esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of Microseismic Monitoring of and Precursor Information on Roof Collapse
## 5 Discussion
### Crack Spatiotemporal Evolution Features  
Using Cluster 6 as an example, we analyze the propagation law of crack clusters. We divide the detected microseismic events into six stages in chronological order for statistical data analysis. From Figure 8, it is evident that the crack cluster primarily extends outward from the center of an ellipsoid. The confidence interval area of the expansion continues to increase, indicating a continuous increase in the propagation radius. In terms of the expansion direction, during the first three stages, there is linear expansion without any deviation in direction. However, starting from stage 4, the upper cracks start to dominate and lean toward the direction of surface crack development. By stage 6, the influence range of the crack clusters expands horizontally, with the lower cracks now dominating and leaning toward the direction of mining crack development. The trend in the propagation direction provides a basis for crack prediction. The center of gravity gradually shifts upward in the first five stages, indicating a significant contribution from the upper impacts and dominance of surface cracks. However, starting from stage 6, the center of gravity shifts downward, indicating an increase in the proportion of lower impacts.  
Figure 7: Time series evolution characteristics of MS event number.  
The three-dimensional crack morphology has been reconstructed based on MS data, as depicted in Figure 9. MS events gather and generate on a plane, forming a crack propagation plane. From the results, it is observed that the cracks primarily extend upward, beginning to contract at an elevation of 800 m and eventually connecting to the surface at 980 m. The contraction of the crack surface downward at 530 m is caused by the MS activity in the mining area and provides further insight into the direction of crack development. The fluctuation characteristics in cracks between 530 and 800 m indicate that the crack plane within this range may be formed by multiple cracks penetrating through. The formation of the goaf of the 32# ore body has alleviated the constraint stress on the adjacent rock mass of the 30# original mining area, resulting in the downward extension of cracks from the surface toward the mining area. By increasing the number of local clusters in microseismic data analysis, the ability to identify and characterize individual fractures and their propagation can be improved. Actual engineering weight may not be able to directly observe the discontinuities in situ, but this method can help distinguish and analyze the behavior of fractures in rock masses.  
Figure 8: 2D clustering results.
## 3 Overview of Rollups
### Zk-Rollups  
Zero-knowledge proofs are the major component of zk-rollups. In cryptography, a zero-knowledge proof is a method by which one party (the prover) can demonstrate to another party (the verifier) that some given statement is true while still avoiding revealing to the verifier any additional information beyond the mere fact of that statement's truth [14]. Generally speaking, a zk-proof can be distinguished by three main characteristics: soundness, completeness, and zero-knowledge. Soundness ensures that no dishonest prover can convince the verifier that a false statement is true. Completeness ensures that a prover can convince the verifier if the statement is true. Finally, zero-knowledge ensures that no information about the statement is revealed except the validity of the statement.  
Using zk-proofs, zk-rollups enhance scalability and privacy by allowing off-chain nodes to prove the validity of transactions without revealing any detail. The results of transaction executions are compressed into a single batch and stored in the L1 blockchain together with a cryptographic proof of state transition, as illustrated in Figure 2. In this case, an on-chain verifier smart contract verifies the transactions and updates the blockchain state. This ensures instant transaction finality without a dispute period.  
The two main types of zero-knowledge proofs used in zk-rollups are Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) and Zero-Knowledge Scalable Transparent ARguments of Knowledge (zk-STARKs). The following two subsections provide some details about these two alternatives.  
Figure 1: General architecture of Optimistic Rollup.
## 5 Simulation Analysis and Test Verification  
According to the results of the parameter optimization, we established a storage tank model. According to Equations (5) and (6), we calculated the volume utilization rate and the weight of the improved storage tanks as 90.08% and 439 g, respectively. We obtained the compressive stress as 312.7 MPa via simulation analysis. In order to prevent a stress concentration between the columnar structure and connecting surface by 3D printing, we increased the fillet at the connecting surface position and, hence, the corresponding performance will change after it loads the same pressure. The compressive stress was 172.1 MPa and the results are shown in Figure 10.  
