esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods  
### Variational Mode Decomposition (VMD)  
VMD is a new non-stationary, signal-adaptive decomposition estimation method. It was proposed by [PERSON] in 2014. The purpose of VMD is to decompose the original complex signal into K amplitude and frequency modulation sub-signals. Because K can be preset, and with a proper value of K, modal aliasing can be effectively suppressed [29]. VMD assumes that each \"mode\" has a finite bandwidth with unique center frequencies. The main process of this method is to: (1) use Wiener filtering to de-noise the signal; (2) obtain the K-estimated center angular frequency by initializing the finite bandwidth parameters and the central angular frequency; (3) use the alternating direction method of multipliers to update each modal function and its center frequency; (4) demodulate each modal function to the corresponding baseband; and (5) minimize the sum of each modal estimation bandwidth. The algorithm can be divided into the construction of a variational problem, subsequently obtaining the solution to the variation problem. The algorithm is described in detail in Sections 2.1.1-2.1.4.
## 4 Proposed Methodology of Hybrid Channel Modeling
### Gallery Model for Underground Mine  
It is very important to consider the structure of the mine before developing its mathematical model. Although mines consist of different portions, in this case the general cross-section area for signal propagation is in rectangular form [30]. In this study, the width of the assumed mine gallery is 2a, and the height is considered to be 2b, and the origin is in the center using the Cartesian coordinate framework. The complex electrical parameters for the mine gallery are given below in (1)-(3):  
\[k_{v} =\epsilon_{o}\epsilon_{v}+\frac{\sigma_{v}}{j2\pi f_{o}} \tag{1}\] \[k_{h} =\epsilon_{o}\epsilon_{h}+\frac{\sigma_{h}}{j2\pi f_{o}}\] (2) \[k_{a} =\epsilon_{o}\epsilon_{a}+\frac{\sigma_{a}}{j2\pi f_{o}} \tag{3}\]  
Figure 4: Layout of an underground coal mine, (**a**) Underground mine galleries, (**b**) Channel measurement setup.  
Figure 3: Flow diagram of proposed hybrid channel-modeling scheme for mines.  
where \(\epsilon_{v}\), \(\epsilon_{h}\), \(\epsilon_{a}\) and \(\epsilon_{o}\) denote relative permittivity parameters for the vertical structure of sidewalls, horizontal floor, air, and vacuum space in the tunnel, respectively. Similarly, \(\sigma_{v}\), \(\sigma_{h}\) and \(\sigma_{a}\) represents the conductivity parameters for vertical side boundaries, horizontal top/bottom, and free space of air, respectively. The \(f_{o}\) denotes the central frequency and the permeability parameter \(\mu_{o}\) is considered to be the same for all areas. The relative electrical parameters \(\overline{k_{v}}\) and \(\overline{k_{h}}\) are considered to generate a brief expression where \(\overline{k_{v}}=k_{v}/k_{a}\) and \(\overline{k_{h}}=k_{h}/k_{a}\).
## 2 The Methodology of the Research
### Results Evaluation
#### 2.2.1 Evaluation Metrics  
We utilized three fundamental metrics to evaluate the performance of our models:  
1. Accuracy: The proportion of correct predictions among all predictions made. This is calculated as follows: \[\text{Accuracy}=\frac{\text{True Positives}+\text{True Negatives}}{\text{Total Samples}}\] (1) While accuracy provides a straightforward measure of performance, it can be misleading in datasets with imbalanced class distributions.
2. F1 Score: The harmonic mean of precision and recall, providing a balance between the two. It is defined as follows: \[\text{F1 Score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}}\] (2) The F1 score is particularly useful when the class distribution is uneven and when both false positives and false negatives are important.
3. Area Under the ROC Curve (AUC): Measures the model's ability to distinguish between classes by plotting the true positive rate against the false positive rate at various threshold settings. An AUC of 1 indicates perfect classification, while an AUC of 0.5 suggests no discriminative power.
