esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Analysis and Results
### The Compensation Target Is Inductive Reactive Power Priority Compensation  
Due to the hysteresis of the inductive current, a certain phase difference occurs with the voltage, so the power factor cannot be 1. When the compensation target is the inductive reactive current, \(w_{r}\) is 0.886 due to the limitation of the peak current of the compensation device. At the same time, \(w_{n}\) and \(w_{z}\) are both zero, and the inductive reactive compensation current is \(i^{*}_{Acc}=w_{r}i_{r}\). In order to make the analysis simple and clear, only the voltage and current in phase \(A\) were selected for the inductive reactive power analysis. The reactive power compensation reference current waveform \(i^{*}_{Acc}\) of phase \(A\) is shown in Figure 4. The phase \(A\) current \(i_{A}\) waveform of the power supply side before compensation and the phase \(A\) current \(i^{\prime}_{A}\) waveform after compensation are shown in Figures 5 and 6, respectively. It can be seen from the diagram that after inductive reactive power compensation, the waveform phase difference of the voltage and current is significantly reduced compared with that before compensation, making the power factor close to 1. If the capacity of the compensation device is large enough, the inductive reactive component can be completely compensated for. At this point, the power factor will be 1. The sum of squares of the amplitudes of all reactive current components after compensation is 1083.65 A\({}^{2}\), which is 92.06% lower than that before compensation. That is, the line loss content is reduced by 5% compared with before compensation.  
\begin{table}
\begin{tabular}{c c} \hline
**Current Component** & **Amplitude (A)** \\ \hline \(l_{\text{$a$max}}\) & 203.56 \\ \(I_{\text{$r$max}}\) & 112.87 \\ \(I_{\text{$n$max}}\) & 21.42 \\ \(I_{\text{$z$max}}\) & 21.42 \\ \hline \end{tabular}
\end{table}
Table 3: The amplitude values of different current components.  
Figure 3: The three-phase current waveform of the power supply side before compensation.
## 3 Demand-Response Aggregator Frameworks in Future US Electricity Markets
### Prosumer Privacy in the Prosumer-Aggregator Game  
When it comes to the prosumer DR aggregator game frameworks available in the literature, not much has been carried out to provide such levels of privacy to prosumers. Specifically, in [235], prosumers only share local optimal solutions and their power flexibility in fulfilling the constraints of the proposed global optimization program for the ancillary service market. In other approaches of the day-ahead energy market, like [73; 74; 209], only optimal solutions from the prosumers are shared with the DR aggregator, which prevents the sharing of objectives, constraints (device models from HEMS/BEMS), preferences and settings, forecasts of demand or generation (including limits), etc., collected from sensors [117] or forecasted with data science methods [236] in accordance with the DR aggregator, protecting prosumer privacy. Another work such as Ref. [237] provides a two-stage framework for day-ahead and real-time market bidding, where only the prosumers' schedules are shared with the aggregator, and the problem is solved distributively with the alternating direction method of multipliers (ADMM), while they exchange both their optimal solutions and their power flexibility. ADMM methods can be slow in convergence and require strong assumptions for primal variables to converge to an optimal solution, while the decomposability of the objective is necessary [238]. Since prosumer privacy has not been broadly studied with DR aggregator frameworks, privacy will become one of the open research questions in Section 3.7, and these frameworks are also necessary for future US electricity markets. Last but not least, the authors in [239] proposed a distributed, prosumer privacy-preserving algorithm to solve an MILP for a manual frequency restoration reserve (mFRR) service, allowing the aggregators and prosumers to participate in the balancing market. Please note that although prosumer blockchain privacy frameworks exist, these community coordination schemes follow a different logic [240; 241; 242].
## 3 Scenario and System Model
### Problem Formulation  
In the indoor positioning scenario, we represent the UE set as \(X=\{x_{n}\}_{n=1,\ldots,N}\). The real global coordinate of UE \(n\) in the indoor area is denoted as \(P_{true}^{i}=[P_{h}^{n},P_{v}^{n}]\), where \(P_{h}^{n}\), \(P_{v}^{n}\) represent the horizontal coordinate and vertical coordinate respectively. Assuming that the coordinate of each UE predicted by the neural network \(f(\cdot)\) is valid, the problem can be restated as finding a way to utilize CIR information, the neural network \(f(\cdot)\), and the true position \(P_{true}\) of each UE to approximate the global coordinate.  
