esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet
## 5 Conclusions and Prospects
### Prospects  
In this paper, rolling bearing fault diagnosis based on deep learning is systematically studied, but there are still many avenues worthy of further exploration and research:  
Figure 18: Recognition accuracies of different transfer task models under different working conditions.  
The ECA-MRANet fault diagnosis model proposed in this paper has made some progress in extracting fault characteristics and performing under different working conditions. However, cross-platform cross-device experiments need to be verified. In addition, the network is more complex and the number of parameters is relatively large, so determining how to carry out lightweight processing on the network without sacrificing the diagnostic accuracy is worth further research.
2. Rolling bearing faults can usually be detected and diagnosed by a variety of signals (vibration, electrical, temperature, etc.). In this paper, the bearing vibration signal is used as input data to study bearing faults. Therefore, determining how to effectively fuse different signal data and studying fault diagnosis under multi-mode data input, thereby improving the accuracy and reliability of fault detection, is another important research direction.
3. Most current deep learning methods rely on offline training and offline inference, which cannot meet real-time requirements. Therefore, studying real-time rolling bearing fault diagnosis based on deep learning is a future research direction.  
Conceptualization, K.W. and X.W.; methodology, B.G.; software, B.G.; validation, B.G. and S.S.; formal analysis, B.G., S.S. and R.W.; investigation, B.G. and R.W.; resources, K.W.; data curation, K.W.; writing--original draft preparation, B.G.; writing--review and editing, K.W., S.S., R.W. and X.W.; visualization, B.G. and S.S.; supervision, K.W. and X.W.; project administration, K.W., S.S. and X.W.; funding acquisition, K.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
# Surface Motion for P-Wave Scattering by an Arbitrary-Shaped Canyon in Saturated Half-Space
## 5 Parameter Analysis
### Semicircular Canyon
#### 5.1.1 Medium Porosity  
For dimensionless frequency \(\eta=2\), Figure 4 shows the effects of incidence angle and porosity on ground surface displacement response. The influence of porosity on the amplitude of \(x\)-component and \(y\)-component displacement is different, but the peak value corresponding to the porosity is the same. Compared with \(y\)-component displacement, the amplitude of \(x\)-component displacement is more affected by porosity, especially on both sides of the canyon, and the amplitude of surface displacement decreases with the increase of porosity on the whole. It should be noted that the surface \(x\)-component displacement peak corresponds to the porosity. Except for the incidence \(\gamma_{1}=5^{\circ}\), the peak displacement occurs when the porosity \(n_{s}=0.1\), and the peak displacement occurs when \(n_{s}=0.34\) at all other incidence angles. The influence of porosity on \(y\)-component displacement is not so obvious, especially at the front side and backside of the canyon; the \(y\)-component displacement amplitude curves are almost similar under different porosity, but the influence of porosity on the canyon boundary is more obvious. However, the porosity corresponding to the peak value of \(y\)-component displacement is consistent with that of \(x\)-component displacement.  
Figure 4: _Cont._  
Figure 3: Calculate the case model: (**a**): semicircular canyon; (**b**) trapezoidal canyon.
# The Development and Analysis of a Multistage Spraying Method for Liquids in an Ultrasonic Field
###### Abstract  
Spraying various liquids (primarily aqueous solutions of various substances) is widely used in various technological processes. For most of them, high dispersibility and a narrow droplet size distribution are essential. Ultrasonic spraying allows these parameters to be achieved. However, the higher the dispersity of droplets produced by ultrasonic spraying, the lower the spray productivity. To solve this problem, we propose a method of multistage spraying, consisting of the generation of a large number of particles of large initial size (to ensure high productivity) and their subsequent destruction by propagation in a periodic ultrasonic field (to ensure small droplet sizes at high productivity). To experimentally determine the capabilities of this technique, a tubular emitter in the shape of a cylinder with a stepped-variable cross-section was designed. The configuration of the ultrasonic field inside the emitter (22.2 kHz; 182 dB) implements three-stage spraying (the number of sputtering stages corresponds to the number of anti-nodes along the emitter axis). The effectiveness of the proposed and developed technique, suitable for the generation of droplets smaller than 40 \(\mu\)m, with performance exceeding the known ultrasonic spray techniques by at least 10-fold, was confirmed in the course of the conducted research.  
