esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Results and Discussion
### Implementation Details  
In ED\({}^{2}\)IF\({}^{2}\)-Net, an RGB image of size \(224\times 224\) is used as input, and the model outputs signed distance values of the query points. The iso-surface mesh is visualized using Marching Cubes with a resolution of \(128\times 128\times 128\). The network is implemented in Pytorch [60] and the training parameters are set as follows: batch_size of 16, Adam optimizer [61] with a learning rate of \(5\times 10^{-5}\), \(\beta_{1}\) = 0.9, \(\beta_{2}\) = 0.999, and weight decay of \(10^{-5}\). During training, 2048 query points are randomly selected based on the weighted sampling strategy for loss calculation and back propagation. The experiments were conducted using PyCharm Community Edition, and the training of ED\({}^{2}\)IF\({}^{2}\)-Net was performed on two Nvidia RTX 3090 graphics cards, taking 1 to 3 days, depending on the specific settings. The hyperparameters \(\omega_{0}\), \(\omega_{1}\), and \(\omega_{2}\) in the deformation loss term \(L_{Def}\) of the network loss function were fixed at 0.5, 0.25, and 0.25, respectively. The value of \(\omega_{0}\) at 0.5 was chosen to emphasize the influence of the deformed implicit field \(g_{st}\) on the final reconstruction results. The training process involved 500 epochs and the learning rate was adaptively optimized using the Adam optimizer as described above. Further details on the implementation can be found in Appendix A.
# Volume Loss Assessment with MT-InSAR during Tunnel Construction in the City of Naples (Italy)
## 4 Results
### Displacements Maps after the Excavation Processes  
In Figure 11a, we present the cumulative displacements along the LOS from 2 July 2020 to 23 March 2021. In this figure, the cumulative displacements were aligned to zero on 2 July 2020 to represent those that occurred as a result of the excavation of the first tunnel only. In this figure, it is possible to notice a concentration of negative LOS displacements (i.e., subsidence) in the proximity of the eastern tunnel axis, which is the first excavated, as expected. Furthermore, the displacements return to almost zero gradually, moving away orthogonally from the axis of the excavation.  
In Figure 11b, we present the cumulative displacements along the LOS from 2 July 2020 to 29 January 2022, representing those occurring as a result of both excavations. In this figure, we can see the presence of many measurement points affected by negative LOS displacements in the proximity of both tunnel axes, as expected, and displacements show movements close to zero away from both axes, as per Figure 11a.  
Figure 11: **(a)** Cumulative displacements in mm along the LOS occurring from to 2 July 2020 to 23 March 2021, owing to the excavation of the first tunnel (right red longitudinal axes); **(b)** cumulative displacements along the LOS occurring from to 2 July 2020 to 29 January 2022, owing to the excavation of both tunnels (images modified after Maxar for Microsoft). Sections A to I are the ones that will be investigated.
## 3 Finite Element Model Creation  
### Numerical Model for Slopes  
The Cao Son open-pit mine is located in the north of Quang Ninh Province, Cam Pha City, Vietnam (Figure 1a,b). Presently, waste rock from the Cao Son coal mine is primarily deposited in two dumping sites, Nam Khe Tam and Bang Nau, with Bang Nau being the principal site. According to the design specifications, the Bang Nau dumping site spans 2920 m in length and 1955 m in width, with the highest point of the site at an elevation of +300 m. As of 1 January 2019, the total volume of waste rock yet to be deposited at the Cao Son coal mine was 1.003532 billion cubic meters, of which 208.5 million cubic meters was directed to the Bang Nau dumping site, as shown in Figure 1c.  
A numerical simulation geometric model was established based on the original geometric features and dumping parameters of the Bang Nau dump. The size of the established numerical computational model is 1150 m \(\times\) 380 m. The dump consists of eight soil-discharging benches with a bench slope angle of 37\({}^{\circ}\) and a side slope angle of 23\({}^{\circ}\).
## 5 Implementation and Evaluation
### HGR Performance Evaluation  
HOS-Re evaluates the recognition performance of the proposed methodology using the machine learning classifier SVM with Gaussian RBF Kernel. SVM classifier evaluates the performance of feature selection algorithms with the combinations of regularization parameter \(C\) and \(\gamma\). \(C\) is the cost parameter, and \(\gamma\) is the non-linear classification parameter of the Gaussian kernel. One value amongst {0.003, 0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30, 100, 300} is assigned to \(C\) at a time and \(\gamma\) takes all values in sequence for each iteration, making \(11\times 11=121\) combinations and validates the results using \(10\times 5\) cross-validation. The recognition accuracy reported for the lab, home, lab + home dataset with 276 gesture and lab data set with 150 gestures using MIFS (\(k=30\)/50/80 cumulant feature) by varying \(\beta\).  
