esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Results and Performance Evaluation
### Evaluation of Base Station-Centric (Centralized) Versus Node-Centric (Decentralized) Approaches
#### 7.6.3 Speed of Jamming Detection  
The decentralized approach is undoubtedly much faster than the centralized approach as the latter is based on distributed processing. However, the jamming detection is done for a greater purpose like mapping the jammed area, localizing and tracking the jammer, and taking counter-jamming actions at different layers. An over-all global picture must be built to meet the purpose. In the decentralized approach, the base station has to build-up the over-all picture almost de-novo, whereas, in the centralized approach, the over-all picture is almost simultaneously built along with the process of detection. This aspect compensates the slow speed of the centralized approach and places it at par with the decentralized approach.  
Let us also do the time complexity analysis of our proposed method which uses two different algorithms: Fuzzy Inference System (FIS) and 2-Means Clustering (TMC).  
For the FIS, the time function is given as:  
\[\textit{T(N)}=k_{l}\textit{Ns}^{u}+k_{\textit{2,}} \tag{15}\]  
where \(N\) is the number of iterations (_i.e._, \(N\) is the number of nodes), \(s\) is the number of fuzzy sets defined for the input (\(s=3\) in our case, _i.e._, LOW, MEDIUM, and HIGH), \(u\) is the number of inputs (\(u=2\) in our case, _i.e._, SNR and PDPT), and \(k_{l}\) and \(k_{2}\) are constants. Therefore,  
\[\textit{T(n)}=9k_{l}\textit{N}+k_{\textit{2,}} \tag{16}\]  
Therefore, the time complexity of FIS is of O(\(N\)).  
In the TMC, we have four un-nested loops defined by equations 4 to 6, each running from 1 to _nj_ or _(n-nj)_, where \(n\) and _nj_ are the 'number of one-hop neighbors' and the 'number of jammed one-hop neighbors' respectively of the node under consideration. Since, the maximum value which _nj_ or _(n-\(\eta\))_ can take in these loops is bounded by _n/2_; the maximum number of possible iterations in any of these un-nested loops is limited to _n/2_. There are a total of \(t\) jammed nodes for which the TMC has to be applied. Therefore, the time taken by the algorithm, \(T\) is:  
\[T=k_{3}\,\text{(n/2)}\ t+k_{4,} \tag{17}\]  
where \(k_{3}\) and \(k_{4}\) are constants. Since both, t and n are bounded by the upper limit N, the time complexity of TMC is of O(\(N^{2}\)). Thus, the overall time complexity of the two algorithms taken together is of O(\(N^{2}\)).  
In absolute terms, the total time taken to process 25 nodes in a WSN configuration as depicted in Figure 6 using vectors and 'tic' and 'toc' commands of MATLAB7 on a 2.4 GHz Pentium computer for FIS and TMC together was found to be 0.797 seconds.
## 4 Simulation Study on Disturbance Transient Control
### Transient Stability Control Experiment for Load Disturbance Rejection  
To validate the disturbance-rejection capability of the proposed method, this paper first designs a simulation experiment for bus voltage tracking under load disturbance. The simulation is designed to change the load disturbance magnitude at 2 s and 4 s while maintaining the same control input bus voltage: from 0 to 2 s, the load impedance is 50 \(\Omega\); at 2 s, it suddenly changes to 100 \(\Omega\) and remains at this value until 4 s; from 2 to 4 s, the load impedance is 100 \(\Omega\)[16]. Then, at 4 s, the impedance changes back to 50 \(\Omega\) and remains until the end of the simulation. A constant 2 V voltage disturbance signal is also introduced at 1 s and 3 s of the simulation. This section sets up four simulation experiments, where four controllers are tasked with tracking bus voltages of 20 V, 30 V, 40 V and 50 V under the same conditions. Figures 11-14 show the bus voltage-tracking performance of each controller.  
From the curves in Figures 11a, 12a, 13a and 14a, it can be concluded that when disturbance signals are introduced, the BP-ADRC bus voltage tracking exhibits the smallest fluctuation range, between 0.15 and 0.3 V, and the shortest recovery tracking time, ranging from 0.042 s to 0.051 s. Additionally, from the curves in Figures 11b, 12b, 13b and 14b, it can be observed that in the presence of external load disturbances, the BP-ADRC bus voltage  
Figure 11: (**a**) The 20 V bus-tracking voltage anti-signal interference curve. (**b**) The 20 V bus-tracking voltage anti-load disturbance curve. (**c**) The 20 V bus-tracking voltage total anti-disturbance curve.  
tracking shows the smallest fluctuation range, between 0.02 V and 0.08 V, and the shortest recovery-tracking time, ranging from 0.012 s to 0.067 s.  
