esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Mesh Resolution and Temperature Effects in Jet Ejector CFD Calculations
###### Abstract  
Recent advancements in computational and experimental techniques have deepened the understanding of ejector dynamics. Working maps, which depict the relationship between operating conditions and the performance metrics of entrainment and pressure, are commonly used in defining the ejectors' performances and designs and enhancing their operational flexibility. This paper investigates the sensitivity of a jet ejector to variations in the inlet temperatures, with a focus on how changes in these temperatures affect its performance and Mach number distribution. Using high resolution CFD (computational fluid dynamics) simulations, this study maps ejector behavior by using the ideal gas approximation, marks Mach number scene responses to those changes, and is aimed at optimizing it to work with refrigeration systems using commercial fluid R1234 vf/2,3,3,3-Tetrafluoropropene. The findings provide valuable insight into operational conditions for jet ejectors. The analysis revealed little dependence on the changes in inlet temperatures, therefore expanding the design working conditions by at least +20% of the temperature at each of the inlets. This study also analyzes the small deviations in ejector behavior due to temperature variations at the inlets. It contributes to the development of sustainable refrigeration systems, as it broadens the operational temperature range and enhances practical applications in this field, as this information is difficult to find in commercial databases.  
jet ejector; CFD; compressible fluid; working maps; adaptive mesh refinement Article
## 2 Experimental Procedure  
### Studied Samples and Temperature Tests Methodology  
The studied samples were IOMs with the waveguide topology in the form of a Y-splitter with the electrodes placed on each side (Figure 1). In fact, with no applied voltage this IOM is a Y-branch, but taking into account the IOM's design features and its application, in this paper we will use the term'modulator'. The modulator's electrodes were made using X-cut congruent LN produced by Crystal Technology (USA). Channels were created using direct photolithography. Channel waveguides with a width of 6 \(\upmu\)m were formed by proton exchange in benzoic acid. Proton exchange was performed in a closed zirconium reactor at 170 \({}^{\circ}\)C for 2 h, followed by cooling at a rate of 10 \({}^{\circ}\)C/min. Annealing was performed at 350 \({}^{\circ}\)C for 5.5 h, followed by cooling together with the furnace. As a result, optical channel waveguides with \(\Delta\)n = 0.017 were formed, measured by mode spectroscopy. The final step involved docking the chip with an optical fiber maintaining the polarization of radiation, type Panda. The pyroelectric effect in the samples was not suppressed. Before the experiment, the samples were thoroughly cleaned to remove possible surface contaminants. The physical length of the modulator is 1 = 35 mm. The waveguides in the modulator are located along the Y-axis in the X-cut plane. The waveguide optical losses at room temperature were measured as 1 dB/cm.  
In all studies, the examined sample was subjected to temperature tests. For this purpose, the IOM was placed in the high/low-temperature chamber Espec 712R, where it was exposed to slow cooling to minus 60 \({}^{\circ}\)C at a rate of 0.5 degrees per minute, and then--to fast heating at a rate of 4 degrees per minute to 60 \({}^{\circ}\)C.
# QoE Optimization in a Live Cellular Network through RLC Parameter Tuning
## 3 Transmission Control Protocol  
TCP is a transport layer protocol that follows a client-server architecture. This protocol is connection-oriented. Thus, it is necessary to establish a connection between the client and the server before beginning the transmission of information. The connection will be released when the communication is finished. The TCP is characterized by providing a reliable and orderly delivery of packets.  
To verify that the information is received correctly, the TCP makes use of ACKs. In this way, if the transmitter receives the ACK of a segment, it will know that it has been received correctly; otherwise, the segment will be retransmitted to the receiver.  
In order to achieve high performance and to avoid network congestion [21], the TCP implements a transmission window and various congestion control algorithms. Moreover, the TCP uses two state variables for each connection: a congestion window (cwnd), limiting the amount of data that the transmitter can send before receiving an ACK, and the receiver's advertised window (rwnd), indicating the free space available to store data in the receiver's buffer. The minimum size between cwnd and rwnd limits the amount of data that can be transmitted through the network. Next, the TCP congestion control algorithms are described, as shown in Figure 3.  
* Slow start: This algorithm consist of exponentially increasing the size of the cwnd, starting with a small size, determined by the maximum segment size (MSS) that can be transmitted by the sender, until a threshold called the slow start threshold (ssthresh), determined by the maximum size of the rwnd, is reached. If the network becomes congested during this phase, the value of the ssthresh will be updated as half of the minimum size between rwnd and cwnd, and the slow start algorithm will be restarted. This algorithm is used when the cwnd is smaller than the ssthresh.
