esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Local Failure Probability Sensitivity Analysis
###### Abstract  
When assessing the reliability of a system, a mathematical model is often defined to replicate the system's behavior. The inputs of the system are then gathered into two categories, random inputs and deterministic inputs. The failure of the system depends on both categories and here we focus on the influence of the deterministic inputs. Local failure probability sensitivity analysis consists in computing the derivatives of the failure probability with respect to these deterministic parameters and is a fundamental step in reliability-based design optimization. These sensitivities also provide valuable insights into how specific model parameters affect the failure probability, allowing engineers and designers to make informed decisions about adjusting those parameters to enhance reliability. This article explores various techniques developed in the literature for assessing the sensitivity of failure probability with respect to distribution or design parameters. Depending on the nature of the deterministic parameters and the selected input space, different methods are available. The statistical characteristics of the resulting estimates as well as their computational cost are discussed here, for comparison purpose.  
failure probability; reliability-based sensitivity analysis; local sensitivity; reliability-based design optimization +
Footnote †: journal: _Sci._
# Optimization Design Study of Pipe Curtain Freezing Scheme for Sanya Estuary Submarine Channel
###### Abstract  
The artificial freezing construction technology, compared to other methods, offers several advantages, including superior waterproofing capabilities and the absence of environmental pollution. This technique is particularly prevalent in the construction of tunnels in challenging environments, where the dynamics of the freezing temperature field during the freezing process have consistently been a key area of interest during actual construction activities. In the Sanya Estuary Channel Submarine Tunnel Project, a three-dimensional transient model was developed using COMSOL finite element software to deeply analyze the formation and temperature distribution of the permafrost curtain. Two alternative schemes were designed to improve the original design by optimizing the layout of the permafrost pipeline. Comparative analysis shows that the isotherm \(-10\)\({}^{\circ}\)C intersected at 14 days in the original scheme, 23 days in Optimized Scheme 1, and 24 days in Optimized Scheme 2, indicating a 10-day delay in Scheme 2 versus the original, yet still meeting the 25-day deadline. After 40 days of active freezing, the greatest difference in permafrost curtain thickness was observed at the east wall (downstream), with Scheme 2 differing by 1.05 m from the original and by 0.23 m from Scheme 1. Scheme 2 achieved an average permafrost curtain thickness of 4.18 m around the tunnel, exceeding the 3.5-m design requirement. The mean temperatures in the strong and weak freezing zones of Scheme 2 were below \(-10\)\({}^{\circ}\)C and \(-8\)\({}^{\circ}\)C, respectively, aligning with design standards. Given the conservative nature of the initial plan, Optimized Scheme 2 is highly practical for implementation and offers significant cost savings.  
pipe curtain freezing method; numerical simulation; optimization design; freezing curtain 14, 11791. [[https://doi.org/10.3390/APpl142411791](https://doi.org/10.3390/APpl142411791)]([https://doi.org/10.3390/APpl142411791](https://doi.org/10.3390/APpl142411791))
# Evaluation and Validation of Viscous Oil Cavitation Model Used in Torque Converter
## 4 Parameter Study Results and Discussion  
### Influence of Cavitation on Hydrodynamic Performance  
The torque of pump, turbine and stator can be obtained by the simulation. The performance metrics are torque ratio (\(K=T_{\mathrm{T}}/T_{\mathrm{P}}\)), efficiency (\(\eta=K^{\star}SR\)) and capacity constant (\(CC=T_{\mathrm{P}}{}^{\star}D^{-5\star}\omega_{\mathrm{P}}{}^{-2}\)), and the overall performance is compared with experimental data (Figure 7). The mass flow rate (MF) was also extracted from the simulation results to reveal the influence of cavitation.  
The results revealed that cavitation deteriorated torque converter performance, which is the same as that of our previous studies [29]. When \(SR\leq 0.4\), the cavitation degree increased with the decrease of speed ratio and reached its maximum at stall operating condition (\(SR=0\)), and the cavitation phenomenon disappeared when \(SR>0.4\). The influence of cavitation on the performance of hydraulic torque converter mainly lies in torque ratio, and capacity constant. As a result of cavitation, the torque ratio decreased by 3.7%, and the capacity constant decreased by 21.9% as shown in Figure 7.  
