esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The results obtained using this technique cover a maximum period of 180 days (6 months) from March to August 2019. To compute the \(S_{4}\) variations maps as explained in Section 2.2, it is required to obtain CYGNSS data from at least 60 days before the first day to be studied, so the average \(S_{4}\) maps were computed from 1 January to 28 August.  
Then, the \(S_{4}\) variations (\(\Delta S_{4}\)) maps were computed for every day between 1 March to 28 August, 180 days in total. As is shown in Figure 2, there is a temporal variation of the region covered after doing the LT filtering (from 0 h to 6 h) and the seasonal detrending. Figure 8 shows this map for one of the days with maximum coverage, almost ranging from \(-40^{\circ}\) to \(+40^{\circ}\).  
The image shows the results of the detrended \(\Delta S_{4}\) values for the selected day over oceanic regions with the values ranging from \(-0.06\) to \(0.06\) as indicated in the color scale. Blue stars mark the epicenter of all earthquakes happening during the period from \(-6\) days to \(+6\) days from the current day \(16\) March.  
Figure 9 shows the \(S_{4}\) variations map for a day in which the coverage is minimum, \(30\) March, in this case corresponding to the southern part. It can be observed that still several regions can be used to correlate ionospheric scintillation anomalous variations with earthquakes.
# Network Attack Path Selection and Evaluation Based on Q-Learning
## 5 Simulation Evaluation  
### Establishment of Simulation Environment  
In order to verify the feasibility and effectiveness of the proposed algorithm, the Supervisory Control And Data Acquisition (SCADA) power distribution system was selected to establish a network attack model based on FPN, as shown in Figure 2. The model mainly includes Demilitarized Zone (DMZ) domains, work stations, and control centers. The network area is divided by installing firewalls, and communication rules between domains are formulated to ensure that external access cannot reach the intranet area. The specific access rules are introduced as follows: (1) The data collected by the terminal can only be accessed to the master station through the DMZ domain; (2) The workstation can realize two-way communication with the DMZ domain and the control center; and (3) The DMZ domain and control center can only communicate with workstations. Before quantitative modeling, one makes the following assumptions about the attacker's capabilities: (1) The attacker understands Direct-Attached Storage (DAS) and has the latest DAS vulnerability information and (2) Attackers can deliberately and effectively use social engineering to attack.  
Figure 2: Attack model. t1: Input validation error; t2: Boundary condition error; t3: Buffer error; t4: Information leakage; t5: Permission acquisition and access control.  
Depending on the ability of the attacker, the attacker can launch attacks on different system hosts. There are two most common modes:  
1. The attacker uses the power distribution terminal equipment as the access point to further invade the DMZ area. By invading the DMZ area, The system's security vulnerabilities are continuously used to increase the authority until entering the control center application server because of the invasion. Deliberate tampering of business data will cause greater losses to the system. At this time, the attacker's target is Hs.
2. The attacker uses the power distribution terminal equipment as the access point to invade the DMZ area and continuously use the system security vulnerabilities to increase the authority until entering the operator's Human Machine Interface (HMI). The business data is randomly tampered on the HMI side, because the attacker does not have detailed physical power grid parameters and data. At this time the attacker's target is H4.  
At the same time, in order to analyze the risks caused by network attacks to the power CPS, the IEEE14-node system shown in Figure 3 is selected as the experimental model. Nodes 1, 2, 5, and 7 of the system are distributed power sources, and nodes 4, 8, and 13 are important load nodes. The total power generation capacity of the distributed power generation is 3.7 MW, and the sum of the power requirements of each load is 3.19 MW.
## 3 Microwave NDT Techniques
### Reflectometry Method  
The reflectometry method involves transmitting microwave signals towards a specimen and analyzing the reflected waves due to discontinuities [98]. In MNDT, such discontinuities could be due to the presence of defects or changes in electrical properties. Variations in the amplitude and phase of the reflected signals are crucial to determining the discontinuities' location and nature. Furthermore, this method has two types: frequency-domain reflectometry (FDR) and time-domain reflectometry (TDR). In FDR, continuous wave signals within a frequency range are transmitted through the specimen, and spectral information of the reflected signals is collected to characterize the specimen's material. In TDR, a fast pulse is sent through the specimen, and the time delay of the signals is analyzed to detect defect locations. Each method has its pros and cons.  
