esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In conclusion, atomistic simulation is used to study the effect of dislocation dipole on the failure strength of graphene with ripples. Mechanical properties are obtained for graphene under tensile loading at 0 K, 300 K, and 1000 K. Fracture mechanisms are analyzed at zero temperature to exclude the effect of thermal fluctuations.  
In the presence of ripples, the fracture process is quite complicated and involves graphene wrinkling with reverse transformation to the planar shape, continuous and very different changes in bond lengths, and the appearance of new defects. All these mechanisms are not contradictory, but act simultaneously, which results in quite a high fracture strain. However, the ultimate tensile strength and Young's modulus of rippled graphene are very low, since wrinkling makes graphene much weaker.  
Temperature, graphene wrinkling, and the presence of defects are the main factors affecting the mechanical properties of graphene. The presence of dislocation dipole with the dipole arm greater than 15 A can affect the special distribution of ripples, for example, prevent ripple formation. However, the increase in the length of the dipole arm does not lead to a considerable decrease in the ultimate tensile strength of graphene. SW defects do not lower strength to such a crucial extent, especially at room and finite temperatures. For graphene with dislocation dipole, temperature increase also does not result in a considerable decrease in the graphene strength.  
Conceptualization, J.A.B.; methodology, L.K.G. and A.K.A.; investigation, A.K.A. and L.K.G.; writing, J.A.B. and A.K.A. All authors have read and agreed to the published version of the manuscript.  
For A.K.A. and J.A.B. this research was funded by the State Assignment of IMSP RAS (Young scientist laboratory); for L.K.G. this research was funded by the Russian Science Foundation, grant no. 22-72-00057.  
Not applicable.  
Figure 8: The same as in Figure 6, but for tension along zigzag direction. Corresponding dots I, II, II’ are presented in Figure 7.
# MC-EVM: A Movement-Compensated EVM Algorithm with Face Detection for Remote Pulse Monitoring
## 2 Related Works
### Eulerian Video Magnification for Pulse Extraction
Finally, a pre-processing solution was introduced to prepare the input video using many pre-processing steps to make it suitable for the EVM process [10]. Therefore, the limitations of EVM depend on the stabilization of the subject patient and the low computational capabilities of mobile devices. Our solution uses a face detection model that is used for movement compensation, and the cropped video is then provided to the EVM process to proceed with the rest of the proposed algorithm. The results and comparison with the pre-existing solutions are stated in Section 4.
# PLC-Controlled Intelligent Conveyor System with AI-Enhanced Vision for Efficient Waste Sorting
## 5 Discussion
### Comparative Analysis with Existing Systems  
A comparative analysis is conducted to contextualize the performance of the developed PLC-Controlled Intelligent Conveyor System with AI-enhanced vision against similar systems reported in the literature. This comparison is not intended to provide a thorough review but rather to underscore the contributions of the current work within the field of automated waste sorting technologies. Key performance metrics, such as accuracy, precision, and recall, are commonly employed to evaluate the effectiveness of these systems.  
In a very recent study, [PERSON] et al. [36] reported a waste classification system using deep learning that achieved an accuracy of 83.11% across 28 distinct recyclable categories and an mAP50 of 63%, reflecting its effectiveness in object detection tasks. In another study, [PERSON] et al. [37] evaluated several deep learning models for the classification of waste materials, including ResNet50, GoogleNet, InceptionV3, and Xception. Among these, the ResNet50 model exhibited an accuracy of 95%, a precision of 95.4%, a recall of 95%, and an F1 score of 94.8%, which shows its robustness in the classification of waste materials. [PERSON] et al. [11] developed an intelligent waste bin with automatic sorting, introducing GECM-EfficientNet for real-time waste classification. The model achieved 94.54% accuracy on a self-built household waste dataset and 94.23% on the TrashNet dataset.  
In comparison, the system described in this study demonstrated a classification accuracy of up to 91% for plastic bottles, a precision of 90%, and a recall of 80%, and achieved an mAP50 of approximately 86%. These results highlight the robust object identification capabilities of the developed system even when the object boundaries are imperfectly aligned. In addition, the integration of an advanced deep learning algorithm in the system with a PLC facilitates faster processing times and excellent accuracy, highlighting its potential contributions to the advancement of automated waste sorting technologies. This comparison underscores the advances presented in this work, situating it within the broader context of existing automated waste sorting systems.
