esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The aim of this study is to evaluate 3D printing techniques for investigating the permeability of brittle rocks. Two mainstream 3D printing methods, stereolithography (SLA) and plaster-based 3D printing (PP), are selected to manufacture rocklike samples. The feasibility and capability of 3D-printed samples for simulating natural rocks are analysed based on their physical properties and seepage characteristics. The main findings of this study are as follows:  
1. SLA is suitable for investigating the seepage characteristics or permeability of natural rocks, while PP can simulate the failure modes of brittle rocks.
2. The mechanical behaviour and failure modes of PP gypsum samples are similar to those of moulded gypsum samples.
3. The mechanical properties and failure modes of PP samples are significantly affected by the printing inclination angle and sintering temperature. This can be used to simulate sedimentary rocks by studying the influence of sedimentary orientation and cementation conditions.
4. The slight difference in mechanical properties from previously published data highlights the importance of verification tests before each physical simulation using 3D printing methods.
5. The SLA 3 DP samples are found to have the capability to simulate the permeability coefficient and the anisotropy of porous basalts.  
Conceptualization, X.Z. and C.Z.; methodology, X.Z., Y.L. and C.Z.; software, C.Z.; data curation: Y.L.; validation, J.L. and L.H.; writing--original draft preparation, X.Z. and C.Z.; writing--review and editing, P.C. and J.L.; funding acquisition, X.Z. and C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Jiangsu Province Carbon Peak Carbon Neutral Technology Innovation Project in China (grant number: BE2022034-3), the National Natural Science Foundation of China (grant numbers: 41372266 and 42220104007), and the State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining & Technology (grant number: SKLGDUEK2115).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
Author [PERSON] was employed by the company Jiangsu East China Geological Construction Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
Figure 11: Porous rocks with high potential for gas or liquid storage: (**a**) basalt rock core (D = 44.5 mm) [80], (**b**) carbonate [79], and (**c**) sandstone [84].
## 5 Conclusions  
Frequent handoffs bring a new challenge of designing a reasonable channel allocation algorithm for LEO satellite networks. EC provides sufficient computing power for LEO satellite networks and makes it possible for the application of reinforcement learning. An adaptive DCA algorithm based on a temporal-spatial correlation analysis was proposed in this paper. First, the temporal-spatial correlation of handoff calls was analyzed. Second, the DCA process was formally described as an MDP. Third, a policy for different call events was designed and the MDP model was solved based on SARSA. Finally, simulation experiments were carried out under different traffic distributions and different traffic intensities. The simulation results showed that a TSCA could greatly reduce the rejection probability of handoff calls and improve the total performance of LEO satellite networks. However, the storage space of the Q-values in the TSCA rapidly increased with an increase in the network scale. In the future, we will optimize the required storage space of the proposed algorithm.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON] and [PERSON]; investigation, [PERSON] and [PERSON]; software, [PERSON] and [PERSON]; supervision, [PERSON], [PERSON] and C.H.; writing--original draft preparation, [PERSON] and [PERSON]; writing--review and editing, [PERSON], [PERSON] and C.H.; project administration, [PERSON], [PERSON] and J.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 7: Performance comparison of TSCA with different values of gamma. (**a**1) The rejection probability of the new call under uniform distribution; (**a**2) the rejection probability of the handoff call under uniform distribution; (**a**3) the rejection probability of the total call under uniform distribution; (**b**1) the rejection probability of the new call under nonuniform distribution; (**b**2) the rejection probability of the handoff call under nonuniform distribution; (**b**3) the rejection probability of the total call under nonuniform distribution.  