Figure 14: (**a**) The 50 V bus-tracking voltage anti-signal interference curve. (**b**) The 50 V bus-tracking voltage anti-load disturbance curve. (**c**) The 50 V bus-tracking voltage total anti-disturbance curve.  
Figure 12: (**a**) The 30 V bus-tracking voltage anti-signal interference curve. (**b**) The 30 V bus-tracking voltage anti-load disturbance curve. (**c**) The 30 V bus-tracking voltage total anti-disturbance curve.  
Figure 13: (**a**) The 40 V bus-tracking voltage anti-signal interference curve. (**b**) The 40 V bus-tracking voltage anti-load disturbance curve. (**c**) The 40 V bus-tracking voltage total anti-disturbance curve.  
To ensure the rigor of the simulation experiments, the simulated curve data was imported into the MATLAB workspace. Subsequently, each set of data was substituted into Equations (31)-(33), resulting in Table 2 as shown below:  
Based on Table 2, the data for BP-ADRC is superior to the other three controllers. This indicates that the method proposed in this paper achieves good control performance for load disturbances in bus voltage.
# Mixed-Carrier Communication for Technology Division Multiplexing
## 1 Introduction
In Section 4, the trade-offs between different design parameters are analyzed, including MCC SNR, as well as the individual SNR per modulation scheme at a target BER threshold. Finally, the optimized MCC design parameters are explained and MCC performance is evaluated in Section 5.
# Preparation and Properties of SBS/REOB-Modified Rejuvenated Asphalt
## 3 Test Method
### Rheological and Microscopic Tests of SBS/REOB-Modified Rejuvenated Asphalt  
#### 3.3.1 Rheological Test  
A dynamic shear rheological test (DSR) and a low-temperature bending beam rheological test (BBR) were conducted on the 70\(\#\) base asphalt and REOB rejuvenated asphalt  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & & & \\ \cline{3-5}
**Experimental** & **Level** & **A** & **B** & **C** & **D** \\ \cline{3-5}
**Group** & **Combination** & **REOB** & **SBS** & **Shear** & **Shear** \\  & & **Dosage/\%** & **Dosage/\%** & **Time/min** & **Rate/t/min** \\ \hline
1 & A\({}_{1}\)B\({}_{1}\)C\({}_{1}\)D\({}_{1}\) & 1 (11\%) & 1 (3.5\%) & 1 (40) & 1 (4000) \\
2 & A\({}_{1}\)B\({}_{2}\)C\({}_{2}\)D\({}_{2}\) & 1 (11\%) & 2 (4.0\%) & 2 (50) & 2 (4500) \\
3 & A\({}_{1}\)B\({}_{3}\)C\({}_{3}\)D\({}_{3}\) & 1 (11\%) & 3 (4.5\%) & 3 (60) & 3 (5000) \\
4 & A\({}_{2}\)B\({}_{3}\)C\({}_{3}\)D\({}_{2}\) & 2 (9\%) & 1 (3.5\%) & 2 (50) & 3 (5000) \\
5 & A\({}_{2}\)B\({}_{3}\)C\({}_{3}\)D\({}_{2}\) & 2 (9\%) & 2 (4.0\%) & 3 (60) & 1 (4000) \\
6 & A\({}_{2}\)B\({}_{5}\)C\({}_{1}\)D\({}_{2}\) & 2 (9\%) & 3 (4.5\%) & 1 (40) & 2 (4500) \\
7 & A\({}_{3}\)B\({}_{1}\)C\({}_{3}\)D\({}_{2}\) & 3 (7\%) & 1 (3.5\%) & 3 (60) & 2 (4500) \\
8 & A\({}_{3}\)B\({}_{2}\)C\({}_{1}\)D\({}_{3}\) & 3 (7\%) & 2 (4.0\%) & 1 (40) & 3 (5000) \\
9 & A\({}_{3}\)B\({}_{3}\)C\({}_{2}\)D\({}_{1}\) & 3 (7\%) & 3 (4.5\%) & 2 (50) & 1 (4000) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Orthogonal design scheme.  
Figure 2: Preparation process of SBS/REOB-modified rejuvenated asphalt.  
and the SBS-modified asphalt and SBS/REOB-modified rejuvenated asphalt, respectively, with the optimal mixing amount, as shown in Table 7, to conduct a comparative study and evaluate the high- and low-temperature rheological properties of SBS/REOB-modified rejuvenated asphalt.