The 3D metal printing of the improved storage tank structure is based on the optimization results in Table 11. The print direction of the storage tank, post-processing steps for the storage tank, and the optimized structure of the storage tank was manufactured via a 3D metal printing process as shown in Figures 11-13. The structural performance is compared before and after optimizing the storage tank. The comparison of the storage tank structural performance before and after optimization is presented in Table 12. It can be determined from Table 12 that the compressive stress was improved by 24.98%, the volume utilization ratio was increased by 26.86%, and the weight was reduced by 26.83%. The optimized storage tank was developed via 3D metal printing. Owing to the error of processing, the improved storage tank of the trial manufacture increases the weight, whereas our storage tank weight was reduced by 11.67%. According to Equation (6), the volume utilization ratio can be increased by 24.52%. According to the calculation, the compressive stress can be increased by more than 58.71%.  
Figure 11: The print direction of the storage tank.  
Figure 10: Stress analysis figure for (**a**) the improved storage tank after optimization; and (**b**) the improved storage tank after post-processing.
## 1 Introduction
Whereas for fiber laser flame cutting, coaxial monitoring is more difficult due to the fact that the cutting distortion occurs outside the observation zone, as monitoring is limited by the field of view through the nozzle [22]. To visualize the cutting process inside the kerr, approaches with high-speed cameras positioned off-axial and aligned in the cutting direction are also reported [23; 24] also a trim-cut technique through a transparent substrate by a side visual monitoring set up for clear visualization of the melt front inside the kerr [25; 26; 27], as well as a high-speed x-ray imaging system [9; 10] have been demonstrated. These methods provide valuable insights into melt pool dynamics, melt flow velocity, and the geometry of the cutting front and the cutting kerr. Next to imaging sensors, monitoring systems based on photodiodes have also been developed for quality measurements of laser process [28; 29; 30] as an alternative to quality monitoring with camera sensors. These are more affordable and favor real-time monitoring, which benefits industrial applications. In addition to the sensor technology, an evaluation algorithm is likewise essential for quality monitoring, in particular for industrial applications. To detect and predict cut interruption, a cross-correlation algorithm [31] and a polynomial logistic regression model [32] have been reported. Recent reports show neural network approaches for cut quality monitoring [33; 34] and to predict the kerr width for remote laser cutting calculated from the laser processing parameters [35]. Against this background, in this article, we present a coaxial in situ high-speed camera setup, which can be integrated and retrofitted into a conventional fiber laser cutting head. In contrast to earlier studies [36], we extend our previous research to observe the melt pool during the laser cutting of zinc-coated steel, stainless steel, and aluminum with a high-speed camera directly from the top view in the visible spectral range on the emitted thermal process radiation, spatially and temporally resolved. Furthermore, we detect complete and incomplete cuts with an algorithm adapted for stainless steel, zinc-coated steel, and aluminum.
## 3 Results
### Results of Synthesis
#### 3.3.2 SE vs. Control  
Due to the heterogeneity of both the outcome measurements and interventions, the meta-analysis could only be performed for the WOMAC pain subscale, comparing before and immediately after the intervention [39; 46; 48]. Two studies [39; 46] showed no effect in either direction. The overall effect across all studies showed a small effect in favor of the SE (effect size = 0.364; \(Z\) = 1.993; \(p\) = 0.046; IC 95%= 0.006 to 0.722; Q-value = 2.4; \(p\) = 0.301). Table 2 shows the effect size (standard error, 95% CI) and the weight for each  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multicolumn{1}{c}{**Study**} & **Effect Size** & **Std. Error** & \multicolumn{1}{c}{\(Z\)} & \multicolumn{1}{c}{\(p\)} & \multicolumn{1}{c}{\(95\%\) Confidence} & \multirow{2}{*}{**Weight**} & \multirow{2}{*}{**Weight (\%)**} \\  & & & & & & **Interval** & & \\ \hline [PERSON] et al., 2019 [30] & 0.875 & 0.3326 & 2.630 & 0.009 & 0.223, 1.526 & 1.956 & 20.1 \\ [PERSON] et al., 2012 [33] & 2.038 & 0.3537 & 5.762 & 0.000 & 1.345, 2.731 & 1.902 & 19.5 \\ [PERSON] et al., 2018 [36] & 0.506 & 0.3599 & 1.406 & 0.160 & \(-\)0.199, 1.212 & 1.886 & 19.4 \\ [PERSON] et al., 2022 [35] & 0.272 & 0.3178 & 0.855 & 0.393 & \(-\)0.351, 0.895 & 1.994 & 20.5 \\ [PERSON] et al., 2008 [32] & 0.351 & 0.3144 & 1.117 & 0.264 & \(-\)0.265, 0.968 & 2.002 & 20.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Effect size estimates for individual studies of the VAS scale.  
