esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Vulnerability Assessment of Hybrid Mold Transformer Based on Dynamic Analyses
## 1 Introduction
Based on the numerical models, fragility curves were also constructed to evaluate the seismic vulnerability of the switchboard cabinets using comprehensive ground motions, and the seismic vulnerability obtained in this way was compared with the empirical data. In addition, a study by [PERSON] et al. (2018) presented an approach for estimating the seismic performance of large-scale Italian electric power system subjected to spatially correlated earthquake ground motions. The network flow theory was used to model the power flow allocation over the grid components, and the physical features and constraints of the power grid were considered. Accordingly, the overall vulnerability of the electric power system was evaluated, and the critical components of the grid system were identified. More recently, [PERSON] et al. (2019) investigated the seismic performance of an industrial frame-tank system using FE models. In this study, a braced frame supporting a cylindrical storage steel tank was selected and designed according to the AISC 360-10 code ([PERSON] et al., 2019) and AISC 341-10 code ([PERSON] et al., 2019). The numerical model was then implemented by FE platform OpenSees (Open System for Earthquake Engineering Simulation) ([PERSON] et al., 2019) and validated by using past experimental data collected by [PERSON] et al. (2019). The storage tank analyses were performed in consideration of the connectivity with the supporting structures to determine the acceleration floor spectra and maximum response of the tank. Moreover, fragility models were derived to evaluate the vulnerability of the case study as well as the effectiveness of the analytical approaches. Nonetheless, the number of studies relevant to the seismic vulnerability assessment of nonstructural elements, particularly electrical elements in buildings, still remains rather limited. In the present study, a simplified dynamic analytical model was proposed to assess the seismic vulnerability of a cast resin-type hybrid mold transformer. An analytical model simulating the hybrid mold transformer was composed of linear springs and plastic beam elements in consideration of the key features and dynamics characteristics of the transformer according to the shaking table test results. The effectiveness of the proposed model was validated with previous experimental data obtained from the shaking table tests with input acceleration time histories generated in accordance with the specifications recommended by the ICC-ES AC156 code ([PERSON] et al., 2019). Comprehensive time history dynamic analyses were performed with a variety of input earthquake ground motions considering the variation of the characteristics of the mold transformers including the effective stiffness and coil mass. Then, the fragility curves were derived to predict the exceedance probability of the hybrid mold transformer at specified performance levels corresponding to the damage states under earthquake load.
# Validity of Anchorage Zone Design for Post-Tensioned Concrete Members with High-Strength Strands
###### Abstract  
This paper evaluated the validity of a design method for anchorage zones in post-tensioned concrete members using Grade 2400 strands from American Concrete Institute (ACI) Building Code, ACI 318-19 and Association of State Highway and Transportation Officials (AASHTO) codes. For this purpose, the behaviors of anchorage zones in ten specimens were investigated and compared with ACI 318-19 and AASHTO codes, considering the jacking force and ultimate flexural load. The anchor shape, number of strands, and reinforcement details of the anchorage zone were considered as variables. The test results showed that ACI 318-19 and AASHTO codes could not predict where the maximum bursting force would occur. In addition, they calculated bursting force by summation of the pre-stressing forces from the individual strands, whereas the experimental results were not linearly proportional to the number of strands. The ACI 318-19 and AASHTO standards were inadequate to calculate bearing force of post-tensioned members using high-strength strands. Furthermore, additional stress in the anchorage zone occurred as the flexural load increased, which may have been caused by a failure in the anchorage zone. The experimental results indicated that the method used to predict stress and design of anchorage zone in current codes should be improved to consider stress distribution and additional stress in post-tensioned concrete members using high-strength strands.  
post-tensioned; high-strength strand; anchorage zone; bursting force; bearing force +
Footnote †: journal: Applied Sciences
## 2 The Peak Shrinking and Interpolation (PSI) Technique
### Detection of a Peak and Its Surrounding  
The rational behind the idea of splitting the signal into smaller slices, is to enable parallelization of the process, and looking at peaks closely, as the extent of the peaks varies considerably between slices. The zoomed window (the call-out) in Figure 5 shows slides overlapping (in this case by 1%), which helps to ensure that a peak point is not being missed by being on the border of a slice. In terms of identification of high peaks, comparison with threshold can be employed which can be considered similar to many other techniques [11]. Another approach for detecting high peaks is to find maximum point of each slice. An example of a particular example slice is shown in Figure 6.  
