esa-sceva/satcom-chunk-collection huggingface dataset:
# WAMS-Based Online Disturbance Estimation in Interconnected Power Systems Using Disturbance Observer
###### Abstract  
In the event of a generator loss or disturbance, the power system frequency declines quickly and overall system stability is at risk. During these scenarios, under frequency load shedding is triggered to restore the power system frequency. The main stage of modern adaptive under frequency load shedding techniques is disturbance estimation. However, the swing equation is widely used in disturbance estimation but has some critical estimation errors. In this paper, instead of using the swing equation we proposed the use of a disturbance observer to estimate the curtailed power. By making use of wide area measurements, a system frequency response model, which is a representative of the whole power system, can be realized in real time. Using different power system states of the developed model, a disturbance observer can be designed as well. The main advantage of the disturbance observer is that it can accurately estimate the disturbance magnitude and its location in a very short time. Further investigations show that by using the disturbance observer disturbances, which occur at the same time or at different times in different areas regardless of the magnitude or size, accurate estimations can be made. To ascertain the efficiency of the proposed scheme, simulations are done for a four-area power system using Matlab/Simulink.  
Keywords: disturbance estimation; disturbance observer; swing equation +
Footnote †: journal: Applied Sciences
# Opportunistic Network Algorithms for Internet Traffic Offloading in Music Festival Scenarios
## 5 Evaluation  
In this section, we evaluate the performance of the opportunistic algorithms proposed and presented in Section 4. We first introduce and explain the metrics used to measure performance and then we focus on the characteristics of the mobility traces that we used for evaluation. In the end, we compare and contrast our two algorithms to other existing solutions, and focus on the most suitable design for a music festival.
## 2 Observation Methods
### Plasmasphere Model and Data  
The Belgian SWIFF plasmasphere model (BSPM) is a 3D kinetic semi-empirical model of the plasmasphere [24], coupled to the ionosphere [25]. It has recently been improved to take into account the most recently identified relevant physical processes [20]. It can be run for any date to obtain for every hour the number density and temperature of the electrons and protons inside and outside the plasmasphere, as well as the position of the plasmapause, as a function of the geomagnetic activity driven by the Kp index. The model uses the kinetic approach for the particle densities and the mechanism of quasi-interchange instability for the formation of the plasmapause. The density in the plasma trough region has recently been improved using observations of Van Allen Probes [26]. The results of the plasmasphere model have been compared to NASA's IMAGE mission (2000-2005) global EUV images of the plasmasphere [27], Cluster [28] and Themis [29], among others, allowing the validation of the plasmasphere erosion and dynamical evolution. The most recent version has been made available on the PITHIA [30] platform: [[https://esc.pithia.eu/](https://esc.pithia.eu/)]([https://esc.pithia.eu/](https://esc.pithia.eu/)) (accessed on 5 November 2024).  
Moreover, the magnetic and plasma observations of the low-Earth orbiting Swarm satellites allow us to derive the midnight plasmapause [31]. Launched on 22 November 2013 into a near-polar low-Earth orbit (LEO), Swarm is a constellation of three identical satellites operated by the European Space Agency (ESA) with the purpose of mapping Earth's magnetic field [32]. The initial altitude of the satellite pair Swarm A and Swarm C was about 490 km in April 2014, and it was about 510 km for Swarm B, and both orbit altitudes do slowly decay with time. Based on in situ electron density and temperature [33], GPS-derived TEC, and auroral field-aligned current observations at the Swarm satellites, new Swarm products have recently been developed to characterize plasmapause-related boundaries in the topside ionosphere. This product also includes a plasmapause index, a proxy for the midnight plasmapause position. This index is derived from Swarm observations similarly to the ones described in [34].
