esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Algorithm Design  
### Leak Detection Algorithm  
#### 4.1.1 Leak Information Preprocessing Based on Wavelet Transformation  
(1) Selection of Wavelet Basis  
In this study, wavelet in series db is adopted to transform the original signals. This sort of wavelet has the following advantages: (i) it is sensitive to the leak signals and can highlight the singular characteristics of signals; (ii) it can reduce the signal distortion and ensure perfect signal reconstruction as well as good performance of time-frequency analysis.  
(2) Determination of Decomposition Scale  
We make the discrete wavelet transform for leak signals with background noise. Generally, the larger the decomposition scale is, the more favorable it is to eliminate the noise [35]. However, some important local singular characteristics may be lost if the decomposition scale is too large. According to the frequency characteristics of detection signals, we suppose that signal sampling frequency is \(f_{s}\) and the lowest identification frequency is \(f_{min}\). Thus the maximum decomposition scale \(n\) should satisfy the following relationship expression:  
\[\frac{f_{s}}{2^{n+1}}\geq f_{\min} \tag{1}\]  
Figure 2: Hierarchical model for leak detection and localization.  
(3) Wavelet Denoising Method of Threshold Selection  
After calculating the maximum decomposition scale \(n\), we decompose the leak signals into \(n\) levels. Considering that wavelet spectrums of leak signals and noise signals have different performance characteristics in every scale, we select an appropriate threshold by a heuristic method. For the points whose value is less than the threshold value, its coefficient is set to 0, while for the points whose value is more than the threshold value, its coefficient is set to the difference between the point value and the threshold value. Then we reconstruct the decomposed signals and obtain the single mode signals without noise.  
(4) Characteristic Parameters Calculation  
We carry out the time domain analysis for single mode signals and extract the characteristic parameters which can completely reflect pipeline leak, such as peak value, average amplitude, variance, root amplitude and so on.
# Parameter Identification of Structures with Different Connections Using Static Responses
## 3 Parameter Identification for Truss
### Parameter Identification for Truss Sample  
A six-member truss (Figure 1) was used to demonstrate the parameter-identification method of the truss structure using static displacements. All members have identical cross-sectional geometric properties. The \"as-built\" condition of the six-member truss has cross-sectional areas of \(A\) = 3 \(\times\) 10\({}^{-3}\) m\({}^{2}\) and a modulus of elasticity of 206 GPa. It is assumed that there is damage in members 1 and 4. The \"as-is\" cross-sectional areas of members 1 and 4 are 2.5 \(\times\) 10\({}^{-3}\) m\({}^{2}\) and 2.75 \(\times\) 10\({}^{-3}\) m\({}^{2}\), respectively. Since the \"as-is\" cross-sectional areas of members 1 and 4 are the parameters that need to be identified, therefore assuming the corresponding cross-sectional areas are \(A_{1}\) and \(A_{4}\), forces of \(-100\) kN and 50 kN were applied along degrees of freedom 2 and 3 to excite the truss structure; subsequently, displacements \(\mathrm{D}_{m}\) were measured along degrees of freedom 1 and 2.  
In this study, the measured displacement was determined on the basis of the structural \"as-is\" condition.  
The member stiffness matrix \(\mathbf{k}^{\prime}\) of the truss member was obtained from Equation (6), and the objective function was obtained from Equation (4). The interior-point method [26]
## 3 Deep-Learning-Based Channel Estimation Methods
### Overview of Neural Networks
#### 3.1.3 RNN  
Figure 9 shows a basic RNN structure. In contrast to the previously discussed DNN and CNN, RNN is uniquely designed to handle sequential data. It can remember information from past moments and utilize it for the current output calculation. In this case, the nodes among the hidden layers are interconnected. Moreover, the hidden layer's input depends on both the input layer's value and the hidden layer's output from the previous moment.  
Figure 8: Basic structure of CNN.  
Figure 7: Basic structure of DNN.  
