esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Selective Deep-Learning-Based Clustering Ensemble
## 1 Introduction
In the deep clustering generation phase, unsupervised deep autoencoder networks with different initializations are used to train the non-clustering loss, and then k-means are applied to initialize the cluster centroids. The similarity between the deep low-dimensional embeddings extracted by the autoencoder and the clustering centroids is calculated as soft cluster assignment through Student t-distribution. The KL divergence loss of the the soft cluster assignment and auxiliary distribution is used as the clustering loss. Here, the auxiliary distribution is computed by normalizing the square of the soft cluster assignment. Finally, the clustering loss is jointly and iteratively optimized to obtain multiple deep clustering results. In the selective clustering ensemble phase, firstly, different deep base clusterings are evaluated. Considering that both the quality and diversity of base clusterings affect the final ensemble performance, we construct the ensemble quality and diversity evaluation metrics of base clusterings. The base clusterings with higher quality and diversity are selected as ensemble candidates. Meanwhile, for the consensus function, we consider the local diversity of clusters within the same base clusterings and construct an entropy-based criterion to measure the reliability of clusters. A weighted graph partition consensus function is utilized to efficiently aggregate the candidate base clusterings. The final consensus clustering result is obtained by using Tcut [18] for graph partitioning. The main contributions of our work are summarized as follows:  
* This paper studies the limitations of clustering ensemble and deep clustering, which is an interest and important topic. Then, we propose an improved selective deep clustering ensemble (ISDCE) method to mitigate the problem. It incorporates deep clustering into selective clustering ensemble to enhance the robustness and clustering performance. * ISDCE constructs the ensemble quality and diversity evaluation metrics of base clusterings. It is able to select higher-quality and rich-diversity base clusterings to improve ensemble performance. In addition, ISDCE measure the reliability of clusters and the local diversity of clusters within the same base clusterings to further improve the integration performance. * Extensive experimental results on various types of datasets confirm that ISDCE performs significantly more robustly and better than existing clustering ensemble approaches. The remainder of this paper is organized as follows: we review related work on deep clustering and ensemble clustering in detail in Section 2. Then, we elaborate on our proposed ISDCE methodology in Section 3. Section 4 presents the experimental results. Finally, Section 5 discusses the conclusions.
## 3 Numerical Examples
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & & **Case 1** & & & & **Case 2** & & \\ \hline
**Feedback** & & \(\mathbf{K_{P}}\) & \(\mathbf{T_{d}}\) & \(\mathbf{T_{i}}\) & \(\mathbf{x_{r}}\) & \(\mathbf{x_{top}}\) & \(\mathbf{K_{P}}\) & \(\mathbf{T_{d}}\) & \(\mathbf{T_{i}}\) & \(\mathbf{x_{r}}\) & \(\mathbf{x_{top}}\) \\ \hline \multirow{3}{*}{Displacement} & HS & \(-4.5516\) & \(0.3865\) & \(-2.4710\) & \(0.1562\) & \(0.8384\) & \(-0.0738\) & \(4.8797\) & \(-3.8628\) & \(0.1161\) & \(0.5930\) \\  & FPA & \(-3.4661\) & \(0.5286\) & \(-3.4663\) & \(0.1562\) & \(0.8360\) & \(-0.2285\) & \(1.5507\) & \(1.3225\) & \(0.1165\) & \(0.5948\) \\ \multirow{3}{*}{Velocity} & HS & \(-1.7584\) & \(-0.0453\) & \(-2.0845\) & \(1.2073\) & \(7.0157\) & \(-0.3618\) & \(-0.0103\) & \(5.000\) & \(0.7644\) & \(4.8030\) \\  & FPA & \(-1.6276\) & \(-0.0502\) & \(1.4383\) & \(1.2061\) & \(7.1166\) & \(-0.3620\) & \(-0.0107\) & \(5.000\) & \(0.7640\) & \(4.8025\) \\ \multirow{3}{*}{Acceleration} & HS & \(-0.0408\) & \(-0.0033\) & \(0.0232\) & \(11.706\) & \(55.678\) & \(-0.0146\) & \(0.0047\) & \(0.0432\) & \(8.7490\) & \(34.218\) \\  & FPA & \(-0.0880\) & \(-0.0014\) & \(0.0549\) & \(11.729\) & \(56.481\) & \(-0.0148\) & \(0.0045\) & \(0.0433\) & \(8.7342\) & \(34.142\) \\ \multirow{3}{*}{Total acceleration} & HS & \(0.0575\) & \(-0.0025\) & \(-0.0551\) & \(16.500\) & \(57.409\) & \(-0.0075\) & \(0.0112\) & \(0.0489\) & \(15.450\) & \(46.829\) \\  & FPA & \(0.0734\) & \(-0.0018\) & \(-3.4079\) & \(16.781\) & \(63.912\) & \(0.0124\) & \(0.0111\) & \(-1.9245\) & \(16.383\) & \(63.400\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The optimum proportional–integral–derivative (PID) parameters according to flower pollination algorithm (FPA) and harmony search algorithm (HS). