esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### RREQ Compare Mechanism  
Assuming we have three intermediate nodes (A, B, C) waiting to forward the RREQ to the next node, each node is required to present three conditions, namely, the angle, distance, and energy level to be calculated by criterion function. If node A has a lower angle, distance, and highest energy level and A is greater than B and C, then the nodes will sort in ascending order. The value of A considers as a threshold. If the node \(n\) is greater than A, the threshold will be terminated and replaced with node \(n\).  
```
1. Input:
2. Node
3. Angle
4. Distance
5. Battery Level
6. \(N\) //number of selected nodes
7. Output:
8. Threshold
9. Start:
10. for = 1: until \(N\) //number of selected solutions
11. Select three individuals Nodes A, B, C intermediate nodes
12. Compute the node Angle (Angle)
13. Compute the node distance (distance)
14. Compute node Energy Level (Energy)
15. Criterion Function
16. better Angle = A\({}_{-}\) Angle < B\({}_{-}\) Angle
17. same Angle = A\({}_{-}\) Angle == B\({}_{-}\) Angle
18. better Distance = A\({}_{-}\) Distance < B\({}_{-}\) Distance
19. same Distance = A\({}_{-}\) Distance == B\({}_{-}\) Distance == C\({}_{-}\) Distance
20. better Battery Level = A\({}_{-}\) Battery Level > B\({}_{-}\) Battery Level > C\({}_{-}\) Battery Level
21. if (better Angle)
22. or (same Angle and better Distance)
23. or (same Angle and Distance and better Battery Level)
24. Then
25. add node A to selected Threshold
26. Else
27. Compare nodes B, C to be Threshold
28. end
29. End
30. End
```  
**Algorithm 1** Pseudocode of Route Request Packet (RREQ) comparison
# Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information
## 3 Preliminaries  
### Channel State Information  
RSSI belongs to the MAC layer information, which is easy to obtain, but the stability is low. When the environment becomes complex, it will be subject to the influence of multipath effects in the environment, and RSSI will be the superposition value of multipath signals. However, CSI is the application of physical layer OFDM with a finer granularity of 30 subcarriers. It can distinguish signals from multiple different paths. Specifically, CSI can be described as:  
\[Y(f_{\kappa})=H(f_{\kappa})X(f_{\kappa})+N \tag{1}\]  
\(X(f_{\kappa})\) is used as the input signal; \(Y(f_{\kappa})\) is the accepted signal; \(H(f_{\kappa})\) is the channel state information matrix on each subcarrier; \(N\) is the environmental noise. Therefore, the CSI of each subcarrier can be approximated as:  
\[H(f_{\kappa})\approx\frac{Y(f_{\kappa})}{X(f_{\kappa})} \tag{2}\]  
According to the 802.11n physical layer protocol, the CSI will be divided into 56 subcarriers in the 20-MHz band mode. Each carrier frequency is \(f_{k}=f_{centre}+0.3125k\), where \(k\) corresponds to the carrier index from \(-\)28-1, 1-28. Each carrier interval is 0.3125 MHz. \(f_{centre}\) is the centre of the carrier frequency.  
Intel 5300 NIC output is the CSI of 30 subcarriers because the CSI value is one output for each of the two adjacent carriers. That is, the received CSI is \(H=[H(f_{1})\cdots H(f_{k})]\). Similarly, if the frequency band of 40 MHz is used, according to the protocol, the number of subcarriers will be 114, and the carrier interval remains unchanged. By using MIMO technology, we assume that the number of transmitting antennas and receiving antennas is N\({}_{tx}\) and N\({}_{rx}\), then the CSI dimension of the multi-antenna is N\({}_{tx}\times\) N\({}_{rx}\times\) 30. \(H_{k}=\parallel H_{k}\parallel e^{-j\sin(\angle H_{k})}\), \(\sin(\angle H_{k})\) and \(\parallel H_{k}\parallel\) are the phase and amplitude of the \(K\) subcarrier respectively.  
The phase will be what we are going to use in the MUSIC algorithm. The Channel Impulse Response (CIR) is obtained by inverse Fourier transform of the Channel Frequency Response (CFR). Then, the multipath channel in the time domain can be expressed as \(h(\tau)=\sum_{i=1}^{N}a_{i}e^{-\beta_{i}(\tau-\tau_{i})}\), and the multipath function CIR reflects the multipath channel condition at the receiver. \(a_{i}\), \(\theta\), \(\tau_{i}\) are expressed as the amplitude, phase offset and transmission delay of the \(i\)-th path. \(N\) is the total number of transmission paths.  
