esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation and Analysis
### INCM Beamforming Performance  
The performance of a beamformer is typically assessed in terms of its beampattern and output signal-to-interference-plus-noise ratio (SINR). The beampattern is a function directly related to spatial information, giving a more intuitive understanding of how the beampformer receives signals from different spatial directions. Ideally, the major lobe of the array pattern should be aligned with the direction of the desired signal, with all interference falling into nulls, and the deeper the nulls, the stronger the interference suppression capability. The output SINR is the ratio of the desired signal power to the total power of the interference plus noise in the beamformer's output, which is a primary metric for evaluating beamformer performance. Under the same conditions, a beamformer with a higher output SINR has better capability to suppress interference and noise.
## 3 Results and Discussion
### BLUF Modules in Association with the Effector Domains
#### 3.3.8 Cytochrome p450/ p450 Superfamily  
Cytochrome p450s (CYPs) are a diverse group of beam-containing monooxygenases responsible for the oxidative degradation of steroids, fatty acid and xenobiotics [114]. These beam-thiolate proteins are named after their spectral absorbance peak at 450 nm, due to linkage with the cysteine thiolate of the protein [114]. In spite of a low sequence conservation, the structures are highly conserved. The cytochrome p450 core is made up of a four-helix bundle, helices J and K, two setsof beta-sheets, and it possesses a beam-binding loop, a proton-transfer groove and the conserved EXXR motif in helix K [115]. The hormone synthesis, cholesterol, and vitamin metabolism are some other pathways that are regulated by cytochrome p450s. In this study, we have selected a protein containing the BLUF domain associated with the p450 domain (accession number WP_04544510.1) from _Psychrobacter sp_. P11F6 (Table 1). A multiple sequence alignment of the sequence representing the p450 domain against the well-characterized CYP for _Bacillus subtilis_ has been performed (Figure S6). The multiple sequence alignment revealed that, among two amino acids (i.e., arginine (Arg242) and proline (Pro243)), essential for substrate binding in the peroxygenase enzyme [116], arginine (Arg108) is also conserved in the BLUF regulated p450 domain (Figure S6). However, in the BLUF controlled p450 domain, hydrophobic proline is replaced by a polar amino acid residue, i.e., serine (Ser109). In several p450 enzymes, an adjacent acidic-polar amino acid pair was reported in the substrate binding site. In _Pseudomonas putida_ camphor hydroxylase (CYP101A1), Asp251 and Thr252 were observed in the substrate binding site and used to relay protons onto iron-oxo species to activate the catalytic cycle [114]. The above observations indicate the different evolutionary routes adapted by these enzymes for the H\({}_{2}\)O\({}_{2}\)-driven catalysis. We also performed a protein-protein interaction analysis, which showed interacting partners for the selected protein (Figure S7f and Table S1). Most of the interacting proteins belonged to the fatty acid metabolism (CypB, CypC, CypD, and YitS) and to the secondary metabolism (PksJ and PksM).
## 4 Simplification of the Proposed Method  
The illustrative example in Table 2 considers only four committed units in the operating condition. The number of units can be quite large in a practical system operation which will result in a large number of contingency states and significantly increase computation burden. The computation effort can be reduced by neglecting higher order contingencies that have a very low probability of occurrence. It is noted in Table 2 that states 11, 12, 14, 15, and 16 are associated with third and fourth order contingencies involving outages of three and four units, respectively, within the exposure time. It can be observed that the CGRR will remain the same, within eight decimal places, if these higher order states are not considered in the evaluation in Table 2.  