**Funding:** This research was funded by the National Natural Science Foundation of China (grant numbers 62272242, 61902237, 61972210) and the Innovation Project for Postgraduates of Jiangsu Province (grant number KYCX17 0782).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank the editor and the anonymous reviewer whose constructive comments will help to improve the presentation of this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Multi-Robot Exploration Employing Harmonic Map Transformations
## 3 Methodology
### Robot Exploration
#### 3.3.2 Multi-Robot Setup
The merging of partial maps generated by multiple robots enhances the exploration process significantly as larger areas can be fully mapped in a more efficient manner. A potential challenge arises post-merging, wherein multiple robots may inadvertently explore the same frontiers simultaneously. To address this issue, a strategy is employed to ensure efficient frontier allocation among robots. At the start of the exploration process, each robot is assigned an arbitrary priority number, with a smaller number indicating higher priority. The priority number assists the frontier allocation process, as constant changes in the goal frontier are avoided when the allocation metric (the distance from the frontier in this case) of two agents is equal. When selecting a frontier to navigate to, the robots with higher priority share a merged map and state their current goal frontier. Then, a check is performed to detect any duplicate frontiers. If duplicates are found, the  
Figure 6: The map merging process visualized. (**a-c**) show three partial maps of the same workspace and (**d**) shows the resulting merged map that the robot (**a**) produces. Note that the blue area is on top. robot's current frontier goal is set as a random frontier that no other robot with higher priority has set as a goal. If all other frontiers are taken, then the robot will not pick a goal frontier and stay stationary until a new frontier is discovered. Using the above rule, the inefficient scenario of multiple robots exploring the same frontier is avoided. An exception is made when the robot with higher priority is located outside a certain range, chosen as the laser scanner's maximum distance. In that case, both robots head to the same goal and it is assumed that a new nearest frontier will be discovered during exploration. This approach minimizes redundancy and maximizes exploration coverage, thereby optimizing the collective exploration efforts of the multi-robot system. Finally, the inclusion of other robots inside the workspace breaks the earlier assumption that all obstacles are static. Since our approach is distributed, a simple solution such as active collision avoidance is executed using the LIDAR data that overrides the HMT controller to avoid an imminent collision when robots are located close to each other.
## 1 Introduction
### MOCT Architecture
Temperature control via a thermoelectric cooler (TEC) and an adjustable reverse bias voltage are used on the APD to control the detector gain. Two programmable gain amplifiers (PGA) are used after the TIA that can each be adjusted to attenuate the electrical pulse by a maximum of \(-\)12 dB or amplify the electrical pulse up to 52 dB. The PGAs enable the detector to have a large dynamic range of received optical power. The entire detector and circuitry fits on a custom board design called Near-infrared Optical detection with Variable Amplification (NOVA) which is a 30 mm \(\times\) 30 mm printed circuit board that consumes \(\sim\)1 W of power during detection. The detector output is a differential pair and is routed to the comparator on the front end of the TDC. The TDC time stamps the arrival of each detected pulse from the APD. The comparator on the front end of the TDC takes the analog differential pair from the APD, compares it to an adjustable threshold, and outputs a low-voltage differential signaling (LVDS) pair to the TDC. When the APD output voltage is below the threshold, the comparator outputs a logical false on the LVDS pair. When the APD output voltage is greater than the threshold, the comparator outputs a logical true on the LVDS pair. The TDC being used on MOCT is the AMS TDC-GPX2 and is capable of 20 ps single shot timing accuracy [11]. For single edge thresholding, the TDC records the moment in time when the LVDS pair transitions from a logical false to a logical true. This time stamp corresponds to when the rising edge of the APD is greater that the comparator threshold. The falling edge of the APD signal can simultaneously be timed with the another channel on the same TDC-GPX2 chip. The event that is timed for the falling edge is when the comparator output transitions from logical true to logical false. The rising and falling edge time can be averaged to reduce arrival time errors associated with optical power fluctuations. The time stamp of the optical pulse arrival is sent from the TDC to the modulator for decoding. Since the TDC measures the arrival of the optical pulse as a moment in time, the modulator only has to decode which PPM slot the time stamp is in and the optical pulse width can be greater than the PPM slot width. The modulator then sends the decoded receiver data to the spacecraft bus. Together, all of the MOCT components work congruently to provide power efficient, pulsed optical communication on a nanosatellite platform at lunar distances. Utilizing PPM for optical communication also helps MOCT achieve a power efficient communication link. Compared to other lasercom mission that design for high data rates (>1 Gbps), MOCT intends to achieve modest data rates of \(\sim\)40 Mbps in a compact, low power consumption platform.