# Harmonic Detection Method Based on Parameter Optimization VMD-IWT Combined Noise Reduction
## 3 Harmonic Detection Process Based on RIME-VMD-IWT  
On the basis of the above methods, this paper presents a harmonic detection method based on the RIME optimization algorithm to optimize VMD combined with wavelet threshold denoising. Firstly, VMD is optimized based on RIME to find the best parameter decomposition layer \(K\) and penalty factor \(\alpha\), so as to achieve the optimal signal decomposition effect. Then, the VMD decomposed the noisy signal to obtain \(K\) modal components and screened the modal components by calculating and analyzing the correlation coefficient between the \(K\) modal components and the original signal. The modal components were denoised by wavelet threshold and reconstructed to obtain the final denoised harmonic signal. Finally, the Hilbert transform is used to extract the amplitude and frequency information.  
Step 1: Read the harmonic signal data and preprocess it with noise.  
Step 2: RIME was employed in the search for the optimal parameters \(K\) and \(\alpha\) in VMD, utilizing sample entropy as the fitness function during the iterative process. Through RIME, the individual with the minimum sample entropy was identified, and the corresponding parameter combination [\(K\),\(\alpha\)] of this individual was designated as the optimal parameter set for VMD.  
Step 3: After the noisy signal is decomposed by VMD, K modal components are obtained. Based on the correlation coefficient of each IMF, the components are categorized into effective IMFs and ineffective IMFs.  
Step 4: The invalid IMFs are eliminated, and the effective IMFs undergo de-noising treatment using IWT, resulting in the reconstruction of the signal and obtaining the final de-noised harmonic signal.  
Step 5: Utilize the Hilbert transform to extract amplitude, frequency, and other information for detecting harmonic signal parameters.
# A Novel Routing Scheme for Creating Opportunistic Context-Virtual Networks in IoT Scenarios +
## 4 Opportunistic Context-Virtual Networks
In particular, the information related to nodes that have a low probability to be met again in the near future is removed from the heap of the node upon the expiration of a timer, \(\Delta_{t}\).
## 2 Materials and Methods
### Dose Linearity  
Linearity measurements were conducted at both the SCCC and SGHCC facilities using a solid water phantom.  
For the SCCC measurements, the evaluation was conducted under standard conditions, at 1.5 cm depth and source-to-surface distance (SSD) of 100 cm. Dose linearity was assessed over a range from 50 MU (50 cGy) to 500 MU (500 cGy).  
On the contrary, at SGHCC, an isocentric setup was used. The array was positioned within a polymethyl methacrylate (PMMA) adapter, which has a thickness of 5 cm. Measurements were performed at a depth of 10 cm with an SSD of 90 cm, resulting in an isocentric setup with a source-to-axis distance (SAD) of 100 cm. Dose linearity was evaluated from 25 MU (19.3 cGy) to 500 MU (386.4 cGy).  
The charge collected by the central diode was plotted against the received dose and the sensitivity in Gy/nC was determined by employing a least-squares fitting method. The measurements were determined based on three repetitions to assess the detector response's uncertainty.  
Figure 2: The image on the left depicts the edgeless array, featuring 25 diodes, while the labels of the diodes are shown on the sides of the detector tail. On the right side, the dimensions of the array are illustrated, showing the horizontal pitch of 3 mm and a vertical pitch of 5 mm. Not to scale.
# Tribological Analysis of Steels in Fuel Environments: Impact of Alloy Content and Hardness
###### Abstract  
The performance and durability of high-pressure fuel systems in combustion engines are critical for consistent operation under extreme conditions. High-pressure fuel systems are traditionally lubricated with fuel that is compressed and delivered to the combustion chamber. However, lubrication with fuel presents significant challenges in these systems when used with low-viscosity fuels, leading to increased wear rates, especially in reciprocating contacts. This study delved into the tribological performance of steels of varying alloy content (annealed and hardened variants of AISI-52100, CF2, and D2) against alumina and hard 52100 counterbody materials in ethanol and decane environments. Friction and wear behaviors were evaluated, highlighting the influence of material interactions and environmental factors. Elastohydrodynamic lubrication analysis of the tested systems indicated that ethanol and decane form lubricating films of nanometer-scale thickness, confirming the boundary lubrication regimes of the performed tests. In summary, the tribological behavior trends were similar for alumina and 52100 counterbodies. Even though soft 52100 steel demonstrated low friction, its wear was the largest for both tested environments and counterface materials. Among all the tested materials, hard D2 experienced the lowest wear. 52100 and D2 steels showed opposite friction change behavior when comparing hard and soft samples, with lower friction observed for softer 52100 steel and harder D2 steel. Meanwhile, the wear was lower for harder candidates than for softer ones independent of the environment and counterbody material. Raman spectroscopy analysis of the formed wear tracks indicated the formation of carbon films with larger intensities of characteristic carbon peaks observed for more wear-resistant materials. These results suggest the synergistic effect of hardness and tribochemical activity in reducing the wear of materials.  