The comparison of recognition accuracy of all feature selection algorithms like MIFS, mRMR, CIFE, JMI, CMIM, DISR, ICAP and CONDRED (\(k=30\)/50/80 cumulant feature) for the lab, home, lab + home environment datasets are discussed below.
## 3 Method
### Vibration-System Simulator
#### 3.2.3 Estimate of Simulator Weights  
The simulator can be considered as a 5-input/4-output filter. We estimated the simulator weights and validated the physical fidelity of the simulator. Because the simulator is composed of 20 single-input/single-output filters, the weights of the 20 filters can be estimated independently. Without losing generality, we take filters \(C_{4j}\), \(j=1,2,3,4\) as examples to show how to estimate the filter weights and validate the filters.  
We turned on the No. 4 actuator and left the other actuators and the vibration exciter off. A narrow-band random white-noise signal, shown in Figure 5, was used as the input signal \(\mathbf{x}_{4}(n)\), and then the signals of the acceleration sensors were collected (\(\mathbf{d}_{1}(n)\), \(\mathbf{d}_{2}(n)\), \(\mathbf{d}_{3}(n)\), \(\mathbf{d}_{4}(n)\)). Because only the No. 4 actuator was turned on, \(\mathbf{x}_{0}(n)\), \(\mathbf{x}_{1}(n)\), \(\mathbf{x}_{2}(n)\), \(\mathbf{x}_{3}(n)\) were zeros, and so Equations (6)-(9) were simplified as follows:  
\[y_{1}(n)=\begin{array}{c}\mathbf{\omega}_{41}^{T}(n)\mathbf{x}_{4}(n)\end{array} \tag{11}\]  
\[y_{2}(n)=\begin{array}{c}\mathbf{\omega}_{42}^{T}(n)\mathbf{x}_{4}(n)\end{array} \tag{12}\]  
\[y_{3}(n)=\begin{array}{c}\mathbf{\omega}_{43}^{T}(n)\mathbf{x}_{4}(n)\end{array} \tag{13}\]  
\[y_{4}(n)=According to Equation (5), the filter weights (\(\mathbf{\omega}_{41}(n)\), \(\mathbf{\omega}_{42}(n)\), \(\mathbf{\omega}_{43}(n)\), \(\mathbf{\omega}_{44}(n)\)) can be obtained.  
It is known that \(\mathbf{y}_{j}(n)\) is the estimate of the desired output (\(\mathbf{d}_{j}(n)\), \(j=1,2,3,4\)). The desired outputs and their estimates by FIR filters are compared in Figure 6. The normalized mean square error (NMSE) [38] was used to evaluate the estimate precision. The NMSEs were all smaller than 20%, which shows that the desired outputs were well estimated, and the filters were validated. It also means that the simulator shown in Equation (10) had enough physical fidelity to describe the vibration system.  
The definition of the NMSE is formulated by:  
\[\text{NMSE}=\frac{\left\|\mathbf{d}_{j}(n)-\mathbf{y}_{j}(n)\right\|^{2}}{ \left\|\mathbf{d}_{j}(n)-mean\big{(}\mathbf{d}_{j}(n)\big{)}\right\|^{2}} \tag{15}\]  
where \(\left\|\left|\cdot\cdot\right|\right\|\) indicates the 2-norm of a vector.  
Next, the other filters can be estimated and validated in a similar way. For instance, we turned on the vibration exciter and left the actuators off, and we gave the same signal shown in Figure 5 to the vibration exciter. The filter weights ( \(\mathbf{\omega}_{01}(n)\), \(\mathbf{\omega}_{02}(n)\), \(\mathbf{\omega}_{03}(n)\), \(\mathbf{\omega}_{04}(n)\)) could be obtained. It should be noted that the simulator can only well estimate the outputs of the vibration system when the input forces are from 50 Hz to 60 Hz.
