esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Analysis
### Real Data Analysis  
In this section, the tested informative frequency band selectors have been applied to the real vibration data. The effectiveness of the considered methods has been verified on the data origins from the bearing of the crushing machine. However, due to the lack of local fault in the considered vibration data, we decided to introduce an artificial component related to local damage. A similar approach has been performed in [21, 42, 49]. The signal has been shown in Figure 7. The length of the signal is 6 s and the sampling frequency is 25 kHz. The local fault has been added with the frequency modulation equal to 30 kHz and carrier frequency equal to 2.5 kHz (2-3 kHz). The spectrogram of the data has been presented in Figure 7(a). As one can see, the data reveals high-energy wide-band impulses around 0.25, 4.5 and 5.25 s which are related to the falling rocks. The real vibration signal contains various components with a complex structure, and the component related to the damage is almost imperceptible above the noise.  
Figure 6: Results of the informative frequency band selection for signal \(s_{4}\) using SE and Squared Envelope Spectrum (SES) infograms.  
Figure 7: Real signal from the crushing machine with the local fault added.  
Figure 8: Results of the informative frequency band selection for 7 different methods for the real data from the bearing of the crushing machine.  
The considered IFB selectors have been applied to the real signal with added fault. The results are presented in Figure 8b-f. Additionally, the results of the SE and SES infograms have been shown in Figure 8g-h. The amplitude of the non-cyclic impulses is smaller than in the considered simulated data but the results of the kurtosis, kurtogram, spectral smoothness index and infogram fail. As it was mentioned in the previous section, the spectral smoothness index is numerically sensitive for small values of the observations. Moreover, the analyzed real signal is 6 times longer than the simulated signals presented in the previous section. Small values and increased signal length cause the selector result to take infinite values. This disadvantage makes it impossible to obtain any sensible result (the infinite values appear up to the 5 level of the decomposition). What is more, the SE infogram does not indicate any impulsiveness in the IFB, only the SES correctly indicates the range of the cyclic impulses occurrence. The Alpha selector and spectral Gini index properly indicate the IFB but with much less selectivity than the CVB selector.  
It may happen that the number of random impulses grows up and/or their amplitudes increase. In this case, the spectral distribution of the energy of the analyzed signal will be different and selectivity of the tested selectors could change. Thus, the study of the influence of the number and amplitudes of the non-cyclic impulses on the selectors' results seems to be interesting.
# Distributed Combinatorial Maps for Parallel Mesh Processing
## 3 \(n\)D Distributed Combinatorial Maps
### Extensions and Discussion  
Our definition of distributed combinatorial maps has two strong properties: (1) each \(n\)-cell of the global combinatorial map belongs entirely to one block; (2) darts can not be duplicated in two (or more) different blocks.  
The first property is important for Proposition 1 in order to show how it is possible to retrieve all the information of the global combinatorial map corresponding to a given distributed \(n\)-map. Moreover, \(n\)-cells usually carry important information of the mesh that are necessary in several applications. For this reason, it seems natural not to split an \(n\)-cell between different blocks. However, it is possible to relax this constraint, if needed, by modifying Definition 1, to have \((n+1)\) sets of critical darts, one per dimension of the \(n\)-dmap. A critical dart in dimension \(i\) will be linked by \(\beta_{i}\) to the other critical dart in dimension \(i\) having the same label.  
The second property implies that the set of darts of the different blocks forms a partition of the set of darts of the global combinatorial map. This avoids the need to apply a same operation several times when there are some duplicated parts of the mesh in several blocks. However, depending on applications, it could be useful to create some halo (or ghost) regions around cells of a given block. To do so, Definition 1 can be modified by adding a set of duplicated darts, one per block of the \(n\)-dmap. These darts can belong to different blocks, and must be labeled with a unique and global name (like for critical darts). When an operation modifies a duplicated dart in a given cluster, a message must be sent to all the clusters containing this dart in order to apply the same modification.  
