esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Base Model Selection in Pa Structure  
As mentioned in Section 3.2, the best base model in each branch of Pa structure was determined through 5-fold cross-validation using the training data. The proposed Pa structure consisted of 5 branches, each utilizing different FCs (FC\({}_{1-5}\)) for model development. Figure 4 illustrates the averaged values of OA and Kappa for various ML models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No** & **Model** & **Description** \\ \hline \multirow{7}{*}{1} & \multirow{7}{*}{Pa-Ca} & This is a special case of the proposed framework of this paper without employing PCA (Figure 3). The best models as base models in Pa branches and a Meta-model in Ca structure were identified first. Additionally, various combinations of input FCs (FC\({}_{1}\)–FC\({}_{5}\)) were tested for this specific architecture. This is the proposed framework of this paper (using the same base models and Meta-models as in Model No. 1), as PCA is applied prior to Ca structure. \\  & & \(\text{FC}_{1}\)–FC\({}_{5}\) were utilized in this model. \\  & & \(\text{R}_{1}\)–FC\({}_{5}\) are stacked together without employing the PCA technique before classification using a single ML model. \\  & & \(\text{R}_{1}\)–FC\({}_{5}\) were classified for this specific architecture. This model is similar to Model No. 3, employing PCA before feeding the entire FCs (FC\({}_{1}\)–FC\({}_{5}\)) to a single ML model for classification. The optimum number of components was found to be six using five-fold cross validation using training data. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Different model architectures tested in this article to compare with the performance of the proposed methodology.  
The optimal hyperparameters of each model were found using the described approach in Section 3.2. It can be observed that the RF model outperformed other ML models in all branches. For instance, RF achieved OA values of 80.15%, 83.57%, 79.64%, 82.83%, and 78.51% in FC\({}_{1-5}\) branches, respectively. Similarly, the Kappa values were 0.766, 0.812, 0.754, 0.780, and 0.741, respectively. Therefore, RF was selected as the base model for all Pa branches. Following RF, the GBT, SVM, and CART models ranked next. Moreover, among the branches, FC\({}_{2}\) demonstrated the highest accuracy in terms of both OA and Kappa for each ML model. FC\({}_{4}\), FC\({}_{1}\), FC\({}_{3}\), and FC\({}_{5}\) were ranked next. The results also indicated that the optical FCs (FC\({}_{1-4}\)) performed better compared to the SAR FC (FC\({}_{5}\)). Similarly, the S2 FCs (FC\({}_{1,2}\)) exhibited better performance than the L8/9 FCs (FC\({}_{3,4}\)). Furthermore, the index-based FCs (FC\({}_{2}\) and FC\({}_{4}\)) demonstrated superior performance compared to the spectral-based FCs (FC\({}_{1}\) and FC\({}_{3}\)) in optical RS data. It should be noted that the optimal hyperparameters of RF in each branch were as follows: FC\({}_{1}\) [NT: 200, MN: 1, VPS:], FC\({}_{2}\) [NT: 100, MN: 2, VPS: 1], FC\({}_{3}\) [NT:300, MN: 1, VPS: 1], FC\({}_{4}\) [NT:200, MN: 1, VPS: 1], and FC\({}_{5}\) [NT:200, MN: 2, VPS: 2].
# Unsupervised Adaptation of Deep Speech Activity Detection Models to Unseen Domains
## 2 Domain Adaptation
### Approaches to Domain Adaptation  
In order to better understand the domain adaptation problem, a variety of works over the years have tried to categorise the diverse conditions found for the problem. We refer the reader to the following survey for a more detailed description of this categorisation [32]. The first level of categorisation refers to the relation between source and target domains. Under the assumption that the source and target domain are directly related, transferring knowledge can be performed in a single step. This is usually called one-step domain adaptation. In case that assumption fails, one-step domain adaptation may not be effective. Multi-step domain adaptation [33] aims to connect two unrelated domains via a series of intermediate bridges, and then perform one-step domain adaptation.  
In this work, as human speech has characteristics that may not vary among domains, we assume that source and target domains are related. Because of that, we focus on one-step domain adaptation solutions. In this scenario, domain adaptation approaches can be summarised intro three big cases according to the work in [34]:  
* **Discrepancy-based**: this family of solutions works under the assumption that fine-tuning a model using labelled or unlabelled data can reduce the shift between source and target domain. Under this idea, several criteria can be used to perform domain adaptation: some authors use class information to transfer knowledge between two domains [35]. The authors of [36; 37] seek to align the statistical distribution shift be tween source and target domain. Other approaches also aim to improve generalisation by adjusting the architectures of DNNs, such as the work presented in [38].
