esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Hybrid Ant Colony Optimization for a Multicast Routing Problem
## 4 ACO&CM Algorithm
### Path Construction
Path construction procedure steps are given as the following:  
**Step 1**: Initialize \(CL=\emptyset\); path set \(PL=\emptyset\); Initialize set \(S=\{s\}\); \(S_{m}=M\), \(N_{ant}=\left\lfloor M\right\rfloor\);
**Step 2**: Select randomly an ant from the ant set \(N_{ant}\);
**Step 3**: Set \(CL=\emptyset\);
**Step 4**: Let the source node \(s\) be the current point, \(v_{i}=s\). Each ant begins from the node \(v_{i}\) to find the path to the destination node;
**Step 5**: Each ant chooses the next node \(v_{j}\) which connects with current node using formula (8). Every path satisfies the QoS constraint conditions. **Step 6**: Check self-loop or inter-loop. If \(v_{j}\) is neither in set \(CL\) nor in set \(PL\), then add \(v_{j}\) to set \(CL=CL\cup\{v_{j}\}\); otherwise go to Step 5;  
**Step 7**: If \(v_{j}\) is included in \(S_{m}\) then add \(v_{j}\) to set \(CL=CL\cup\{v_{j}\}\), and move \(v_{j}\) from \(S_{m}\), \(S_{m}=S_{m}/\{v_{j}\}\), add the \(CL\) to \(PL\), \(PL=PL\cup CL\); otherwise, the node \(v_{j}\) is regarded as the current node, \(v_{i}=v_{j}\), go to Step 5;  
**Step 8**: If the termination condition \(S_{m}=\emptyset\) is met, all the destination nodes are put into the paths and \(N_{ant}\) paths have been constructed; otherwise, go to go to Step 2.
## 4 Numerical Experiment Results and Discussions  
The finite element method (FEM) computation plane, as depicted in Figure 4, is a 2D representation of a rectangular aperture (i.e., open window) in a rigid thin wall. Eight secondary sources are placed 0.125 m apart within the 1-m wide window to control the incident plane waves entering the window at angle \(\theta\), according to the guidelines in [27]. The entire computation plane is enclosed by a perfectly-matched layer so that all waves are damped to simulate a free-field condition. The acoustic power radiation from the window due to the noise can be calculated based on the far-field formulation as [21]  
\[\mathbf{W}_{d}=\frac{\left|\mathbf{D}\right|^{2}}{2\rho c}\pi r, \tag{23}\]  
where \(r\) is the radius of the far-field evaluation arc, \(\rho\) is the density of air, and \(c\) is the speed of sound in air. The acoustic power radiation after ANC can be expressed as  
\[\mathbf{W}_{e}=\frac{\left|\mathbf{E}\right|^{2}}{2\rho c}\pi r, \tag{24}\]  
Finally, the acoustic power attenuation (i.e., noise reduction NR) can be calculated as  
\[\text{NR}=10\log_{10}\frac{\mathbf{W}_{d}}{\mathbf{W}_{e}}=10\log_{10}\frac{ \left|\mathbf{D}\right|^{2}}{\left|\mathbf{E}\right|^{2}}. \tag{25}\]  
In this simulation, we evaluate the acoustic power radiated through the aperture by minimizing the sum-of-the-squared error at 1100 points in the far-field arc with a radius \(r=10\) m. Hence, the 1100 evaluation points in the optimal control formulation is analogous to the error microphones in a physical adaptive ANC system. In this scenario, the 'error microphones' are arranged such that the total sound power radiating from the aperture is minimized, which results in global control in the entire room interior. The optimal secondary source output is calculated based on (3), where the secondary paths are determined from FEM simulation. This is similar to the process as detailed in [28]. The'reference' signals are retrieved from the FEM simulations at the position of the secondary sources depicted by \(q_{n}\) in Figure 4. This arrangement replicates a collocated reference microphone and secondary source. Incidence angles of the plane waves are considered from \(-90^{\circ}\) (noise from the top) to \(90^{\circ}\) (noise from the bottom) at an interval of \(1^{\circ}\).