During the training process, RNNs may suffer from long-term dependency, leading to gradient vanishing or gradient explosion. Therefore, the literature [49] conducted further research and proposed a special RNN called the long short-term memory (LSTM) network to effectively resolve this issue.
## 4 Results
### Principal Component Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Component** & **PC1** & **PC2** & **PC3** & **PC4** & **PC5** & **PC6** & **PC7** & **PC8** \\ \hline CGPA & 0.013 & \(-\)0.003 & **0.998** & \(-\)0.028 & 0.011 & 0.017 & \(-\)0.024 & \(-\)0.014 \\ Attempt Count & 0.002 & \(-\)0.004 & 0.007 & 0.031 & 0.005 & **0.145** & 0.067 & \(-\)0.082 \\ High Risk = 1 & \(-\)0.007 & 0.005 & \(-\)0.014 & \(-\)0.117 & 0.017 & \(-\)0.151 & 0.025 & 0.044 \\ High Risk = 2 & 0.007 & \(-\)0.005 & 0.014 & 0.117 & \(-\)0.017 & 0.151 & \(-\)0.025 & \(-\)0.044 \\ Term Exceeded = 1 & \(-\)0.006 & \(-\)0.002 & \(-\)0.015 & \(-\)0.005 & 0.005 & \(-\)0.012 & \(-\)0.019 & 0.050 \\ Term Exceeded = 2 & 0.006 & 0.002 & 0.015 & 0.005 & \(-\)0.005 & 0.012 & 0.019 & \(-\)0.050 \\ At Risk = 1 & \(-\)0.020 & 0.010 & 0.016 & -0.204 & 0.057 & \(-\)0.534 & **0.154** & **0.261** \\ At Risk = 2 & 0.020 & \(-\)0.010 & \(-\)0.016 & **0.204** & \(-\)0.057 & **0.534** & \(-\)0.154 & \(-\)0.261 \\ At Risk SSC = 1 & 0.000 & 0.004 & 0.004 & \(-\)0.027 & -0.003 & \(-\)0.049 & 0.016 & 0.002 \\ At Risk SSC = 2 & 0.000 & \(-\)0.004 & \(-\)0.004 & 0.027 & 0.003 & 0.049 & \(-\)0.016 & \(-\)0.002 \\ Other Module Count & \(-\)0.008 & 0.013 & \(-\)0.011 & 0.075 & 0.071 & \(-\)0.284 & \(-\)0.265 & 0.043 \\ Plagiarism & 0.016 & \(-\)0.005 & 0.003 & 0.027 & **0.242** & **0.287** & **0.172** & **0.346** \\ CW1 = 1 & \(-\)0.011 & \(-\)0.005 & \(-\)0.012 & \(-\)0.065 & 0.038 & -0.132 & -0.113 & \(-\)0.446 \\ CW1 = 2 & 0.011 & 0.005 & 0.012 & 0.065 & \(-\)0.038 & **0.132** & **0.113** & **0.446** \\ CW2 = 1 & \(-\)0.004 & \(-\)0.003 & \(-\)0.007 & \(-\)0.084 & 0.066 & -0.109 & \(-\)0.075 & \(-\)0.311 \\ CW2 = 2 & 0.004 & 0.003 & 0.007 & 0.084 & \(-\)0.066 & **0.109** & 0.075 & **0.311** \\ ESE = 1 & \(-\)0.004 & 0.059 & \(-\)0.025 & \(-\)0.638 & \(-\)0.002 & **0.232** & \(-\)0.130 & 0.052 \\ ESE = 2 & 0.004 & \(-\)0.059 & 0.025 & **0.638** & 0.002 & \(-\)0.232 & **0.130** & \(-\)0.052 \\ Played & **0.398** & **0.582** & \(-\)0.002 & 0.039 & \(-\)0.022 & \(-\)0.020 & 0.006 & \(-\)0.010 \\ Paused & **0.825** & \(-\)0.560 & \(-\)0.014 & \(-\)0.060 & 0.001 & \(-\)0.024 & \(-\)0.012 & 0.002 \\ Likes & 0.008 & 0.013 & \(-\)0.012 & **0.134** & **0.522** & \(-\)0.019 & \(-\)0.748 & **0.235** \\ Segment & **0.398** & **0.582** & \(-\)0.002 & 0.039 & \(-\)0.022 & \(-\)0.020 & 0.006 & \(-\)0.010 \\ Moodle Online In campus = 1 & \(-\)0.009 & 0.008 & 0.011 & \(-\)0.094 & 0.012 & \(-\)0.104 & \(-\)0.047 & \(-\)0.130 \\ Moodle Online In campus = 2 & 0.009 & \(-\)0.008 & \(-\)0.011 & 0.094 & \(-\)0.012 & **0.104** & 0.047 & **0.130** \\ Moodle Outside campus = 1 & \(-\)0.012 & \(-\)0.017 & 0.004 & 0.033 & \(-\)0.568 & \(-\)0.046 & \(-\)0.325 & **0.144** \\ Moodle Outside campus = 2 & 0.012 & 0.017 & \(-\)0.004 & \(-\)0.033 & **0.568** & 0.046 & **0.325** & \(-\)0.144 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: PCA variances. Figure 5: Principal component analysis (PCA) selection.
