esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
More recently, advanced deep-learning techniques have been applied for prognostic purposes, and these use multiple traditional methods combined and neural networks to perform RUL classification [40, 45, 56, 57, 58, 59]. In [59], an RUL prediction method was proposed based on a long short-term memory (LSTM) neural network framework and deep features, which were learned adaptively from the two health states. [PERSON] [48] proposed a method to characterize and classify seven different bearing classes using statistical features, EMD energy entropy and an artificial neural network (ANN). [PERSON] [56] used a combination of two supervised ML techniques; a regression model and multi-layer ANN to predict the RUL of rolling element bearings. Here, in this paper, a novel ML method for RUL is proposed, using non-linear signal processing techniques to perform feature engineering based on STFT and EA with One-third octave band feature compression. The rationale for using Fourier and EA-based  
Figure 1: (**a**) The main components of an electric machine, (**b**) percentage occurrences of failure modes from the statistical data presented in [2, 6] and (**c**) typical degradation trend and remaining useful life of an operating machine. feature extraction was as a result of detailed vibration signal analysis conducted on the bearing signals from the dataset. This motivated the incorporation of non-linear feature compression of the multidimensional feature space using Octave bands as the prognostic information signatures are highly concentrated in the lower portion of the spectra. These features form part of the ML method recipes alongside twelve different supervised learning algorithms based on \(k\)-NN and SVM to determine the optimal choice for RUL classification. SVM and \(k\)-NN algorithms were chosen because of their robustness for supervised learning problems, in particular problems with datasets of limited size, which greatly limits the suitability of applying other more advanced deep-learning approaches, e.g., ANN and LSTM. The work also highlights the importance of using non-linear wear-state models to track the degradation severity levels; this has been shown to greatly improve the performance of the ML classifiers overall for this RUL task. The time frequency analysis conducted on the vibration signals also motivated the investigation of non-linear wear state models as the bearing degradation typically does not follow linear trends. The remainder of this paper is organised as follows. Section 2 presents a graphical and statistical analysis of the vibration signals. The proposed ML method is detailed in Section 3. The experimental procedure is illustrated in Section 4. In Section 5, the results from the proposed CbM system are presented. Section 6 presents the major trends and findings from the results and statistical analysis, and the limitations of this work are discussed. Finally, Section 7 concludes the work and highlights future research avenues to explore.
# Robust Estimators in Geodetic Networks Based on a New Metaheuristic: Independent Vortices Search
## 2 Classical and Robust Approaches
### LMS Median Position and LTS/LTS-RC Summation Limit  
As seen above, the LMS has a median position defined by \(h\) by applying Equation (7). Likewise, the trim limits (residuals for summation) for LTS and LTS-RC are defined, based on the same equation.  
Occasionally, a variation of this equation is used [19; 21; 43], replacing Equation (7) by Equation (18) [38], below:  
\[h=\left(n+1\right)/2, \tag{18}\]  
where \(n\) is the number of observations.  
It is clear that the only difference, regarding Equation (7), is the absence of the variable \(p\) (the number of parameters). By defining \(h\) as above, more residuals are trimmed in LTS and LTS-RC, and the true median position of the residuals vector becomes the median value for LMS.  
We can also think about other possible values for \(h\). In the case of LTS and LTS-RC, a lower limit considers fewer residuals in the estimation (i.e., it will ignore more values in the residuals vector). For the LMS, the limit defines a single residual position to be taken into consideration. With a greater limit, the residual location considered the center will be further from the median, toward the larger values.  
Thus, besides testing and analyzing the REs presented in this study, we also will verify the behavior of both Equations (7) and (18) for LMS, LTS, and LTS-RC estimators.