Let the predicted coordinate for UE \(n\) with the position error be denoted as \(f(\theta,x_{n})\), where \(\theta\) represents the weights of the neural network. The position error is defined as \(|f(\theta,x_{n})-P_{true}^{n}|\). Our optimization objective for a set of \(N\) training samples is to minimize the Mean Squared Error (MSE) value between the predictions and true labels, which can be expressed as:  
\[min\frac{1}{N}\sum_{n=1}^{N}(f(\theta,x_{n})-P_{true}^{n})^{2}. \tag{5}\]
## 3 Intelligent Tuning Based On DDPG
### Formulations
The reward function consists of three parts, which take into account the process of tuning target and human experience. The main algorithm is concluded in Algorithm 1. For details of the DDPG algorithm, readers can refer to [26]. ```
1:Randomly initialize critic network and actor network with weights \(Q\big{(}s,a|\theta^{Q}\big{)}\) and \(\mu(s|\theta^{\mu})\). 2:Initialize the target network \(Q^{\prime}\) and \(\mu^{\prime}\) with the same weights as above. 3:Initialize the replay buffer \(D\). 4:Randomly initialize inserted screw positions. 5:for episode = 1,\(M\)do
6: Read S-parameters from the VNA and formulate the initial state \(s_{1}\). 7:for step = 1, \(T\)do
8: Select action \(a_{t}\) according to the current policy and exploration noise. 9: Tune corresponding screws to execute action \(a_{t}\) and compute reward \(r_{t}\), and observe new S-parameters to formulate new state \(s_{t+1}\). 10: Store the transition \((s_{t},a_{t},r_{t},s_{t+1})\) in \(D\). 11: Sample a random minibatch of \(N\) transitions \((s_{i},a_{t},r_{i},s_{i+1})\) from \(D\). 12: Set \(y_{i}=r_{i}+\gamma Q^{\prime}\big{(}s_{i+1},\mu^{\prime}\big{(}s_{i+1}|\theta^ {\mu^{\prime}}\big{)}\big{|}\theta^{Q^{\prime}}\big{)}\). 13: Update the critic network by minimizing the loss: \(L=\frac{1}{N}\sum\limits_{i=1}^{N}\big{(}y_{i}-Q\big{(}s_{i},a_{i}|\theta^{Q} \big{)}\big{)}^{2}\). 14: Update the actor network using the sampled policy gradient: \(\
abla_{\theta^{\mu}}J\approx\frac{1}{N}\sum\limits_{i=1}^{N}\
abla_{a}Q\big{(} s,a|\theta^{Q}\big{)}\big{|}_{s=s_{i},\mu=\mu(s_{i})}\
abla_{\theta^{\mu}}\mu(s| \theta^{\mu})\big{|}_{s_{i}}\). 15: Update the target networks: \(\theta^{Q^{\prime}}\gets\tau\theta^{Q}+(1-\tau)\theta^{Q^{\prime}}\) \(\theta^{\mu^{\prime}}\leftarrow\tau\theta^{\mu}+(1-\tau)\theta^{\mu^{\prime}}\)
16:endfor
17:endfor
```  
**Algorithm 1** Intelligent Tuning Based on DDPG
## 4 Imaging Algorithm for Mbf-Sar  
In the aforementioned derivation, Equation (16) represents the phase of the 2-D frequency spectrum after LRWC. Then, the residual RCM, SRC, and high-order coupling terms are compensated in 2-D frequency domain, the function can be expressed as:  
\[H_{2}(f_{\rm r},f_{\rm a})=\exp\Big{[}-j\Big{(}\psi_{1}f_{\rm r}+\psi_{2}f_{ \rm r}^{2}+\psi_{3}f_{\rm r}^{3}+\psi_{4}f_{\rm r}^{4}\Big{)}\Big{]} \tag{22}\]Transforming the result by range IFFT, the azimuth compression is completed by multiplying the function given as:  
\[H_{3}(t_{\mathrm{r}},f_{\mathrm{a}})=\exp(-j\psi_{0}) \tag{23}\]  
Finally, the 2-D focused SAR image can be obtained after the azimuth IFFT operation, which can be written as:  
\[s_{\mathrm{r5}}(t_{\mathrm{r}},t_{\mathrm{a}})=\mathrm{sinc}\bigg{[}B_{\mathrm{ r}}\bigg{(}t_{\mathrm{r}}-\frac{R_{\mathrm{new}}}{c}\bigg{)}\bigg{]}\mathrm{sinc} \bigg{[}B_{\mathrm{a}}\bigg{(}t_{\mathrm{a}}-\frac{y_{\mathrm{new}}}{v_{\mathrm{ x}}}\bigg{)}\bigg{]} \tag{24}\]  
where \(R_{\mathrm{new}}=R_{\mathrm{t}}+R_{\mathrm{r0}}+\frac{1}{2}\Big{(}A_{1}k_{1}^{2 }-\frac{2}{3}A_{2}k_{1}^{3}+\frac{1}{2}A_{3}k_{1}^{4}\Big{)}\) and \(y_{\mathrm{new}}=A_{1}k_{1}-A_{2}k_{1}^{2}+A_{3}k_{1}^{3}\).  