A study was carried out considering different loading conditions of the IEEE-RTS to determine an appropriate number of contingencies to be included in the CGRR evaluation in order to reduce computation effort while achieving reasonable accuracy. Table 3 shows nine operating schedules to meet the load ranging from 900 MW to 2459 MW. The appropriate numbers of units are committed from the priority loading order for each load condition to meet a probabilistic risk criterion of UCR <0.001 [7]. The committed units are loaded using the economic load dispatch method. The CGRR was calculated in 11 decimal places considering all the contingency states and is shown in the third column of Table 3. The table also shows the percent error when the number of contingencies in the evaluation is reduced. It can be seen that the error decreases as the order of the contingencies included in the evaluation is increased. The last column shows that there is no error when CGRR value is expressed  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**State \(k\)** & **Outage MW \(Z_{k}\)** & **Reduction MW \(X_{k}\)** & **Probability \(p(Z_{k})\)** & **RM MW** & \(RR|Z_{k}\) & \(p(Z_{k})\times RR|Z_{k}\) \\ \hline
1 & 0 & 0 & 0.9993152 & 30 & 0 & 0 \\
2 & 11 & 4 & 0.0001141 & 23 & 0 & 0 \\
3 & 20 & 7 & 0.0001  
in 11 decimal places when contingencies up to the fourth order are considered in the evaluation. It can therefore be concluded that considering up to the fourth order contingency in CGRR evaluation provides acceptable results with less than 0.00005% error.
## 5 Simulation Results
### Simulation Results
#### 5.2.4 Average Delay per Packet  
For an urgent monitoring application, the delay can be a crucial parameter. Average delay per packet can be calculated as the sum of the duration of time that all packets take to reach the Base Station (BS) divided by the total number of packets that successfully reach the Base Station. It is also called end-to-end delay. Figure 13 displays the relationship between average delay per packet and the number of asymmetric links.  
\[AverageDelay=\frac{\sum\text{Time to send all data in BS}}{\text{Number of data successfully reached at BS}} \tag{9}\]  
Figure 13 shows that in COASYM-MAC the average delay is decreased more than 5% compared to ASYM-MAC. When link asymmetry surpasses 80%, most nodes must select a relay node to send data to the intended receiver. The COASYM-MAC protocol gives better performance compared to ASYM-MAC. In ASYM-MAC, if a sender fails to transmit a packet due to an asymmetric link, every time it waits for \(t\) time, switches to sender-initiated mode, and sends a long preamble each  
time. In the COASYM-MAC protocol, however, when link asymmetry occurs it waits for \(t\) time and chooses a sibling. If it does not find any sibling as helper node, it waits for a neighbor node's beacon. This neighbor node sends data to the BS as a helper node. The sender node immediately changes its parent to the neighbor node. As a result, the average delay decreases in COASYM-MAC compared to ASYM-MAC. The percentage of reduction in average delay per packet for COASYM-MAC in the case of 90% link asymmetry is 7.45%, 9.28%, and 11.08% for 27 nodes (Figure 13a), for 54 nodes (Figure 13b), and for 90 nodes (Figure 13c), respectively, as compared to the ASYM-MAC protocol.  
Figure 12: Packet delivery ratio: (**a**) for 27 nodes; (**b**) for 54 nodes; and (**c**) for 90 nodes.
# A Space-Borne Ka-Band Tile-Type Phased Array Transmitter Module Based on HTCC Technology
## 2 T/R Module Scheme  
### Brick-type T/R Module  
The brick-type T/R modules combine and integrate multiple transceiver RF channels side by side, as shown in Figure 1. The signal transmission in each channel is planarly interconnected, ensuring good circuit continuity, excellent high-frequency performance, and reliable interconnections. Each channel still maintains a circuit layout similar to a single channel, resulting in a relatively simple circuit design. However, the planar integration density and packaging efficiency are relatively low, leading to the limited scale of the antenna array [22; 23]. At the same time, due to the low integration level of the channels, the single assembly process requires mixed assembly of multiple chips on the substrate, which poses challenges for automated assembly production. The common issues encountered by brick-type T/R modules can significantly impact RF performance as operating frequencies increase, especially above the Ka-band.  
Figure 1: The schematic diagram of brick-type architecture.
# Dichotomy Graph Sketch: Summarizing Graph Streams with High Accuracy Based on Deep Learning +
## 4 Dichotomy Graph Sketch Mechanism
### Edge Classifier
#### 4.3.1 Node Embeddings Using a Graph Auto-Encoder  
Graph representation learning [40; 41] typically involves obtaining node embeddings which are essentially feature representations, in order to help accomplish downstream tasks. Thus, using a graph auto-encoder (GAE) [40] model, we obtain node embeddings to assist the classifier in classifying heavy edges and light edges accurately.  