## 5 ML Intersection with 6G Enabling Technologies
### ML and VLC Integration for Optimization of 6G Wireless Network
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref** & **Application** & **ML Model** & **Outcome** \\ \hline
[182] & Phase reconfiguration & D-PSC & Obtained the optimal phase shifts for maximum data rate with over 25\% higher data rate compared to existing approaches. \\ \hline
[183] & Convergence rate & DRL & The proposed model’s learning rate at 0.005 outperforms the existing methods in terms of both converging rate and average energy consumption. \\ \hline
[184] & Phase shifts & DDPG & Improved both the convergence and the reward performance of the network when compared to conventional model-free learning approaches. \\ \hline
[177] & Dimensionality reduction & DDPG & The proposed approach outperformed the other approaches. \\ \hline
[171] & Optimized sensing & CNN & The developed method significantly improved the sensing quality of the network. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._method (Q-learning) was integrated to optimize the route selection performance of the system. The authors implemented the Q-learning route selection algorithm using the VLC controller, and the D2D provides enhancements for the VLC network. The results show that ML provided significant improvement for both D2D and VLC for overall system improvement. The performance of a hybrid network composed of radio frequency (RF) and visible light communication (VLC) APs has been optimized by integrating a multi-agent Deep Q-Network (DQN) approach for effective power allocation [193]. The newly developed DQN approach has over 90% shorter convergence time compared to the QL-based approach. Similar results have been reported for resource management [194]. A PDS-ERT algorithm accelerated the learning rate and improved the learning efficiency of the RF/VLC network. The authors reported that the proposed deep PDS-RET learning algorithm-based heterogeneous RF/VLC significantly improved the energy efficiency of the network. A summary of ML applications for VLC-enhanced systems has been provided in Table 9.
# Closed-Form Performance Analysis of the Inverse Power Lomax Fading Channel Model
## 5 Conclusions  
This research introduces and examines the inverse power Lomax fading channel model for wireless communications, demonstrating its versatility in describing a range of fading scenarios from severe to mild. The IPL model, defined by its probability density function, cumulative distribution function, and moment-generating function, captures the statistical characteristics of fading channels more flexibly than traditional models like Rayleigh, Rice, Nakagami-m, etc. This study provides exact analytical expressions for key performance metrics such as the amount of fading, outage probability, average bit error rate, and ergodic capacity. These metrics are crucial for evaluating the reliability and efficiency of wireless communication systems. The findings reveal that the IPL model can effectively operate under various fading conditions by adjusting its parameters, thereby encompassing the behavior of several well-known models. Additionally, this research confirms the accuracy of the derived expressions through extensive numerical analysis and statistical simulations, underscoring the practical applicability of the IPL model in real-world communication systems.  
Figure 14: \(\Delta\overline{C}\) contour map for various channel parameters \((\alpha,\beta)\); black dashed line corresponds to \(\Delta\overline{C}=0\), i.e., Rayleigh fading.  
**Funding:** This work was supported by the Russian Science Foundation under grant 24-29-00516 ([[https://rscf.ru/en/project/24-29-00516/](https://rscf.ru/en/project/24-29-00516/)]([https://rscf.ru/en/project/24-29-00516/](https://rscf.ru/en/project/24-29-00516/)), accessed date: 7 August 2024).  
**Data Availability Statement:** The original contributions presented in the study are included in the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Building Penetration Losses at 3.5 GHz: Dependence on Polarization and Incidence Angle
## 5 Conclusions  
We measured the building penetration losses for four different kinds of facade as a function of the incidence angle and polarization, in a band from 3 to 4 GHz, that is of interest for 5G deployment. Results show a relevant polarization and angle dependence of this attenuation. We also found that, as a general trend, the attenuation increases with the incidence angle if the field is vertically polarized, while for horizontal polarization, the angular variation is lower and not monotonous. We detected variations up to 8 dB comparing normal to 60\({}^{\circ}\) incidence with vertical polarization.  
These results should be taken into account when estimating indoor 5G coverage at 3.5 GHz provided by outdoor base stations. They will also be useful for calculating the shielding provided by building facades and exploring the possibility of frequency reuse in a 5G mobile network at 3.5 GHz. To this extent, this paper would be of significant value to those who are required to deploy 5G coverage predictions for realistic indoor scenarios.  
Figure 14: Attenuation as a function of incident angle \(\theta\) for horizontal polarization and 30 cm facade without windows (red solid line); and with windows (blue dashed line), 15 cm facade (green dash-dot line) and glass facade (purple dotted line).  