liquid atomization; ultrasonic; droplet dispersion; spraying; emitter; droplet deformation; droplet dispersion; multistage spraying +
Footnote †: journal: Applied sciences
## Abbreviations  
The following abbreviations are used in this manuscript:  
1P One-Pulse, response with two monotonic segments (one extreme point)  
2 DoF Two Degrees of Freedom  
AR Automatic Reset  
ARC Automatic Reset Controller  
AW Anti-Windup  
ADRC Active Disturbance Rejection Control  
DOB Disturbance Observer  
HO Higher-Order  
HO-AR Higher-Order Automatic Reset  
_IAE_ Integral of Absolute Error  
IPDT Integrator Plus Dead Time  
MFC Model-Free Control  
MRDP Multiple Real Dominant Pole  
PD Proportional-Derivative  
\(PD_{n}^{m}\) Proportional with \(m\)th order Derivative, \(n\)th-order filter  
\(PD_{n}^{m}R\) Proportional with \(m\)th order Derivative, \(n\)th-order filter and automatic Reset  
PI Proportional-Integral  
PID Proportional-Integral-Derivative  
PIDA Proportional-Integral-Derivative-Accelerative  
POCD Practice-Oriented Controller Design  
SE Speed-Effort  
SOPDTUZ Second Order Plus Dead Time with Unstable Zero  
SW Speed-Wobbling  
TV Total Variation  
TV\({}_{1}\) Deviation from 1P Shape
# Passive Bistatic Ground-Based Synthetic Aperture Radar: Concept, System, and Experiment Results
## 3 Signal Processing
### Imaging and Displacement Estimation
Finally, according to Equation (31), SAR image can be obtained by the 2D inverse Fourier-transform as  
\[\mathbf{\chi}_{p}=\mathbf{F}^{H}s_{p}(\mathbf{f})=\mathbf{F}^{H}\mathbf{\sum}_{m=1}^{M}s_{p}^{m}( \mathbf{f})=\mathbf{F}^{H}\mathbf{\sum}_{m=1}^{M}s_{surv}^{p,m}(\mathbf{f})s_{ref}^{p,m}(\mathbf{f })^{*} \tag{18}\]0  
where \(\mathbf{F}\mathbf{x}\) and \(\mathbf{F}\mathbf{y}\) are the Fourier-transform matrices in the \(x\) and \(y\) directions, respectively. Alternatively, instead of interpolation and then FFT in the \(y\) direction, the type-I NUFFT [48] can also be used, based on which Equation (32) can be modified to  
\[\mathbf{\chi}_{p}=\mathbf{F}^{H}s_{p}(\mathbf{f})=\mathbf{F}^{H}\mathbf{\sum}_{m=1}^{M}s_{p}^{m}( \mathbf{f})=\mathbf{F}^{H}\mathbf{\sum}_{m=1}^{M}s_{surv}^{p,m}(\mathbf{f})s_{ref}^{p,m}(\mathbf{f })^{*} \tag{18}\]1  
Compared to BPA, RMA can get a comparable result, while the computing time can be reduced for imaging a local area with fine grid size. However, it should be noted that for a large scene and a proper grid size, the computational complexity of BPA can be lower than RMA, since, in this case, zero padding is necessary along the azimuth direction for RMA, resulting in a lot of samples in the azimuth direction and a time-consuming 2D interpolation or 1D NUFFT process. Based on BPA or RMA, a time series of SAR images can be obtained with a temporal baseline of about 15 min by the PB-GB-SAR system. For each pair of SAR images, an interferometric phase image can be generated by  
\[\mathbf{\chi}_{p}=\mathbf{F}^{H}s_{p}(\mathbf{f})=\mathbf{F}^{H}\mathbf{\sum}_{m=1}^{M}s_{p}^{m}( \mathbf{f})=\mathbf{F}^{H}\mathbf{\sum}_{m=1}^{M}s_{surv}^{p,m}(\mathbf{f})s_{ref}^{p,m}(\mathbf{f })^{*} \tag{18}\]2  
Then, the two-way displacement of the target can be estimated by \(D=\lambda\Delta\varphi_{D\text{-}In}(x,y)/2\pi\). For GB-SAR, the relationship \(D=2\) (\(\delta x\text{sin}\theta+\delta y\text{cos}\theta\)) = \(2\delta r\) can be used, where \(\delta x\) and \(\delta y\) are the target displacements in the \(x\) and \(y\) directions and \(\theta=\arctan(x/y)\) is the angle of the target. Therefore, for GB-SAR, the one-way target displacement along the LoS direction can be estimated as \(\delta r\) = \(D\)/2. For PB-GB-SAR, there is not a single LoS direction since the bistatic geometry is used. However, for the currently used geometry, based on the plane wave approximation and given that the target only hasthe displacement along the LoS direction of the surveillance antenna (i.e., \(\delta x/\delta y=\tan\theta\)), the two-way displacement estimated by PB-GB-SAR can be expressed as  
\[\mathbf{\chi}_{p}=\mathbf{F}^{H}s_{p}(\mathbf{f})=\mathbf{F}^{H}\mathbf{\sum}_{m=1}^{M}s_{p}^{m}( \mathbf{f})=\mathbf{F}^{H}\mathbf{\sum}_{m=1}^{M}s_{surv}^{p,m}(\mathbf{f})s_{ref}^{p,m}(\mathbf{f })^{*} \tag{18}\]3  
Therefore, the LoS displacement with respect to the surveillance antenna can be calculated by \(\delta r=D/(1+\cos\theta)\).
