esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis and Discussion
### Comparison Analysis  
In this research, decision-making for intelligent collision avoidance of ships based on ship manoeuvring process deduction is proposed. This method can efficiently resolve ship collisions in a variety of multi-ship encounter scenarios. In the past, many methodologies and techniques for the collision-free problem were presented by various relevant scholars [16; 20; 25; 28; 31; 35; 36], however, this paper offers unique diversity and superiority compared to the previous studies.  
A lot of related studies pay more attention to the collision avoidance for a single ship or open sea, and ignore the multi-ship scenario and confined sea [31; 36; 37]. Compared to a single ship, collision avoidance is quite complicated in multi-ship circumstances. Although some studies [26; 27; 28; 29] have provided some algorithms and models for the multi-ship situation which consider the ship motion model insufficiently, most of current research pays less attention on the model in which the ship's speed and rotation of propeller are not matched. The core model provides two benefits for decision-making of collision avoidance. One is using the CRI model to identify the most dangerous ship out of a group of ships with varying degrees of collision risk. Another is that the decision-making by the proposed algorithm will not create additional collision risk, which will not impede the safe navigation of other TSs.  
In addition, few studies consider the characteristics of ship motion in the variable speed of the ship. Therefore, it is highly necessary to deduce the next state of OS for collision avoidance. In this research, an improved MMG model is introduced to derive the ships' manoeuvre motion process, which reduces the gap between the collision-free algorithm and practical applications. Furthermore, the COLREGs and good seamanship are also taken into account for the proposed method for intelligent collision avoidance.  
In summary, this paper presents an intelligent collision avoidance method based on an IVO algorithm, which utilises alternation of course and changing of speed to present collision for the ship. Many factors are taken into account, including the improved MMG model, COLREGs, and good seamanship. Moreover, this model can resolve the conflict between ships in complicated circumstances, which is also effective for multi-ship encounter situations.
# Fault Prediction Modeling for High-Impact Recorders Based on IPSO-SVM
## 3 Experiments and Results
### Comparison with Other Models  
In recent years, deep learning and other artificial intelligence methods have made significant advancements in fault prediction. Long Short-Term Memory (LSTM), a specialized type of Recurrent Neural Network (RNN), has been widely used in fault prediction due to its ability to capture long-term dependencies in time-series data. In this study, an LSTM-based fault prediction model was constructed to evaluate its performance in scenarios involving small samples, high dimensionality, and nonlinearity [19]. The basic parameters of the LSTM model are shown in Table 10:  
Additionally, to provide a more comprehensive comparison, a commonly used machine learning algorithm--Random Forest--was selected as another baseline [20]. The parameters of the Random Forest model are shown in Table 11:  
The data indicate that IPSO-SVM outperforms LSTM and Random Forest in fault prediction tasks. IPSO-SVM achieved the highest accuracy (90.5%), precision (95.5%), and F1 score (89.9%), followed by LSTM, which, despite having a higher recall rate (86%), had slightly lower precision and F1 score. Random Forest exhibited the lowest performance, with an accuracy of 83.5% and precision of 86.0%. These results suggest that IPSO-SVM is more effective than LSTM and Random Forest in handling small samples and nonlinear data.
# Cloud Computing in Free Route Airspace Research
## 3 FRA Data Structures and Algorithms
### FRA Optimization Algorithm  
After defining the FRA optimization task, we have the input points \(P_{1},P_{n}\in NFRA\). Now, our task is to find the optimal flight from \(P_{1}\) to \(P_{n}\). We proceed by finding the shortest path between vertices in the GRFA graph \(P_{1}\), \(P_{n}\) and so we get the sequence \(A_{p}=(V_{1},V_{2},\ldots,V_{k})\). Here, it is necessary to determine the entry and exit points for each airspace \(V_{j}\) and then we can solve the problem (5) in parallel in each space \(V_{j}\). When we find multiple shortest paths, we perform a calculation for each path and find the optimal flight. Within the airspace, we consider a flight to be optimal if we can make it along a line segment that has the minimum length.  
FRA optimization algorithm diagram:  
Input: \(P_{1}\) _departure point_, \(P_{n}\) _arrival point_ of flight in _NFRA_.  