Recently, [PERSON] obtained the Channel State Information (CSI) by modifying the Intel 5300 network card driver from the ordinary WiFi device. In 802.11a/g/n, the CSI is the sampled version of CFR. Meanwhile, the Atheros CSI tool proposed by [PERSON] is supposed for all types of Atheros 802.11n WiFi chipsets now. Since the subcarriers of different frequencies are not exactly the same in space, it is possible to better characterize the space by CSI and improve the accuracy of WiFi localization.
## 4 Results
#### 4.1.2 Correlation Between Antenna Geometry and Performance Metrics
Antennas classified as \"other\" demonstrate the highest average gain, approximately 8 dBi, along with a wide distribution and significant outliers on both ends. This pattern reflects the heterogeneity of this category and the potential for both high and low gain, depending on the specific design. Slot antennas, with a mean gain of roughly 3 dBi, show a tight distribution with low outliers, indicating generally stable and moderate gain values. Spiral antennas also have a mean gain around 3 dBi, coupled with a narrow distribution and low outliers, reflecting low variability and typically low gain characteristics. Fractal antennas, averaging around 5 dBi, exhibit a relatively narrow distribution but include both high and low outliers, suggesting generally stable behavior with occasional deviations that may lead to either enhanced or reduced gain, depending on the application and design specifics. Comparative insights  
Comparative analysis of antenna designs for energy harvesting reveals distinct performance profiles in terms of area, efficiency, operating frequency, and gain. Spiral antennas stand out due to their exceptional compactness (mean \(\approx\) 1000 mm\({}^{2}\)) and the highest efficiency (mean \(\approx\) 96%) with minimal variability, although they typically operate at lower frequencies (\(\approx\)1.8 GHz) and offer modest gain. Similarly, fractal designs achieve high efficiency (mean \(\approx\) 80%) and stable performance at higher frequencies (\(\approx\)2.5 GHz), despite moderate variability in area. In contrast, the \"others\" category, while requiring the largest mean area (\(\approx\)7000 mm\({}^{2}\)), yields the highest average gain (\(\approx\)8 dBi), albeit with significant variability across all metrics. Array antennas offer a good mean gain (\(\approx\)6 dBi) and stable frequency behavior (\(\approx\)2.4 GHz), balanced by moderate variability in both area and efficiency. Rectangular, slot, and circular designs generally exhibit moderate requirements in terms of area, efficiency, and gain, with varying degrees of spread and presence of outliers, reflecting diverse performance consistency. Finally, bow-tie antennas consistently deliver stable and modest performance in efficiency (\(\approx\)60%), lower operating frequencies (\(\approx\)2.0 GHz), and gain (\(\approx\)5 dBi), along with moderate area and limited variability.
## 5 Simulation Results
### Single-Entry Interference from Starlink to NTN  
Within this scenario, we estimated interference levels and throughput losses over several minutes as the NTN satellite served a user while an interfering Starlink satellite passed nearby. The average distance between the NTN satellite and the Starlink satellite was approximately 1900 km. Our results are presented in CDF curves illustrating the levels of signal-to-noise degradation and the associated throughput losses. Figure 10 displays the CDFs of the single-entry interference from the Starlink satellite operating in the 1990-1995 MHz frequency band to the NTN system.  
Our results indicate that, in the case of single-entry interference, the throughput loss amounts to 2.5%. Generally, a 5% throughput loss is considered acceptable. However, this criterion is typically applied to terrestrial LTE/NR systems. Given the NTN satellite's limited uplink link budget for D2D services, even a 2.5% throughput loss can substantially impact the performance of the NTN system.
# Numerical Investigation of Tonal Trailing-Edge Noise Radiated by Low Reynolds Number Airfoils
###### Abstract  
A high-fidelity computational analysis carefully validated against concurrently obtained experimental results is employed to examine self-noise radiation of airfoils at transitional flow regimes, with a focus on elucidating the connection between the unsteady behavior of the laminar separation bubble (LSB) and the acoustic feedback-loop (AFL) resonant interactions observed in the airfoil boundary layers. The employed parametric study examines AFL sensitivity to the changes in the upstream flow conditions and the airfoil loading. Implicit Large-Eddy Simulations are performed for a NACA-0012 airfoil in selected transitional-flow regimes for which experimental measurements recorded characteristic multiple-tone acoustic spectra with a dual ladder-type frequency structure. The switch between the tone-producing and no-tone-producing regimes is traced to the LSB size and position as a function of the flow Reynolds number and the airfoil angle of attack, and further substantiated by the linear stability analysis. The results indicate a strong multi-tonal airfoil noise radiation associated with the AFL and attributed to the switch from the slowly-growing Tollmien-Schlichting to the fast-growing Kelvin-Helmholtz instabilities occurring in thin LSB regions when those are localized near the trailing-edge (TE) on either side of the airfoil. Such a process eventually results in the nonlinearly saturated flapping vortical modes (\"rollers\") that scatter into acoustic waves at the TE.  