Figure 4: DSI-SLM PAPR reduction technique [20].  
Figure 5: Block diagram of proposed PSI technique [24].  
The location of the peak occurring is indicated as \(P_{index}\). In a similar way, as seen in Algorithm 1, the address or index for second point before and after the high peak on the left side and right side is indicated as \(L_{index}\) and \(R_{index}\), where \(L_{index}=P_{index}-SUR\) and \(R_{index}=P_{index}+SUR\) and \(SUR\) indicates the surrounding or skirt of a peak. In should be noted that, unlike previous techniques that consider peaks only by the raised point, here, a peak is defined by its surrounding sample values also. As seen in Algorithm 1, peaks and surrounding points are detected. It should be noted that the process of finding peak points indicated by \(P_{index}\), can be performed in two ways. The first approach is to enforce a threshold value, which is essentially a crest detection technique. This approach results in a matrix containing a number of points, and each point is addressed in terms of surrounding points  
the slice with address of \(N\) is considered for extraction (\(R_{index}=N\)). It should be noted that in the cases mentioned, the number of extracted points will be less than five, and when interpolating the processed peak back to the signal, careful addressing should be preformed.  
```
\(SUR\gets 2\); \(P_{index}\leftarrow\) index of the peak; \(L_{index}\leftarrow\) Left side border; \(R_{index}\leftarrow\) Right side border; if\(L_{index}\leq 0\)then \(L_{index}=1\); else \(L_{index}=P_{index}-SUR\); endif; if\(R_{index}\leq N\)then \(R_{index}\gets P_{index}+SUR\); else \(R_{index}\gets N\); endif;
```  
**Algorithm 1** Defining the borders of a peak (surrounding borders)
# A Numerical Investigation of Laser Beam Welding of Stainless Steel Sheets with a Gap
## 3 Beam Shape Optimization
Different parameter combinations of the secondary beam have been found within given technological restrictions, which improved the results of every tested scenario so much as to meet all of the quality requirements given in Table 2. To optimize the beam size, it was found that different parameters of the secondary beam had effects on certain aspects of the final weld bead. For example, focusing most of the available secondary beam power onto a \"slit\" in front of the primary welding beam improved the penetration depth, while widening the beam decreased the amount of incompletely filled groove seen in the cross-sections. Finally, the offset (i.e., distance between primary and secondary beam center in welding direction) was set to half the length of the secondary rectangular beam in scan direction, so that its rear edge (considering the weld direction to be forward-facing) coincided with the center of the primary working beam and thus only applied preheating. No substantial positive influence was found when altering this value, and no offset in the direction orthogonal to the weld was tested due to symmetry. In Figure 9, the result of simulation S2-opt is shown, where an optimized secondary laser beam was added to the conditions of EX2 to reach the quality requirements listed in Table 2. Three exemplary cross-sections and the longitudinal section from the simulation are given, together with the experimental cross-section that was already partly depicted in Figure 5, now with measurements being provided to quantify the improvements through the use of a secondary beam in terms of achieving the quantitative quality requirements on the weld bead. Figures 10 and 11 analogously show the results with an optimized secondary beam for different experimental conditions; all experimental parameters that are not explicitly given within the Figures can be found in Table 1.  
Details about the final secondary beam parameters used to fulfill all quality requirements are summarized in Table 3.  
The methodology presented here--using a well-calibrated, three-dimensional and transient CFD model to optimize a multidimensional field of industry-scale LBW process parameters--proved to be computationally affordable and helpful for process design. Figure 10: Longitudinal and cross-sections of simulation S3-opt, where a secondary laser beam was added to the conditions of EX3 and iteratively optimized. All measurements in (mm). Figure 9: Results of simulation S2-opt, where a secondary laser beam was added to the conditions of EX2 and iteratively optimized. All measurements in (mm).
