esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Optical Conductivity  
Once we have the tight-binding models for the four compounds, we can use them to calculate physical observables as, for example, the optical conductivity \(\sigma(\omega)\). For this aim, we use the Kubo formula  
\[\begin{array}{c}\sigma(\omega)=\frac{e^{2}}{\hbar}\frac{\hbar}{A\omega}\sum_{ mn,\mathbf{k}}(f(E_{n})-f(E_{m}))\times\\ \qquad\qquad|\langle\Psi_{n}(\mathbf{k})|\vartheta|\Psi_{m}(\mathbf{k})\rangle| ^{2}\,\delta[\hbar\omega-(E_{n}(\mathbf{k})-E_{m}(\mathbf{k}))],\end{array} \tag{5}\]  
where \(A\) is the area of the unit cell, \(\Psi_{n}(\mathbf{k})\) is the eigenstate of energy \(E_{n}\), \(f(E_{n})=1/(1+e^{\beta E_{n}})\) is the Fermi-Dirac distribution function, in terms of the inverse temperature \(\beta=1/k_{B}T\) and considering that the Fermi energy lies in the gap, and \(\vartheta=(1/\hbar)\partial\mathcal{\hat{H}}/\partial\mathbf{k}\) is the velocity operator. The results are shown, for the approximate range of validity of our TB models (\(\sim\)1 eV above and below the band gap), in Figure 5. We observe that, for all the compounds, there is a threshold for the onset of optical transitions that is equal to the gap \(\Delta\). The steplike structure of \(\sigma(\omega)\) at low energies (see insets of each panel in Figure 5) is due to the SOC, which leads to two sets of optical transitions in the spectrum. Due to the stronger SOC of heavier W atoms, the effect is especially visible in WS\({}_{2}\) and WSe\({}_{2}\), with plateaus of \(\sim\)0.4 eV in \(\sigma(\omega)\), corresponding to the energy splitting of the valence band at the K and K\({}^{\prime}\) points of the Brillouin zone (see Figure 3). These transitions lead to the well-known A and B absorption peaks observed in photoluminescence [16]. We further notice that our results for the optical conductivity are in good agreement, even for the onset energy, with experimental measurements (see e.g., Reference [53]). At higher energies, the optical conductivity shows a series of peaks that are associated with optical transitions between flat bands in the spectrum (van Hove singularities). Such van Hove singularities are clearly evident for the valence and conduction bands at the M point of the Brillouin zone (see Figure 3). We notice that disorder (vacancies, adatoms, etc.), not included here, can lead to the creation of midgap states that allow for additional optical transitions in the spectrum [29].  
Figure 5: Optical conductivity of the four compounds, calculated from the TB model. The insets show the respective low energy zooms of \(\sigma(\omega)\) around the onset of optical transitions.
## 3 Proposed Approach  
### Algorithm Framework  
The proposed methodology comprises three distinct modules: the dispatching rule mining module, the evaluation module, and the feature selection module. Figure 1 depicts the structured framework of the proposed methodology. In the dispatching rule mining module, the GEP algorithm (integrated with an adaptive variable neighborhood search algorithm (IGEP)) is used to extract new dispatching rules by learning from the different training scenarios. The IGEP algorithm starts with an initial population, consisting of randomly generated rules. The candidate rules are subsequently presented to the evaluation model, which defines the various scheduling scenarios and assesses them, using quantitative performance metrics. After all the candidate rules are evaluated, the values of the performance measures of all rules are sent back to the dispatching rule mining module, where a new population of candidates is reproduced and modified from the current high-performing rules using genetic manipulation and a variable neighborhood search in the IGEP algorithm. Subsequently, the succeeding set of rules is transmitted to the evaluation module to assess its efficacy. This process is repeated until the stopping criteria is satisfied. In this way, the IGEP algorithm continuously searches for the optimal solution to update the rule base.