## 2 Basic Theory of the TPOD Method and the Rotor Model with Coupling Faults
### The Rotor Model with Coupling Faults
\(f_{x}\) and \(f_{y}\) are the model of dimensionless non-linear oil-film force, \(f_{x}=\frac{F_{x}}{sP}\), \(f_{y}=\frac{F_{y}}{sP}\). \(G(x,y,\alpha)=\frac{2}{(1-x^{2}-y^{2})^{1/2}}\big{[}\frac{\pi}{2}+\arctan \frac{y\cos\alpha-x\sin\alpha}{(1-x^{2}-y^{2})^{1/2}}\big{]}\) and \(V(x,y,\alpha)=\frac{2+(y\cos\alpha-x\sin\alpha)G(x,y,\alpha)}{1-x^{2}-y^{2}}\) are the \(S(x,y,\alpha)=\frac{x\cos\alpha+y\sin\alpha}{1-(x\cos\alpha+y\sin\alpha)}\), \(\alpha\) directional components of bearing non-linear oil-film force. \(c\) is the bearing clearance, \(s=\frac{\mu\omega R}{P}\Big{(}\frac{R}{c}\Big{)}^{2}\Big{(}\frac{L}{2R}\Big{)}^ {2}\) is the Sommerfeld number. \(\mu\) is the lubricating oil viscosity, \(L\) is the bearing length, \(R\) is the radius of the bearing, \(\omega\) is the external excitation, \(P\) is the loading, and \(\tau\) is the dimensionless time. The detailed values of the corresponding parameters of the rotor system are shown as in the Appendix A. The non-linear oil-film force [56] along \(x\) and \(y\) directions can be found in Appendix B. We also provide the formulas of the corresponding parameters in Appendix B.
# Commonsense-Guided Inductive Relation Prediction with Dual Attention Mechanism
## 3 Methodology
### Experimental Results
#### 4.2.1 Main Results  
The main experimental results are presented in Tables 3 and 4. \"Avg.\" denotes the average metric values of the four versions of the same KG. The optimal and suboptimal results are marked in bold and underlined text, respectively. \"--\" represents the results that were not provided in the original work (and cannot be reproduced). The two tables present the results of the comparative experiments. CNIA demonstrated optimal performance in both AUC-PR and Hits@10 metrics across most datasets, thus confirming the effectiveness and sophistication of CNIA. Specifically, the CNIA model outperformed the GraIL, TACT, and CoMPILE models on all datasets, and it surpassed the SNRI and ConGLR models on most datasets, albeit with suboptimal results on individual datasets. When averaging the metrics across the three datasets, it was evident that the average AUC-PR value of CNIA was optimal on all three datasets, while the Hits@10 value was suboptimal on WN18 RR and optimal on the other two datasets. Compared with the base model SNRI, the experimental results on both WN18 RR and FB15k-37 were significantly improved, and the average AUC-PR and Hits@10 values were greatly increased, thereby indicating that CNIA is superior and can enhance the performance of inductive link prediction. Compared with the state-of-the-art model RMPI, CNIA is constantly better on the majority of datasets. In comparison to the ConGLR model, the average AUC-PR improved by 0.44% on the WN18 RR dataset, 0.25% on the FB15k-237 dataset, and 2.25% on the NELL-95 dataset. However, the average Hits@10 was lower than ConGLR on the WN18 RR dataset, but it improved by 1.95% on the FB15k-237 dataset and 0.99% on the NELL-95 dataset. ConGLR only performed well on WN18 RR-v2,v4 as on other datasets it did not perform well, which indicates that ConGLR's biased logical reasoning method is specifically suited for certain WN18 RR datasets. In contrast, our model CNIA consistently performed well across various datasets, thus demonstrating its versatility and stability across different scenarios. Among the AUC-PR metric values, CNIA exhibited more pronounced advantages on the WN18 RR and NELL-995 datasets. Upon observing Table 2, it was noted that the #R value of WN18 RR and NELL-995 datasets consistently remained lower than that of FB15k-237, thus indicating that the subgraphs in these two datasets were more likely to be sparse and lack the structural information for reasoning. CNIA effectively leveraged the adjacent relationship information when compared to the baseline model, thereby addressing the issue of subgraph sparsity. However, the improvement effect on the FB15-237 dataset was not as noticeable, possibly because the subgraph density of this dataset was high and due to the fact that the effective structural information could be extracted using closed subgraphs, thus making the baseline model easily predictable.