1. Suppose that \(P_{1}\in\underline{V}\), \(P_{n}\in\overline{V}\).
2. Compute the shortest path from \(\underline{V}\) to \(\overline{V}\) in graph \(GFRA\) (the shortest path contains the minimum amount of airspace).
3. If there are more shortest paths, find the optimal shortest path, and let it be the path \(A_{p}=(\underline{V}=V_{1},V_{2},\ldots,V_{k}=\overline{V})\) (optimal shortest path selection--according to the criterion of optimization).
4. Find entry-exit points \(P_{je},P_{jx}\in V_{j}\subseteq NFRA\) for \(j=1,2,\ldots,k\). \(P_{1e}=P_{1}\ P_{jx}=P_{(j+1)e^{\prime}}\), \(j=1,2,\ldots,k-1\), \(P_{kx}=P_{n}\) (the point \(P_{jx}=P_{(j+1)e}\in V_{j}\cap V_{j+1}\) can be selected as the point which lies at the smallest distance from the line \(P_{1}P_{n}\)).
5. Find the shortest path between points \(P_{je},P_{jx}\in V_{j}\subseteq NFRA\) for \(j=1,2,\ldots,k\) (can be calculated in parallel).  
The program code of algorithms can be found in the MATLAB File Exchange database. One such algorithm is here [13]. All research (project) algorithms are located in the Cloud ([4], Algorithms section).
## 4 Use Cases for Multimedia Analysis in Archiving Community Memories
### Recognising Social Events in Social Media Streams  
Combining items from social media streams, such as Flickr photos and Twitter tweets, into meaningful groups can help users to contextualise and effectively consume the torrents of information now made available on the social web. From the point-of-view of a web archive, this grouping of data makes it much more amenable to later browsing and analysis. Performing this grouping can also help separate irrelevant information from relevant information in a guided crawl. The task of performing such a grouping is made challenging because of the scale of the streams and the inherently multimodal nature of the information being contextualised. The problem of grouping social media items into meaningful groups can be seen as an ill-posed and application-specific unsupervised clustering problem. A fundamental question in multimodal contexts is determining which features best signify that two items should belong to the same grouping.  
Recently, we have been developing a methodology that approaches social event detection as a multi-modal clustering task. The various challenges of this task are addressed by our approach: the selection of the features used to compare items to one another; the construction of a single sparse affinity matrix; combining the features; the relative importance of features; and clustering techniques that produce meaningful item groups, whilst scaling to cluster very large numbers of items. Our state-of-the-art approach developed in the ARCOMEM project was evaluated using the dataset defined by the 2013 MediaEval (MediaEval is an international multimedia analysis benchmarking initiative [42]) Social Event Detection task [43]. With a near-optimal configuration, we achieved an F\({}_{1}\) score of 0.94, showing that a good compromise between precision and recall of clusters can be achieved. At the time of writing, this is the highest reported score on this dataset. Full details on our approach can be found in [44].  
Figure 4: Visualising trending images on Twitter. The image in the centre was the most popular image at the time of capture; on the right-hand side are some of the instances of this image. The small piles of images on the left correspond to other images that are currently trending (_i.e._, they appear multiple times in the time-window); these can be clicked on to expand them. The text of Tweets related to the currently selected trend are shown at the bottom.
# Sentiment Classification Using Convolutional Neural Networks
## 5 Result and Discussion
### Discussion  
#### 5.2.1 Comparison with Other Models  
In terms of the F1 scores, our network was about 10% greater than the traditional machine models. We believe that the biggest reason is the inherent ability of the CNN to capture local patterns. The CNN has the ability to capture local patterns and higher level patterns through its convolutional and pooling layers, as explained in [48]. We believe that such an ability of the CNN model produces the performance gap. The proposed network was also better than the state-of-the-art deep learning models  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **Weighted-F1** \\ \hline Decision Tree & 46.8 \\ Naive Bayes & 27.2 \\ Support Vector Machine & 34.9 \\ Random Forest & 46.2 \\ \hline [PERSON] [1] & 68.2 \\ [PERSON] et al. [42] & 67.5 \\ \hline Emb+Conv+Conv+Pool+FC & 68.3 \\ Emb+Conv+Pool+FC & 55.0 \\ Emb+Conv+Conv+Conv+Pool+FC & 64.6 \\ Emb+Conv+Pool+Conv+FC & 66.9 \\ Emb+Conv+global+FC & 65.3 \\ Emb+Conv+Conv+globalpool+FC & 67.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of ternary classification with the MR data, where Emb, Conv, Pool, globalpool, FC stand for embedding layer, convolutional layer, pooling layer, global pooling layer, and fully-connected layer, respectively.  
