esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on PF-IFF-Based Diagnosis Model of Plant Equipment Failure
###### Abstract  
There are two types of maintenance policies for equipment: breakdown maintenance and preventive maintenance. In the case of applying preventive maintenance, the maintenance is carried out based on time or the condition of the equipment. However, with the development of Information and Communications Technologies (ICT) and the Internet of Things (IoT) technology, the data collected from equipment has rapidly increased and the use of Condition-Based Maintenance (CBM) to perform appropriate maintenance based on the condition of the equipment is increasing. In this study, based on gathered sensor data, we introduce an approach to diagnosing the condition of the equipment by extracting specific data features related to the types of failures that occur with equipment. To this end, we used the K-means clustering method, support vector machine (SVM) classifier, and Pattern Frequency-Inverse Failure mode Frequency (PF-IFF) method with the Term Frequency-Inverse Document Frequency (TF-IDF) method. As a case study, we applied the proposed approach to a centrifugal pump and carried out computational experiments for assessing the performance and validity of the proposed approach.  
CBM; diagnosis; SVM; TF-IDF; plant equipment failure +
Footnote †: journal: Medical of Plant Equipment Failure  
occurs, whereas PM conducts maintenance activities based on time or equipment condition as a preventive measure before a failure occurs. PM can be further divided into Time-Based Maintenance (TBM) and Condition-Based Maintenance (CBM). Considering the risk, the CBM is appropriate for the plant equipment that can cause significant damage to the environment in the event of a failure [1].  
This study introduces an equipment diagnosis approach for identifying failure modes with gathered sensor data from plant equipment under the CBM policy. The proposed approach diagnoses what kinds of failures will appear in the future by extracting specific patterns associated with equipment failure modes based on gathered sensor data and establishing diagnosis models using them. In this study, the Term Frequency-Inverse Document Frequency (TF-IDF) technique, mainly used in text mining, is applied to efficiently detect anomalous patterns associated with equipment failure modes. The TF-IDF value tells us how critical a word is in a specific document. Many research works use it as a method for evaluating the degree of importance of words existing in the document. In this study, we used the TF-IDF technique to identify the patterns related to a specific failure mode among the patterns that occurred before the failure mode, and also applied the K-means clustering method and support vector machine (SVM) classifier to establish the diagnosis model.  
This study is organized as follows: the relevant previous works are reviewed in Section 2 and the proposed approach is described in Section 3. The case study carried out is discussed in Section 4. Finally, Section 5 concludes the study with a summary.
## 5 Parametric Analysis
### Length of Span \(L\)  
Specimens with same shear span ratio but different \(L\) were studied according to the established FE model. Table 5 presents the \(M_{u}\), \(\Delta_{s}\), and failure modes of the specimens with \(L\) values of 3000 mm, 3600 mm, 4200 mm, and 4800 mm. The \(\Delta_{s}\) of the specimens and the deflection limit required by GB 50608 [32], \(L\)/200, are depicted in Figure 22. As expected, specimens with the same \(\rho_{f}\) but varied \(L\) values exhibited the same failure mode and similar \(M_{u}\) (\(M_{s}\)) values. The \(\Delta_{s}\) was not linearly altered with the increase of span for beams with the same reinforcement ratio. This was consistent with the deflection equation based on the fundamental theory of material mechanics [34], in which the deflection was a quadratic function of \(L\) for a given moment. As the deflection limit was in proportion to the \(L\), the \(\Delta_{s}\) of specimens with larger values were more likely to exceed its limit. Intuitively, as displayed in Figure 22, the \(\Delta_{s}\) values of beams with \(L\) that ranged from 3000 to 4200 mm were acceptable, while that  
Figure 21: Influence of reinforcement ratio: (**a**) Ultimate capacity of finite-element (FE) models; (**b**) Moment-deflection response of FE models.  
of their counterpart with an \(L\) of 4800 mm was unconservative. Therefore, a larger reinforcement ratio was needed for SFLC beams reinforced with GFRP bars with larger span lengths according the current provisions.
