esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Exergy Analysis  
Since exergy analysis has been widely studied (e.g., [41]), only a brief description of the methodology is presented here.  
Exergy is defined as the maximum useful theoretical work that can be obtained by a stream or a system when the stream or the system is brought to thermodynamic equilibrium with the environment. The environment is a system in equilibrium with constant temperature, pressure (\(T_{0}\), \(p_{0}\)), and chemical potential, and a total exergy of zero. Exergy can be divided into four main components: physical (\(\dot{E}^{PH}\)), chemical (\(\dot{E}^{CH}\)), kinetic (\(\dot{E}^{KN}\)), and potential (\(\dot{E}^{PT}\)) [42]:  
\[\dot{E}=\dot{E}^{PH}+\dot{E}^{KN}+\dot{E}^{PT}+\dot{E}^{CH} \tag{2}\]  
Usually, changes in kinetic and potential exergy are very small and can be neglected. Physical exergy is the maximum useful work that can be obtained by passing the unit of mass of a substance from a state with specific temperature and pressure (\(T\) and \(p\)) to that of the environment (\(T_{0}\) and \(p_{0}\)) (restricted dead state) by means of physical processes [42]. Chemical exergy is the maximum useful work that can be obtained when passing from the restricted dead state with \(T_{0}\) and \(p_{0}\) to the dead state, which is the state of complete equilibrium (mechanical, thermal, and chemical) with the environment, which may not imply a chemical process.  
In this work, the reference stream with zero exergy used was the air entering the boiler, with \(T_{0}=306\) K and \(p_{0}=1.013\) bar. Standard chemical exergy values of the main substances were obtained using the model of [PERSON] [43]. Air, flue gas, and molten salt streams were treated as ideal mixtures [42]. The chemical exergy of coal was calculated considering the reaction of its combustion [42], following the procedure described by [PERSON] et al. [44].  
To realize an exergy analysis of a power plant, it was required to define an exergy balance for each of the components in the plant and a balance for the overall system. The balance for each component \(k\) is defined as follows:  
\[\dot{E}_{F,k}-\dot{E}_{P,k}-\dot{E}_{D,k}=0 \tag{3}\]  
where \(\dot{E}_{F,k}\) is the exergy of the fuel (required resources to generate the desired product), \(\dot{E}_{P,k}\) is the exergy of the product, i.e., the desired output of a process, and \(\dot{E}_{D,k}\) is the exergy destruction of the component due to thermodynamic inefficiencies within it [42; 45]. The corresponding equation for the overall system is written as:  
\[\dot{E}_{F,sys}-\dot{E}_{P,sys}-\dot{E}_{D,sys}-\dot{E}_{L,sys}=0 \tag{4}\]  
where \(\dot{E}_{L,sys}\) is the exergy loss of the overall plant. The specific performance evaluation of a component/system was based on the study of its exergetic efficiency, defined as the ratio between its product and the resources used to generate this desired product [42].  
\[\varepsilon_{k/sys}=\frac{\dot{E}_{P,k/sys}}{\dot{E}_{F,k/sys}} \tag{5}\]Therefore, an energetic analysis is useful to locate the inefficiencies of a thermodynamic system and find their possible cause, facilitating the analysis and optimization of power plants.
# Analyzing the SAR in Human Head Tissues under Different Exposure Scenarios
## 2 Materials and Methods
### SAR Simulations
A three-dimensional view of the designed antenna is shown in Figure 2 and the geometrical dimensions of the antennas for the 900 and 1800 MHz frequencies are provided in Table 4. Human (e.g., adult male) interaction with the antenna is depicted in Figure 3. In SAR simulations, the antenna is typically placed in a location that represents its actual position on the mobile phone, considering factors such as distance from the body and its specific position. In the simulations, the excitation signal level is determined according to reference SAR values measured at the laboratory for the tissue equivalent fluids. SAR values in different tissues were calculated using CST where [PERSON]'s equations were solved through the finite integration technique (FIT) [34]. E distribution in the head region [35] is calculated by using the simplified Maxwell's equation as given in (2):  
\[\
abla\times\mu_{r}^{-1}\
abla\times\mathrm{E}-k_{0}^{2}\varepsilon_{r}\mathrm{ E}=0 \tag{2}\]  
where, \(\mu_{r}\), \(\varepsilon_{r}\) and \(k_{0}\) represent the relative permeability, relative permittivity, and wave number, respectively. Additionally, hexahedral mesh type and perfect boundary approximation are applied. In order to adjust accuracy and simulation duration, automatic mesh \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Frequency (MHz)**} & \multicolumn{3}{c}{**Antenna Dimensions (mm)**} \\ \cline{2-4}  & **da** & **dl** & **dc** \\ \hline
900 & 1.5 & 117 & 12.9 \\ \hline
1800 & 1.5 & 50 & 12.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Dimensions of the antenna used in the simulations. Figure 3: The interaction of a human with the antenna. Figure 2: The antenna geometry used in the simulations. generation with an acceptable ratio limit is used. SAR values are averaged according to the IEEE/IEC 62704-1 standard [36].