* **Adversarial-based**: in this case, a domain discriminator tries to identify whether a data point belongs to the source or the target domain. This is used to encourage domain confusion through an adversarial objective that minimises the distance between source and target domain distributions. Two main groups can be observed when implementing this idea: those relying on generative models such as generative adversarial networks (GAN) [39], or those that rely on non-generative models that aim to obtain domain invariant representation through a domain confusion loss [40].
* **Reconstruction-based**: This approach is based on the idea that data reconstruction of the source or target samples may be helpful in order to improve the domain adaptation process. This way the reconstructor is able to ensure both specificity of intra-domain representations and indistinguishability of inter-domain representations. Some examples of these methods can be cited, such as the use of an encoder-decoder reconstruction process [41], or an adversarial reconstruction obtained via a cycle GAN model [42].
# An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields
## 5 Discussion
### Directions for Future Research  
Future studies could explore the following avenues:  
Incorporating advanced deep learning techniques, such as Long Short-Term Memory (LSTM) networks or Transformer-based architectures, to capture sequential dependencies and long-range temporal patterns in macro-financial data [14].  
Ensembling diverse machine learning models beyond random forest, such as Gradient Boosting Machines (e.g., XGBoost, LightGBM) or Stacked Generalization (Stacking) frameworks, which could enhance performance through model diversity and meta-learning.  
Expanding the feature set to include international capital flows, term premium components, yield curve slope, market volatility indices (e.g., VIX), and global macroeconomic indicators, which may improve model responsiveness to external shocks and reflect global interdependencies in bond markets.  
Scenario analysis and stress testing, using simulation-based forecasts under hypothetical macroeconomic conditions (e.g., policy rate shocks or geopolitical crises), to assess model stability and provide decision-support tools for risk managers and central banks.  
Enhancing the interpretability of complex models using tools such as SHAP or LIME to make high-performing ensemble or neural models more transparent and suitable for policy analysis and regulatory oversight [22].  
These directions can help build more robust, explainable, and globally informed forecasting frameworks, aligning predictive modeling efforts with the evolving complexity of financial systems.
# Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging
## 2 Materials and Methods
### Methodology
#### 2.2.5 Ensemble and SWA  
The optimal implementation of hyperparameters for the seven models is carried out using an ensemble learning approach that combines Stacking and Voting classifiers. The models are wrapped within a pipeline that includes feature selection through SelectKBest, and the models themselves are retrained and simulated with varying random states. The final probabilities are then evaluated using the Stochastic Weighted Averaging (SWA) technique to enhance model performance.  
Ensemble Approach (Stacking and Voting): The ensemble method leverages the strengths of individual models by combining them to improve predictive accuracy and robustness. In stacking, multiple base models are trained on the same training data, and a meta-classifier is used to make the final prediction based on the outputs of these models. Voting combines the predictions of different models using a soft voting mechanism, where the final prediction is based on the average predicted probabilities from all models.  
Stochastic Weighted Averaging (SWA): SWA enhances the generalization of the model by averaging the weights of several models trained with slight variations, such as different random states or random restarts. By retraining the models and averaging their predictions, SWA smooths out the fluctuations in model weight updates, leading to improved generalization and more stable predictions. Performance metrics are calculated using test data, and the results are compared before and after applying SWA to assess its impact on model generalization and accuracy.
# Analysis of Grating Lobe Effects on GEO DSC Distributed Antennas
###### Abstract  
Traditional single-antenna systems have inherent limitations in terms of antenna gain, anti-interference capability, and flexibility. To overcome these challenges, satellite-mounted distributed antenna systems disperse multiple antennas at different positions on the satellite to improve the reception quality and signal-to-noise ratio of satellite signals, enhancing the performance of the satellite communication system without additional bandwidth or transmission power. However, the dispersed locations and long distances between antennas on the satellite result in less compact spacing compared to terrestrial distributed antennas, leading to the generation of a significant number of grating lobes. The distributed satellite cluster (DSC) approach revolutionizes the traditional mode of satellite utilization, enabling close collaboration among distributed loads. In this study, we analyzed the impact of grating lobes produced by DSC distributed antennas in geostationary Earth orbit (GEO) and simulated the grating lobe patterns of two 1 m circular aperture satellite antenna arrays in GEO. The simulation results revealed that the relative position change of the satellites affected the width and number of interference fringes in a certain ground area, while change in the carrier phase led to the translation of the interference fringes. To mitigate the grating lobes, we employed a sparse array technique. The simulation results demonstrated that the sparse array effectively suppressed the grating lobes but at the expense of a decrease in the sidelobe level and beamwidth.  