An example of the reflectometry method is that of [PERSON] et al. [98], which presented a K-band microwave reflectometry tool introduced into a robot designed to traverse pipelines. This robot employs a systematic scanning method to detect and assess defects and external wall degradation in a high-density polyethylene (HDPE) pipe. The specific pipe examined had a diameter of 150 mm and a wall thickness of 9.8 mm. The investigation in [98] shows that signals from minor defects were masked by fluctuations due to the intense background signal emanating from the metal surface of the plastic-lined metal pipe.  
Similarly, [PERSON] et al. introduced a novel NDT technique that uses a TDR for detecting delamination in metal-backed GFRP laminates [99]. By utilizing the Inverse Fast Fourier transform (IFFT) algorithm to analyze reflections in the time domain, delamination was shown to be effectively identified by examining the magnitude of signal reflection at specific time steps. Experimental validation demonstrated the method's ability to evaluate delamination at depths as low as 1 mm, marking a significant advancement in the rapid and precise inspection of GFRP laminates [99]. In a continuation to their previous work, [PERSON] et al. [100] developed a microwave TDR technique that involves scanning a coated surface with an open-ended rectangular waveguide (OERW), and then examining the time domain reflections by connecting the OERW to a vector network analyzer (VNA). The proposed method, shown in Figure 10, accurately estimated variations as small as 0.1 mm in the thickness of the dielectric material beneath each scanned area.  
Another work by [PERSON] et al. [101] introduced an innovative MNDT technique for detecting material defects utilizing TDR, wavelet decomposition analysis, and artificial neural networks. This technique had notable broad applicability across various dielectric materials with varying thicknesses, demonstrating significant versatility and generalization capabilities in defect detection.
## 2 Related Work
### Neural Radiance Fields (NeRFs)  
Neural Radiance Fields, commonly referred to as NeRFs, represent a novel approach in the realm of 3D reconstruction, employing deep neural networks to model volumetric scenes using sparse sets of 2D images [28]. This method capitalizes on the capability of neural networks to encode complex data relationships, thereby allowing for the generation of intricate 3D scenes. One of the main attractions of NeRFs is their ability to produce high-fidelity and continuously viewable scenes without the necessity for mesh-based representations, setting them apart from conventional 3D modeling techniques.  
NeRFs have heralded significant advancements in the domain of 3D modeling and reconstruction. By leveraging a scene's sparse radiance samples and optimizing over viewing angles and light directions, NeRFs synthesize novel views with impressive accuracy. Their applications span a diverse range, from virtual reality and augmented reality to film production and architectural visualization. Within an industrial context, NeRFs can be especially advantageous for product modeling and prototyping, offering a more detailed and adjustable representation compared to traditional methods [29].  
While NeRFs present remarkable capabilities, their implementation, particularly in large-scale industrial scenarios, presents challenges. The computational intensity of NeRF algorithms, given their reliance on deep neural networks, can lead to longer rendering times, which might be infeasible in real-time applications [30]. Additionally, the quality of the reconstruction can sometimes be contingent on the diversity and number of 2D input images. Researchers are actively exploring methods to enhance the efficiency of NeRF implementations and ensure consistent quality across diverse input conditions. Recent research has aimed at improving the computational efficiency of NeRFs, leading to variants like FastNeRF and MicroNeRF that target real-time and embedded applications [31]. There is also ongoing exploration into combining NeRFs with other 3D reconstruction techniques, aiming to harness the strengths of multiple methods. Given the rapid advancements in the field, NeRFs and their derivatives are poised to redefine the landscape of 3D modeling and visualization in the forthcoming year.