The error comparison of turbine torque, torque ratio, efficiency and capacity constant between no cavitation and cavitation at various speed ratio under cavitating conditions is shown in Figure 8. It can be concluded that the error reaches the maximum under stall when cavitation was ignored, and the maximum capacity constant error was reduced from 19.6% to 6.7% by considering cavitation (Figure 8). However, a higher accuracy model could be achieved if proper empirical coefficients were adopted. Therefore, we studied the influence of empirical parameters to reveal the influence of the model parameters on  
Figure 6: Micro pressure test of internal flow field in torque converter.  
Figure 7: Comparison of torque ratio, efficiency, and capacity constant between no cavitation, cavitation, and test at various speed ratio under cavitating and non cavitating conditions.  
the cavitation performance and flow state and optimized the cavitation model for torque converter cavitation simulation.
# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles
## 5 Traffic Flow Models
### Macroscopic Models  
Macroscopic models consider traffic flow as continuous, connected with continuous models for fluids. They omit individual vehicles using variables such as density and flow.  
The first macroscopic traffic flow model was derived in hydrodynamic analogy by [PERSON] and [PERSON] [29] and, independently, [PERSON] [30]. Their model was later named the LWR model. But the hydrodynamic approach to traffic flow can be justified as a continuum only with a high density of vehicles. A partial differential equation for the conservation of vehicles is as follows:  
\[\frac{\partial k}{\partial t}+\frac{\partial q}{\partial x}=0 \tag{17}\]
# FODIT: A Filter-Based Module for Optimizing Data Storage in B5G IoT Environments
## 2 Background
### IoT Architecture in B5G Environments  
The IoT has evolved into a critical enabler of intelligent systems, with applications ranging from smart homes and cities to industrial automation and autonomous transport. At its core, IoT architecture is composed of five key components--devices and sensors, connectivity, data processing, user interface, and security--organized across four hierarchical layers: perception, network, edge computing, and application, as shown in Figure 1.  
The emergence of B5G technologies significantly augments this architecture by providing ultra-low latency, massive connectivity, and enhanced network intelligence. B5G introduces features such as AI-native networking [26], intelligent slicing [27], and advanced edge computing capabilities [20], which directly support dynamic and real-time IoT workloads. These advancements open new possibilities for distributed intelligence, collaborative edge processing, and context-aware data handling. However, they also exacerbate the challenge of managing the exponential growth in data volume generated by billions of interconnected devices.
# EEG-Based Measurement for Detecting Distraction in Coal Mine Workers
## 4 Results
### EEG Features Extraction
#### 4.2.2 Frequency Domain: Spectral Amplitude  
We examined the average FFT results from the whole-brain electrodes of both groups. Since no significant differences were observed in the overall average power across electrodes, we conducted comparative analyses at the channel level. Figure 7 illustrates the mean power spectra within the 1-40 Hz frequency band at the O1 channel for both groups, along with their significance levels. The power spectral intensity shows that, compared to the focused group, the distracted group exhibited elevated EEG power spectra in the mid-to-high frequency range (8-40 Hz, encompassing \(\alpha\) and \(\beta\) bands). This aligns with the findings of [PERSON] et al., suggesting that increased \(\alpha\) and \(\beta\) band power may be associated with a decline in alertness and attention levels [39].  
To elucidate the differential EEG power distribution across frequency bands between the two groups, we segmented the power spectra into four key bandwidths: \(\delta\) (1-4 Hz), \(\theta\) (4-8 Hz), \(\alpha\) (8-12 Hz), and \(\beta\) (12-30 Hz). We excluded the \(\gamma\) band (above 30 Hz) due to weak signal strength and susceptibility to noise. Figure 8 shows topographical maps illustrating the average power in these bands and their statistical significance. The focused group exhibited higher mean power in the \(\delta\) and \(\theta\) bands compared to the distracted group, with significant differences in the \(\delta\) band over the right temporal region (T8) and in the \(\theta\) band across the frontal areas (FP1, FP2). Conversely, the distracted group showed elevated mean  
Figure 6: P300 peak amplitudes and \(p\)-values across channels for two groups.  