# Investigating Master-Slave Architecture for Underwater Wireless Sensor Network
## 4 System Model
### Data Delivery Scheme  
For the process of packet generation and packet forwarding, we follow a certain data delivery scheme. In this scheme, every node can generate packets (NR, ACK, CTRL, DATA) in the whole process. When a node becomes a source (i.e., node that holds/generates the DATA packet), it will first check the sink in its range for its existence, and then handover the DATA packet to sink without following the forwarding scheme. If there is no sink in its proximity, it follows the scheme in which the nodes are in the neighborhood. The nodes are divided into Suppressed and Potential Forwarding Nodes (PFN) based on depth greater than and less than the source node, respectively. The PFNs are further subdivided into masters and slaves based on MSF values. The MSF values are calculated on each PFN using Equation (10). The source node fetches these values from the PFNs through the neighbor request packet and evaluates the PFNs into masters and slaves based on higher and lower values, respectively. Algorithm 1 plays a major role in selecting the desired best master nodes for the forwarding of the data packet. Slave nodes are suppressed by the CTRL packet broadcast by the source node, containing the IDs of the masters. After the mentioned  
Figure 8: Holding time and activation time calculation.  
selection process, the clocks are allocated to the master nodes using the activation time and holding time Equations (10)-(15). The node that beats the timer forwards the data and then follows an Algorithm 2 and the flowchart in Figure 9, for forwarding data until it finds the sink, and marks the successful data delivery to the sink.
# Two-Stage Intelligent Model for Detecting Malicious DDoS Behavior
## 2 Related Work
### Statistical Analysis Detection Method  
The current research trends show that many researchers tend to use information entropy [25] and information distance [26] for statistical anomaly detection. Researchers statistically analyze traffic features to distinguish attacks from legitimate traffic [27].  
[PERSON] et al. [28] summarized the work on modeling malicious activities from various perspectives, discuss the pros and cons of current models. [PERSON] et al. [29] proposed a second-order statistics-based discrimination algorithm to detect botnet attacks. However, [28; 29] mainly focus on analyzing the botnet model. [PERSON] et al. [30] used the flow correlation coefficient to measure the similarity among suspicious flows to differentiate DDoS attacks from flash crowds. However, they lack that have a fine-grained classification of DDoS.  
[PERSON] et al. [31] used Kullback-Leibler Divergence (KL) to evaluate the different histograms similarity to obtain the best performance. [PERSON] et al. [32] proposed a new measure that they have called the Bhattacharyya distance. They verified that the distance measure is easier to evaluate than the divergence. [PERSON] et al. [8] calculated the flow score through the distribution of packet length. This method's results demonstrate its effectiveness in detecting the botnet. However, a single feature will not stand in for all kinds of attacks. [PERSON] et al. [PERSON] et al. (2018) used four features to detect DDoS with high accuracy: backward packet length standard deviation, flow duration, average packet size, and flow inter-arrival time standard deviation. [PERSON] et al. [PERSON] et al. (2019) selected flow rate, duration, average packet length, and bytes per second to provide the highest detection rate.  
[PERSON] et al. [PERSON] et al. (2019) exploited the EWMA to evaluate the statistical distance, but did not verify the effectiveness of the proposed detection method. We combined the methods in [PERSON] et al. (2018); [PERSON] et al. (2019) to measure flow similarity based on previous knowledge (MFPK). According to the attack behavior, we selected five features standing for the attack, and used the probability distribution of five features to represent normal traffic and attack behavior. We deployed a similarity statistics model based on previous knowledge at the ingress router, and calculated the benign traffic baseline. Then, the incoming traffic was judged as benign or abnormal. Finally, the abnormal traffic was fed into the neural network detection model. We evaluated the effectiveness of the MFPK method at detecting DDoS attacks and compared the proposed method with the existing methods [PERSON] et al. (2018); [PERSON] et al. (2019). In order to prove the effectiveness of the features selected, we compared the performance with that of existing feature selection methods [PERSON] et al. (2018); [PERSON] et al. (2019).