# The Behavior of Tree-Width and Path-Width Under Graph Operations and Graph Transformations
## 3 Unary Graph Operations and Graph Transformations
### Vertex Deletion and Vertex Addition
#### 3.1.2 Vertex Addition  
Let \(G\) be a graph, \(N\subseteq V(G)\) be a subset of vertices from \(G\), and \(v\
ot\in V(G)\) be a newly introduced vertex. By \(G+_{N}v\), we denote the graph we obtain from \(G\) when inserting \(v\) with neighborhood \(N_{G}(v)=N\), i.e.,  
\[G+_{N}v=(V(G)\cup\{v\},E(G)\cup\{\{v,u\}\mid u\in N\}).\]  
In the special case when \(N_{G}(v)=\{u\}\) holds for a vertex \(u\in V(G)\), we call \(v\) a _pendant vertex_ of \(G\). If \(N_{G}(v)=V(G)\setminus\{v\}\) is true, we call \(v\) a _dominating vertex_ of \(G\). With _vertex addition_ defined, we consider the tree-width and path-width of graph \(G+_{N}v\) in the following theorem.  
Theorem 3.2: _For a graph \(G\), a subset of vertices \(N\subseteq V(G)\), and a vertex \(v\
ot\in V(G)\), it holds that_  
\[\begin{split} tw(G)&\leq tw(G+_{N}v)\leq tw(G)+1 \quad\text{and}\\ pw(G)&\leq pw(G+_{N}v)\leq pw(G)+1.\end{split}\]  
Proof: By introducing \(v\) to all bags of a tree-decomposition (path-decomposition) of \(G\), we obtain a tree-decomposition (path-decomposition) of \(G+_{N}v\). Thus, it follows that \(\operatorname{tw}(G+_{N}v)\leq\operatorname{tw}(G)+1\) and \(\operatorname{pw}(G+_{N}v)\leq\operatorname{pw}(G)+1\) are true. By removing \(v\) from\(G+_{N}v\), we obtain \(G\). Consequently, the lower bounds follow from the upper bounds of Theorem 2.  
We can always add a pendant vertex to a graph without increasing the graph's tree-width. To do so, we introduce a new bag that contains the new vertex and its sole neighbor. Then, the new bag is connected to any bag of a tree-decomposition for the graph that already contains the neighbor.  
Corollary 1: _For a graph \(G\), a vertex \(u\in V(G)\), and a newly introduced vertex \(v\
ot\in V(G)\), it holds that_  
\[tw(G+_{\{u\}}v)=\max(tw(G),1).\]  
Conversely, the following example shows that the previous statement does not hold with respect to path-width. Introducing a pendant vertex to a graph can increase its path-width.  
Example 3: _The path-decomposition \(\mathcal{X}=(\{a,b\},\{b,c\},\{c,f\},\{c,d\},\{d,e\})\) shows that the path-width of graph \(C\) in Figure 2c is one. By introducing pendant vertex \(g\) to \(C\), as depicted in the graph \(I(K_{1,3})\) in Figure 2b, the path-width increases to two: \(\mathcal{X}^{\prime}=(\{a,b\},\{b,c\},\{c,f,g\},\{c,d\},\{d,e\})\)._
## 4 Experimental Results and Discussion
### Obtained Results
#### 4.6.2 Comparative Analysis  
From this analysis and the Figure 14a-j, the following specific comments are listed:  
* **Original YOLOv5s6 Standard:** the heatmaps show limited focus areas with low accuracy in detecting loop and protrusion fibers.
* **YOLOv5s6 + C2f (backbone):** adding the C2f block improved the detection accuracy, especially for subtle patterns like protrusion fibers.
* **YOLOv5s6 + Bot-Transformer:** the integration of the Bot-Transformer module enhanced the focus on the relevant image parts, improving loop fiber detection.
