esa-sceva/satcom-chunk-collection huggingface dataset:
# A Population-Based Search Approach to Solve Continuous Distributed Constraint Optimization Problems
## 1 Introduction
In this paper, we customize a population-based local search approach for C-DCOPs, which is more suitable for distributed optimization. Our motivations and contributions to PLSA can be summarized as follows:  
* **Motivation:** The pseudo-tree is a communication structure commonly used in C-DCOP algorithms. The root agent can be aware of the aggregate utility according to the utility passing between all agents. We have two worries: (i) the root agent requires more computational and memory overhead. (ii) The privacy violation during utility passing, even if the privacy violation is minor [40]. **Contribution:** Therefore, we use the graph structure in PLSA. The agent only passes its value assignments (instead of utilities received from other neighbors) to neighbors and optimizes the sum of local utilities through the neighbors' value assignments. An important benefit is that each agent can guarantee equal overhead and privacy. * **Motivation:** In distributed optimization, since none of the agents are aware of the quality of the aggregate utilities, the quality of aggregate utility fluctuates with the changes in value assignments. However, the sum of local utilities affects aggregate utility, so we consider that local stability promotes global stability to a certain extent. **Contribution:** Hence, we propose a _STATE mechanism_ for each agent to control the changes in value assignments. Specifically, each agent changes its state (updates or holds values) based on historical values, which can make the current agent terminate the update early and keep local stability. * **Motivation:** Falling into the local optimum is always an awkward problem in approximate optimization algorithms, which limits the exploration ability of the algorithm. Similarly, PLSA also faces the problem of falling into the local optimum as an approximate optimization algorithm. **Contribution:** With a simple idea, we introduce a classical mutation operator to jump out of the local optimum. The agent resets each value with probability to a random value from its domain. PGSA is an extension of PLSA, which considers introducing _Breadth First Search_ (BFS) pseudo-tree technology to guarantee its anytime property while extending local search to global search. Extensive comparative experiments on three randomly generated benchmark problems show that PGSA can improve the solution quality of PLSA, and our algorithms are superior to the state-of-the-art C-DCOP algorithms in terms of solution quality. The remainder of this paper is organized as follows. Firstly, we introduce the background of the problem, the distributed population, and the BFS pseudo-tree in Section 2. In Section 3, we present the details of our algorithms. Section 4 presents the theoretical analysis. Then, Section 5 exhibits the comparative experiments of our algorithms and the state-of-the-art C-DCOP algorithms. Finally, we give the conclusion and future avenues of research in Section 6.
## 3 Results
### Adsorption Isotherm  
In this work, the dosage of GA-FK gel was 0.05 g, the initial concentration of Fe(III) solution was 50, 100, 200, 300, and 500 mg:L\({}^{-1}\), and the adsorption experiments were carried out at 303 K. The experimental data were fitted and calculated by [PERSON] (Formula (3) [25]) model and [PERSON] (Formula (4) [25]) model, and the results are shown in Figure 8 and Table 2.  
\[\frac{C_{e}}{Q_{e}}=\frac{1}{K_{L}Q_{m}}+\frac{1}{Q_{m}}C_{e} \tag{3}\]  
\[\ln Q_{e}=\ln K_{F}+\frac{1}{n}\ln C_{e} \tag{4}\]  
where \(Q_{e}\) is experimental equilibrium adsorption capacity (mg:g\({}^{-1}\)); \(C_{e}\) is experimental equilibrium concentration (mg:L\({}^{-1}\)); \(Q_{m}\) is saturated adsorption capacity (mg:g\({}^{-1}\)); \(K_{L}\) is Langmuir adsorption constant (L\(\cdot\)mg\({}^{-1}\)); \(K_{F}\) is [PERSON] adsorption constant (mg:g\({}^{-1}\)), and 1/n is heterogeneity parameter.  
The adsorption pattern of GA-FK gel to Fe(III) is more fitting with the [PERSON] model (R\({}^{2}\) = 0.9855) (see Figure 8). Because the adsorption process of GA-FK gel to Fe(III) is a two-stage mode, in the second stage the GA-FK gel begins to swell, transforming from rigidity to flexibility until it is complete swelling. The volume of GA-FK gel particles gradually expanded, the pores disappeared, and the specific surface area changed  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**\(T\) (K)**} & \multicolumn{2}{c}{**[PERSON]**} & \multicolumn{3}{c}{**[PERSON]**} \\ \cline{2-7}  & \(K_{F}\) (L \(\cdot\) mg\({}^{-1}\)) & **1/n** & \(R^{2}\) & \(Q_{m,cal}\) (mg:g\({}^{-1}\)) & \(K_{L}\) (L \(\cdot\) mg\({}^{-1}\)) & \(R^{2}\) \\ \hline
303 & 40.516 & 0.4433 & 0.9855 & 178.6 & 0.201 & 0.9253 \\ \hline \end{tabular}
\end{table}
Table 2: Isotherm parameters of Fe(III) adsorption by GA-FK gel.  
