esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Transformer Fault Diagnosis Based on Dissolved Gas Analysis by Correlation Coefficient-DBSCAN
## 4 Example Analysis and Results
### CCDBSCAN Method Classification Result Analysis
When the original data are used for clustering in Figure 8c, the low-energy discharge DGA data of 8c will be mistakenly clustered into the high-energy discharge fault, resulting in misdiagnosis. However, after applying the method to the gas data amplification, the correlation between the data and the high-energy discharge cluster is obviously reduced. There were four data correlation coefficients between the data and the high-energy discharge cluster that were more than 0.95, and now their correlation coefficients are reduced to 0.9. The correlation coefficient of the low-energy discharge cluster is different from that of high energy discharge cluster, and this data is successfully diagnosed as low energy discharge fault. See Table 6 for CCDBSCAN results and average correlation coefficient of all 30 data. Figure 8: Line chart of correlation coefficient before and after improvement. (**a**) Correlation coefficient of T3; (**b**) Correlation coefficient of PD; (**c**) Correlation coefficient of D1; (**d**) Correlation coefficient of D2. It can be seen from the table that among the 30 fault data clusters of CCDBSCAN, the diagnosis results of 26 data are completely consistent with the actual fault types. In the other four data, two data are misjudged, No.5 data was actually D2, but is diagnosed as T3, No.17 data is actually T3 but is diagnosed as PD, and the rest of the diagnosis results of No.s7 and 8 data are close to the actual results. Although the thermal fault is artificially divided into three types with 300 and 700 \({}^{\circ}\)C as clear boundaries, T1, T2, T3, there is in fact no such clear physical boundary. The distinction between temperature of T1, T2, T3 is a qualitative fuzzy description, and there is a transition state between them. Similarly, D1 and D2 are distinguished by the level of discharge energy. There is also a transition state between them, and there is no clear physical boundary. Therefore, using a correlation coefficient to describe the similarity degree of DGA data to each fault, rather than an absolute diagnosis result, may be more practical in engineering. The diagnosis results of the above two data can still be regarded as valid. Therefore, Table 6 shows a high accuracy rate of fault diagnosis. According to the diagnostic rule [35] of IEC60599\(-\)2015 (see Table 5 for the diagnostic results), 10 of the above 30 DGA data were diagnosed as errors, with the numbers of 2, 5, 7, 8, 9, 10, 14, 19, 23 and 27 respectively. The accuracy rate of fault diagnosis was relatively low.
# A System Architecture of a Fusion System for Multiple LiDARs Image Processing
## 3 Proposed System Architecture for Multiple LiDARs Image Processing
### Inter-Process Communication  
To effectively implement multiprocessing, it is most important to optimize the internal communication method between processes. Communication between multiple processes involves a shared memory model and a message-passing model. In shared memory, communication between multiple processes can read and write to the shared memory area through sharing a part of the address space. When the shared memory is set, communication between multiple processes is possible without utilizing the kernel. As a result, the processing is fast and freedom of communication is ensured. However, a unique algorithm for synchronization between processes is needed without the kernel. In order to control concurrent access, an access control method such as locking or semaphores should be introduced. In this study, it is difficult to solve the synchronization problem of shared memory because there are many processes for multiple clients and a server.  
Message passing can exchange data using buffering in the kernel without sharing memory between processes. It has the disadvantage of slower processing speed than shared memory, but it can be implemented in a simple manner. Message passing has a variety of models such as PIPE, Message Queue, and Socket. In this study, the socket method is used to implement communication between multiple clients and a single server. In the socket method, multiple processes can access through the abstract port provided by the operating system. In the IPC socket method, a process can communicate with another process using the port. Unlike other IPC methods, it is independent of process location and machine boundary. Communication between multiple clients and a server is conducted by UDP. In a fusion system for multiple LiDAR sensors, it is more important to transmit all the data in real time than to ensure the reliability of a packet. A UDP socket does not have a transmission buffer, so data are copied to the kernel area and deleted immediately after transmission. In this system, there is no transmission buffer in the client socket because of unidirectional communication. Figure 4 shows the inter-process communication between a client and a server through UDP protocol.  
