esa-sceva/satcom-chunk-collection huggingface dataset:
# Progress and Summary of Photodarkening in Rare Earth Doped Fiber
## 3 Phenomena and Problems Caused by Photodarkening
### Refractive Index Change  
The phenomenon of PD leads to the RIC. The reason for the RIC is the structure change [71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82] and the generation of color center [83]. Experiments showed that when the photosensitive optical fiber was irradiated by ultraviolet light, the glass structure in some areas was more compact [71]. Studies performed on Tin-doped silica optical fibers proved that the RIC could be ascribed to structural rearrangements induced by photochemical  
Figure 7: PD-induced absorption coefficient change (\(\Delta\alpha\)) at 600 nm (primary \(y\)-axis) and respective fiber temperature (secondary \(y\)-axis) measured with a thermal camera. Adapted with permission from [38] © The Optical Society.  
reactions [72]. Different color centers have different photoconductivity characteristics, which will lead to the change of refractive index.  
In 1990, [PERSON] and [PERSON] [53] used Kramers-Kronig relation to relate the variation of the refractive index of optical fiber with the spectral variation of optical fiber in ultraviolet absorption.  
\[\delta n(\omega)=\frac{c}{\sqrt{\pi}}\underset{i}{\sum}\frac{\delta\alpha_{i}( \omega_{i})b_{i}} \tag{1}\]  
where \(\delta n(\omega)\) was the RIC when the angular frequency was \(\omega\), \(c\) was the speed of light in vacuum, \(\delta\alpha_{i}\) was the absorption coefficient change, \(\omega\) was the center frequency of the work and \(\omega_{i}\) was the center frequency of each absorption peak. It indicated that any change in absorption at any wavelength introduced RIC at all wavelengths. Thus, the existence of RIC induced by PD and TB could be anticipated.  
In 2010, [PERSON] et al. [28] conducted high-precision measurement and analysis of RIC caused by light aging and TB in ytterbium-doped fiber based on the modal interference method. PD-induced RIC was positive at the ytterbium lasing wavelengths near 1080 nm, and it approached a saturated level, which was in the order of 10\({}^{-6}\)-10\({}^{-5}\) for the tested fiber samples.
## 1 Introduction
* DIFTOS is designed in a hierarchical fashion, thus short length path requests are resolved locally, and does not need to travel long distances. * To demonstrate the validity of DIFTOS, we have conducted a thorough comparison with existing approaches. The rest of the paper is organized as follows: Section 2 highlights recent related works, In Section 3, we present the proposed system's architecture. Whereas Section 4 sets up the theoretical foundation of DIFTOS. Furthermore, Section 5 gives details about the simulation environment. While in Section 6 we discuss the performance evaluation of DIFTOS and analyze the obtained results. Finally, we conclude the paper and present the future directions in Section 7.
## 4 Biosensors in the Field of Healthcare
### Smart Implants for Cardiovascular Disease
Importantly, these two types of pacemakers can function wirelessly [121; 122]. Currently, leadless pacemakers rely on batteries with limited lifetime. On average, the NanostimTM (Figure 6D) can last up to 15 years, while the MicraTM has a lifespan extend to approximately 12.5 years [123]. The limited lifespan and size of batteries has always been a limitation of such devices and makes their inclusion in smart stents impractical, whereas their use is more feasibly in smart grafts or with the use of wireless power [124]. The heart consists of four chambers: two atria that receive blood from the body and two ventricles that pump blood out. These chambers have unique pressures that differ from one another based on the chamber's function. For example, the systolic pressure in the left ventricle and right ventricle is roughly 120 and 20 mmHg, respectively. This is because the left ventricle has to pump blood throughout the entire systemic blood circuit. Oppositely, the right ventricle pumps blood only throughout the pulmonary blood circuit (short distance) [125]. Evaluating the left atrial pressure (LAP) is important in patients with HF, congestive heart disease, and mitral valve disease. To measure intracardiac pressure, a special catheter, called Swan Ganz, is inserted into the femoral vein and threaded up to the right atrium, right ventricle, and pulmonary artery to record the pressure in each chamber. This conventional method does not enable the real measurement of the left atrium and ventricle but rather provides an estimate [126]. A new wireless technology was created by Victoria, named V-LAP (Table 3), for monitoring LAP remotely. The V-LAP device can penetrate and stay in the interatrial septal wall. Such a device is capable of communicating with the external world at rest and ambulation [127].