## 4 Materials and Methods
### Recombinant DNA Work and Gene Expression  
Cloning of plasmids was done in _E. coli_ DH5\(\alpha\) using PCR-generated fragments that were purified using the NucleoSpin kit (Macherey-Nagel, Duren, Germany). Oligonucleotides were ordered from Metabion GmbH (Planegg/Steinkirchen, Germany) (Table 2). For plasmid construction standard PCR, restriction and dephosphorylation reactions [75] were performed as well as Gibson Assembly [76]. Transformation of _E. coli_ was performed via the RbCl method [42]. Cloned DNA insert fragments were verified by sequencing. Transformation of _C. glutamicum_ was performed via electroporation using a Gene Pulser Xcell(tm) (Bio-Rad Laboratories GmbH, Munich, Germany) at 2.5 kV, 200 \(\Omega\) and 25 \(\upmu\)F [74]. For expression of CrtR in the pTEST vector (NA, Tables 1 and 2) and the production of His-tagged CrtR from various organisms in the pET16b vector (HN, Tables 1 and 2), the respective genes were amplified from chromosomal DNA using primer pairs NA25/26 and HN83/HN84 for _C. glutamicum_, NA27/28 and HN85/HN86 for _C. callunae_, NA31/32 and HN87/HN88 for _A. jensenii_, NA33/34 and HN89/HN90 for _P. nicotinovorans_, NA39/40 and HN93/HN94 for _M. luteus_ and NA41/42 and HN95/HN96 for _P. chlorophenolicus_, respectively. The purified PCR products were cloned into pTEST restricted with _BamHI_ and pET16b restricted with _Ndel_ using Gibson assembly [76], respectively. Chromosomal DNA was extracted from DSMZ (see Table 1).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Oligonucleotide (\(\mathbf{5^{\prime}}\)–\(\mathbf{3^{\prime}}\))** \\ \hline NH45 & CATGCCTCGCAGCTGACTACTAGAGAAGGCCCTTCAGATGGAATTCTGAACAGATTTTCAA \\ NH46 & GTTGCGTGGCAGTTATTATCCCCAACGGGAACAGAG \\ NH47 & AACTGCACCAGACAAGAAGGCCTTCAAGTGTACAGCTTAGAGGTGTACAGGTGTACAGGTGTACAGGTGTACAG \\ NH48 & ATGCAGCTGACCGGGGAGAGAGGGCCCTTACAGGGGTACGGGTACAGGTGTACCGCTATGGTAC \\ NH49 & ATGGAAATTGCAGCTGACCGGGGAAAGGCCCTTACAGAGGTGTACCGCAAAAGCAA \\ NH50 & GCATGCCTGACAGGTCGACACGTAGAGACAGAGAGAGAGAGAGCCCCTTACGGGGAAAGCAA \\ NH51 & GTTCGTTGGCGCAGTTG
## 3 Methodology  
### System Design and Configuration  
In order to create an integrated energy storage system, battery energy storage systems (BESSs) and hydrogen energy storage systems (HESSs) must be combined into a comprehensive framework. This process includes detailing the technical specifications of both BESSs and HESSs, as well as developing an integration architecture that ensures these systems function cohesively within the building's energy grid. The architecture features advanced control systems equipped with sensors and communication protocols to optimize performance, manage efficient energy storage, facilitate smooth charge/discharge cycles for BESSs, and oversee effective hydrogen production and usage for HESSs. This integrated strategy aims to improve energy management and sustainability for the Engineering 5 Building (Building Number 120) at Kangwon National University (Figure 1):  
* Technical Specifications for BESS: Details about the battery energy storage systems (BESSs) used in this research are provided according to their lifespan, energy density, capacity, and charge/discharge efficiency. Typically, lithium-ion batteries are chosen for their high energy density and efficiency. The technical specifications include nominal voltage, capacity (in kWh), maximum discharge rate, and cycle life. These specifications are essential to guarantee that the BESS can supply the energy needed for Kangwon National University's Engineering 5 Building.
* Technical Specifications of HESS: The hydrogen energy storage systems (HESSs) are defined by their hydrogen production rate, storage capacity, electrolysis efficiency, and fuel cell performance. The system produces hydrogen via electrolysis, stores it, and later converts it back to electricity using fuel cells. The effectiveness of the electrolysis process, storage pressure, hydrogen storage capacity (in kg), and fuel cell power output are important parameters. The successful integration of HESS with the building's energy management system depends on certain specifics.