Figure 8: Adsorption isotherm of GA-FK gel to Fe(III) (Freundlich model).  
dramatically. Fe(III) ions diffused into gel particles with the solvent. As GA bonded to FK chains, the chemical adsorption capacity was greatly improved, so the adsorption is non-specific adsorption.  
It can be seen from Table 2 that the Freundlich isotherm adsorption empirical constant \(K_{\text{F}}\) is 40.516. It indicates that GA-FK gel has a high adsorption affinity to Fe(III), and 0 \(<\) 1/n = 0.4433 \(<\) 1, indicating that adsorption is easy to take place. It is due to the GA molecules that it has strong complexation ability. The [PERSON] isothermal adsorption empirical constant \(K_{\text{L}}\) is 0.201, and the calculated maximum adsorption capacity of GA-FK gel (\(Q_{m,\alpha l}\) = 178.6 mg.g\({}^{-1}\)) is far less than the experimental value (\(Q_{m,exp}\) = 319.0 mg.g\({}^{-1}\)). It indicates that the adsorption of GA-FK gel to Fe(III) is more consistent with Freundlich model, and the adsorption is mainly non-specific adsorption.
###### Abstract  
In this study, to visualize damage and defects, such as cracks and voids in concrete, the SIBIE (stack imaging of spectral amplitudes based on impact echo) procedure is applied and numerically improved to construct a three-dimensional (3D) model of elastic wave propagation behavior. A unit of arrayed accelerometers is installed to detect multi-channel signal waveforms in the frequency domain. The resonant frequencies due to reflections at each node in 3D lattice nodes are computed by using the distances from elastic wave input to multiple output locations. The amplitudes corresponding to the resonant frequencies in the spectrum are summed up as the reflection intensity of elastic wave at each node. The reflection intensity distribution is visualized finally in the targeted area three-dimensionally. Case studies are carried out on the proposal of the improved 3D-SIBIE procedure, applied to a concrete specimen with simulated-damage as well as in-situ highway RC (Reinforced Concrete) slabs in service. As for the signal detection, a non-contact elastic wave detecting system using a laser doppler vibrometer is also introduced to consider and validate the promising remote sensing and inspection technique for damage evaluation in concrete with the 3D SIBIE procedure.  
Keywords:non-destructive testing; concrete; impact-echo; elastic wave; SIBIE; 3D visualization; accelerometer; laser doppler vibrometer; remote sensing +
Footnote †: journal: Applied
# The Bearing Performance and Sectional Design Method of Yielding U-Shaped Steel Support
## 4 Analysis of the Influence Mechanism on the Bearing Performance of Yielding U-Shaped Steel Support
### Principles of Sectional Design and Engineering Recommendations  
#### 4.3.1 Principles of Sectional Design  
According to the analysis of the influencing mechanism of the bearing characteristics of the support, the sectional design principles for yielding U-shaped steel support are proposed, as shown Figure 19.  
Figure 18: Bearing capacity–displacement curve of steel support.  
Based on the above analysis of the influence mechanism on the bearing characteristics of support, the sectional design principles for yielding U-shaped steel support are proposed. The process is as follows:  
Firstly, conduct investigations into engineering conditions such as roadway anchoring parameters and cross-section dimensions, as well as geological conditions like in situ stress and rock mass properties, and then establish an engineering geological model.  
Secondly, carry out numerical tests to clarify the internal force distribution forms and deformation failure states of the support. Based on the principle that the axial force of the support is the source of the shrinkage movement of the nodes while the bending moment is the source of resistance against the shrinkage movement, put forward the overall sectional areas.  
Finally, taking into account comprehensive factors such as the transportation, installation, and handling of the support, propose the specific sectional methods for the support.
# Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review
## 4 Experimentation Methodology
### Single Host Emulation
#### 4.3.1 Mininet  
The first step to build an experimentation framework with Mininet is to set up an environment with the required tools and routing protocols. Since Mininet is developed for Linux, the open-source Internet routing protocol suite FRR was chosen. FRR implements BGP (with the needed configuration knobs for the data center), Openfabric, and IS-IS. In order to run the RIFT protocol, the open-source implementation rift-python [60] was installed.  
Once the environment is ready, the experiment must be designed. To this end, we coded a topology generator based on an open-source project [61], using the Mininet Python API [62]. Altogether, this allows for generating a fat-tree topology with the \(k\) factor as the input parameter.  
With this tool in place, we can emulate a fat-tree topology with a selected \(k\) and (manually) configure the necessary routing protocol on each node. Of course, the manual configuration does not scale properly with the \(k\) factor. Consequently, a configuration layer was added to the fat-tree generator, which achieves the configuration for the chosen routing protocol and initiates the protocol daemons on each topology node, enabling to experiment over a fat-tree topology in Mininet, running a data center aware routing protocol.  
As part of the experiments, control plane traffic is collected for further analysis, as detailed in Section 4.6.
## 3 Energy Management Strategy for HEV/PHEVs
### Optimization-Based EMS
#### 3.2.2 Global Optimization Methods  
The concept of Dynamic Programming (DP) is essential for developing strategies to find an optimal global solution. DP utilizes Bellman's optimality principle and decomposes complex problems into simpler subproblems. Normally, Deterministic Dynamic Programming (DDP) has been used to calculate a theoretical lower bound for consumption on specific speed profiles [31]. However, DDP has a curse of dimensionality, meaning that the computational cost increases exponentially with the number of state and control variables, thereby making it impractical for use in real-time applications, particularly for large systems. Some works have proposed techniques for reducing the dimension of the state or control space to overcome this limitation [32; 33]. Additionally, future trips information can be considered in the EMS formulation in order to minimize the total trip fuel consumption, either through a mixed-integer linear programming problem [34], or through a Stochastic Dynamic Programming (SDP) framework [35; 36].  
In conclusion, EMSs are a crucial aspect for HEV/PHEVs, and they play a vital role in achieving their energy efficiency and reducing their environmental impact. The various optimization techniques offer a range of options for designing EMSs that meet the specific requirements of different HEV/PHEV applications. Furthermore, the integration of eco-driving strategies with EMSs can result in improvement in more aspects, such as significant fuel savings, increased safety, and driving comfort. In order to fully understand the complex integration technique, we will explore the various eco-driving strategies that are not limited by HEV/PHEV first in the next section.
## 2 The Critical State Line Considering the Effect of Principal Stress Axis Rotation
### The Influence of Principal Stress Axes Rotation on the Anisotropic State Variables  
When the principal stress axis rotates from 0 to 180 degrees, the corresponding anisotropic state variable \(A\) changes systematically. The following curve depicts the relationship between the rotation angle and \(A\) under different anisotropic conditions (with different values of \(A\)).  
Figure 1 depicts the effect of the rotation angle on \(A\) when \(a_{1}=a_{2}\). The calculations are conducted using an intermediate principal stress coefficient of \(b=0.5\), a failure stress ratio \(M_{f}\) of 1.25, and a confining pressure of 100 kPa. Notably, \(A\) remains consistently 0 when \(a_{1}\) = \(a_{2}\) = 0, implying isotropic material behavior. In this particular scenario, the rotation of the principal stress axis has no effect on \(A\), and by extension, it does not alter the failure strength. Conversely, when \(a_{1}\) = \(a_{2}\) \(\
eq\) 0, \(A\) varies monotonically within the 0 to 90 degrees range, highlighting the anisotropic nature of the material. The failure strength changes in accordance with the rotation angle, and the magnitude of this variation is proportional to the magnitude of the change in \(A\) value.  
Figure 2 shows the impact of the rotation angle on \(A\) when \(a_{1}\) is not equal to \(a_{2}\). Figure 2a,b represent the influence of varied \(a_{2}\) on \(A\) while \(a_{1}\) remains constant (for example, when \(a_{1}\) equals 0.1 or 0.5). It is evident that there is a gradual increase in \(A\) with an increase  
Figure 1: The influence of rotation angle on the anisotropic state variables.  
in \(a_{2}\), albeit the amplitude of the rise is relatively minor. On the other hand, Figure 2c,d displays the influence of varied \(a_{1}\) on \(A\) while \(a_{2}\) remains constant (for instance, when \(a_{2}\) equals 0.1 or 0.5). The changes are comparable to those seen with \(a_{2}\) variation--i.e., there is an increase in the anisotropic state variables with an increase in \(a_{2}\). However, \(a_{1}\) has a more significant impact than \(a_{2}\).  