## 6 Thematic Taxonomy of UWSNs
### Deployment
#### 6.1.3 Enabling Technologies
\begin{table}
\begin{tabular}{c l l c c c} \hline \hline \multirow{2}{*}{**Author**} & \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Objective**} & \multicolumn{3}{c}{**Deployment Criteria**} \\ \cline{3-6}  & & &
\begin{tabular}{c} **Energy** \\ **Consumption** \\ \end{tabular} & \multirow{2}{*}{**Coverage**} & \multirow{2}{*}{**Connectivity**} \\ \hline
[35] & Self-deployment Particle swarm & Optimize events coverage & Yes & Yes & Yes \\ \hline
[36] & Construction of initial infrastructure & Node placement strategy to minimize & No & Yes & Yes \\  & transmission cost & & & \\ \hline
[37] & Uneven cluster deployment & Improves network reliability and prolongs network lifetime & Yes & Yes & Yes \\ \hline
[38] & Fisher information matrix (FIM) & Target positioning precision & Yes & No & No \\ \hline
[39] & A three-dimensional coverage pattern and deployment scheme & Preserve network coverage & Yes & Yes & Yes \\ \hline
[32] & Game theory & Optimize mobility of nodes and targets & No & Yes & Yes \\ \hline
[40] & Integration of a realistic model and gradient descent method & Improve sensor node placement & Yes & Yes & No \\ \hline
[41] & Multiobjective optimization framework & optimal deployment of a sparse network of sensors against moving targets & No & Yes & Yes \\ \hline
[42] & Autonomous deployment algorithm for k-barrier coverage & Utilize self-deployment method to improve coverage & Yes & Yes & Yes \\ \hline
[43] & Greedy Iterative Approach (GFCND) & Improve network connectivity and coverage & Yes & Yes & Yes \\ \hline
[44] & Stratified Connected Tree & Optimize leaf nodes position to improve coverage and connectivity & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mobile Deployment Algorithm in UWSNs.
## 6 Numerical Results
The coaxial circular current loop source has a radius \(R=5\) cm and is placed at a distance \(h=30\) cm from the disk. The magnetic field is sampled along the \(z\) axis at \(z=-30\) cm. Finally, the dependence of \(\mathrm{SE_{H}}\) on the loop radius \(R\) is reported in Figure 7 for radius values between \(R=5\) cm and \(R=50\) cm. While this always results in \(R<a\), the loop radius can be smaller or larger than the fixed source-to-screen distance \(h=30\) cm. In all of the cases shown here, the PEC limit is achieved at frequencies higher than about 100 KHz; however, the position and shape of the \(\mathrm{SE_{H}}\) peak in the intermediate range depends on \(R\), becoming more pronounced and shifting towards high frequencies as \(R\) decreases. On the other hand, at the limit of large \(R\) the peak of \(\mathrm{SE_{H}}\) tends to disappear. In summary, in the above figures the variations of \(\mathrm{SE_{H}}\) with frequency, thickness-to-skin-depth ratio, source-to-screen distance, and loop radius are illustrated. Although these results do not span the entire parametric space, they are both relevant and representative of the typical electromagnetic response of the considered configuration. Thus, it is worthwhile to point out that although we have considered only copper as a typical conductive material, the validity of the method is general and has been tested on different values of the disk conductivity. Figure 5: Magnetic shielding effectiveness \(\mathrm{SE_{H}}\) as a function of the thickness-to-skin depth ratio \(d/\delta\). The other parameters are the same as in Figure 4.  
Figure 6: Magnetic shielding effectiveness \(\mathrm{SE_{H}}\) as a function of the source-to-screen distance \(h\). The other parameters are the same as in Figure 4.
## 6 ML and the Security of Blockchain
### Sybil Attack Mitigation  
Sybil attacks, which involve the creation of multiple fake identities to gain undue influence over a network, can also be countered using ML techniques. Graph-based ML algorithms, such as graph neural networks (GNNs), are effective in analyzing peer-to-peer relationships within the network. These algorithms can detect anomalous patterns indicative of clusters of fake or suspicious nodes [109]. By classifying nodes based on behavior and contribution, ML models can help prevent adversaries from manipulating consensus through artificial node creation.