friction; ethanol; decane; steel; hardness; lubrication; tribochemistry; elastohydrodynamic lubrication analysis; wear +
Footnote †: journal: Applied Sciences
## 5 Discussion
### Limitations and Future Work  
There are several limitations to this study that need to be addressed. Out of the 5-day diary study, one of the days fell on Valentine's Day. As this is an extraordinary day, the results obtained during this period might have had an impact on the perception of the participants towards their notification experiences.  
Secondly, it is important to note that only users with iOS devices were recruited in this study. For a more accurate sampling, further research should be conducted with a homogeneous mix of smartphone operating systems.  
Thirdly, the vast majority of participants who took part in the diary study identified as female. Of the 28 people recruited for the study, 21 were females while only 7 identified as males. This uneven distribution of genders may have an impact on the results. We expect that a more balanced representation could improve the overall representativeness.  
Fourthly, the sample also consisted of an unbalanced occupational representation. While only 4 students contributed to the study, 11 had the occupational status of an employee, and 13 participants indicated having both working and studying occupations. Occupations can have differences in environment or settings and these can affect how people react or interact with push notifications.  
Future work could be conducted on how a particular user shifts between various characteristics throughout different time frames. Shorter shifts could occur over the same day, while longer ones could potentially emerge over a period of days, thus indicating the importance of evaluating push notification experiences over time as further investigation.  
Last but not least, evaluation of how successfully the provided strategies regarding each characteristic should be conducted to ensure reliable notification experience improvements.
# Malicious File Detection Method Using Machine Learning and Interworking with MITRE ATT&CK Framework
## 3 Materials and Methods
### Dataset  
The experimental dataset was used on the public kaggle site. This was achieved using a raw PEByte stream and a csv file containing tens of thousands of data points obtained by downloading the merged PE malware dataset into a 32-byte vector. To detect static-analysis-based malicious files, we imported various modules (os, pandas, pickle, numpy, pefile, etc.) based on Python, utilized the analysis environment to extract features from files, and applied them to pre-trained ML models. They were classified into normal and malicious files. Table 1 lists the classified legitimate and malicious file results.  
Malicious Code Analysis and Feature Extraction  
Dataset analysis was conducted using Google Colab to build an experimental environment, and analysis was attempted by importing various modules (os, pandas, numpy, pickle, pefile, joblib, etc.) based on Python. As a result, the data in the dataset were successfully classified into a normal file and a malicious file. The results are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Legitimate** & **Malicious** & **Data Type** \\ \hline
96,724 & 41,323 & Int 64 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Legitimate file classification results.  
Figure 10: Experimental environment and configuration diagram.  
By using 0 for normal files and 1 for malicious files, it is easy to distinguish between normal files and malicious files. The first column of the dataset is Name, which indicates the file name. The second column indicates MD5 hash values for normal and malicious files. In the third column, legitimate files are classified as 0 and malicious files as 1. The fourth column indicates the size of the file, and finally, Magic indicates the PE format. It was clearly classified into one of two categories: normal and malignant. The accelerated classifier is expressed as Equations (5) and (6) [11].  
\[TNR\;:\;\frac{TN}{TN+TP}\;\times\;100=1 \tag{5}\]  
\[TPR\;:\;\frac{TP}{FN+TP}\;\times\;100=0 \tag{6}\]  
In the interpretation of the above formula, when the probability of predicting maliciousness is greater than or equal to the true-negative rate (TNR), the file is determined to be malicious. Moreover, if the probability of a normal prediction is greater than or equal to the true-positive rate (TPR), the file is determined to be normal. For the remaining probabilities, the file is determined to be unknown.