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Story** & \(\mathbf{M_{i}}\) **(t)** & \(\mathbf{K_{i}}\) **(KN/m)** & \(\mathbf{C_{i}}\) **(KNs/m)** \\ \hline
1–8 & 346.6 & 680,000 & 734 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The story properties of the structure. Figures A1-A4 in Appendix A. Maximum displacement, velocity and total acceleration values of uncontrolled structures values under near-fault ground motions with a pulse are 55.24 cm, 4.54 m/s, and 37.98 m/s\({}^{2}\). By using active control with the acceleration feedback strategy, these values are reduced to 49.30 cm, 4.05 m/s, and 34.45 m/s\({}^{2}\) for Case 1 and 42.39 cm, 3.33 m/s, and 27.99 m/s\({}^{2}\) for Case 2, respectively. Under near-fault ground motions without a pulse, the displacement values of these uncontrolled and controlled cases (Case 1 and Case 2) are calculated as 97.53, 84.51 and 60.39 cm, respectively, while 8.15, 6.97 and 4.85 m/s are the velocity values and 64.72, 55.49 and 37.02 m/s\({}^{2}\) are the acceleration values, respectively. In addition, top story values (x\({}_{\text{top}}\)) are reported for the critical excitation in Table 4. As expected, Case 2 has significant superiority comparing to Case 1 since Case 2 contains active control in all stories.
## 3 Control Effect of Ground Improvement on Tunnel Displacement
### Simulation Conditions of Ground Improvement
#### 3.4.3 Effect of Ground Improvement Width  
Figure 13 shows the evolution curves of \(\eta_{\text{h}}\) with \(w\). It is observed that under the four conditions with different \(h\) values, \(\eta_{\text{h}}\) increases linearly with \(w\), and the greater the ground improvement strength \(q_{\text{u}}\) is, the more obvious the increase of \(\eta_{\text{h}}\) is obtained. Under the condition of \(h=1.5H_{\text{t}}\), when the \(w\) increases from \(L_{\text{t}}/6\) to \(2L_{\text{t}}/3\), the \(\eta_{\text{h}}\) value has a maximum increase of 100%. Under the conditions of \(h=2.0H_{\text{t}}\), \(h=2.5H_{\text{t}}\) and \(h=3.0H_{\text{t}}\), when \(w\) increases from \(L_{\text{t}}/6\) to \(2L_{\text{t}}/3\), the \(\eta_{\text{h}}\) value has a maximum increase of 70%, 55%, and 48%, respectively.  
It is concluded from the above analysis that the control effect on tunnel displacement can be increased by increasing the ground improvement width. In actual engineering, if there is enough space between the deep excavation and the tunnel to allow ground improvement and ensure the safety of the tunnel, it is suggested to increase the ground improvement width as much as possible.
# GAN-LSTM Joint Network Applied to Seismic Array Noise Signal Recognition
## 8 Conclusions  
We established a joint GAN + LSTM network for identifying seismic array noise signals. We used the GAN discriminator network to learn the features of seismic phases, then input these features into the LSTM network and, by training the joint network, successfully identified and classified seismic phases and noise signals. The following understandings were obtained:  
1. Using beamforming data of regional seismic and teleseismic P and S phases of an array, the GAN can effectively learn the main features of seismic phases. Its discriminator can be used to extract features of seismic phases;
2. By training the GAN discriminator and LSTM joint network on mixed seismic and noise signal data while fixing the discriminator network parameters and training only the LSTM network parameters, we can achieve the reliable classification of seismic phases and noise signals, with 97.46% recall and 95.28% precision for seismic signal identification and classification;
3. The real-time monitoring array data processing method developed by this model can classify IDC seismic phases and noise signals. The test with half-year data of 2019 shows that the identification rate of seismic phases is 94.12%, and 95.03% for noise signals. Improved noise signal identification performance can further improve the quality of phase association and event detection.  