## 3 The Conventional Critical Distance Method  
### Theory of Critical Distance (TCD)  
The concept of the critical distance was originally proposed by [PERSON] [16] and [PERSON] [3], who considered that the effective stress for notch fatigue does not depend entirely on the peak stress at the notch root, but on the stress distribution near the notch root. [PERSON] summarized and developed their views and proposed the classical critical distance method based on the theory of linear elastic fracture mechanics (LEFM). According to the range of effective stress selection, the critical distance method is divided into the critical point method (PM), the critical distance line method (LM), critical area method (AM) and critical volume method (VM), as shown in Figure 6. Specifically, PM defines the stress at L/2 from the root of the notch as the effective stress for notch fatigue. When the effective stress of notch fatigue is equal to the fatigue strength of the smooth specimen, the corresponding applied load is the notch fatigue limit; LM is the average stress within the 2L length of the notch root as the effective stress of notch fatigue. AM and VM take the average stress in the critical semicircle and critical volume as the effective stress, respectively.  
The mechanical expression of the critical distance method is as follows:  
\[\sigma_{eff}(PM)=\sigma(L/2)=\sigma_{0} \tag{1}\]  
\[\sigma_{eff}(LM)=\frac{1}{2L}\int_{0}^{2L}\sigma(r)dr\ \ =\sigma_{0} \tag{2}\]  
\[\sigma_{eff}(AM)=\frac{2}{\pi(1.32L)^{2}}\int_{-\frac{\pi}{2}}^{\frac{\pi}{2} }\int_{0}^{1.32L}\sigma(r,\theta)rdrd\theta\ \ =\sigma_{0} \tag{3}\]  
\[\sigma_{eff}(VM)=\frac{3}{2\pi(1.54L)^{3}}\int_{0}^{\pi}\int_{-\frac{\pi}{2}}^ {\frac{\pi}{2}}\int_{0}^{1.54L}\sigma(r,\theta,\varphi)r^{2}drd\theta d\varphi \ \ =\sigma_{0} \tag{4}\]  
where \(L\) is material constant, which can be expressed as:  
\[L=\frac{1}{\pi}\left(\frac{K_{th}}{\sigma_{0}}\right)^{2} \tag{5}\]  
where \(K_{th}\) is the stress intensity factor threshold, \(\sigma_{0}\) is the fatigue strength of the smooth specimen.
## 1 Background
In the literature, there are many multimodal analgesic protocols for postoperative pain in TKA, based on different combinations (type and dosage) of preintra- and postprocedural interventions: systemic drugs (nonsteroidal anti-inflammatory drugs, NSAIDs, or cyclooxygenase-2 inhibitors, opioids, gabapentins), local infiltration analgesia, neuraxial anesthesia, peripheral nerve blocks, and other nonpharmacological strategies. This approach reduces the dependence on a single medication and may reduce or eliminate the need for opioids. Compared with monotherapy, multimodal analgesia in TKA patients provides superior postoperative pain relief. A postdischarge multimodal pain regimen significantly reduces the level of opioid used without compromising pain control [13]. To determine which are the most effective analgesic interventions, it is necessary to take into account the control of the pain, the effect on function, the length of stay, the costs, the patient satisfaction, the risk of addiction, and the complications [14]. Since there is no consensus on the optimal perioperative analgesic method, the present article provides a brief and practical synthesis on current multimodal pre-postsurgery analgesia protocols for primary TKA (excluding intraoperative anesthetic techniques) reported especially in recent meta-analyses and reviews [15]. The focus is not only on the traditional aims (pain scores and opioid sparing), but also the functional recovery and patient-reported outcomes. For this narrative review, a comprehensive search of PubMed, MEDLINE, EMBASE, and the Cochrane Database of Systematic Reviews was performed.
# Na-Doping Effects on Thermoelectric Properties of Cu\({}_{2-x}\)Se Nanoplates
###### Abstract  
For this work, a \(\beta\)-phase Cu\({}_{2-x}\)Se nanowire and nanoplate, and a Na-doped Cu\({}_{2-x}\)Se nanoplate were successfully synthesized using solution syntheses. The morphologies of the Cu\({}_{2-x}\)Se nanowire and nanoplate could be easily controlled by changing the synthetic condition. The Na-doped Cu\({}_{2-x}\)Se nanoplate was prepared by a simple treatment of the Cu\({}_{2-x}\)Se nanoplate with a sodium hydroxide-ethylene glycol solution. The nanopowders were then consolidated to bulk materials using spark plasma sintering (SPS). The phase structure and the microstructure of all of the samples were checked using X-ray diffraction (XRD), high-resolution transmission electron microscope (HR-TEM), and scanning electron microscope (SEM) analyses. The thermoelectric transport properties, such as the electrical conductivity, Seebeck coefficient, carrier concentration, carrier mobility, and thermal conductivity, were measured at temperature ranges from 323 to 673 K. The results show that Na played two important roles: one is reducing the carrier concentration, thereby improving the Seebeck coefficient, the other is reducing the thermal conductivity. Overall, the maximum thermoelectric figure of merit (\(ZT\)) of 0.24 was achieved at 673 K in the Na-doped Cu\({}_{2-x}\)Se nanoplate.  