satellite communications; distributed antennas; DSC; GEO; grating lobe effects; grating lobe suppression 2023
###### Abstract  
Metamaterials are familiar in life sciences, but are only recently adopted in structural health monitoring (SHM). Even though they have existed for some time, they are only recently classified as smart materials suitable for civil, mechanical, and aerospace (CMA) engineering. There are still not many commercialized metamaterial designs suitable for CMA sensing applications. On the other hand, piezoelectric materials are one of the popular smart materials in use for about 25 years. Both these materials are non-fiber-optical in nature and are robust to withstand the rugged CMA engineering environment, if proper designs are adopted. However, no single smart material or SHM technique can ever address the complexities of CMA structures and a combination of such sensors along with popular fiber optical sensors should be encouraged. Furthermore, the global demand for miniaturization of SHM equipment, automation and portability is also on the rise as indicated by several global marketing strategists. Recently, Technavio analysts, a well-known market research company estimated the global SHM market to grow from the current US $ 1.48 billion to US $ 3.38 billion by 2023, at a compound annual growth rate (CAGR) of 17.93%. The market for metamaterial is expected to grow rapidly at a CAGR of more than 22% and the market for piezoelectric materials is expected to accelerate at a CAGR of over 13%. At the same time, the global automation and robotics market in the automotive industry is expected to post a CAGR of close to 8%. The fusion of such smart materials along with automation can increase the overall market enormously. Thus, this invited review paper presents a positive perspective of these non-fiber-optic sensors, especially those made of metamaterial designs. Additionally, our recent work related to near field setup, a portable meta setup, and their functionalities along with a novel piezoelectric catchment sensor are discussed.  
metamaterial; robot; piezoelectric; sensors; non-fiber optics; automation; structural health monitoring +
Footnote †: journal: Sensors  
0000-0002-4891-9900-5888][PERSON]
## 3 System Model
### Simulations Results
#### 4.3.2 Average Energy Consumption versus Simulation Time  
Average energy consumption over different simulation times is shown in Figure 6. The comparison of three protocols, i.e., MRRP, ATEAR, and Enhanced ATEAR, indicates average network energy consumption at different simulation times. The initial energy of each node was 0.3 j. From results, it was observed that ATEAR consumed 0.139 j, MRRP consumed 0.184 j, and Enhanced ATEAR (E-ATEAR) consumed 0.125 j of energy at 1000 s. At the end of simulation at 5000 s, the energy consumption of ATEAR, MRRP, E- ATEAR protocols reached 0.184, 2.456 j (which was practically not possible for implanted sensors), and 0.184 j respectively. This investigation showed the performances of ATEAR and Enhanced ATEAR were far better than MRRP. The neglected factor of energy consumption in MRRP was overcome in ATEAR and Enhanced ATEAR.  
Figure 5: Average temperature rise versus simulation.  
Figure 6: Average energy versus simulation time.
## 3 Dynamic Modelling
### Mechanical Efficiency  
The energy loss due to friction in a mechanical system is expressed by mechanical efficiency, as a measure of how the mechanical input power is transmitted through the mechanism to the outputs. The mechanical efficiency is a variable quantity in the transient regime, significantly influenced by the system's inertial properties, which tends asymptotically towards a maximum value specific to the steady-state system operation.  