# Deep Learning for Joint Adapatations of Transmission Rate and Payload Length in Vehicular Networks
## 4 Deep Learning for AMCPLS in IEEE 802.11p V2V Networks
### Features Preparation  
For the sake of training our DCNN model, we need to construct a training set that includes \(M\) training examples, \(\{f^{m},a^{m}\}_{m=1}^{M}\), where \(f^{m}\) is the features vector of the training example \(m\), \(a^{m}\) is the class label of the training example \(m\), and \(m\in\{1,2,...,M\}\). Each class \(a^{m}\) incorporates the optimal combination of the MCS and the payload length for the corresponding features vector \(f^{m}\). The features of the training example \(m\) can be extracted from the corresponding estimated OFDM channel coefficients, \(\hat{H}^{m}_{p}[n]\), at the all \(U_{s}\) used subcarriers and the estimated noise standard deviation, \(\hat{\sigma}^{m}\). It can be represented by  
\[f^{m}=[|\hat{H}^{m}_{p}[1]|,|\hat{H}^{m}_{p}[2]|,...,|\hat{H}^{m}_{p}[U_{s}]|, \hat{\sigma}^{m}] \tag{9}\]  
where \(|\hat{H}^{m}_{p}[n]|\) is the magnitude of the estimated channel coefficient \(\hat{H}^{m}_{p}[n]\). The key performance metrics that we use to classify the training examples are the effective throughput, \(\Gamma\), and the FER. The class label \(a^{m}\) of training example \(m\) is the optimal combination of the MCS and the payload length that achieves the maximum throughput and complies with a predefined FER constraint, \(\epsilon\), as expressed by the following equation  
\[a^{m}=\operatorname*{argmax}_{a}\{\Gamma(a,f^{m}):FER(a,f^{m})<\epsilon\}, \tag{10}\]  
where \(\Gamma(a,f^{m})\) is the throughput achieved at the channel condition corresponding to the features vector \(f^{m}\) with the transmission class \(a\), and \(FER(a,f^{m})\) is the FER at the channel condition corresponding to the features vector \(f^{m}\) with the transmission class \(a\). Note that (10) is similar to (8), except that the class \(a^{m}\) replaces \(\{i^{*},l^{*}\}\) and the features vector \(f^{m}\) is used instead of the intractable actual channel condition. With (9) and (10), we can generate many training examples \(\{f^{m},a^{m}\}_{m=1}^{M}\) to train the DCNN for AMCPLS. (The generation of the training set is described in detail in Section 5.2).
# Explainability of Automated Fact Verification Systems: A Comprehensive Review
## 3 Explainable AFV
### Methodological Perspective
#### 3.2.3 Attention-Based Approach  
Different from the summarization and the logic-based techniques, explainable AFV systems such as [24; 35] use visualizations to illustrate important features or evidence utilized by AFV models for predictions. This provides users with a means to understand the relationships that influence the decision-making process. For example, the AFV model proposed in [35] introduces an attention mechanism that directs the focus towards the salient words in an article in relation to a claim. This enables the generation of the most significant words in the article as evidence (words with more weights are highlighted in darker shades in the verdict) and [35] claims that this strategy enhances the transparency and interpretability of the model. The explanation module of the fact checking framework in [24] also utilizes the attention mechanism to generate explanations for the model's predictions, highlighting the important features and evidence used for classification.  
However, ref. [3] illustrated several critical concerns associated with the reliability of attention as an explanatory method, citing pertinent studies [36; 37; 38] to reinforce the argument. The authors point out that the removal of tokens assigned high attention scores does not invariably affect the model's predictions, illustrating that some tokens, despite their high scores, may not be pivotal. On the contrary, certain tokens with lower scores have been found to be crucial for accurate model predictions. These observations collectively indicate a possible 'fidelity' issue in the explanations yielded by attention mechanisms, questioning the reliability and interpretability of attention mechanisms in models. Furthermore, ref. [3] argue that the complexity of these attention-based explanations can pose substantial challenges for people lacking an in-depth understanding of the model architecture, compromising readability and overall comprehension. This scrutiny of the limitations inherent to attention-based explainability methods highlights the pressing need to reevaluate their applicability and reliability within the realm of AFV.
# Ground Target Detection and Damage Assessment by Patrol Missiles Based on YOLO-VGGNet
## 4 Target Damaged Components Analysis Based on Convolutional Feature Clustering  
After evaluating the damage level of the targets in the image blocks, the proposed convolutional feature clustering will be activated to analyze the damaged components for functionally damaged targets, which can cluster convolution kernels with large response values at the target's specific components. We can judge whether the component is destroyed according to activation intensity of the clustered convolution kernels.  
