esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Response Surface Optimisation of the Extraction  
The design of experiment was implemented with the aim at assessing the effect of three selected parameters (independent variables), the DES concentration (\(C_{\text{DES}}\), termed as \(X_{\text{1}}\)), the liquid-to-solid ratio (R\({}_{\text{L/S}}\), termed as \(X_{\text{2}}\)), and the stirring speed (S\({}_{\text{S}}\), termed as \(X_{\text{3}}\)), on the extraction yield of total polyphenols (Y\({}_{\text{TP}}\)), which represented the response [19]. The mode chosen was Box-Behnken with three central points and the independent variables were coded between \(-1\) (lower limit) and \(+1\) (upper limit), as follows:  
\[\text{x}_{\text{i}}=\frac{\text{X}_{\text{i}}-\text{X}_{0}}{\Delta\text{X}_{ \text{i}}},\text{ i}=1,\ 2,\ 3. \tag{1}\]  
The term \(\text{x}_{\text{i}}\) represents the dimensionless value of the independent variable i and \(\text{X}_{\text{i}}\) the actual one. \(\text{X}_{0}\) is termed the actual value of the independent variable i at the central point of the design, and \(\Delta\text{X}_{\text{i}}\) the step change of \(\text{X}_{\text{i}}\) (Table 1). The range of values for the variables used was decided on the basis of previously published results [13; 14]. Model significance, as well as the significance for each polynomial coefficient and the overall coefficient \(R^{2}\) for the mathematical model were evaluated by performing ANOVA, at least at 95% significance level. Non-significant dependent terms (\(p>0.05\)) of the model were not considered and were omitted. Model validation was accomplished by runs performed under the predicted optimal conditions. The actual (measured) values were then compared with the predicted ones.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Independent** \\ **Variables** \\ \end{tabular} } & \multirow{2}{*}{**Code Units**} & \multicolumn{3}{c}{**Coded Variable Level**} \\ \cline{3-5}  & & \(-1\) & \(0\) & \(1\) \\ \hline \(C_{\text{DES}}\) (\%, _w/v_) & \(\text{X}_{\text{1}}\) & \(55\) & \(70\) & \(85\) \\ R\({}_{\text{L/S}}\) (mL g\({}^{-1}\)) & \(\text{X}_{\text{2}}\) & \(20\) & \(60\) & \(100\) \\ S\({}_{\text{S}}\) (rpm) & \(\text{X}_{\text{3}}\) & \(200\) & \(500\) & \(800\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Process (independent) variables used for the experimental design and their codified levels.
## 1 Introduction  
High-order modulation formats together with coherent detection and digital signal processing (DSP) have attracted significant attention to increase spectral efficiency in coherent optical transmission systems [1]. Carrier phase recovery (CPR) algorithms play a key role in these systems for the estimation and compensation of the phase noise induced by free running lasers. High-order modulation formats impose stringent requirements on the performance of these algorithms, as the distance between constellation points reduces with the increase in modulation order. The blind phase search (BPS) algorithm [2] and the N-_th_ power approach [3] have typically been proposed for CPR in square multilevel quadrature amplitude modulations (Sq-mQAM) [4; 5; 6]. Although the BPS algorithm achieves a high phase noise tolerance, it requires a large computational complexity especially for high-order modulations where the required number of test phases increases. On the other hand, the N-_th_ power concept requires less hardware complexity but comes at the expense of a poorer phase noise tolerance, as the relative number of suitable constellation points for phase estimation decreases with the modulation order. Different two-stage CPR schemes, which include both approaches, have been proposed to achieve similar phase noise tolerance as single-stage BPS while relaxing its computational complexity [7; 8; 9; 10; 11; 12].  