Thus, the relation for the mechanical efficiency of the wind turbine is established starting from its definition, in which Equations (22) and (35) are replaced:  
\[\eta=-\frac{T_{G}\omega_{G}}{T_{R}\omega_{R}}=-\frac{-a_{G}(i_{1\_2}-1)\omega_ {R}+b_{G}}{-a_{R}\omega_{R}+b_{R}}(i_{1\_2}-1) \tag{38}\]  
Equation (36) is solved as a function of \(\omega_{R}\) and the mechanical efficiency in the transient regime is obtained after the replacement of the obtained solution in Equation (38):  
\[\eta=\frac{(i_{1\_2}-1)a_{R}b_{G}-(i_{1\_2}-1)^{2}a_{G}(b_{R}-\varepsilon_{R} J^{*})}{\Big{(}\frac{i_{1\_2}}{n_{2,1}}-1\Big{)}a_{R}b_{G}-(i_{1\_2}-1)\Big{(} \frac{i_{1\_2}}{n_{2,1}}-1\Big{)}a_{G}b_{R}-a_{R}\varepsilon_{R}J^{*}} \tag{39}\]  
where \(J^{*}\) is the equivalent mechanical moment of inertia of the system reduced at body 2:  
\[J^{*}=J_{1}\frac{i_{1\_2}^{2}}{n_{2,1}}+J_{2}+s_{3}J_{3}\frac{i_{3\_2}^{2}}{ n_{2,3}} \tag{40}\]Equation (39) shows the dependence of efficiency on the angular acceleration \(\varepsilon_{R}\) of the wind rotor; in a steady-state regime (i.e., \(\varepsilon_{R}=0\)), the expression of the mechanical efficiency can be simplified as follows:  
\[\eta=\eta_{2\_1}\frac{i_{1\_2}-1}{i_{1\_2}-\eta_{2\_1}} \tag{41}\]  
Unlike the conventional wind turbines with a wind rotor and conventional generator (with fixed stator), in which the speed increase has the efficiency independent of the speed amplification ratio, the stationary mechanical efficiency of wind turbines with counter-rotating generator depends on both the efficiency of the speed increase SI with fixed axes (i.e., \(\eta_{2\_1}=\eta_{2\_3}\eta_{3\_1}\)) and the kinematic ratio \(i_{1\_2}\), according to Equation (41). Given that the kinematic ratio \(i_{1\_2}\) has a negative supraunitary value and \(\eta_{2\_1}<1\), it is obvious that \(\eta>\eta_{2\_1}\). Thus, it can be concluded that wind turbines with counter-rotating generator have a better mechanical efficiency in a steady-state regime than the conventional systems that integrate the same speed increase.
## 4 Experimental Results
### The EMR-SF Construction Results by LRBF
#### 4.3.2 Accuracy Comparison
While the EMR-SF constructed by LRBF is more in line with the EMR-SF constructed by the propagation characteristics of EMR, the construction accuracy of LRBF is higher than that of IDW and OK.  
Figure 16: Construction results of EMR-SF by different space field methods. (**a**) The interpolation results by IDW. (**b**) The interpolation results by OK. (**c**) Interpolation results by LRBF.
## 2 Materials and Methods
### Frequency Spectrum Features  
Nine biologically significant EEG frequency bands were used: \(\delta\) or delta (1-3 Hz), \(\theta\) or theta (3-7 Hz), \(\alpha\) or alpha (7-10 Hz), \(\mu\) or mu (10-13 Hz), \(\beta\)1 or beta-1 (13-25 Hz), \(\beta\)2/\(\gamma\)1 or beta2-gamma1 (25-45 Hz), \(\gamma\)2 or gamma2 (55-70 Hz), \(\gamma\)3 or gamma3 (70-90 Hz), and \(\gamma\)4 or gamma 4 (90-110 Hz). PSDs (power spectral densities) of these bands were calculated with the Welch method (Hanning filtering window, 50% overlap of consecutive windows) [35]. Once the PSDs were calculated, they were converted to the feature vector by summing the PSDs of frequencies lying between the boundaries of each band.
