esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on an Improved Method for Galloping Stability Analysis Considering Large Angles of Attack
## 3 Verification of the Critical Wind Speed of Galloping
### Comparison and Analysis of the Theoretical Results  
The measured tri-component force coefficients shown in Figure 7 were processed according to the method described in the section on the theoretical solution. Then, the galloping force coefficients, which are shown in Figure 8, were calculated with Equation (18) and the Den Hartog criterion with the processed data. It can be seen in Figure 8 that with wind attack angles within the range of 0\({}^{\circ}\)-5\({}^{\circ}\), the values of the galloping force coefficient calculated with the dC\({}_{\rm V}\)/d\(\alpha\) criterion and the Den Hartog criterion were very close. With wind attack angles within the range of 5\({}^{\circ}\)-25\({}^{\circ}\), the error of the galloping force coefficients calculated with the two criteria gradually increased, but it was still relatively small. When the wind attack angle was larger than 25\({}^{\circ}\), the galloping force coefficients obtained with the Den Hartog criterion were all greater than those obtained with the dC\({}_{\rm V}\)/d\(\alpha\) criterion, and the difference was huge, which is consistent with the conclusions obtained in the section on the theoretical solution.  
Figure 6: H-shaped section: (**a**) wind tunnel test on the segment model; (**b**) parameters of the cross section and direction of the wind attack angle.  
Figure 7: Tri-component force coefficients.  
Considering that the curve of the measured tri-component force coefficient contained discrete data with increases of 5\({}^{\circ}\), errors could easily occur in the curve-fitting and derivation process, which would distort the calculation results. Therefore, the calculated galloping force coefficient under working conditions of 70\({}^{\circ}\), which had a relatively small difference, was selected to calculate the critical wind speed of galloping. Then, the calculated critical wind speed of galloping was compared with the measured critical wind speed of galloping. The wind speed-displacement curve at a wind attack angle of 70\({}^{\circ}\) is shown in Figure 9.  
According to the results in Table 2, the (dC\({}_{\text{V}}\))/d\(\alpha\) method had a small error between the calculated critical wind speed of galloping and the measured critical wind speed at large angles of attack, and the result was more conservative, which could prove the validity and reliability of the (dC\({}_{\text{V}}\))/d\(\alpha\) criterion. In addition, to improve the calculation accuracy and make the calculation results more reliable, the amplitude of the variations in the wind attack angle should be reduced. For example, a force measurement test was performed for every additional 1\({}^{\circ}\) in the wind attack angle, and enough samples of the tri-component force coefficient were collected in the wind tunnel tests to ensure--to some extent--that the calculated parameters were not distorted during the curve-fitting and derivation process.  
Figure 8: Galloping force coefficients.  
Figure 9: Wind speed–displacement curve.
## 5 Results and Analyses
### Prediction Models
The basic findings are consistent with research showing that the sampling techniques improved the overall performance [8; 9]. Regarding the overall results of the datasets, mcMMO and mct datasets achieved the highest prediction accuracy in both the third and fourth scenarios. Tables 12-15 present the results of AUC for prediction models across seven datasets in the first, second, third, and fourth scenarios, respectively. The prediction accuracy in terms of AUC measurement was evaluated, and the results of each scenario were presented separately in these tables considering the following aspects. First, the performance of the investigated prediction model was compared with the baseline, which is based on the dependent variable only (i.e., change-proneness) and predicts the mode value of this variable. Second, the best model in each dataset was identified (the highest AUC) using boldface. Third, the best model in all datasets was identified using boldface with underlined values. Finally, we investigate the impact of applying ensemble FS, sampling, and both ensemble FS and sampling techniques on the performance of prediction models, and the best model to predict change-proneness was determined. Figure 3: (a) Box plot of the AUC for prediction models on the first scenario. (b) Box plot of the AUC for prediction models on the second scenario. (c) Box plot of the AUC for prediction models on the third scenario. (d) Box plot of the AUC for prediction models on the third scenario.