a 2021 11 2257 11.11.11.11.11.11.11.11.11.1.11.1.11.1.11.1.11.1.1.1.11.1.  
and its connection to the acoustic feedback-loop process still requires further clarification. Thus, the current work focuses on numerically investigating the underlying physical phenomena and their effects on the mechanisms responsible for the generation of airfoil tonal noise in transitional flow regimes. To achieve this, the paper is organized as follows. In Section 2, we first reference the concurrent experimental study employed for validation of our numerical analysis. As the current work focuses on an Implicit Large-Eddy Simulations (ILES) approach for numerical experiments, the method and its implementation are discussed in detail. The validation of the 2D numerical analysis through comparison with 3D results is presented in Section 3. Section 4 is split into multiple parts describing the effects of the angle of attack (AoA) and Reynolds number (\(Re_{c}\)) on the airfoil transition from the tone-producing to no-tone-producing regimes, with the focus on the linear-stability analysis of the behavior of the laminar separation bubble (LSB) and evolution of viscous/inviscid instabilities during the transition process. The frequency selection process and the resulting spectral ladder-type frequency staging are examined as well. The final discussion of the obtained results is presented in Section 5.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 5 Evaluation Protocols and Metrics
### Challenges in Evaluation and Benchmarking  
Despite the advancements in evaluation metrics, there are still challenges in evaluating and benchmarking DVC models.  
1. Diversity of Video Content Videos contain a wide range of events, contexts, and interactions, making it difficult to create a comprehensive set of reference captions that capture all possible descriptions. This diversity can lead to inconsistencies in evaluation results.
2. Semantic Similarity Current metrics rely heavily on n-gram matching and may not accurately capture the semantic similarity between captions. This can result in models being penalized for generating captions that are semantically correct but differ in wording from the references.
3. Temporal Localization DVC involves not only generating accurate captions but also locating the events in the video. Evaluating the temporal localization of events is challenging, as it requires precise annotations and may not be fully captured by existing metrics.
## Materials and Methods
### Bentchtop Transceiver Testing
#### 2.3.2 BER Measurement
In Equation (4), \(p\) is the packet position from Equation (3), \(T_{\text{symbol}}\) is the duration of a PPM symbol, \(S_{p}\) is the modulated string value for position \(p\), and \(T_{\text{slot}}\) is the duration of a PPM slot:  
\[m\cdot T_{\text{string}}+p\cdot T_{\text{symbol}}+S_{p}\cdot T_{\text{slot}} \approx n\cdot T_{\text{counter}}+t_{\text{recorded},1} \tag{4}\]  
Equation (4) is solved by successively increasing \(m\) and \(n\) until both sides match with an acceptable margin of error, within less than 1 ns. The time stamps are now on a common timebase, with no uncertainties due to counter length or string repetition. The Bit Error Ratio (BER) can be estimated by building an histogram of the time errors. The average of the error is removed from histograms, since each set of the acquired data covers several minutes, and a real-time decoder clock recovery mechanism would cancel long term clock slips. For each time stamp, the error from the expected time of the pulse is used to compute the number of errors by symbol. If the error \(\varepsilon\) is smaller than \(1/2\cdot T_{\text{slot}}\), then there is no error. If \(\varepsilon>1/2\cdot T_{\text{slot}}\), but \(\varepsilon<3/2\cdot T_{\text{slot}}\), then there is up to 1 bit wrong in the symbol. If \(\varepsilon>3/2\cdot T_{\text{slot}}\), but \(\varepsilon<5/2\cdot T_{\text{slot}}\), then there is at most 2 bits wrong in the symbol. This was repeated for the number of bits contained in the PPM symbol. The average error over the recorded pulses gives the Bit Error Ratio.
