esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Mandel System for Prefabricated Vertical Drain Installation: A Macro to Micro Analysis
## Materials
### PVD Characteristics  
The PVD consists of a core, drainage channel, and filter. A schematic illustration of the PVD is provided in Figure 1. The material of the PVD was made by polypropylene core. Physical modeling was performed to study particular aspects of the behavior of prototypes.  
A scaled-down model conducted for large-scale consolidation testing is recommended by assuming a related governing equation [21]. Therefore, the coefficient of PVD depth and permeability are the significant factors influencing PVD performance. The similarity theory for the scaling factor of PVD can be determined using Equations (1) and (2).  
\[\frac{z_{p}}{z_{m}}=n_{z} \tag{1}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Soil Properties** & **Kaolinite** \\ \hline Water content (\%) & 135 \\ Plastic limit \(w_{p}\) (\%) & 28.8 \\ Liquid limit \(w_{L}\) (\%) & 48.1 \\ Plasticity index \(l_{p}\) & 19.3 \\ Specific gravity (\(G_{s}\)) & 2.58 \\ Grain size particle less than 0.002 mm (\%) & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The kaolinite properties.  
\[\frac{k_{p}}{k_{m}}=n_{k} \tag{2}\]  
where \(z_{p}\) is the embedded length of the PVD prototype, \(z_{m}\) is the embedded length of the PVD model, \(n_{z}\) is the scale factor of embedded length, \(k_{p}\) is the permeability of the prototype, and \(k_{m}\) is the change permeability of the model. The scale factor implies a necessary initial specific volume or void ratio, a scale factor of F = 20 was determined, as expressed in Equation (3).  
\[\Delta v=\Delta e=n_{k}\ln(F) \tag{3}\]  
where \(\Delta v\) is the change in initial specific volume, and \(\Delta e\) is the change in void ratio. The scale factor parameter for three types of PVD is shown in Table 2.
## 2 Network Model and Operation
### Time Schedule  
Time scheduling in a TDMA network concerns how to assign time slots to nodes for transmission. If the assignment is periodic, the shortest complete set of slots without repetition is the so-called frame.  
Figure 3: Collision-free constraints when node \(n\) transmits in time slot \(k\). (**a**) In the previous slot \(t-1\), node \(j+1\) cannot transmit because node \(j\) will be busy transmitting in time slot \(t\) and will not receive the data. (**b**) Node \(j+1\) is not allowed to receive in time slot \(t+1\) to avoid the collision between the wave coming from node \(j\) and the expected wave from node \(j+2\). (**c**) Node \(j-1\) must be receiving the wave from node \(j\) in time slot \(t+1\), so that it cannot transmit in that time slot (**d**).  
Figure 2: Network operation: (**a**) half-duplex operation means a sensor node can be in three states: transmission, reception, or idle; (**b**) omnidirectional propagation (with one hop power control): when node \(j\) transmits, the propagated wave reaches both nodes \(j+1\) and \(j-1\), but only node \(j-1\) receives the data (following the routing path); (**c**) the transmission time equals the propagation delay.  
In a TDMA network, a schedule is a periodic sequence of time slots (frame), where every time slot can be assigned to a set of nodes. Due to the constraints mentioned in the previous Section, transmission is not possible during all the time slots in a frame. A first goal in scheduling is to make as many transmissions as possible within a frame to increase the throughput in the network, while keeping the frame as short as possible, avoiding the collisions already mentioned.
### Evolution of Research Techniques  
Objective and quantitative research on visual quality is becoming increasingly important in urban planning [28] (Appendix A). Previous studies have relied on user evaluations within specific geographical areas to assess visual perception quality. However, subjective evaluations are influenced by constantly changing psychological preferences and experiences [29]. The lack of robust generalizability in empirical surveys based on subjective evaluations makes them unsuitable for supporting more rigorous visual quality research.  
The widespread application of computer technology has created new data landscapes, providing quantitative tools for detailed studies of color and visual quality. Compared to traditional urban color research methods, these tools can efficiently process larger volumes of data than manual methods, thereby expanding the scope of research [14]. [PERSON] and [PERSON] [30] utilized street view images to explore the relationship between the urban green view index and visual comfort, aiming to guide future street greening planning. [PERSON] et al. [20] employed street view technology combined with metrics, such as regional saturation, visual entropy, green view index, sky index, and sky openness index, to measure the visual quality of urban landscapes. [PERSON] et al. [31] used machine learning and human-machine adversarial models to assess residents' perceptions of urban neighborhood visual quality. [PERSON] et al. [32] collected visual and audio files, employing deep learning and standard algorithms to measure perceptions of audiovisual environments within urban areas.  