## 4 Discussion
The phenomenon of net CO2 absorption is closely tied to the growth activities of reeds. Furthermore, related studies have shown that the response of NEE to solar radiation is also influenced by other limiting factors, such as the atmospheric vapor pressure deficit and occasionally frost [28; 29]. The influence of the radiation intensity on the net CO2 exchange is more pronounced within a suitable temperature range. Additionally, a larger leaf area index corresponds to a more distinct response of net CO2 exchange to radiation intensity. Most studies suggest that changes in WL and temperature increases can significantly affect the production of CO2 in wetland ecosystems [30; 31; 32]. Research on wetlands in southern Finland found that a high temperature is the primary factor leading to an increase in net carbon uptake [33]. This study found that nighttime carbon flux exhibits an exponential growth trend in response to Tair. Temperature affects ecosystem CO\({}_{2}\) flux through both direct and indirect mechanisms, ultimately influencing ecological processes [34]. Firstly, the seasonal variation in photosynthesis is primarily controlled by temperature [35]. Within a certain temperature range, higher temperatures are more favorable for photosynthesis [36]. For example, studies on the seasonal cumulative GPP of corn and wheat ecosystems found that higher mean temperatures are associated with larger cumulative GPP values [37]. Research on temperate bulrush marshes also showed that temperature is a key indicator for daily GPP [38]. Furthermore, ecosystem respiration is primarily controlled by temperature, with respiration increasing exponentially as temperature rises [39]. This is because higher temperatures can stimulate dark respiration in both soil and vegetation. Research on northern marshes revealed that higher temperatures correspond to higher ecosystem respiration rates [40]. At both interannual and seasonal scales, the Reco of salt marshes was found to be significantly correlated with temperature [41]. Furthermore, CO\({}_{2}\) fluxes are controlled by temperature through its effects on vegetation cover, the leaf area index, and the length of the growing season. An increase in Tsoil is accompanied by an increase in AGB [42]. The temperature in the environment controls the humidity by regulating the vapor pressure. Research on wetlands in the southwestern plateau of China found that temperature can explain most of the seasonal variability in carbon fluxes. Warming can increase both GPP and Reco, thus influencing the carbon balance of the plateau wetlands [43]. Although water level conditions significantly influence the uptake and release of CO\({}_{2}\) in the ecosystem, temperature remains the primary factor for carbon fluxes in the Momoge salt marsh.
# Time Domain Investigation of Hybrid Intelligent Controllers Fed Five-Phase PMBLDC Motor Drive
## 3 Design of Controllers
### Fuzzy Logic Controller  
The fuzzy logic controller helps the designer to linguistically express the general characteristics of any non-linear system by forming IF-THEN rules. The fuzzy logic controller consists of four functional blocks, namely fuzzification, defuzzification, fuzzy inference engine, and fuzzy rule-base. A triangular membership function for input parameters, namely error (M) and change in error (\(\Delta\)M), has been considered. Similarly, for the output parameter, a torque reference (\(\mathrm{T_{ref}}\)) is considered with the same type of triangular membership.  
The Fuzzy Associate Memories (FAM), as given in Table 1, depict the rules of the fuzzy logic controller. Based on the literature review on the selection of FAM, for simplicity and accuracy, we chose a triangular-shaped membership function with a 50% overlapping rate for fuzzy linguistic sets, namely Negative Large (NL), Negative Medium (NM), Negative Small (NS), Zero (Z), Positive Large (PL), Positive Medium (PM), and Positive Small (PS). This function is used for both the input variable, namely, error (M) and change in error (\(\Delta\)M), as well as the output variable, namely, the torque reference (T\({}_{\text{ref}}\)). The final fuzzy output (T\({}_{\text{ref}}\)) is converted into a crisp output using the Centroid method of defuzzification process,  
\[Z=\frac{\sum_{\text{x}=1}^{\text{n}}\mu(\text{x})\text{x}}{\sum_{\text{x}=1}^{ \text{n}}\mu(\text{x})} \tag{8}\]  
where Z is the defuzzified value, and \(\mu(\text{x})\) is the membership value of member x.