# Connectivity Maintenance Based on Multiple Relay UAVs Selection Scheme in Cooperative Surveillance
## 3 Multiple Relay UAVs Selection Scheme Based on Fuzzy Optimum Selection
### Multiple Relay UAVs Selection Scheme in Cooperative Surveillance
#### 3.3.2 Role Conversion Stage  
Role conversion state is developed to dynamically adjust communication links by revising the relay UAVs number, and it is assumed that just only one UAV changes its role at the same time for simplicity. As network is connected, all UAVs can exchange information with each other via single and multiple hops, and \(r_{comm}\) is the communication range. The role conversion stage is implemented according to the relationship among SUAVs swarm, AUAV and the \(N_{R}\)th RUAV, which is depicted in Figure 4, and the relay UAVs number and communication links are dynamically adjusted in cooperative surveillance.  
\(r_{rts}\) is the threshold distance for AUAV converting role to continue to perform surveillance task, and the blue dotted circle with radius \(r_{str}\) reflects the threshold distance for AUAV changing role to serve as RUAV. According to the relationship among the \(N_{R}th\) RUAV, AUAV and SUAVs swarm, the role conversion stage is realized as follows.  
If there is no SUAV in the circle zone \(z_{1}=\{\mathbf{x}:\|\mathbf{x}-\mathbf{x}_{u}\|\leq r_{str}\}\) of AUAV, it represents that SUAVs swarm is away from ground station, and the network connectivity will be broken if AUAV does not change its roles to serve as RUAV. At this time, it is essential that supplying a RUAV to prevent communication interruption. Correspondingly, the relay UAVs number should be revised as \(N_{R}+1\), and a new AUAV is selected to connect SUAVs swarm and RUAVs.  
It is obvious that supplying RUAV can effectively extend surveillance scope, which is considered more important than removing RUAV for surveillance range contraction. If no relay UAV supplement, it is concerned that whether there are some UAVs in the circle zone \(z_{2}=\{\mathbf{x}:\|\mathbf{x}-\mathbf{x}_{u}\|\leq r_{rts}\}\) of the \(N_{R}th\) RUAV. If there is at least one UAV in this zone, it represents network is effectively connected and the AUAV can change role to perform surveillance task again. Accordingly, relay UAVs number should be revised as \(N_{R}-1\), and the most remote RUAV adjusts its role to serve as a new AUAV.  
In Figure 4, the ring domain \(z_{buffer}=\{\mathbf{x}:r_{rts}\leq\|\mathbf{x}-\mathbf{x}_{u}\|\leq r_{str}\}\) can be seen as a buffer zone to prevent UAVs role converting frequently, which is beneficial for effectively achieving tradeoff between surveillance payoff and network connectivity. For some special cases, it is emphasized that relay UAV supplement is ineffective when \(N_{R}=N_{U}-2\), and relay UAV removal should not be triggered when \(N_{R}=0\) in contrast.  
Figure 4: Geometry relationship among RUAV, AUAV and SUAVs in role conversion stage.
## 4 Methodology
### Measurement Model Assessment
#### 4.3.3 Model Fit Criteria  
In accordance with [PERSON] et al. [63], at least one fitness index from each category (absolute, incremental, and parsimonious) of model fit indices has to be met. As literature stresses the importance of selecting fit indices unaffected by sample size [68,69], three such indices were selected for use in this study [69]: (1) Chi-Square/df (CMIN/DF) \(<\) 3.0, (2) comparative fit index (CFI) \(>\) 0.90, and (3) root mean square error of approximation (RMSEA) \(<\) 0.08.  