## 3 Results  
Descriptive statistics on anthropometric data, biochemical analysis, free PUFA concentrations in plasma, and dietary intakes are presented in Table 1. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**All _n =_ 196**} & \multicolumn{2}{c}{**Females**} & \multicolumn{2}{c}{**Males**} & \multirow{2}{*}{_p Value * (Males vs. Females)_} \\ \cline{2-2} \cline{5-6}  & **Median** & **IQR** & **Median** & **IQR** & **Median** & **IQR** \\ \hline \multicolumn{6}{c}{**Anthropometric data**} \\ \hline Age (years) & 12 & 5 & 12.00 & 5.0 & 12.00 & 4.75 & 0.787 \\ zBMI & 3.13 & 1.20 & 2.83 & 1.21 & 3.44 & 1.37 & 0.001 * \\ \hline \multicolumn{6}{c}{**Biochemical analysis**} \\ \hline TC (mg/dL) & 174.00 & 54.00 & 166.00 & 52.00 & 182.50 & 55.50 & 0.109 \\ TG (mg/dL) & 129.00 & 86.50 & 125.00 & 88.00 & 133.00 & 99.75 & 0.237 \\ HDL (mg/dL) & 46.00 & 18.00 & 43.00 & 19.00 & 48.00 & 6.75 & 0.085 \\ AST (U/L) & 29.00 & 14.00 & 29.00 & 14.00 & 29.50 & 15.50 & 0.127 \\ ALT (U/L) & 32.00 & 15.00 & 31.00 & 16.00 & 33.50 & 14.75 & 0.167 \\ CRP (mg/dL) & 4.70 & 7.90 & 4.80 & 8.70 & 4.75 & 7.72 & 0.315 \\ Homocysteine (\(\mu\)mol/L) & 14.78 & 8.78 & 14.16 & 8.08 & 15.33 & 9.28 & 0.398 \\ \hline \multicolumn{6}{c}{**Measurements of free PUFA in plasma**} \\ \hline ALA (\(\mu\)mol/L) & 3.72 & 4.27 & 3.24 & 3.95 & 4.09 & 4.54 & 0.145 \\ EPA (\(\mu\)mol/L) & 0.23 & 0.14 & 0.2 & 0.13 & 0.26 & 0.14 & 0.008 * \\ DHA (\(\mu\)mol/L) & 6.26 & 4.82 & 5.88 & 4.01 & 6.76 & 6.09 & 0.217 \\ LA (\(\mu\)mol/L) & 137.2 & 122.12 & 126.39 & 105.35 & 159.08 & 124.62 & 0.215 \\ ARA (\(\mu\)mol/L) & 7.15 & 4.44 & 6.8 & 4.05 & 7.78 & 4.82 & 0.025 * \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Descriptive statistics for anthropometric data, biochemical analysis, measurements of free PUFA in plasma, and dietary intakes. The frequencies of _FADS2_ gene single nucleotide polymorphisms studied within 196 subjects are shown in Table A1. There were significant differences in the levels of plasma free EPA and ARA between males and females (\(p\) = 0.008 and \(p\) = 0.025 respectively), with males having higher levels of these fatty acids than females. [PERSON]'s rho test results are presented in Table 2, indicating the degree of correlation between plasma free PUFAs species and the corresponding estimated intake amounts. Positive correlations were found between EPA and DHA intakes, between ALA and EPA levels in plasma, between ALA and DHA levels in plasma, and between EPA and DHA in plasma. Significant differences in the distribution of free DHA levels were noted between the rs526126 _FADS2_ genotypes, with heterozygotes having higher levels of this fatty acid as compared with the CC genotype (Figure 1A). Following data grouping according to DHA and EPA dietary, significant differences in DHA plasma levels were found between GC
## 2 Technology Selection
### Demonstration and Future Planned Projects Worldwide
[30] considered two control loops to also maintain a CO\({}_{2}\) recovery of 87% or higher where equipment sizing was based on this criterion and a stream purity of 95%. [PERSON] et al. [31] investigated three possible control strategies, namely: a standard PID feedback, a cascade PID scheme, and a model predictive control scheme. All three strategies exhibited acceptable disturbance rejection and setpoint tracking, given that the MPC showed the best performance in terms of settling time. [PERSON] et al. [32]developed a mathematical black-box model to analyze the dynamic behavior of the PCC pilot plant through manipulating the lean solvent flow rate and reboiler duty while varying the flue gas flow rate and feed CO2 concentration as disturbance variables. Results were based solely on the modeled pilot plant and its operation during the data collection but not referring to the theory behind the PCC process. [PERSON] et al. [26] developed a rate-based model for the absorber column using the Kent-Eisenberg model for simplicity, improving the previously published model [33] by introducing the transfer of MEA. The model was validated against steady-state experimental operation data, and no control structure parameters were discussed. [PERSON] et al. [34] developed a dynamic model using the simulation tool gPROMS accounting for mass transfer with chemical reactions assuming they were in equilibrium. The data logs were based on a pilot plant, and the validated model was used to study the effect of an increase in flue gas moisture content and the effect of intercooling on the process. The majority of the studies focused on the dynamic behavior for the stand-alone model or in response to disturbances from the power plant. In the current work, which includes the P2J pathway, other aspects such as curtailed energy, electrolysis etc., are included. In this work, a detailed process design and optimization of the complete capture process is described. The outcome study is then used to operate the process units dynamically and investigate open- and closed-loop strategies. The proposed control scheme is tested for disturbance rejection and set point tracking. The novelty of the model lies in the final integration with a parallel electrolysis stage where the carbon capture model keeps track of the hydrogen production by producing the stoichiometric required CO2 to eliminate the need for any underground storage requirement.