# Analysis of the Most Relevant Factors for Routing in Internet of Space Things Networks
## 3 Methodology
### Evaluation Metric  
The \"Packet Delivery Ratio\" (PDR) was the main routing metric used in this paper to measure the impact of the different considered factors previously described. This metric measures the ratio between the received and sent packets, as shown in (2)  
\[\text{PDR}=(\text{Received\_Packets\_At\_Destiny})/(\text{Send\_Packets\_In\_Sender\_Node}) \tag{2}\]  
Note that although there are other relevant routing performance metrics such as end-to-end delay or overhead (among others), PDR provides a general assessment of routing performance, in the sense that a properly working routing algorithm will provide a high PDR.
## 5 Cumulative Damage Model
### Strain-Cycle Relationship of BRBs  
[PERSON] (1962) and [PERSON] (1966) defined the relationship between the number of cycles until the failure, \(N_{\!f}\), and the strain amplitude, \(\Delta\epsilon_{t}\), which leads to fatigue failure, as given in Equation (13). Usually, this relationship is derived from experimental results or empirical formulas based on statistical data. The fatigue life of BRBs, manufactured in Japan, has been studied extensively under different loading scenarios such as the shaking table test ([PERSON] and [PERSON], 1968), the constant amplitude fatigue test ([PERSON] and [PERSON], 1968), the BRB frame shaking table test under random amplitude ([PERSON] and [PERSON], 1968), and the truss frame test under gradually increasing amplitude ([PERSON] and [PERSON], 1968). [PERSON] et al. (2016) observed that the fatigue properties of BRBs are not considerably changed for different types of steel material, wherein the approximate strain-failure cycle relation is given as below,  
Figure 14: Probable multi-hazard scenarios in the target building lifetime.  
\[\Delta\varepsilon_{t}[\%]=0.5N_{f}^{-0.14}\ (\Delta\varepsilon_{t}<0.1\%) \tag{13}\] \[\Delta\varepsilon_{t}[\%]=20.48N_{f}^{-0.49}(0.1\%\leq\Delta \varepsilon_{t}<2.2\%)\] \[\Delta\varepsilon_{t}[\%]=54.0N_{f}^{-0.71}(\Delta\varepsilon_{t} \geq 2.2\%).\]
# Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things
## 7 Conclusions and Future Works  
Energy conservation is important role in Internet of Things (IoT). Therefore, routing plays an essential role in IoT. We proposed Energy and Delay Aware Data aggregation in Routing Protocol (EDADA-RPL) for IoT. It has two processes, namely parent selection and data aggregation. The parent selection process uses the routing metric residual energy (RER) to select the optimal parent for data transfer. The data aggregation process uses the compressed sensing (CS) theory in the parent node to combine data packets from the child nodes. Then, the aggregated data is transmitted to the DODAG root from the downward parent. The DODAG root node collects all the aggregated data and performs the reconstruction operation to get original data. The performance of EDADA-RPL is compared with RPL and LA-RPL. The EDADA-RPL decreases the delay and packet loss ratio by 8-15 s and 6-10%, respectively.  
In our future work, we plan to deploy sky motes in a real-time environment and toanalyse EDADA-RPL efficiency in various scenarios.  
Conceptualization and methodology, S.S., S.B.; software and validation, A.K.L. and N.C.; formal analysis and investigation, Y.N.; writing--original draft preparation, S.S., S.B., S.R.; writing--review and editing, S.S., S.R., A.K.L.; funding acquisition, Y.N.  
This research was supported by the KIAT (Korea Institute for Advancement of Technology) grant funded by the Korea Government (MOTIE: Ministry of Trade Industry and Energy). (No. N0001791, HRD Program for ICT Convergence Smart Rehabilitation Industrial Education Program) and the Soonchunhyang University Research Fund.  
The authors declare no conflict of interest.
## 3 Analysis of the Numerical Results
### Characterization of Erosion and Wear  
The discrete phase particle density was set to \(\rho=2650\) kg/m\({}^{3}\), the particle size was set to 0.45 mm, the viscosity was set to 0.001 Pa-s and the flow rate was set to 20 m/s.  