study included in the meta-analysis. For publication bias, a funnel plot is shown in Figure S2. Only [PERSON] et al. [39] assessed the WOMAC pain at follow-up (weeks 32 and 52) and found that the improvement was maintained in the intervention group compared to the control group at week 32.  
The forest plot graph showed the overall results and the confidence interval for the effect size measure chosen in each study (Figure 5).  
The VAS did not improve immediately after the intervention, [39, 41]. Conflicting results were found for the NRPS, which improved in one study [45], but not in another [38]. Other outcomes were only assessed in one study. For both the ICOAP [38] and JKOM [44], no differences were found immediately after the intervention. The WOMAC improved immediately after the intervention [45], as did the knee PPT [43]. Pain scores improved in the intervention group both immediately and at follow-up [42]. No statistical difference was found by [PERSON] et al. at follow-up for the VAS [39].  
The overall level of evidence was low or very low due to the small number of studies and the imprecision and high heterogeneity in the results. Only the WOMAC pain subscale after intervention showed high quality of evidence (Table A5).
# Proactive Service Caching in a MEC System by Using Spatio-Temporal Correlation among MEC Servers
## 5 Performance Evaluation
### Effect of Service Popularity Variation  
To assess the impact of the degree of service popularity change at each MECS on the caching performance, we conduct the following simulations. We extend the topology by deploying \(M=16\) MECSs in a \(4\times 4\) grid, which ranges from (0,0) to (3 km,3 km). We locate each MECS at each grid point. We also increase the number of services from 64 to 128 and set \(\eta\) to 16. For each service \(k\) and each MECS \(m\), we randomly change the popularity of a service \(k\) at a MECS \(m\) at each time slot. To control the degree of service popularity change at each MECS, at the beginning of each time slot, we randomly configure \(v_{m,k}(t+1)=v_{m,k}(t)+x(t)y(t)\) (\(\forall m\in\mathcal{M},k\in\mathcal{K}\)), where \(x(t)\) is determined with a probability of \(1/2\) to be either \(1\) or \(-1\). We randomly select \(y(t)\) from \([0,\xi]\) according to the Uniform distribution.  
In Figure 11, we show the distribution of the cache similarity degree (\(a_{m}(t)\)) with different \(\xi\). We oberve that as the degree of service popularity change at each MECS (i.e., \(\xi\)) increases, the cache similarity degree decreases. In the case of the proposed method, the average \(a_{m}(t)\) is \(98.47\%\) when \(\xi=0.05\), while is is \(96.19\%\) when \(\xi=0.15\). We also observe in this figure that the proposed method outperforms the LSTM method. For all MECSs and \(\xi\)s, the cache similarity degree is higher when using the proposed method than when using the LSTM method.  
Figure 10: Distribution of the relative cache hit rate at each MECS when a cache size is \(\eta=8\).
## 3 Parametric Modeling of GSPCW Walls
### Height-to-Width Ratio  
The height-to-width ratio is one of the main factors that determines the failure type of shear walls and affects their seismic performance. The height-to-width ratios of the model studied in this paper were selected in the range from 1.5 to 3.5, as listed in Table 3, thus being representative of common height-to-width ratio values in engineering practice for shear walls. Figure 8 shows the hysteresis and skeleton curves for the parametric wall models with different height-to-width ratios. It can be seen from the figure that the height-to-width ratio has a distinct effect on the load capacity and stiffness of the wall  
Figure 6: Influence of steel ratio: (**a**) load–displacement hysteresis curves, and (**b**) skeleton curves.  