## 5 Generation of Multiple Spots across the Field of View  
One major advantage of the presented setup is the capability of potentially activating several cells simultaneously at different freely selectable locations on a given sample. To test the capabilities of the setup, we used both beam paths to generate 1 to 100 foci at random positions in the field of view. For these measurements, the laser power was kept constant and camera acquisition times were adjusted to fully use the dynamic range. Again, we measured the \(D_{4\sigma}\) beam widths and extracted the average gray value on the camera for each focus in a radius smaller than \(D_{4\sigma}\). Assuming a linear response of the camera, we normalized the resulting mean gray values to the acquisition time to get a measure for the mean intensity per focus. Theoretically, for \(n\) generated foci, the overall laser energy should split evenly, resulting in a \(n^{-1}\) decay of the mean intensity. As can be seen in Figure 4 on the left in a double-logarithmic plot, both beam paths are able to generate foci almost according to theory with a \(n^{-1.03}\) behavior up to a wavelength-dependent threshold of \(n=20\) for the yellow beam path and \(n=60\) for the blue beam path, after which mean intensities drop more quickly, resulting in a decrease in signal-to-noise ratio.  
Figure 4 (right) shows the increasing diameters for an increasing number of generated foci. The increase from 1 to 100 foci is much higher for the yellow laser at 19.4% than for the blue laser at 3.8%. While no clear connection can be made from increasing focus size to the wavelength-dependent SNR drop, one can conclude that, for higher wavelengths, the setup projects higher spatial frequencies with lower fidelity than for lower wavelengths.
## 3 Example Analysis and Experiment
### Method Comparison  
To better validate the effectiveness of the proposed multimodal representation method, we input the vibration, sound, and image features (after feature representation) into a ResNet. The training and testing of the samples were conducted, and the recognition  
Figure 11: Comparison of recognition accuracy for different optimizers.  
Figure 10: Comparison of recognition loss for different optimizers.  
Figure 9: Confusion matrix.  
accuracy and average loss value for each modality on the test set were compared. The results are shown in Figures 12 and 13.  
As shown in Figures 12 and 13, when the image modality is used as input, the model achieves the lowest recognition accuracy. This phenomenon occurs because the temporal information in the image modality is not fully represented. When the vibration modality is used as input, the model's recognition accuracy and loss values are closest to those of the multimodal input. However, the multimodal approach achieves a higher average recognition accuracy than the vibration modality. The confusion matrix for the recognition results of different modality performances is shown in Figure 14.  
As shown in Figures 9 and 14, the recognition performance of the four compared modalities is arranged in ascending order as follows: image, sound, vibration, and multimodal. Additionally, in the confusion matrix in Figure 14, it can be observed that among the misclassified health states, the early degradation state is most often misclassified as  
Figure 14: Confusion matrix of recognition results for different methods.  
Figure 12: Comparison of recognition accuracy for different modalities.  
Figure 13: Comparison of loss values for different modalities.  
the healthy state. This is because the feature changes in the early degradation stage are relatively subtle, making it harder to distinguish. On the other hand, the fault state has the highest recognition rate, as its feature differences are more pronounced, making it easier for the model to accurately identify.  
The comparison of average recognition accuracy, average precision, average recall, and other evaluation metrics for different modalities (vibration, sound, image, and multimodal) is presented in Table 5.  
As shown in Table 3, although the sound modality achieves relatively high recognition accuracy, its training speed is slower. The vibration modality has a recognition accuracy close to the multimodal approach, but it requires a longer training time. In contrast, the multimodal approach not only maintains high recognition accuracy and low loss values but also exhibits a faster convergence speed, making it the superior choice in terms of both efficiency and performance.
