esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Partial discharge (PD) is a primary factor leading to the deterioration of insulation in electrical equipment. However, it is hard for traditional methods to precisely extract PD signals in increasingly complex engineering environments. This paper proposes a new PD signal denoising method combining Aquila Optimizer-Variational Mode Decomposition (AO-VMD) and K-Singular Value Decomposition (K-SVD) algorithms. Firstly, the AO algorithm optimizes critical parameters of the VMD algorithm. For the PD signal overwhelmed by noise, the AO-VMD algorithm can decompose it and reconstruct it by using kurtosis. In this process, the majority of the noise is removed, and the characteristics of the original signal are shown. Subsequently, the K-SVD algorithm performs sparse decomposition on the signal after OA-VMD, constructs a learned dictionary, and captures the characteristics of the signal for continuous learning and updating. After the dictionary learning is completed, the best matching atoms from the dictionary are selected to precisely reconstruct the original noiseless signal. Finally, the proposed method is compared with three traditional algorithms, Adaptive Ensemble Empirical Mode Decomposition (AEEMD), SVD-VMD, and the Adaptive Wavelet Multilevel Soft Threshold algorithm, on the simulated signal and the actual engineering signal. The results both demonstrate that the algorithm proposed by this paper has superior noise reduction and signal extraction performance.  
partial discharge; AO; VMD; sparse decomposition; K-SVD; kurtosis
## 2 Materials and Methods  
### Gene Expression Datasets  
This study used publicly available gene expression profiles from the NCBI GEO database ([[https://www.ncbi.nlm.nih.gov/geo/](https://www.ncbi.nlm.nih.gov/geo/)]([https://www.ncbi.nlm.nih.gov/geo/](https://www.ncbi.nlm.nih.gov/geo/)); accessed on 1 April 2025) using the \"GEOquery\" package (v2.70) from R (v4.3.3) [15]. Three datasets (GSE3790_GPL96, GSE3790_GPL97, and GSE26927) were considered and generated from distinct sequence  
Figure 1: Workflow of the study for identifying key biomarkers in [PERSON]’s disease (HD). The study comprises three main steps: (1) Meta-analysis: Three brain tissue-related GSE datasets from distinct platforms were retrieved using the GEOquery package in R, preprocessed, and batch-corrected. Meta-analysis was performed using limma and MetaVolcanoR under both relaxed and stringent criteria. (2) Machine learning: Normalized batch corrected data is split into training and test sets. Nineteen machine learning (ML) classifiers were evaluated, and the top three models (based on AUC) were selected. SHAP analysis of these models identified six candidate genes. Five genes are considered robust biomarkers that overlap with candidate genes from ML, with the stringent meta-analysis of differentially expressed genes (DEGs). (3) Validation: A nomogram based on the five biomarkers was trained on the training data and validated using an external dataset. ROC curve analysis on the external set was performed, and the selected final biomarkers with strong discriminative power for HD were identified.  
ing platforms. The GSE3790 dataset contains 404 human brain tissue samples profiled from two different microarray platforms [16; 17]. The first platform, GPL96 ([HG-U133A] Affymetrix Human Genome U133A Array), includes 100 HD samples and 101 healthy controls, whereas the second, GPL97 (Affymetrix Human Genome U133B Array), contains 101 HD and 102 healthy brain tissue samples. Lastly, the GSE26927 dataset, generated using the GPL6255 platform (Illumina HumanRef-8 v2.0 Expression BeadChip), includes 20 samples in total, containing an equal number of HD (n = 10) and control (n = 10) [18; 19]. For external validation, we incorporated two datasets, which include both Human (GSE33000) [20] and Mouse (GSE9857) [21] related to the brain tissue. The GSE33000 dataset comprises 304 samples (157 HD and 157 control) profiled on the Rosetta/Merck Human 44K microarray (GPL4372), and the mouse GSE9857 dataset comprises 18 mouse brain tissue samples profiled using the GPL1261 platform (Affymetrix Mouse Genome 430 2.0 Array), including 9 samples from HD models and 9 from control mice. All samples from HD were considered diseased (HD) regardless of their subtypes.