As illustrated in Figure 8, the fracturing fluid erosion of the pipe confluence occurs mainly in the two bent pipe areas of 2# and 4#. The bent pipe section is more easily eroded than the straight pipe section. At the bends, the sand particles are affected by centrifugal force and the local vortex zone, which makes the outer wall of the pipe confluence more likely to be impacted than the inner wall, resulting in serious erosion and wear.  
Figure 8: Erosion cloud map.  
Figure 7: Cloud diagram of the particle trajectory.  
As displayed in Figure 9, the 4# bend has no erosion phenomenon in the 0\({}^{\circ}\)\(\sim\)10\({}^{\circ}\) range, while the erosion intensity is the greatest in the position of 70\({}^{\circ}\)\(\sim\)90\({}^{\circ}\). As the particles enter the bend section, the direction of fluid velocity is changed and is affected by the vortices in the A and B regions. The flow direction is shifted to the inside, and the wall surface of the bend is hit by a large number of particles positively. The position of 70\({}^{\circ}\)\(\sim\)90\({}^{\circ}\) has a large number of particles, the flow velocity is fast and the erosion and wear are intensified.  
The main erosion and wear location of the 2# bend is located at 0\({}^{\circ}\)\(\sim\)30\({}^{\circ}\) and 70\({}^{\circ}\)\(\sim\)90\({}^{\circ}\) of the bend, and the particle trajectory reveals that a large number of sand particles fall vertically along the straight pipe section. The bend is affected by the centrifugal force of sand particles as well as gravity, and the wall surface of the bend within the range of 0\({}^{\circ}\)\(\sim\)30\({}^{\circ}\) is impacted, resulting in serious erosion at the inlet end of the bend. In addition, the \"secondary flow\" and the local vortex impact, part of the particles in the impact and then the fluid movement trend to the high angle position of the bend results in the 70\({}^{\circ}\)\(\sim\)90\({}^{\circ}\) position of the bend also appearing in the serious erosion area. However, the main erosion area of the 2# elbow is located on one side of the elbow centre axis section, rather than in the middle or evenly distributed on both sides. This is because the convex wall vortex region B causes the 4# elbow side of the outer wall to form a clear fluid extrusion surface, and most of the particles will be along the outer pipe wall down to the 2# elbow unilateral outer wall surface for shear effect, resulting in serious cutting wear. Moreover, because of the existence of a secondary flow vortex at the exit of the 2# elbow, the straight pipe section of the exit section will also produce a certain degree of erosion wear.
###### Abstract  
Terahertz antennas can greatly enhance the near fields and enable strong light-matter interactions, and thus have been widely used in applications such as terahertz sensing and detection. Here we propose a novel approach to further enhance the near fields in terahertz antennas. We show that by sandwiching hyperbolic metamaterials that are composed of InSb and SiO\({}_{2}\) multilayer and that are dressed with hole arrays, between a terahertz dipole antenna and the substrate, the near-field electric field intensities in the antenna can be further enhanced by more than three times. Simulations reveal that this enhancement originates from the doubly enhanced in-plane electric field component and the significantly enhanced out-of-plane electric field component. We expect this work will advance the design of terahertz antennas that are widely used in sensors and detectors.  
Keywords:near-field enhancement; terahertz antenna; hyperbolic metamaterials +
Footnote †: journal: Applied Physics Letters
# Framework for Malware Triggering Using Steganography
## 4 Evaluation
### Results and Analysis  
The suggested scheme's two primary components are the geolocation condition (trigger) and the stego-image. First, we compared the accuracy of the different geolocation systems used for evaluation based on the devices, browsers, and access network types. Then, to describe the features of stego-images, we analyzed the Mean Square Error (MSE), Structured Similarity Index Measure (SSIM), and Peak Signal to Noise Ratio (PSNR).
## 5 Conclusions  
The state-of-the-art modified SeM and traditional prediction approaches of CF cannot lead to decent recommendations to the active user as they cannot compute the similarity value of those two users whose ratings are non-co-rated and disjoint to each other. In this  
Figure 17: RMSE Value at Dataset2.  
case, the results of top-n similar neighbors of an active user become unattainable; therefore, the accuracy of CF-based RS considerably drops in a sparse dataset.  