## 3 Experimental Results and Discussions
### Discussion  
Through the analysis of experimental results, the proposed ODS method demonstrates its effectiveness across three remote sensing scene datasets. In comparison to generative and policy-based approaches, ODS exhibits superior accuracy in five-way tasks with varying sample sizes. Notably, among the selected feature types, optimizing the distortion magnitude of edge features provides the model with the most significant gains. This suggests a substantial discrepancy in the contribution of distinguishable features generated by different types of feature distortions. Hence, exploring and optimizing combinations of distortion amplitudes for different features in the feature distortion space holds the potential for further accuracy improvement. However, constructing a higher-dimensional feature distortion space will inevitably result in a significant increase in computational complexity, necessitating a specific task analysis and hardware ability assessment.  
Simultaneously, the test results of ODS-DC on the three datasets indicate an enhancement in model robustness. In comparison to the single-path strategy, the dual-path strategy in five-way, five-shot tasks showed a potential improvement of approximately 7-18% in classification accuracy. Furthermore, the classification efficiency of ODS-DC is contingent on the number of support set samples. While an increase in the number of support set samples enhances model accuracy, it also leads to a substantial reduction in model inference efficiency. For instance, in a five-way scenario, the inference time for a single-path model only increased about twice from 1-shot to 10-shot tasks, while for a dual-path model, the inference time increased by over six times. Therefore, in tasks with fewer samples, the advantages of ODS-DC are more readily evident.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Framework Type** & **1 Shot** & **5 Shot** & **10 Shot** \\ \hline Single path & 72 \(\pm\) 17 & 106 \(\pm\) 19 & 134 \(\pm\) 19 \\ Dual path & 98 \(\pm\) 21 & 177 \(\pm\) 28 & 685 \(\pm\) 39 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of mean inference time (ms) of the proposed model in different settings.  
Figure 8: The confusion matrices for single-path (**A**) and dual-path (**B**) classification of 5 randomly selected urban scenes, as well as single-path (**C**) and dual-path (**D**) classification of 5 randomly selected nonurban scenes (5-way, 5-shot).
## 1 Introduction
Correlation maps [15] are employed to assess the vulnerability of infrastructure and identify the zone of influence of extreme events on this infrastructure. Vulnerability is assessed using an index-based approach, acknowledging that certain aspects of vulnerability may be difficult to directly observe or measure. Moreover, the complex task of assessing cumulative vulnerabilities resulting from mutual dependencies and correlations among different elements is particularly challenging. To address this, GIS mapping has been developed to estimate the effects of non-functioning infrastructures on neighboring infrastructures, as well as on the urban and social scale.