# Damage Detection Method Based on Continuous Wavelet Transformation of Lamb Wave Signals
## 2 Damage Estimation Method Based on CWT
### Normalized Coefficient of CWT  
The coefficient of CWT for square-integrable function \(f(t)\) in the time domain can be written as,  
\[CWT_{f}(a,b)=\ \frac{1}{\sqrt{a}}\int_{-\infty}^{+\infty}f(t)\overline{\psi} \bigg{(}\frac{t-b}{a}\bigg{)}\mathrm{d}t, \tag{2}\]  
where \(a>0\) and \(b\in\mathbb{R}\) are scale and time-shift parameters of wavelet function \(\psi(t)\), respectively. During the transform, a dynamic signal is represented using these dual parameters. In this research, the scale is set as the ratio between sampling frequency and central frequency as  
\[a=\ \frac{F_{s}}{F_{c}} \tag{3}\]  
Figure 1: Time-of-flight (ToF)-based damage triangulation.  
where \(F_{s}\) is the sampling frequency calculated using the sampling period as \(F_{s}=1/\Delta\), and \(F_{c}\) is the central frequency of the excitation signal. The bar accent represents complex conjugate of the wavelet function \(\psi(t)\). The selection of the mother wavelet function \(\psi(t)\) is crucial for CWT applications. The Gabor wavelet function was chosen in this research for its higher time resolution to capture the ToF accurately. It has an accurate time-frequency feature extraction capability in ToF-based damage localization problems [11]. The Gabor wavelet function can be expressed as  
\[\psi_{G}(t)=\ \frac{1}{\sqrt[4]{\pi}}\sqrt{\frac{\omega_{0}}{\gamma}}\exp[- \frac{(\omega_{0}/\gamma)^{2}}{2}t^{2}]\exp(i\omega_{0}t), \tag{4}\]  
where \(\omega_{0}\) and \(\gamma\) are the real constants. \(\omega_{0}=2\pi\), so that the value \(1/a\) becomes equal to frequency \(f=\ \omega/2\pi\), and \(\gamma=\pi\sqrt{2/\ln 2}\) was set to satisfy the following admissibility condition:  
\[\int_{-\infty}^{\infty}\abs{\psi(\omega)}^{2}/\omega\mathrm{d}\omega<\infty. \tag{5}\]  
For the monitoring region discretized into a number of grid points, the possibility of damage existence at each grid point is directly defined by the coefficient of CWT for each actuator-sensor pair. The application of the coefficient of CWT, based on the Gabor wavelet, on damage-scattered signals constructs a CWT envelope for each pair. The possibilities of damage existence are expressed within the range from 0 to 1 with the maximum possibility at a grid point with value 1. Therefore, all damage-scattered signals, \(f_{D}(t)\), were normalized by dividing with the maximum absolute value of amplitude of the corresponding signal as,  
\[f_{D}(t)=\frac{f_{D}(t)}{\max(\mathrm{abs}(f_{D}(t)))} \tag{6}\]  
The Gabor wavelet coefficient modulus of the normalized damaged-scattered signal then extracts the damage information for further processing of damage image. It is performed as  
\[I(b)=\mathrm{abs}\bigg{(}CWT_{f_{D}(t)}(a,b)\bigg{)} \tag{7}\]
# Enhancing Reactive Ad Hoc Routing Protocols with Trust
## 3 The Proposed Method of Trust-Aware Reactive Ad Hoc Routing
### Definition of the Time Unit  
Our simulation does not model real devices. Instead, it reproduces the results of their behavior. Thus, the simulation does not include the precise timing of the communication in a network. We decided to establish a reference operation that takes for simplicity constant time. This operation is the retransmission of the message to the next hop under ideal conditions. In reality the retransmission is not an atomic operation, and it is influenced by many factors such as node performance or the amount of data traffic going through that node. We expect that all nodes are identical and the data traffic is not too dense, therefore we could neglect these factors. We define _Time Unit (TU)_ to represent the retransmission time. All metrics, which are related to time, use _TU_ as a unit.