Figure 2: Classification performance according to the depth of the convolutional layer, where the horizontal axis means the number of stacked convolutional layers.  
such as those of [PERSON] [1] and [PERSON] et al. [42]. [PERSON] [1] and [PERSON] et al. [42] commonly utilized three filters (sizes of 3, 4, and 5) in parallel, in the hope to capture the filters' arbitrary features with different sizes. On the other hand, our network had consecutive filters that had two benefits: (1) hierarchical (higher) features were extracted, and (2) the high-level filters would have a broader range to see local patterns, where such advantages of consecutive filters were already reported in many previous studies [56; 57; 58]. The three-way approach, employed in [42], gave worse performance than the model of [1], although it was designed to combine of a CNN model and a traditional machine learning model. The reason can be explained by the fact that the CNN model itself was already sufficient to achieve the high performance, as shown in Table 3, so the combination with other traditional models (e.g., Naive Bayes) degraded the overall performance. In [PERSON] et al. [42], among the sentences classified by CNN [1], the sentences with weak confidence were re-classified by the traditional machine learning models, and it improved the overall performance. On the other hand, for the three datasets in this paper, we found that the ratio of weakly-classified instances was biased, as shown in Figure 3; this means that the entropy of the weakly-classified instances was small, so it was difficult to expect that the performance would improve by the combination with the traditional models.
# MCIDN: Deblurring Network for Metal Corrosion Images
## 3 Method
### Frequency Channel Attention Module (FCAM)  
In addition to the SCAM, we introduce the FCAM for image deblurring, as illustrated in Figure 4. Recognizing the differences in high-frequency information between blurred and sharp images, we initially employ convolutional operators to enhance the high-frequency features, similar to high-pass filters. Based on the learned attention weights, we then amplify the useful frequency components and merge these enhanced features to facilitate image restoration.  
Specifically, given an input \(I\), the FCAM utilizes a sequence of convolutional layers to enrich the high-frequency information. The process can be formalized as follows:  
\[F_{1} =f_{3\times 3}^{1}(I) \tag{4}\] \[F_{2} =f_{3\times 3}^{2}(F_{1})\] (5) \[F_{3} =f_{3\times 3}^{2}(F_{2}) \tag{6}\]  
where \(f_{3\times 3}^{n}\) denotes a \(3\times 3\) convolutional layer. This explanation is initially presented within the context of a dual-frequency scenario for simplicity, yet the approach is readily extendable to accommodate multifrequency scenarios. Following this, the network is tailored to modulate individual frequency components and subsequently amalgamate them. To this end, channel attention weights are computed as follows:  
\[W_{i}=L_{i}(L_{0}(AAP(\sum_{i}F_{i}))),i\in\{1,2,3\} \tag{7}\]  
Figure 4: The architecture of proposed FCAM.  
where \(F_{i}\) represents the input feature shape, and \(L\) denotes a fully connected layer. We use concat operation to add up the attention weights of the three channels, and then generate the final attention weights.  