###### Abstract  
Cognitive sensors are embedded in home appliances and other surrounding devices to create a connected, intelligent environment for providing pervasive and ubiquitous services. These sensors frequently create massive amounts of data with many redundant and repeating bit values. Cognitive sensors are always restricted in resources, and if careful strategy is not applied at the time of deployment, the sensors become disconnected, degrading the system's performance in terms of energy, reconfiguration, delay, latency, and packet loss. To address these challenges and to establish a connected network, there is always a need for a system to evaluate the contents of detected data values and dynamically switch sensor states based on their function. Here in this article, we propose a reinforcement learning-based mechanism called \"Adaptive Scheduling in Cognitive IoT Sensors for Optimizing Network Performance using Reinforcement Learning (ASC-RL)\". For reinforcement learning, the proposed scheme uses three types of parameters: internal parameters (states), environmental parameters (sensing values), and history parameters (energy levels, roles, number of switching states) and derives a function for the state-changing policy. Based on this policy, sensors adjust and adapt to different energy states. These states minimize extensive sensing, reduce costly processing, and lessen frequent communication. The proposed scheme reduces network traffic and optimizes network performance in terms of network energy. The main factors evaluated are joint Gaussian distributions and event correlations, with derived results of signal strengths, noise, prediction accuracy, and energy efficiency with a combined reward score. Through comparative analysis, ASC-RL enhances the overall system's performance by 3.5% in detection and transition probabilities. The false alarm probabilities are reduced to 25.7%, the transmission success rate is increased by 6.25%, and the energy efficiency and reliability threshold are increased by 35%.  
reinforcement learning; adaptive scheduling; cognitive sensors; energy efficiency; Internet of Things; latency; false alarm rate; detection; transmission probabilities +
Footnote †: journal: Applied Sciences  
1
# Molecular Dynamics Study on the Crystallization Process of Cubic Cu-Au Alloy
## 1 Introduction
Scientists observed in the phase transition temperature region a decrease in the Cu-Au gravity [48; 60; 61]. Previous studies have shown the fine-tuning of the catalytic activity of Au nanoparticles [62; 63]. The plasmonic response of Cu-Au nanocrystals with low Cu content has also been used in photochemotherapy [62], and a tunable plasmon response is also predicted for precious metals [64]. Recently, researchers studied the 2D structure of Au at an evaporation temperature of T\({}_{\text{m}}\) = 1353 K directly on graphene films containing Cu from the growth of the base material. Under electron irradiation, Au atoms are liberated and bound to the Cu substrate to form Cu-Au alloys [33]. Scientist have also successfully studied the electronic structure, phase transition, and crystallization of Al [65], Fe [66], AlNi nanoparticles [67], NiCu bulk [68], NiFe nanoparticle [69], AgAu alloy [70], NiCu nanoparticle [71], AuCu bulk [72], NiAu alloy [73], and polymer [74]. In particular, with NiCu and NiAu alloys, it has been shown that when increasing the temperature and heating rate, the length of the link r decreased, g(r) decreased while increasing the number of atoms, the annealing time increased, then r and g(r) increased [68; 71; 73]. From the considerations mentioned above, there appears the following question: if the metals are doped together, can the phenomenon of the length of the links be decreased? To answer that question, we continue to work with Cu-Au alloy to elucidate that problem by studying the influence of factors on the structure, crystallization, and phase transition of cubic Cu-Au. Crystallization rate is the ability of the process to happen quickly or slowly, depending on many factors. We will show that, when the temperature increased, the crystallization process decreased, the structure gradually turns to the liquid state, and vice versa. The consideration of the change of crystallization process is of great significance in determining structural changes and material properties, such as electrical and magnetic properties. As the crystallization process increased, the electrical conductivity and magnetic of the material increase. When the time of each heating step increased, and the annealing time (the extension of the heating time at each heating step), the crystallization process increased, then it advances the most at the glass temperature T\({}_{\text{g}}\) = 550 K. During the phase transition, link length (r), total energy (E\({}_{\text{tot}}\)), size (l), number of FCC, HCP, and Amor structural units have a significant change. The obtained results are evidence for the research and fabrication of this material for photocatalytic devices in the future.
## 5 Proposed Rescheduling Methods
### Rescheduling for Machine Breakdown (Dynamic Event)  
Figure 2 shows the workflow for machine breakdown. A machine breakdown, referred to as a dynamic event, occurs when a machine fails unexpectedly at a specific time of scheduling. When it comes to flexible job-shop scheduling, if there is sudden (unplanned) unavailability of a machine, it can have a significant impact on the production process and lead to a delay in processing. In the proposed method, initially, basic information on machine breakdown is required, like the broken machine number and breakdown time of the machine. At the time of machine breakdown, all upcoming operations are rescheduled, and the starting time of operations is defined immediately after the breakdown time with interruption of the operations that had already started to process on the broken machine. After eliminating the machine breakdown, with the help of extracted data from static scheduling, the remaining operations that need to take place are rescheduled.  