## 5 Discussion
### The Effect of Intermediate Principal Stress on the Ground Response Curve (GRC)  
The present study applied the ground response curve (GRC) to reflect the relationship between radial displacement around the cavern and tunnel support pressure, thereby allowing analysis of the stability of surrounding rock. Figure 9 shows the ground response curves under different intermediate principal stress. The radial displacement, which is inversely proportional to the supporting pressure, gradually decreased with increasing supporting pressure. Furthermore, there were evident effects of intermediate principal stress on the GRC, particularly under \(b\) = 0, as shown in Figure 9. As the intermediate principal stress coefficient \(b\) increased from 0 to 0.75, the dimensionless \((u/r_{0})2G/(\sigma_{0}-\sigma_{r})\) at the tunnel opening gradually decreased by 63.43%, 51.52%, and 16.30%, respectively. Nevertheless, with an increase in \(b\) from 0.75 to 1, the dimensionless \((u/r_{0})2G/(\sigma_{0}-\sigma_{r})\) increased by 37.33%. In addition, the maximum radial displacement occurred for \(b\) = 0 when \(pi=0\), which could be attributed to a minimum restraining effect of the intermediate principal stress on the displacement in the plastic zone.  
In summary, suppression of tunnel deformation by intermediate principal stress showed an interval effect and gradually decreased with increasing \(b\). After \(b\) exceeded a certain threshold, it promoted deformation of the surrounding rock. Similarly, the restraining effect of supporting pressure \(pi\) on tunnel deformation gradually weakened with increasing pi.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(b\) & **0** & **0.25** & **0.5** & **0.75** & **1.0** \\ \hline \(K_{\psi p}\) & 1.1805 & 1.2061 & 1.2312 & 1.2404 & 1.2330 \\ \hline \end{tabular}
\end{table}
Table 2: Peak dilatancy coefficient \(K_{\psi p}\) under different parameter \(b\) stresses for \(p_{i}=2\).
# Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM
## 4 Proposed Method
### Stacked-LSTM
#### 4.2.2 Source Signal Mask Generation  
The separation part is the process of obtaining the source signal mask. The mask of each source signal is obtained by training the network. The physical meaning of the mask actually corresponds to the mixing matrix A in the mathematical model of blind source separation. The traditional blind source separation method obtains the mixing matrix through iterative calculation, while the deep learning method obtains the multi-dimensional mapping of the mixing matrix in the neural network by training the weight coefficients. The specific steps of the separation part are as follows:  
Step 1 First, perform Group Normalization (GN) (100) on the input. In the group normalization operation, the channels are grouped and the mean and variance are calculated in each group for normalization. According to the size of the batch size, the calculation of GN is independent. As an alternative to batch normalization (BN), GN can naturally transit from pre-training to fine-tuning. Its performance is better than BN, which enhances the generalization ability of the model while avoiding gradient disappearance and gradient explosion. Then the 1-D convolution is used to extract features.  
Step 2 Subsequently, the feature sequence enters the stacked Union-LSTM. A stacking block contains six LSTM blocks. Then the 1-D convolution is used to further extract features.  
Step 3 PReLU is used as the activation function. Since there is no zero point in the derivative of PReLU, the problem of neuron not learning in the negative interval can be prevented. Next, a 2-D convolution operation is performed to further extract features. Next, through the tanh activation function, the multi-dimensional characteristics of the mixed source signal are obtained. The result obtained by Tanh is the characteristic expression of the source signal. The result obtained by Sigmoid is the mask information of the two source signals. In this way, the time domain masks of the two source signals are obtained after training:  
\[\mathbf{s}_{mask}=f_{mask}^{stacked-LSTM}(\mathbf{x}_{encoder}) \tag{12}\]  
In the formula, \(f_{mask}^{stacked-LSTM}(\cdot)\) is separation module containing stacked-LSTM, which is used to generate a time domain mask \(\mathbf{s}_{mask}\).  
Step 4 The coding feature of the mixed signal obtained in Section 4.2.1 actually contains two coding features of the source signal. Each source signal has a potential time-domain mask [18], and the coding feature of each source signal can be extracted through the time-domain mask. The obtained time-domain mask of each source signal is multiplied with the encoding feature representation of the mixed signal to obtain the feature encoding of the two communication source signals:  
\[\mathbf{s}_{sep}=\mathbf{x}_{encoder}\circ\mathbf{s}_{mask} \tag{13}\]  
In the formula, \(\circ\) is the Hadamard product, which is the element-wise product of the two operands.