# Influence of Earthquakes on Landslide Susceptibility in a Seismic Prone Catchment in Central Asia
###### Abstract  
Central Asia is one of the most challenged places, prone to suffering from various natural hazards, where seismically triggered landslides have caused severe secondary losses. Research on this problem is especially important in the cross-border Mailuu-Suu catchment in Kyrgyzrast, since it is burdened by radioactive legacy sites and frequently affected by earthquakes and landslides. To identify the landslide-prone areas and to quantify the volume of landslide (VOL), Scoops3D was selected to evaluate the slope stability throughout a digital landscape in the Mailuu-Suu catchment. By performing the limit equilibrium analysis, both of landslide susceptibility index (LSI) and VOL were estimated under five earthquake scenarios. The results show that the upstream areas were more seismically vulnerable than the downstream areas. The susceptibility level rose significantly with the increase in earthquake strength, whereas the VOL was significantly higher under the extreme earthquake scenario than under the other four scenarios. After splitting the environmental variables into sub-classes, the spatial variations of LSI and VOL became more clear: the LSI reduced with the increase in elevation, slope, annual precipitation, and distances to faults, roads, and streams, whereas the highest VOL was observed in the areas with moderate elevations, high precipitation, grasslands, and mosaic vegetation. The relative importance analysis indicated that the explanatory power reduced with the increase in earthquake level and it was significant higher for LSI than for VOL. Among nine environmental variables, the distance to faults, annual precipitation, slope, and elevation were identified as important triggers of landslides. By a simultaneous assessment of both LSI and VOL and the identification of important triggers, the proposed modelling approaches can support local decision-makers and householders to identify landslide-prone areas, further design proper landslide hazard and risk management plans and, consequently, contribute to the resolution of transboundary pollution conflicts.  
earthquake impacts; volume estimation; Scoops3D; limit equilibrium method; radioactive contamination; Tien Shan Mountains
# 1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System
###### Abstract  
Ultra-Wideband (UWB) technology is crucial for indoor localization systems due to its high accuracy and robustness in multipath environments. However, Non-Line-of-Sight (NLoS) conditions can cause UWB signal distortion, significantly reducing positioning accuracy. Thus, distinguishing between NLoS and LoS scenarios and mitigating positioning errors is crucial for enhancing UWB system performance. This research proposes a novel 1D-ConvLSTM-Attention network (1D-CLANet) for extracting UWB temporal channel impulse response (CIR) features and identifying NLoS scenarios. The model combines the convolutional neural network (CNN) and Long Short-Term memory (LSTM) architectures to extract temporal CIR features and introduces the Squeeze-and-Excitation (SE) attention mechanism to enhance critical features. Integrating SE attention with LSTM outputs boosts the model's ability to differentiate between various NLoS categories. Experimental results show that the proposed 1D-CLANet with SE attention achieves superior performance in differentiating multiple NLoS scenarios with limited computational resources, attaining an accuracy of 95.58%. It outperforms other attention mechanisms and the version of 1D-CLANet without attention. Compared to advanced methods, the SE-enhanced 1D-CLANet significantly improves the ability to distinguish between LoS and similar NLoS scenarios, such as human obstructions, enhancing overall recognition accuracy in complex environments.  
channel impulse response; non-line-of-sight classification; ultra-wideband; deep learning; indoor positioning system +
Footnote †: journal: Applied sciences