The conditions to filter out the operation that participates in rescheduling are as follows:  
The operations involved in the machine breakdown:  
* Operations that end before breakdown time (i.e., stop time of operation < breakdown time) will not be involved in the rescheduling strategy.
* Operations that end after breakdown time (i.e., stop time operation > breakdown time) will be involved in the rescheduling strategy.  
The operations that are not involved in the machine breakdown:  
* Operations that had already started (i.e., start time of operation > breakdown time) will not be involved in the rescheduling strategy.
* Operations that started after the breakdown time (i.e., start time operation > breakdown time) will be involved in the rescheduling strategy.  
After eliminating the non-required operation, we must reschedule all remaining operations using the proposed hybrid optimization method with a new start time for the machines to obtain the optimization solution for rescheduling the sequence.
## 3 Experimental Setup and Tests
### The Establishment of the Test Rig  
Arcing is the main factor causing the micro-switches to fail [28]. Whenever the switch contacts are separated, the arc will be generated, and the contact voltage will continue to rise until failure [29].  
When it starts to work, there exists two phenomenon in the process. One is increasing in contact voltage: For micro-switches, the arc generated on the contacts will be as high as 4000 K or more, making the contact material partially melted and sputtered. In the meanwhile, it generates complex physical and chemical processes. With the increase of the work cycle, there are thousands of these repeated effects and superposition, and the contact resistance gradually increases until the conduction capability is lost. The other is insulation performance reduction: When micro-switches are in operation,due to the repeated action of the arc, melting, vaporization, and splashing of the electric shock material, the metal compound adheres to the surface of the insulating part near the contact. With the increase in the number of work, the attached crop will grow thicker until it connects with the insulated conductors. The working period of typical drive controller micro-switch is shown in Figure 2.  
Due to the actual working conditions of the micro-switches, some basic physical quantities were selected. Rated voltage is chosen as 110V direct current (DC), the rated current was chosen as 1A DC, the time constant was chosen as 15 ms and the operating frequency was chosen as 120r/min [30].  
The test rig used in this experiment was identical with the one used by [PERSON] et.al. [30], designed to test the life of micro-switches showing in Figure 3.
# An Efficient Multi-Lane SpaceFibre Core for Spacecraft Data-Handling Networks
## 3 Architecture of the E-ML-SpFi Core
### Key Technologies
#### 3.2.1 Hierarchical Error Recovery
Consequently, the hierarchical architecture provides a top-down description of the retry mechanism and combines a variety of strategies to simplify the complex implementation logic required by the retry process.
# Accident Risk Analysis of Gas Tankers in Maritime Transport Using an Integrated Fuzzy Approach
## 3 Methodology
### Fuzzy Delphi Method
\[d=\frac{1}{n}{\sum_{i=1}^{m}D_{ij}} \tag{5}\]  
The threshold value (\(Th_{d}\)) for each prediction field is verified using Equation (6). \[Th_{d}=\frac{1}{n}{\sum_{i=1}^{m}d_{j}} \tag{6}\]  
The acceptability of the prediction domain is determined by the value of \(Th_{d}\). In this study, the prediction domain is accepted if \(Th_{d}\leq 0.2\)[47]. Expert group agreement (\(EA_{j}\)) is tested by calculating expert agreement for each factor with Equation (7) [43; 45]. \[EA_{j}=\frac{E_{j}}{n}\%_{0} \tag{7}\]  
Here, \(n\) represents the total number of experts, \(E_{j}\) represents the distance between the experts' fuzzy prediction values (\(\widetilde{\omega_{ij}}\)) on a given factor j and the average fuzzy number (\(\widetilde{\omega_{j}}\)) for all experts on that factor [43] If this distance \(\leq 0.2\), the expert group consensus was assessed according to the criterion \(EA_{j}\geq 75\%\) or more, based on the agreement of the experts for each factor. Otherwise, factors with an expert agreement of less than 75% will be ignored [43; 45; 47]. Expert Specifications  
The objective of this study was to ascertain the reliability of the Delphi method by employing the insights of experts in the respective domains. Consequently, all individuals involved were required to possess extensive knowledge of gas tanker operations, safety, and chartering. A total of eight professionals were selected for this study, each of whom possesses significant experience in their respective fields. These experts have held roles such as operations manager, technical manager, and chartering manager in GCT shipping companies across various countries for a minimum of ten years. All selected experts participated in both rounds of the Delphi study. The following details for the experts are provided: years of experience, current departments, gas tankers' tonnage, and number of vessels. These details can be found in Table 3.  