Due to its particular shape, circular multilevel quadrature amplitude modulation (C-mQAM) constellations provide a higher phase noise tolerance compared to Sq-QAM constellations. The n-PSK partitioning CPR scheme for C-mQAM constellations proposed in [13; 14] achieves a relatively high linewidth tolerance with a low computational complexity. However, the algorithm requires _a priori_ amplitude discrimination for symbol classification, which undermines its performance at low optical signal-to-noise ratios (OSNRs). In this paper, we propose a novel two-stage n-PSK partitioning CPR algorithm for C-mQAM constellations to alleviate this problem. The first stage of the algorithm provides an initial constellation estimation utilizing the n-PSK partitioning algorithm based on ring selection. The second stage utilizes this estimated constellation to classify the received symbols employing optimal symbol decision boundaries and applies the rest of the n-PSK partitioning process. The combined linewidth symbol duration product (\(\Delta vT_{s}\)) tolerance of the proposed algorithm is studied through extensive simulations and is compared with different CPR algorithms to evaluate its relative performance improvement. The performance of the proposed algorithm is also experimentally evaluated in a 28-Gbaud back-to-back C-16 QAM transmission system and compared with that of the other CPR schemes. The computational complexity of the proposed algorithm is finally studied in detail and a modification in the algorithm to reduce its computational complexity is also proposed.
# Many-Objective Optimization for Anomaly Detection on Multi-Layer Complex Interaction Networks
## 2 Related Work
### Many-Objective Optimization  
Optimizing many-objective functions (\(\geq\)3) simultaneously is called many-objective optimization, e.g., [21; 22; 23]. It is applied to tackle a wide range of problems not only in science and engineering, e.g., [24; 25], but also in the social sciences. Here, different approaches have been developed, such as reducing the complexity, e.g., [7; 26]. Other approaches apply multi- and many-objective optimization for network analysis such as [27], in order to identify key players in large social networks; [8] utilized modularity maximization in multiplex networks using many-objective optimization; [28] performed community detection from signed social networks using a multi-objective evolutionary algorithm; and finally, [29] presented an approach for many-objective optimization using eigenvector centrality in multiplex networks.  
In this paper, we adapt and extend the approaches proposed by [PERSON] et al. [8; 9; 29]. The first step of the approach starts by measuring the centrality of all nodes on all layers of multi-layer network. This is followed by applying many-objective optimization with full enumeration of all layers based on a _maximization problem_ to find the Pareto Front. In contrast to this approach, we utilize the Pareto Front as a non-dominated solution generated by many-objective optimization for _minimization_ as a basis to extract a set of anomaly candidates, i.e., a set of suspected anomalous nodes from the network.
## 2 System-Level Design Optimization
### System-Level Optimization Methods  
There are two approaches regarding system-level optimization based on the procedure of optimization and evaluation. These are the Single-Level Optimization Method (SLOM), which treats the system as a unified entity, and the Multi-Level Optimization Method (MLOM), which optimizes each component sequentially and evaluates the system holistically [19; 22]. Both methods aim to optimize the entire system, with the latter being computationally less expensive due to the division of the optimization process into the following two separate parts: machine and controller. Nevertheless, both methods have been utilized in relevant studies, with the former approach being preferred due to its simplicity in implementation.  
Figure 2: System-level design optimization framework.
# Video Interpolation-Based Multi-Source Data Fusion Method for Laser Processing Melt Pool
## 4 Results and Discussion
### Video Frame Interpolation Module Evaluation
#### 4.1.2 The Effectiveness of the Video Frame Interpolation Module  
To verify the effectiveness of the video frame interpolation module in generating intermediate frames, this study downsampled the original 60 FPS infrared videos to 30 FPS, creating a video dataset (Dataset 4), as shown in Table 1. We compared and analyzed the consistency between the interpolated frames generated by the different models and the ground truth on the test set of Dataset 4 to evaluate the effectiveness of the models. In terms of qualitative validation, the consecutive image pairs (a-c) in Figure 7 demonstrate the actual frame interpolation effect, where (a) is the previous frame of the infrared source video, (b) is the next frame, (c) is the ground truth, and (d-f) display the intermediate frames generated by SuperSloMo, IFRNet, and the pre-trained MTVFI model, respectively.  