# An Opportunistic Network Routing Algorithm Based on Cosine Similarity of Data Packets between Nodes
## 3 An Efficient Forwarding Strategy Based on Cosine Similarity between Nodes
### Building the Opportunistic Network Topology  
Assume that the selected sub-network topology is as shown in Figure 2. There are 12 nodes in total. The set of nodes is \(V=\{V_{1},V_{2},\cdots,V_{12}\}\). All nodes are relay nodes, all of which have the characteristics of mobility and the ability to carry and forward information. In current period, it is assumed that node \(V_{1}\) needs to send information as the source node, and the speed of the message transmission between nodes is far greater than the speed of node movement. When the message is transmitted in sub-network, the sub-network topology will not change essentially.  
In the OppNet, neighbor nodes of each node can transmit information, and each neighbor node may become the next-hop. Compared with the dynamically changing network topology, in the OppNet with people as the mobile carrier, the social relationships between nodes are relatively stable, it does not change with change of the network topology. The sociality of nodes are embodied in the data packets they carry. Therefore, it is necessary to select some nodes with relatively stable social relations among numerous neighbor nodes for information transmission.  
This paper uses the cosine similarity to calculate the similarity of the data packets carried by nodes. The similarity of data packets between nodes is equal to the similarity between nodes, so as to define the strength of social relationships between nodes.
# Comparison between Laser and Stamping without Die (SWD) for Micro Tapered Hole Forming
## 4 Experimental Results and Discussion
### Punch Morphology after Stamping  
The punch head after stamping is as shown in Figure 17. As the punch head is formed by reverse EDM, there are discharge pits and cracks on the surface of the punch head. As the EDM process produces debris, bubbles, and secondary discharge, the punch head tip would be rounded. Therefore, the line contact is produced in the micro stamping process instead of the point contact that causes unbalance and punch head breakage.  
According to the experimental results, the Al6061 and C2680 workpieces are only punched 18 times with stamping depths of 300-380 \(\upmu\)m. Figure 17a is an OM picture of a punch head after stamping and it shows that the punch head is not broken. Figure 17b is a magnified SEM picture of a punch head tip. It shows the punch head is not broken or worn, as the WC has higher strength and wear resistance, and aluminum and copper alloy materials are soft and have high ductility. Therefore, the experimental results prove the feasibility of a tapered punch for micro stamping.
## 3 Results and Discussion
### Application of the Proposed Model to the G-Band Images Dataset
#### 3.7.2 Validation of the ANN Models  
Figure 16 shows the confusion matrices for the MLP-ANN that obtained the best accuracy in both stages of the proposed system. For each group, a confussion matrix with the false positives (FP), false negatives (FN), true positives (TP), and true negatives (TN) is shown. In Figure 16a-c, the confussion matrices for the first classification stage are shown. The lower accuracy was found in the group 2, where a 0.91 accuracy is obtained. In Figure 16d, the confussion matrix has a minimum accuracy of 0.88 for chromosomes C4 and C5. In Figure 16e, the minimum obtained accuracy is 0.51 for the class C23. Finally, in Figure 16f, the lower accuracy is 0.5 for the class C24. The group that exhibits the lowest accuracies is the group 2 of the multi-class classification, where most of the tested classifiers have scores below 60%. Finally, the chromosome number 10 presented the lowest accuracy (35%).  
In this section an ANN two-stages classification model applied to G-band chromosomes images was proposed. This model obtained a accuracy of 88.45% over a Q-band image dataset. The same model attained a percentage of 82.02% when it was applied over a G-band image dataset. It is worth noting that the G-band dataset was composed of only 24 karyotypes, while the number of karyotypes of the Q-band dataset was 119. A classification model trained with more information will obtain better results.  