## 3 Equivalent Circuits and Matching Network
This relationship is identical to dependence \(L_{1}\), \(L_{2}\) = _f(k)_ at IPT idle running and also identical to \(L_{2}\) = _f(k)_, at equivalent circuit with two inductances, when IPT is connected to the power supply and load (Figure 6);
* row 3--shows the relative change of mutual inductance \(L_{M2}\) of T equivalent circuit (Figure 5);
* row 4--the change of leakage inductances \(L_{N1}\) and \(L_{N2}\) of T equivalent circuit;
* row 5--the change of leakage inductance \(L_{L1}\) from equivalent circuit with two inductances. Its change is a much smaller range than the leakage inductances \(L_{N1}\) and \(L_{N2}\) from row 4. Analyzing the results recorded in Table 1, the expression of equivalent circuits with two and three inductances and their compensating circuits can lead to the following conclusions:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{k}\) & \(\mathbf{0.2}\) & \(\mathbf{0.3}\) & \(\mathbf{0.4}\) & \(\mathbf{0.5}\) & \(\mathbf{0.6}\) \\ \hline g(k)/g(k\({}_{0}\)) & 0  
there is a strong change of magnetic coupling and equivalent inductance values from current position of the coils. This requires the use of additional measures for dynamic matching at different x-y-z coils positions;
* in case of magnetic coupling change, T equivalent circuit inductances change over a wider range than in the equivalent circuit with two inductances (rows 3 and 5, Table 1);
* at \(k\) values in the range 0.2 \(<\)\(k\)\(<\) 0.6, relative change of \(\Delta L_{\text{N1}}\)(\(k\))/\(\Delta L_{\text{L1}}\)(\(k\)) is about 1.68.
# Critical Review of Techniques for Food Emulsion Characterization
## 4 Emulsion Fractionation and Characterization Methods
### Field-Flow Fractionation (FFF)
The limitations of conventional FFF systems have driven the research towards the development of hybrid platforms combining two or more external fields for improved separation, such as electrical asymmetrical flow field-flow fractionation (ElasFIFFF or EAF4) [244]. ElAsFIFFF is a separative platform that combines electrical field-flow fractionation and asymmetrical flow field-flow fractionation, allowing for separation based on both diffusion coefficient and the surface charge of droplets (or analytes). As a result, this approach enables the separation of droplets based on their charge, even if they have similar sizes. This feature has the potential to improve separation resolution compared to traditional FFF methods. Other advantages of ElAsFIFF include improved control of the electric field and sample velocity, the ability to separate both cationic and anionic species in one run, lower voltage requirements compared to EIFFF, and reduced interference from electrode effects. Furthermore, ElAsFIFF is not limited to low ionic strength samples and thus provides more versatility in emulsion characterization. However, a downside of ElAsFIFF is that it introduces additional process parameters influencing the outcome, due to the combination of the two external fields driving the separation.
## 5 Experiment
### Comparison with the OCP Path with Shorter DTT  
Several cases with a trajectory Desired Terminal Time (DTT) shorter than or equal to 0.903 s are taken into consideration in the experiment, including 0.451 s, 0.541 s and 0.903 s. The experiments are executed three times for each case. The Actual Terminal Time (ATT) is shown in Table 4. The executive time increased by 81.08%, 64.51% and 31.79%. Because the model is not exactly the same as the experimental robotic manipulator, ATT is always longer than DTT. As shown in Figure 7a, the joint position of each joint in each experiment is given, and we find that the shorter the time, the higher the slope of the trajectory. What is more, when DTT = 0.451 s, the trajectory of joint 1 is interrupted half way because of self-collision, which means the trajectory with a time shorter than 0.451 s is not allowed.  
The current of each joint actuator is shown in Figure 7b, and the current over 0.21 A is marked in Figure 7c. It is found that the current of the standard OCP trajectory is stable; only four sampling points in joint 1 are over the rated maximum. As a result, the motion is quick and smooth without too much vibration. However, the current of the OCP path with a shorter DTT becomes not stable any more, and the period of current over rated maximum increases greatly, which causes violent vibration. It was found that the case with the same path and shorter time requires higher voltage to achieve the trajectory, which may go beyond the nominal voltage and cause a vibration. The default case takes a much longer time to finish the motion, and the voltage curve is more smooth, which means the capabilities of the manipulator are not fully utilized. As the result, the validity of the proposed method is proved.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DTT (s)** & **Order** & **ATT (s)** & **Rate** & \\ \hline  & 1 & 1140 & 26.25\% & \\
0.903 & 2 & 1180 & 30.68\% & 31.79\% \\  & 3 & 1250 & 38.43\% & \\ \hline  & 1 & 0.840 & 55.27\% & \\
0.541 & 2 & 0.930 & 71.90\% & 64.51\% \\  & 3 & 0.900 & 66.36\% & \\ \hline  & 1 & 0.820 & 81.82\% & \\
0.903 & 2 & 0.790 & 75.17\% & 81.08\% \\  & 3 & 0.840 & 86.25\% & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Table of DTT and ATT in the experiments.  
Figure 6: The OCP trajectory and default trajectory with different maximum velocity in the experiment.