**Author Contributions:** Conceptualization, D.H. and X.W.; methodology, J.L. and G.C.; simulation and testing, M.H. and J.W.; data collection, Z.L. and J.S.; writing--review and editing, W.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Acknowledgments:** It is grateful to all colleagues who have contributed to this research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion  
### XRD Analysis  
Most of the CdS or CdS/PbSe-performed characterizations were carried out on corning glass substrates to avoid wasting glass substrate coated with ITO. Only I vs. V characterization was performed using ITO as a front contact to close the circuit of the CdS/PbSe device.  
The XRD diffractograms of synthetized CdS and CdS/PbSe are presented in Figure 2. A hexagonal crystalline structure was observed in the CdS thin film (PDF# 80-0006, Cu X-ray source), with a preferential orientation crystalline plane (002) at 26.87\({}^{\circ}\) (Figure 2a). We also observed another diffraction peak at 48.24\({}^{\circ}\) corresponding to the same crystalline structure and the crystalline plane (103). No other diffraction peaks appear, indicating the absence of impurities during the deposition of CdS. Figure 2b shows the XRD of the heterojunction CdS/PbSe, obtained by converting plumbonacrite to PbSe by ionic exchange The XRD pattern match with the mineral phase of PbSe was called clausthalite (PDF# 65-0327, Cu X-ray source), which corresponded to a cubic crystalline structure with a preferential orientation crystalline plane (200) at 29\({}^{\circ}\). As can be shown, only one diffraction peak of CdS was detected and denoted by \(\Delta\), which corresponds to the crystalline plane (002) as aforementioned. Additionally, two extra diffraction peaks at 20.5 and 34\({}^{\circ}\), labeled with *, appeared, which correspond to crystalline planes (\(-\)101) and (111) of the PbSe molybdenum monoclinic crystalline phase (PbSe + 4O\({}_{3}\)), according to the PDF# 15-0462 (Cu X-ray source). Therefore, the conversion from plumbonacrite to PbSe can be stated as successful, since no other diffraction peaks related to plumbonacrite appeared [26].  
Figure 1: Scheme of the device based on the ITO/CdS/PbSe heterostructure.
## 4 Seepage Control System in the Xigou Tailings Dam with an Increased Height
### Numerical Results
#### 4.4.5 Sensitivity Analysis of the Drainage Pipe Spacing  
The seepage flow behaviors in the tailings dam with an increased height are usually related to the spacing of the drainage pipes. If the drainage pipes are more densely installed, the elevation of the phreatic surface can be more drastically lowered. However, with a decrease in the pipe spacing, the construction becomes more difficult, and the construction costs increase. To design a reasonable drainage pipe spacing, a spacing from 6 m to 14 m with an increment of 2 m was simulated to assess the performance of the seepage behavior. Currently, the drainage pipe spacing was taken to be 10 m according to the design requirements.  
Figure 16: The comparison of the phreatic surface at a typical cross-section (**a**) for the cases with a change in the permeability coefficient and with different pipe spacings (**b**).  
Figure 15: The phreatic surface envelope diagram of drainage elements at (**a**) 5% blockage (**b**) 10% blockage.  
The numerical results with different pipe spacings are depicted in Figure 16b, indicating that with a decrease in the pipe spacing, the phreatic surface was significantly reduced in the tailings dam, especially when the pipe spacing was larger than 10 m. The change in the pipe spacing in a certain range had no significant effect on the phreatic surface. When the pipe spacing decreased from 10 m to 6 m, the location of the phreatic surface showed marginal sensitivity and increased by only 1.7 m for a 6 m pipe spacing and by 2.5 m for an 8 m pipe spacing compared to a 10 m pipe spacing. However, when the pipe spacing was increased from 10 m to 14 m, the increase in the free surface was rapid, and the phreatic surface increased by 7.2 m and 10.6 m for the 12 m pipe spacing and 14 m pipe spacing, respectively. Therefore, the designed 10 m pipe spacing was suitable for the drainage pipe arrangement.