A two-stage classification scheme was proposed. In the first stage, the chromosomes are classified in 3 wide groups, based on the ISCN length characterization of chromosomes. In a second stage, the output of the first stage is used as input for a multi-class classifier applied over each wide class. This allowed us to improve the accuracies. The ANN used in this scheme was tuned by reducing the number of neurons in the hidden layer without affecting the final classification result.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Original Configuration**} & \multicolumn{2}{c}{**Modified Configuration**} \\ \hline \multicolumn{5}{c}{**Binary Classification**} \\ \hline
**Group** & **Neurons** & **Accuracy** & **Neurons** & **Accuracy** \\ \hline
1 & 66 & 95.26 & 4 & 96.44 \\
2 & 66 & 90.56 & 20 & 90.29 \\
3 & 66 & 93.60 & 34 & 93.60 \\ \hline \multicolumn{5}{c}{**Multiclass Classification**} \\ \hline
1 & 68 & 91.12 & 17 & 92.89 \\
2 & 71 & 77.08 & 67 & 79.24 \\
3 & 70 & 84.40 & 30 & 86.40 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of best classification accuracy versus hidden neuron number.  
Figure 15: Training time for the GID when using an increasing number of hidden-layer neurons.
## 4 Methodology
### Pacf  
The grouting power time series is decomposed by the EWT into serval subseries, and the correlation between the data in each subseries is different. Therefore, in the design of the prediction model corresponding to different subseries, the correlation between the data in each subseries needs to be analyzed, and the optimal input variables of each subseries can be determined by analyzing the correlation. Suppose that \(x_{t}\) is the value needed to be predicted, and only \(x_{t-1}\) can be selected as the input variable if all the PACF values are within the 95% confidence interval \(\left(-1.96/\sqrt{N},1.96/\sqrt{N}\right)\). Meanwhile, \(x_{t-p}\) is taken as one of the input variables if the partial autocorrelation at lag \(p\) exceeds the 95% confidence interval. For details of the PACF process, see [44].  
The PACF value at lag \(p\), denoted by \(\alpha_{pp}\), can be calculated as follows:  
\[\left\{\begin{array}{l}\hat{\alpha}_{11}\ =\ \hat{\rho}_{1}\\ \hat{\alpha}_{p+1,p+1}\ =\ \frac{\hat{\rho}_{p+1}-\sum\limits_{j\ -1}^{p}\hat{\rho}_{j+1-j}\hat{\alpha}_{pj}}{ \frac{1-\sum\limits_{j\ -1}^{p}\hat{\rho}_{j}\hat{\alpha}_{pj}}{1-\sum\limits_{j\ -1}^{p}\hat{\rho}_{j}\hat{\alpha}_{pj}}}\\ \hat{\alpha}_{p+1,j}\ =\ \hat{\alpha}_{pj}-\hat{\alpha}_{p+1,p+1}\cdot\hat{ \alpha}_{p,p-j+1}\end{array}\right. \tag{5}\]  
where \(j\) = 1, 2,..., \(p\); \(p\) = 1, 2,..., \(M\), where \(M\) is the maximum lag; and \(\rho_{p}\) is the autocorrelation function (ACF) at lag \(p\).
## 2 Use Cases
Environmental sampling using settle plates is one of the prevalent environmental monitoring methods [98; 99]. Typically in a commercial-scale biomanufacturing facility, microbiologists routinely place hundreds of open Petri dishes containing growth media to collect the samples of the viable particles present in the environment [100]. These Petri dishes are later scanned for detecting the label (QR code), collected, and incubated, and are subjected to qualitative and quantitative analysis. The data relating to the sampling is stored manually on a Laboratory Information Management System (LIMS). Hence, labelling, transport, correct placement, and collection of settle plates are of utmost importance. Considering humans are the primary source of contamination, another manual intervention with the picking up of the Petri dishes to scan them is far from an optimal method of recording information for environmental monitoring. In this use case, a mobile collaborative robot is utilised to collect the sampled Petri dishes and read a QR code attached to the Petri dish. The information is then updated in the LIMS for future reference. The information from the QR code can further be used to fetch data from a cloud database or to initiate a digital twin [77]. Each use case is an example of a mundane task that can be automated but requires a degree of flexibility to locate specific samples, deliver them to the correct location, and navigate an environment that contains human operators and mobile equipment. They are also tasks where human errors or contamination can be eliminated through the use of the robot. The following sections report experiments to demonstrate that a mobile robot could perform some of the tasks involved in the above three use cases.