We divide the convolutional feature clustering into three steps. The first is to select the convolutional layer, which contains the richest weight information of the targets' components. We compared the weight visualization of three CAM methods using ablation experiments and chose the best to visualize the weights of four targets layer by layer. Finally, the 26 th convolutional layer in Score-CAM is selected after comparison.  
The second is the improvement of deconvolution for clustering the convolution kernels of different types of targets. Deconvolution is used to analyze the feature extracted by convolution kernels. We found that different targets of the same class tend to activate specific convolution kernels due to similar components and structures. Therefore, by the guided backpropagation, we collected the (top 10 activation intensity kernels of the layer) clustered convolution kernels of a certain number of radars, tents, supply vehicles, and tanks. The result shows that different radar targets tend to activate the same part of the convolution kernels because they contain the same characteristics (the radars all contain antenna and bracket).  
At last, we deepen the study from different targets to the targets' different components by guiding the backpropagation of convolution kernels. It is experimentally demonstrated that for different components of the same target, partial convolution kernels exist, which are focused on extracting features of the same component. Therefore, we proposed the novel convolutional feature clustering method, which can cluster the convolution kernels with large response values at the target's specific components and determine whether that component is destroyed by detecting the sum of the activation intensity of the clustered convolution kernels. Furthermore, we evaluate eight damaged components of four types of targets based on the convolutional feature clustering method and verify the effectiveness of our method.
# Multiple Observations for Secret-Key Binding with SRAM PUFs
## 7 Sequential MO Helper Data Scheme  
We have seen in Section 5 that a better performance (smaller reconstruction error probability) is often achieved when the number of enrollment observations is increased. However, each enrollment observation requires a full reset (power off and power up) of the SRAM-PUF. Therefore, considering more enrollment observations results in an increased duration of the enrollment phase of the MO helper data scheme. This may be undesirable in practice as the enrollment phase must be completed in a secure environment and limited time is available.  
In this section, we present a variation on the MO helper data scheme that we call the Sequential Multiple-Observations (SMO) helper data scheme. We exploit the fact that a new SRAM-PUF observation is required for each key reconstruction, and, after the reconstruction, we use this additional observation to update the helper data. As a result, the helper data are updated (sequentially) after each reconstruction, and thus the reconstruction error rate is reduced over time. The SMO scheme improves the efficiency regarding the MO scheme, since observations are used both for reconstruction and enrollment. Furthermore, the SMO scheme enables the usage of a reduced number of observations during the enrollment phase. The reduced number of enrollment observations comes at an initial cost of larger reconstruction error probability (FER); however, this is quickly reduced (see Figure 8) during the lifetime of the device.
# Product Demand Prediction with Spatial Graph Neural Networks
## 3 Methodology
### Attention-Aware Message Propagation  
In the intricate landscape of retail, not all interactions between locations bear equal significance in shaping product demand. The influence of one retail location on another can vary dramatically based on a multitude of factors, such as the similarity of the products offered, the competitive dynamics between the stores, or even the demographic characteristics of the surrounding areas. Traditional methods of aggregating information across nodes in a graph often assume uniformity in these relationships, potentially glossing over the subtleties that could inform more nuanced demand predictions. To address this limitation and embrace the complexity of real-world retail networks, it becomes urgent to introduce a mechanism that can discern and weigh the varying degrees of influence between neighboring locations. By implementing an attention-aware message propagation system, we can selectively amplify or attenuate the information flow between nodes, ensuring that the aggregation of features across the network accurately reflects the heterogeneity of inter-node impacts. This approach not only enhances the model's ability to capture the essence of spatial interactions but also fine-tunes the predictive accuracy by focusing on the most relevant signals for demand prediction in each specific location. Following this rationale, the introduction of an attention mechanism becomes a critical advancement in our methodology, allowing for a dynamic and context-sensitive representation of retail locations within the graph.  
We introduce an attention mechanism to capture the varying impact that different neighbors have on a node's feature representation. The attention coefficients \(\alpha_{ij}\) quantify the importance of node \(v_{j}\)'s features to node \(v_{i}\), computed as  
\[\alpha_{ij}=\frac{\exp\left(\text{LeakyReLU}(\mathbf{a}^{T}[\mathbf{Wh}_{i} \|\mathbf{Wh}_{j}])\right)}{\sum_{k\in\mathcal{N}(v_{i})}\exp\left(\text{LeakyReLU }(\mathbf{a}^{T}[\mathbf{Wh}_{i}\|\mathbf{Wh}_{k}])\right)}, \tag{2}\]  
where \(\mathbf{a}\) is a learnable weight vector, \(\mathbf{W}\) is a shared linear transformation applied to each node's features, and \(\|\) denotes concatenation.
