esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods and Data Sources
### Multi-Objective Optimization Algorithm
#### 3.5.2 Nsga-ii  
The non-dominated ranking genetic algorithm with elite strategy (NSGA-II) is an improvement on the genetic algorithm framework, proposed by [PERSON] et al. in 2002, which is also the mainstream algorithm in multi-objective optimization problems nowadays. It is also able to obtain a series of optimal non-dominated rankings and crowding distances with uniform distribution and good diversity when solving large-scale optimization problems, and its main features are as follows:  
* Fast non-dominated sorting: NSGA-II employs an efficient non-dominated sorting algorithm, which classifies the population into different ranks (fronts) based on their dominance relationships. Individuals in each rank are not dominated by any other individuals in the same rank for all objectives. This sorting method significantly reduces the computational complexity of the algorithm from O(MN3)O(MN3)O(MN3) to O(MN2)O(MN2)O(MN2), where MMM is the number of objectives, and NNN is the population size.
* Crowding distance calculation: To maintain solution diversity, NSGA-II introduces the concept of crowding distance. Crowding distance is a measure of how close an individual is to its neighbors, used to ensure an even distribution of solutions in the objective space. Within the same non-dominated rank, individuals with larger crowding distances are preferred, thereby ensuring population diversity.
* Elitist strategy: NSGA-II adopts an elitist strategy by selecting individuals not only from the offspring but also from the combined pool of parents and offspring in each generation. These individuals are selected based on non-dominated sorting and crowding distance. This strategy ensures that the best solutions are not lost and accelerates convergence.
* Binary tournament selection: During the selection process, NSGA-II uses a binary tournament selection mechanism. In each selection, two individuals are randomly chosen and compared. The individual with the higher rank is selected. If both individuals belong to the same non-dominated rank, the one with the larger crowding distance is selected. This approach ensures both selection directionality and population diversity.
* Crossover and mutation operations: NSGA-II employs classical genetic algorithm operations, including simulated binary crossover (SBX) and multi-point mutation. These operations explore a broader solution space by recombining and randomly altering the genes of individuals.  
Few and stable parameters: NSGA-II has low parameter dependency, and the impact of parameter settings on algorithm performance is relatively stable, making the algorithm robust across different problems and datasets.
# A Programmable SRv6 Processor for SFC
## 3 Protocol-Independent Pipeline Design
### Programmable MAT  
A match-action table (MAT) is the core module in RMT architecture. As shown in Figure 2, each stage looks up fixed-size keys constructed by the key extractor through exact matches in MAT, which are the actual entries looked up in the match table. The lookup result is used as an index to the VLIW action table to identify the appropriate action to perform. Each VLIW action table entry indicates which fields from the PHV to use as ALU operands (i.e., the configuration of each ALU's operand crossbar) and what opcode should be used for each ALU controlled by the VLIW instruction (i.e., addition, subtraction, etc.). There is one ALU per PHV container, removing the need for a crossbar on the output, because each ALU's output is directly connected to its corresponding PHV container. After a stage's ALUs have modified its PHV, the modified PHV is passed to the next stage.
## 3 Use Case Demonstration Overview
### Energy Reduction Using ECO-Features  
Three energy management techniques are considered to reduce the energy requirements of the buses, namely, ECO-comfort [8], ECO-driving [9], and ECO-charging [10; 11]. ECO-comfort optimizes the thermal management system of the bus responsible for the cabin and battery cooling systems, ECO-driving optimizes the EMS of the bus responsible for vehicle traction and regeneration, and ECO-charging optimizes the charging management system of the vehicle responsible for battery charging. Figure 4 highlights the effects of the ECO-features on the SoC; and details about the functionality of the three ECO-algorithms are presented in the Appendix B. Based on the SoC profile shown in the top row of Figure 4, ECO-driving has a significant effect on energy reduction, as seen from the smaller drop in the battery SoC with ECO-driving compared with the baseline. This is because the baseline driving profile featured aggressive driving, i.e., high speed (max. velocity of 18.7 m/s) and acceleration (max. acceleration of 2.39 m/s2), and??these see the highest reduction in the energy requirement due to the application of ECO-driving. There is modest energy savings due to ECO-comfort, as it was simulated for moderate springtime weather conditions. ECO-charging does not change the energy requirement of the vehicle compared to the baseline, but as can be seen from the bottom row of Figure 4, it does spread out the charging duration, resulting in a lower average load on the electricity grid; this is important during fleet charging so as not to put undue stress on the electricity grid. Overall, the 12 m bus saw an average reduction of 0.4 kWh/km from the baseline energy requirements, while the 18 m bus had a reduction of almost 1.8 kWh/km from the baseline. On average, at least three quarters of the reduction was achieved due to ECO-driving, while barely 2% is due to ECO-charging, as shown in Figure 5.  