\[[W_{1}^{\prime},W_{2}^{\prime},W_{3}^{\prime}]_{c}=\frac{e^{[W_{1}W_{2},W_{3}]_{ c}}}{\sum_{j=1}^{3C}e^{[W_{1},W_{2},W_{3}]_{j}}} \tag{8}\]  
In Equation (8), \([\cdot,\cdot,\cdot]\) denotes the concatenation operation. The final output of FCAM can be generated using the following equation:  
\[\hat{l}=W_{1\times 1}\big{(}F_{1}W_{1}^{\prime}+F_{2}W_{2}^{\prime}+F_{3}W_{3}^ {\prime}\big{)}+I \tag{9}\]
# An Introduction to Nonlinear Integrated Photonics: Structures and Devices
## 4 Nonlinear Photonics Devices
### Nonlinear Sources
This limit was overcome by the same research group by using two mutually coupled resonators, again realized in the TFLN platform: the fabricated device proven to be capable of generating an on-chip EO comb with a line spacing of 30.925 GHz, a pump-to-comb conversion efficiency of 30%, and a wide comb span of 132 nm [200]. Early attempts of micro-comb-based communications used simple on-off keying (OOK) modulation [201; 202], where information is carried by the optical intensity. In this case, low comb intensity noise is essential for achieving high performance. Error-free transmission for each individual line of a low-noise micro-comb, transmitted over tens of kilometers of single-mode fiber, has been demonstrated with a power penalty of less than 0.5 dB [202]. Coherent data transmission, which typically poses stringent requirements on the spectral purity of the optical carrier, has also been demonstrated with phase-locked micro-combs. In the first work reported by [PERSON] et al. in 2014 [203], a data stream of 392 Gbit s\({}^{-1}\) was encoded on six lines of a micro-comb using quadrature phase-shift-keying (QPSK) and 16-state quadrature amplitude modulation (16 QAM). A second experiment demonstrated the feedback stabilization of the comb and transmission of a 1.44 Tbit s\({}^{-1}\) data stream over  
Figure 15: Supercontinuum generation in a 4.7 mm long silicon photonic wire waveguide for several input central wavelengths at P 0 \(\approx\) 1W. The inset shows that spectral broadening increases as \(\lambda_{0}\) approaches the ZGVD wavelength of 1290 nm. Reprinted with permission from [193] © The Optical Society. up to 300 km with twenty comb lines. These results showed that micro-combs can indeed meet the highly demanding requirements of coherent communications and thus offer an attractive route towards chip-scale terabit/s transceivers [204].
###### Abstract  
The conversion of abandoned butted well salt cavities into underground storage facilities holds immense significance for safeguarding energy security and improving the ecological environment. A significant barrier to the reconstruction of these old cavities is the limited comprehension of their complete morphology, caused by residue coverage. The three-dimensional seismic techniques excel in identifying complex geological structures but have a limited understanding of underground old salt cavity morphology, thus the seismic forward simulation method is utilized to study their seismic response patterns. Based on 3D seismic data, well logging data, and measured cavity shape parameters from the Yexian salt mine region in Henan Province, China, a geological model and observation system were established. The seismic response characteristics of the butted well salt cavern model, encompassing five distinct morphological attributes such as cavity spacing, cavity diameter, cavity height, sediment height, and horizontal connection channel height, were thoroughly investigated. The findings show that the cavity roof exhibits a distinctive \"two peaks sandwiching a strong valley\" feature, with the positions of the valley and roof remaining aligned and serving as a reliable indicator for identifying the cavity's top surface. The width of the roof waveform exhibits an exponential amplification effect relative to the cavern width. The residue's top surface presents an \"upward-opening arc\" wave peak with a downward shift that diminishes as the residue's height increases. This peak forms a circular feature with the cavity roof reflection waveform, and the residue's top surface is always located in the upper half of this circular waveform. The horizontal connection channel's top and bottom surfaces exhibit contrasting reflection patterns, with the top position aligning with the reflection trough and the bottom reflection waveform shifting downward as the channel height increases. The brine cavern, residue, and bottom of the salt cavern mainly exhibit chaotic reflections. There are distinct identification characteristics on the cavity top, residue top, and connecting channel top in forward simulation. The research findings provide valuable guidance for identifying the morphology of the underground real butted well salt cavity based on 3D seismic data and accelerating the construction of underground energy storage facilities.  