# Algebraic Point Projection for Immersed Boundary Analysis on Low Degree NURBS Curves and Surfaces
## 6 Conclusions  
In this paper, novel first- and second order-algebraic point projection methods for low degree two-dimensional NURBS curves and three-dimensional NURBS surfaces are proposed. The procedure utilizes the recently developed algebraic level sets. As a first step, the differential property of the resultant matrix is used to obtain the footpoint in the physical space. Next, the parameter value of the footpoint is computed by solving the over-constrained resultant system. Algebraic point projection eliminates inefficient iterative computations and the need for a good initial guess by providing an exact on-curve solution and good approximate solution for points near the curve. Through numerical examples, the algebraic method, especially the second-order point projection is demonstrated to be faster and more smooth than Newton-Raphson iterations.  
The proposed algebraic point projection technique possesses two limitations. As illustrated in Section 4, the proposed method is inaccurate or will fail when query points are far away from the curve/surface due to the inaccuracy of the algebraic distance measure. Algebraic point projection will also fail at points where the gradient of the algebraic level-sets is not defined. Such a point occurs, for example, at the center of a circle or sphere. At these locations, a correction to the gradient or a perturbed point is needed. Although algebraic point projection is an approximation, particularly for test points far away from the curve/surface, it has utility for isogeometric analysis, where small inaccuracy in point inversion solution may be acceptable as a trade-off against smooth, robust, and efficient projection. Such a projection is critical given the large number of quadrature points at which point projection is necessary during analysis.  
**Author Contributions:** Conceptualization, T.S. and G.S.; Methodology, H.L., P.K.V. and T.S.; Software, H.L., P.K.V. and T.S.; Validation, H.L., P.K.V., T.S. and G.S.; Formal analysis, P.K.V.; Investigation, H.L., P.K.V., T.S. and G.S.; Resources, G.S.; Data curation, H.L., P.K.V., and T.S.; Writing--original draft preparation, H.L., P.K.V., T.S. and G.S.; Writing--review and editing, G.S.; Visualization, H.L., P.K.V. and T.S.; Supervision, G.S.; Project administration, G.S.; Funding acquisition, G.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors wish to thank the reviewers for their suggestions that improved the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Study on Reinforcement Learning-Based Missile Guidance Law
## 3 Proportional Navigation Guidance Law  
Proportional navigation guidance (PNG) is the most widely used guidance law on homing missiles with an active seeker [2; 11; 12]. PNG generates an acceleration command by multiplying the LOS rate and velocity with a predesigned gain. The mechanism reduces the look-angle and guides the missile to hit the target.  
The acceleration command by PNG in the planar engagement scenario is as follows:  
\[a_{c}=NV_{m}\Omega \tag{10}\]  
where \(N\) is navigation constant (design factor), \(V_{m}\) is the speed of the missile, and \(\Omega\) is the LOS rate. Figure 4 shows the conceptual diagram of PNG.  
Meanwhile, Pure PNG, which is one of the branches of PNG, is adopted in this paper. Pure PNG is used for a missile to hit a relatively slow target like a ship. The acceleration axis of the Pure PNG is attached perpendicular to the missile velocity vector as it is shown in Figure 1. Additionally, the velocity term in Equation (10) of Pure PNG is the inertial velocity of the missile. PNG is very useful, and it basically is even optimal for missile guidance. Technically, creating a missile guidance law that has better performance than PNG, despite sharing the same engagement scenario that PNG is specialized for, is not possible under ideal circumstances. However, PNG is not optimized for practice. Thus, we will figure out whether RL-based is able to exceed the performance of PNG when the simulation environment gets more practical, i.e., applying a controller model. Of course, we did not apply the practical model to training but applied them to the simulation part.  
The overall structure of the PNG missile simulation is shown in Figure 5.