# Unlocking the Potential: How Flying Taxis Will Shape the Future of Transportation
## 3 Customer Need to Use Flying Taxis
### Flying Taxis Provide Transportation to Areas with Poor Road Conditions or Infrastructure  
Flying taxis have the potential to provide transportation to areas with poor road conditions or infrastructure. Since flying taxis can take off and land vertically, they do not require a runway or traditional infrastructure like airports. This means they can operate in areas where traditional aircraft cannot, such as remote locations or areas with limited road infrastructure. Additionally, flying taxis have the ability to fly over traffic congestion, which can be especially beneficial in areas with poor road conditions or traffic management [180].
## 5 Recognition and Validation of Inadequate Grouting Section  
### Analysis of Grouting Record  
The field grouting processing is recorded to recognize the inadequate reinforcement section. The drill length, actual grouted holes length, grouting volume and the grouting volume per meter of the right and left lanes of the Yujingshan tunnel are listed in Tables 4 and 5, respectively.  
According to the grouting quantity of C-S slurry listed in Table 4, the grouted sections of right lane can be divide into two cases. The first case is D3K279 + 872-D3K279 + 900.5 section, in this section, the strata are mainly injected by SAC slurry, indicating that the groundwater have little influence on the section. The calculation result of Equation (5) shows that the total grouting volume per meter is theoretically no less than 1.02 m\({}^{3}\)/m, and except the D3K279 + 898 and D3K279 + 900.5 sections, the total grouting volume per meter of other sections is satisfied the designed value, indicating the grouting effect of  
Figure 12: Grouting hole drilling design in typical section of (**a**) D3K279 + 883 (**b**) D3K279 + 888 (**c**) D3K279 + 903 and (**d**) D3K279 + 938.  
other sections are good. The other case is D3K279 + 903-D3K279 + 935.5 section, where the C-S slurry is dominant grouting material, indicating the stratum is significantly affected by groundwater. Except for the D3K279 + 903 and D3K279 + 905.5 sections, the total grouting quantity per meter of other sections meets the designated value. The analysis results show that the D3K279 + 898-D3K279 + 905.5 surrounded by dotted line in Table 4 is the transition section of groundwater, due to the lack of grouting quantity of the section, the grouting effect should be further studied. In addition, the D3K279 + 925.5 section is only grouted by C-S slurry which plays a major role in sealing the hydraulic connection. The error value between the grouting record and the designed value is 18.63%, the grouting effect of this section needs to be analyzed by other methods.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mileage**} & \multirow{2}{*}{**Drill**} & \multirow{2}{*}{**Actual Grouted**} & \multirow{2}{*}{**Grouting Volume/m3**} & \multicolumn{2}{c}{**Grouting Volume Per Meter**} \\ \cline{5-8}  & & & & **SAC** & **C-S** & **Total** & **SAC** & **Total** \\ \hline D3K279 + 872 & 175 & 175 & 240.79 & 0 & 240.79 & 1.38 & 1.38 \\ \hline D3K279 + 875.5 &  
Table 5 is the grouting record of left lane of Yujingshan tunnel. The grouting record of left lane shows that the section are grouped by C-S and SAC slurry. However, the grouting quantity ratio of SAC slurry is larger than the C-S slurry, which indicates that the influence of groundwater on left lane is characterized by a small degree of impact, but a large range. It is noted that the total grouting quantity per meter of D3K279 + 938 section reaches 3.62 m\({}^{3}\)/m, which suggests the existence of the through crack and resulting the leakage of grout, the reason should be discussed.