The complexity of several algorithms can be improved by using our distributed combinatorial maps, as illustrated in this paper for an adaptive hexahedral mesh refinement method. Generally speaking, all algorithms that can be split in independent computations will be improved. When there are few dependent parts, global consistency can be preserved by adding a few communications between blocks and in this case, the complexity is still improved. However, operations that have huge dependent parts, that require global computation of the entire mesh, will not be improved when applied on distributed combinatorial maps. Indeed, in such a case, a lot of messages will be sent, possibly with a lot of information to exchange between the different blocks. However, this is a well known limitation of parallel computing and our solution does not provide a magic solution to this problem. Moreover, we would like to remind that the first goal of distributed combinatorial maps is to enable dealing with huge maps that do not fit in the memory space of a single computer, and this goal is achieved as illustrated in our experiments.
## 3 Proposed System Model
### DAWPC-MAC Model Overview
Prioritization is based on packet type in Equation (9):  
\[\text{Priority}=\Bigg{\{}\begin{array}{ll}1&\text{if }\text{Dtype}=\text{ Critical}\\ 0&\text{ \  
The duration of each slot (\(T_{s}\)) is carefully calculated to include the transmission time for the data packet (\(T_{\text{dp}}\)), the acknowledgment packet (\(T_{\text{a}}\)), a guard time (\(T_{\text{g}}\)) to handle slight variations in timing, and twice the maximum propagation delay (\(2\cdot\tau_{\text{p}}\)) to account for bidirectional communication. This design ensures efficient and synchronized communication within the network. This index indicates the theoretical capacity available at the courier node as receiver node for receiving data packets during the portion of a frame allocated for data transmission as depicted in Equation (12) [37].  
\[B=\frac{S_{s}\times(2\times\tau_{\text{p}}+T_{dp})}{N\times T_{dp}}, \tag{12}\]  
where, N is the number of nodes and \(s_{s}\) is the size of frame. The transmit energy consumption for a transmission between the transmitter and the receiver can be determined using the following equation as depicted in Equation (13). \[E_{t}=P_{0}\cdot(T_{\text{WS}}+T_{\text{RTS}}+T_{\text{SA}}+T_{\text{g}}+T_{ \text{a}})+P_{\text{required}}\cdot T_{\text{d}}, \tag{13}\]  
where, \(P_{0}\) is the power consumption that is used for the waking signal, \(T_{SA}\) for the slot allocation signal, \(E_{Tx}\) denotes the energy consumed by a node to transmit data during a specific time slot. It is a crucial parameter for evaluating energy efficiency in UWSNs. \(P_{Tx}\) represents the transmission power, which is the power level used by the node while sending the data packet, and \(T_{slot}\) refers to the duration of the time slot allocated for the node's transmission. This formula highlights the direct relationship between transmission energy, power, and time. Efficient energy usage is crucial in UWSNs since sensor nodes are usually powered by batteries with limited capacity, and recharging or replacing them in underwater environments is challenging. Reducing energy consumption extends the operating lifetime of the network.
## 3 Results
### Spatial-Temporal Distribution of Chl-a  
#### 3.2.1 Spatial Variations of Chl-a  
Based on the inversion results of the Chl-a concentrations from 2013 to 2020, the distribution of the Chl-a concentrations in the coastal waters of the Guangxi Beibu Gulf was obtained by taking the average value (Figure 7). The average Chl-a concentration in each bay in the coastal waters of the Guangxi Beibu Gulf from 2013 to 2020 is shown in Table 5.  
The concentration of Chl-a in the coastal sea surface of the Beibu Gulf of Guangxi was higher in the nearshore coastal waters and lower in the offshore waters, and it gradually decreased from north to south. The concentration of Chl-a in the Naniu Estuary Bay was the highest, and the average of the whole region was 11.469 \(\mu\)g/L. The means of the Dafeng Estuary Bay, Beihai, Qinzhou Bay, Pearl Bay, and Fangcheng Bay were 8.198, 7.461, 6.600, 5.031, and 3.372 \(\mu\)g/L, respectively.  