* Congestion avoidance: Once the slow start phase is finished, the congestion avoidance phase is started. The congestion avoidance algorithm lineally increases the cwnd until the network becomes congested. In this phase, there are two reasons for network congestion: the reception of three duplicate ACKs or the expiration of the waiting time of receipt of an ACK. In the first case, the size of the cwnd and the ssthresh will be reduced and the linear increase will continue. In the second case, the size of the cwnd is reset and the slow start algorithm is started.
* Fast retransmit: If three duplicate ACKs are received, it is determined that the loss of a segment has occurred. When this occurs, the retransmission of the lost segment will be performed, without the need to wait for the retransmission timer to expire.
* Fast recovery: After the retransmission performed by the previous algorithm, the cwnd will continue its growth in the avoidance congestion phase.
###### Abstract  
In robot navigation and manipulation, accurately determining the camera's pose relative to the environment is crucial for effective task execution. In this paper, we systematically prove that this problem corresponds to the Perspective-3-Point (P3P) formulation, where exactly three known 3D points and their corresponding 2D image projections are used to estimate the pose of a stereo camera. In image-based visual servoing (IBVS) control, the system becomes overdetermined, as the six degrees of freedom (DoF) of the stereo camera must [PERSON] with nine observed 2D features in the scene. When more constraints are imposed than available DoFs, global stability cannot be guaranteed, as the camera may become trapped in a local minimum far from the desired configuration during servoing. To address this issue, we propose a novel control strategy for accurately positioning a calibrated stereo camera. Our approach integrates a feedforward controller with a Youla parameterization-based feedback controller, ensuring robust servoing performance. Through simulations, we demonstrate that our method effectively avoids local minima and enables the camera to reach the desired pose accurately and efficiently.  
P rpi problem; stereo camera system; image-based visual servoing; eye-in-hand configuration; feedforward and feedback control; accurate camera pose +
Footnote †: journal: Applied Sciences  
Article  
A Novel Feedforward Youla Parameterization Method for Avoiding Local Minima in Stereo Image-Based Visual Servoing Control [PERSON] and [PERSON]
## 4 The Influence of System Parameters on Response Characteristics  
### The Relationship between System Parameters and Optimal Parameters  
Considering the needs of practical engineering applications, we give the relationship between the optimal parameters in Equations (28) and (29) and the system parameters \(\mu\), \(L\), and \(\beta\) under different cases. The coupling term \(\mu L^{2}\) is taken as abscissa, and the inerter-mass ratio is taken as ordinate. It can be observed from Figures 6 and 7 that the system is stable within the range of the selected parameters.  
Figure 4: The normalized amplitude–frequency curves for different cases.  
Figure 5: Transient response to initial displacement \(x_{0}\): (**a**) \(\alpha_{1}>0\). (**b**) \(\alpha_{1}<0\).  
Figure 6: The relationship between optimal parameters and system parameters when \(\alpha_{1}>0\): (**a**) \(\alpha_{lept}\). (**b**) \(\alpha_{2 opt}\). (**c**) \(v_{opt}\). (**d**) \(\xi_{opt}\).
# SACRB-MAC: A High-Capacity MAC Protocol for Cognitive Radio Sensor Networks in Smart Grid
## 5 Conclusions  
In this paper, we have proposed SACRB-MAC, which is a spectrum aggregation based MAC protocol. SACRB-MAC employs spectrum aggregation technique to improve the throughput of CRSNs in smart grid. Besides, SACRB-MAC is a receiver-based MAC protocol, which exploits the broadcast nature of wireless medium to improve reliability of CRSNs. Hence, SACRB-MAC has the potential to address the unique challenges of CRSNs in smart grid. Analytical and simulation results demonstrate that under smart grid AMI network environments, SACRB-MAC can have a high-capacity and reliable performance. Therefore, SACRB-MAC provides a promising solution for CRSNs in realizing the vision of smart grid. The coexistence and fairness for several secondary networks sharing the same primary user resources will be the focus of our future work.  
**Acknowledgments:** This work was supported by the Fundamental Research Funds for the Central Universities (Grant No: HIT.NSRIF.2015023), Aerospace Support Technology Fund of China (2014-HT-HGD-13), and National Natural Science Foundation of China (Grant No: 61102084).  
**Author Contributions:** [PERSON] proposed the idea and wrote the paper. [PERSON] did the simulation. [PERSON] revised the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Conclusions  
This work discussed the ultrasonic velocity and transmission loss in graphite rods. Based on solid elastic mechanics, the differential equation of motion of longitudinal waves propagating in a viscoelastic rod was derived. Additionally, the derived differential equations of motion were used to make COMSOL numerical models.  