Figure 3: The proposed architecture of a fusion system.
## 5 Simulations and Assessments
### Comparative Analysis of Different Control Methods  
In order to verify the effectiveness of the proposed DTCC-ESC method, as shown in Table 3, the active damping compensation control (ADCC) method, the active damping compensation control with engine active control (ADCC-EAC) method, and the DTCC-ESC method proposed in this paper were selected for simulation and comparative analysis on the established compound power-split PHEV model. In addition, taking point D of the engine starting condition as an example, the simulation results were analyzed in detail, and then six typical engine-starting condition points in Figure 18 were taken as examples to analyze the vehicle jerks under different engine starting conditions.
# Deep Reinforcement Learning for Intelligent Penetration Testing Path Design
## 3 Proposed Method
### MDDQN Algorithm
The Q network is updated as follows:  
\[Q(S_{t},A_{t},\theta)\leftarrow Q(S_{t},A_{t},\theta)+\alpha[R_{t+1}+\gamma max _{\alpha}\hat{Q}(S_{t+1},a_{t},\theta)-Q(S_{t},A_{t},\theta)] \tag{10}\]  
where \(\alpha\) is the learning rate. In this paper, the method of generating the transfer matrix is improved from [PERSON] et al. [23]. That is, all nodes in the attack graph are mapped into a matrix that includes the common vulnerability scoring system version 3 (CVSS 3) [36] values of the vulnerabilities. It also includes other operations, such as accessing predefined scores for files. For a clearer understanding of the MulVAL double deep Q network( MDDQN) algorithm, pseudocode is added, as shown in Algorithm 1. ```
1:Obtain target system topology information;
2:Set sensitive host value;
3:Use MulVAL to create attack tree;
4:Search attack graph paths using depth first algorithm;
5:Establish transfer matrix; /*Equation (6)*/
6:Neutral networks initialization;
7:for episode = 1 to maxepisode do
8:for t = 1 to T do
9: Use \(e\)-greedy to select action \(a\) according to Q value /*Equation (7)*/
10: Execute action \(a\), receive next state \(s^{\prime}\), reward \(r\), and whether done
11: Add transition tuple (\(s\),\(\mu\),\(r\),\(s^{\prime}\)) to \(D\)
12:\(s=s^{\prime}\)
13:if\(|D|>\) batchsize then
14: Sample and construct target Q value /*Equation (8)*/
15: Do a gradient descent step with loss function /*Equation (9)*/
16: Replace target parameters \(\theta^{-}\leftarrow\theta\) every N steps /*Equation (10)*/
17:endfor
18:endfor
```  
**Algorithm 1** MDDQN algorithm  
If the corresponding host h to the state s is not a sensitive host, the host value is 0 and the immediate reward Reward(s,a) obtained by action a is negative. If the state corresponding to s' is a sensitive host, the immediate reward result is shown in Equation (11). \[Reward(s,a)=Reward_{Init}-Cost_{val} \tag{11}\]  
\[Reward_{Init}=Score_{val}+Value_{h} \tag{12}\]  
where \(Reward_{Init}\) represents the predefined scores of the vulnerability exploitation and privilege escalation in the transfer matrix, h represents all hosts invaded by the agent, and \(Cost_{val}\) denotes the action cost of the vulnerability exploited by the agent. The action cost is set to 6, 4, and 2 based on the high, medium, and low access complexity of the vulnerability common vulnerability s system. As shown in Equation (12), \(Score_{val}\) is the CVSS 3 value of the vulnerability exploited by the agent, and \(Value_{h}\) denotes the asset value of host h. Based on these reward value settings, the goal of agent training is to attack the host with the most valuable host by using fewer operations.