Figure 6: Accuracy verification results of seven characteristic variables. (**a**) Inversion results. (**b**) Fitting results.
# Bearing Fault Diagnosis Using Grad-CAM and Acoustic Emission Signals
## 2 Proposed Method  
Figure 1 illustrates the process of diagnosing bearing faults by the proposed method as a flowchart. In step 1, the envelope power spectra are calculated from pre-acquired AE signals containing healthy and faulty conditions. In step 2, frequency magnitudes are extracted from the characteristic frequency range of the bearing and used as features. In step 3, the CNN is trained using the extracted features. Here, the envelope power spectra of new AE signals are classified into healthy or faulty condition using the trained CNN. Finally, in step 4, the importance weights with frequency are generated including valuable regions in the envelope spectrum using Grad-CAM for the acquired AE signals.
## 1 Introduction
### Contributions and Paper Organization  
In this work, we aim to improve the swarm robustness and consistency under sparsely available communications. The scale of robot swarms is only a dozen or less. Both the communication bandwidth and the communication distance of robots are limited. Besides, since the paper focuses on the flocking control mechanism, the swarm will be represented in an agent-level form. The main contributions of this paper include:  
1. The graph importance entropy is defined to quantitatively evaluate the swarm robustness. A quantitative evaluation protocol, where the graph importance entropy is one of the core metrics, is established to determine the best learning-based strategy and rank different distributed flocking control strategies;
2. The importance-correlated directed graph convolutional networks (IDGCNs) are constructed for multidimensional feature extraction and aggregation of graph data under restricted communications. Furthermore, the weighted link contraction degree matrix is designed to portray the distribution of graph importance in real-time while extracting graph structure information for backbone network maintenance. In addition, based on IDGCNs, time-varying topology relations and swarm scale can be described uniformly, which ensures the scalability of the distributed flocking control strategies;
3. The centralized expert strategy is modified to realize the balanced distribution of graph importance entropy. A distributed strategy with performance close to the expert template is generated based on imitation learning, enabling scalable graph importance consistency and scalable swarm robustness.  
The rest of the paper is structured as follows: Section 2 formulates the description of robot swarm with restricted communications and the flocking task. Section 3 introduces the quantitative evaluation metric of swarm robustness and the imitative learning approach based on IDGCNs. Section 4 designs several simulation experimental scenarios to compare the distributed strategy obtained in this paper with other distributed flocking control strategies. Section 5 concludes this paper.
# Two-Path Spatial-Temporal Feature Fusion and View Embedding for Gait Recognition
## 6 Conclusions  
In this paper, we propose a novel gait recognition framework that combines two-path spatial-temporal feature fusion with view embedding. In the two-path spatial-temporal feature fusion module, MSFE is firstly utilized to extract feature representations with different granularities from the input frames. Then, the two-path parallel structure is designed to obtain the muti-scale spatial-temporal features, where FLSFE extracts both global and local features by R-convs and MSTFE interacts temporal features with multiple scales for achieving the strong ability of muti-scale spatial-temporal modeling. Additionally, a view embedding module is put forward to make the muti-scale spatial-temporal features  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Short-Term** & **Long-Term** & **NM** & **BG** & **CL** \\ \hline ✓ & & 96.8 & 91.9 & 80.6 \\  & ✓ & 95.5 & 90.3 & 74.3 \\ ✓ & ✓ & **97.0** & **92.9** & **81.6** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Study of the effectiveness of multi-scale temporal features on CASIA-B dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**R-conv1** & **R-conv2** & **R-conv3** & **NM** & **BG** & **CL** \\ \hline \(1\) & \(1\) & \(1\) & 95.3 & 86.1 & 71.9 \\ \(2\) & \(2\) & \(2\) & 95.3 & 86.9 & 72.8 \\ \(2\) & \(4\) & \(4\) & 95.4 & 87.8 & 73.9 \\ \(2\) & \(8\) & \(8\) & 95.6 & **88.6** & **75.2** \\ \(4\) & \(8\) & \(8\) & 95.9 & 87.9 & 74.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Study of the effectiveness of the value of \(n\) in each R-conv on CASIA-B dataset.  
under the best viewpoint. Extensive experiments conducted on various public datasets validate superior performance of our approach compared to recent counterparts.  