The observation results indicate that the intermediate frames generated by the IFRNet model have an extremely high similarity to the next frame. This suggests that the model failed to accurately capture the motion characteristics of the melt pool. Although the SuperSloMo model can recognize certain motion characteristics of the melt pool, its generated intermediate frames exhibit blurriness in the edge areas of the melt pool, which indicates that the model has deficiencies in the precise recognition of melt pool motion and texture details. In contrast, the pre-trained MTVFI model has successfully learned the non-linear motion characteristics of the melt pool; the intermediate frames it generates are most similar to the ground truth in terms of texture details and temperature gradients,  
\begin{table}
\begin{tabular}{l|l l} \hline \hline
**Model** & **PSNR** & **SSIM** \\ \hline Super-SloMo & 36.917 & 0.982 \\ IFRNet & 39.114 & 0.991 \\ MTVFI (our model) & 38.908 & 0.992 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the video frame interpolation module for MTVFI and other models on Dataset 3.  
proving the effectiveness of the MTVFI model in the task of video frame interpolation for melt pool temperature distribution data.
# Wideband-Narrowband Switchable Tapered Slot Antenna for Breast Cancer Diagnosis and Treatment
###### Abstract  
In this paper, a wideband-narrowband switchable tapered slot antenna (TSA) with a compact meander line resonator for an integrated microwave imaging and hyperthermia system was proposed. A compact meander line resonator, which exhibited band-pass characteristics and provided narrowband characteristics by using one PIN diode, was fabricated beneath the tapered slot of the wideband TSA to minimize the degradation of the wideband characteristics. Moreover, the electromagnetic energy was transferred to the meander line resonator with a coupling effect to ensure effective frequency switching. By adapting a PIN diode on the meander line resonator, frequency switching could be achieved. In this way, the proposed antenna could operate in a real-time frequency switching mode between the ultra-wideband (UWB; 3.1-10 GHz), which is used for microwave imaging, and the 2.45 GHz band (industrial, scientific, and medical, ISM band), which is used for microwave hyperthermia. Frequency and time-domain results proved the applicability of the proposed antenna to an integrated breast cancer detection and treatment system.  
tapered slot antenna; frequency reconfigurable antenna; wideband to narrowband switching; tumor treatment; microwave imaging; hyperthermia +
Footnote †: journal: Applied  
17 March 2021  
15 April 2021  
**Publicher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**Copyright:** 2021 by the authors. License MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
**Citation:** [PERSON]; [PERSON].  
Wideband-Narrowband Switchable Tapered Slot Antenna for Breast Cancer Diagnosis and Treatment.  
_Appl. Sci._**2021**, _11_, 3606. [[https://doi.org/10.3390/app11083606](https://doi.org/10.3390/app11083606)]([https://doi.org/10.3390/app11083606](https://doi.org/10.3390/app11083606))
# Towards Deterministic-Delay Data Delivery Using Multi-Criteria Routing over Satellite Networks
## 2 System Model  
### Constellation Model  
This study considers a uniformly distributed low Earth orbit (LEO) satellite network constellation, where all satellite nodes form a set \(S=\{S_{i}|1\leq i\leq N\}\). Each satellite in this network is equipped with four full-duplex inter-satellite links, and each satellite can connect with up to four neighboring satellites, including two intra-plane inter-satellite links within the same orbit and two cross-plane inter-satellite links between different orbits [21]. For polar orbit satellites, there are two counter-moving orbits, and the gap formed between these two orbits is known as a cross-seam [22; 23]. When satellites move to the cross-seam and high-latitude regions, the relative angular velocity between inter-plane satellites increases significantly, leading to the temporary closure of inter-plane inter-satellite links in these regions [24]. Furthermore, as the LEO satellite network moves periodically along its orbits, the Earth undergoes rotation, resulting in relative motion between satellites and the Earth. This causes the ground area beneath a satellite to change over time. To represent the time-varying topology of the satellite network, this study adopts a snapshot graph model to characterize the time-varying link resources of the satellites. Although the satellite network topology changes over time, within sufficiently small time intervals, the topology remains stable [25; 26; 27]. Therefore, the time range \(T\) can be divided into \(N\) adjacent time slots \(\{\tau_{1},\ldots,\tau_{N}\}\), ensuring that within each time slot, the satellite network topology remains unchanged. Based on this time-slot division mechanism, by characterizing the network topology within each time slot \(\tau\), the time-varying satellite network snapshot graph model over time \(T\) can be represented. The snapshot graph model is represented by \(\mathrm{G}=\{\mathcal{V}_{T},\mathcal{E}_{T}\}\), where \(\mathcal{V}_{T}\) is the set of nodes in the snapshot graph, and node \(u_{\tau}\in\mathcal{V}_{T}\) represents the satellite node \(u\) within the time slot \(\tau\). \(\mathcal{E}_{T}\) represents the set of links in the snapshot graph, where link \((u_{\tau},\mathrm{v
## Appendix A Experimental Basis for Configuration Selection
### Valid-Area Threshold  
The valid-area threshold defines the minimum proportion of farmland pixels within a patch required for it to be considered valid during training and inference. If the threshold is set too low, patches may contain mostly background, introducing noise during training and providing insufficient visual cues during inference. This aligns with previous findings that a low proportion of the target class within a patch can reduce classification accuracy [60].  