Figure 7: Spectral amplitude and statistical analysis for two groups. (**a**) Spectral amplitude on O1 channel; (**b**) corresponding \(p\)-values at each frequency band.  
power in the \(\alpha\) and \(\beta\) bands, with significant differences in the \(\alpha\) band in the left temporal and occipital areas (TP7, T7, P3, P1) and in the \(\beta\) band in the occipital and parietal areas (O1, PO7, C3, C4). We incorporated the average power from these significant electrode frequencies as indicators for subsequent investigations.
# Directional Paging for 5G Communications Based on Partitioned User ID
###### Abstract  
The millimeter-wave (mmWave) spectrum is one of the important propositions of 5G mobile networks due to its ability to accommodate massive traffic demands and an ever-increasing number of wireless devices. The beam-formed directional technique overcomes the propagation and path loss challenges of mmWave high frequencies. Though the directional convergence is expected to unleash new possibilities, it cannot be implemented with conventional power-saving solutions optimized over omnidirectional communications. Paging broadcast, for users in Idle Discontinuous Reception (IDRX) mode for energy saving, is one such necessary function in a wireless communication that needs modification in highly directional beam-based transmissions. Due to the limited spatial coverage of directional beams, the paging transmission takes place over multiple beams, which increases the paging resource overheads of the network substantially. In this article, we present a novel paging mechanism for the directional air interface in mmWave-enabled 5G communications. Numerical analysis of the proposed Partitioned UE ID-based Directional Paging (PIDP) mechanism reduces the paging resource overheads of the network, resulting in a 15% gain in power savings compared to directional paging transmission without the UE ID partition.  
5G communications; DRX; UE ID; paging; beams; mmWaves +
Footnote †: journal: Sensors
## 4 Discussion  
### CGAN Performance Towards Predicting S2-like Spectral Bands  
CGANs were effective in translating S2-like spectral bands from L8 bands. They reduced the spectral difference between the spectral bands available in both sensors (i.e., green and NIR). CGANs also spatially enhanced the original L8 images to match the target S2 spectral bands. Of the two approaches used to predict the additional S2-like RE bands from L8, the multistep method produced qualitatively better and more consistent results than the direct approach. Breaking the more complex task of predicting S2-like bands from L8 into two smaller tasks--translating the corresponding S2-like band from the L8 band, and then translating the S2-like band from the previous step to the target S2-like red edge band, produces better quality images. However, the direct approach performed quantitatively better. Multistep CGANs benefited from the intermediate step since there is a stronger relationship among the bands of each step (L8 NIR to S2 NIR and S2 NIR to S2 red edge) compared to the relationship between L8 NIR and the S2 RE bands. CGANs learned the finer spatial features from S2 bands, and this was most evident for multistep methods since the network could learn from two sets of data (intermediate and target S2 images). This feature of CGANs should be exploited for generating images at a higher spatial resolution than the original images by incorporating additional finer resolution imagery if possible. Therefore, CGANs can effectively learn the spatial as well as spectral representations between the two  
Figure 8: S2-like RE1, RE2, and RE3 bands predicted by direct and multistep methods. (**A**) L8, (**B**) S2-like predicted by direct, (**C**) S2-like predicted by multistep, and (**D**) target S2. All images are stacked as, R: RE3, G: RE1, and B: RE2.  
satellite products and do not simply reduce the per-pixel average error between the input and target images.
# ODIN112-AI-Assisted Emergency Services in Romania
## 3 Method
### Speech Emotion Recognition Model  
#### 3.4.1 Speech Pre-Processing  
Our aim is to develop a system for emergency services. As a consequence, the speech data to be processed is recorded from a telephone. Hence, it passed through several coding/decoding steps in the communication network and is sampled at 8 kHz. The training and testing datasets must reflect the nature of the real speech signal. For this reason, we coded and decoded all recordings with a Full Rate GSM (GSM-FR) codec, assuming that currently, most calls to emergency services are from mobile terminals. We consider the Full Rate GSM codec as a worst-case scenario. The speech quality (measured using mean opinion score-MOS) of the decoded GSM-FR speech is lower than the quality of current codecs (e.g., Adaptive Multirate Nawor Band-AMR-NB and Adaptive Multirate Wide Band-AMR-WB) [39; 40]. As such, our model will also work with better codecs if satisfactory results are obtained when classifying emotional speech samples coded/decoded with the GSM Full Rate codec.  