**Author Contributions:** Conceptualization, M.G.S. and I.C.; methodology, M.G.S. and I.C.; software, C.I.; validation, M.G.S., C.I., I.C. and I.E.; formal analysis, M.G.S. and I.C.; investigation, M.G.S., C.I., I.C. and I.E.; resources, M.G.S. and I.C.; data curation, M.G.S.; writing--original draft preparation, M.G.S.; writing--review and editing, C.I., I.C. and I.E.; supervision, M.G.S.; project administration, M.G.S.; funding acquisition, M.G.S. and I.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Spanish Government, Ministerio de Ciencia e Innovacion, Secretaria General de Investigacion, grant number PID2020-112545 RB-C52; Xunta de Galicia, project ED431C 2019/26; atlantTlic Research Centre and the European Regional Development Fund (ERDF); FCT/MCTES through national funds and when applicable, co-funded EU funds under the projects UIDB/EEA/50008/2021.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN +
###### Abstract  
Accurate identification of partial discharge (PD) and its types is essential for assessing the operating conditions of electrical equipment. To enhance PD pattern recognition under imbalanced and limited sample conditions, a method based on a Deep Autoencoder-embedded Generative Adversarial Network (DAE-GAN) is proposed. First, the Deep Autoencoder (DAE) is embedded within the Generative Adversarial Network (GAN) to improve the realism of generated samples. Then, complementary PD data samples are introduced during GAN training to address the issue of limited sample size. Lastly, the model's discriminator is fine-tuned with augmented and balanced training data to enable PD pattern recognition. The DAE-GAN method is used to augment data and recognize patterns in experimental PD signals. The results demonstrate that, under imbalanced and small sample conditions, DAE-GAN generates more authentic PD samples with improved probability distribution fitting compared to other algorithms, leading to varying levels of enhancement in pattern recognition accuracy.  
partial discharge; data augmentation; DAE; GAN; DAE-GAN Article
# Using Explainable Machine Learning to Explore the Impact of Synoptic Reporting on Prostate Cancer
## 1 Introduction
#### Synoptic Reporting  
Histopathological reports are of uttermost importance for providing timely and proper information to oncologists, which is crucial in the diagnostic process and delivery of high quality cancer care [32; 33; 34]. Traditional pathology reports are in the form of narrative text, which means that they are written without a fixed, structured format [35; 33]. Unfortunately, these narrative reports are susceptible to data incompleteness, inferior understandability, and misinterpretation [36], since they lack a fixed structure of scientifically validated data items.  
To alleviate these disadvantages, synoptic reporting (SR) has been introduced. SR occurs in the form of a report in which the information elements are presented in a pre-defined tabular form and stored as discrete data items in a database [37; 38]. This structure makes the most important information more accessible to clinicians, which has the potential to reduce medical errors [39; 38]. Additionally, SR allows for faster detection of essential data, higher completeness of reported information, improved reporting of clinically relevant data, uniformity, and making information computer readable [40; 41].  
In the Netherlands, SR was introduced in 2008 by the Nationwide Network and Registry of Histo- and Cytopathology (PALGA), starting with pathological reporting of breast and colorectal cancer. Currently, SR has been implemented in more than 31 different protocols, all of which have been formally approved by the Dutch Society of Pathology. Although the benefits of SR for Dutch patients has been investigated in a few types of cancer (e.g., colorectal [35; 42], gallbladder [43]), this has not been done in prostate cancer.  
In this paper, we used XML to study the effect of SR on predicted ranked survival of Dutch prostate cancer patients. Additionally, we compared its performance with that of a classical statistical method. The manuscript is organized as follows. Section 2 gives a detailed description of how the data were curated and how the models were developed and validated. Section 3 presents the obtained results, which are further discussed in detail in Section 4. Section 5 closes the paper with our overall conclusions.
## 5 Experiments and Discussions
### Background: Software Design  
To get the samples, for convenience, we used the above oscilloscope instead of commercial ADCs. Firstly, according to the Nyquist sampling theory, we set the sampling rate at 100 MHz, which is the minimal appropriate sampling rate of the oscilloscope. As soon as the waveform is stored, it is transfered to the back-end computer for software processing. In designing the software, we divided the whole process into several steps as follows:  
1. Calibration. This step is essential, because the relation between the samples and the original signal becomes unknown in real hardware implementation. Physical effects have a considerable impact on the sampling process, which may lead to detection failure [24]. However in [25], an automated calibration algorithm is presented applying to MWC. Similarly, in SCMWC, same process is adopted and implemented in our software. Generally, once the circuit board is fixed, the relation becomes a constant matrix. So the calibration process only need once.
2. Digital Finite Impulse Response filtering. Actually, the SCMWC requires ideal analog low pass filters to accomplish the detection process. however, in practice, implementing ideal filters is generally difficult. Usually, people use digital filters to compensate for the analog filters. It is proved in [26] that with only a moderate amount of oversampling, the imperfections caused by non-ideal filters can be effectively corrected in the digital domain. In our software, the samples from the oscilloscope are firstly put through a digital Finite Impulse Response (FIR) filter with cutoff frequency 7.75 MHz (\(1/2f_{p}\)). Then, we resample the output data, to ensure the actual sampling rate is a little higher than \(f_{p}\). At this point, the preprocessing of the data is complete.