Previous studies that have integrated various metrics for urban visual quality have validated the feasibility of using street view data and machine learning in visual quality research. While these methods offer a reliable approach for comprehensive quantitative analysis in urban color studies, macro-level color research based on big data often lacks insight into the psychological cognition of individual users regarding spatial colors. This presents potential limitations for research on visual perception.
# Hierarchical LSTM-Based Network Intrusion Detection System Using Hybrid Classification
## 3 Proposed Algorithm
### Proposed NIDS
#### 3.2.2 LSTM-Based Session Classifier  
The session classifier bundles the packet features generated by the packet classifier into one session feature and processes them. Therefore, the input of the \(n\)-th cell of the session classifier is the output value of the packet classifier for the \(n\)-th packet of the session. Because the session classifier uses the output value of each cell, it determines whether the corresponding session is intruded upon by using various output combinations. For example, classification may be performed using only the output of the last cell; however, the outputs of all cells may be considered together.  
Therefore, to determine the most effective method, the classification performance of various methods was measured based on the CIC-IDS2017 dataset, and the method that achieved the highest performance was selected. To this end, we experimented with different cases: using the output values of all cells of the session classifier, using only the last output value, using the average score for each class of all output values, and selecting the most selected class for all output values. The results of each experiment are listed in Table 3.  
The results of this experiment confirmed that the highest F1-score was achieved when only the output of the last cell was used in both the training and test datasets. Therefore, according to the experimental results, the proposed method was designed to use only the output value of the last cell of the session classifier. The final output value was provided as an input to the DNN, and the output of the final DNN determined whether to invade. This method has several advantages. First, the overall structure of the classifier is simplified and the classification speed is increased. In addition, when packets being processed belong  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Combination Type** & **Train Dataset** & **Test Dataset** \\ \hline All cells & 98.7396 & 98.3010 \\ Last cell & 98.7421 & 98.3109 \\ Average score & 98.7396 & 98.3089 \\ Most selected & 98.7396 & 98.3089 \\ \hline \hline \end{tabular}
\end{table}
Table 3: F1 score for each combination method of cell output for the session classifier.  
Figure 7: Dual LSTM-based packet classifier when the receive packet is partitioned into three inputs to Dual LSTM.  
to one session, memory space can be saved because it is not necessary to store the output result of the cell for the mid-session packet of the session classifier.
## 4 Industry-Related Challenges and Opportunities
Indeed, thanks to the improvements provided by the previous objective and to new technologies such as MCS and big data analysis, new innovative services will be provided. This will not only create new value opportunities but also enable diverse and flexible career paths that will allow people to work and remain productivefor longer. Furthermore, this new flexibility will enable more flexible work organisation models, which will gradually meet the growing need of employees to strike a better balance between their work and private lives;
* new technologies, particularly with reference to MCS and advanced analysis, will be key not only to improve production and working conditions but also for an **improvement in customer satisfaction**. First off, optimized production processes and supply chain management will lead to a supply that perfectly fits the demand. Furthermore, individual, customer-specific criteria will be included in the design, configuration, ordering, planning, manufacturing and operation phases, also incorporating last-minute changes, indeed enabling mass customization to be implemented.
# Towards Machine Learning for Error Compensation in Additive Manufacturing
## 4 Error Compensation in Additive Manufacturing
Model regression is used to improve the product quality or do failure prognosis for the machines. Very few of these algorithms have considered the regression of time-varying systems. The proposed model in [120], addresses the online model regression for time-varying manufacturing systems with random unknown model variations during production. For comparison, the suboptimal method is also tested in another scenario and defined the following average MSE over all points as the metric to compare the performance of the two methods. In this study, a standard Gaussian Process Regression (GPR) method was adapted for the model regression of time varying manufacturing systems with shift and drift parameters unknown. In addition, a suboptimal GPR method was proposed which ignores the correlation between the latest measurement and the historical data as a trade-off between the storage expense and the estimation optimality. Verification of the effectiveness of the two proposed methods have been simulated and compared. A new method of predictive model based on Gaussian process approach is proposed by [PERSON] et al. using selective laser melting [121]. The estimate of statistical model parameters is collected via Bayesian inference framework, and porosity of the parts predicted using the Kriging method for a range of pre-defined setting. A predictive modeling framework is proposed based on Gaussian process models to predict the resulting density in parts manufactured using selective laser melting as a function of the processing parameters. The most impactful parameters that have been reported on SLM parts' porosity include laser power, scanning speed, powder layer thickness, hatch distance and laser beam size. A Gaussian process-based predictive model that assumes the porosity of the fabricated part as a function of laser power and scanning speed is formulated, which have been identified as two of the most significant processing parameters affecting part porosity. The proposed methodology was validated using a real-world case study of predicting the porosity of 17-4 PH stainless steel specimens manufactured on a _ProX 100 TM SLM_ system.