# GDC-Based Infiltrated Electrodes for Solid Oxide Electrolyzer Cells (SOECs)
###### Abstract  
In this work, porous complex and metal-free cathodes based on a (La\({}_{0.6}\)Sr\({}_{0.4}\)) (Cr\({}_{0.5}\)Mn\({}_{0.5}\)) O\({}_{3}\) (LSCM) screen-printed backbone infiltrated with Ce\({}_{0.9}\)Gd\({}_{0.1}\)O\({}_{2}\) (GDC) were fabricated for solid oxide electrolyzer cells. GDC infiltration has been optimized by structural and microstructural investigation and tested by electrochemical measurements in CO/CO\({}_{2}\) mixtures. Infiltrated electrodes with a non-aqueous GDC solution showed the best electro-catalytic activity towards CO\({}_{2}\) reduction, exhibiting a much lower polarization resistance, i.e., R\({}_{\rm pol}\) = 0.3 \(\Omega\)-cm\({}^{2}\) at 900 \({}^{\circ}\)C. The electrochemical performance of LSCM/GDCE in terms of R\({}_{\rm pol}\) is comparable to the best-performing Ni-YSZ cathode in the same operating conditions (R\({}_{\rm pol}\) = 0.23 \(\Omega\)-cm\({}^{2}\)).  
SOEC; infiltration; doped-ceria; cathodes +
Footnote †: journal: Applied Physics Letters
## 4 Simulation Experiment and Results Analysis  
### Experimental Parameter Setting  
The DAG is randomly generated by setting parameters such as task number, communication calculation ratio (CCR) [3], parallel factor, and heterogeneous factor, which are used for experimentation. The HSP-QGA algorithm is both an improved genetic algorithm and a heuristic algorithm. Since ant colony algorithms are more often used in heuristic algorithms, we chose the genetic algorithm PA-CGA [29], the ant colony algorithm ACO [30], and the improved ant colony algorithm ACOQ [31] for comparison experiments  
Figure 8: Flow chart of the HSP-QGA.  
with the HSP-QGA. To limit the dimension of a task graph in randomly generated DAGs, set the specific parameters as follows: the parallel factor \(\alpha\ =\ \{2,4,6,7\}\), representing the number of tasks in each layer of DAG; the heterogeneous factor \(\beta\ =\ \{0.1,0.35,0.65,0.8,1\}\), representing the difference in the ability of heterogeneous processors to handle tasks. As the various DAGs were generated randomly and independently, and the meta-heuristic algorithm is stochastic in nature, the method of averaging among 100 Monte Carlo runs is used in this paper. The primary parameter settings of each algorithm are shown in Table 5.
## 2 Results and Discussion
### Optical Properties  
Diffuse reflectance spectra are normally determined by a UV-Visible spectrophotometer. In order to investigate the calcination effect on the optical properties of TiO\({}_{2}\) nanoparticles, the diffuse reflectance spectra were measured in the range of 200-800 nm at room temperature for all calcined samples, as shown in Figure 6a. The optical band gap values for all samples calcined at different temperatures were determined from reflectance spectra using the Kubelka-Munk equation:  
\[\left(F\left(R_{\infty}\right)hv\right)^{2}=A\left(hv-E_{g}\right) \tag{2}\]  
Figure 5: Transmission electron microscopy (TEM) images of TiO\({}_{2}\) nanoparticles at different calcination temperatures and the size distribution histogram for samples calcined at 500 and 800 \({}^{\circ}\)C, respectively.  
where \(F(R_{\infty})\) is the so-called remission parameter or Kubelka-Munk function, \(Inv\) is the incident photon energy, \(A\) is a constant depending on the transition probability, and the diffuse reflectance \(R_{\infty}\), \(R_{\infty}\) is the diffuse reflectance that is obtained from \(R_{\infty}=R_{sample}/R_{standard}\)[38; 39].  
The values of \((F(R_{\infty})Inv)^{2}\) versus \((Inv)\) were plotted and are illustrated in Figure 6b. Straight lines were drawn to fit the experimental band gap curves and were extended to intercept the \((Inv)\) axis in order to determine the optical band gap values of the TiO\({}_{2}\) nanoparticles at different calcination temperatures. It was found that the titania nanoparticles have a direct optical band gap that underwent a decrement as calcination temperature increased from 3.40 eV at 500 \({}^{\circ}\)C to 3.12 eV at 800 \({}^{\circ}\)C, as shown in Table 2. A decrease in the energy band gap with increasing calcination temperatures is attributed to the increase in the particle size and crystallinity improvement, according to the XRD and TEM analysis. It is supposed that, as the particle size increases, the number of atoms that form a particle also increase; consequently, the valence and conduction electrons is rendered more attractive to the ion core of the particles, and the band gap width of the particles thus decreases [40].