3. Power spectrum sensing. Every piece of the data is then processed automatically by the software. As described in Section 3.3, we divide the sensing algorithm into several steps shown in Figure 11. Firstly, we get the autocorrelation matrix \(\mathbf{r}_{y}(n)\) from samples \(y(n)\). The frame \(\mathbf{Q}\) is then calculated. Next we perform eigenvalue decomposition to get \(\mathbf{V}\) which has the same column space as \(\mathbf{r}_{y}(n)\), and the rank of the two matrixes are the same. Finally, we use some common CS algorithms to solve the frequency support. When the support \(S_{x}\) is known, the signal frequency position is certain.  
Figure 10: Photograph of the experiment environment.  
\[\hat{r}_{x}^{S_{x}}(f)=\mathbf{\Phi}_{S_{x}}^{\dagger}r_{y}(f),\;f\in F_{s}\;, \tag{20}\]  
where submatrix \(\mathbf{\Phi}_{S_{x}}\) contains the columns of \(\mathbf{\Phi}\) indexed by \(S_{x}\) and the \(\mathbf{\Phi}_{S_{x}}^{\dagger}=(\mathbf{\Phi}_{S_{x}}^{H}\mathbf{\Phi}_{S_{x}})^{-1}\mathbf{ \Phi}_{S_{x}}^{H}\) is the Moore-Penrose pseudoinverse of \(\mathbf{\Phi}_{S_{x}}\)
## 4 Analysis of Power Loss in Topologies
### Buck-Boost Topology  
In Table 5, both the switching and conducting semiconductors of the conventional TSBB converter are S\({}_{1}\)/S\({}_{2}\)/D\({}_{1}\)/D\({}_{2}\). Therefore, the switching loss \(P_{CON,SW}\) and conduction loss \(P_{CON,CD}\) can be expressed as:  
\[P_{CON,SW}=P_{SW,S1}(V_{i})+P_{SW,S2}\left(V_{o}\right)+P_{SW,D1}(V_{i})+P_{SW, D2}(V_{o}) \tag{25}\]  
\[P_{CON,CD}=P_{CD,S1,ON}+P_{CD,S2,ON}+P_{CD,D1,OFF}+P_{CD,D2,OFF} \tag{26}\]  
Both the switching and conducting semiconductors of the proposed TSBB converter are S\({}_{1}\)/S\({}_{2}\)/D\({}_{1}\). Therefore, the switching loss \(P_{PRO,SW}\) and conduction loss \(P_{PRO,CD}\) can be, respectively, expressed as:  
\[\begin{array}{c}P_{PRO,SW}=P_{SW,S1}\Big{(}\frac{C_{oss,D2}}{C_{oss,S1}+C_{ ass,D2}}V_{i}\Big{)}+P_{SW,S2}\Big{(}\frac{C_{oss,D2}}{C_{oss,S2}+C_{ ass,D2}}V_{o}\Big{)}\\ \\ \hskip 142.26378 pt+P_{SW,D1}(V_{i}+V_{o})\\ \\ P_{PRO,CD}=P_{CD,S1,ON}+P_{CD,S2,ON}+P_{CD,D1,OFF}\end{array} \tag{27}\]  
The voltage stress of each semiconductor is indicated in parentheses since the switching loss varies by the voltage stress. The loss difference between the conventional and proposed TSBB converters is determined by the switching loss of D\({}_{1}\)/D\({}_{2}\) and the conduction loss of D\({}_{2}\) in the switch-off section, and it can be expressed as:  
\[P_{CON}-P_{PRO}=P_{SW,D1}(V_{i})+P_{SW,D2}(V_{o})+P_{CD,D2,OFF}-P_{SW,D1}(V_{i }+V_{o}) \tag{29}\]  
There are no switching and conduction losses by D\({}_{2}\) because the proposed TSBB converter D\({}_{2}\) does not operate. However, the voltage stress of D\({}_{1}\) increases to \(V_{i}+V_{o}\), the voltage stress and reverse current of the diode in a linear section are \(P_{SW,D1}(V_{i})+P_{SW,D2}(V_{o})\cong P_{SW,D1}(V_{i}+V_{o})\). Therefore, the loss is determined by the conduction loss of the switch-off section of D\({}_{2}\). The power efficiency of the proposed TSBB converter is higher than that of the conventional TSBB converter. In contrast, the switching loss by D\({}_{1}\), \(P_{SW,D1}(V_{i}+V_{o})\), increases in the section where the reverse current of the diode increases sharply owing to the voltage stress. Therefore, the power efficiency of the proposed TSBB converter is lower than that of the conventional TSBB converter.