* Integration Architecture and Control Systems: Integrating BESS and HESS involves a complex architecture that manages their interaction with the building's energy grid. The control systems are designed to optimize energy storage and discharge cycles for BESS and manage hydrogen production and utilization for HESS. For real-time monitoring and control, the architecture consists of controllers, sensors, and communication protocols, guaranteeing a smooth integration and optimizing the advantages of both storage technologies.  
Figure 1: Kangwon National University Samcheok Campus.
## 2 Materials and Methods
### Statistical Analysis  
Descriptive statistics of the data obtained through counts, percentages, means, and standard deviations (SD) were performed, depending on the nature (qualitative or quantitative) of the variable. The intra-operator concordance was evaluated in 25 random samples (45.45% of the sample) with the intraclass correlation coefficient (ICC). Qualitative data were analyzed with [PERSON]'s exact test. Quantitative data were subjected to normality tests (Shapiro-Wilk test with Lilliefors correction) and, later, to the Mann-Whitney U test and Kruskal-Wallis test, for independent samples, and the Bonferroni post hoc test was used for the comparisons between study groups. The effect of the type of tooth and the depth of the dentin was analyzed with the ANOVA test of repeated measures (with Bonferroni multiple comparisons adjustment), including analysis with the test of inter-subject effects, test of intra-subject effects and the Geisser-Greenhouse (G-G) test for paired comparisons. Data were analyzed with the SPSS(tm) version 25 for Windows program (IBM Corp. Released 2017. IBM(r) SPSS(tm) Statistics for Windows, Version 25.0. IBM Corp., Armonk, NY, USA) with a 95% confidence level (\(p\) value < 0.05) and asymptotic or bilateral significance (except [PERSON]'s test, with exact significance).
## 4 Discussion
It is suggested that practitioners utilize the coordinate data from the GPS devices to ensure instances of straight-line running are selected when applying the current findings to field-based running style analysis [39]. Treadmill running was selected in the study design to control the running speed and to induce intra-subject variations in running kinematics by altering the speed between each trial. As a result, ground reaction forces were not captured due to the absence of an embedded force platform. Considering the residual effects of the running speed observed, especially in the category 2 fixed effect variables, understanding the associated changes in joint/segment kinetics with each running speed and how they differed across a wider variety of running styles would have provided a more comprehensive insight into the effect of different running styles on the GPS-based accelerometer accelerations. The outcome of this study was favorable, considering the significant associations found with relatively small participants. In the future, having a bigger sample size would allow for further verifications of the outcome of this study.
# A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information
## 2 Bearing Fault Diagnosis Method Based on Knowledge Reasoning
### Construction of Multi-Local Fault Diagnosis Model Based on Bi-LSTM  
The structure of the multi-local fault diagnosis model based on Bi-LSTM proposed in this paper is shown in Figure 1. The multi-local fault diagnosis model based on Bi-LSTM firstly uses the noise reduction encoder to clean the vibration signal and current signal of the industrial motor bearing, respectively, to obtain the vibration reconstruction signal and the current reconstruction signal. Afterward, the reconstructed vibration signal and the reconstructed current signal are input into the local fault diagnosis model based on Bi-LSTM and the attention mechanism, and the characteristic information of the reconstructed signal in each fault state is deeply mined to form a local diagnosis decision. Finally, the local diagnostic decisions are fused using the D-S evidence theory to obtain the final diagnostic results.