Based on the analysis, the following conclusions can be drawn: \(A\) varies with the change in rotation angle, and its absolute value monotonically increases from \(0^{\circ}\) to \(90^{\circ}\); additionally, \(A\) increases as amplitude parameters \(a_{1}\) and \(a_{2}\) increase, with \(a_{1}\) having a greater impact than \(a_{2}\).
# Efficient Support Vector Regression for Wideband DOA Estimation Using a Genetic Algorithm
## 4 Performance Analysis
### Generalization Performance  
This experiment is designed to analyze the generalization performance of the proposed algorithm by assessing its DOA estimation accuracy under conditions where the signal types in the testing dataset differ from those in the training dataset. Maintaining the same training configuration as that in Section 4.1, the testing phase employs signal intervals of [10\({}^{\circ}\), 20\({}^{\circ}\), 30\({}^{\circ}\)] and two-signal angle ranges of [\(-60^{\circ}\), 60\({}^{\circ}\)] with 1\({}^{\circ}\) increments, generating a total of 303 test samples. The test signal is an LFM signal with the same bandwidth and an SNR of 6 dB. Figure 9 shows the DOA estimation results and corresponding errors obtained by the proposed algorithm.  
Figure 9 shows that, when trained on BPSK signals, the proposed algorithm produces DOA estimation results for LFM signals that closely match the true signal angles across all signal intervals. The estimated angles almost coincide with the actual values, which demonstrates the robustness of the algorithm to variations in signal types. Moreover, the majority of estimation errors remain within [\(-0.5^{\circ}\), \(0.5^{\circ}\)], indicating strong generalization ability and high estimation accuracy even when the signal type in the testing phase differs from that in the training phase. These results validate the adaptability and practical applicability of the proposed method in complex and diverse signal environments.
# The Antimicrobial Potential of Hexane Oils and Polyphenols-Rich Extracts from _Pistacia vera_ [PERSON].
###### Abstract  
Pistachio (_Pistacia vera_ L.) nuts contain nutrients and phytochemicals which have been linked to several positive outcomes. The aim of this research was to examine the antimicrobial effect of natural raw and roasted unsalted polyphenols-rich pistachio extracts (NRRE and RURE) and hexane oil fractions. American Type Culture Collection (ATCC), food and clinical isolates of Gram-positive bacteria (_Listeria monocytogenes_ and _Staphylococcus aureus_), Gram-negative bacteria (_Pseudomonas aeruginosa_, _Escherichia coli_ and _Enterococcus ficetium_) and yeasts (_Candidia albicans_) were used. In addition, the influence of the extraction method was evaluated. Generally, NRRE extracts were richer in polyphenolic compounds compared with RURE extracts. NRRE extracted with \(n\)-hexane was the most effective on _Listeria monocytogenes_ food isolates strains (MIC values between 0.25 and 2.0 mg mL\({}^{-1}\)). All extracts, except for RURE extracted with \(n\)-hexane, were active against _Listeria monocytogenes_ ATCC 13932. Both hexane oil fractions were active against _Listeria monocytogenes_ ATCC 13932 and _Enterococcus ficetium_ DSZM 17050. The oil obtained from natural pistachio was active against three food isolates of _Listeria monocytogenes_. In conclusion, the present study indicates an inhibitory effect of pistachio polyphenols against _Listeria monocytogenes_, one of the most serious pathogens causing foodborne disease.  
Keywords:polyphenols-extracts; _Pistacia vera_ L.; antimicrobials
## 2 Security Requirement and S-Box Design Methods
### S-Box Design Methods  
In this section, we review several \(8\times 8\) S-box constructions available in literature and classify them into three generic methods which are random generation, evolutionary or heuristic, and a mathematical function. In general, the classification of each S-box proposal is based on its construction. As an example, random methods that use a pseudo-random generator to generate a key and/or select a key randomly from its construction elements are categorized as a random generation approach. Likewise, using mathematical functions as the core of S-box construction will be categorized as a mathematical function approach. The S-box constructions that use neither a random generator nor mathematical function can be categorized as heuristic approaches. In addition, we also classify S-box constructions that use more than one generic method as a _Combination Method_.  