# Analysis of AC DC Four-Switch Boost-Buck Battery Charger Converter for EV Applications
## 6 Boost-Buck Converter Using Snubber RC Circuit  
To mitigate the internal instability caused by high-Q oscillation peaks, a solution is proposed in the form of an RC snubber circuit. This section focuses on the introduction of the RC snubber circuit to dampen the resonance peak. Figure 10 presents the modified converter configuration with the addition of a damping RC circuit (\(R_{d}\), \(C_{d}\)) connected in parallel to the intermediate capacitor C. The RC snubber circuit is strategically placed to provide effective damping of the resonance oscillations. Other options are reducing the gain  
Figure 8: The intermediate capacitor \(v_{c}(t)\) in the boost mode. (**a**) The intermediate capacitor \(v_{c}(t)\) waveform shows the internal instability behavior. (**b**) Normalized FFT of intermediate voltage \(v_{c}(t)\).  
Figure 9: The intermediate capacitor \(v_{c}(t)\) in the buck mode. (**a**) The intermediate capacitor \(v_{c}(t)\) waveform shows the internal instability behavior. (**b**) Normalized FFT of intermediate voltage \(v_{c}(t)\).  
and the bandwidth of the controller. The proposed method is a simple and cost-effective solution to mitigate the high-Q oscillation peaks.  
The subsequent analysis will involve the small-signal modeling of the converter in both buck and boost modes. This modeling approach enables the evaluation of stability and operational regions. By incorporating the RC snubber circuit into the system, it is expected that the damping effect will suppress the high-Q oscillation peaks, leading to improved stability and overall system performance. Through the investigation of the small-signal models and stability analysis, the effectiveness of the RC snubber circuit in enhancing the stability of the converter will be assessed.
# Effects of Visually Induced Self-Motion on Sound Localization Accuracy
## 3 Results  
The total number of the left (0) and right (1) responses under each condition were recorded. First, for each angle of the subjective midline and just noticeable difference (JND) to be calculated, we plotted the correct-answer rate as a function of the angular distance between the loudspeaker position and the physical straight-ahead orientation of the participant. The normal cumulative distribution function was then fitted to the plots as a psychometric model function, using maximum-likelihood estimation [27].  
The point of subjective equality was calculated as the 50% point of the psychometric function, i.e., the estimated mean represents the subjective midline. Moreover, the JND of the subjective midline, defined as the detection threshold yielding 75% correct 2 AFC responses, was estimated as follows. As the normal cumulative distribution function was used in the estimation, the point at a distance of +0.6745 times the standard deviation (\(\sigma\)) from the mean crosses 0.75 of the accumulated distribution probabilities of the estimated normal cumulative distribution function. Thus 0.6745\(\sigma\) is an approximate representation of the JND of the angle of the subjective midline. One participant was excluded from the analysis owing to the non-estimation of reasonable psychometric functions.  
Figure 2 shows the average of the subjective midline and standard errors. One-way repeated-measure analysis of variance (ANOVA) was performed on the subjective midline with the experimental condition as a factor; three experimental conditions were considered--counterclockwise, control, and clockwise. The main effect of the experimental condition (counterclockwise, \(M=-1.86\); control, \(M=-2.44\); clockwise, \(M=-2.03\)) was not significant (\(F\) (2, 18) = 0.51, \(n.s.\), \(\eta^{2}=0.05\)).  
Figure 3 shows the average number of JNDs and standard errors. A one-way repeated-measures ANOVA was performed on the JNDs with the experimental condition as a factor. As in the case of the subjective midline, three experimental conditions were considered. The results showed that the main effect of the experimental condition was significant (\(F\) (\(2,18\)) = 5.50, \(p\) < 0.05, \(\eta^{2}\) = 0.38), and multiple comparison analysis ([PERSON]'s method, \(p\) < 0.05) [28] revealed that JND was smaller for the control condition (\(M\) = 1.06) than for the vector condition (counterclockwise, \(M\) = 1.77; clockwise, \(M\) = 1.68). No significant difference was noted between the counterclockwise and clockwise conditions.