Conversely, if the threshold is set too high, boundary patches may be filtered out more than necessary, resulting in reduced data diversity and degraded generalization performance. This also corresponds to observations in prior studies showing that removing patches with a high proportion of background can lead to the loss of boundary information and cause overfitting to interior-dominant patterns [60].  
To identify a balanced setting, we compared valid-area thresholds of 5%, 10%, and 15%. At 5%, many low-quality patches were included, degrading performance due to insufficient contextual information. At 15%, boundary patches were filtered out more than necessary, limiting spatial variation. In contrast, a threshold of 10% provided the best trade-off between maintaining contextual integrity and ensuring patch diversity. Accordingly, we adopted 10% as the default valid-area threshold throughout both the training and inference pipelines.
# A Graph Neural Network Node Classification Application Model with Enhanced Node Association
## 3 Model Analysis
### ENode-GAT Model
#### 3.1.1. Input Layer  
After employing external node references in the graph convolution layer, the input layer consists of the process of inputting the multi-source heterogeneous graph structure. The graph structure information, which consists of node data and connections between nodes, is converted into an adjacency matrix and fed to the convolution layer for the initial convolution operation. Figure 2 depicts the operation diagram of external node reference.  
In the structure depicted in Figure 2, nodes are converted into graph structures by establishing virtual edge connections between the central nodes based on their keywords. However, this graph structure is not stable, and there may be isolated nodes, which can reduce the classification accuracy of the model. To address this issue, we suggest  
Figure 1: Diagram of the ENode-GAT classification model.  
Figure 2: External node reference operation diagram.  
introducing similar feature word nodes to reconstruct the structure of the graph and strengthen the association between the nodes. The experimental results indicate that this method can significantly enhance the precision of the classification.
## 2 Optimization
### Adaptive Learning Rate Optimizers
#### 2.2.6 Nadam  
Nesterov-accelerated adaptive moment estimation (NADAM) [29] incorporates Nesterov momentum into Adam.  
\[\mathbf{m}_{0} =\mathbf{0},\ \mathbf{v}_{0}=\mathbf{0}\quad\text{(initialize 1 st and 2 nd moment estimates)}\] \[\mathbf{m}_{t} =\beta_{1}\mathbf{m}_{t-1}+(1-\beta_{1})\
abla J(\mathbf{\theta}_{t-1}) \quad\text{(update biased 1 st moment estimate)} \tag{29}\] \[\mathbf{v}_{t} =\beta_{2}\mathbf{v}_{t-1}+(1-\beta_{2})\,(\
abla J(\mathbf{\theta}_{t-1} ))^{2}\quad\text{(update biased 2 nd moment estimate)}\] (30) \[\hat{\mathbf{m}}_{t} =\frac{\mathbf{m}_{t}}{1-\prod\limits_{i=1}^{t+1}\mu_{i}},\ \ \hat{\mathbf{v}}_{t}=\frac{\mathbf{v}_{t}}{1-\beta_{2}^{t}}\quad\text{(bias corrections)}\] (31) \[\hat{\mathbf{g}}_{t} =\frac{\
abla J(\mathbf{\theta}_{t-1})}{1-\prod\limits_{i=1}^{t}\mu_ {i}}\] (32) \[\overline{\mathbf{m}}_{t} =(1-\mu_{t})\hat{\mathbf{g}}_{t}+\mu_{t+1}\hat{\mathbf{m}}_{t}\quad\text {(calculate update vector incorporating Nesterov)}\] (33) \[\mathbf{\theta}_{t} =\mathbf{\theta}_{t-1}-\frac{\eta}{\sqrt{\hat{\mathbf{v}}_{t}}+\epsilon} \circ\overline{\mathbf{m}}_{t}\quad\text{(apply update)}. \tag{34}\]  
According to the TensorFlow documentation ([[https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Nadam](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Nadam)]([https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Nadam](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Nadam))), parameters \(\mu_{t}\) are calculated using  
\[\mu_{t}=\beta_{1}\left(1-0.5\times 0.96^{\frac{t}{20}}\right), \tag{35}\]  
which is similar to the momentum schedule used in [30], while, in [29], \(\mu_{t}\) are additional hyperparameters required in advance.