## 6 Results and Discussion
### Reason Generation Results  
In this subsection, we analyze the performance of GCLTE in reason generation. We quantify the experimental results into specific values to evaluate text quality using metrics such as BLEU-1, BLEU-4, and ROUGE-1 (Precision, Recall, F1-score).  
According to the evaluation results presented in Tables 4-6, GCLTE surpasses most other models across all three datasets. Specifically, GCLTE demonstrates significantly superior performance compared to the baseline models in terms of BLEU-1 and BLEU-4 scores. It is noteworthy that Att2 Seq achieves comparable BLEU scores to GCLTE on the REASONER and Amazon_Music datasets and even slightly higher BLEU-4 scores on the REASONER dataset. However, it performs poorly on the Amazon_Instant_Video dataset. In contrast, GCLTE demonstrates consistent high performance across all datasets, indicating strong generalization capabilities.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **RETER** & **NRT** & **LighGCN** & **SGL** & **EMER** & **GCLTE** \\ \hline
**RMSE** & 1.3173 & 1.1926 & 1.2158 & 1.2030 & 1.3147 & **1.1147** \\
**MAE** & 1.0978 & 0.9484 & 1.0035 & 0.9617 & 1.1275 & **0.9189** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ratings prediction based on REASONER dataset (test set).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **RETER** & **NRT** & **LighGCN** & **SGL** & **EMER** & **GCLTE** \\ \hline
**RMSE** & 1.1269 & 1.1191 & 1.1194 & 1.0645 & 1.1191 & **1.0816** \\
**MAE** & 0.9050 & 0.8891 & 0.8840 & 0.8432 & 0.8878 & **0.8476** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ratings prediction based on Amazon_Instant_Video dataset (test set).  
Regarding ROUGE scores on the REASONER dataset, GCLTE does not achieve the highest Precision and Recall, but it leads in F1-score among all models. The F1-score, a harmonic mean of Precision and Recall, indicates that while GCLTE may not be more optimal in a single metric, it maintains the highest overall performance. Analyzing the results for the Amazon_Music and Amazon_Instant_Video dataset, GCLTE's ROUGE scores significantly outperform those of other models in all metrics except Precision. Specifically, we observe that some models with high Precision scores perform poorly in both Recall and BLEU-4 scores. This is interpreted as an indication of poorer fluency or readability in the generated text, potentially leading to more repetitive word usage. GCLTE demonstrates consistent stability across all metrics, achieving strong scores without any significant anomalies. This stability reflects the robust feature extraction capability of our graph convolutional encoder, which uses a contrast strategy. Additionally, the transformer, enhanced with the encoder's embedding state, produces high-quality text explanations.
# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 2 Critical Research Agenda
### Supply-and-Demand Balancing
#### 2.1.1 Timeframes  
Traditionally, supply-and-demand power balancing is a major operational challenge; energy and capacity adequacies were addressed in the planning stage, as discussed in Section 4.1. However, in a zero-carbon power grid, wind and solar PV energy vary significantly, increasing the extent and duration of supply-and-demand imbalance compared to those of a traditional grid producing weather-independent energy. Thus, apart from energy provision, a zero-carbon grid must feature the flexibility and reliability required to maintain the supply-and-demand balance at the desired level from the planning stage to the scheduling, and to the real-time operation [23]. In other words, as we move toward 100% renewable electricity, power balance and system adequacy must be simultaneously tackled in the planning and operational stages, and on both the supply and demand sides.