The random generation approach is the simplest technique to construct an S-box; however, its disadvantage is that most of the final S-boxes generated do not achieve our desired value for cryptographically strong S-boxes, as stated in Table 1. Conversely, the mathematical function approach is the best technique with which to achieve the desired S-box properties; however, it is difficult to find this function. For the heuristic approach, the latest S-box construction proposals show an improvement compared to the early proposal in terms of its S-box properties. Therefore, nowadays, researchers focus their attention on exploring heuristic approaches to generate cryptographically strong S-boxes since the implementation of this approach is convenient in both hardware and software.  
To facilitate the classification of S-box construction, we divide the S-box design methods into two subsections. The first subsection reviews the S-box constructions which are implemented in the block ciphers proposal, while the second subsection reviews other S-box constructions available in literature.
# The Linac Coherent Light Source: Recent Developments and Future Plans
## 4 Future Developments and Science Opportunities--LCLS-II-HE
### LCLS-II-HE Overview and Technical Capabilities  
#### 4.1.1 The Envisioned LCLS-II-HE Upgrade Will:  
* Deliver two to three orders of magnitude of increase in average spectral brightness in the hard X-ray range beyond any proposed or envisioned diffraction-limited storage ring (DLSR) and exceed the anticipated performance of the European-XFEL.
* Provide temporal coherence for high-resolution spectroscopy near the Fourier transform limit with more than a 300-fold increase in average spectral flux (ph/s/meV) for high-resolution studies beyond any proposed or envisioned DLSR.  
Figure 11: The performance of LCLS-II-HE will allow access to the ‘hard X-ray’ regime, providing atomic resolution capability, with an average brightness roughly 300 times the ultimate capability of a diffraction-limited storage ring (DLSR) [72]. Self-seeding will further increase the average brightness of the X-ray free-electron laser (XFEL) facilities by an additional factor of 20 to 50.  
* Generate ultrafast hard X-ray pulses in a uniform (or programmable) time structure at a repetition rate of up to 1 MHz.
* Double the electron beam energy of the CW-SCRF linac to 8 GeV, thereby creating three independent accelerators within a single facility: (1) a new 8 GeV superconducting linac; (2) a separately tunable 3.6 GeV by-pass line for the LCLS-II instruments; and (3) the existing 15 GeV Cu-linac.
# Incorporating External Knowledge Reasoning for Vision-and-Language Navigation with Assistant's Help
## 3 Method
### Problem Setup and Overview  
#### 3.1.1 Setup  
Our goal is to design a VLN agent that is capable of reasoning with the knowledge graph and requesting additional help when in need. The agent receives an initial language instruction and real-time visual observations as input signals, but does not know the map of the environment or its own specific location. The language instruction \(I\) is formulated as a set of words \(\{\omega_{0},\cdots,\omega_{L}\}\), where \(L\) is the number of words. The real-time visual observation is a \(360^{\circ}\) panoramic view, including 36 image views \(\{o_{t,i}\}_{i=1}^{36}\), and each view \(o_{t,i}\) is composed of an RGB image \(v_{t,i}\) and its orientation \((\theta_{t,i},\varphi_{t,i})\), where \(\theta_{t,i}\) and \(\varphi_{t,i}\) are the heading and elevation angles, respectively. The agent is initially spawned at a random location in the scene. A requester, simulating the real human user, asks the mobile agent to find an object in a specific room by sending a high-level language command (\"Find [object(s)] in [room]\"). There is at least a target object instance in the environment that satisfies the end-goal, making the task always feasible. During the specified time steps,if the agent stays within \(R_{S}\) meters from the target object along the shortest path, the task is considered to have been successfully fulfilled. Here, \(R_{S}\) represents the length of the success radius, a task-specific hyperparameter. There is a simulated human assistant that the agent can turn to in the environment, who is present at both the training and evaluation time, providing the agent with single-step teacher decision-making through direct intervention. During execution, the agent is able to decide whether to ask the simulated human assistant for help through prior general knowledge, if the query budget allows. The query budget is a hyperparameter that determines the number of times the agent can ask.  
In our example, as shown in Figure 1, the agent is lost and asks for help at Position 2; the simulated human assistant gives a \"go forward\" action so that the agent can navigate along the hallway to approach the dining room. Direct intervention assistance may not ensure that the end-goal is fulfilled, but is guaranteed to get the agent closer to the target location.