In addition to the fitness indices, the threshold for item factor loading must be satisfied. In this study, items with factor loadings below 0.50 or greater than 1.0, and those with a negative value, were deleted. If all items meet these criteria but the fitness index is not satisfied, items associated with highest modification index were sought [70,71]. It was possible to either delete one of the factor loadings, or constrain both factor loadings [72,73]. The deletion process was preferred to improve construct definition with respect to the high factor loadings and uncorrelated items [73]. If the items in one of the construct are consistently below the three items, the researcher can apply an alternative process, the constraint procedure [73,74]. Figure 2 summarizes the steps for evaluating the model fit in this study.
# Zinc Plating on Inkjet-Printed Ti3C2 Tx MXene: Effect of Electrolyte and PEG Additive
###### Abstract  
Zinc-ion batteries (ZIBs) are currently being studied as an alternative to lithium-ion batteries (LIBs). The nucleation and growth of the zinc deposition mechanism is a critical field of research in ZIBs, as it directly affects the battery efficiency and lifespan. It is of paramount importance in mitigating the formation of porous, dendritic Zn structures that may cause cell inefficiency and, eventually, short-circuiting failures. Interfacial engineering plays a key role in providing reversible plating and stripping of metallic Zn in ZIBs through the proper regulation of the electrode-electrolyte interface. In this work, we investigated the behavior and characteristics of Zn plating on inkjet-printed Ti3C2 Tx MXene-coated substrates according to the different electrolyte compositions. Specifically, ZnCl2 and ZnSO4 solutions were employed, evaluating the effect of a relatively low-molecular-weight polyethylene glycol (PEG400) addition to the electrolyte as additive. Electrochemical analyses demonstrated higher deposition kinetics in chloride-based electrolytes rather than sulfate ones, resulting in lower nucleation overpotentials. However, the morphology and microstructure of the plated Zn, investigated via scanning electron microscopy (SEM) and X-ray diffraction (XRD), revealed that the electrolytic solution played a predominant role in the Zn crystallite formation rather than the Ti3C2 Tx MXene coating. Specifically, the preferential Zn [002] orientation could be favored when using additive-free ZnSO4 solution, and a PEG addition was found to be an efficient texturing agent only in ZnCl2 solution.  
zinc-ion batteries; inkjet printing; Ti3C2 Tx MXene; coating; nucleation; overpotential
## 1 Introduction
The support vector machine (SVM) algorithm has been applied in many TCM systems for classifying tool conditions [54; 55], and the artificial neural network (ANN) algorithm has been widely used for predicting tool conditions and workpieces [56]. A recently evolved deep learning AI algorithm finding its way into TCM systems is the convolutional neural network (CNN) [57]. The CNN algorithm takes in input data in the form of an image, processes it by extracting its features, and evaluates the cutting tool's condition. [PERSON] and [PERSON] [58] developed a tool wear prediction system to monitor the flank wear of a cutting tool using a CNN, and concluded that the method gives a good response to the data in the form of images, with accuracy of 87.26%. Several studies have been carried out on the evaluation of tool conditions and workpiece roughness. In this study, tool conditions were classified based on flank wear. The corresponding quality output of each tool class was experimentally determined by measuring the surface roughness parameters of the workpiece. The vibration signals were captured during machining operations using IoT-enabled sensors and gateways. The signals were decomposed by applying the EMD method to the raw vibration signals. This method was applied because of the nature of the vibration signals captured from the machining process. The Hilbert transform (HHT) was applied to the resulting IMFs from the decomposition to extract the feature vectors that were used to classify the cutting tool. To optimize the classification algorithm and make it less expensive, a genetic algorithm (GA) was used for feature selection. Finally, the features selected using the GA model after applying HHT to the decomposed signals were fed into machine learning (ML) models to classify each tool condition and the corresponding quality output. The classification was performed using three different ML models, and the error loss of each model was evaluated. Neural networks with SCG, KNN, and SVM algorithms were used to develop the classification models. The error loss of each model was evaluated to determine the optimal algorithm for the classification problem. The study can provide an alternative solution to intermittently stopping the machining process to evaluate the tool and workpiece condition during machining.