Conceptualization, C.H.; Data curation, D.G. and X.Z.; Formal analysis, C.H. and D.G.; Funding acquisition, C.H.; Investigation, D.G.; Methodology, C.H. and D.G.; Project administration, C.H.; Resources, C.H.; Software, D.G.; Supervision, C.H.; Validation, C.H. and D.G.; Visualization, D.G. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Research Foundation of Education Bureau of Liaoning Province (Grant No. LZD202001) and the Science and Technology Project of Department of Science & Technology of Liaoning Province (Grant No. 2021 JH1/10400029).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request. The data are not publicly available due to personal information and privacy about the data.  
The authors declare no conflict of interest.
# Techno-Economic Potential of Plasma-Based CO\({}_{2}\) Splitting in Power-to-Liquid Plants
## 3 Methodology
### Modelling
#### 3.2.2 Plasma-Based CO\({}_{2}\) Splitting  
The plasma reactors operate at ambient pressure, and ambient temperature was present at the inlet. Depending on the plasma technology, the gas temperature across the reactor increased to 80 \({}^{\circ}\)C (DBD) or 400 \({}^{\circ}\)C (GA), as shown by experimental investigation. The electrical power \(P_{Plasma}\) needed to split the CO\({}_{2}\) was calculated using Formulas (1)-(3), under the use of \(\eta_{CO_{2}}\) and \(\chi_{CO_{2}}\) as parameters.
## 2 Materials and Methods
### User Profiling Based on Frequent Subgraphs
#### 2.2.3 Mining Frequent Subgraphs  
An FSM algorithm [24] was applied to each user graph, chunking the overlapping parts until there were no more candidate patterns with a frequency over the minimum support. An isomorphic part to count the frequency was required, as the item instances included in the user graph were essentially all different. Therefore, we applied abstraction to them. Abstraction is similar to conceptualization, wherein a specific instance is replaced with its class. Then, the algorithm finds common triples, counts their frequency, and selects the triples that exceed the minimum support as candidates. The most dominant candidate is replaced with a new node (or chunk), and the graph updates to reflect this replacement. This process is repeated until no more candidates appear, and a frequent subgraph is obtained by reconstructing the original shape of the last chunk.  
Algorithm 1 is the pseudo code of the FSM algorithm.  
```
user graphU candidates \(\leftarrow\) {} chunks \(\leftarrow\) {} min_support s abstract(item instances inU)  count initial frequencies of triples inU for each triple t inU if frequency(t) > s then candidates \(\leftarrow\) candidates + t while! candidates! > 0 do target \(\leftarrow\) choose_from(candidates) chunks \(\leftarrow\) chunks + target candidates \(\leftarrow\) {} U \(\leftarrow\) update(U) # replace triples around target count triple frequencies inU for each triple t' inU if frequency(t') > s then candidates \(\leftarrow\) candidates + t' if! \(\mid\) chunks! > 0 then subgraph \(\leftarrow\) restructure(chunk) return subgraph
```  
**Algorithm 1** FSM algorithm.  
The main parameter to be set for this process was the minimum support for mining patterns. The minimum support allowed decisions to be made differentially by considering the distribution of the number of items each user interacts with. For example, in the movie domain, if the minimum support was set to two for those who watched 10 movies and those who watched 100 movies, the degree of preference might be reflected differently. Therefore, it was appropriate to differentiate the minimum support.  
The resulting set of subgraphs comprised common triples belonging to the items that a user commonly interacted with. Figure 4 shows a simple example of an extracted subgraph applied to a movie domain. As a movie is an item, it is abstracted. The numbers next to the properties indicate how many times there have been interactions with the movie with the corresponding triple.