This study of the proposed recommendation approach adopts a new attribute in the similarity computation as well as considers only the _'most'_ similar neighbor in a rating prediction. As a result, the algorithm can determine how similar two users are based on their shared interests, but their ratings are non-co-rated and distinct from each other. Finding a _'most'_ similar neighbor is a comparatively easy task than the findings of top-n close neighbors in a sparse situation, Therefore, we mainly think about the most similar neighbor in a rating prediction approach to attain improved recommendation accuracy.  
For the justification of the proposed recommendation approach, we conduct a number of comparisons on the collected MovieLens datasets. The comparative analysis is divided into two parts and uses the MAE and RMSE performance indicators. In the first section of the study, it is explained why categorical attributes of items must be taken into account when calculating similarity. As a result, a similarity measure using items' categorical attributes provides considerably enhanced recommendation outcomes than other traditional CF algorithms. In the second part of the analysis, we justify that a modified prediction approach with the aforementioned similarity measure can also play a major role in enhancing the accuracy of CF. Therefore, all improved CF algorithms we get from the first part of the analysis are compared with the proposed recommendation approach. The comparative results of the above two sections show the effectiveness of the proposed recommendation over other CF algorithms. Furthermore, our study allows us to ensure the future direction of improving the recommendation accuracy of CF by modified similarity measures with an improved prediction approach. In the proposed approach, we have used the balance factor value of \"0.5.\" The effectiveness of ReS might be enhanced by an optimized algorithm for selecting the value of this balancing factor.  
Conceptualization, P.K.S., A.K.S. and R.K.S.; methodology, R.K.S. and J.P.S.; validation, P.K.S., A.K.S., R.K.S. and S.D.; formal analysis, J.P.S. and S.D.; investigation, P.K.S.; resources, R.K.S., P.K.S. and P.K.S.; writing--original draft preparation, S.D. and J.P.S.; writing--review and editing, R.K.S., P.K.S., A.K.S., J.P.S. and S.D.; visualization, P.K.S. and S.D.; final version writing--review, editing, S.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
There are no conflict of interest among authors.
# Artificial Neural Network Framework for Hybrid Control and Monitoring in Turning Operations
## 1 Introduction
Despite significant progress, several shortcomings persist, including insufficient emphasis on energy efficiency, limited data generalization, challenges in real-time applicability, inadequate model interpretability, and barriers to industrial integration. Other critical gaps include the lack of standardized validation protocols, computational complexity for practical deployment, and incomplete multi-objective optimization frameworks that balance cutting force, surface quality, and energy use. Addressing these challenges requires comprehensive and diverse datasets, hybrid modeling approaches that combine physical insights with data-driven techniques, standardized benchmarking for consistent evaluation, and industry-focused solutions to enhance scalability and adoption. Overcoming these hurdles will be essential to fully harnessing the potential of machine learning and related advancements in intelligent, sustainable manufacturing ecosystems. The primary research objective of this study about a hybrid control and monitoring framework for turning operations using artificial neural networks is to develop a robust system that enhances real-time decision-making and machining efficiency. Specifically,this study aims to design an inverse ANN model for accurate real-time cutting force prediction using electrical parameters, enabling dynamic optimization and tool protection. It also seeks to construct a forward ANN model to predict and optimize energy consumption based on cutting force and machining parameters, promoting sustainable operations. Additionally, the research will explore implementing an adaptive recalibration mechanism that leverages incremental learning, transfer learning, and full retraining to maintain model accuracy amidst changing conditions. Furthermore, the framework will initiate a dynamic model management system for the efficient storage and selection of trained models, ensuring adaptability across diverse turning scenarios. Finally, the study will evaluate the framework's integration with Industry 4.0/5.0 principles, assessing its impact on machining performance, energy efficiency, and tool life within intelligent manufacturing ecosystems. The methods adopted include data-driven modeling, employing forward and inverse ANN models trained using structured and stratified datasets. These datasets were systematically collected through controlled experiments that simulate various loading conditions by applying calibrated braking forces at different spindle speeds, thus enabling the measurement and correlation of machining parameters such as cutting force, power consumption, and current intensity.