The compensation function sets the center point as the reference target point with an acceptable error, which will be discussed in the next section.  
To summarize the operations above, the block scheme of the proposed BFL-SAR imaging algorithm is shown in Figure 2.
## 1 Introduction
For the purpose of this work, the Bohme disc method that complies with European Standard EN 13892-3 [15] was used to determine the depth of wear. The samples used were mortars containing fly ash, granite powder, and their combinations as partial substitutes for cement, aiming for reducing cement content, together with carbon footprint [16]. According to the literature, abrasion resistance of concrete with fly and/or granite powder addition has already been examined by some researchers in cases as follows the influence on abrasion resistance such as replacing 50 and 70% of the cement with fly ash [17; 18], replacing up to 100% of the cement with granite waste in self-compacting concrete [19], using waste granite as a coarse aggregate [20], and a combination of using granite as a coarse aggregate and replacing 10% of the cement with fly ash [21]. Unfortunately, because of the fact that granite powder rarely increases the compressive strength of concrete, the granite added to the concrete is more often a coarse aggregate than the replacing cement eco-admixture. Additionally, investigating things other than compressive strength, the mechanical properties of the cementitious composite modified with granite powder is not the point of interest of other researchers. Another important fact is that the current methods of determining abrasion resistance share similar disadvantages, including damaging sample surfaces and time-consuming testing. The possible transformation of some tests into prediction methods is performed and, therefore, optimization can be achieved by artificial intelligence (AI). Due to the increasing value of using AI to solve various problems in civil engineering, algorithms of artificial neural networks became very popular for predicting the mechanical properties of cementitious composites with eco-friendly admixtures, such as compressive strength [22], elastic modulus [23], and the wear of a modified cementitious composite [24]. Unfortunately, only a few researches have been focused on predicting abrasion resistance using neural networks [25; 26]. Reducing the amount of destructive test methods in favor of prediction is important and, therefore, a sustainable approach should be studied deeply and more complexly.
## 4 Discussion
Ethanol ingestion increases intestinal epithelial permeability, releasing bacteria and lipopolysaccharide (LPS) into the circulation, and LPS induces the secretion of proinflammatory cytokines by Kupffer cells in the liver [22; 45]. Conversely, apple polyphenols upregulate the expression of intestinal tight junction proteins, reducing intestinal permeability and inflammation [46; 47]. Ethanol intake impairs hepatic lipid metabolism [48]. Further, ethanol disrupts lipid homeostasis by increasing triglyceride and free fatty acid synthesis and decreasing the elimination of free fatty acids from hepatocytes, leading to lipid deposition in the liver [49]. _Fabp2_ promotes the production of n-3 polyunsaturated fatty acids in the intestine by regulating the genes involved in fatty acid absorption and the synthesis of triacylglycerglycerol and cholesterol [50]. Acetyl-CoA carboxylase 1 (ACC1), encoded by the _Acac_ gene, is a rate-limiting enzyme involved in the ab initio synthesis of fatty acids and catalyzes the conversion of acetyl-CoA to malonyl-CoA [51]. _Cpt1a_ is implicated in hepatic fatty acid oxidation [52]. In our study, apple intake significantly downregulated _Fabp2_ and _Acac_ and upregulated _Cpt1a_ in ethanol-treated rats. Apples improve lipid metabolism and reduce lipid accumulation in the liver, and these effects may be due to dietary fiber and polyphenols [53; 54; 55]. Liver fibrosis is a chronic inflammatory process triggered by several factors, including alcohol abuse. Fibrosis is characterized by the deposition of extracellular matrix components, especially fibrillar collagens, by activated hepatic stellate cells and the increased expression of markers of liver fibrosis, including COL1A1 and \(\alpha\)-SMA [56]. In this study, apple intake reduced alcohol-induced liver inflammation and decreased the expression of _Col1a1_ and _Acta2_. Polyphenols improve fibrosis by decreasing inflammation, OS, and the levels of fibrosis markers [57]. Dietary fiber reduces liver fibrosis in mice [58]. Nonetheless, the effects of other compounds in apples and mechanisms of action need to be further studied.