## 1 Introduction
Given the great potential of this method, deep reinforcement learning algorithms such as trust region policy optimization (TRPO) [16] and proximal policy optimization (PPO) [17] are used in robotic arm control. [PERSON] et al. [18] applied the PPO algorithm to model training; the information that can be obtained in the process of training is limited, which leads to low exploration efficiency of the robotic arm and the final learning effect has difficulty meeting the requirements. In this paper, the recognition method of YOLOv5 [19] is used for visual guidance to quickly and accurately identify the door handle target. For the application of the deep reinforcement learning method in the control of the mobile robotic arm, the training convergence speed is slow and the stability is poor because of the sparse reward value of the algorithm. To reduce training time and help the algorithm converge, this paper proposes an improved PPO algorithm. It can learn complex behaviors, handle continuous action spaces, and find good policies in environments with high-dimensional state spaces. The research adopts the co-simulation method to realize the interaction between the mobile robotic arm and the virtual environment. It also applies the trained strategy to the door-opening task of the mobile robotic arm platform. The effectiveness of the method is verified by comparative experiments. The contributions of this work are as follows:  
1. The method of YOLOv5 is applied to door-handle recognition, and the relationship between distance and pixel ratio is experimentally fitted for door-handle localization. 2. The state space, action space and reward function are designed to realize the interaction between the robotic arm and the environment. 3. An improved PPO algorithm is presented by expanding the experience pool to reduce the exploration of the useless environment. 4. The comparison experiments show that the improved PPO algorithm is faster and more stable than the TRPO and PPO algorithms. The rest of this paper is organized as follows. Section 2 describes the method of handle identification and positioning. Section 3 proposes the improved PPO algorithms. Section 4 establishes a simulation environment to test reinforcement learning algorithms such as TRPO, PPO, and improved PPO. In Section 5, the trained model is applied to the recognition and grasping task of the mobile robotic arm in the real scene. Section 6 concludes the paper and discusses the future work.
# Propagation, Scattering and Defect Detection in a Circular Edge with Quasi-Edge Waves
## 3 Experimental Study
### Reflection and Transmission at Edge Defects  
In this section, the experimental results were compared with the corresponding FE simulation results for validation purposes. The reflected and transmitted waves were collected at MP1 and 2, respectively, which was the same as the FE configuration shown in Section 2.3.  
Figure 12a shows a comparison between numerical and experimental time domain signals in intact conditions, while Figure 12b compares the FE and experimental time domain signals in the presence of a 2 mm deep edge defect. Both experimental and numeral data were normalised by the corresponding maximum absolute amplitude in the time domain. The arrival time of the incident wave and reflected wave was precisely predicted  
Figure 11: (**a**) Experimental setup. (**b**) Configuration of wedge excitation.  
by the FE simulation, as shown in Figure 12b. The minor phase shift in the incident and reflected wave can be attributed to the slight misalignment of the wedge in the experiment.  
Figure 13a,b present the ratio of amplitudes of reflected (\(A_{r}\)) and transmitted (\(A_{t}\)) waves from the edge defect to the amplitude of the incident wave (\(A_{i}\)), respectively. The amplitudes of reflected and transmitted waves were normalised by the amplitude incident wave at MP1. The reflection (\(R_{r}\))-and-transmission (\(R_{t}\)) ratio was defined as \(R_{r}=A_{r}/A_{i}\) and \(R_{t}=A_{t}/A_{i}\), respectively. It can be observed that the 3D FE model well predicted the trend of reflection-and-transmission ratio with the change in defect depth-to-wavelength ratio. The discrepancy can be attributed to the misalignment of the laser beam. In the experiment, it is not pragmatic to ensure that the laser beam is perfectly perpendicular to the mid-plane of the edge due to the thin edge thickness of the specimen, which consequently causes differences between amplitudes of experimentally measured signal and corresponding numerical data; the reason for this phenomenon is discussed in Section 2.2.2. Overall, there is good agreement between FE simulated results and the experimental data, which means that the developed 3D FE approach is adequate and capable of investigating the interaction between the incident \(QES_{0}\) wave mode and the narrow edge defect in the curved edge.