Using the experimental ultrasonic measurement device, we successfully measured graphite rods' ultrasonic velocity and transmission loss at different frequencies. The ultrasonic velocity in the graphite rod was 2275 m/s at the four selected frequency points. The transmission loss varied significantly with frequency emission. The graphite rod had a smaller transmission loss when the ultrasonic wave frequency was between 25 and 55 kHz. At 47 kHz, the transmission loss was only 1.837 dB/m. This means that when the ultrasonic wave propagated 10 m, it still had 12% intensity.  
The COMSOL model is based on a double silencing terminal, which includes a sound source room and a receiving room filled with air. The influence of ultrasonic reflection on the calculation of sound field intensity was eliminated. The results showed that the transmission loss calculated by the COMSOL model was in good agreement with the experimental measurement results. This provides a practical and effective calculation method for estimating the sound transmission loss in the rod. Comparing the transmitted intensity drawn in COMSOL with the displacement of the structure, it was determined that the displacement of the structure controls the transmitted intensity.
## 3 Materials and Methods
### Time Series Decomposition
The bottom figure—the reconstructed signal itself. Explanations: e-05 means \(10^{-5}\). As shown, this wavelet provides excellent localization of frequencies in time, making it highly useful for the analysis of oscillations and the detection of sinusoids. Additionally--and importantly--not every time series is stationary, and this wavelet yields good results even when analyzing non-stationary sequences. The output of this wavelet transform consists of complex numbers, which, similar to the Fourier transform, allow for the easy extraction of amplitude and phase information at a specific frequency. Therefore, it is frequently used in studies of processes occurring on and within the Earth's crust [61; 62]. The analysis was performed using MATLAB under license no. 40911196. The graph in Figure 10 shows the results of the signal processing using the selected wavelet. The Morlet wavelet method was also applied to the periodicity analysis of all SRDN-3 probes [63; 64], and the results are presented in Figure 6:5 For most radon probes, the period slightly exceeded one calendar year. This was due to the high level of noise in the data. The signal-to-noise ratio (SNR) for all probes did not exceed 1.7 dB (Figure 11). The reduction of the series by a 365-day period was observed to leave high frequencies, but of low significance and an unknown source. When interpreting the results of the spectral analysis, it is challenging to identify which frequencies are significant. Phenomena of \"geo\"-type origin are often characterized by red noise; therefore, it is reasonable to compare the spectral characteristics of our signal to that of red noise [64] (18). Figure 6:6  
If a power peak at a given frequency exceeds the corresponding red noise level, the detected signal component can be considered significant. Figure 11 shows the spectrum of the analyzed signal along with the power of the red noise (represented by the red dashed line). It is evident that none of the signal components exceeded the red noise power. Figure 6:7 Spectrogram with significance levels. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **SRDN-3 No. 2** & **SRDN-3 No. 3** & **SRDN-3 No. 4** & **SRDN-3 No. 5** & **SRDN-3 No. 6** \\ \hline
**Period [day]** & 365.5 & 367.2 & 376.8 & 372.3 & 369.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Time periods defined for individual radon probes. Figure 10: Radon probe SRDN-3 No. 2 with removed period-related trend. The subsequent wavelet analysis performed on a time series shortened to five months included the period from 1 June 2015, to the end of November 2015. As the 1-day period could begin or end before or after the date included in the analysis, the analyzed time period was extended by 14 (Figure 12). Although the analyses did not show a 24-h period with significance exceeding the noise spectrum in the analyzed time, they indicated a residual period of about 260 h, which was also below the significance level (Figure 12).
# Time Aware F-Score for Cybersecurity Early Detection Evaluation
## 3 Materials and Methods
### Models  
The set of models used in the experimental evaluation of the proposed selectors and metric is composed by some well known of-the-shelf state-of-the-art machine learning algorithms. The selection of this set was made based on the work presented in [25] for detection of cyberbullying in Vine social network. This methods were also tested on the same datasets for the early detection problem as shown in [3]. Particularly, the implementation by _scikit-learn_[26] was used, and the description of the models with the parameters used is listed below:  
* Parameters: \(C=1\), \(class\_weight=\)'\(balanced\)', \(dual=False\), \(max\_iter=1000\)
* Parameters: \(n\_estimators=50\), \(bootstrap=False\), \(class\_weight=None\)
* Parameters: \(n\_estimators=1000\), \(learning\_rate=2.0\), \(algorithm=\)'\(SAMME.R^{\prime}\)
* Parameters: \(n\_estimators=500\), \(class\_weight=None\), \(max\_features=\)'\(sqrt\)', \(max\_depth=7\), \(bootstrap=False\)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Entities \& Items** & **Normal** & **Anomalous** & **Total** \\ \hline \multirow{3}{*}{Depression} & Entities & 752 & 135 & 887 \\ \cline{2-5}  & Items & 481,837 & 49,557 & 531,394 \\ \cline{2-5}  & Items per entity & 640.7 & 367.1 & 599.1 \\ \hline \multirow{3}{*}{Network attacks} & Entities & 10,045 & 65,655 & 75,700 \\ \cline{2-5}  & Items & 1,566,602 & 131,249 & 1,697,851 \\ \cline{1-1} \cline{2-5}  & Items per entity & 155.96 & 1.99 & 22.43 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the main characteristics of the datasets.  