All recordings are segmented in 1-second frames, with a stride of 10 ms. For each frame, a log-spectrogram (see Figure 8) is computed. The log-spectrograms are transformed to RGB images after applying dynamic range normalization between \(-90\) dB and \(-7\) dB. This processing is similar to [30].  
Figure 8: Sample spectrogram used as input to the neural network.  
Figure 7: Training pipeline for Automatic Speech Recognition system.
# Stock Price Prediction Using a Frequency Decomposition Based GRU Transformer Neural Network
## 4 Experiment
### Experiment Results
#### 4.4.2 Comparison with Baseline Frequency Decomposition  
The experiments with different frequency decomposition techniques have been done for comparison purpose. CEEMD, together with wavelet transform [55], Fourier transform [56], and no decomposition have been studied respectively for each experiment. The result is showed in Table 3. It is indicated that CEEMD is the optimal choice. Wavelet is also a strong frequency decomposition technique, which can remove high frequency noise from the original time series. However, in this case, it is tricky to decide the threshold for the noise, as it might lose some information compared with CEEMD, while the latter keeps all the information of the price series. As the reason for Fourier transform, it is because that the transform has the assumption that the original series can be divided into several periodical series, but this is not reasonable for stock price series, otherwise everyone can make profits according to the fixed price movement.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Hyper-Parameter** \\ \hline Optimization & Adam \\ Initial Learning Rate & 0.001 \\ Exponential Linear Decay & 0.9 \\ Epoch Number & 75 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiment Hyper-parameter Details.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & \(\mathbf{r^{2}\) (\%) \(\uparrow\)} & **MSE (\%) \(\downarrow\)** & **MAE (\%) \(\downarrow\)** \\ \hline DeepLOB [28] & \(2.36\pm 0.214\) & \(0.0302\pm 0.00219\) & \(0.0945\pm 0.00450\) \\ DeepAtt [54] & \(1.75\pm 0.157\) & \(0.0317\pm 0.00218\) & \(0.0932\pm 0.00451\) \\ MHF [29] & \(1.29\pm 0.104\) & \(0.0337\pm 0.00219\) & \(\mathbf{0.0896\pm 0.00460}\) \\ FDG-Trans & \(\mathbf{2.88\pm 0.168}\) & \(\mathbf{0.0291\pm 0.00217}\) & \(0.0917\pm 0.00446\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performances of FDG-Trans and 3 other hybrid models.
## 3 Results  
### Fatty Acid Composition  
The fatty acids (FAs) present in oils are crucial for the evaluation of oils, as they exert a substantial impact on the quality and application of oils. The FA profiles of all cold-pressedhemp seed oils are shown in Table 1. Oil pressed from the _Henola_ cultivar had 88% to 90% UFA, with the dominating PUFA accounting for 74% to 76% and saturated fatty acids (SFAs) accounting for 10% to 11%. These results are comparable with the FA profile of another Polish hemp variety, _'Bialobrzeskie'_[6].  
UFA, SFA, MUFA, and PUFA totals did not show notable differences in samples pressed from stored (HP_HLC an HP_HLD) and fresh (HP_HLA, HP_HLB, and HP_HLE) seeds. However, individual FA content showed significant differences (\(p\leq 0.05\)) in terms of the most abundant PUFA linoleic acid (C18: 2; LA) and \(\alpha\)-linolenic acid (C 18: 3 n\(-\)3; ALA). The lowest LA content for HP_HLA (47%) and the highest for both HP_HLC and HP_HLD (55%) were determined. Amongst other PUFAs, ALA ranged from 17 to 25%, with the highest concentration noted for HP_HLA. The presence of a unique acid was detected, i.e., steardionic acid (C18: 4, SDA), in the range of 1.23 to 1.58%, which makes this oil nutritionally more valuable. A comparatively lower presence of steardionic acid (1.2%) was previously reported by other researchers [18]. SDA acts as a biological precursor for longer-chain n\(-\)3 fatty acids, which can be rapidly synthesized in the human body, i.e., eicosapentaenoic acid (EPA; C20: 5 n\(-\)3) and docosahexaenoic acids (DHA; C22: 6 n\(-\)3) [19]. Oleic acid (C18: 1) was found in the concentration range of 12 to 14%. The most abundant SFAs were found to be palmitic acid (C16: 0) and stearic acid (C18: 0), accounting for a range of 6.59-6.99% and 2.69-2.71% of total FAs, respectively. Among the remaining fatty acids, none exceeded a 1% contribution to the total FA content. The ratio of n\(-\)6 to n\(-\)3 fatty acids ranged from 2: 1 to 3: 1, as shown in Table 1. A similar result regarding the n\(-\)6/n\(-\)3 ratio was obtained for the same _Henola_ variety by [PERSON] et al. [6] at the level of 2.87 and for commercial hemp seed oils from 2.96 to 3.27 by [PERSON] [20]; however a lower quantity of PUFAs was reported.