Table 4 shows that there is a high correlation between the amount of energy savings due to ECO-driving and average speed of the vehicle in the baseline scenario. There is also a link between the size of the bus and possible energy savings. However, the data also show that there is no link between the top speed of the vehicle in the baseline scenario and possible energy reduction; this may be because the vehicle does not spend sufficient time at its top speed for it to matter. These results prove that there is a lot of room for improvement when it comes to driving behavior and low-speed driving is recommended for optimum traction energy utilization.  
Figure 5: Breakdown of energy savings due to ECO-features for 12 m and 18 m electric bus.
## 3 Results
### Effect of Vanadium Complexes on EYL Liposome Membranes
#### 3.2.2 16 doxyl Probe  
Figure 9 shows the rotational correlation time parameter (\(\tau\)) for the 16 doxyl probe as a function of the concentration of **V1**, **V2**, and **V3** additives. Similar to Figure 8, to illustrate additive-induced changes more clearly, changes in \(\tau\) were normalized to 1, where \(\tau_{0}\) is the value for liposomes without additive and \(\tau\) is the parameter value for a given additive concentration. A \(\tau/\tau_{0}\) above 1 shows that the probe slowed down the rotation, which may indicate a decrease in membrane fluidity in the central part of the bilayer. The data  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**V3**} & \multicolumn{3}{c}{**V1**} & \multicolumn{3}{c}{**V2**} \\ \hline
**Line No.** & **dH [mT]** & **Centre [mT]** & **g** & **dH [mT]** & **Centre [mT]** & **g** & **dH [mT]** & **Centre [mT]** & **g** \\ \hline
1 & 1.496 & 306.146 & 2.249766 & 1.554 & 306.145 & 2.249773 & 1.902 & 306.225 & 2.249186 \\ \hline
2 & 1.369 & 316.382 & 2.176979 & 1.353 & 316.406 & 2.176814 & 1.647 & 316.484 & 2.176277 \\ \hline
3 & 1.211 & 326.947 & 2.106632 & 1.245 & 326.963 & 2.106529 & 1.47 & 327.022 & 2.106149 \\ \hline
4 & 1.241 & 337.945 & 2.038074 & 1.232 & 337.97 & 2.037923 & 1.484 & 338.027 & 2.03758 \\ \hline
5 & 1.393 & 349.364 & 1.971459 & 1.365 & 349.268 & 1.972001 & 1.669 & 349.405 & 1.971228 \\ \hline
6 & 1.634 & 361.147 & 1.907137 & 1.633 & 361.135 & 1.907201 & 1.997 & 361.156 & 1.90709 \\ \hline
7 & 1.925 & 373.247 & 1.845311 & 1.932 & 373.241 & 1.845341 & 2.408 & 373.186 & 1.845613 \\ \hline
8 & 2.359 & 385.759 & 1.785459 & 2.417 & 385.736 & 1.785566 & 2.951 & 385.683 & 1.785811 \\ \hline
0 & & & 2.842 & 345.424 & 1.993946 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spectroscopic parameters of EPR spectral lines of vanadium complexes.  
presented in Figure 8 shows that the vanadium complexes significantly increase the fluidity of the membrane in its central region. This process was monotonic in the concentration range of 0% to 2-3%, causing a change in the parameter of 20% for **V3**, 25% for **V1**, and 30% for **V2**. Above these concentration values, the curve flattened, indicating a saturation of the fluidity process.  