Figure 7: Influence of axial load ratio: (**a**) load–displacement hysteresis curves, and (**b**) skeleton curves.  
models. As the height-to-width ratio increases from 1.5 to 3.5, the load capacity decreases by 59%, and its lateral stiffness decreases by 88%. This is because as the height-to-width ratio increases, the failure mode of the specimen transfers from shear failure to bending failure.
## 2 Materials and Methods  
The workflow of our similarity-based approach for predicting the aging of lithium-ion batteries in second life is described as follows:  
* Aging datasets must be previously generated from accelerated tests to illustrate aging trajectories (capacity fade over cycle number) defined by several combinations of stress factors. A comprehensive set of cycling and calendar test matrices capturing the operational envelope of LiBs was proposed by [4]. Alternatively, electrochemical-based performance models with embedded aging capabilities (side reactions) can be used to generate aging data artificially [20].
* Knees in each capacity fade curve must be detected to frame the second life window for each trajectory. Therefore, the first knee point of the curve indicates EoL, while the second knee point indicates the battery's non-useful cycle or time. Second life is then framed between these two knee points, and subsets of the original datasets must be created by extracting the corresponding data within this window.
* Cycling aging curves from real-world second life applications are then compared to each aging trajectory by computing their similarities, and they are later normalized to estimate the contribution percentage of each aging trajectory (associated with a given combination of stress factors) to the cycling aging curve. In this work, we use two approaches to estimate similarity: (i) time series similarity or distance-based similarity approaches and (ii) tree-based similarity approaches. Time series similarity approaches are widely used in different tasks, including clustering, classification, anomaly detection, and forecasting, while tree-based similarity approaches are interpretable, scalable, and can effectively handle complex patterns.
# Research on Leaf Area Density Detection in Orchard Canopy Using LiDAR Technology
## 5 Conclusions  
This research proposes an orchard canopy leaf area density detection algorithm based on leaf fitting and constructs a detection system utilizing this algorithm. Through secondary orthogonal regression experiments conducted on laboratory-simulated canopy, a mathematical calculation model for orchard canopy leaf area density was established. The results indicate that this algorithm is suitable for detecting the leaf area density of orchard canopies.  
Leaf area density detection was conducted for both laboratory-simulated canopies and orchard canopy under designed conditions. Experimental results showed that the absolute value of the average relative error was 11.58% in the laboratory-simulated canopy experiments and 16.75% in the orchard canopy experiments. These results verified the applicability and accuracy of the proposed algorithm compared to existing measurement methods.  
The actual orchard experiment results indicated that the leaf area density detection algorithm based on leaf fitting performed well within a leaf area density range of 1.0-4.0 m\({}^{2}\)/m\({}^{3}\). When the leaf area density index of the orchards was high, the algorithm was affected by increased leaf overlap, leading to a gradual increase in detection error.  
This research established a mathematical calculation model for canopy leaf area density of orchard, further validating the capability of LiDAR technology in smart agriculture applications. In future research, a multi-sensor fusion algorithm will be incorporated to compensate for errors caused by leaf overlap and angle, further improving measurement accuracy. An integrated measurement and controlled spraying algorithm will be developed to facilitate the practical application of this technology.  
Not applicable.  
The data presented in this study are available within the article.  
The authors thank the Faculty of Agricultural Equipment of Jiangsu University for its facilities and support. The authors declare no conflicts of interest.
## 5 Performance Analysis of the CL-APCC Protocol
### Time Complexity Analysis of CL-APCC Protocol  
**Theorem 1.** In any grid, the time complexity of period \(t\) is \(O(N)\), the time complexity is \(O(fN)\) which is from data generated to be sent to BS, and \(f\) is the biggest hop of network routing.  