## 5 Challenges, Open Research Problems, and Future of WBANs
### Privacy  
Privacy protection of patients in WBANs has been extensively researched and developed. Authorization of the users in the WBANs system should not be overlooked and the users should have autonomy and control over their data of any type [97]. In addition to, especially in image processing applications which are becoming more available from day to day, the privacy-preserving methods should be developed for the comfort of the monitored people. On-node processing of images can be a solution in which no images are transferred, only the information about the image is sent over the network. [PERSON] _et al_. [98] has discussed a wireless sensor system privacy leak called Fingerprint and Timing-based Snooping attacks that can happen even if the wireless communication is encrypted.
# Fast and Reliable Burst Data Transmission for Backscatter Communications
## 4 CRFID Operating Procedure Optimization
### CRFID Operating Procedure  
We introduce a burst transmission mechanism to optimize the EPC C1G2 communication procedure for bulk transmission. The core idea is fragmenting large data packets into blocks and letting a tag occupy all time slots for burst transmission when there are data to be transmitted. In addition, in order to deal with the error of data frames, we introduce erasure codes to improve system reliability and to reduce ACK delay.  
An overview of erasure coding is given in Figure 3. The node encodes \(N\) source data frames that are required to transmit into \(N+M\) frames through the XOR of several random source data frames, where \(M\) is the number of redundant frames [34]. Each frame is sent only once, and the reader can decode and restore the original data by successfully receiving any \(N\) frames. Thus, there is no need to acknowledge each frame for the reader, which reduces ACK delay. If the number of errors in the data frame is greater than \(M{+}1\), it means the data restoration failed.  
The improved communication procedure is shown in Figure 4. The Query and QueryRep commands are used to initialize data requests and to set related parameters. The RN16 contains a 16-bit random number generated by the CRFID. After being sent to the reader, the reader returns it as a field of the ACK to the tag to indicate agreeing to the data transmission request of the tag. Then, the tag selects the optimal sleep time and the number of transmitted data frames according to the current energy-harvesting condition for burst transmission. Then, the reader and CRFID repeat the above duty cycle operation until all data in the current round have been sent or wait until the reader returns a QueryRep command for the next round of sending request.  
Figure 3: Overview of erasure coding.
## 2 System Reliability Evaluation Method of a Single Failure Criterion
### Copula Theory
#### 2.1.2 Meta-Elliptical Copulas  
Meta-elliptical copulas, first proposed by [PERSON] [38], are derived from the elliptic distribution function. It is an extension of the multivariate normal distribution function. Meta-elliptic copulas can fit the abnormal distributions well, such as a multivariate extremal distribution. The meta-elliptic copula functions can be divided into the Gaussian copula, t-copula, Cauchy copula, etc. In this study, the Gaussian copula was adopted as the candidate meta-elliptic copula. The d-dimensional Gaussian copula function is defined as follows:  
\[\begin{array}{ll}C(u_{1},u_{2},\ldots,u_{d}|\Sigma)&=\Phi_{\Sigma}(\Phi^{-1} (u_{1}),\Phi^{-1}(u_{2}),\ldots,\Phi^{-1}(u_{d}))\\ &=\int_{-\infty}^{\Phi^{-1}(u_{1})}\ldots\int_{-\infty}^{\Phi^{-1}(u_{2})} \frac{1}{(2\pi)^{d/2}|\Sigma|^{1/2}}\exp\Bigl{(}-\frac{1}{2}w^{T}\Sigma^{-1} w\Bigr{)}dw.\end{array} \tag{3}\]  
where \(C(u_{1},u_{2},\ldots,u_{d}|\Sigma)\) represents the Gaussian copula function. \(\Phi^{-1}(\cdot)\) is the inverse function of the standard normal distribution function. \(\Phi_{\Sigma}(\Phi^{-1}(u_{1}),\Phi^{-1}(u_{2}),\ldots,\Phi^{-1}(u_{d}))\) is the multivariate standard normal distribution function. \(w\) represents the matrix of variables of the integrand. \(\Sigma\) represents the correlation coefficient matrix.