Cu\({}_{2-x}\)Se; doping; thermoelectric properties +
Footnote †: journal: Applied Physics Letters
# Neuroevolution for Parameter Adaptation in Differential Evolution
## 2 Materials and Methods
### Neuroevolution
To avoid deleting new solutions and still keep the population evolving, the speciation (or niching) procedure is applied, which uses innovation numbers to determine similarities and differences between individuals and create subgroups within population. In NEAT, the first individual creates the first species, and the subsequent individuals are either added to this species or create their own based on compatibility or similarity distance. If this distance is smaller than a certain threshold, then an individual belongs to the species. The distance combines the information about the number of excessgenes \(E\), the number of disjoint genes \(D\) and the average difference in weights of the matching genes \(\overline{W}\) as follows:  
\[\delta=c_{1}\cdot E+c_{2}\cdot D+c_{3}\cdot\overline{W}, \tag{12}\]  
where \(c_{1}\), \(c_{2}\) and \(c_{3}\) are the importance coefficients. At the end of each generation, the offspring individuals are assigned to species. After this, the best representatives of each species create the new generation. The general scheme of NEAT is shown in Algorithm 1. ```
1:for\(gen=1\) to \(NG\)do
2: Initialize population \(P\) with minimal solutions, calculate fitness \(fit_{i}\), \(i=1,\ldots,N\)
3: Assign species numbers to every individual
4:for\(i=1\) to \(N\)do
5: Perform tournament-based selection (\(t=2\)) to get index \(tr\)
6: Perform crossover between \(P_{i}\) and \(P_{tr}\), save offspring to \(O_{i}\)
7: Select the mutation operator to be used
8: Perform mutation on \(O_{i}\) and calculate fitness
9:endfor
10: Perform speciation of combined parents \(P\) and offspring \(O\) populations
11: Create new population from the representatives of every species
12:endfor
13: Return best found solution
```  
**Algorithm 1** NEAT  
A variety of studies have investigated ways of improving NEAT and applying it to different fields, including studies on fitness landscape [44], using different activation functions [45], meta-analysis [46] and using it as hyper-heuristic [47]. In the next subsection, the proposed approach for evolving parameter adaptation heuristics with NEAT is described.
## 4 Experimental and Discussion
### Comparison with State-of-the-Art Algorithm  
In this section, we compared the coupling algorithm with the state-of-the-art evolutionary algorithms NSGA-II [22], SPEA2 [25], PNIA [26], MOEA/D [23]. The parameter values of these algorithms are listed in Table 2. For more details about these algorithms, please refer to the literature [25; 26].  
In our research work, each work compares MGSO-BFO with NSGA-II, SPEA2 PNIA, and MOEAD the typical simulation results are shown in Table 11. As we can be seen from Table 11, in terms of convergence, the algorithm proposed in this paper is superior to the other three classical algorithms in terms of SCH, ZDT1, ZDT2, ZDT3 and ZDT4, especially in terms of the convergence of ZDT1, ZDT2, ZDT3 and ZDT4. For ZDT6, however, the convergence of the MGSO-BFO is not as good as that of the other three algorithms. In terms of diversity, the MGSO-BFO algorithm in this paper shows good diversity on SCH, ZDT2, ZDT4 and ZDT6.However, the diversity of MGSO-BFO on ZDT1 and ZDT3 was lost to PNIA. In order to further demonstrate the effectiveness of the algorithm proposed in this paper, IGD indices of the four algorithms were tested. The experimental results are shown in Table 12. The experimental results also show that the proposed algorithm is superior to other algorithms on SCH, ZDT1, ZDT2, ZDT4 and ZDT6. For ZDT3, they are in the same order of magnitude and show the same performance as other algorithms. From what has been discussed above, we can come to the conclusion that the MGSO-BFO algorithm shows good performance whether for convergence or diversity.  
Figure 3 shows the dynamic performance of the MGSO-BFO, NSGA-II, SPEA2, PNIA and MOEA/D. This figure demonstrates the abilities of those algorithms in converging to the true Pareto front and in finding diverse solutions in the front. For SCH, ZDT1, ZDT2, ZDT3 test functions, NSGA_II, SPEA2, PNIA, MOEA/D and MGSO-BFO show strong convergence and distribution, indicate the similarity between algorithms. It can be seen from the performance diagram of ZDT4 and ZDT6 that our algorithm can well converge to its real front surface, especially in ZDT4, NSGA-II and PNIA algorithms may be trapped in local optimization and cannot well converge to the real front.