## 3 Simulation Results and Analysis
### Results and Discussion: Torque Performance
The torque ripple is caused by the 5 th and 7 th air-gap harmonic magnetic fields and MMFs produced by the currents of the stator windings. It is indicated that the harmonic torque produced by air-gap harmonic magnetic fields and slot effects influences the machine performance most seriously and should be reduced by slot or rotor skewing. Due to the use of the fractional-slot winding in the optimized structure, the torque ripple can be effectively reduced compared to the original structure according to the simulation results. Similarly, the torque ripples of the two structures significantly decreased after the slot is skewed while with a slight decrease in the average output torque. It shows that the optimized structure is beneficial to reduce the torque ripple and noise in the aircraft application. Figure 10: Simulation results of cogging torque waveforms under the no-load condition: (**a**) skewed-slot angle is 0\({}^{\circ}\); and (**b**) skewed-slot angle is 7.5\({}^{\circ}\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Skewed Angle** & **The Original** & **The Optimized** \\ \hline Cogging torque & 0\({}^{\circ}\) & 3.1 N\(\cdot\)m & 2.9 N\(\cdot\)m \\ (peak-to-peak value) & 7.5\({}^{\circ}\) & 0.9 N\(\cdot\)m & 0.8 N\(\cdot\)m \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cogging torques with or without skewed slot of both structures.
# Forward Link Optimization for the Design of VHTS Satellite Networks
## 1 Introduction
In addition, there are currently well-studied methodologies that estimate and optimize wireless communication network parameters, particularly those applied to Wireless Local Area Network (WLAN) [18; 19; 20]. However, the general objective of this paper is to evaluate and optimize VHTS systems whose link conditions, architecture, and cost structure are very specific and rather different to other wireless communication systems. This work proposes an optimization methodology to design VHTS systems based on geostationary platforms in order to obtain the minimum cost per Gbps in orbit, where the optimization depends not only on the number of beams, but also on the user \(G\)/\(T\) and the link availability, according to a set of technical and commercial requirements. This methodology minimizes the cost per Gbps in orbit by maximizing the capacity of the channel per beam based on an analytical expression of the average \(\mathit{CINR}\) as a function of the number of beams, the user \(G\)/\(T\) and the link availability. Therefore, optimal values can be obtained straightforwardly. It is also important to notice that the ground segment is included in the calculation of the system cost and cost per Gbps in orbit, as well as the impact of using CCM or VCM (Constant and Variable Coding and Modulation, respectively) in the satellite capacity. The main contributions of this work are listed below:  
* An analytical model that calculates the \(\mathit{CINR}\) in the forward link, depending on the number of beams, the user \(G\)/\(T\), and the link availability;
* The use of Q/V bands in the feeder link is considered;
* The beamwidth of the satellite beams is not fixed, allowing the number of spots in the service area to be changed;
* The user station \(G\)/\(T\) can be selected from a set of possible values;
* System cost model considers both satellite and ground segment costs;* Link availability in the service area is considered to evaluate system performance with CCM and VCM. This paper is organized as follows: Section 2 analyzes the VHTS scenario and presents the optimization problem. Section 3 details the models and requirements that will be used to evaluate the VHTS system and proposes an analytical model for the calculation of the CINR. Section 4 explains the evaluation methodology for dimensioning and optimizing VHTS systems. Section 5 evaluates the methodology in two scenarios presenting the trade-offs and decision factors for selecting optimal values of system parameters and evaluating the capacity of the system using CCM and VCM. Finally, Section 6 draws the conclusions of the paper.