esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Outlooks  
The non-linear dynamic characteristics of the rotor system with the coupling faults have been discussed in this paper. A 24-DOFs rotor system supported by a pair of sliding bearings with both crack and rub-impact faults has been established by the [PERSON]'s second law. The coupling faults have been studied via comparing the single fault (crack or rub-impact). The effects of the systematic parameters (cracked depth, clearance between rotor and stator, stator stiffness, eccentricity) to the coupling faults have also been presented by comparing with the bifurcation and amplitude-frequency characteristics. The POM energy method has been used to confirm the DOF number of the reduced system. The TPOD has been applied to reduce the original system to a 3-DOFs one, the efficiency of the optimal order reduction model has been verified via the comparison of the bifurcation diagrams. The POM energy is a direct method to obtain the optimal reduced system of high-dimensional non-linear dynamic system. The dynamic characteristics analysis of the rotor system with coupling faults and the applications of the TPOD method can provide qualitative guidance to the common faults in rotor system and prior information of fault diagnosis. Further studies on this subject are being carried out by the present authors in the three aspects: the first is to study the dynamical characteristics of the ball bearing rotor model with the coupling faults; the second is to study the coupling faults of the rotor system with uncertainties based on the polynomial dimensional decomposition and other methods. Figure 23: Bifurcation diagrams. (**a**) The original system. (**b**) The reduced system. **Author Contributions:** Data curation, N.W., K.Z. and C.F. ; Formal analysis, K.L., C.F. and Y.J. ; Funding acquisition, K.L. ; Investigation, N.W. ; Methodology, K.L., N.W., C.F. and Y.J. ; Project administration, K.L. and Y.Y. ; Resources, Y.J. and H.Z. ; Software, K.Z., Y.Y. and H.Z. ; Visualization, H.Z. ; Writing--Original draft, K.L. and Y.J. ; Writing--Review & editing, K.Z. and Y.Y. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by National Natural Science Foundation of China (Grant No. 12072263, 11802235, 11972295), the Natural Science Foundation of Shaanxi Province (Grant No. 2020)Q-129) and Aviation Engine Innovation Center of National Defense Science, Technology and Industry (Grant No. CXZX-2019-001). **Conflicts of Interest:** The authors declare that they have no conflict of interest.
## 4 Classification and Core Components of DT Systems in Transportation Infrastructure
### Classification of DT Systems in Transportation Infrastructure
#### 4.1.2 Data Integration Methods  
1. Static Data-Based DTs  
These DTs rely heavily on static information, such as design specifications, geographic data, and historical usage patterns. In new infrastructure planning and design, the static data-based DT may simulate the long-term implications of different design choices, such as road layout and material selections [51]. However, these models react poorly to real-world, real-time changes and do not capture the true dynamic nature of these transportation systems [52].  
2. Dynamic Data-Based DTs  
Dynamic DTs make use of real-time data streams from sensors, IoT devices, GPS systems, and more. They have become vital for every traffic management system, which requires minute-by-minute updates on traffic conditions, the state of the weather, and accidents on roads [53]. The main challenges regarding the integration of dynamic data include the consistency of data, reduction in latency, and handling voluminous real-time data efficiently [54]. Among these problems, edge computing has been proposed to serve the purpose of enabling the local processing of data near the source and reducing constant transmissions to higher-end centralized servers [55].  
3. Hybrid Data-Based DTs  
Hybrid DTs are built on an integrated view of both static and dynamic data within a more holistic transportation system. For example, a railway network DT might integrate design blueprints, static data, with real-time data on train loads and track conditions to evaluate system health and predict maintenance needs [3]. The net effect is more accurate and flexible modeling, but there is a concomitant increase in integration complexity [53]. Developing standardized data formats and communication protocols is essential for enabling hybrid data integration, although achieving full interoperability remains challenging.