# Evolutionary Game for Content Cache in a mm-Wave-Based Vehicular Fog
## 6 Conclusions  
A platoon of autonomous vehicles constitutes a vehicular fog which collaboratively shares resources and data for vehicle control. In such dense vehicular fog, recent mm-Wave-based V2X communications can suffer from more interferences from neighbor vehicles forming beams using directional antennae. To avoid such mutual beam interference, we propose to distribute contents over vehicle caches to diversify mm-Wave beam directions. For this, we build a model of a sectorized multi-lane road and investigate throughput of the mm-Wave V2V communications using content caches. Experiments on the model show that a narrower beam and more caches can improve the connectivity but both have limitations in terms of increment. To decide the caching probability of each vehicle, we adopt an evolutionary game theory that allows vehicles to update their probability based on global information of caching strategy or only information of its past strategy. With the global information, homogeneous vehicles update caching probability together based on differential payoff between cooperation and defection. Otherwise, heterogeneous vehicles select their own strategy without interactions. Both approaches show that a stable proportion of vehicles cooperate for content cache to maximize their own utility after several hundred repeated games. This convergence speed needs to be enhanced to satisfy the dynamic topology of vehicular fog.  
This work was partly supported by the Technology development Program of MSS (no. S2901002) and the ICT development R&D program of MSIT (no. NRF-2017R1C1B1006607).  
**Acknowledgments:** In this section you can acknowledge any support given which is not covered by the author contribution or funding sections. This may include administrative and technical support, or donations in kind (e.g., materials used for experiments).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 11: Evolution of \(p_{c}\) based on payoff of cooperation and defection.  
Figure 12: Content cache probability using distributed algorithm based on local replicator dynamics.
## 4 Discussion
### Implant Performance  
While each implant was trialled on a different hand specimen, their performances were normalized against the baseline suture-based scenario conducted on the same specimen. This allowed us to comfortably compare their relative performances.  
Table 1 shows that the implants evened out finger forces (i.e., reduced average differences across fingertip forces). The rod implant (with \(>\)20% MAE reductions) consistently outperformed the U implant (with \(<\)20% MAE reductions) for a wide range of paddle orientations ([\(-30^{\circ}\), \(-12^{\circ}\)] and [\(9^{\circ}\), \(30^{\circ}\)]). This remains true even in the glued configuration, which simulated tissue adhesion resulting from scarring. Additionally, in Figure 8 panel (A), we see that with the U implant, the index finger did not apply much force at lower tendon tensions. This problem may have arisen due to minor surgical inconsistencies that the U implant was unable to accommodate, as well as the rod implant.  
Considering these promising preliminary results, the _rod implant_ is the clear candidate for future work and implant development as we move towards testing the biomechanical efficacy of arranging these implants hierarchically to achieve differential action across four fingers in a large-scale human cadaver study, before eventual clinical trials and United States Food and Drug Administration (FDA) approval.
## 2 Related Work  
Leak detection equipment has been developed over the past few decades following the ever-increasing demands of the industry. The current real-world vacuum leak detection method in the semiconductor industry is based on mass spectrometers. In theory, every kind of mass spectrometer can be used for leak detection. In many situations, it is more practical to use mass spectrometers that are devoted to leak detection by using helium tracer gas [24; 25], which require the chamber to be in an isolated state with full pumping and make it unsuitable for in situ vacuum leak detection during the ongoing process [26]. Moreover, the cost of the He vacuum leak detector and He gas is too high, which makes the whole detection system not cost effective. Therefore, it is important and necessary to create a leak detection system that does not require high cost measurement tools, such as a helium leak detector.  
To overcome this problem, data-driven anomaly detection has been extensively studied. [PERSON] and [PERSON] proposed a partial least squares (PLS) method [27]. PLS is a regression model between predictors and responses in a reduced feature space using orthogonal latent vectors. PLS studies orthogonal latent vectors that best explain the covariance between responses and predictors. The latent vectors serve as new predictors and regress the responses on these new predictors. In fault detection, the tool state features are predictors and the wafer class is the response. [PERSON] et al. [28] proposed a decision tree classification model to detect faults in dry etch equipment. The model was built from the five sensor signal data. [PERSON] et al. [29] proposed to build a specific control chart for detecting specific types of faults. They collected tool state data directly from the etcher. These data consisted of 19 variables. [PERSON] et al. [30] proposed a technique based on the statistical method. They adapted the multivariate control chart method to maintain changes in the mean and standard deviation coefficients by remodeling the technique.