# The Electric Vehicle Routing Problem with Time Windows, Partial Readers, and Parcel Lockers
## 5 Experimental Results
### Parameter Tuning  
To tune the parameters of our proposed ALNS, we utilize the one factor at a time (OFAT) approach, successfully employed in tuning parameters for solving VRP variants [50; 52]. For this experiment, we randomly selected six large EVRPTW-PR-PL instances (i.e., c103, c202, r110, r205, rc106, and rc207) to evaluate parameter settings. Table 1 summarizes all the parameters used in the proposed ALNS. In this experiment, eight parameters (i.e., \(\omega^{penalty}\), \(\omega^{proba}\), \(\eta^{noimp}\), \(\eta^{proba}\), \(\eta^{penalty}\), \(\epsilon_{b}\), \(\epsilon_{f}\), \(\epsilon_{i}\), \(\epsilon_{w}\), and \(\delta_{L}\)) are selected for tuning, while the remaining parameters are referenced from previous works. In particular, parameters related to removal and insertion operators (i.e., \(\phi_{1}\), \(\phi_{2}\), \(\phi_{3}\), and \(\delta^{noise}\)) are set based on [53]. Parameters associated with the SA acceptance criteria (i.e., \(T_{0}\) and \(\alpha\)) are discussed in Section 4.5. The initial weight penalties and their ranges are adopted from [53] (see Section 4.2). To ensure a good trade-off between computational time and solution quality, the ALNS algorithm is terminated after reaching \(\eta^{max}=30,000\) iterations. We next vary the values of the eight selected parameters, which are produced by an ad hoc trial-and-error procedure [48] (see Column \"Value\" of Table 1), and their initial values are randomly selected (see Table 2). All parameters are tuned sequentially, with each parameter being tuned in turn. The tuning order of parameters follows the order reported in Table 2.  
For each tuned parameter, we consider a set of four candidate values to test each of them, while the remaining parameters are fixed to their initial values unless their final values have been found. This therefore provides four different parameter settings for the ALNS algorithm. The final value of the tuned parameter is selected based on the best performance observed among the four parameter settings. For each parameter setting, the performance of the ALNS algorithm is determined based on the average gap of average solution values after 5 runs, denoted by \(\Delta\tilde{f}_{avg}[\%]\) (see Table 2). This process is repeated until all parameters are tuned. Table 2 summarizes the detailed results of the tuning procedure.
# A Two-Port Dual-Band Dual-Circularly-Polarized Dielectric Resonator Antenna
## 1 Introduction
Experimental measurements demonstrate that the proposed antenna exhibits broad dual AR bandwidths of more than 16% with dual-CP performance, making it become an excellent candidate for downlink and uplink SATCOM. Simulation and measurement results of reflection coefficients, ARs, gains, and radiation patterns are in reasonable agreement. The paper is structured as follows: In Section 2, the DRA design and analysis are presented. This includes Section 2.1, which introduces the configuration of the proposed DBDCP DRA with the final optimal parameters. Section 2.2 covers the working mechanism, evolution process, and AR enhancement techniques for the DRA structure, which is designed to achieve dual-band and dual-circular polarization. To obtain optimal performance, parametric studies have been performed, as shown in Section 2.3. The experimental results of the fabricated DRA are summarized and discussed in Section 3. Finally, in Section 4, the proposed DRA is compared with some previously published DBDCP DRAs, and conclusions are drawn.
# VR as a 3D Modelling Tool in Engineering Design Applications
## 1 Introduction  
Virtual reality (VR) is a mature technology, which has established itself as an important tool in many engineering applications, such as design review, inspection, and worker training. It was previously determined that VR 3D modelling (VR modelling) is suitable for design purposes [1]. Moreover, some studies report that modelling in VR software is easier to learn, more natural and intuitive to use and enables faster modelling in comparison with traditional computer-aided design (CAD) desktop 3D modelling software [2]. However, research regarding the use of VR in product design has been limited to the conceptualisation phase and design review. Moreover, previous design phase studies expressed doubt that VR tools can be used for mechanical design and engineering work as they lack the required accuracy and the capability to transfer the model into later stages of product development [3].  