# Hybrid CNN-NLP Model for Detecting LSB Steganography in Digital Images
## 1 Introduction  
Over the years, forensic computing has evolved from being an auxiliary discipline to becoming a relevant and crucial pillar in the digital field since it not only combines computer science and forensic sciences to collect legally admissible digital evidence in criminal and civil cases, but also provides essential evidence to determine the what, how, when, and where of events [1] through scientific techniques that allow the identification of malicious manipulations in systems, files, or data during the different phases of an investigation [2]. In essence, it seeks to guarantee the authenticity, originality, sameness, and integrity of digital evidence.  
Steganography has emerged as a significant challenge in forensic analysis due to its ability to hide information without perceptibly altering the carrier files. Thanks to this ability, it is a tool that can be misused to hide illicit communications or manipulate digital evidence [3, 4, 5, 6], which poses a risk, since the chain of custody can be affected, and it can even induce judicial errors and compromise decisions based on apparently legitimate evidence [7].  
Adopting advanced technological approaches to address these threats is essential as investigations become more complex. Steganography based on the least significant bit (LSB) method is particularly difficult to detect as the alterations it introduces in digital images are minimal and practically imperceptible to the naked eye.  
Faced with this problem, the need arises to implement a variety of techniques ranging from pixel-to-pixel comparison to the use of convolutional neural networks (CNNs), whose implementation has shown significant results since, with the application of deep learning, these networks are capable of identifying complex patterns and subtle characteristics in digital images, allowing the detection of imperceptible modifications and, consequently, the presence of hidden messages with high precision [3; 6; 8].  
From the above, the development of a CNN-based model is proposed for the detection of hidden messages in grayscale digital images (PNG type) affected by the least significant bit (LSB) steganographic method to find out with certainty whether the images are genuine or not and, at the same time, a semantic validation process for the extracted message is integrated using natural language processing (NLP) techniques, to verify if the hidden message has linguistic coherence in the Spanish language.
# An Actor-Based Formal Model and Runtime Environment for Resource-Bounded IoT Services +
## 4 Operational Semantics
### Transition Rules
#### 4.2.6 Rejecting a Service Request  
A service request is rejected if none of the existing services match the target service's requirements expressed in \(\rho\), and there do not exist sufficient contributing services for serving \(\rho\) (i.e., there are no sufficient contributing services for serving any member of \(\rho\)). If these two conditions hold, a request rejection message is sent to the client service which sent \(\rho\).  
If \(search(\rho,s)\) evaluates \((\emptyset,\emptyset)\), then the transition rule for rejecting a service request is as follows:  
\[\begin{split} search(\rho,s)\xrightarrow[]{\lambda}_{X}( \emptyset,\emptyset)\\ \langle[I:\ [R[search(\rho,s)]]_{a_{s_{d}}}\,\ \alpha:\ :\ \ \emptyset]_{s_{d}}\,\ S\mid C\mid M\rangle\rightarrow\\ \langle[[I:\ [R[(\emptyset,\emptyset)]]_{a_{s_{d}}}\,\ \alpha:\ :\ \ \emptyset]_{s_{d}}\,\ S\mid C\mid\langle s \Leftarrow\emptyset\rangle,\ M\rangle\end{split} \tag{12}\]  
where \(X=\{a_{s_{d}}\}\) is the context in which \(search(\rho,s)\) is reduced to \((\emptyset,\emptyset)\), \(s_{d}\) is the directory service, \(a_{s_{d}}\) is \(s_{d}\)'s coordinator actor, and \(s\) is the client service which sent service request \(\rho\). This transition happens if and only if \(search(\rho,s)\) is evaluated to \((\emptyset,\emptyset)\). This leads to the creation of message \(\langle s\Leftarrow\emptyset\rangle\) which is a rejection message sent to \(s\) indicating that there is no way of serving \(\rho\).
# Calibration for Sample-And-Hold Mismatches in \(M\)-Channel TIADCs Based on Statistics
## 1 Introduction
### Review of Literature  
Foreground methods. In [18; 36], the mismatch coefficients are identified with the help of training signals, which is more accurate than the background methods but requires interruption of the normal operation. An additional channel is needed to assist the calibration in [36], which brings about additional hardware cost.  