# Topological Navigation and Localization in  
Tunnels--Application to Autonomous Load-Haul-Dump Vehicles Operating in Underground Mines  
[PERSON]  
Mobile robots are no longer used exclusively in research laboratories and indoor controlled environments, but are now also used in dynamic industrial environments, including outdoor sites. Mining is one industry where robots and autonomous vehicles are increasingly used to increase the safety of the workers, as well as to augment the productivity, efficiency, and predictability of the processes. Since autonomous vehicles navigate inside tunnels in underground mines, this kind of navigation has different precision requirements than navigating in an open environment. When driving inside tunnels, it is not relevant to have accurate self-localization, but it is necessary for autonomous vehicles to be able to move safely through the tunnel and to make appropriate decisions at its intersections and access points in the tunnel. To address these needs, a topological navigation system for mining vehicles operating in tunnels is proposed and validated in this paper. This system was specially designed to be used by Load-Haul-Dump (LHD) vehicles, also known as scoop trans, operating in underground mines. In addition, a localization system, specifically designed to be used with the topological navigation system and its associated topological map, is also proposed. The proposed topological navigation and localization systems were validated using a commercial LHD during several months at a copper sub-level stoping mine located in the Coquimbo Region in the northern part of Chile. An important aspect to be addressed when working with heavy-duty machinery, such as LHDs, is the way in which automation systems are developed and tested. For this reason, the development and testing methodology, which includes the use of simulators, scale-models of LHDs, validation, and testing using a commercial LHD in test-fields, and its final validation in a mine, are described.
# On the Performance of Partial LIS for 6G Systems
## 1 Introduction
Instead of using an LIS between the user and a BS, the work published in [8] considers a full-duplex unmanned aerial vehicle relay employed to increase the communication capacity of millimetre-wave networks. Channel encoding is proposed as a solution for handling complexity. In low-density parity-check (LDPC) codes, complexity reduction is emphasized for efficient operation in LIS scenarios. LDPC codes empower a coded MIMO system to achieve a data rate nearly approaching that of the Shannon limit. LDPC codes boost remarkable error-correction prowess while maintaining minimal decoding complexity, making them highly compatible with LIS environments. In this article, we analyse an LIS [9; 10; 11; 12; 13] system associated with a single carrier with frequency domain equalization (SC-FDE) [14; 15], which is an alternative to orthogonal frequency division multiplexing (OFDM). Due to the high peak-to-average power ratio (PAPR) of OFDM signals, their use in uplink scenarios is not well suited. SC-FDE transmission presents a less significant PAPR, and is thus more suited for the uplink scenario. Four different receivers are studied in this article and are compared in terms of performance: zero forcing (ZF), minimum mean squared error (MMSE), maximum ratio combining (MRC), and equal gain combining (EGC) [16; 17]. Moreover, the LIS system, combined with SC-FDE transmission, is also studied in scenarios associated with LDPC codes. This article is an extension of the research published in [10], using a subset of the antenna LIS elements (referred to as a partial LIS), instead of the whole LIS. Because it is a function of the number of antennas, the computational complexity decreases when using only antenna elements that are closer to the user equipment (UE) instead of the whole LIS. Moreover, with a partial LIS, a reduction of energy consumption is achieved, and mitigation of the interference levels is obtained, allowing a performance that is approximately the same as that obtained with the whole LIS system. The outline of this article is as follows: system and signal characterizations are presented in Section 2; the performance outcomes are shown and discussed in Section 3; and, finally, the conclusions of this article are presented in Section 4.