## 2 Materials and Methods
### Fatty Acid Analysis by GC-MS  
The GC-MS analysis of fatty acids was done by the standard transesterification procedure followed by separation of ethyl esters (FAMEs) using a PerkinElmer Clarus 600 T GC-MS (PerkinElmer, Inc., Shelton, CT, DOOR) with a column Supelcowax 10 (60 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m thickness (Supelco Inc., Bellefonte, PA, USA). Temperature program: initial temperature 140 \({}^{\circ}\)C, then increase to 220 \({}^{\circ}\)C by 7 \({}^{\circ}\)C/min, the final temperature remained constant for 23 min up to the end. Injector temperature: 210 \({}^{\circ}\)C, split 1:24. Injected volume: 0.5 \(\upmu\)L. Fluent: He, 0.8 mL/min. MS conditions: ionization energy 70 eV, trap current 100 \(\upmu\)A, source temperature 150 \({}^{\circ}\)C. Mass scanning range \(m/z\): 22-395, with 0.14 scan/s. The identification of fatty acids was based on retention times, by comparison with retention times of fatty acids from a standards mixture (37 component FAME Mix, SUPELCO# 47885-U) and by comparing the mass spectra with those from the MS database (NIST MS Search 2.0). Individual fatty acid levels were expressed in % of area of the total identified fatty acid peak areas.  
\begin{table}
\begin{tabular}{c c c} \hline
**Botanical Origin/Group nr.** & **Commercial Samples: Labelled Quality/Sample Code** & **Authentic Samples Code** \\ \hline Sunflower (I) & Cold-pressed/SFO1, SFO2, SFO3 & aSFO4 \\ Pumpkin (II) & Cold-pressed/PO1, PO2, PO3 & aPO4 \\ Linseed (III) & Cold-pressed/LSO1, LSO2 & aLSO3 \\ Hempseed (IV) & Cold-pressed/HIO1, HO2, HO3 & aH04 \\ Soybean (V) & Cold-pressed/SO2, SO3 & aSO1 \\ Walnut (VI) & Cold-pressed/WO1, WO2, WO3 & aWO4 \\ Sea buckthorn (VII) & Cold-pressed/SBO2, SBO3, SBO4 & aSBO1 \\ Olive (VIII) & Extra virgin Italy (EVOO1), virgin Spain (EVOO2), Extra & - \\ virgin oil Greece (EVOO3), virgin Greece (EVOO4) & - \\ \hline \end{tabular}
\end{table}
Table 1: The botanical origin, the labelled quality, and codes used in all investigations and statistical classifications of commercial and authentic oils.
# Effect of Micropiles on Clean Sand Liquefaction Risk Based on CPT and SPT
###### Abstract  
Liquefaction is a hazardous seismic-based phenomenon, which causes an abrupt decrease in soil strength properties and can result in the massive destruction of the built environment. This research presents a novel approach to reduce the risk of soil liquefaction using jet-grouted micropiles in clean sands. The saturated soil profile of the study project mainly contains clean sands, which are suitable to more reliably employ simplified soil liquefaction analyses. The grouting is conducted using 420 microplies to increase the existing soil properties. The effect of jet grouting on reducing the potential of liquefaction is assessed using the results of the cone penetration test (CPT) and the standard penetration test (SPT), which were conducted before and after jet grouting by implementing micropiles in the project sites. According to three CPT-based liquefaction analyses, the [PERSON] method predicts the most effective improvement range of the factor of safety in the clean sand. The Boulanger and Idriss, and Eurocode methods show comparable evaluations. Results of the SPT-based analyses show the most considerable increase of the factor of safety following the Boulanger and Idriss, and NCEER approaches in the SP soil. CPT- and SPT-based analyses confirm the effectiveness of jet grouting by micropiles on enhancing soil properties and reducing the risk of liquefaction.  
soil liquefaction; micropile; cone penetration test; CPT; standard penetration tests; SPT; soil improvement; jet grouting; clean sand Article
## 3 Key Time-Domain NMR Methods
### NMR Cryoporometry
#### 3.2.3 NMRC Protocol  
The protocol of an NMR Cryoporometry experiment involves absorbing a liquid into pores; freezing the liquid, and then warming the sample. The NMR echo amplitude is monitored, so as to determine the amount of liquid that has melted as a function of temperature. This gives a measure of the pore volume vs. melting temperature, and hence, via the Gibbs-Thomson equation, the pore size distribution.  