Formally, given a graph \(G=(V,E)\) with \(|V|=N\), we denote its degree matrix as \(\mathbf{D}\), and its adjacency matrix as \(\mathbf{A}\). In addition, node features are summarized in an \(N\times M\) feature matrix \(\mathbf{X}\).  
GAE utilizes a two-layer graph convolutional network (GCN) [42] as encoder to form the node embeddings of a graph. Formally, the node embeddings are denoted by \(\mathbf{Z}=GCN(\mathbf{X},\mathbf{A})\) where \(GCN(\cdot)\) denotes the two-layer graph convolutional network. The two-layer GCN is defined as  
\[GCN(\mathbf{X},\mathbf{A})=\tilde{\mathbf{A}}ReLU(\tilde{\mathbf{A}}\mathbf{ X}\mathbf{W}_{0})\mathbf{W}_{1}, \tag{1}\]  
where \(ReLU(\cdot)=max(0,\cdot)\), and \(\tilde{\mathbf{A}}=\mathbf{D}^{-\frac{1}{2}}\mathbf{A}\mathbf{D}^{-\frac{1}{2}}\) is the symmetrically normalized adjacency matrix. To reconstruct the graph, GAE adopts \(\tilde{\mathbf{A}}=\sigma(\mathbf{Z}\mathbf{Z}^{\mathbf{T}})\) as the decoder where \(\tilde{\mathbf{A}}\) denotes the adjacency matrix of the reconstructed graph \(G^{\prime}\).  
To train a GAE for a graph \(G\), we can feed the \(G^{\prime}\)s adjacency matrix \(\mathbf{A}\) together with its node feature matrix \(\mathbf{X}\) into the GAE, and obtain the adjacency matrix \(\tilde{\mathbf{A}}\) of the reconstructed graph \(G^{\prime}\). Then, we minimize the following cross-entropy loss:  
\[\mathcal{L}=\frac{1}{N}\sum y\text{log}\hat{y}+(1-y)\text{log}(1-\hat{y}), \tag{2}\]where \(y\) denotes the element in \(\mathbf{A}\), and \(\hat{y}\) denotes the element in \(\hat{\mathbf{A}}\). After the GAE is well trained, we can obtain the current node embeddings \(\mathbf{Z}=GCN(\mathbf{X},\mathbf{A})\).  
Note that constructing a lossless graph \(G\) for every graph stream session is both time-consuming and space-expensive. Since a graph sketch can be regarded as the compression of an original graph, we use the temporal graph sketch \(\mathcal{K}_{s}\) in the sample generator as input to train the GAE to reduce the complexity.
## 6 Numerical Experiments
### Comprehensive Evaluation of the Algorithms
#### 6.4.1 Objective Function and Computational Time Values
Along with the analysis of the average objective function and computational time values, the scope of this study includes a detailed statistical analysis of the objective function values, obtained over ten replications, for each solution algorithm and each large size problem instance. More specifically, the one-way analysis of variance (ANOVA) was conducted, assuming the null hypothesis stating that the objective function values, obtained over ten replications by the SAEA algorithm, were equal to the objective function values, obtained over ten replications by an alternative algorithm (i.e., EA, DPCEA, and AEA), for a given problem instance. A total of three tests were performed throughout the one-way ANOVA analysis for each large size problem instance, including the following: (i) SAEA vs. EA; (ii) SAEA vs. DPCEA; and (iii) SAEA vs. AEA. The one-way ANOVA analysis results are reported in Table 6, which provides the following information for each large size problem instance: (1) instance number; (2) number of vessels; (3) number of berths; (4) \(F\)-statistic for each test; and (5) \(p\)-value for each test. In the conducted one-way ANOVA analysis, \(F\)-statistic represents the ratio of the mean squared errors (errors quantify the differences between the objective function values, which were obtained by the algorithms), while \(p\)-value represents the probability that the test statistic may take a value greater than the value of the computed test statistic. It can be observed that small \(p\)-values were obtained for all ANOVA tests and all the generated large size problem instances (i.e., the maximum \(p\)-value did not exceed 9.08 \(\times\) 10\({}^{-3}\)). The latter finding highlights that the objective function values, obtained over ten replications by the SAEA algorithm, were statistically lower than the objective function values, obtained over ten replications by the alternative algorithms (i.e., EA, DPCEA, and AEA), for each large size problem instance (i.e., the null hypothesis of the ANOVA test was rejected).