# The Big Picture: An Improved Method for Mapping Shipping Activities
## 3 Generating Density Maps from Raw AIS Data  
In this section, we illustrate the steps followed to produce highly accurate density maps of shipping activity from raw AIS data. The overall view of the process, as depicted in Figure 2, begins with the decoding of the data and the removal of erroneous and irrelevant messages. This step is followed by the calculation of the traffic density with the subsequent maps generated according to the grid resolution selected. For more accurate results, the option of improving the quality of the AIS dataset through additional vessel positions (orange component in Figure 2) may be considered. The two techniques proposed in this work for this step are described in detail in the following sections.  
For the implementation of our pipeline, we relied mainly on open-source libraries in Python for all steps. Moreover, to boost the performance of our system we used Python's process parallelization option for the steps of cleaning, filtering and density calculation. For the handling of the geometries, during the grid generation and land-masking, the Shapely library was utilized, along with the GDAL library for the rasterization process.
# Design of a SIW Variable Phase Shifter for Beam Steering Antenna Systems
## 2 SIW Variable Phase Shifter  
SIW phase shifters have the advantages of being compact, low loss, and planar which make them suitable for integration with other components on a single layer. Phase shifters are critical components in the mm-wave and microwave area for applications like phased array systems in radio astronomy, and beam steering systems for tracking applications in wireless and satellite communications. Phase shifters divide into two main groups: Fixed phase shifters and variable phase shifters. This paper's focus is on the design of a variable SIW phase shifter.
# Rotate Vector Reducer Fault Diagnosis Model Based on EEMD-MPA-KELM
## 1 Introduction
[PERSON] [14] from Nanyang Technological University introduced a kernel function idea from a support vector machine into ELM to improve the stability and generalization ability of ELM neural network model training results, and proposed Kernel Extreme Learning Machine (KELM). [PERSON] [15] used the nuclear extreme learning machine to classify turbine rotor faults. [PERSON] [16] proposed a fault diagnosis method based on IBOA-KELM to improve the accuracy and efficiency of diesel engine fault diagnosis. [PERSON]. [17] proposed a fault diagnosis method based on the Whale Optimization Algorithm (WOA) to optimize KELM, aiming at the problems that fault features are difficult to extract and time-frequency features cannot fully represent state information. [PERSON]. [18] proposed a fault diagnosis method for the coal mill of a nuclear extreme learning machine based on feature extraction of a variational model. The above studies combined various methods with the nuclear extreme learning machine for fault diagnosis, and achieved good results, but most of them have poor universality. Moreover, because KELM introduced kernel functions, it is very sensitive to parameters and has randomness, so the results are not stable every time, which is easy to affect the prediction results. The above fault diagnosis method for the RV reducer has a high accuracy, but the content of the method is more complex, with more complicated calculation, and a large number of data support is needed. Often, the data characteristics of the RV reducer are fewer when the initial fault occurs, resulting in the reduced accuracy of the RV reducer in operation, and the fault cannot be detected. In this paper, a MPA-KELM fault diagnosis and classification model for RV reducer of a small sample of industrial robots based on EEMD was proposed. In view of the characteristics of the industrial robot RV reducer where the fault is not obvious and the sample data are sparse, the spectrum diagram was used to diagnose the fault from the measured data of RV reducer, the data measured by the RV reducer test platform was decomposed by EEMD, and several Intrinsic Mode Functions (IMFs) are obtained. After the overall average checking and optimization of each IMF, multiple groups of characteristic values were obtained. The eigenvalues are imported as inputs into the KELM optimized by the Marine Predators Algorithm (MPA) in order to accurately evaluate the RV reducer working state and establish a fault diagnosis model.