# Development of an Intelligent Voltage Control System for Bulk Power Systems
## 2 Intelligent Voltage Control System
### Knowledge Base of the Expert System  
Knowledge in an expert system is classified according to facts and rules, which are stored in the database and rule base, respectively. The database in the backward rule-based system, i.e., PROLOG, is divided by the static database and dynamic database and stored as immutable facts in a specific domain or hypothetical facts derived from the inference process. Here, the knowledge base stores the system information obtained from the load flow and uses it for searches and inferences. The contents of the database and rule base are as follows:  
(A) Static and dynamic database:  
* Open/closed status of transmission lines.
* Upper and lower limits of each bus voltage.
* Installed capacity of each reactive power compensation device.
* Current usable capacity of each reactive power compensation device.
* Dynamic data from SCADA or any other monitoring system.
* Values of the sensitivity matrices, which are functions of each bus voltage and tap position, calculated from the numerical subsystem at the request of the inference engine.
* Load bus voltage profile calculated from the load flow.  
(B) Rule base:
* Rules to compare the current voltages with their specified limits and to find the buses with voltage violations.
* Rules to check the voltage violation according to the following criteria:  
* Normal voltage (p.u.): 0.95 \(\leq\) V \(\leq\) 1.05  
* Abnormal voltage (p.u.): V < 0.95 or V > 1.05
* Like many other countries, a \(\pm\)5\(\%\) variation is allowed in 765 and 345 kV high-voltage transmission networks in Korea. These limits can be changed using input data for general purposes.
* Rules to establish the sensitivity tree for each abnormal bus voltage find the most effective (highest weighted sensitivity) control in the bus with the worst voltage violation, and calculate the controls needed to remove the voltage violation.
* Rules to check the operation of the controller considering its constraints. If the controls exceed the limits, then the control value is fixed to the control limit.
* The criteria of the generator terminal voltage: upper and lower limits of the generator terminal voltage (p.u.).
* Transformer tap and shunt capacitors or reactors operated in the range of control limits.
* According to the selected control actions, voltage variations at each bus are estimated using the sensitivity tree, and the bus voltages recalculated by the load flow are refreshed under the appropriate control actions.
* The above procedures are repeated for each load bus in the overall system until all bus voltages satisfy their own limits.
# Dual Head and Dual Attention in Deep Learning for End-to-End EEG Motor Imagery Classification
###### Abstract  
Event-Related Desynchronization (ERD) or Electroencephalogram (EEG) wavelet is essential for motor imagery (MI) classification and BMI (Brain-Machine Interface) application. However, it is difficult to recognize multiple tasks for non-trained subjects that are indispensable for the complexities of the task or the uncertainties in the environment. The subject-independent scenario, where an inter-subject trained model can be directly applied to new users without precalibration, is particularly desired. Therefore, this paper focuses on an effective attention mechanism which can be applied to a subject-independent set to learn EEG motor imagery features. Firstly, a custom form of sequence inputs with spatial and temporal dimensions is adopted for dual headed attention via deep convolution net (DHDANet). Secondly, DHDANet simultaneously learns temporal and spacial features. The features of spacial attention on each input head are divided into two parts for spatial attentional learning subsequently. The proposed model is validated based on the EEG-MI signals collected from 54 subjects in two sessions with 200 trials in each sessions. The classification of left and right hand motor imagery in this paper achieves an average accuracy of 75.52%, a significant improvement compared to state-of-the-art methods. In addition, the visualization of the frequency analysis method demonstrates that the temporal-convolution and spectral-attention is capable of identifying the ERD for EEG-MI. The proposed machine learning structure enables cross-session and cross-subject classification and makes significant progress in the BMI transfer learning problem.  
machine learning: brain machine interface; attention; motor imagery; classification +
Footnote †: journal: Medical Image and Image Processing  
1 Center for Digital Media Computing, Informatics School, Xiamen University, 422 Siming South Road, Xiamen 361005, China; [EMAIL_ADDRESS]  
2 Computer and Science School, Minnan Normal University, Xiang Qianzhi Street 36, Xiangcheng District, Zhangzhou 363000, China  
3 OYMotion Technologies Co., Ltd., Floor 6, Building 2, 222 Guangdong Road, Shanghai 201318, China; [EMAIL_ADDRESS]