## 6 Experimental Results
### Experimental Results  
This section is mainly to test the performance of the QWDO algorithm for solving the problem of UCAV path planning. In this section, a total of two test instances were carried out. In the simulation experiment, the dimension \(D\) and the maximum number of iterations _Maxgen_ are used as the two control variables. We look at the performance of QWDO algorithm as compared with other optimization algorithms, for instance, quantum bat algorithm (QBA) [24], quantum particle swarm optimization (QPSO) [25], wind driven optimization (WDO), bat algorithm (BA), particle swarm optimization (PSO), and differential evolution (DE). All of the test cases are carried out with 50 independent experiments. We use the battlefield environment parameters described in [5]. UCAV starts at (10,10) and the destination is (55,100). In this battlefield environment, there are five threat centers. Table 8 presents information about known threats for the first test instance. Unmanned combat air vehicle (UCAV) path planning aims to calculate the optimal or suboptimal flight path. When the dimension of the algorithm is not the same, the results will be different. Table 9 shows the mean results, the best fitness value and the worst fitness value between the algorithms of 50 independent runs. In the following tables, bold results indicate that the algorithm performed the best. From Table 9, we see that the mean normalized optimization results of DE algorithm performed the best in \(D=5\) and \(D=10\). In the rest of the cases, the mean normalized optimization results of QWDO algorithm performed best. We can see that the best-normalized optimization results of DE algorithm performed the best in \(D=5\), and in the rest of the cases, the best normalized optimization results of QWDO algorithm performed best. As can be seen in Table 9, the worst normalized optimization results of QWDO algorithm is the best, except in \(D=15\) and \(D=25\). In summary, the performance of QWDO algorithm is better than other optimization algorithms. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Constant inertia & 0.7298 \\ The first acceleration coefficients & 1.4962 \\ The second Acceleration coefficients & 1.4962 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The parameters setting of particle swarm optimization (PSO). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Threat Center (km)** & **(45,50)** & **(12,40)** & **(32,68)** & **(36,26)** & **(55,80)** \\ \hline Threat radius (km) & 10 & 10 & 8 & 12 & 9 \\ Threat grade & 2 & 10 & 1 & 2 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Information about known threats for the first test instance. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Mutation scale factor & 0.2 \\ Crossover probability & 0.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The parameters setting of difference evolution (DE).
## 1 Introduction
In the domain of concrete T-beams, [PERSON] [24] conducted full-scale blast tests on prestressed T-beams and identified localized shear failure of the concrete as the primary failure mode. [PERSON] [25] conducted model tests on prestressed T-beams simulating terrorist explosions on bridge decks, noting that with increasing explosive charge, the damage to prestressed steel tendons intensified, the frequency of anchorage failures increased, and the steel tendons ultimately fractured. As for steel box girders, [PERSON] [26] analyzed the damage modes of various components of steel box girders when car bombs were detonated inside them, suggesting that measures such as the proper arrangement of stiffeners, rebar of critical components, and installation of blast-resistant layers are effective in enhancing the blast resistance of bridges. [PERSON] [27] analyzed the dynamic behavior of a cable-stayed steel box girder bridge under different TNT charges and blast locations, highlighting that the detonation height significantly influences the damage modes and plastic deformation of the main girder. In terms of deck systems, [PERSON] [28] performed large-scale explosion tests to determine the resistance of composite concrete bridge decks when subjected to explosive charges at close range. Currently, the research on the blast damage effects of bridge girders primarily focuses on steel box girders and concrete T-beams, with relatively limited studies addressing the blast damage behavior of multi-chamber concrete box girders. Furthermore, most of these studies rely on numerical simulations, with a lack of corresponding experimental investigations. In light of the proven effectiveness of polyurea blast protection in masonry and steel structures, there is a pressing need for research on its application in the field of bridge engineering. However, the effectiveness of applying polyurea protective coatings to enhance the blast resistance of concrete box girders in self-anchored suspension bridges remains unclear at present. In this study, three blast tests were conducted on two identical scaled-down (1:3) specimens of a typical single-box, triple-chamber concrete box girder, based on an actual bridge engineering background. The tests involved two 3 kg of TNT charges and one 5 kg TNT charge. The specimens were designated as G (box girder without polyurea), PCG (box girder coated with polyurea, first detonation), and PCGR (box girder coated with polyurea, second detonation). A comparative analysis was performed to examine the damage characteristics and dynamic response of specimens G, PCG, and PCGR. Simulations were performed utilizing the LS-DYNA software to investigate the damage behavior of concrete box girders coated with polyurea under near-field blast loads. The blast response of polyurea-coated wide box girders was analyzed considering variations in standoff distance, charge position, and detonation method. The findings provide insights that could serve as a reference for the blast-resistant design of bridges. The technical flowchart of this study is shown in Figure 1.