# Band Gap Analysis for Materials with Cookie-Shaped Auxetic Microstructures, Using Finite Elements
## 4 Results
### Verification on a Continuum with Microstructure  
The study continued with the construction of a two-dimensional continuum, including microstructures. In particular, five rows and five columns with the following characteristics have been considered (Figure 16):  
* Dimensions of outer ellipse: 200 \(\times\) 100 (mm)
* Dimensions of inner ellipse: 170 \(\times\) 80 (mm)
* Rod thickness: 6 (mm)  
In order to study the behavior of the mesh, a boundary load of 10 MPa has been applied on the five sides of each protruding bar in the left part of the mesh. The displacement results are collected for a point at the center of the grid, which will move as long as the applied force produces a wave capable of penetrating the grid. The frequency response of the grid is in accordance with the band gap analysis of the unit cell,.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Inner Ellipse** & **Rod Thickness** & **Rod Thickness** & **Rod Thickness** \\
**170 \(\times\) 90 (mm)** & **3 mm** & **6 mm** & **12 mm** \\ \hline
1 st bandgap & 5 th–6 th (850–1570 Hz) & 3 rd–4 th (840–1160 Hz) & 3 rd–4 th (950–1530 Hz) \\
2 nd bandgap & - & 5 th–6 th (1220–1570 Hz) & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parametric investigation. Inner ellipse 170 \(\times\) 90.  
Figure 16: Mesh structure for verification with 25 representative volume elements.  
Figure 17 shows how the grid geometry is affected for different frequencies of boundary load application. The grid was tested at frequencies between 0 Hz-2000 Hz, with a step of 25.
## 4 Results Analysis
### BIM Integration with Virtual Environment  
Augmented reality (AR) and virtual reality (VR) are two essential components that will boost the construction industry and have the ability to introduce innovative productivity in the construction industry [112]. AR is comprised of real-world experience, whereas VR is comprised of a completely immersive experience that completely isolates the user from the physical world. The interviewers asked, 'In high-rise buildings, construction workers are working on dangerous scaffolding and scaffolding failures, how does the virtual environment help to identify construction workers during construction?' Interviewees believed that BIM integration with a virtual environment, such as AR and VR, enables the development of fully computer-generated environments that give the user the feeling of being truly immersed in a virtual environment. In addition, AR technology allows the overlay of digital information in real time and in the correct spatial location to increase or improve the real environment. VR can also be used to display portions of buildings that are superimposed on a real-world view. Responses included 'I think it will be possible to create a virtual environment', 'I think a BIM-based framework will be needed to monitor the situation', and 'I think BIM has the potential to integrate with AR and VR to improve safety monitoring in high-rise buildings'. Moreover, BIM integration with a virtual environment can be used to make construction safer, including the simulation of different accident scenarios in order to prevent the occurrence of an accident due to a safety hazard in building projects. The simulation of common unsafe acts and conditions are supplemented with accident reports, regulations, and precautions as well as virtual experiments of innovative construction technologies, systems, and processes, and the modelling of a dynamic construction site environment to enable dynamic construction site management.
# Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks
###### Abstract
Using DRL to improve the performance of large-scale dynamic video analysis is quite interesting, however, the video analytic system lacks an effective mechanism to optimize the video configuration adaptively, which causes low resource utilization. Specifically, we use the video frame resolution as an example to illustrate the impact of the video configuration on the accuracy and latency of video analysis. In this paper, we divide the total time into several equal time slots and define \"fps\" as the numbers of video frames for each time slot. Images with a higher resolution may be more accurate, however, they may cause a long transmission delay. When the network bandwidth is time-varying, translating video with a higher resolution may increase resource consumption and cause high latency, while translating video with a lower resolution may reduce the network utilization and the analysis accuracy. Therefore, it is necessary to propose an algorithm to select the video frame resolution adaptively, according to the state of the network. Based on the above discussion, this paper proposes the joint video frame resolution scaling, task offloading, and resource allocation algorithm based on Deep Q-Learning Network (JVFRS-TO-RA-DQN) to optimize video edge offloading and the resource allocation decision. The key contributions of this paper are as follows. * A two-layer NOMA-enabled video edge scheduling architecture is proposed, where UEs are divided into different clusters of NOMA, and the tasks generated by UEs in the same cluster are offloaded over a common subchannel to improve the offloading efficiency. * An attempt is made to optimize the QoE of the UE by formulating a cost-minimization problem composed of delay, energy, and accuracy in order to weigh up the relationship between these three parameters. * The JVFRS-TO-RA-DQN algorithm is proposed to solve the joint optimization problem. The JVFRS-TO-RA-DQN algorithm contains two DQN networks; one is used to select offloading and resource allocation action, and the other is used to select video frame resolution scaling action, which effectively overcomes the sparsity of the single-layer reward function and accelerates the training convergence speed. * The experimental results show that the JVFRS-TO-RA-DQN algorithm can achieve better performance gains in terms of improving video analysis accuracy, reducing total delay, and decreasing energy consumption compared to the other baseline schemes. The rest of this article is organized as follows. In Section 2, we review the relevant work carried out in other studies. Section 3 provides a description of the problem and the scheduling model, and Section 4 depicts the details of our algorithm's implementation. In Section 5, the results of the simulation are analyzed, and finally, we summarize our work in Section 6.