## 4 Results Analysis  
### Parallel Electrical Resistivity Imaging Analysis  
Comparing and analyzing Figure 7a,b, it can be observed that the actual grouting body in Figure 7a is not a perfect circle but appears as a circular disk. Due to the influence of gravity and the direction of the grouting pipe, the slurry does not form a perfect spherical shape. Therefore, when measuring the spherical radius, the horizontal diffusion radius of the slurry was selected as the slurry's diffusion radius. In Figure 7b, the blue area outlines the morphology of slurry diffusion within the gravel. The inversion imaging is used to visualize the diffusion of the slurry within the gravel. The slurry's horizontal diffusion in the experiment approximates a circular shape. From Figure 7, it is evident that the Bingham fluid slurry permeates and spreads uniformly in a spherical manner from the grouting point. The macroscopic control of slurry diffusion range can be achieved during infiltration grouting by using the resistivity method.
## 3 Discussion of Results
### Low Damping Case
Figure 7: Comparison of non-dimensional amplitudes obtained by different fitting methods for EO = 5 and \(\zeta_{1,2}=0.0001\), (**a**) SD = 0.05, (**b**) SD = 0.10. For evaluating in more detail the performance of the 2 DOF fitting model, further cases have been investigated with different damping properties. Increasing damping reduces the vibration amplitudes and, as a consequence, one large peak could be present instead of two peaks, even if for a single mode the peaks should be split due to mistuning. In this condition, the curve fitting and the parameters identification become complicated. Figure 11 shows the fitted curves for damping ratios of 0.002 and 0.003 for two different EOs. Since in this case only one peak seems to be present, the fitting was also done with the SDOF method. It can be observed that both 2 DOF and Sine fit models exhibit perfect approximations. On the contrary, the 1 DOF model is not able to completely capture the response since the model 1 DOF is not correct, being here two peaks partially overlapped. The values of the identified parameters (resonance frequency and damping) using both 2 DOF and 1 DOF model are also listed in Table 3. It can be observed that the 2 DOF evaluation method is able to accurately identify the vibration parameters for each mode, whereas the 1 DOF model provides a good prediction for frequency but overestimate damping. It should be noticed that in the case of the Sine fitting method the same parameters are not obtained directly by the fitting procedure, in fact in this case the resonance frequency and the associated damping are estimated not during the fitting but after, by post processing the obtained reconstructed response curve. The damping can be obtained by means of techniques  
Figure 10: Comparison of non-dimensional amplitudes obtained by different fitting methods for EO = 10 and \(\zeta_{1,2}=0.0001\), (**a**) SD = 0.05, (**b**) SD = 0.10. Figure 9: Comparison of non-dimensional amplitudes obtained by different fitting methods for EO = 7 and \(\zeta_{1,2}=0.0001\), (**a**) SD = 0.05, (**b**) SD = 0.10.  
like half-power method, but in a case like this, where two close peaks are partially overlapped, using half-power method can lead to not accurate results. More accurate results can be reached by doing a further FRF curve fit analysis [32; 33; 34] which takes extra effort. However, in particular for the identification of the damping value associated to a certain resonance, the 2 DOF method gives more accurate results than Sine fitting (based on half-power method), because the damping parameters are variables of the model obtained directly by the fitting procedure. This holds if the experimenter uses the appropriate model, that is 2 DOF instead of 1 DOF model when the shape of the peak suggests that there might be two partially overlapping peaks due to the presence of mistuning.
## 4 Results
### Error Estimate
The comparisons are performed with TerraSAR-X velocity maps retrieved from single SAR repeat pass pairs. For S1 velocity data acquired from single image pairs and velocity data from the velocity mosaic are used. Because of possible temporal variations in velocity the lower sections of glacier tongues are not used for the comparison. We computed the root mean square error (RMSE) of velocity magnitude between TerraSAR-X and S1 for 28 areas with mean velocities ranging from 1 m\(\cdot\)d\({}^{-1}\) to 10 m\(\cdot\)d\({}^{-1}\). The RMSE for this data set corresponds to 7.4% of the mean velocity for S1 velocities from a single image pair and to 4.8% for the merged S1 velocity map. These values include the total uncertainties both for the S1 velocities and the TerraSAR-X velocities. [PERSON] _et al._[34] compare velocity maps from TerraSAR-X/TanDEM-X data with _in-situ_ velocity records measured with GPS receivers on several Greenland glaciers. For measurements over 11-day intervals they report a mean difference of 1.5% between TerraSAR-X/TanDEM-X and GPS velocities. Assuming similar uncertainty for our TerraSAR-X retrievals, the RMSE can primarily be attributed to uncertainty in the S1 velocity data. The uncertainty for slow moving areas was calculated by deriving the RMSE for 12 areas with mean velocities between 0.1 m\(\cdot\)d\({}^{-1}\) to 0.5 m\(\cdot\)d\({}^{-1}\). For S1 velocities retrieved from a single image pair the RMSE is 0.068 m\(\cdot\)d\({}^{-1}\), and for the merged S1 ice velocity map the RMSE is 0.047 m\(\cdot\)d\({}^{-1}\). The higher relative error at slow velocities is due to the impact of ionospheric noise in the total error budget.
# Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ACF & Auto correlation function \\ ANN & Artificial neural networks \\ AOA & Arithmetic optimization algorithm \\ APE & Absolute percentage error \\ APSK & Amplitude phase shift keying \\ AR & Autoregressive \\ ARE & Average relative error \\ ARIMA & Autoregressive integrated moving average \\ ARMA & Autoregressive moving average \\ ARMSE & Average root mean square error \\ BFA & Bacterial foraging algorithm \\ BP & Backpropagation \\ BPNN & Backpropagation neural network \\ CBAM & Convolutional block attention module \\ CBAS & Chaos beetle antennae search algorithm \\ CCO & Cluster chaotic optimization \\ CGO & Chaos game optimization \\ CMSE & Cumulative mean square error \\ CVRMSE & Coefficient of variance of the root mean square error \\ DCSK & Differential chaos shift keying \\ DMI & Delayed mutual information \\ EKF & Extended Kalman filter \\ EMD & Empirical mode decomposition \\ ESN & Echo state network \\ FFNN & Feed-forward neural network \\ GA & Genetic algorithm \\ GPF & Gaussian particle filtering \\ GRNN & Generalized regression neural network \\ GWO & Grey wolf optimization \\ HBO & Honey be optimization \\ HEA & Hybrid evolutionary adaptive \\ HFD & Higuchi’s fractal dimension \\ IGWO & Improved grey wolf optimizer \\ ITSM & Improved time series method \\ LLE & Largest Lyapunov exponent \\ LLNF & Locally linear neuro-fuzzy \\ LS & Least square \\ MAD & Mean absolute deviation \\ MAE & Mean absolute error \\ MAPE & Mean absolute percentage error \\ MARE & Mean absolute relative error \\ MCMC & Monte Carlo Markov chain \\ MLE & Machine learning ensembles \\ MLP & Multilayer perceptron \\ MMSE & Minimum mean square error \\ MPSV & Minimum phase space volume \\ MRE & Mean relative error \\ MRFO & Manta ray foraging optimization \\ MRPE & Maximal relative percentage error \\ MSD & Mean squared deviation \\ MSE & Mean squared error \\ MSLE & Mean squared logarithmic error \\ MSP\({}_{\text{a}}\)E & Mean squared prediction error \\ NARX & Nonlinear autoregressive exogenous model \\ NMAE & Normalized mean absolute error \\ NMAPE & Normalized mean absolute percentage error \\ \end{tabular}
# A Software Defined Radio Evaluation Platform for WBAN Systems
## 2 Preliminary and Related Work
### SDR Testbed  
The traditional industrial process of developing a digital communication system is to use an ASIC to perform the RF test. However, despite having the advantages of great performance, low power, and reduced footprint, using an ASIC, on the other hand, raises a high non-recurring cost and dramatically increases the development time. Unlike the ASIC implementation, SDR testbeds provide users with great flexibility to evaluate the RF performance of a certain design. For a common SDR system, the data processing task is performed by a Digital Signal Processor (DSP) or by an FPGA. Due to the programmable nature of the DSP and FPGA, users working with SDR platforms have the ability to change the parameters of the system based on their requirements [17]. In industry, SDR testbeds have been widely adopted as verification tools in wireless communications by the researchers working with other technologies such as Long-Term Evolution (LTE) [18], WLAN [19], Bluetooth [20], and 5G technologies [21]. For instance, in the article [22], the authors proposed a new design architecture to provide effective spectrum management for 5G wireless networks by applying it on an SDR platform. On the contrary, there is still no existing SDR evaluation platform for the latest IEEE 802.15.6 standard. Meanwhile, the IEEE 802.15.6 standard defined a wide range of frequency channels and multiple modulation methods for applications under different circumstances [12]. This highly adaptive nature raises the need for an equally flexible evaluation platform. Due to the configurable nature of the SDR platform, switching between different frequency channels and modulation methods can be achieved easily, which was earlier impossible without manufacturing multiple ICs that implement different configurations.