* **YOLOv5s6 + GeLU:** the GeLU activation function increased the sensitivity to subtle texture variations, improving the accuracy in complex, noisy backgrounds.
* **YOLOv5s6 + C2f + Bot-Transformer:** this combination significantly improved focus areas, providing more detailed and accurate detection in complex image parts.
* **YOLOv5s6 + C2f + GeLU:** enhanced fine detail detection and sensitivity to texture variations, leading to a better overall performance.
* **YOLOv5s6 + Bot-Transformer + GeLU:** refined focus areas, resulting in greater accuracy and the better handling of complex scenarios, such as protrusion fibers.
* **YOLOv5s6 + C2f + Bot-Transformer + GeLU:** a comprehensive performance improvement in the focus, accuracy, and sensitivity to detecting loop and protrusion fibers.
* **YOLOv5s6 + Hyperparameters:** hyperparameter tuning improved focus areas and detection accuracy, reducing false positives in noisy backgrounds, though some defects were only detected in models with previous combinations.
* **YOLOv5s6 Improved Hairiness:** the final model shows significantly enhanced detection, identifying seven hairiness defects compared to only one in the original model.
## 2 Materials and Methods
### Feature Extraction
#### 2.4.2 Riesz Wavelet Coefficients  
Furthermore, the texture features from the image are extracted by calculating the Reisz wavelet coefficients, which are helpful in the classification of the malignancy. Images may have either smooth regions interrupted by edges or abrupt changes, and these abrupt changes in an image are most crucial because they represent boundaries within the image. Fourier transform is a powerful tool for data analysis, but it does not represent those changes significantly because sine waves are not localized in time, nor in space. Therefore, in order to analyze these changes, wavelet transforms may be used [29]. A wavelet is a rapidly decaying wave-like oscillation that has zero mean; unlike sinusoids, which extend to infinity, a wavelet exists for a finite duration. The availability of a wide range of wavelets is a key strength of wavelet analysis.  
To generate Riesz coefficients [30], a 3D steerable wavelet transform composed of two operators is used in this proposed research. A 3D isotropic wavelet transforms will perform multiscale decomposition and yields vanishing moments, and then a 3D Riesz transform adds directionality and steerability [31,32]. The calculation of Riesz wavelet coefficients involves the wavelet frame, \(\left|\psi_{i,k}\right|\) I \(\in\) Z, k \(\in\) Z\({}^{3}\), whose basis functions in L\({}_{2}\)(R\({}^{3}\)) are generated by a single mother wavelet, \(\psi\)(x). The normalized wavelets at scale \(i\) and translation parameter, \(k\), are given by:  
\[\psi_{i,k}(x)=\psi_{i}\Big{(}x-2^{i}k\Big{)}\text{ with }\psi_{i}(x)=2^{-\frac{ 3i}{2}}\psi\Big{(}x/2^{i}\Big{)}. \tag{9}\]  
The computation of Riesz wavelet coefficients is divided in two steps: (1) discrete convolution of the image with the sampled filters, (2) down sampling by a factor of \(T_{i}\).  
The set of Riesz wavelet coefficients, \(\left.s_{i,k}^{n}=\left\{\mathcal{R}^{n}\psi_{i}*f\right\}(x)\right|_{x=T_{i} k}\), is computed as:  
\[\begin{split} s_{i,k}^{n}=\Bigg{\{}&\mathcal{R}^{n }\sum\limits_{k^{\prime}\in\mathbb{Z}^{3}}\omega_{i,k^{\prime}}\frac{1}{T_{i} ^{n}}\text{sinc}\Big{(}\frac{-T_{i}k^{\prime}}{T_{i}}\Big{)}\Bigg{\}}(x) \Bigg{|}_{x=T_{i}k}\\ =\sum\limits_{k^{\prime}\in\mathbb{Z}^{3}}\omega_{i,k^{\prime}} \{\mathcal{R}^{n}\text{sinc}(k-k^{\prime})\}\end{split} \tag{10}\]  
where \(\mathcal{R}^{n}\) denotes the \(N^{\text{th}}\) order Riesz transform with \(n=(n_{1},n_{2},n_{3})\in N^{3}\), such that \(\left|n\right|=N\) with pulsation vector \(\omega=(\omega_{1},\omega_{2},\omega_{3})\in R^{3}\):  
\[\hat{\mathcal{R}}^{n}\left(\omega\right)=(-j)^{N}\sqrt{\frac{N!}{n_{1}n_{2}!n_ {3}!}}\frac{\omega_{1}^{n_{1}}\omega_{2}^{n_{2}}\omega_{3}^{n_{3}}}{\left( \omega_{1}^{2}+\omega_{2}^{2}+\omega_{3}^{2}\right)^{N/2}} \tag{11}\]  
From the above equations, the Riesz wavelet coefficients can be obtained by scaling eight times because each image is resized into 256 \(\times\) 256 dimensions.