## 3 Results
### Comparison of FEA and Analytical Models
To assist in maximizing the energy harvesting efficiency of slitted beams, this work explored the effect of slit characteristics on the frequency response of the beams. Figure 5 compares the FRF of a slitted beam fitted with PZT using the analytical model and FEM. The analytical model shows two primary resonance peaks at 26 and 42 Hz and an anti-resonance drop at 35 Hz. With minor differences in the precise peak positions (28 and 44 Hz) and amplitudes, the FEM results show closely similar results. The first peak of both the analytical model and FEM nearly match, but the second peaks show small differences. In comparison to the analytical model, the FEM model's anti-resonance dip is noticeably less, and the difference in frequency more pronounced. However, in general, the analytical model accurately depicts the fundamental features of the beam's dynamic behaviour; nevertheless, additional refinement is required to better align the model with the FEM.
# Parameter Estimation for Some Probability Distributions Used in Hydrology
## 2 Probability Distributions
### Pearson V Distribution (PV2)  
Two parameters, Pearson V distribution, represent a particular form of PV5 when the position parameter is 0 [21].  
\[\mathrm{f(x)}=\frac{\exp\left(-\frac{\beta}{x}\right)}{\beta\cdot\Gamma( \alpha-1)}.\left(\frac{x}{\beta}\right)^{-\alpha} \tag{36}\]  
\[\mathrm{F(x)}=\frac{\Gamma\Big{(}\alpha-1,\frac{\beta}{x}\Big{)}}{\Gamma( \alpha-1)}=1-\mathrm{pgamma}\bigg{(}\frac{\beta}{x},\alpha-1\bigg{)} \tag{37}\]  
\[\mathrm{x(p)}=\frac{\beta}{\Gamma^{-1}(\mathrm{p};\alpha-1)}=\frac{\beta}{ \mathrm{qgamma}(\mathrm{p},\alpha-1)} \tag{38}\]  
where \(\alpha,\beta>0\) are the shape and the scale parameters; \(\mathrm{x}>0\).
## 5 Conclusions  
To solve the bottleneck problem of the low permeability of deep coalbeds and the difficulty in mining CBM, this work proposes a method of directional hydraulic fracturing and permeability enhancement for deep CBM mining that combines hydraulic fracturing technology with directional drilling technology. The selection equation of the included angle for the bidirectional hydraulic splitting of deep coalbeds was constructed through theoretical derivation. The joint propagation law of hydraulic splitting double cracks was revealed using the numerical simulation results. The following conclusions are drawn:  
1. According to the basic knowledge of theoretical mechanics, the hydraulic splitting angle selection equation was deduced. The split angle is negatively correlated with the crack length. The longer the crack, the smaller the split angle. The split angle is positively correlated with the width of the disturbed region and the wider the split angle.
2. The characteristics of the coal body disturbance area are different. The combined action of the double fissure damage disturbance region will lead to asymmetric stress distribution on both sides of the fissure, resulting in recurrent stress fluctuation during fissure development and forming a zigzag fracture. When the split angle is 90\({}^{\circ}\), the disturbance occupies 2/3 of the area around the borehole, and the overall crack effect is the best.
3. The designed new hydraulic splitting device is combined with directional drilling technology. By installing the new hydraulic cracking device at the end of the directional drilling rod, the hydraulic splitting device is transported to the designated position inside the coal body, and deep directional cracking is realized, and the gas extraction efficiency of the coal seam is improved. After the new directional hydraulic splitting technology, the CBM extraction efficiency can reach 5.37 m\({}^{3}\)/min, which is improved by about 5.08%. At the same time, the deeper the drilling distance, the higher the CBM extraction efficiency.  
**Author Contributions:** Conceptualization, methodology, visualization, J.Z.; software, validation, formal analysis, investigation, writing--original draft preparation, Y.N.; resources, data curation, supervision, project administration, funding acquisition, J.C. and Y.G.; writing--review and editing, Y.N. and L.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (52174182, 52274202), the Central Guidance on Local Development Science and Technology Funds of Hebei Projects (226Z4601G), the Youth Foundation of Hebei Provincial (E2022209051), and the North China University of Science and Technology Youth Support Program.  
**Data Availability Statement:** Not applicable.  
The first author would like to thank [PERSON], [PERSON], [PERSON], and [PERSON] for their technical help.  
The authors declare no conflict of interest.
# A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
## 2 Background and Algorithm Overview
### Overview of GNSS-R Open Loop Tracker Algorithm
#### 2.4.2 Estimating Open Loop Delay and Doppler  
When the best estimate of the surface specular point is obtained, the reflected signals delay code phase and Doppler frequency can be estimated as described in Section 4. The delay and Doppler of the reflected signal are estimated separately as described in Sections 4.2 and 4.3, respectively. Importantly, the delay estimation calculation is relative to the tracked direct signal and requires a specific instrument hardware configuration with is described and illustrated in Section 4.1 (Figure 2). The reflected Doppler estimate is made using only the reflection geometry and a correction for the drift of the receiver clock.