# Effects of Propanol on the Performance and Emissions of a Dual-Fuel Industrial Diesel Engine
## 3 Results and Discussion
### Emission Characteristics
#### 3.3.3 Carbon Monoxide  
Carbon monoxide is produced at high temperatures in the fuel-rich zones of the combustion chamber. The formation of CO in an internal combustion engine is favored by a mixture that is too rich, generating the formation of local fuel-rich zones; a low combustion temperature leading to a slower reaction rate, even in zones with high excess air; as well as a low level of turbulence and low mixture turbulence. Figure 10 shows changes in the carbon monoxide emission of the engine powered by diesel and propanol in relation to the engine powered by D100. The figure shows that increasing the propyl alcohol content in the mixture combusted in a compression-ignition engine causes a decrease in the carbon monoxide content in the exhaust gas. This fact is due to two factors. First, the additional oxygen provided in the alcohol fuel has a positive effect on achieving complete combustion, and thus improves the oxidation of CO to CO\({}_{2}\), reducing the concentration of CO. Secondly,  
Figure 9: Emission of NO and HC for dual-fuel engine fueled by diesel and propanol.  
as the amount of alcohol is increased, the amount of carbon in the combustible mixture is reduced, and therefore the possibility of forming its compounds, including CO, is also limited. For DP65, compared to D100, there was a decrease in CO concentration by 1.3%.
## 4 Performance Results
(**a**) Position of \(i\)th vehicle. (**b**) Velocity of \(i\)th vehicle. (**c**) Velocity error \(e_{v_{i}}\). (**d**) Spacing error \(e_{i}(t)\). To achieve a quantitative comparison of the performance between the proposed method and the DO-based sliding mode control strategy, the overshoot and integral of the time-weighted absolute error (ITAE) index of \(e_{i}\) are incorporated as shown in Table 1. According to Table 1, we can observe that compared to the DO-based sliding mode controller in [38], the FTDO-based controller can reduce the overshoot of \(e_{i}\) by 57.3% and improve the tracking accuracy by 38.1%. A quantitative comparison of the performance between FTDO and DO is presented in Table 2. According to Table 2, compared to the DO in [38], FTDO can improve the estimation accuracy of the mismatched disturbance by 31.2% and that of the matched disturbance by 17.3%. By analyzing the results, we can conclude that the proposed control strategy enhances the tracking accuracy and reduces the overshoot of \(e_{i}\), thereby enabling the vehicle platoon to achieve better control performance. Moreover, the proposed FTDO can effectively improve the estimation accuracy of the disturbances.
## 3 Methodology
### Resilience Assessment of Sparse Road Networks
#### 3.5.2 Evacuability  
Rapid evacuation is a crucial component of disaster management and system resilience. Geological disaster events in sparse road networks can lead to severe congestion, disrupting emergency responses, transportation of supplies, and resident evacuation. Thus, evacuability refers to the ability to quickly clear congested vehicles using secondary roads and smaller networks, improving the response time of emergency management. It is measured by the total time taken for emergency response vehicles to reach blocked locations after each disaster event, indicating the system's effectiveness in managing congestion and rapid response.  
\[A_{\text{Evacuability}-\sum\limits_{j=1}^{J}}\Big{(}t\Big{(}s\mid z^{j}\Big{)}-t \Big{(}d\mid z^{j}\Big{)}\Big{)} \tag{12}\]  
here, \(\psi\big{(}t\mid z^{j}\big{)}\) is the system performance after the \(j\) disaster, \(t\big{(}s\mid z^{j}\big{)}\) is the time when recovery starts, and \(t\big{(}s\mid z^{j}\big{)}\) is the time when recovery reaches an acceptable level. \(J\) is the total number of disaster events observed.
## 2 Wideband Antenna Designing
### Results of Unit Elements
#### 2.2.3 Radiation Pattern  
The proposed ultra-wideband antenna's observed and modelled radiation patterns at resonance frequencies of 30 GHz and 28 GHz are shown in Figure 7a,b. It is clear that the antenna provides both frequencies with a broad side radiation pattern in the E-plane and a slightly inclined radiation pattern in the H-plane. The tiny distortion in the radiation pattern is due to loading several stubs. Both the generated and observed radiation patterns exhibit strong correlations with one another. When testing, a little discrepancy that results from manufacturing flaws or connection loss is noticed. Because of the results and correlation between measured and simulated outcomes, future 5G devices running at broadband and high gain may be interested in the proposed study. The antenna offers a low cross polarization of <\(-\)14 dB for both resonating frequencies.  
Figure 5: Comparison among simulated and experimental 1S11 results.  
Figure 6: Comparison among simulated and experimental gain results.