## 2 Materials and Methods
### Machine Learning Algorithms Used in the Study  
In this section, a comprehensive comparison is conducted among the algorithms employed in this study, including decision tree regression (DTR), random forest regression (RFR), gradient boosting regression (GBR), support vector regression (SVR), and artificial neural networks (ANNs). These algorithms were chosen for their common use in previous studies.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \(LL\) & \(Fs\) & \(e_{0}\) & \(SR\) & \(\gamma_{d}\) & \(E_{t}\) & \(q_{u}\) \\ \hline count & 225 & 225 & 225 & 225 & 225 & 225 & 225 \\ mean & 59.7 & 25.3 & 0.73 & 76.2 & 15.66 & 42,267 & 517.1 \\ std & 13 & 17.06 & 0.14 & 15.68 & 1.228 & 33,966 & 393.7 \\ min & 41.1 & 0 & 0.45 & 50 & 13.3 & 950 & 17.79 \\
25\% & 48.6 & 10 & 0.63 & 60 & 14.66 & 14,717 & 238.3 \\
50\% & 55.8 & 30 & 0.74 & 74.25 & 15.3 & 34,418 & 416.97 \\
75\% & 72.1 & 40 & 0.858 & 90 & 16.6 & 61,706 & 665.11 \\ max & 78.8 & 50 & 1.032 & 100 & 18.451 & 159,238 & 2277.9 \\ \hline \end{tabular}
\end{table}
Table 4: The statistical attributes of the selected variables.  
Figure 9: Assessing the impact of different input parameters for \(q_{u}\) and \(E_{t}\) via correlation coefficients: (**A**) Pearson for \(q_{u}\), (**B**) Pearson for \(E_{t}\), (**C**) Kendall for \(q_{u}\), (**D**) Kendall for \(E_{t}\), (**E**) Spearman for \(q_{u}\), and (**F**) Spearman for \(E_{t}\).  
The dataset's complexity influenced the selection of machine learning (ML) models. ML models that are adept at handling such complexities may be necessary because traditional models may not be able to capture these subtleties. Aiming primarily for higher predictive accuracy for variables such as \(q_{u}\) and \(E_{t}\) in expansive soils, machine learning models were chosen due to their capacity to efficiently navigate intricate relationships.  
This study employs a variety of machine learning models, including extreme gradient boosting (XGBoost), gradient boosting regression (GBR), artificial neural networks (ANN-GMDH, and ANNs), support vector regression (SVR), decision tree regression (DTR),an ensemble of varied, randomly formed, and unpruned decision trees. An RFR is built by assembling a collection of decision trees that have been systematically altered. Random feature selection emerges as a critical component of the training process. The careful selection of different attributes is required to ensure a rich diversity of decision trees. As a result, each decision tree contributes a unique evaluative perspective to the collective forest, culminating in the outcome being determined by the aggregated votes of these trees. The random forest approach is explained in detail in earlier research [73; 86].
# Cascaded Machine-Learning Technique for Debris Classification in Floor-Cleaning Robot Application
## 3 Preliminary
### Support Vector Machine (SVM)  
SVM is commonly used in classification problems [24; 25; 26]. SVM constructs a hyperplane as a separation between two or more classes. This hyperplane is constructed based on the distances from various points of different classes from it. The standard form of a hyperplane is given in Equation (1).  
\[\vec{w}.\vec{x}-b=0 \tag{1}\]  
where \(\vec{w}\) is a vector normal to the described hyperplane, referred to as the weight, and \(b\) represents the bias. For any data set containing points \((\vec{x},y)\), with the class label \(y\) having two values \(+1\) and \(-1\), the hyperplane separating the two classes satisfies Equations (2) and (3).  
\[\vec{w}.\vec{x}_{i}-b\geq 1\quad\text{if}\quad y_{i}=+1 \tag{2}\]  
Figure 1: Structure of CNN.  
\[\vec{w}.\vec{x_{i}}-b\leq 1\quad\text{if}\quad y_{i}=-1 \tag{3}\]  
However, this boundary may not be perfect. This can be due to the data not being completely separable. Hence, determining the hyperplane becomes a minimization problem where the hinge loss is minimized. Equation (4) describes the hinge loss function.  
\[\sum max(0,1-y_{i}(\vec{w}.(x_{i})-b)) \tag{4}\]  
Another scenario when the boundary may not be perfect is when the data is not linearly separable. Here, kernel functions such as Radial Bias Function (RBF), linear, Polynomial, Gaussian, etc., can be employed for classification with SVM.
# Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
## 7 Experimental Results and Analysis
### Evaluation of Autoencoder Performance on PC
The reconstructed shapes closely match the inputs, with only slight differences in clarity, resulting in low reconstruction errors that are not flagged as anomalies. In contrast, EMNIST samples, which were not seen during training, are usually unsuccessfully reconstructed. These significant differences lead to high reconstruction errors and, consequently, high AD rates. However, AD on EMNIST is not perfect due to certain letters resembling digits in structure, such as \"O\", \"I\", \"L\", and \"C\". As a result, some EMNIST characters are partially reconstructed and not  
Figure 3: Input and reconstruction examples on autoencoder Model 1 [196, 16, 196]. Figure 2: Anomaly detection rate distribution for different datasets using Model 1 [196, 16, 196] across batch sizes 1, 10, and 50.  
flagged as anomalies. This phenomenon limits the achievable F1 score on EMNIST, even for well-trained models. This behavior can be explained by the nature of the learned latent space. Even though the AEs are trained only on digits, they often begin to capture more general visual features--such as strokes, loops, and corners--that are shared across both digits and some alphabetic characters. Over time, the model may learn to abstractly represent these components, allowing it to partially reconstruct letter-like inputs as well. Figure 4 illustrates the results from testing the first AE model, showing AD rates across EMNIST letter classes. Letters that are structurally similar to digits are detected less frequently as anomalies, making them harder to detect reliably. Interestingly, when evaluating the MNIST-C dataset, we observe different behaviors. As illustrated in Figure 3, in the fog variant, global distortion leads to severe reconstruction failure, causing the AE to misinterpret the input entirely and correctly assign it a high AD rate. On the other hand, localized corruptions such as dotted line, spatter, or motion blur preserve most of the original digit shape. The model reconstructs the underlying digit relatively well while removing or ignoring the added noise during reconstruction. This demonstrates the AE's ability to perform implicit denoising, which is a useful byproduct of its training objective. However, the AD rates in such cases remain elevated since the model compares the corrupted input with its clean output and detects the difference. This highlights the sensitivity of reconstruction error: even if the digit is preserved, the presence of artifacts increases the AD rate. These observations align with the results in Table 3, where EMNIST and MNIST-C fog consistently yield high F1 scores, confirming that the AE effectively flags them as anomalous. Other corrupted variants, being structurally closer to clean MNIST, result in more variable detection outcomes.
# Estimating Tetrachloroethene Sorption Coefficients Based on Soil Properties in Organic-Poor Soils
## 2 Materials and Methods
### Sorption Experiments  
Sorption experiments were conducted using a batch equilibrium technique based on established methods [39; 40]. Each soil sample (10 \(\pm\) 0.1 g, dry mass) was placed into 40 mL borosilicate glass tubes (28 \(\times\) 95 mm) equipped with PTFE septa. To each tube, 5 mL of 1 M CaCl\({}_{2}\) solution and 30 mL of distilled water were added, maintaining a soil-to-solution ratio of 1:3.5 (\(w/v\)). An aliquot of the standard PCE solution in methanol (1000 mg L\({}^{-1}\)) was then injected below the water level to achieve relative concentrations (C\({}_{\rm w}\)/S) of 0.015, 0.06, 0.25, 0.42, 0.831, and 1.35. These six points were used to construct the sorption isotherm. Duplicates of each sample were prepared and analyzed under the same experimental conditions. Following spiking, the tubes were shaken on a Laboratory Shaking Machine LT 2 (Kavalierglass, Sazava, Czech Republic) at a temperature range of 20 to 25 \({}^{\circ}\)C. The samples were allowed to equilibrate for 24 to 48 h before phase separation, which was achieved by centrifugation at 800 rpm for 3 min using a Jouan Cii Multifunction Centrifuge (Juan GmbH, Berlin, Germany). The extraction method followed EN ISO 10301. PCE concentration was measured using gas chromatography; further experimental parameters and details of the analysis method are provided in Texts S3 and S4.
## 1 Introduction  
In this paper, we demonstrate how the partial-wave method, as first introduced by [PERSON] and [PERSON] [1], can be used to gain a new perspective on guided waves. This new perspective adds to our understanding of the structure of guided waves, and provides valuable insight into the leaky characteristics of Lamb waves (which are not to be confused with the study of leaky Lamb waves and their corresponding dispersion curve solutions).  