# TSCH Multiflow Scheduling with QoS Guarantees:
## 3 Results
### Radio Duty Cycle  
The tests described above clearly demonstrate that SDN WISE-TSCH performs much better than Orchestra, however, it also requires higher power consumption. The RDC can be seen in Figure 11b, for each node using the two schedulers. In the first case in node 8, Orchestra has a consumption 40% lower than that obtained by SDN WISE-TSCH. For the case of higher congestion, Orchestra's consumption is lower by 75%, but the difference in performance is also significant. Finally, in the case of node 10, where the performance is similar, the difference in performance is 28%.  
Figure 11: Deadline Satisfaction Ratio obtained in testbed for each Tx node.  
Figure 10: End-to-End Delay and Packet Inter-arrival time in testbed for SDN WISE-TSCH and Orchestra.
# Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
## 7 Trustworthy AI Solution for Intrusion Detection in Smart Buildings
### ML Models  
The development of an FL setting begins with the selection of a suitable ML algorithm. In the context of analyzing sensor and network state images, CNNs were identified as an effective choice due to their ability to capture spatial hierarchies and extract complex  
Figure 5: Network images with separated (**top**) and unified (**bottom**) channels.  
Figure 6: Telemetry images with separated (**left**) and unified (**right**) channels.  
patterns within image data. This led to the creation of two distinct CNN architectures, both adhering to the standard CNN framework comprising input, Conv2D, MaxPooling2D, flatten, dense, dropout, and output layers [66].  
The CNN architectures for the network and sensors were developed using Keras, following the standard CNN structure. However, there are notable differences between the two networks:  
* **Input Layers**: The input shapes vary, with network images sized at \(6\times 7\) pixels and sensor images at \(6\times 3\) pixels, both in RGB format.
* **Output Layers**: The sensor architecture's output layer has dimensions of \(6\times 3\), predicting one of three states (normal traffic, attack, or sensor unavailability) for each of the six sensors. The network architecture's output layer has dimensionsof \(1\times 8\), predicting across eight distinct classes. Additionally, the sensor architecture incorporates a reshape layer to transform the input from a \(1\times 18\) matrix to a \(6\times 3\) matrix, facilitating individual predictions for each sensor.
# Activation Process of ONU in EPON/GPON/XG-PON/NG-PON2 Networks
## 5 Simulation Results
### Simulations for ITU Standards  
The ITU activation processes are always described within the TC layer and are divided into several states. The simulation was based primarily on the ONU's activation description (see Section 4). Information was transmitted in PLOAM messages for \(125\,\upmu\)s. The OLT operating status was the time granted to the ONU by the OLT to process the received message. It had a duration of \(750\,\upmu\)s. The total ONU activation time consisted of the following: a synchronization time that was given to the synchronization state machine, the time of the transmission of the messages and their processing by the ONU, the generated window, the propagation delay that was added to each message, the calculated equalization delay and the activation time of all previously activated ONUs. All time constants were specified by the standards [24; 26].  
In Stage O1 of the GPON standard, synchronization with the downstream signal provided by the synchronization state machine was required. In the simulation, only the initial synchronization was solved. In all cases, two consecutive frames with the correct PSync were needed. Random and incorrect PSync were generated. If the PSync was correct, the N variable was incremented. Otherwise, it was set to zero. Synchronization ended when N was equal to M (M was set to two). Next, the OLT operation status followed. Because the simulation did not consider collisions, there were no standard timers to prevent the ONU from remaining in any part of the activation for an indefinite time.  
Propagation delay was the time it took to move a message from the OLT to the ONU. It depended on the length of the distribution network to the unit and the transmission rate in the environment. In the simulation, the propagation delay was added to the downstream and upstream messages and was calculated for each unit separately according to the following formula:  
\[T_{pd}=\frac{l_{i}}{\frac{c}{n}}, \tag{1}\]  
where \(l_{i}\) represents the distance between the ONU and OLT, \(c\) is the speed of light and \(n\) is the refractive index.