# Study on Water Inrush Characteristics of Hard Rock Tunnel Crossing Heterogeneous Faults
## 3 Fault Water Inrush Model of Igneous Rock
### Governing Equation for Global Seepage  
The dual-porosity model is based on the concept that a fractured porous medium consists of two distinct regions with contrasting characteristics [42]. One region contributes significantly to permeability but has limited capacity, while the other region has low permeability but determines the overall capacity. In the case of the igneous rock fault fracture zone, the fault core acts as a pathway for groundwater flow, enhancing the permeability and conductivity of the fault. However, it has a narrow width and limited water storage capacity. On the other hand, the medium-scale fracture network within the damage zone has lower permeability but facilitates local water storage and communication [43; 44]. Hence, the hydrological conditions within the fault meet the criteria for the generalized dual-porosity model, as shown in Figure 3c. By employing the concept of extended dual-porosity media, the governing equations for global seepage within the fault can be derived.  
A separate [PERSON]'s equation for each medium can be written as:  
\[\mathbf{q}_{fc}=-\mathbf{k}_{fc}\frac{\partial p_{fc}}{\partial x_{j}};\mathbf{q}_{dz}=- k_{dz}^{equ}\frac{\partial p_{dz}}{\partial x_{j}} \tag{10}\]where \(\mathbf{q}_{fc}\) and \(\mathbf{q}_{dz}\) are the fluid flow in the fault core and damage zone, respectively. The fluid mass balance, accounting for the fluid flow in the fault core (fc) and the damage zone (dz) as well as the interporosity fluid transfer, can be expressed separately as:  
\[\frac{\partial\zeta_{fc}}{\partial t}+v^{l}\frac{\partial\mathbf{q}_{fc}}{\partial x}=\Gamma \tag{11}\]  
\[\frac{\partial\zeta_{dz}}{\partial t}+v^{dz}\frac{\partial\mathbf{q}_{dz}}{ \partial x}=-\Gamma \tag{12}\]  
where \(\Gamma=\Lambda\Big{(}p_{fc}-p_{dz}\Big{)}\) represents the interporosity fluid flux transfer, \(\Lambda\) is a characteristics of fractured formation such as matrix's permeability, fractures' geometry, distribution, and size. \(\zeta\) represents the fluid mass in the pore space, and \(\zeta=\Big{(}\frac{\Delta m_{f}}{\rho_{f,0}}\Big{)}=-\alpha_{ij}\epsilon_{ij}+ \frac{p}{M}\), \(M\) is the Biot's modulus.
## 3 Results  
### Data Collection  
On 29 May 2024, the search strategy identified a total of 222 articles from the Medline Embase (n = 57) and Scopus (n = 165) databases. After the removal of 65 duplicates, 157 articles remained for the initial title and abstract screening. The screening phase resulted in the exclusion of 129 articles due to nonhuman study models (n = 30), lack of real-time ultrasound imaging data (n = 51), irrelevance to cancer (n = 95), absence of XAI applications (n = 25), focus on impertinent tasks (n = 18), inappropriate study types (n = 19), and no medical application (n = 51). Excluded articles complied with one (n = 29) or multiple exclusion (n = 100) criteria.  
Subsequently, the remaining 28 articles underwent full-text analysis. Two articles were excluded during the retrieval, due to their unavailability of full text. The full-text analysis of the remaining 26 articles resulted in an additional exclusion of 9 articles based on the predefined criteria. These exclusions encompassed articles that (i) investigated nonhuman subjects i.e., objects (n = 2) and animals (n = 1); (ii) utilized inappropriate imaging data types such as static photographs (n = 2), monomodal CT scans (n = 1), schematic images (n = 1), or the use of ultrasound for navigational purposes instead of image analysis (n = 1); (iii) lacked sufficient relevance to cancer or oncology (n = 4); (iv) only mentioned XAI as a future recommendation (n = 2) or in mathematical expressions (n = 2); (v) focused on peripheral tasks such as captioning (n = 1), eye-gaze mapping (n = 1), or lacked a specific task (n = 1); and (vi) were directed towards nonmedical image description applications (n = 3). Excluded articles complied with one (n = 4) or multiple (n = 4) exclusion criteria. Ultimately, 17 articles were eligible and selected for further in-depth analysis [37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53]. Figure 4 provides a visual overview of the article selection process.  