This study aims to bridge this gap and investigate the usability of the VR tool in the product design stage and to research the possibility of converting and using the VR-created model in later phases such as detailing while ensuring an adequate level of accuracy. The study explores the possibilities the technology offers for the design of complex geometrical models. Given that VR supports easier navigation through the design environment (space), it should be simpler and faster for designers to create designs with complex curved geometries. It was investigated if objects created in VR can satisfy the required model quality and if they can be simply transferred to desktop applications, so that further detailing and editing of the model can be done seamlessly, quickly leading to simulation or manufacture and thus speeding up the development process. The aim was to extract the advantages and disadvantages of desktop CAD modelling and VR modelling in supporting the embodiment design starting from the product concept to its final form.  
This work consists of the following parts: (1) Background and objectives introduce the topics and relevant contributions related to CAD modelling, VR technology and itsdifferent applications in engineering design and states the objectives of this research; (2) Methodology includes the description of methods and tools used in the study, which consists of a preliminary study aimed at identifying key steps and evaluation criteria in the 3D modelling process, and an experimental research study which explores the modelling capabilities in VR environment; (3) Results of the study; (4) Discussion of the results, and (5) Conclusion.
# Improvements of Computational Ghost Imaging by Using Sequenced Speckle
## 2 Experiment Setup and Measurement Principle  
### Experiment Setup  
The experiments were conducted using a DMD-based computational ghost imaging (CGI) setup, specifically the Vialux(tm) DLP V- module V4100 board. The setup consisted of a 0.7-inch diagonal array comprising 1024 \(\times\) 768 micromirror elements with a pitch of 13.6 \(\upmu\)m, as illustrated in Figure 1. The diameter of a He-Ne laser beam was expanded using a beam expander and directed onto the DMD surface. The sequenced speckle patterns were then projected onto the object using the DMD, which had a refresh rate of 40 Hz. The programmed series of sequenced speckle patterns on the DMD approximated the light field's intensity distribution on the object plane. Each random speckle pattern provided a unique configuration of image intensities. In the experiment, a set of 1024 \(\times\) 768 digitally microscanned patterns was sequentially displayed on the DMD. These sequenced random speckle patterns passed through the object being imaged, which was positioned at a distance of 0.5 m from the DMD. The transmitted intensity signals were detected using a photodiode acting as a bucket detector (Thorlabs, PDA36A-EC). The system could be easily controlledand triggered using a data acquisition card. A computer was employed to generate the sequenced speckle patterns, control their exhibition, record the light intensity data from the data acquisition card, and reconstruct the image. The retrieved ghost image was reconstructed by correlating the intensity signals with the sequenced random speckle patterns. To underscore the feasibility and performance of sequential ghost imaging (SGI), we fabricated a laser-cut window-shaped transmitting object. Importantly, the window structure employed in our experimental setup simultaneously captured different target sizes. The depicted cross in the figure symbolizes detailed target information, while the four rectangles exemplify the imaging quality for relatively larger targets. This selection of targets serves to highlight the robustness of our sequential speckle method. The actual object profile is presented in Figure 1.
# Performance Evaluation of IMERG GPM Products during Tropical Storm Imelda
## 3 Results and Discussion
### Probabilistic Statistical Indices
Table 3 summarizes the probabilistic statistical indices of the performance of the Early-, Late-, and Final-run products. A comparison of the summarized data with an earlier study [44] conducted over the central United States showed similar values of POD (~0.91), slightly lower values of FAR (~0.33), and slightly higher values of CSI (~0.62) for fall months (September, October and November).