## 1 Introduction
Modulated wideband converters (MWC) structure is a sub-Nyquist sampling scheme for acquiring sparse wideband signals [31; 32]. It is flexible and can be realized by commercial components. Many improved structures, such as distributed MWC (DMWC) [33], random partial Fourier structured MWC (RPFMWC) [34], and array based MWC [35], have been proposed for specific signal processing. In our previous work [36], a method for detecting FH signals based on MWC was proposed, based on channelization and energy detection. However, the method focuses on single-target FH signal detection and is not applicable to multi-target signals. Furthermore, the method mainly processes the signals in the frequency domain and losses the time information. In this paper, we propose methods for the tracking and network sorting of FH signals based on the estimation of TOA and power by MWC in the time domain. A dynamic programming multi-frequency function is designed and applied in the system to identify the signals and obtain the support set directly. Instead of caching data for further operations, the proposed system processes the input signals in real time, which is necessary for signal tracking. Furthermore, separation of the multiple signal is implemented by the results of tracking and comparative power. The rest of this paper is organized as follows. In Section 2, we provide the basic information related to multiple FH signals. A detailed design of the dynamic programming MWC method for signals tracking and sorting is presented in Section 3. The effects of some parameter settings and simulation results are given in Section 4 and the conclusions are presented in Section 5.
## 4 Result Analysis
### Analysis of the Document Collection
#### 4.1.4 Sources Analysis  
The 70 documents were published in 46 different sources. [PERSON]'s law was used to better comprehend the impact and quality of the sources used. Three clusters (Cluster 1-3) were created to categorize the documents, with Cluster 1 having the most relevant sources. Particularly, 7 sources (15.2%) that published 25 documents (35.7%) comprised Cluster 1, 16 sources (34.8%) that published 22 documents (31.4%) comprised Cluster 2, and 23 sources (50.0%) that published 23 documents (32.9%) comprised Cluster 3. Table 2 presents the sources of the first cluster, where \"Source\" is the name of the source, \"Rank\" is the overall rank of the source, \"Freq.\" is the volume of documents published in the specific source, \"cumFreq.\" is the cumulative volume of published documents, and \"Cluster\" represents the cluster in which each source was categorized in. Based on the outcomes of applying Bradford's law, \"IEEE International Conference on Advanced Learning Technologies (ICALT)\" (rank = 1 and freq. = 6), \"ACM International Conference Proceeding Series\" (rank = 2 and freq. = 5), \"CEUR Workshop Proceedings\" (rank = 3 and freq. = 4), \"IEEE International Conference on Teaching, Assessment and Learning for Engineering (TABLE)\" (rank = 4 and freq. = 3), \"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)\" (rank = 5 and freq. = 3), \"British Journal of Educational Technology\" (rank = 6 and freq. = 2), \"Communications in Computer and Information Science\" (rank = 7 and freq. = 2), \"British Journal of Educational Technology\" (rank = 8 and freq. = 2), and \"Communications in Computer and Information Science\" (rank = 9 and freq. = 2) were the top sources.  
Figure 6: Annual distribution of published studies.
# CNN Based Image Classification of Malicious UAVs
## 3 Results and Discussion
### Error Analysis  
It is important to understand under what circumstances the trained models are making errors in their predictions. In particular, it is useful to know whether the assumption that the models will struggle to distinguish between the DJI Mavic 2 Enterprise and DJI Mavic Air is valid. This can be achieved by examining the confusion matrix for each model, which shows a count of predicted object versus true actual object. Table 4 illustrates the confusion matrix for one of the two best performing models in our study, Resnet-18, and Table 5  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Number of Test** & **Number of Correct** & **Test Accuracy** \\  & **Images** & **Predictions** & \((n_{correct}/n_{total})\) \\  & \((n_{total})\) & \((n_{correct})\) & \\ \hline VGG-16 & \(3\times 26=78\) & 67 & 0.859 \\ \hline Resnet-18 & \(3\times 26=78\) & 66 & 0.846 \\ \hline AlexNet & \(3\times 26=78\) & 61 & 0.782 \\ \hline MobileNet v2 & \(3\times 26=78\) & 61 & 0.782 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test Accuracy Using Trained Models.  
illustrates the confusion matrix for one of the other two models in our study, MobileNet v2. The counts value in the main diagonal of the matrix (i.e., from top left to bottom right) represent correct predictions (i.e., predicted UAV type = actual UAV type), whereas the count values in other cells represent an error in prediction.  