Figure 4: Flowchart visualizing the results of the PRISMA-based article selection process.
## 5 Intelligent Fault Diagnosis of Sensor Faults
### Artificial Intelligence-Based Fault Diagnosis Methods
#### 5.2.2 Expert System Methods  
The method based on an expert system is used to diagnose faults based on the experience of experts and maintenance personnel involved in producing agricultural sensors. It is primarily divided into rule-based expert systems and expert systems with fuzzy reasoning. An expert system is a structured knowledge system that imitates human experts to solve problems in a particular domain. Expert system components include a knowledge base, a rule base, an inference engine, a human-computer interface, and an explanation facility [95]. The knowledge base is the key to ensuring the expert system's accuracy, which comprises the operators' theoretical and professional knowledge. The system operates as follows: the data to be diagnosed is input through a human-computer interface, the inference engine matches the current known conditions, information, and rules of the knowledge base, and the user is then presented with the conclusion of the matching rules [96; 97]. [PERSON] [98] proposed a WSN fault node classification and management scheme based on fuzzy rules, which detected the sensor fault state and divided it into various categories to guarantee the reusability of fault nodes. [PERSON] et al. [99] proposed a hardware fault diagnosis model for sensor nodes based on a fuzzy inference system with three inputs, developed 27 fuzzy rules based on the status of the transmitter, receiver, and battery, and categorized the nodes as normal nodes, end nodes, and dead nodes. This scheme's detection accuracy and misdiagnosis rate have been enhanced.  
A mathematical model is not required by the expert system-based technique for determining what is wrong. It employs the rules and information we already possess to determine what is wrong with an agricultural sensor system. It has outstanding advantages in the application of nonlinear systems because it has good performance and strong learning ability in dealing with known faults. Nevertheless, there are still things that could be improved with this method: As more and more types of sensors are utilized, the complexity of the sensor system must increase. This makes it difficult to determine why the sensors are malfunctioning. The current expert systems for fault diagnosis lack universality and adaptability, and each system operates independently. This is extremely wasteful. Users are interested in the location and timing of agricultural sensor failures.
# Security Awareness in Software-Defined Multi-Domain 5G Networks
## 6 Characteristics of Application-Specific Data in the Mobile Network Testbed
### EPC Data from Video and IoT scenarios  
Data was collected from different measuring points in our testbed (as depicted in Figure 9). The measured time was roughly two minutes. The feature vector used in the analysis contained four individual packets: the destination and source IP address and timestamp, as well as used protocols. Most of the traffic consisted of IoT or video traffic over CoAP or RTP protocols, respectively. In the IoT only scenario, the number of CoAP packets comprised 53-57% at all measurement points except at net_d, where CoAP packets presented only 36% of all packets. The Address Resolution Protocol (ARP) covered also 36% at that point. In the video and IoT scenario, video produced 96-97% of all packets at all measurement points, while only 1% were from CoAP. This is due to the much greater CoAP packet interval of two seconds, which also explains the rather low packet percentages for CoAP in the IoT-only scenario. In the video scenario, also some SIP packets that control the video stream were detected. Otherwise, the rest of the packets belonged to the Spanning Tree Protocol (STP), the Internet Control Message Protocol (ICMP, ICMPv6), the Stream Control Transmission Protocol (SCTP), the Address Resolution Protocol (ARP), and the Domain Name System (DNS).  
The heterogeneity of the data collected from the testbed is visualized in Figure 10 (data seen when only IoT applications were used) and Figure 11 (data seen in combined video and IoT case). The illustrations were done using a Multidimensional Scaling (MDS) algorithm [67; 68] that places our multi-dimensional data objects into a four-dimensional diagram (where colour is the fourth dimension) in a manner that the distances between objects are preserved as well as possible. The MDS algorithm provides a visual mean--by comparing the shapes of result diagrams--to measure the level of similarity of datasets.  
Figure 10: Multidimensional scaling illustration of the testbed communication with Internet of things (IoT) applications only.  
The figures illustrate the increased diversity between the two scenarios. In practice, the difference comes from the fact that in the video and IoT scenario there are more packets, more protocols, as well as more data flows going from different destinations to different sources.