# Measurement and Modeling of 3D Solar Irradiance for Vehicle-Integrated Photovoltaic
## 2 Model
### Partial and Dynamic Shading Model
It is important to note that the probability modeling of the partial shading, corresponding to the fourth through sixth rows of Table 2, is the key to the simulation. To minimize the evil influence of the partial shading, the PV and car manufacturers try to place the series strings as close as possible to the shape of the partial shading or non-uniform illumination pattern caused by the curvature of the car body. In a way, the distribution of the shaded cells are not uniformly distributed but leaves some distribution pattern. To mimic this situation, the random model is made by three steps (see fourth to sixth rows of Table 1). Specifically, the number of the shaded cells in each string is allocated using Equation (21), and the short-circuit current from each cell impacted by shading is given in Equation (2). Note both Equations (1) and (2) contain Boolean algebra. It is also important to note that the vector and matrix data that are generated by the above calculations and contain the information of the shaded ratio of each cell in the module are only used as the circuit calculation of the module by series-parallel connection with bypass diodes. Therefore, in the downstream calculation step, the position of the shaded cell is not referenced, but they may be redistributed in the series connection by order of the shaded ratio. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Section** & **Distribution** & **Type** & **Range** \\ \hline Date (day number) 1  & Uniform distribution & Integer & 0–364 (day) \\ Time 1  & Uniform distribution & Integer & 0–23 (h) \\ Number of cells partially & & & \\ shaded 2  & Uniform distribution & Integer & 0 (Number of cells in the string) \\ Number of cells fully shaded 2  & Uniform distribution & Integer & 0 (Number of cells in the string) \\ Shading ratio of each & & & \\ partially-shaded cell 3  & Uniform distribution & Real & 0–1 \\ Car orientation 4  & Uniform distribution & Real & 0\({}^{\circ}\)–360\({}^{\circ}\) \\ Isc of each cell & Normal distribution & Real & – \\ Voc of each cell & Normal distribution & Real & – \\ Diode ideality 5  & Normal distribution & Real & Greater than 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of random variables used for the Monte Carlo simulation. To acquire a dataset for comparison, a conventional static irradiance measurement system with a pyrheliometer, a tracking pyranometer mounted onto a sun tracker, a horizontal pyranometer, and two vertical pyranometers facing eastward and westward was installed at the University of Miyazaki, Japan (31\({}^{\circ}\)49\({}^{\prime}\) N, 131\({}^{\circ}\)24\({}^{\prime}\) E). The elevation angle and azimuth angle of the sun position are calculated based on the conventional method, including the day angle, declination angle, time equation, hour angle, latitude, and longitude.
# A Prototype of a Portable Gas Analyzer for Exhaled Acetone Detection
## 4 Conclusions and Further Perspectives  
We developed a portable gas analyzer prototype for exhaled acetone detection employing an application-suited gas sensor array. The device was shown to be capable of serving as a tool for non-invasive diabetes monitoring with the additional advantage of having a hand-held form factor and battery powered operation. The prototype is currently patent pending in the European Patent Office and is specified within a non-disclosure agreement between the designers and investors, therefore, the presented description gives only a general overview of the system. However, all components have been widely discussed, including the gas sensor array, gas chamber design, 3D printable housing design, hardware and software design, and correction for temperature and humidity. Finally, the calibration and measurement technique as well as the analyzer operation was shown, validating the proposed type of exhaled acetone-detection meters. After the certification process, the prototype will be validated in clinical tests in the University Hospital in Krakow, Poland.  
A.R. conceptualization; J.S. and A.R. methodology; J.S. software; A.R. validation; A.R. and J.S. investigations; J.S. and A.R. writing--original draft preparation and writing--review and editing; A.R. supervision; A.R. project administration.  
The work was financially supported by the National Centre for Research and Development under LIDER/252/L-6/NCBR/2015.  
The authors declare no conflict of interest.  
Figure 8: The calibration curves of all four sensors (S1–S4) in the gas sensor array under exposure to 2–20 ppm of acetone. Optimal operating temperature (given by the manufacturer) was ensured and 50% relative humidity was stabilized.
# Energy-Efficient Channel Coding Strategy for Underwater Acoustic Networks
## 3 Channel and System Model
In practice, a minimum transmit power level is always required [31], independent of the link distance \(l\). The total electric power for a single transmit attempt over the acoustic link can be written as  
\[10\log_{10}\big{[}a(f)\big{]}=\frac{0.11f^{2}}{1+f^{2}}+\frac{44f^{2}}{4100+f^ {2}}+\frac{2.75f^{2}}{10^{4}}+0.003\ \ \mathrm{dB/km}. \tag{2}\]1  
where \(P_{PA}^{min}\) is the minimum transmit power, \(P_{tx}^{el}\) and \(P_{rx}^{el}\) are the electric power consumptions (in Watts) of the transmit and receive circuits, respectively.