## 1 Introduction
### Experimental Setups and Parameters
The results presented in Section 3.4 show that for the case considered here, both the Rayleigh and Brillouin backscattering effects can only cause negligible channel capacity reductions when upstream channels are reallocated to inter-ONU communications.
# Accelerate Incremental TSP Algorithms on Time Evolving Graphs with Partitioning Methods
## 6 Conclusions and Summary  
In this research work, our focus was to study different partitioning strategies and compute the TSP tour incrementally. Each partitioning strategy divides the graph with different objectives. The first strategy is vertex attribute partitioning, which is a balanced way of partitioning where each sub-graph consists of an equal number of vertices. This is the most common method of getting equal-sized partitions on any graph. The second strategy is edge attribute, where apart from equal-sized partitions, the focus is also on minimizing the number of edges across partitions. In our case, we used the nearest neighbor algorithm. In this strategy, the vertices that are closest to a randomly chosen center vertex are placed in one partition. This results in an unbalanced partition because of an uneven number of vertices in each partition. The experiments results of these three strategies were compared, and we have observed that the vertex attribute has the fastest computation time. Finally, the proposed algorithms are summarized in Table 4.  
The results of the incremental approach on time-evolving graphs are promising and have shown significant improvement as compared to traditional TSP algorithms. This leads to various directions in future research. Some of the future research directions are to perform experiments on real graphs and use prediction algorithms to predict the change happening in the graph. Another future objective is to extend incremental TSP algorithms to map real-world problems and apply an incremental approach to solve Generalized TSP, Clustered TSP, or Multiple TSP.  
Figure 21: Supersteps in Pg-TSP for a varied number of partitions.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Algorithm** & **6-Vertex** & **7-Vertex** & **8-Vertex** & **9-Vertex** \\ \hline I-TSP & 11.33 & 28.57 & 86.04 & 528 \\ \hline Ig-TSP & 6.33 & 18.00 & 48.60 & 174.18 \\ \hline Genetic Algorithm [25] & 42.00 & 91.35 & 148.67 & 204.71 \\ \hline P-TSP(Node size) & 0.0013 & 0.0015 & 0.006 & 0.009 \\ \hline P-TSP(Edge size) & 0.0012 & 0.006 & 0.009 & 0.01 \\ \hline P-TSP(k-means) & 0.009 & 0.015 & 0.019 & 0.022 \\ \hline Pg-TSP(Node size) & 0.0011 & 0.0013 & 0.0019 & 0.003 \\ \hline Pg-TSP(Edge size) & 0.001 & 0.003 & 0.003 & 0.004 \\ \hline Pg-TSP(k-means) & 0.002 & 0.004 & 0.005 & 0.008 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Run-time comparison(seconds).
# A Robust Scheduling Framework for Re-Manufacturing Activities of Turbine Blades
## 3 Re-Manufacturing of Turbine Blades  
Gas turbine (Figure 1), in which burning of an air-fuel mixture produces hot gases that spin a turbine to produce power, is one of the most widely-used power generating technologies. Turbine blades (Figure 2), whose individual price is close to a middle-class car (i.e., about ten-thousand euros), is one of the most important and expensive parts in a gas turbine. To maximize the performance of a gas turbine, they are constituted by multiple stages, each of them equipped with specially-designed turbine blades. According to the specific OEM (Original Equipment Manufacturer), the number of stages can vary, as an example, a F-class turbine [15] needs about 400 blades for 3 to 6 stages. Blades belonging to the same stage of the turbine are usually manufactured/re-manufactured in batch to guarantee homogeneous characteristics and the balancing of the portion of the rotor for the stage.  
During the functioning of the turbine, blades are exposed to an extremely strenuous environment (high temperatures, stresses, vibration and corrosion) constituting a major cause for wear and failures of the blades (Figure 3) and, consequently, reduced energy conversion efficiency, and potentially disruptive failures of the whole turbine.  
Figure 1: GE H-series power generation gas turbine [16].  
Figure 2: New turbine blade [17].  
Thus, planned maintenance operations are enforced for gas turbines, requiring all the blades in the rotor to be disassembled, inspected and, if needed, re-manufactured or replaced with new ones.