_Proof._ The period \(t\) of each grid is decided by the situation of the local network, the sending rate of each node, the length of data flow and the conflict regions of each node. Node \(i\) is analyzed in a certain grid \(k\). When the period \(t\) is at the maximum, the sending conflict times of each node in the grid is also at the maximum. So, it is necessary that the sending probability is 1 when node \(i\) finally sends a data packet, it's available by Equation (24):  
\[R_{i}\left(t\right)+J\times\left(1-R_{i}\left(t\right)\right)/n_{{}_{con}}^{i}= 1\Rightarrow J=n_{{}_{con}}^{i} \tag{24}\]  
From Equations (23) and (24) we can see the maximum conflict times of node \(i\) is as below:  
\[Max\left(J\right)=\left(N\times\pi\times r^{2}\right)/L^{2} \tag{25}\]  
If we know the existing waiting time it takes node \(i\) to send data, we can obtain the complexity equation of the period \(t\). In the conflict region of node \(i\), the time length of packet sending for each node is \(\left\lceil\,L_{{}_{Data}}^{q}\left/\mu_{{}_{con}}^{i}\left(t\right)\,\right\rceil\). The packet length \(L_{{}_{Data}}^{q}\) is fixed, so if it makes \(\left\lceil\,L_{{}_{Data}}^{q}\left/\mu_{{}_{con}}^{i}\left(t\right)\,\right\rceil\) maximal, then \(\mu_{{}_{con}}^{i}\left(t\right)\,\)needs to be minimal. Assume that the data inputting rate \(\lambda_{{}_{con}}^{i}\left(t\right)\,\)which is generated by data sources is already known. From the Queuing theory (subsection 4.1.1), \(0\leq\rho=\lambda_{i}^{k}\left(t\right)/\ \mu_{i}^{k}\left(t\right)\leq 1\), it can be obtained \(Min\left(\mu_{{}_{con}}^{i}\left(t\right)\right)=\lambda_{i}^{k}\left(t\right)\), so the longest time of period \(t\) is attained by the Equation (22):  
\[Max\left(t\right)=Max\left(J\right)\times L_{{}_{Data}}^{q}\left/Min\left(\mu _{{}_{con}}^{i}\left(t\right)\right)=\frac{N\times\pi\times r^{2}\times L_{{}_ {Data}}^{q}}{Min\left(\mu_{{}_{Con}}^{i}\left(t\right)\right)} \tag{26}\]  
So the time complexity of arbitrary grid complexity is \(O(N)\). Similarly, the maximum time of data from generated until being sent to BS is shown as below:  
\[Max\left(i_{{}_{i}}\right)=\frac{f\times N\times\pi\times r^{2}\times L_{{}_ {Data}}^{q}}{Min\left(\mu_{{}_{con}}^{i}\left(t\right)\right)} \tag{27}\]  
Its time complexity is \(O\left(fN\right)\). Therefore, the theorem 1 is proved to be correct.
# Detecting Social Media Bots with Variational AutoEncoder and k-Nearest Neighbor
## 4 Experiments
### Data Description  
At present, many scholars are engaged in research on social media bots, and some have collected datasets and published them on the bot repository platform. We selected the author profiling task 2019 (CLEF2019) dataset [32] for this paper, which aims to identify the nature of the Twitter account, detect whether the account is a social media bot or a human, and determine the gender of the account.  
The CLEF2019 dataset consists of two groups of Twitter accounts, one in English and the other in Spanish. Each account in the dataset is a collection of 100 tweets. The datasets of different languages are divided into a training set and a test set. Each account is described by two aspects, as shown in Figure 4. The first column defines the nature of the account, and the second column gives the gender. Tweets are not processed to maintain a real scene so that one can know whether a tweet is original or forwarded. The English part of the dataset consists of 4120 accounts, of which 2880 belong to the training set and 1240 belong to the test set. The Spanish part has 3000 users; the training set has 2080 accounts, and the remaining users are the test set. In all the partitions, users are divided into bots and humans, while humans are divided into men and women. The proportion of social bots and human users in all the English datasets was half and half. As shown in Table 1, the length of tweets from different accounts varies greatly: the smallest tweet has only one character, while the longest tweet has more than 900 characters.  
The original feature matrix X and the decoded new feature matrix \(X^{*}\) are fused to get \(\text{Y}=\{y_{i}\}_{i=1}^{N}\) as the input of the anomaly detection part. In order to better describe the overall performance of the model, this paper selects AUC (area under the curve), precision, recall, and training time as evaluation indexes. AUC is the area under the ROC curve (the subject working characteristic