## 3 Numerical Simulation of Deep Foundation Pit
### Selection of Model Parameters  
Existing practices and studies have indicated that the strain within the soil during the excavation process typically remains in a small strain state. As depicted in Figure 4, the hardening soil small strain (HSS) model exhibits the characteristic whereby the shear modulus of the soil increases with strain. It can effectively capture the hydrostatic pressure and expansibility of soft clay. This model allows for the differentiation of unloading stiffness during loading and reloading, thereby enabling a relatively accurate representation of the behavior induced by soil excavation. Therefore, the HSS model was adopted in this study. The model parameters are all derived from laboratory data, especially the small strain parameter G\({}_{0}\). Through a series of dynamic triaxial tests, small-amplitude cyclic shear stresses were applied to the soft soil samples, and the relationship between the shear modulus of the soil samples and the shear strain was measured to determine the value of G\({}_{0}\). The main soil parameters obtained are shown in Table 3.
## 4 Conclusions  
In summary, we investigated the electrical and optical properties of ultraviolet-ozone-irradiated RuO\({}_{2}\) NS conducting films. The sheet resistance of the five-layered nanosheet coating films increased by 10% in comparison with the normal films owing to the removal of the residual organic materials via UVO exposure. It seems that a 10% reduction in sheet resistance is relatively small. However, it should be noted that this sheet resistance was reduced without the degradation of the optical transmittance in the RuO\({}_{2}\) NS films. Moreover, the optical transmittance was found to increase simultaneously, by 1.5%, in the five-layered nanosheet coating films. Our results clearly indicate that the precise control of ultraviolet-ozone treatment is extremely facile and efficient in enhancing the electrical and optical properties of RuO\({}_{2}\) NSs for their application as transparent conduction electrodes. In particular, the RuO\({}_{2}\) NS conducting films can be applied as transparent electrode materials in a flexible touch screen or solar cell due to their good electrical and optical properties and strong mechanical durability.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Initial** & **24 h** & **48 h** & **120 h** \\ \hline With 30-min UVO & 2286 \(\Omega\)/sq & 2286 \(\Omega\)/sq & 2427 \(\Omega\)/sq & 2423 \(\Omega\)/sq \\  & (4.5\%) & (5.8\%) & (5.7\%) \\ Without UVO & 2522 \(\Omega\)/sq & 2672 \(\Omega\)/sq & 2738 \(\Omega\)/sq & 3031 \(\Omega\)/sq \\  & (5.9\%) & (7.9\%) & (11.9\%) \\ \hline \end{tabular}
\end{table}
Table 1: Sheet resistance changes with time in the five-layered RuO\({}_{2}\) NS films treated with or without 30-min UVO treatment.  
**Author Contributions:** Conceptualization, J.W.R. and S.-i.K.; Data curation, J.W.R.; Formal analysis, W.H.S.; Funding acquisition, J.W.R.; Investigation, H.-S.K. and S.Y.K.; Methodology, W.H.S.; Project administration, S.-i.K.; Supervision, S.-i.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP), Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20193091010110), National Research Foundation of Korea (NRF) (NRF-2019R1C1C1005254), and by the Hongik University new faculty research support fund.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
Driven by a large number of emerging diversified services, in the 5G and beyond era, concurrent direct inter-ONU and upstream communications inside a PON-based mobile access network are highly desirable to provide dynamic, ultra-dense, and fast ONU-to-ONU (without involving an OLT) and ONU-to-OLT connections. To cost-effectively deliver highly dynamic and low latency direct inter-ONU communications, this paper proposes and experimentally demonstrates novel concurrent direct inter-ONU and upstream communications in an upstream 27 km, >62.47 Gbit/s IMDD PON. For supporting inter-ONU communications between a large number of ONUs, an advanced passive remote node is also proposed. Based on different passive optical components, this remote node can be implemented using two approaches, which can, respectively, reduce the inter-ONU signal power losses by >12.2 dB and >16.6 dB (for 128 ONUs) in comparison with existing inter-ONU communication techniques' remote nodes. In each ONU and OLT, a single pair of cascaded IFFT/FFT-based point-to-multipoint (P2 MP) flexible optical transceivers are employed to simultaneously and dynamically establish multiple ONU-to-ONU and ONU-to-OLT communications according to actual users' requirements. Experimental results show that the proposed network has excellent robustness against various transmission system impairments, including chromatic dispersion, the Rayleigh and Brillouin backscattering effects, and the channel interference effects. For each ONU, dynamic channel allocation can be made without compromising its overall performance.  