In the selection of experts, the primary criteria were their employment within gas tanker companies and their demonstrable experience within the sector.
## 3 Case Study
### Result Analysis
#### 3.3.1 Hierarchical Evaluation of Node Importance Based on Single Indicators
Therefore, it is essential to develop a comprehensive node importance evaluation method that accommodates the varying requirements of different network layers and parameters. At the same time, such an approach should support flexible applications based on single-parameter evaluations to address specific research needs.
# Microseismic Data-Direct Velocity Modeling Method Based on a Modified Attention U-Net Architecture
## 2 Methods
### Attention Gate (AG) Module  
The attention mechanism is a simulation of the human visual system, designed to focus on local information. It dynamically adjusts the weights of different positions in feature maps to enhance specific information. This mechanism can be categorized into three main types: spatial attention, channel attention, and spatial and channel mixed attention models. Currently, it finds extensive applications in various domains [35]. In 2018, [PERSON] et al. introduced a novel module called AG in the field of medical imaging. This module was integrated with the U-Net architecture, resulting in the Attention U-Net architecture [33].  
Specifically, in the Attention U-Net, the AG module is used to adjust the output features of the encoder before concatenating them with the corresponding features from the decoder. The AG module generates a gating signal that automatically calculates the weights of features at different spatial positions, allowing the model to focus on specific regions.  
The computational principle within the red box in Figure 1 is as follows: Firstly, the gating tensor controlling encoder features is mapped to the intermediate layer using Formula (1). Secondly, the input tensor representing decoder features is mapped to the intermediate layer using Formula (2):  
\[phi\_g=W_{g}^{T}g \tag{1}\]  
\[theta\_att=W_{x}^{T}x^{l} \tag{2}\]  
where \(W_{g}\) and \(W_{x}\) represent linear transformations, \(l\) corresponds to the number of layers. Afterward, \(phi\_g\) and \(theta\_att\) undergo correlation operations according to Formula (3) to calculate the query \(Q_{att}^{l}\). To compute the attention coefficients \(\alpha\) using Formula (4), which is finally multiplied with the input tensor \(x\) to obtain the weighted encoder feature vector \(\widetilde{x}\), the following is used:  
\[Q_{att}^{l}=\psi^{T}(\sigma_{1}(theta\_att+phi\_g+b_{g}))+b_{\psi} \tag{3}\]  
\[\alpha^{l}=\sigma_{2}(Q_{att}^{l}(x^{l},g;\Theta_{att})) \tag{4}\]  
where \(\sigma_{1}=\max(0,x)\) corresponds to ReLU activation function, \(\sigma_{2}=\frac{1}{1+\exp(-x)}\) corresponds to sigmoid activation function. AG is characterized by a set of parameters \(\Theta_{att}\), which includes linear transformation parameters \(W_{g}\), \(W_{x}\), \(\psi\) and bias parameters \(b_{\psi}\) and \(b_{g}\)[34].
## 4 Results and Discussion
### Throughput Comparison  
Figure 5 reveals the throughput comparison, where all frameworks showed a relatively higher throughput; ACRLPC enabled a peak throughput of 95 Mbps and TCH-MAC closely followed with a throughput of 90 Mbps. FDU-MAC dropped slightly, obtaining a throughput of 85 Mbps, whereas UW-ALOHA-QM had the lowest throughput of 80 Mbps. This outcome shows that while ACRLPC achieved better throughput than TCH-MAC, TCH-MAC was also competitive with ACRLPC and that both of these schemes outperformed FDU-MAC and UW-ALOHA-QM. When throughput is essential for high data rates, then the server can either use LRMAC, ACRLPC, and TCH-MAC, which make them suitable for these types of environments.  
Figure 4: Energy consumption comparison of ACRLPC with benchmark techniques.  
Figure 5: Throughput comparison of ACRLPC with benchmark techniques.