It can be seen from Table 4, and more so from Table 5, that the majority of prediction errors are one of the two following cases:  
* actual UAV is a DJI Mavic 2 Enterprise, predicted UAV is a DJI Mavic Air (row 1, column 2).
* actual UAV is a DJI Mavic Air, predicted UAV is a DJI Mavic 2 Enterprise (row 2, column 1).  
Furthermore, when the actual UAV in the image is a DJI Phantom 4, it is almost always predicted correctly by the model (24 out of 26 times in both Tables 4 and 5).  
These statistics confirm the previous intuition that the trained models are very good at correctly predicting a DJI Phantom 4 in an image due to its distinctive appearance, but sometimes get confused in distinguishing between the DJI Mavic 2 Enterprise and DJI Mavic Air due to their similar appearance. However, the trained models still correctly predict a DJI Mavic 2 Enterprise and DJI Mavic Air in images more often than they make errors.
## 2 Case Study and System Model  
The real case of a large cruise ship, built by one of the major international players, has been considered for testing the proposed approach, performing two independent optimizations for two weekly cruises, one representative of summer and one of winter conditions.
## 3 Results
### Pressure  
This study found that the fluid injection pressure did not significantly differ between the bolus and infusion techniques at 35.8 (31.5 to 40.2) kPa and 38.4 (36.4 to 40.4) kPa, respectively. The difference in pressure between the two techniques was only 2.6 (from 1.9 to 7.2) kPa, and the \(p\)-value was 0.254, indicating no statistically significant difference. However, the bolus technique showed a higher fluid injection pressure when it came to gentle contact with the epineurium and at a 60-degree angle. The difference in pressure when gentle contact was made with the epineurium was (95% CI) 9.6 (2.0-17.0) kPa, with a \(p\)-value of 0.02. Similarly, at a 60-degree angle, the bolus technique showed a difference in pressure of 8.9 (1.2 to 16.6) kPa, with a \(p\)-value of 0.024. These results are shown in Figure 4 and Table 2 of this study.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Technique** & **Force (SD)** & **Comparison** & **Difference** & \(p\)**-Value** \\ \hline \hline Bolus injection & 0.96 (0.61) & Bolus vs. infusion & 0.19 (0.06–0.32) & 0.003 \\ \hline Continuous injection & 0.77 (0.54) & & & \\ \hline Angle & & & & \\ \(30^{\circ}\) & 0.76 (0.47) & \(30^{\circ}\) vs. \(45^{\circ}\) & 0.08 (\(-\)0.10 to 0.27) & 0.52 \\ \(45^{\circ}\) & 0.67 (0.46) & \(30^{\circ}\) vs. \(60^{\circ}\) & 0.40 (0.20 to 0.60) & \(<\)0.001 \\ \(60^{\circ}\) & 1.16 (0.69) & \(45^{\circ}\) vs. \(60^{\circ}\) & 0.49 (0.29 to 0.69) & \(<\)0.001 \\ \hline Tissue & & & & \\ Muscle & 0.45 (0.36) & Epineurium—touch vs. muscle & 0.45 (0.24 to 0.65) & \(<\)0.001 \\ Epineurium—touch & 0.90 (0.49) & Epineurium—indentation vs. muscle & 0.78 (0.56 to 1.02) & \(<\)0.001 \\ Epineurium—indentation & 1.24 (0.59) & Epineurium—indentation vs. touch & 0.34 (0.14 to 0.54) & \(<\)0.001 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of force and statistical analysis between bolus and continuous injection techniques at different angles and tissue locations.  
Figure 3: Peak axial force and fluid injection pressure measured during bolus injection and continuous infusion; effect of tissue location (gentle touch on epineurium, epineural indentation and muscle) and angle. Image (**a**) shows difference in axial force on gentle touch of epineurium, but not in muscle or on epineural indentation. Image (**b**) shows difference in force at \(60^{\circ}\) angle, but not \(30^{\circ}\) or \(45^{\circ}\).