# Research on Vibration Comfort of Non-Motorized Lane Riding Based on Three-Axis Acceleration
## 3 Experimental Data Collection and Processing  
### Experimental Program  
(1) Experimental Tool  
In this study, smartphones serve as the data acquisition terminals, utilizing their built-in global positioning system (GPS) modules and acceleration sensor modules to gather GPS positional coordinates and triaxial acceleration data. As per the Android developer's official documentation, the sensor module's accuracy is at 1 in 10,000, satisfying the data accuracy requirements for this research. During data collection, the smartphones were vertically attached to the torsos of experimenters seated on two different types of vehicles: electric bicycles and bicycles. This study mainly used electric vehicles for experiments and compared and analyzed non-motorized vehicle types using bicycles.  
(2) Experimental Subject  
The study focused on a segment of Youyi Avenue in Wuchang District, Wuhan City, as shown in Figure 1. Data such as the locations of stations, intersections, access points, and the presence of segregated or non-segregated non-motorized traffic lanes along the route were acquired through Baidu Live Map.  
Figure 1: Schematic diagram of the experimental section.  
Experimental Program  
Data collection was conducted from 11:00 to 13:00 on weekdays. The methodology involved spatially dividing the non-motorized paths into segments, each 100 m in length, for analytical purposes. The moments of stopping and starting at stations and intersections were meticulously recorded. To minimize the impact of extraneous variables on the results, each experiment utilized vehicles of the same model with consistent seat heights.
# Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment
## 3 Experimental Design
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Operating frequency (\(f_{op}\)) & 1876.6 MHz \\ Downlink frequency (\(f_{\uparrow}\)) & 1872.6 MHz \\ Uplink frequency (\(f_{\uparrow}\)) & 1776.6 MHz \\ Receiver antenna height (\(h_{\textbf{a}}\)) & 1.5 m \\ Transmitter antenna height (\(h_{\textbf{t}}\)) & 30 m \\ Average building height (\(h_{\textbf{b}}\)) & 15 m \\ Average inter-building distance (\(d_{\textbf{b}}\)) & 20 m \\ Transmitter-receiver distance (\(d_{\textbf{r}}\)) & 100 m \\ Base station transmitter power (\(P_{\textbf{t}}\)) & 46 dBm \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Measurement Parameters. Figure 1: Flowchart of optimization and measurement. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Statistical Measure** & **RSRP** **(dB)** & **RSRQ (dB)** & **PCC/SINR** **(bps)** & **RSSI** **(dBm)** & **DL EARFCN** **(bps)** & **PCC PHY** **(bps)** \\ \hline Range & 53.90 & 16.69 & 34.71 & 46.31 & 0.00 & 28,822.94 & 28,040.11 \\ Minimum value & \(-112.95\) & \(-18.62\) & \(-8.63\) & \(-82.23\) & 1876.00 & 68.00 & 0.00 \\ Maximum value & \(-59.05\) & \(-1.93\) & 26.08 & \(-35.92\) & 1876.00 & 28,890.94 & 28,040.11 \\ Median value & \(-92.21\) & \(-8.96\) & 4.84 & \(-69.25\) & 1876.00 & \(-\) & \(-\) \\ Mean value & \(-91.74\) & \(-9.35\) & 5.53 & \(-68.06\) & 1876.00 & \(-\) & \(-\) \\ Standard deviation & 8.51 & 2.25 & 6.43 & 7.85 & 0.00 & \(-\) & \(-\) \\ Variance & 72.49 & 5.05 & 41.40 & 61.63 & 0.00 & \(-\) & \(-\) \\ Skewness & 0.74 & \(-0.83\) & 0.54 & 0.91 & \(-\) & 2.08 & 2.11 \\ Number of Missing & 0 & 0 & 0 & 0 & 0 & 87 & 27 \\ values & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 2.41 & 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical data of the measurements from UNILAG.
# An Efficient Person Search Method Using Spatio-Temporal Features for Surveillance Videos +
## 4 Experiments and Results
### Evaluation Criteria  
In the surveillance video database, two different criteria were used to evaluate the performance of the proposed method. First of all, Average Precision (\(AP\)) was utilized as the performance metric for a query person, and Mean Average Precision (\(MAP\)) was employed to evaluate the overall performance and computed as:  
\[MAP=\frac{1}{N_{q}}\sum_{i=1}^{N_{q}}AP_{i} \tag{9}\]  
where \(N_{q}\) is the total number of query persons.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Database** & **Video Clips** & **Person Identities** & **Training Data** & **Testing Data** \\ \hline Surveillance Video & 15,000 & 897 & 11,546 & 3454 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The statistics of surveillance video database.  