At present, our understanding of guided waves is primarily informed by the derivation of characteristic equations and dispersion curve solutions. The dispersion curve solutions in particular have become significantly more accessible with the widespread use of the semi-analytical finite element (SAFE) method. However, the SAFE method does not provide the same level of understanding that would be possible by analyzing a characteristic equation. The most well-known example of this is that the two-part Lamb wave characteristic equation shows that all of the Lamb waves can be divided into two types: symmetric and anti-symmetric. Hence, the importance of the present work. However, this paper will not cover the calculation of dispersion curves, attenuation, energy loss, or anisotropic materials. Neither will it cover the derivation of characteristic equations for various guided waves.  
For the purpose of clarity, in this paper, bulk waves are plane waves that travel through the bulk of a media, and surface waves are plane waves that travel along the surface of a media. It is emphasized that all of the waves are considered to have a planar wavefront.  
Sections 2 and 3 will provide a brief history and the fundamentals of the partial-wave method and the Christoffel equation, respectively. These sections give special attention to the dictionary used and the assumptions made, as they are pertinent to how the interpretation presented in this paper deviates from the literature. Section 4 describes this new method of interpretation. It discusses in detail how the slowness curve solutions of the Christoffel equation can be used to interpret the characteristics of most guided waves, and the Lamb wave solutions in particular. Sections 5 and 6 apply the new method of interpretation to a variety of guided waves, which include Rayleigh waves, Stoneley waves, Lamb waves, and shear horizontal waves.  
Section 7 focuses on Lamb waves, and a method developed in Section 6 is used to investigate the contributions of each partial wave to Lamb waves from 50 kHz to 20 MHz for a 1-mm thick aluminum plate. This section also identifies characteristics of Lamb waves that have not been formally discussed in the literature.  
Section 8 uses the results from the previous sections to develop an understanding of the leaky characteristics of Lamb waves. Various characteristics of the acoustic leakage from the waveguide can be easily determined, including the angle of propagation and the amplitude of emission based on the interface conditions. A correlation between the amount of acoustic leakage and the refraction qualities of obliquely travelling bulk waves in the guided wave is also identified.
###### Abstract  
Thin-walled conical parts with variable-section are usually made of superalloy material, with poor plasticity and complex forces, which are difficult to form and control. In this research, a thin-walled conical casing of superalloy GH1140 with variable-section is studied; the real stress-strain curve of the material is fitted and the load-displacement curves of superalloy GH1140 are obtained through a universal testing machine. To clarify the equivalent stress distribution on the upper surface of the thin-walled casing during the forming process and after unloading the rotary wheel, the finite element model of the thin-walled conical casing during the spinning forming process is established with the Simufact Forming finite element analysis software. The effects of processing parameters, such as the mandrel rotational speed \(\omega\), the roller feed ratio \(f\) and the gap deviation rate \(\delta\) between the roller and mandrel, on the spinning forming load were obtained. The distribution and numerical trend of the tangential residual stress after forming were detected by X-ray diffraction, and the causes of defects such as flange instability were analyzed. The results of the forming test and the test of residual stress conform well with the simulation, which verifies the stability of the model. The research provides a theoretical basis for improving the forming quality of thin-walled parts with variable-section.  
process parameters; spinning; forming load; variable-section thin-walled conical part; residual stress +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Weather Variations  
The average annual temperature and annual total precipitation of Guangzhou are shown in Figure 7, which indicates that the temperature fluctuation was stable and smaller than the precipitation fluctuation. It is demonstrated by variations of monthly temperature and total precipitation in Figure 7, and precipitation reached the bottom in 2003 and then increased significantly. NPP change had a  
Figure 6: Spatial mapping of three effects patterns in the central area, suburb and exurb. **(a)** 2001–2003 weather variation. **(b)** 2001–2003 LULC change. **(c)** 2003–2006 weather variation. **(d)** 2003–2006 LULC change.  
positive correlation with temperature and a negative correlation with precipitation, and the correlation coefficient between temperatures, precipitation was higher in the latter period than in the early period (Table 4). It is probably a lag NPP response to the strong El Nino event in 2003. The highest negative correlation coefficient between precipitation and NPP occurred in the suburb, and the weak relation occurred in the central and exurb during both periods. However, from 2001 to 2003, the correlation coefficient was only \(-0.27\) with standard deviation \(0.46\), and it reached to \(-0.85\) with standard deviation only \(0.14\) from 2003 to 2006. The correlation coefficient suggested that the relationship between precipitation and NPP was more closed in the large suburb area during the latter period, which was consistent with the effects dominated by weather variation.