## 3 Analysis and Discussion of Results
### Comparative Study with Multi-Criteria Using the TODIM Method  
The TODIM (an abbreviation for Interactive and Multi-Criteria Decision Making in Portuguese) application is based on a multi-criteria global value function proposed by [PERSON] and [PERSON] (1991) [PERSON] and [PERSON] (1991). The TODIM method depicts a prominent tool to appraisal MCDM problem situation [37]. In fact, this mathematic function is built in parts, with their mathematical descriptions reproducing the profit/loss function of prospect theory. The multi-attribute global value function then aggregates all win and loss measures across all criteria.  
**Step 1:** As a first step for the implementation of TODIM, the corresponding decision matrix must be exposed.  
Table 9 describes the decision matrix to be with the variable to be analyzed.  
Figure 11: Effect of time towards the response variable of the humidity factor.  
Figure 10: Effect of time towards the response variable of the temperature factor.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Item** & **Levels** & **Humidity** & **Temperature** & **Coffee** & **Ovine** & **Bovine** \\ \hline N1 & Low & 70 & 15 & 4920.1 & 0 & 0 \\ N2 & Low-Medium & 70 & 20 & 4911.1 & 0 & 0 \\ N3 & Low-High & 70 & 25 & 0 & 0 & 5078.2 \\ N4 & Medium-Low & 80 & 15 & 5296 & 0 & 0 \\ N5 & Medium-Medium & 80 & 20 & 0 & 0 & 5475.3 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Decision matrix.  
**Step 5:** Finally, the alternatives are ranked in descending order of their dominance scores and the alternative with the highest dominance score obviously becomes the best option.  
Table 21 describes the rankings of the alternatives.  
Figure 12 depicts that level **N6** denoted in yellow color corresponds to the combination of levels of humidity of 80 and temperature of 25.  
Level N6 is equivalent to the optimal decision-making, as shown in Figure 13.  
Finally, the validations of the results are consistent to confirm that level \(N6\) depicts the best solution. The findings obtained with the TODIM method and the statistical tests are significant to validate the results.
# Review of Machining Equipment Reliability Analysis Methods based on Condition Monitoring Technology
## 3 Failure Physical Analysis for Signal Selection
### The Relevance Analysis between Failure Mechanism and Output Signal
#### 3.1.3 Tool Wear  
Tool wear is one of the most typical phenomena in the machining process. Numerous studies have shown that there are mechanical, thermal and chemical effects in the tool wear process, as well as friction, adhesion and diffusion. It is the result of the combined action of one or more forms of wear. The main cause of tool wear is mechanical heat and chemical wear [57]. Mechanical wear is caused by the scoring of hard spots in the workpiece material. Thermal and chemical wear is caused by the bond and the diffusion.  
When the tool is severely worn, the amplitude and the energy of the AE signal generated by the friction between the tool and the workpiece increases due to the increased contact area between the tool flank and the workpiece. And the frequency components will be diversified. At the same time, due to the complexity of the tool wear form, tool wear can also lead to the increase of cutting forces, cutting temperatures and vibration.  
Therefore, scholars usually combine a variety of signals, such as cutting force signals, vibration signals, acoustic emission signals, power or current signals to study the wear state of the tool [58].