Figure 7: Spectroscopic lines of vanadium complexes (**V3**, **V1**, **V2**) dissolved in DMF.
## 2 Materials and Methods
### Optimization Problem  
The primary objective of this section is to minimize the total cost associated with the SAPV. The objective function, as described in Equation (9), incorporates various cost components:  
\[\text{Annual}_{\text{cost}}=\text{PV}_{\text{cost}}+\text{Battery}_{\text{cost}}+ \text{Generator}_{\text{cost}} \tag{9}\]  
Each of these components must be optimized to achieve the minimum annual cost. This optimization involves selecting the most efficient combination of installed PV panel power and battery capacity. The specific definitions for these terms are provided as follows:  
\[\text{PV}_{\text{cost}}=\frac{\text{P}_{\text{installed}}*\text{cost}\text{W} _{\text{installed}}}{\text{useful}_{\text{Life}_{\text{panels}}}} \tag{10}\]  
\[\text{Battery}_{\text{cost}}=\frac{\text{Q}_{\text{b,max}}*\text{cost}\text{W} _{\text{installed}}}{\text{useful}_{\text{Life}_{\text{battery}}}}*\frac{\text{ useful}_{\text{Life}_{\text{panels}}}}{\text{useful}_{\text{Life}_{\text{battery}}}} \tag{11}\]  
\[\text{Generator}_{\text{cost}}=\text{Fuel}_{\text{cost}}(\frac{\text{euros}}{ \text{I}})*\text{ENS}(\text{kWh})\]  
\[*\text{Generator}_{\text{consumption}}(\frac{\text{I}}{\text{kWh}}) \tag{12}\]  
In order to solve this optimization problem, certain constraints must be satisfied. These constraints ensure that the equipment sizes are physically feasible and prevent the system cost from escalating to excessively high values. Specifically, the constraints are shown in Equations (13)-(16).  
Equipment size constraints:  
\[\text{P}_{\text{installed}}\geq 0 \tag{13}\]  
\[\text{Q}_{\text{b,max}}\geq 0 \tag{14}\]  
Size limit constraints:  
\[\text{P}_{\text{installed}}\leq 20,000 \tag{15}\]  
\[\text{Q}_{\text{b,max}}\leq 35,000 \tag{16}\]  
The final mathematical formulation of the optimization problem can be summarized as follows:  
\[\text{min}\hskip 28.452756 pt\text{Annual}_{\text{cost}}\]  
\[\text{subject to}\hskip 5.690551 pt\left\{\begin{array}{c}\text{P}_{\text{installed }}\geq 0\\ \text{Q}_{\text{b,max}}\geq 0\\ \text{P}_{\text{installed}}\leq 20,000\\ \text{Q}_{\text{b,max}}\leq 35,000\end{array}\right. \tag{17}\]  
This formulation defines the objective function and constraints required to optimize the SAPV system's design while minimizing annual costs, ensuring equipment sizes are positive, and preventing sizes from exceeding predefined limits.  
The optimization problem addressed is formulated as a mono-objective optimization problem. It is aimed to minimize a single cost function, which represents the overall cost associated with the design and operation of the stand-alone photovoltaic (SAPV) system. However, the implications of the design results on reliability are also studied, taking advantage of Monte Carlo simulations.