## 1 Introduction
[13] predicted the availability of CO2 liquefaction processes for a ship-based carbon capture and storage (CCS) chain and they converted the availability to unavailability cost to calculate the life-cycle cost (LCC). [PERSON] et al. [14] estimated the availability of LNG fuel gas supply systems to evaluate economics of them. They concluded that one of the significant factors was mechanical devices. [PERSON] et al. [15] reviewed the studies related with the profitability, reliability, and condition based monitoring of liquefied natural gas-floating production storage and offloading unit (LNG-PFSO). They assumed that the efficiency of LNG-PFOS depends on LNG liquefaction process type, system reliability, and maintenance approach, and reviewed the paper at theses points. They concluded that the literature was not sufficient to improve efficiency of LNG-PFSO. [PERSON] et al. [16] developed the condition-based maintenance system to perform proactive maintenance in advance to avoid the abnormal states. They addressed the system architecture, main components, diagnostics, and prognostic methods of the system. The methodologies for the availability estimation has been improved to increase its accuracy and to apply to various systems. Precise availability estimation is important because it directly influences the owner's decision. The availability estimation is performed several times depending on the design stages (conceptual design, basic design, and detailed design stages). In the early design stage, the results of availability estimation are effective for design improvement, but it is hard to estimate it precisely due to the limited data. On the contrary, accurate availability estimation is possible at the end of the design stage using sufficient data, but it accompanies high costs for the system modification. Therefore, it is an important to estimate the availability in the early design stage accurately. Although many studies improved the methods to increase their accuracy, there was little effort to practically estimate the availability in the early design stage  
The purpose of this study is to investigate the availability gap between in the early and late design stages by estimating it with the design stages to find practical manner of availability estimation in the early design stage. The structure of this study is as follows. The target systems are described. In Section 3, methodologies for the availability estimation are discussed. The results of the availability estimation and the sensitivity analysis are indicated in Section 4. Finally, the conclusions are presented.
## 3 Results
The second highest value is for the surface temperature from the MODIS satellite, with a coefficient of 0.68, followed by the surface temperature from the Landsat satellite, which has an adjusted R2 value of 0.55. The higher adjusted R2 coefficient for the monthly average temperature from the MMD (0.75) can be attributed to the larger amount of data available for this parameter (386 data points), compared to 238 MODIS data points and 37 Landsat data points. The number of data points plays a significant role in determining the adjusted R2 value. However, it is important to note that the adjusted R2 for the surface temperature of the MODIS satellite is 0.68, and for the Landsat satellite, it is 0.55. Although the adjusted R2 values differ among the variables, all three regression models are considered reliable as they exceed \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Sum of Squares** & **Df** & **Mean Square** & **F** & **Sig** \\ \hline Regression & 191.949 & 1 & 191.949 & & \\ Residual & 63.779 & 382 & 0.163 & 1149.6 & 0.00 \\ Total & 255.728 & 383 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: ANOVA for the effect of ENSO occurrence on temperature from MMD. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Dependent Variable** & **Model Prediction Impact ONI** & **Adjusted R2** & \(p\) **Value** \\ (Y) & **(X)** & & \\ \hline Monthly mean temperature MMD & Y = 0.828 \(\times\) (Q) + 26.071 & 0.75 & 0.000 \\ LST MODIS & Y = 0.832 \(\times\) (Q) + 26.087 & 0.68 & 0.000 \\ LST Landsat & Y = 1.139 \(\times\) (Q) + 26.610 & 0.55 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Single regression formula for understanding the effect of ONI on three dependent parameters to predict the effect of ENSO occurrence on air temperature and land surface temperature. the threshold of 0.50 or 50% [31]. This means that 50% of the variation in the air temperature from the MMD, the surface temperature from MODIS, and the surface temperature from Landsat satellites can be explained by the ONI. The positive relationship between the ONI and the dependent variables is demonstrated by the adjusted R\({}^{2}\) values. This indicates that an increase in the ONI is associated with an increase in parameters such as the air temperature from the MMD, the surface temperature from the MODIS satellite, and the surface temperature from the Landsat satellite. Thus, 50% of the variation in these variables can be explained by the ONI. In summary, the adjusted R\({}^{2}\) values in Table 5 illustrate a positive relationship between the ONI and the monthly average temperature from the MMD, as well as the surface temperature from the MODIS and Landsat satellites. This implies that an increase in the ONI corresponds to higher values for the mentioned parameters.