# A3C-R: A QoS-Oriented Energy-Saving Routing Algorithm for Software-Defined Networks
## 5 Experimental Evaluation
### Performance Analysis of Routing Algorithms
Due to the uniform transmission of traffic, its energy saving advantage is still better than that of ECMP algorithm. The DDPG-EEFS algorithm takes the minimum network energy consumption and the average completion time of network flow as the joint optimization goal in modeling. Therefore, compared with the I-DQN algorithm, it has a better energy-saving advantage and a delay optimization effect, but the throughput and packet loss rate effects are slightly weaker than the I-DQN algorithm. Our A3C-R algorithm takes QoS and energy consumption as the joint optimization goal and uses the asynchronous update advantage of the A3C algorithm to fully perceive the network status and select an energy-saving path that meets the QoS requirements. Therefore, the A3C-R algorithm has better QoS performance indicators and energy-saving advantages than the ECMP algorithm, I-DQN algorithm, and DDPG-EEFS algorithm. Figure 5: Performance comparison of routing algorithms under different traffic loads.
# Personal Picocell Scheme Using Adaptive Control CRE in Heterogeneous Mobile Networks +
## 3 User Throughput in Single-Band HetNet
### Parameter Optimization in Single-Band HetNet  
We first determine the parameter optimization, (i.e., the best combination of \(\alpha\), \(\text{CSO}_{\text{high}}\), and \(\text{CSO}_{\text{low}}\)), of adaptive control CRE to provide the best average and 5-percentile user throughput. Here, the 5-percentile user throughput is defined as the 5 th percentile point of the CDF of user throughput. Figure 7a-d shows the average and 5-percentile user throughput versus \(\text{CSO}_{\text{high}}\) as a function of \(\text{CSO}_{\text{low}}\) for \(\alpha=0.2\), 0.4, 0.6, and 0.8, respectively. As shown in Figure 7a,b, the performance of \(\alpha=0.2\) and 0.4 is better than that of \(\alpha=0.6\) and 0.When \(\alpha=0.2\) and 0.4, a lower \(\text{CSO}_{\text{low}}\) provide a better average user throughput and a higher \(\text{CSO}_{\text{low}}\) provide a better 5-percentile user throughput. Considering both the average and 5-percentile  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Value**} \\ \cline{2-3}  & **Macro-eNB** & **Pico-eNB** \\ \hline Cell layout & Hexagonal grid, 19 cell sites, 3 sectors per site & 4 picos per sector \\ Cell radius (ISD) & 289 m (500 m) & - \\ Tx power & 46 dBm & 30 dBm \\ Tx antenna gain & 14 dBi & 5 dBi \\ Antenna height & 32 m & 10 m \\ Carrier frequency & 2.0 GHz & 2.0 or 3.4 GHz \\ System bandwidth & 10 MHz & 10 MHz \\ UE distribution & 30 UEs per sector, 2/3 clustered distribution \\ Scheduling algorithm & Proportional fairness \\ Link adaptation & 15 MCS (QPSK to 256-QAM) \\ Traffic model & Full buffer \\ Link to system mapping & EESM \\ MIMO & \(2\times 2\) SU-MIMO \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters [20].  
user throughput, the parameters of \(\alpha=0.4\), \(\text{CSO}_{\text{low}}=4\) dB, and \(\text{CSO}_{\text{high}}=6\) dB are determined to be the best.
## 2 Materials and Methods
### State-Space Modelling in the Frequency Domain  
Consider the state-space minimal realization of a single-input, single-output (SISO) linear time-invariant system in the frequency domain:  
\[G(s)=C(sl-A)^{-1}B+D \tag{5}\]  
The state-space frequency-domain system identification problem consists of obtaining a set of parameters [\(\hat{A}\), \(\hat{B}\), \(\hat{C}\), \(\hat{D}\)], so that the estimated frequency response function represents closely the frequency response of the continuous state-space model determined by [A, B, C, D].  