# Salient Region Detection Using Diffusion Process with Nonlocal Connections
###### Abstract  
Diffusion-based salient region detection methods have gained great popularity. In most diffusion-based methods, the saliency values are ranked on 2-layer neighborhood graph by connecting each node to its neighboring nodes and the nodes sharing common boundaries with its neighboring nodes. However, only considering the local relevance between neighbors, the salient region may be heterogeneous and even wrongly suppressed, especially when the features of salient object are diverse. In order to address the issue, we present an effective saliency detection method using diffusing process on the graph with nonlocal connections. First, a saliency-biased Gaussian model is used to refine the saliency map based on the compactness cue, and then, the saliency information of compactness is diffused on 2-layer sparse graph with nonlocal connections. Second, we obtain the contrast of each superpixel by restricting the reference region to the background. Similarly, a saliency-biased Gaussian refinement model is generated and the saliency information based on the uniqueness cue is propagated on the 2-layer sparse graph. We linearly integrate the initial saliency maps based on the compactness and uniqueness cues due to the complementarity to each other. Finally, to obtain a highlighted and homogeneous saliency map, a single-layer updating and multi-layer integrating scheme is presented. Comprehensive experiments on four benchmark datasets demonstrate that the proposed method performs better in terms of various evaluation metrics.  
s  
saliency detection; Gaussian model; diffusion process; nonlocal connections +
Footnote †: journal: Applied Sciences
## 5 Model Checking LTL and \(\omega\)-Regular Properties
### The Model Checking Problem  
We start with the model \(\mathfrak{M}\) and the property \(\varphi\) we want to verify. The property is usually given as an LTL formula, that is, a formula expressible according to the following grammar:  
\[\varphi\eqsim=a\mid\varphi\land\varphi\mid\
eg\varphi\mid\mathbf{X}\varphi \mid\varphi\,\mathbf{U}\,\varphi,\]  
where \(a\in AP\) is taken from the given finite set of atomic propositions. In addition, the syntactic sugar shared with CTL, we also consider the Globally/Always temporal operator \(\mathbf{G}\varphi=\
eg\mathbf{F}\
eg\varphi\), sometimes written as \(\square\varphi\).  
The semantics of LTL is analogous to the one of CTL, in particular for the temporal operators. We call _word_ an infinite sequence of sets of atomic propositions \(w\in(2^{AP})^{\omega}\). We say that \(w\)_satisfies_\(\varphi\), denoted by \(w\models\varphi\), in the following cases:  
\[w\models a \text{if }a\in w[0];\] \[w\models\varphi_{1}\land\varphi_{2} \text{if }w\models\varphi_{1}\text{ and }w\models\varphi_{2};\] \[w\models\
eg\varphi \text{if it is not the case that }w\models\varphi;\] \[w\models\mathbf{X}\varphi \text{if }w[1\cdots]\models\varphi;\text{ and }\] \[w\models\varphi_{1}\,\mathbf{U}\,\varphi_{2} \text{if }\exists n\in\mathbb{N}:w[n\cdots]\models\varphi_{2}\text{ and }\forall 0\leq i<n:w[i\cdots]\models\varphi_{1},\]  
where \(w[i\cdots]=w[i]w[i+1]\cdots\). We denote the set of all words satisfying \(\varphi\) by \(\mathit{Words}(\varphi)=\{\,w\in(2^{AP})^{\omega}\mid w\models\varphi\,\}\).  
The model checking problem for LQMCs against an LTL formula \(\varphi\) is formalized as follows.  
Definition 5: Let \(\mathfrak{M}\) be an LQMC, \(s\in S\) be a state, and \(\varphi\) be an LTL formula. Given \(\sim\in\{\lesssim,\gtrsim,\approx\}\) and \(\mathcal{E}\in\mathcal{S}^{\mathcal{I}}(\mathcal{H})\), the model checking problem for \(\mathfrak{M}\) against \(\varphi\) asks to verify whether \(\mathcal{Q}_{s}^{\mathfrak{M}}(\{\sigma\in\operatorname{Path}^{\mathfrak{M}} \mid L(\sigma)\in\mathit{Words}(\varphi)\,\})\sim\mathcal{E}\) holds.  
The model checking problem for LQMCs against an \(\omega\)-regular property \(\mathcal{W}\subseteq(2^{AP})^{\omega}\) is defined analogously.  