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 6 Experimental Setup
### Performance Metrics  
We used two metrics for performance evaluation: accuracy of either benign or poisoned model on clean data (\(Acc\)), and attack success rate (\(ASR\)), the accuracy of a back-doored model on poisoned data samples. The best-performing backdoored model should maintain the same accuracy on clean data as that of the benign model while achieving an \(ASR\) on poisoned data samples for the target class.
# Collision Avoidance Geographic P2P-RPL in Multi-Hop Indoor Wireless Networks
## 3 Collision Avoidance Geographic Point-to-Point RPL  
In this section, the Collision Avoidance Geographic P2P-RPL is proposed to discover the on-demand P2P route based on the location information from IR-UWB-based cooperative self localization. Collision Avoidance Geographic P2P-RPL was operated in RPL-based networks to find a P2P path with location information of each node. All nodes were UWB-enabled RPL routers and each node was able to estimate its location information by the fully-distributed Bayesian cooperative multi-hop positioning method. A node exchanged the statistical characteristics of the location error with neighbors by piggybacking a location error field on the standard RPL control messages (DIO and DAO).  
Location error characteristics are represented by the location state, which includes the two-dimensional position information of the lower and upper bounds of the position estimates respectively. For P2P route discovery, the location-constrained forwarding zone is defined as the smallest rectangular region including the estimated region of the source and destination nodes. A node joins the temporary DAG rooted at the source and forwards a P2P-DIO only if it belongs to the location-constrained forwarding zone. Only the nodes within the location-constrained forwarding zone can participate in the route discovery procedure, resulting in reduced routing overhead of the P2P-RPL algorithm.  
To mitigate the long listen-only period and non-deterministic suppression problem, the collision avoidance elastic trickle timer algorithm is proposed. Each node is given a different length of listen-only period depending on the transmission priority. The order of the transmission priority is determined by the distance from the source node, and the angle between the line connecting the sender and the destination nodes and the line connecting the sender and the target nodes. If a higher order of transmission priority is given, then a shorter listen-only period is selected in the trickle timer. The shorter listen-only period allows a node to transmit a P2P-DIO earlier than the other nodes and the probability of collisions becomes significantly lower.
# Weakly Supervised Conditional Random Fields Model for Semantic Segmentation with Image Patches
## 3 The Proposed Method  
We propose a novel framework for weakly supervised ISS based on image patches and CRF. As shown in Figure 1, the flowchart of the proposed framework contains three main parts, i.e., superpixel generation and image segmentation, CRF model construction, and CRF based semantic inference of image patches for ISS. First, the improved SLIC algorithm is used to segment the training images into superpixels, which are merged into image patches based on the weakly supervised information. Second, the class correlation function and similarity function are introduced into the CRF model to construct a CRF model for inferring semantic class labels. Finally, the trained PBCRF model is applied for weakly supervised semantic segmentation of images. Relevant details of these three parts within the proposed framework are presented as follows.
# Development and Application of Unesaled borehole Leakage Detection Device Based on Flow Method
## 2 Research and Development of Unsealed Borehole Leakage Detection Technology Based on Flow Method  
### Components of Air Leakage Detection Device  
Most of the previous leakage detection is based on judging the concentration of seam gas extracted and negative pressure to determine whether the borehole is leaking. In this paper, a flow-based device was proposed for unsealed borehole leakage detection. The device (Figure 1) includes mainly a pressure limiting valve, pressure gauge, connecting rod, expandable capsule, sealing rubber plug, wet flow gauge, and other components. The specific leakage position and leakage amount can be judged based on the variation in the flow rate at different measurement points.  
The specific operation of the leakage detection device is as follows.  
1. Remove the original connection device of the extraction pipe from the borehole, and the expanding capsule is then pushed to measurement point 1 using a connecting rod;2. Pass the outermost connection rod and the purple copper pipe (connected to wet flow gauge) through the double-hole rubber plug, and completely seal with the extraction pipe; 3. After all components are connected, open the underway press-air system to inflate the expanding capsule to seal the section of the borehole to be tested for leakage; 4. After the inflation is completed, open the extraction system valve, and observe the number of wet flow gauges; 5. When the number of wet flow gauges is stable (the number remains unchanged within 3 min), turn off the extraction system valve, and deflate and depressurize the expanding capsule. Then, send the expanding capsule to the next measurement point for the next set of flow tests by mounting a link.