## 3 Architecture
### Cost Aspects of the Proposed Solution  
The proposed solution was implemented with cost effectiveness as one of the most important factors. The retail value of the ICT hardware employed in the solution should not exceed USD 100 per square meter (10.76 square feet). One square meter would occupy nine tiles, around USD 20 for the controller and less than USD 80 for the nine pressure sensors. The solution scales linearly with no additional cost.
## 2 Methodology  
### G1 Method  
The G1 method [9; 15] is a subjective weighting technique that serves as an improvement over the AHP. Unlike AHP, it does not require the construction of a judgment matrix, which simplifies the computation and allows for consistency testing. The G1 method involves experts ranking evaluation indices based on their subjective judgment and quantifying the relative importance between adjacent indices to derive the weights.  
The process begins by determining the order of importance among the evaluation indices. Experts select the most important index from the entire set \(\{E_{1},E_{2},\ldots,E_{n}\}\) according to the evaluation criteria and denote it as \(E_{1}^{*}\). Next, the most important index among the remaining \(n-1\) indices are selected and denoted as \(E_{2}^{*}\), and the process continues until all indices are ordered. This results in an importance sequence of evaluation indices \(E_{1},E_{2},\ldots,E_{n}\) with respect to the evaluation objective, which is formed as \(E_{1}^{*}>E_{2}^{*}>\ldots>E_{n}^{*}\). Subsequently, the importance between each pair of adjacent indices in the sequence is quantified. This step forms the basis for calculating the subjective weights.  
Second, the relative importance of adjacent indices must be quantified. Suppose the expert-assigned importance ratio of evaluation index \(E_{i-1}^{*}\) to \(E_{i}^{*}\) is denoted as \(\gamma_{i}\), where the value of \(\gamma_{i}\) can be selected based on standard reference scales [15; 16]. The weights of the evaluation indices can then be calculated using the following equations:  
\[w_{n}=(1+\sum_{i=2}^{n}\prod_{j=i}^{n}\gamma_{j})^{-1}, \tag{1}\]  
\[w_{i-1}=\gamma_{i}w_{i}\quad i=n,n-1,\cdots,2. \tag{2}\]
# The Protective Effect of Polyunsaturated Fatty Acids against Dry Eye Disease: A Literature Review
## 3 PUFAS Supplementation and DED
### Topical Application of PUFAs  
The topical administration of PUFAs might be an intriguing option as it can avoid accumulation of extra calories as well as gastrointestinal side effects caused by oral PUFAs intake [16]. PUFAs may be able to penetrate into the lipid layer of the tear film and exert influence on the structure and physicochemical properties of the lipid layer [67].  
In a murine DED model, topical ALA application alleviated corneal epithelial damage and ocular surface inflammation reflected by the expression of inflammatory cytokines (e.g., IL-1\(\alpha\) and TNF- \(\alpha\)), and an increase in CD11b(+) cells [68]. Combination of topical 0.2% \(\omega\)3 PUFA and artificial tears provided significant additional effect for a restoration of corneal epithelial integrity and regularity and a reduction in the levels of conjunctival inflammatory cytokines (e.g., IL-10 and IL-17) and oxidative stress markers (e.g., 4-hydroxynonenal) in a mouse DED model [69].  
The topical application of PUFAs may be beneficial for DED associated with corneal damage after keratorefractive surgery [16]. Topical treatment with DHA combined with nerve growth factor (NGF) stimulated regeneration of corneal epithelium and nerve after PRK in a rabbit model [70]. The application of DHA/PEDF combination eye drops stimulated synthesis of NPD1 and promoted recovery of corneal epithelial cells and nerve plexus, and restoration of corneal sensation after corneal stromal dissection in a rabbit model [71; 72]. The corneal epithelial cells released PEDF in response to injury, and the addition of DHA resulted in the stimulation of the NPD1 synthesis through an autocrine mechanism [73]. The DHA/PEDF combination eye drops also inhibited the production of LTB4, a pro-inflammatory cytokine, which subsequently attenuated the inflammation associated with corneal damage [73].  
A clinical trial showed that the addition of trehalose and flaxseed oil to artificial tears facilitated recovery after ocular surface damage with fewer adverse effects [74]. A recent study showed that \(\omega\)6 LA at low concentrations (20 mol%) in topical preparations enhanced the spreadability and stability of the tear lipid layer, which can be beneficial for stabilization of tear film in DED patients [75].