inter-ONU communications; point-to-multipoint (P2 MP) transceivers; multi-channel aggregation; intensity modulation and direct detection (IMDD); passive optical networks +
Footnote †: journal: _Photonics_
## 6 Algorithms and Frameworks in Federated Learning
### Model Aggregation Methods
#### 6.3.2 Weighted Averaging  
An evolution of the simple averaging method is weighted averaging, where each client's updates are weighted by the number of samples that it possesses. This approach gives more importance to clients with more data, which can be beneficial in non-IID settings.  
\[\theta_{global}=\frac{\sum_{i=1}^{N}w_{i}\theta_{i}}{\sum_{i=1}^{N}w_{i}}\]  
where \(w_{i}\) is the weight (often the number of samples) of the \(i\)th client.  
Advantages:  
* More robust to non-IID data scenarios.
* Reduces the impact of stragglers or clients with fewer data samples.
* Challenges:
* Still vulnerable to adversarial attacks if a malicious client possesses a significant amount of data.
## 6 Discussion
### Accuracy  
No significant difference in accuracy was found between the MVF and the EBC, and the accuracy decreased with the increase in cluster size. We suggest that the MVF could increase efficiency while maintaining accuracy. When the cluster size increases, more nodes and attributes are involved, which is why the error probability is higher.
# Large Deformation Modeling of Wing-Like Structures Based on Support Vector Regression
## 2 Theory and Method
### SVR Prediction Model of Large Deformation  
Based on the SVR model, the static prediction model for large deformation of flexible wing-like structure can be established. The modal excitation force is defined as a feature and the generalized coordinates in modal space are given as the target value. The dataset is formulated as:  
\[G^{r}=\left\{(\overline{f}_{1},q_{r1}),(\overline{f}_{2},q_{r2}),\cdots,( \overline{f}_{NT},q_{rNT})\right\}\ \ r=1,2,\cdots m \tag{19}\]  
Figure 1: The schematic diagram of support vector regression (SVR).  
Figure 2: The principle of kernel mapping in nonlinear SVR model.  
where \(m\) is the order of modal space, \(NT\) is the number of samples, which are also called test cases, and \(q_{r}\) is scalar and denotes the r-th generalized coordinates in modal space. For a modal space with \(m\) modal shapes, the number of SVR models required is also \(m\).  
Choosing of samples is important for the prediction model. The samples include given loads and corresponding deformations, which is the same as traditional ROM construction. Deformations are generated in nonlinear FEM software. Two factors are important in choosing the given load in samples. The first is the spatial distribution of the load over the FEM model. The second is the overall magnitude of the FEM loading. With the absence of a priori information about the exact nature of the nonlinear relationship, a sum of a number of weighted modal shapes is often used as load in samples:  
\[\overline{f}=\Gamma(\sum_{r=1}^{m}a_{r}\lambda_{r}\varphi_{r}) \tag{20}\]  
\[\lambda_{r}=\frac{2i}{g-1}-1,\;\;i=0,1,\cdots,g-1 \tag{21}\]  
where \(a_{r}\) are scalar weighting factors for each selected modal shape and \(g\) is grid density applied to define intermediate loads in order to have a wide range of sampling space.  
The Gaussian kernel is used as a kernel function in this research to build the nonlinear SVR model. After samples are obtained, the suitable parameters regularization \(c\) in Equation (13) and kernel parameters \(\gamma\) are selected by classical grid search techniques [30]. A simple grid search, with grids value from \(2^{-5}\) to \(2^{15}\) for \(c\) and \(2^{-13}\) to \(2^{3}\) for \(\gamma\), both in interval \(2^{1}\), is used for parameters selection. \(\varepsilon\) is given empirically before \((c,\gamma)\) selection and those parameters are assumed fixed in the SVR model.  
The samples are divided into training samples and test datasets. The training sample is used for establishment of the SVR model and the test dataset is used for assessing the performance of the SVR model. The procedure illustration of the SVR model's construction is presented in Figure 3.  
Figure 3: Procedure of SVR model construction.  
Once the prediction model has been built and tested, static deformation in modal space can be solved after giving any force input. The deformation in physical space of wing-like structures can be obtained by Equation (4).