Background methods. In [19], a test tone is injected near the Nyquist frequency for the coefficient estimation. The drawback of this semi-blind method is that the input signal can only occupy the middle part of a Nyquist band, causing a low utilization of the frequency band. In [27], the coefficients are tracked with the help of low-pass filters and fractional delay filters, whose bandwidth utilization efficiency is a little higher than [19]. An input-free band (IFB) is utilized in [23; 35] for the coefficient identification in 2-channel TIADCs. This method consumes fewer resources than [27], but it fails for some narrow-band signals, which will be further explained in Section 5.3. The in-phase/quadrature (I/Q) mismatch calibration technique is borrowed for dual- and quad-channel TIADC's frequency response mismatch calibration in [24; 25]. However, when the input signal contains mainly sinusoidal components, the calibration is less satisfactory, which will be further explained in Section 5.3.  
Among others, ref. [3; 5] propose timing mismatch identification algorithms based on wide sense stationary (WSS) property and modulo \(M\) quasi-stationary property of the input signal, but they do not mention the calibration of the S/H mismatch.
# Overview of Distributed Machine Learning Techniques for 6G Networks
###### Abstract  
The main goal of this paper is to survey the influential research of distributed learning technologies playing a key role in the 6G world. Upcoming 6G technology is expected to create an intelligent, highly scalable, dynamic, and programable wireless communication network able to serve many heterogeneous wireless devices. Various machine learning (ML) techniques are expected to be deployed over the intelligent 6G wireless network that provide solutions to highly complex networking problems. In order to do this, various 6G nodes and devices are expected to generate tons of data through external sensors, and data analysis will be needed. With such massive and distributed data, and various innovations in computing hardware, distributed ML techniques are expected to play an important role in 6G. Though they have several advantages over the centralized ML techniques, implementing the distributed ML algorithms over resource-constrained wireless environments can be challenging. Therefore, it is important to select a proper ML algorithm based upon the characteristics of the wireless environment and the resource requirements of the learning process. In this work, we survey the recently introduced distributed ML techniques with their characteristics and possible benefits by focusing our attention on the most influential papers in the area. We finally give our perspective on the main challenges and advantages for telecommunication networks, along with the main scenarios that could eventuate.  
machine learning; distributed learning; telecommunications; 6G +
Footnote †: journal: _Algorithms_
## 3 Materials and Methods
### Evaluation Metrics  
To comprehensively assess the performance of the proposed method, a set of evaluation metrics was employed, including precision, recall, F1 score, accuracy, mean Average Precision (mAP@50 and mAP@75), and Frames Per Second (FPS). These metrics quantify classification and detection performance, as well as computational efficiency, from different perspectives. Precision measures the proportion of correctly predicted positive samples among all predicted positive samples, serving as an important metric for assessing prediction accuracy. Recall indicates the proportion of actual positive samples correctly identified, reflecting the sensitivity of the model. The F1 score, as the harmonic mean of precision and recall, is particularly useful in scenarios with imbalanced datasets, providing a balanced measure of a model's ability to correctly identify positive samples while avoiding false positives. Accuracy, the most commonly used classification metric, measures a comprehensive evaluation of overall model performance. mAP is the key metric in object detection tasks. Specifically, mAP@50 and mAP@75 were adopted in this study to evaluate the average precision at Intersection over Union (IoU) thresholds of 0.5 and 0.75, respectively, illustrating performance under different levels of stringency. Finally, FPS was utilized to measure the real-time computational efficiency of the method, representing the number of images processed per second, which is critical for deployment in practical scenarios. The mathematical definitions of these metrics are as follows:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}, \tag{28}\]\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}, \tag{29}\]  
\[\text{F1}=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{\text{Precision}+ \text{Recall}}, \tag{30}\]  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}, \tag{31}\]  
\[\text{AP}=\int_{0}^{1}\text{Precision}(r)\,dr, \tag{32}\]  
\[\text{mAP}=\frac{1}{C}\sum_{c=1}^{C}\text{AP}_{c}, \tag{33}\]  
\[\text{FPS}=\frac{N}{T}, \tag{34}\]  
where TP, FP, and FN denote the number of true positives, false positives, and false negatives, respectively. The F1 score provides a robust metric for balancing precision and recall, particularly when one metric dominates the other in scenarios such as highly imbalanced datasets. \(r\) represents the recall, \(\text{Precision}(r)\) denotes the precision at recall \(r\), \(C\) is the total number of classes, \(\text{AP}_{c}\) is the average precision for class \(c\), \(N\) is the total number of frames processed, and \(T\) is the total processing time. By integrating these metrics, including the F1 score, the performance of the proposed method was thoroughly evaluated in terms of predictive accuracy, sensitivity, overall classification capability, object detection precision, and computational efficiency, providing a holistic view of the model's strengths and limitations.