## 2 NOAA Ocean Satellite Course
### Extending Our Reach  
Until recently, the NOAA Satellite Courses offered by WCN have been held almost exclusively on the U.S. West Coast, in either Corvallis, Oregon (2006-2013), or Seattle, Washington (2014-2017). As a consequence, participation has been biased towards those residing regionally or those who could secure travel funds to attend. To begin addressing this issue, WCN offered a Satellite Course in March at the NMFS Laboratory in Juneau, Alaska, in March 2019. A significant number of scientists there had never been able to attend the course in Seattle as its timing conflicted with their operational field work. In addition, WCN will partner with the CoastWatch Atlantic Node for a 2019 course in Galveston, Texas, and a 2020 course in Puerto Rico. Beginning in 2017, WCN began working with personnel from other CoastWatch regional nodes to develop courses tailored to specific regions of the United States. As a consequence, Central Pacific OceanWatch Node offered a satellite data training course in Hawaii in autumn 2018. The East Coast Node offered a course in Narragansett, Rhode Island, in August 2018, and a second course in College Park, Maryland, in May 2019.  
In addition to training the NOAA operational workforce, engaging an international workforce is an important element in increasing use of satellite data in oceanography. Towards this end, elements of the NOAA Satellite Training Course have been incorporated into the tutorial sessions offered by the Pan Ocean Remote Sensing Conference (PORSEC). Since 2014 the WCN manager has been the lead instructor in the PORSEC tutorials, which have been held in Indonesia (2014), Brazil (2016), and South Korea (2018); the next tutorial is planned for Malaysia in 2020. The tutorials attract an international group of advanced undergraduate students, graduate students, and young professionals who want to expand their expertise in using ocean satellite products.
## 2 Related Works  
To the best of our knowledge, only the works of [PERSON] _et al_. [8] and SELDA [9] incorporate secure routing into the secure data aggregation schemes. They employ distributed monitoring schemes to detect misbehaving nodes. When any compromised node is detected by its neighborhood, a secure routing protocol is launched to choose safe path(s) to the considered honest data aggregators. Their secure routing protocols are an on-demand or reactive protocol type. Their main advantage is that, as long as no intrusion is detected, the expensive secure data transmission is not needed. However, it is very obvious that their monitoring scheme requires low-cost sensor nodes to keep watching their neighborhood. Keep sensor nodes awaking all the time consumes a lot of energy.  
Different from previous works, proactively providing a secure routing protocol as the foundation for secure data aggregation can be another consideration. This approach makes a secure routing algorithm available since the beginning of network operation and the secure data aggregation scheme can work on top of it. It protects the transmission for the secure data aggregation scheme. We found that there has been no work that lays a secure routing protocol as the foundation for a secure data aggregation protocol.  
Due to its energy-efficient feature, we exclusively studied a clustering-based routing protocol, the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol [10]. Our previous work on LEACH-based secure routing protocols in [11] revealed an interesting fact. Every scheme still trusts the CHs to do the data aggregation. Since the network setting is a homogenous network, this approach is risky.  
If we look carefully at the secure data aggregation protocols presented in [6, 7], those schemes provide techniques (such as interactive proof, witness-based scheme, reference-based data aggregation, verification or privacy homomorphic encryption scheme) to verify the correctness of the aggregated data or not to fully authorized the aggregators to do the data fusion. This teaches us not to trust the aggregators blindly. Secure data aggregation for a low-cost homogeneous WSN is highly challenging.  
We also found that those secure data aggregation protocols are not built on a secure routing protocol. In [12], we stated our premise that although some assumptions are made, clearly defining the secure routing protocol for the secure data aggregation to be based on, or at least stating the minimum requirements for the secure routing schemes to meet in the case for flexible choice of secure routing, is necessary. Therefore we argue that the role of secure routing would be rendered useless if the data aggregation scheme built on it is not secure (i.e. by fully trusting data aggregators). Conversely, the secure data aggregation protocol needs a robust underlying routing protocol as its foundation in order to be effectively optimal.