# A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
## 1 Introduction  
Currently, industrial robots are the workhorses of highly automated production systems [1]. A challenge to the productivity of such systems remain faults of industrial robot gears as they can cause extended downtimes. Condition monitoring (CM) of the gears can be a measure for countering this issue. CM describes a maintenance strategy in which sensor data is used to determine the health state of a robot gear. For this, sensor data is transformed into health indicators that correlate with the gear's health state. Critical monitored values within the time series of the health indicators form the decision criterion for a maintenance action [2]. Usually, there are many industrial robots operating in a production system and the health state of each of the axes must be monitored. Hence, manual monitoring is not feasible and an automated system is required. Such a system must be able to detect anomalies and trends in the health indicator data reliably. Anomalies in the data can be related to faults that occur abruptly (e.g., breaking of a gear tooth) and trends can be an indicator for increasing wear [3]. The occurrence of such events should be presented to the maintenance crew while showing only few false alarms. To the best of our knowledge, such a combined system does not yet exist for industrial robot gear condition monitoring. Hence, the contribution of our publication is threefold. Firstly, a combined anomaly and trend detection system (CATS) for industrial robot gear CM and secondly a method for selecting suitable anomaly detection (AD) and trend detection (TD) models for this defined application are presented. Thirdly, the suitability of different AD and TD models for the defined use case is evaluated by applying the method. Thus, the remainder of this publication is structured as follows: in Sections 1.1 and 1.2 an overview of industrial robot CM systems, AD and TD models is given and the addressed research gap is refined. In Section 2, CATS and the AD and TD model evaluation method is described. In Section 3, the method is applied to state-of-the-art AD and TD models and suitable models for CATS are selected. In Section 4, the limitations of the presented approach are discussed. In doing so, the outlook discussed in Section 5 is derived, which also includes a summary of ourcontribution. Through the remainder of this publication the term application refers to the condition monitoring of industrial robot gears.
# Integrating Geometric Dimensioning and Tolerancing with Additive Manufacturing: A Perspective
## 2 Adaptation of GD&T Principles to AM-Specific Geometries  
As mentioned above, differently from subtractive manufacturing, AM introduces some design complexities such as organic geometries, the presence of internal features, and, possibly, lattice structures. Dealing with non-Euclidean surfaces, these are difficult to dimension and tolerate using conventional GD&T standards. By way of example, lattice structures or gyroids used in lightweight aerospace components may not have clearly defined planes, axes, or cylinders for referencing traditional datum systems. For what concerns internal features, such as channels, cavities, and voids, the main issue is related to their inaccessibility for measurement using traditional inspection tools like coordinate measuring machines (CMMs); as a consequence, it is not trivial to determine the actual dimensions of such features, despite the fact that this challenge is shared with the field of precision casting, where several techniques can also be adapted to AM. Finally, the layer-by-layer process in AM results in anisotropic material properties and dimensional variation due to factors like residual stress, heat distortion, or powder bed characteristics. This makes it difficult to define a set of geometrical tolerances on the part.  
To address these challenges, GD&T is rapidly evolving to encompass functional datum systems that can reference freeform surfaces instead of relying on idealized planes or cylinders [12, 13]. Therefore, future GD&T practices will integrate surface profile tolerancing for freeform surfaces using methods such as point cloud analysis or mesh comparison to verify the manufactured geometry against the design intent. Not by chance, ISO 21920-1:2021 [14] and ASME Y14.46 [15] (under development for AM-specific GD&T) define a set of parameters for surface texture, including those suitable for irregular shapes produced by AM. AM has been used by businesses like Rolls-Royce and GE Aviation for manufacturing engine parts with complex geometries, such as fuel nozzles. Tolerances for freeform surfaces and functional interfaces need to be defined for these components using GD&T modifications. Another example is provided by freeform designs customized to each patient's anatomy, which are used in patient-specific implants, such as those made with AM for orthopedics. Instead of using traditional dimensions, GD&T standards should be adopted to include tolerances for fitness and function.