**Keywords:** underground energy storage; butted well salt cavern morphology; seismic forward simulation; seismic reflection characteristics; horizontal connection channel
## 4 Numerical Results and Discussion
### Evolution of Transient Dynamic Response of Blade off
#### 4.2.3 Frequency-Domain Response Characteristics  
In order to learn about the instantaneous response frequency component and the steady-state response frequency components, the time-domain responses within 0.05-0.12 s  
Figure 13: The motion orbits at different positions over time: (**a**) 1# bearing; (**b**) node 11 (C.G. position); (**c**) 2# bearing.  
and 1-1.5 s are respectively taken by FFT to obtain the corresponding frequency-domain responses, as shown in Figure 14. The results show that some natural vibration modes of the rotor system are excited by the sudden imbalance induced by the blade off occurring at the first stage gas turbine. The first natural frequency (\(f_{n1}\), the backward and forward frequencies are basically the same due to the weak gyroscopic effect on the first natural mode), the second backward frequency (\(f_{n1-}\)), and the forward frequency (\(f_{n2+}\)) exist in the spectrums of both bearings, while only the first natural frequency exists in the spectrum of Node 11, which is because the second natural mode is difficult to excite by the excitation near the C.G. position as mentioned above. Importantly, the high-order natural frequency components decay faster than the lower-order, while the rotational frequency (\(f_{t}\)) is always present, and its vibration amplitude remains unchanged. The reason for this is self-explanatory.
## 3 Results and Discussion
### The Grating Constant \(\Lambda\)
#### 3.1.3 Mutual Consistency: \(\Lambda(I,\,t_{\mathrm{exp}})\)  
Although the recording dose, _i.e._, the product of recording intensity and exposure duration, represents the decisive value, determining the grating formation, an independent investigation of \(\Lambda(I)\) and \(\Lambda(t_{\mathrm{exp}})\) provides a descriptive characterization of the material response. Nevertheless, results from Section 3.1.1 and 3.1.2, which are not contingent on each other, merge into a consistent picture of the material performance. Moreover, the independent results can be unified as follows.  
According to the symmetry of second derivatives ([PERSON]'s theorem), the partial derivations of a function with continuous second partial derivatives are commutative. This takes shape in the equivalence of Equations (4) and (6):  
\[\frac{\partial^{2}\Lambda}{\partial I\;\partial t_{\mathrm{exp}}}=\frac{ \partial^{2}\Lambda}{\partial t_{\mathrm{exp}}\;\partial I}<0 \tag{8}\]  
Specifically, Equation (8) manifests in the following results. On the one hand, the effect of a laterally decreasing periodicity is enhanced with increasing duration of exposure (Equation (4)). On the other hand, the increase of the periodicity with the exposure duration is diminished with increasing intensity of exposure (Equation (6)).
# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
###### Abstract  
Road extraction from high-resolution satellite images has become a significant focus in the field of remote sensing image analysis. However, factors such as shadow occlusion and spectral confusion hinder the accuracy and consistency of road extraction in satellite images. To overcome these challenges, this paper presents a multi-scale fusion-based road extraction framework, HRU-Net, which exploits the various scales and resolutions of image features generated during the encoding and decoding processes. First, during the encoding phase, we develop a multi-scale feature fusion module with upsampling capabilities (UMR module) to capture fine details, enhancing shadowed areas and road boundaries. Next, in the decoding phase, we design a multi-feature fusion module (MPF module) to obtain multi-scale spatial information, enabling better differentiation between roads and objects with similar spectral characteristics. The network simultaneously integrates multi-scale feature information during the downsampling process, producing high-resolution feature maps through progressive cross-layer connections, thereby enabling more effective high-resolution prediction tasks. We conduct comparative experiments and quantitative evaluations of the proposed HRU-Net framework against existing algorithms (U-Net, ResNet, DeepLabV3, ResNet, HRNet) using the Massachusetts Road Dataset. On this basis, this paper selects three network models (U-Net, HRNet, and HRU-Net) to conduct comparative experiments and quantitative evaluations on the DeepGlobe Road Dataset. The experimental results demonstrate that the HRU-Net framework outperforms its counterparts in terms of accuracy and mean intersection over union. In summary, the HRU-Net model proposed in this paper skillfully exploits information from different resolution feature maps, effectively addressing the challenges of discontinuous road extraction and reduced accuracy caused by shadow occlusion and spectral confusion factors. In complex satellite image scenarios, the model accurately extracts comprehensive road regions.  
high-resolution remote sensing images; road extraction; shadow occlusion; spectral confusion; multi-scale fusion Article