In the electro-analytical method, measurement of the electrochemical cell's response results in a set of magnitude (Z(\(\omega_{\mathrm{i}}\))) and phase (\(\theta(\omega_{\mathrm{i}}\))) measurements (i.e., G(\(\omega_{\mathrm{i}}\))), corresponding to the \(i\)th excitation signal applied frequency (\(\omega_{\mathrm{i}}\)). To ensure that a measured data set is sampled at a frequency above the Nyquist frequency to reproduce correctly the system dynamics, the sampling rate is adjusted to 10 times the applied frequency. A common approach to minimizing the L\({}_{2}\)-norm error is to minimize the least-squares cost function [29]:  
\[\min_{\hat{A},\hat{B},\hat{C},\hat{D}}\sum_{i=1}^{l}(W(\omega_{1}))^{2}\big{|} \hat{G}(\omega_{\mathrm{i}})-F(\omega_{\mathrm{i}})\big{|}^{2} \tag{6}\]  
where \(W(\omega_{\mathrm{i}})\) is the weight array, \(F(\omega_{\mathrm{i}})\) is the measured data, \(\hat{G}(\omega_{\mathrm{i}})\) is the estimated transfer function and \(l\) is the number of measurement data frequencies.
## 2 Network Architecture and Training
### Experimental Procedure  
To quantitatively evaluate the model's performance in gravity field inversion tasks, the Root Mean Square Error (RMSE) (see Equation (18), where \(y_{i}\) and \(\hat{y}_{i}\) denote the true and predicted values, respectively) and Signal-to-Noise Ratio (SNR) (see Equation (19), where \(\text{Var}(y)\) is the variance of the clean data and \(\text{Var}(\hat{y}-y)\) is the variance of the residuals) were selected as the core evaluation metrics. RMSE measures the overall deviation between the model predictions and the ground truth, while SNR evaluates the energy ratio between the signal and noise. Together, these metrics characterize the model's accuracy and generalization ability from the perspectives of error magnitude and signal quality.  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2}} \tag{18}\]  
\[SNR=10\cdot log_{10}\Big{(}\frac{\text{Var}(y)}{Var(\hat{y}-y)}\Big{)} \tag{19}\]  
The experiment was set to run for 300 epochs as a complete training cycle. The evolution of training loss and validation loss is shown in Figure 7. In the early stages of training, both losses exhibited a significant downward trend, indicating that the model rapidly learned the global features and patterns of the data through gradient updates. As the iterations progressed, the loss curves gradually entered a stable convergence phase, with values maintained in a low error range, suggesting that the model parameters had approached the neighborhood of the optimal solution and that further gradient updates had a diminished effect on loss optimization. Although the overall convergence trend was clear, minor oscillations in the loss curves persisted, attributable to the statistical fluctuations in gradient estimates caused by mini-batch random sampling in each epoch of stochastic gradient descent. In the later stages of training, the two-loss curves nearly overlapped, indicating that the generalization error on the training and validation sets had converged to similar levels, with no signs of overfitting (i.e., training loss remaining consistently lower than validation loss). This demonstrates the robustness and generalization capability of the model during training.
# A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry
## 2 Hybrid Unsupervised Exploratory Plots
### Sammon Mapping  
Sammon Mapping (SM) [45; 46] or Sammon Projection is a projection method for analyzing multivariate data. It can be seen as a type of MDS method, using a non-linear metric that is frequently used for EPP. SM maps a high-dimensional dataset to a lower dimensionality one, conserving the intrinsic structure of the data when the patterns are projected.  
Unlike standard PCA and other EPP techniques, SM is a non-linear approach, as the result cannot be represented as a linear combination of the original variables. Nevertheless, SM minimizes the differences between corresponding pairwise point distances in the two spaces. PCA applies optimal mapping to the dataset, while SM tries to obtain a lower-dimensional dataset that keeps the original structure as much as possible. Because of that, the SM algorithm has a high computational load O(n\({}^{2}\)).  
The source dataset is represented as \(N\) vectors in \(L\)-dimensional space, given by \(X_{i}\), \(i=1\),..., \(N\). It is sought to map these into \(d\)-dimensional space (with \(d<L\)), to obtain vectors \(Y_{i}\), \(i=1\),... \(N\); \(d_{ij}\) is the pairwise distance between \(Y_{i}\) and \(Y_{j}\), and \(d_{ij}^{*}\) is the distance between \(X_{i}\) and \(X_{j}\).  
SM aims to minimize the following error function, which is often called Sammon's stress or Sammon's error:  
\[\text{E}=\frac{1}{\sum_{i<j}d_{ij}^{*}}\sum_{i<j}^{n}\frac{\left(d_{ij}^{*}-d _{ij}\right)^{2}}{d_{ij}^{*}} \tag{1}\]  
As originally suggested [45], the minimization could be achieved by gradient searching techniques or by other methods, but these frequently involve iterative procedures. Convergent results are not always reached, and the number of iterations is decided experimentally [47].