In real-world surveillance, we pay more attention to the top-_K_ search results. Thus, the top-_K_ accuracy is defined as:  
\[Accuracy=\frac{D_{q}}{N_{q}} \tag{10}\]  
where \(D_{q}\) is the number of query persons that obtain top-_K_ positive search results, and \(N_{q}\) is the total number of query persons. In the following experimental results, the value of \(K\) is set to 1, and top-1 accuracy is used to evaluate the performance.  
To evaluate search efficiency, the average time for \(N_{q}\) query persons is used to compare our proposed method with the existing person search methods.
###### Abstract  
The failures of tailings dams have a major negative impact on the economy, surrounding properties, and people's lives, and therefore the monitoring of these facilities is crucial to mitigate the risk of failure, but this can be challenging due to their size and inaccessibility. In this work, the deformation processes at Zelazny Most tailings dam (Poland) were analyzed using satellite Advanced Differential SAR Interferometry (A-DInSAR) from October 2014 to April 2019, showing that the dam is affected by both settlements (with a maximum rate of 30 mm/yr), and horizontal sliding in radial direction with respect to the ponds. The load of the tailings is pushing the dam forward along the glacio-tectonic shear planes located at depth, in the Pliocene clays, causing horizontal displacements at a rate up to 30 mm/yr, which could lead to a passive failure of the dam. The measured displacements have been compared with the ones observed by in situ data from the 90s to 2013, available in the literature. The outcomes indicate that intense localized deformations occur in the eastern and northern sectors of the dam, while the western sector is deforming evenly. Moreover, although the horizontal deformation had a slowdown from 2010 until 2013, it continued in 2014 to 2019 with recovered intensity. The upper and the recent embankments are affected by major settlements, possibly due to a lower consolidation degree of the most recent tailings and a larger thickness of compressible materials.  
A-DInSAR; tailings dam stability; Zelazny Most; remote sensing monitoring; ground deformation +
Footnote †: journal: Journal of Applied Mechanics and Physics  
1
Footnote †: journal: Journal of Applied Mechanics and Physics
# A Novel Approach for Improving XML Querying over Wireless Broadcast Channels
## 3 Proposed Work
### Detailed Description of the Architecture Components  
The proposed architecture for improving XML querying over wireless broadcast channels involves several components working together seamlessly.  
1. Mobile Device: On the mobile device, users enter queries into a Query Interface, which then routes these queries to the appropriate channels or retrieves cached results if available. Furthermore, the Query Decomposer divides complex queries into sub-queries and communicates with the cache to check for existing results or replace them with new ones.
2. Query Interface: Where users input their queries.
3. Query Router: Routes queries to the appropriate channels or cached results.
4. Query Decomposer/Cache: Decomposes complex queries into sub-queries and checks/updates the cache.
5. XML Data Partitions: The XML data are divided into segments based on specific criteria such as document size, type, or content, and each partition is assigned to a separate wireless broadcast channel. This approach distributes the load and allows each channel to broadcast its assigned XML data segment efficiently.
6. Document Size: XML documents of comparable sizes are grouped together by document size partitioning. For instance, one partition might include smaller documents, while another might contain larger documents. Because smaller files may be stored and retrieved faster, this method can effectively improve response times for simpler queries in networks where smaller files are visited more frequently. Larger papers, on the other hand, can be streamed independently to prevent using resources that are required for smaller, faster-access files.
* Document Type: Document type partitioning arranges the XML data according to the role or schema structure of the document. \"Customer records,\" \"product catalogs,\" and \"transaction logs\" are a few examples. Sorting by type eliminates the need to search through irrelevant data kinds by directing particular query types to the appropriate channel. When data have different access patterns for each type or when queries are type-specific, this method works very well.
* Document Content: Documents are divided using content-based partitioning according to specific important attributes or metadata found in the XML content itself. XML documents could be divided, for instance, by date, topic, or geographic location. This method makes it possible for queries aimed at particular content categories to be effectively routed to the right channels by enabling partitioning that accurately reflects the data being queried.
* Wireless Broadcast Channels: Each partition is assigned to a different channel to balance the load and broadcast the data.
* XML Query Results Aggregator: After the queries are processed, the mobile device's XML Query Results Aggregator combines the results from the various channels or cached data to create the final query result, which is then displayed to the user.