###### Abstract  
The multi-constellation Global Navigation Satellite Systems (GNSS) offers promising potential for the retrieval of real-time (RT) atmospheric parameters to support time-critical meteorological applications, such as nowcasting or regional short-term forecasts. In this study, we processed GNSS data from the globally distributed Multi-GNSS Experiment (MGEX) network of about 30 ground stations by using the precise point positioning (PPP) technique for retrieving RT multi-GNSS tropospheric delays. RT satellite orbit and clock product streams from the International GNSS Service (IGS) were used. Meanwhile, we assessed the quality of clock and orbit products provided by different IGS RT services, called CLK01, CLK81, CLK92, GFZC2, and GFZD2, respectively. Using the RT orbit and clock products, the performances of the RT zenith total delays (ZTD) retrieved from single-system as well as from multi-GNSS combined observations were evaluated by comparing with the U.S. Naval Observatory (USNO) final troposphere products. With the addition of multi-GNSS observations, RT ZTD estimates with higher accuracy and enhanced reliability compared to the single-system solution can be obtained. Compared with the Global Positioning System (GPS)-only solution, the improvements in the initialization time of ZTD estimates are about 5.8% and 8.1% with the dual-system and the four-system combinations, respectively. The RT ZTD estimates retrieved with the GFZC2 products outperform those derived from the other IGS-RT products. In the GFZC2 solution, the accuracy of about 5.05 mm for the RT estimated ZTD can be achieved with fixing station coordinates. The results also confirm that the accuracy improvement (about 22.2%) can be achieved for the real-time estimated ZTDs by using multi-GNSS observables, compared to the GPS-only solution. In the multi-GNSS solution, the accuracy of real-time retrieved ZTDs can be improved by a factor of up to 2.7 in the fixing coordinate mode, compared with that in the kinematic mode.  
multi-GNSS; tropospheric delays; IGS-RT; orbit and clock products; precise point positioning 1
# Improving the Robustness of Model Compression by On-Manifold Adversarial Training
## 4 Methodology  
Our main objective is to train a compressed DNN model robust to natural noise in data. This is a critical consideration for deploying DNNs in small embedded devices in IoT environments, where a small model size is desirable due to limited computation resources, and sensor noise occurrence can be relatively frequent.  
However, it is not an easy task to model the noise distribution in observations without strong assumptions, for example, that noises are approximately normally distributed [48], and such an assumption may not be appropriate in all data: also, prior knowledge about the noise distribution is often not available [49]. To deal with this issue, we propose using _on-manifold adversarial examples_ (abbreviated as adv-on, to be defined later) as the noise samples with the largest deviation. We use adv-on for two purposes:  
* To evaluate the robustness of compressed DNN models against natural noise.
* To improve the robustness of a compressed DNN model based on sparse coding with adversarial training.  
This makes a clear distinction of our work from the existing literature [17; 18; 19; 20] on robust model compression where the robustness has been considered against (regular) adversarial examples. As discussed in [PERSON] et al. [21], the regular adversarial examples (generated by, for example, FGSM or PGD as discussed earlier) are likely to deviate from the data manifold--the subspace that includes natural samples well. In this sense, we regard the regular adversarial examples as off-manifold (we denote them by off-manifold adversarial examples, adv-off).
## 3 Experimental Results and Analysis
### Dynamic Stress-Strain Curve  
Figure 6 shows the dynamic stress-strain curves of the temperature-water-coupled sandstone specimens under eight axial pressure conditions and no axial pressure condition in the SHPB test.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Axial Pressure (MPa)**} & **Specimen** & \begin{tabular}{c} **Dynamic** \\ **Number** \\ **Strength (MPa)** \\ \end{tabular} & \begin{tabular}{c} **Peak Strain** \\ **(\(\times 10^{-3}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Dynamic** \\ **Modulus of** \\ **Elasticity (GPa)** \\ \end{tabular} &
\begin{tabular}{c} **Average Strain** \\ **Rate (s\({}^{-1}\))** \\ \end{tabular} \\ \hline  
end{tabular} & 58.69 & 8.14 & 9.40 & 145 \\ \hline \end{tabular}
\end{table}
Table 1: Temperature–water-coupled sandstone SHPB test results.  
Figure 5: SEM images of sandstone under different axial pressures.  
Figure 6 shows that under different axial pressures, the dynamic stress-strain curve of sandstone after experiencing the temperature-water-coupled effect exhibits strong regularity. As the axial pressure increases, the cluster of the dynamic stress-strain curve of sandstone samples gradually shifts to the lower right, indicating a decrease in the peak stress value and an increase in the strain.