## 3 Proposed Method
### Loss Function  
The loss function compares the segmentation map generated by the deep learning model with the groundtruth and outputs the error. The EAR-Net uses focal loss as the loss function. Small and thin objects in segmentation are difficult to learn because of the small number of pixels. In particular, when a large object that is easy to learn in an image occupies most of the area, the sample that is easy to segment dominates the learning.  
Figure 7: Decoder architecture.  
Therefore, we utilize focal loss with improved standard cross entropy to direct focus on the difficult-to-learn samples. Equation (6) represents the cross-entropy loss function.  
\[CE(p,y)=\begin{cases}-\log(p)&ify=1\\ -\log(1-p)&otherwise.\end{cases} \tag{6}\]  
Here, \(p,y\) are the groundtruth and output of the proposed method, respectively. The cross-entropy loss function compares the segmentation map generated by the proposed method with the groundtruth and outputs the error. In this case, if the standard cross-entropy loss function is used, the easily divided samples dominate the overall loss. Therefore, the loss function is improved to reduce the loss of well-segmented samples and to relatively increase the loss of difficult-to-segment samples. Equations (7) and (8) represent the focal loss.  
\[p_{t}=\begin{cases}p\\ 1-p\end{cases} \tag{7}\]  
\[FL(p_{t})=-\alpha_{t}(1-p_{t})^{\gamma}\log(p_{t}) \tag{8}\]  
As for the focal loss, when \(p_{t}\) is correct or close to 1, it is significantly smaller than the conventional cross-entropy loss function. Conversely, when \(p_{t}\) is close to 0, the loss increases again. \(\alpha,\gamma\) are hyperparameters that control the contribution of the focal loss in the loss function. If \(\gamma\) is 0, it is the same as the existing cross-entropy loss function. We set the optimal value \(\alpha=\gamma=1.0\) based on the data analyzed by [PERSON] and [PERSON] [26] and our experimental data.
## 3 Results  
### Description of the Test Area  
The test area is located in the hamlet Lueg near the village of Gries am Brenner in Tyrol, Austria. We took measurements of the south-facing slopes of the Padauner Kogel. Below this mountain slope runs the railway track of the Brennerbahn, which is today a part of one of the most important railway connections between Germany and Italy. It connects Munich via Innsbruck with Verona on the shortest route. This section of the track is strongly endangered by rockfall and is protected by numerous protective structures. Due to their rapid evolution, high velocity and impact energy and proximity to infrastructure, mass movements can pose a significant natural hazard. Such mass movements of different types have been surveyed extensively by terrestrial laser scanning in the past [17; 18; 19]. According to [PERSON] et al. [20], the key insights into the use of terrestrial laser scanning (TLS) in rock slope investigations include: (a) the capability of remotely obtaining the orientation of slope discontinuities, which constitutes a great step forward in rock mechanics; and (b) the possibility to monitor rock slopes which allows not only the accurate quantification of rockfall rates across wide areas but also the spatio-temporal modelling of rock slope deformation with an unprecedented level of detail. The purpose of this laser scanning is to be able to carry out an improved risk analysis for this area by means of a very detailed surface model.  
In addition to the automated filtering, we manually categorized points into ground and non-ground points. The manually filtered results are therefore suitable for comparison with results from automated filtering methods.  
The surveyed slope is approximately 1 km long and has a height difference between valley and mountain tops of approximately 450 m. Most of the area has vegetation, manly the steep parts are rock without vegetation with slope up to 70\({}^{\circ}\). Because of the diverse terrain, automated filtering of laser scanning data is difficult.  
Figure 7 shows an inclination map [21] of the test site. Flat areas have brighter colours as mostly barren slopes with more than 60\({}^{\circ}\) inclination. Two significant, large rock formations are on the upper side and at the left below.  
Figure 7: Test area (inclination map); image source: © Land Tirol, tiris, www.tirol.gv.at/tiris.