## 3 Results
### Calculation of \(CO_{2}\) Emissions
This value results in 5.32 \(\times\) 10\({}^{-2}\) kWh/km at 0.1119 kWh/km. Using the EU mix (596 gCO2e/kWh), emissions of 31.68 gCO2e/km to 66.69 gCO2e/km for V2G mode are obtained. Table 5 presents the summary of the emissions calculations made up to this point and the calculation of the total (partial) without the contribution of impacts on the distribution network. These results show that V2G mode increases CO2 emissions and therefore reduces the benefits of using EVs. V2G mode infrastructures can be used for various purposes, including controlling EV charging and discharging, using EV batteries as energy storage, and auxiliary services. This may lead to the assumption that emissions from communications, measurement, and processing infrastructures should not be affected by EVs; however, for the G2V or V2G mode to work, the infrastructures must exist, so they are affected by EVs in these integration modes. These two modes emerged in response to the problems that EVs in NC mode can cause in the functioning of the energy system, which is why they are attributable to EVs. Furthermore, there are studies that indicate that the use of EVs, for example, for energy storage, is not the cheapest option, nor the most efficient, and therefore will not be the most sustainable either [38]. Another question that can be raised regards V2G mode's implication that the EV batteries would need to be oversized compared to the autonomy that the EV needs; otherwise, they would not be able to serve as temporary energy storage. As batteries are the main source of CO2 emissions during the manufacturing phase and at the end of life, the [30] recommends that they be as small and adjusted as possible for the desired autonomy. According to this recommendation, it is not expected that there will be much scope for storage, unless owners accept that their EV's charging will be used to satisfy grid needs rather than guarantee the EV's autonomy. However, owners are not very receptive to losing control of their EV [32]. If EV batteries are oversized, then manufacturing emissions will be greatly increased, as the battery is responsible for 31 to 46% of the manufacturing emissions of each EV and 14 to 23% of end-of-life emissions [25]. Batteries also have losses during their use that contribute 4% to EV emissions in the circulation phase. Globally, batteries are responsible for 13 to 22% of an EV's life cycle emissions [25]. If the battery increases, the weight that the EV will have to support also increases, which implies an additional consumption [39] of 5.6 Wh/km per additional 100 kg [25]. Therefore, increasing batteries will have an impact on global EV emissions. Given this, the EU recommends that manufacturers be encouraged to reduce the size of batteries to what is strictly necessary [30]. This indication makes it difficult to use EVs in V2G mode, because it limits the capacity of the batteries to function in a relevant way to temporarily store energy with the aim of returning it to the grid.
# Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations
## 3 Motivation
Therefore, it is important to state that the purpose of using these distortions, and combining them in a perceptual experiment, is to investigate how human participants perceive them and at what level the audio and visual distortions influence their perception of the audio-visual quality. Then, recreating a particular application scenario is out of the scope of this study. It is expected that using diverse audio and video distortions and making available the annotated material will contribute to the study of multimodal quality perception, which is commonly restricted to a single type of audio distortion (i.e., audio compression). Based on these findings, more specific studies can be performed considering a particular use-case scenario, which will include specific types of degradation and content material.
# Progress on Single-Feed Quality Wideband Linear Wire Array
## 3 Driven Element  
The WHEMS antenna serves as the driven element for the QWL antenna and is fundamental to the broadband and high-gain performance of the QWL antenna. The following will introduce the input impedance characteristics and radiation properties of the linear WHEMS antenna and, for the first time, provide the analytical expression and a quick design formula for its input impedance.
## 4 Application Example
In order to determine the categories relevant to acceptability, tolerance, or control of risk and enable comparisons between water distribution systems, an analysis of the kind carried out here will help set threshold values for the acceptability of probabilities. The effectiveness of assessments of specific treatment stations will be aided by a comparison between the probability value attained and the value considered acceptable.