The NMR measurement required is very simple, just a [\(\pi/2-\tau-\pi-\tau-\) Echo] NMR sequence, with the echo at an NMR 2\(\tau\) measuring time of typically 500 us to 20 ms, so as to avoid any signal from solid ice. The amplitude of the NMR Echo is recorded as a function of temperature. The Echo is  
Figure 5: NMR Cryoporometric Melting Point Depressions, for different pore geometries: Sol-Gel (spherical), SBA-15 (hexagonal) and MCM-41 (hexagonal), plotted against inverse pore diameter, as determined by gas adsorption [50]; This figure was published in Progress in NMR Spectroscopy, 56, 1, [PERSON], Studies of nano-structured liquids in confined geometry and at surfaces, Page 80, Figure 6, Copyright Elsevier (2010).  
polynomial fitted, and the polynomial solved to determine the peak amplitude. We can then apply the Gibbs-Thomson equation and the Rahman-Strange transformation to obtain the pore volume distributions, as a function of pore diameter.  
A recent calibration is frequently employed, as appropriate to water in Sol-Gel silica, at an NMR 2\(\tau\) measuring time of 2 ms [50]:  
\[\mathrm{k}_{GT}\quad=580\ \mathrm{K.\AA}\]
## 3 Results and Discussion
### Optimum Operational Parameters of CSR  
Keeping optimization criteria in the view, there were 16, 18 and 16 solutions available for response parameters L* (light), L* (medium) and L* (dark) with their corresponding MC respectively. However, only the solutions at the highest desirability were considered. The response surface quadratic model suggested the optimum feeding capacity at 15 kg and thermal oil flow rate at 0.01 Ls\({}^{-1}\) for all three responses whereas the optimum conveyor speed was predicted at 1.42, 0.99, and 0.69 rpm for light, medium and dark roast conditions respectively. The optimum percentages of MC were predicted 1.33, 1.72, and 1.98% respective to L* values of 53, 48.5, and 43 against the selected solutions.
# A Deep Learning Approach for Intrusion Detection Systems in Cloud Computing Environments
## 4 Experimental Result  
In this section, we delve into the findings that highlight the remarkable accuracy of a deep learning model based on the CNN blocks model in classifying network traffic. The study employs rigorous data analysis techniques, specifically convolutional neural network modeling, to provide a solid foundation for data interpretation. The reshaped CSE-CICIDS2018 dataset, consisting of seven data files divided into different scenarios, was meticulously formatted for CNN input. The model underwent thorough training and analysis to ensure optimal performance.  
The performance of the model was evaluated by differentiating between various categories of network traffic. Performance metrics were then calculated for each scenario. The resulting confusion matrix, precision, recall, F1-score, and overall accuracy were significantly high, which indicates an effective classification.
# Transformer Self-Attention Change Detection Network with Frozen Parameters
###### Abstract  
The purpose of change detection is to recognize changed areas from a pair of two remote sensing images. However, since change areas often include multiple terrain features, this demands enhanced feature extraction capability from the model. This paper proposes a frozen-parameter Transformer self-attention change detection network (ZAQNet). The network integrates four innovative modules: a GIAU (Generalized Image Attention Unit) which can effectively fuse the features of two remote sensing images and accurately focus on changing areas; a GSAU (Global Spatial Attention Unit) which performs self attention processing in the image spatial dimension to enhance the model's ability to capture global change information; a GSCU (Global Semantic Context Unit) which performs self-attention operations in the channel dimension to enhance the model's attention to feature maps containing changing information; and a PRU (Patch Refinement Unit) which extracts and refines spatial position information from the underlying feature map, optimizing the restoration effect of the feature map. The experiments on the BTRS-CD and LEVIR-CD datasets show that ZAQNet performs excellently in change detection tasks. Among them, the change detection index F1 and IOU are better than the comparison model. These results fully demonstrate the superiority, robustness, and generalization ability of ZAQNet in change detection tasks and provide an efficient and reliable solution for remote sensing image analysis.  
chapter 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol., pp. 2018-2020, 2018. External Links: Document Cited by: SS1.
# Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks
## 2 Literature Review
### Spectrum Handoff
#### 2.2.2 Reactive Handoff  
Authors in [18], projected a reactive handoff approach named as dynamic frequency hopping communities (DFHC). DFHC is believed to enhance QoS demands of CRUs while giving suitable and reliable sensing of the spectrum for assuring the PU defense. In this research, a wireless regional area network cell or a CRU examined the accessibility of the new objective channel while transmitting on an ongoing channel. To avoid intervention with the PUs, the CRU maintains its communication on the selected channel and opens sensing for other channels. Authors in [50], proposed a reactive handoff approach which uses an M/G/1 queuing model [18] to examine the channel utilization of CRN. In this approach, a user can concurrently use various vacant channels for transmitting CR. In [21], the authors proposed an M/G/1 queuing model to resolve the spectrum handoff problem. The interarrivals to the M/G/1 queue are modeled and shared various vacant channels, for instance, the overall interarrivals from all CRUs. The handoff delay occurs at the maximum level when the PU arrived, hence the handoff judgment is completed. In [51], author presented a framework comprises of probabilistic algorithms, and some other novel approaches including CRU clustering and PRP M/G/1 queuing to attain better competence in spectrum handoff in a CRN. Authors claim that the proposed strategy performs better than existing schemes in terms of accuracy in sensing the right channel, handoff latency, and energy consumption. The authors of [52] projected a reactive handoff scheme for Zigbee in which a CRU can access various channels via incessantly sensing and spectrum handoff. The presented sensing and handoff technique provides better results in a situation where the noise is at a minimal level. However, as an energy detector, it cannot distinguish between noise and PU signal, hence a false alarm can be activated which produces the useless handoffs.
## 3 Results and Discussion
### Analytical Results  
The analytical results for samples with horizontal isotropy planes (\(\beta=0^{\circ}\)) are presented in Section 3.1.1. Sections 3.1.2 and 3.1.3 discuss the influences of single elastic parameters on the developing radial strains \(\varepsilon_{r}\) and the vertical strains \(\varepsilon_{z}\) respectively, for samples with inclined (\(\beta=45^{\circ}\)) and vertical isotropy planes (\(\beta=90^{\circ}\)).
## 2 System Reliability Evaluation Methods
### System Reliability Evaluation Method Based on the Modified AHP
#### 2.2.4 Calculations of Weight Vector and Largest Eigenvalue  
The weight vector \(\mathbf{W}=(\omega_{1};\omega_{2}\cdots\omega_{n})\) and the largest eigenvalue \(\lambda_{max}\) of the judgement matrix are calculated according to Equations (9) and (10):  
\[\omega_{i}=\frac{\sqrt[n]{\prod\limits_{j=1}^{n}a_{ij}}}{\sum\sqrt[n]{\prod \limits_{j=1}^{n}a_{ij}}}, \tag{9}\]  
\[\lambda_{max}=\frac{1}{n}\sum\limits_{i=1}^{n}\frac{\left(\mathbf{A}\mathbf{W}\right)_ {i}}{\omega_{i}}. \tag{10}\]
# Applications of Machine Learning and Computer Vision in Industry 4.0
## 2 Industry 4.0
### Machine and Deep Learning in Industrial Processes  
In recent years, the real possibilities and capabilities of artificial intelligence--AI--have become more and more visible. By combining advanced intelligent technologies, AI enables devices to perform tasks that were previously only possible with the assistance of human intelligence. It is advancing rapidly and exponentially, and soon there will be no limit to what AI can achieve in many industries and functions [9].  
As a result of digitalization and the advent of the Industry 4.0 concept, it is undergoing a significant transformation, and AI is one of the tools bringing about this change. AI has evolved in its capabilities over the years and has found application in various areas of industrial manufacturing and automation. The integration of AI with other advanced technologies in the industrial ecosystem will enable manufacturers to gain a strong foothold in the Industry 4.0 concept. Automation as a part of manufacturing is implemented through systems such as the programmable logic controller (PLC), distributed control system (DCS) and SCADA system. However, smart manufacturing is the interconnectedness of advanced AI, Industrial Internet of Things (IIoT) and analytics technologies embedded in these traditional systems to increase manufacturing automation, improve process quality, improve process optimization and achieve higher cost savings [9].  
The introduction of AI into industrial manufacturing will result in machines no longer requiring special programming to perform their tasks. Instead, these machines will have the potential to learn from their own experiences. This concept has been gaining a lot of momentum in recent years due to its ability to leverage the vast amount of data that can be generated as a result of new industrial concepts such as the Industrial Internet of Things--IIoT. In order to be competitive, it is essential that businesses develop the skills that can help them exploit the opportunities of digitalization. These applications of AI vary for different manufacturing industries [10].