**Definition 6**.: _Let \(\mathfrak{M}\) be an LQMC, \(s\in S\) be a state, and \(\mathcal{W}\subseteq(2^{AP})^{\omega}\) be an \(\omega\)-regular property. Given \(\sim\in\{\lesssim,\gtrsim,\approx\}\) and \(\mathcal{E}\in\mathcal{S}^{\mathcal{I}}(\mathcal{H})\), the model checking problem for \(\mathfrak{M}\) against \(\mathcal{W}\) asks to verify whether \(Q_{s}^{\mathfrak{M}}(\{\sigma\in\operatorname{Path}^{\mathfrak{M}}|\;L( \sigma)\in\mathcal{W}\,\})\sim\mathcal{E}\) holds._
## 3 Results
### The Impact of Soil Temperature on CO\({}_{2}\), CH\({}_{4}\) and N\({}_{2}\)O Fluxes  
Air and soil temperatures at a depth of 10 cm reached their maximum in June in both birch (25.8 \({}^{\circ}\)C and 20.6 \({}^{\circ}\)C, respectively) and black alder (24.2 \({}^{\circ}\)C and 17.9 \({}^{\circ}\)C, respectively) stands. The average air and soil temperatures in birch stands were 15.1 \({}^{\circ}\)C and 11.3 \({}^{\circ}\)C, respectively, and in black alder stands--14.3 \({}^{\circ}\)C and 10.7 \({}^{\circ}\)C, respectively.  
The largest CO\({}_{2}\) fluxes from tree stems are detected when the soil temperature rises above 10 \({}^{\circ}\)C, but some activity can be observed already when the soil has warmed up to 5 \({}^{\circ}\)C. As the soil cools to 0 \({}^{\circ}\)C, CO\({}_{2}\) emissions decrease. Comparing emissions from both of the tree species, the largest CO\({}_{2}\) fluxes were measured in birch stands, where 112.7 mg CO\({}_{2}\)-C m\({}^{-2}\) h\({}^{-1}\) was released at 17.7 \({}^{\circ}\)C, but in black alder stands emissions did not exceed 95.4 mg CO\({}_{2}\)-C m\({}^{-2}\) h\({}^{-1}\) at 19.1 \({}^{\circ}\)C (Figure 7). For both birch and black alder, a significant difference was found between the amount of CO\({}_{2}\) released when the soil is cooler than 5 \({}^{\circ}\)C and when the soil is warmer than 5 \({}^{\circ}\)C (\(p<0.01\)). As the interval of comparable temperatures increases to 10 \({}^{\circ}\)C, the \(p\)-value decreases. A significant, strong, positive correlation was found between the emitted CO\({}_{2}\) and soil temperature in birch and black alder stands (Spearman correlation coefficient \(\rho\) = 0.89 and \(\rho\) = 0.91, \(p<0.001\), respectively).  
Evaluating the impact of temperature on CH\({}_{4}\) emissions, the coefficient of determination in silver birch stands was R\({}^{2}\) = 0.10, but in black alder stands it was R\({}^{2}\) = 0.06 Moreover, an insignificant, weak, negative correlation (\(p=-0.056\), \(p=0.761\)) was found between the released CH\({}_{4}\) from the tree stem surface and the soil temperature up to 10 cm in birch stands, while an insignificant, weak, negative correlation was found in black alder stands (\(\rho=-0.18\), \(p=0.45\)).  
Evaluating the impact of temperature on N\({}_{2}\)O emissions, the values of the determination coefficients are R\({}^{2}\) = 0.02 in birch stands and R\({}^{2}\) = 0.04 in black alder stands. An insignificant, weak, negative correlation (\(\rho=-0.22\), \(p=0.24\)) was found between soil temperature and N\({}_{2}\)O flux in birch stands. In black alder stands, an insignificant, moderate, negative correlation (\(\rho=-0.37\), \(p=0.12\)) was found between the N\({}_{2}\)O flux from the surface of tree stems and soil temperature.  