# Topic Detection Based on Sentence Embeddings and Agglomerative Clustering with Markov Moment
###### Abstract
We have also suggested their application to network-like representation of texts and sentiment analysis [7; 8]. In this paper, we test the proposed method of agglomerative clustering by [PERSON]'s method with Markov stopping moment for several options of text representation, including more traditional tf-idf and improved word2 vec and the sentence embeddings-based text representation. As quality metrics (in particular, V-measure) show, our method (Appendix A) stabilizes the result at the level of \(>\)0.5, the number of expected clusters being 10 and 20. Thus, the model performs much better than the existing baseline ones, including DBSCAN and OPTICS. Raw data that are collected from social media provide additional challenges in terms of topic detection, due to their high noise, post length variations, author-induced grammar and lexicon distortions, and user-by-user dialogue fragmentation. The problem is especially significant for short user texts, like tweets [9]. Thus, before applying the model to data from social media for social science tasks, we used more structured real-world datasets for our experiment; these datasets had to be pre-labeled, as our goal was to test the method. To test our methodology, we have used the well-known real-world labeled dataset \"20 newsgroups\" (or, in short, News20) ([[http://qwone.com/~jason/20](http://qwone.com/~jason/20) Newsgroups/]([http://qwone.com/~jason/20](http://qwone.com/~jason/20) Newsgroups/)) with pre-marked topicality. Four News20 subsets were used as the model text corpora, namely News3 (three classes and 83 documents), News5 (five classes and 121 documents), News10 (10 classes and 268 documents), and News20 (20 classes and 18,846 documents). We have also employed another real-world dataset, namely the BBC one ([[http://mlg.ucd.ie/datasets/bbc.html](http://mlg.ucd.ie/datasets/bbc.html)]([http://mlg.ucd.ie/datasets/bbc.html](http://mlg.ucd.ie/datasets/bbc.html))), with five thematic clusters [10], to ensure that our results are dataset-independent. For numerical experiments, we used a program code that was written in the Python 3.7 programming language. The NumPy and SciPy libraries were used for calculating distance matrices. The calculations were carried out using the PyCharm shell by JetBrains based on IntelliJ IDEA, and Collaboratory, a free environment for Google's Jupiter Notebook. The remainder of the paper is organized, as follows. Section 2 describes the methodological design, including data pre-processing with USE-based text representation, the approximation-estimation criterion and test, and the Markov stopping moment for finding the optimal number of clusters for the texts represented via embeddings. In Section 3, we report on the results as compared to the DBSCAN and OPTICS baselines by V-measure, NMI, and the number of clusters' inference accuracy. In Discussion (Section 5) and Conclusion (Section 6), we re-assess the method and reflect upon its use on real-world data.
# Roll Motion Prediction of Unmanned Surface Vehicle Based on Coupled CNN and LSTM
## 3 Experiments
### Reference Models  
To demonstrate the advantages of the proposed model, four classic models (i.e., ARIMA, DNN, univariate LSTM, and multivariate LSTM) were compared. The USV roll motion status time-series data were used as the data set for the ARIMA model, DNN, and univariate LSTM. Multivariate LSTM used the same data set as the proposed model.