## 6 Application of the Optimization Techniques: Routing Protocols
### Attribute-based or Data-centric Routing Protocols  
In this category, the following protocols stand out:
## 1 Introduction
### The Proposed Approach and Contributions  
The EDRP and DGs help the DSOs in a crisis time. Since the power system topology in each repair time interval is changeable, the optimal values of the DGs and EDRPs in each load point will be changed. Unfortunately, this has not been addressed in previous studies. In this paper, a novel smart outage management system considering EDRPs and DGs denoted by \(SOMS_{DGs}^{EDRPs}\) is proposed. The \(SOMS_{DGs}^{EDRPs}\) works in a two-stage process. In the first stage, the optimization problem is solved to cluster the outages in each depot. In this stage, the ability of each crew, the capacity of each crew vehicle, the limitations of the depot resource and the distance between the outages and depots are considered. In the second stage, the multi-objective cost function is provided to optimize the DG cost, EDRP cost, load shading cost and RRC time. In summary, the contributions of this paper are the following:  
* A novel two-stage multi-objective optimization is provided for outage management.
* The EDRP, as an essential program to decrease the cost of an outage in a smart distribution system, has been considered.
* The combination of the RRC problem, DG dispatch problem and EDRPs considering topological power system constraints are investigated.
* The proposed method is tested on an IEEE 34 bus test system as well as an actual Iranian 66 bus power distribution feeder.
## 2 Silicon Photonics Micro-Transceiver with Multimode Interface
### Mach-Zelnder Modulator Design for High-Temperature Operation  
Figure 11a shows the schematic design of the silicon photonic Mach-Zehnder modulator (MZM) with distributed push-pull phase change nodes along each arm. The modulator was designed to provide on-off keying (OOK) over a wide temperature and frequency range. Figure 11b shows the frequency roll-off characteristics of the modulator. IOCore operation at 25 and 32 Gbps NRZ per channel has been reported [3], and the development of 50 Gbps NRZ per channel capability is underway.
# Dynamic Interaction Analysis of Long-Span Bridges Under Stochastic Traffic and Wind Loads
## 4 Application Examples
### Characteristics of Bridge Dynamics Under Different Traffic Densities
#### 4.2.3 Wind Load Effects  
The wind loads have a negligible impact on vertical displacements of bridges but significantly influence horizontal displacements. As illustrated in Figure 13, the maximum lateral displacements induced by wind loads are 2.00 cm. When wind loads are factored in, they contribute an additional force that affects the bridge's horizontal vibration. This increase is a result of the extra excitation provided by wind loads, which can notably affect the dynamic response of sea-crossing bridges. When these environmental loads are combined with dense traffic, the superposition of vehicle and environmental loads further intensifies the vibration response.  
The interaction between wind and traffic loads substantially elevates the bridge's horizontal vibration volatility, especially under conditions of dense traffic and high wind speed action. Under such complex scenarios, the vibration response exhibits stronger nonlinear characteristics and a heightened amplitude. The combined effects of these loads accentuate the vibration intensity, underscoring the necessity of considering these interactions in the design and safety assessment of large-span bridges.  
While the horizontal vibration amplitude of the bridge increases moderately when wind loads are considered, it becomes more pronounced under conditions of high wind speeds (12 m/s). The complexity and fluctuation of the response are significantly amplified. Therefore, the design and safety assessment of large-span bridges must incorporate the combined effects of the vehicle and wind loads to ensure the bridges' safety and stability under complex operational conditions.