* _Logistic Regression_: Conformed by a _logit MaxEnt_ classifier, where the maximum entropy classifier is combined with a logistic function.
* Parameters: \(C=0.1\), \(class\_weight=\textit{`balanced'}\), \(dual=False\), \(penalty=\textit{`12'}\), \(solver=\textit{`sag'}\)  
After models for detection in individual items are trained, we apply a selector system to take the final decision based on individual items. Those decision functions are tested by using the results previously obtained with standard final deciders as baselines. In this case, four different functions are applied to this task, particularly:  
* _mean_: Uses an aggregation of probabilities for each class, and decides one or the other based on those values.
* _bigger_: Uses the bigger class probability to provide the output of the classifier.
* _last_: Uses the last item processed to decide if the result is normal or anomalous
* _2 last_: Uses the combination of the class probabilities of the last two values in order to provide the final output.
###### Abstract  
Variable tooth thickness gears have significant effects on the characteristics of the flow field inside the gearbox and the lubrication efficiency under high-speed operating conditions due to their complex parameters, such as tooth profile, cone angle, rotational speed, and oil injection speed. To investigate the impact mechanism of oil injection velocity on the working flow field of high-speed variable tooth thickness gears under varying parameters, this paper establishes an oil injection lubrication model under high rotational speeds of variable tooth thickness gears, based on computational fluid dynamics (CFD) methods and the Volume of Fluid (VOF) model, combined with the dynamic mesh technique. This paper analyzes the lubrication issues at the initial oil injection moment of involute variable tooth thickness gears. By computing the lubricant distribution state at 0.1 s after the oil injection onset based on the stabilized flow field under no-oil-injection condition, discussions are conducted on the single-phase and two-phase flow fields within the gear casing at different cone angles and rotational speeds separately examining the flow states near the oil nozzle and the distribution patterns of lubricant at the meshing portions. The results indicate that, without oil injection, the pressure near the oil nozzle gradually increases with an increase in rotational speed and decreases with an increase in cone angle; at the initial oil injection moment, the lubricant volume fraction at the gear meshing portions gradually increases with an increase in rotational speed and rises with an increase in cone angle.  
variable tooth thickness gear; cone angle; oil injection lubrication; VOF multiphase flow technique; lubricant distribution Article
During periods when wind turbines are operating close to their rated capacity (i.e., generating electricity of an amount equal to their nameplate) and so are rotating at their maximum speed (Figure 1a), the \(v_{t}\) of hydrometeors (Figure 1b) contributes \(\leq\)10% of the net closing velocity between the droplets and blade. A possible exception to this may occur if hydrometeors are caught up in downdrafts or downwards from convective systems [47]. 1. The number, size and phase of hydrometeors that impact the blade leading edge. The incubation, transition and steady-state progression of damage on leading edge [48] differs as a function of precipitation climate. There is evidence that larger drops are of greater importance in dictating the incubation period and that smaller drops are critical in the transition and steady-state progression [49]. Further, the materials response to hail (ice) differs from that to collisions with rain (liquid) droplets [35]. The maximum von-Mises stress created by impact of a 10 mm diameter hailstone on a blade leading edge greatly exceeds that from a rain droplet of equivalent size and closing velocity due to differences in mass and hardness [35]. Recent laboratory-based research found that for hailstone with diameters of 15 and 20 mm as few as five impacts at a closing velocity of \(\geq\) 110 ms\({}^{-1}\) were needed to cause damage to a glass fibre reinforced plastic composite coated with polyurethane [50]. Larger wind turbines with higher rated capacity and longer blades are now being deployed [51; 52; 53]. These wind turbines typically use lower rotational speeds, but the length of the blade means they typically have higher tip-speeds than smaller, and lower rated (or nameplate) capacity wind turbines, and thus they may experience higher rates of LEE. That coupled with increased deployments offshore [52] where maintenance costs are considerably higher [54; 55; 56], led to the International Energy Agency Wind Technology Collaboration Programme (IEA Wind TCP) to create Task 46. This task is designed to undertake cooperative research in the key topic of blade erosion. The goal is to improve understanding of the drivers of LEE, the geospatial and temporal variability in erosive events, erosion mechanics, the impact of LEE on wind plant performance and the cost/benefit of possible mitigation strategies.