Figure 7: Relationship between CO\({}_{2}\) fluxes from birch and black alder stems and soil temperature.
# Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM
## 8 Evaluation
### Impact of LNN-RLSTM Technology  
Compared with other models, LNN-RLSTM is improved by three points:  
1. Numerical visualization of anomalous data using quartiles.
2. Identification of anomalous data and anomalous cycles using LNN neural network training data.
3. Anomalous frequencies can be identified using RLSTM neural network training data.  
We used these three techniques to optimize our model and experiments. Adding these three improvements step by step gives us the LNN-RLSTM model.
## Radiosynthesis and _in Vivo_ Evaluation of Two PET Radioligands for Imaging \(\alpha\)-Synuclein  
**[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1,Jinda [PERSON] 1, [PERSON] 1, [PERSON] 2 and [PERSON] 1,***  
1 Department of Radiology, Washington University School of Medicine, 510 S. Kingshighway Boulevard, St. Louis, MO 63110, USA; E-Mails: [PERSON]_ADDRESS] (X.Z.); [EMAIL_ADDRESS] (H.J.); [EMAIL_ADDRESS] (P.K.P.); [EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (H.Y.); [EMAIL_ADDRESS] (J.F.); [EMAIL_ADDRESS] (R.H.M.)
2 Department of Neurology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO 63110, USA; E-Mail: [EMAIL_ADDRESS]
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-314-362-8487; Fax: +1-314-362-8555.  
_Received: 30 April 2013; in revised form: 7 February 2014 / Accepted: 27 February 2014 / Published: 17 March 2014_  
**Abstract:** Two \(\alpha\)-synuclein ligands, 3-methoxy-7-nitro-10\(H\)-phenothiazazine **(2a, \(K_{i}=32.1\pm 1.3\) nM) and 3-(2-fluoroethoxy)-7-nitro-10\(H\)-phenothiazazine **(2b, \(K_{i}=49.0\pm 4.9\) nM)**, were radiolabeled as potential PET imaging agents by respectively introducing \({}^{11}\)C and \({}^{18}\)F. The syntheses of [\({}^{11}\)C]**2a** and [\({}^{18}\)F]**2b** were accomplished in a good yield with high specific activity. _Ex vivo_ biodistribution studies in rats revealed that both [\({}^{11}\)C]**2a** and [\({}^{18}\)F]**2b** crossed the blood-brain barrier (BBB) and demonstrated good brain uptake 5 min post-injection. MicroPET imaging of [\({}^{11}\)C]**2a** in a non-human primate (NHP) confirmed that the tracer was able to cross the BBB with rapid washout kinetics from brain regions of a healthy macaque. The initial studies suggested that further structural optimization of [\({}^{11}\)C]**2a** and [\({}^{18}\)F]**2b** is necessary in order to identify a highly specific positron emission tomography (PET) radioligand for _in vivo_ imaging of \(\alpha\)-synuclein aggregation in the central nervous system (CNS).  
**Keywords:** Lewy bodies; Parkinson's disease; PET; phenothiazazine; radiosynthesis; \(\alpha\)-synuclein  
**Abbreviations:**\(a\)-syn, \(a\)-synuclein; BBB, blood-brain barrier; calcd., calculated; CNS, central nervous system; DBU, 1,8-diazabicyclo[5.4.0]undec-7-ene; DCM, dichloromethane; DMF, \(N\),_N_-dimethylformamide; DMSO, dimethyl sulfoxide; EOB, end of bombardment; [\({}^{18}\)F]FEOTs, [\({}^{18}\)F]fluoroethyltosylate; HPLC, high performance liquid chromatography; HRMS, high resolution mass spectrometry; LBs, Lewy bodies; LNs, Lewy neuritis; NHP, non-human primate; PD, Parkinson's disease; PET, positron emission tomography; QC, quality control; RCY, radiochemical yield; SAR, structure-activity relationship; SD, Sprague Dawley; SUV, standardized uptake value; TAC, time-activity curve; VMAT2, vesicular monoamine transporter type 2.