body balance, walking, functional improvement, pain, cognition, quality of life
* Time--last 5 years (January 2015-December 2020)
* (Type of study)--randomized controlled trials  
The primary search keywords were \"elderly\", \"virtual reality\", \"exergames\" \"rehabilitation\", \"falls\", and their synonyms. Keywords were combined using the Boolean operators \"AND\" and \"OR\". The search strategy was flexible and adapted to the database search engines. For the PubMed, we used the following search scheme: (((virtual reality OR augmented reality OR video games OR game * OR computer games OR wi OR exergames OR vr))) AND (((rehabilitation OR exercise * OR physical therapy OR physiotherapy OR physical activity OR activity OR training))) AND ((elderly OR aged OR older OR elder OR geriatric OR elderly people OR old people OR senior))) AND (((fall prevention OR preventing falls OR prevent falls OR falls OR falling))) AND (Randomized Controlled Trial[ptyp]). Each search strategy was checked by two authors.
## 3 Results and Discussion
### Case Study A: Hybridization with an ICE (Internal Combustion Engine) Fed with External Fuel
On the other hand, the heat capacity of the stream decreases when higher glycerol concentrations are fed, from 3.70 kJ/kg.K at 10 wt.% glycerol to 2.79 kJ/kg.K at 30 wt.% glycerol. This trend is because the effect of heat capacity variation is predominant over the reactor outlet temperature change. Regarding the syngas stream, operating under the conditions described previously, the H\({}_{2}\), CH\({}_{4}\), and CO flowrates produced are the same as for the base scenario in the operating temperature range considered, established by the exhaust gases temperature. With these results, the total electrical power generated is the sum of electricity obtained directly from the ICE and the electricity generated in the cycle using the thermal power of the hot effluent leaving the reactor. To evaluate the electrical efficiency, three equations are proposed to show different points of view about products obtained and resources consumed:  
* Total electrical Power = Electricity from ICE + Electricity from the reactor hot effluent
* Electrical Efficiency 1 = \(\frac{\text{Total electrical Power}}{\text{Fuel thermal power}}\)
* Electrical Efficiency 2 = \(\frac{\text{Total electrical Power}}{\text{Glycerol thermal power + Fuel thermal power}}\)
* Electrical Efficiency 3 = \(\frac{\text{Total electrical Power + Electrical power generation from syngas}}{\text{Glycerol thermal power + Fuel thermal power}}\). The thermal powers of glycerol and natural gas (fuels) are calculated assuming lower heating values of 16 MJ/kg and 47.5 MJ/kg, respectively. To calculate the electrical power generation from the syngas stream produced in the plant, it has been considered the product of the H\({}_{2}\), CH\({}_{4}\), and CO flowrates and each of the corresponding lower heating values (120.0, 50.0, and 10.1 MJ/kg, respectively) and applying an efficiency of 40% [55]. Taking into account the results shown in Figure 10, the plant with an ICE could produce a total electrical power in the range from 1612 to 2381 kW with a variable electrical efficiency shown in Table 4, which includes the main results obtained as a summary for the case where the reactor inlet temperature is 530 \({}^{\circ}\)C.  
Figure 10: (**a**) Electricity generation in the ICE and (**b**) electricity generation from the hot effluent leaving the reactor versus the inlet reactor temperature in the range from 450 to530 \({}^{\circ}\)C. Both figures are parametrized as functions of the glycerol mass concentration in the feed stream.
## 2 Methods
### Calculating the Inner Products of the Unit Normal Vector and the Ray Vector for Each Face Element  
The inner product of the unit normal vector for each face element and the ray vector was calculated for all restricted surface elements. This process yielded the following two critical details:  
(1) Calculation of incident light intensity for each element  
Due to the cosine characteristic of incident light intensity, when the incident light intensity from a vertical direction is set to one, the intensity of light striking at angle \(\theta\) is represented by \(cos(\theta)\). Based on this principle, the inner product of the unit normal vector and the unit ray vector can be calculated for each plane element, resulting in a value of \(cos(\theta)\). This value indicates the rate of incident light intensity. By calculating the inner product for all planes, we determined the incident light intensity rate for each plane. Furthermore, by summing the values for all planes, we calculated the overall incident light intensity rate for the entire study area.  
(2) Shadow surface processing  
Calculating the inner products is also beneficial for managing self-shadowing areas and shadow surfaces. As illustrated in Figure 7, a positive cosine value indicates an acute angle, which implies that light can reach a particular surface element. In contrast, a negative value indicates an obtuse angle, suggesting that light does not reach it. However, implicit surfaces can produce positive inner products for complex surfaces, even when light is blocked. Figure 8 shows an example of this phenomenon and describes the method used to process hidden surfaces.  
The face element was assumed to extend infinitely to determine the inner points, with the intersection point \"a\" representing the location where the face and ray vector intersect. The outer product is calculated between the vector connecting two adjacent vertices and the vector connecting the starting vertex to point \"a\". This process is repeated for all  
Figure 7: Calculation of cosine loss on curved surface elements.  
vertices. If the result of the outer product matches either the positive or negative of the unit normal vector, point \"a\" is considered to be internal; if none of the results match, it is considered external.  
This method employed the self-shadow calculation algorithm referenced by [PERSON] et al. [21]. In their study, the calculation involved transforming the surface into UV coordinates, whereas in this study, we performed the calculation using Cartesian coordinates. However, the fundamental principles remained identical.