## Materials and Methods
### Mathematical Models
#### 2.3.4 Reassigning Improperly Assigned Exceptional Machines (EMs) and Redundant Machines (RMs)  
The CF based on the solution from model 3 and the part family formation may result in an unsatisfactory block diagonal solution due to improperly assigned Ems, which process most parts in other cells, or RMs, which process no parts in their parent cell. Therefore, a subsequent refinement procedure is used to improve the quality of incumbent block diagonal solutions through an inspection by reassigning them to their most appropriate cells if there are any improperly assigned EMs. The reassignment of improperly assigned EMs/RMs can lead to higher GE by decreasing EEs and voids.  
The improperly assigned EMs/RMs are categorized as follows:  
* An absolute RM which processes no parts in any cell;
* A type I RM which processes most parts in other unique cells except for its parent cell;
* A type II RM which processes most parts in two or more other cells except for its parent cell;
* A type I EM which processes some parts in its parent cell but processes most parts in other unique cells except for its parent cell;
* A type II EM which processes most parts in two or more cells including its parent cell.  
To illustrate the types of improperly assigned EMs/RMs, consider a block diagonal matrix as shown in Figure 3. From this matrix, we can observe the set of machine cells, MC\({}_{1}\) = {1, 2, 3}, MC\({}_{2}\) = {4, 5, 6}, and MC\({}_{3}\) = {7, 8}; and the set of part families, PF\({}_{1}\) = {1a, 2b, 3c}, PF\({}_{2}\) = {4a, 5b, 6a}, and PF\({}_{3}\) = {7b, 8c, 9b}. According to the above definition, machines 1, 2, and 3 are type I RM, type II RM, and absolute RM, respectively, and machines 6 and 8 are type I EM and type II EM, respectively.  
Figure 3: A block diagonal matrix for identifying the type of exceptional machines (EMs)/redundant machines (RMs).  
To determine the type of EM and RM, the following elements need to be evaluated for each machine:  
* Its parent cell;
* Cells processing most parts;
* The total number of parts processed; and
* The number of parts processed in its parent cell.  
Figure 4 is a flow chart showing the determination of the type of EM or RM for a machine. The reassignment procedure for improperly assigned EMs/RMs is then stated as follows:  
* Reassign a type I RM or type I EM to another unique candidate cell;
* Reassign an absolute RM, type II RM or type II EM to the cell with the fewest 1s. If ties occur, reassign them to the cell with the lowest number of machines.
* Remove an absolute RM from the current cell since it does not process any parts under the incumbent cell configuration.
## 3 Results and Discussions
### Polyphenols
#### 3.1.4 Quantification of Phenolic Compounds in Almond Skin
For almond blanching, we have noticed that the amount of polyphenols extracted is low compared to natural almonds. Results in [PERSON] et al. paper [12] presented that blanched skin is rich in polyphenols compared to blanching water. This can be explained by the low diffusion of polyphenols, since heat does not affect their stability [28]. Another statement in [PERSON] et al. paper [29] showed that heat appears to be an effective method for polyphenol extraction. The low diffusion can be explained by the short-time exposure to the high temperature (5 min), leading to a low concentration of polyphenols extracted. The percentage of phenolic compounds extracted with the reference method is negligible (4.08%) due to low temperature (20 \({}^{\circ}\)C) and the absence of stirring, thus low diffusion. These previous results can be supported by mDP. For NS, NUA, UA and BA, mDPs were 8, 7, 7 and 5, respectively. It could be noted from these results that high temperature used for blanching depolymerized procyanidins and reduced the mean polymerization degree from 8 to 5. Clearly, procyanidins amount in almond skin is the highest among other compounds, in addition to flavanols in their aglycone forms. As a matter of fact, procyanidins represent 90.50%, 83.71%, 87.91% and 67.47% of total polyphenols extracted for each almond skin extracts (in NA, NUA, UA, and BA, respectively). Flavonol glucosides were also abundant and represented the second most important phenolic compound in the four extracts.