esa-sceva/satcom-chunk-collection huggingface dataset:
# GPS/BDS RTK Positioning Based on Equivalence Principle Using Multiple Reference Stations
## 1 Introduction
[38] proposed a partial ambiguity resolution (PAR) method for a subset of ambiguities selected according to the successively increased elevations, and the ratio test was used for ambiguity validation. [PERSON] and [PERSON] [39] also presented a new method to determine the subset with maximum ambiguities in an iterative process, validating each subset by applying the success rate and ratio tests. [PERSON] et al. [40] proposed a modified PAR method based on the method of [PERSON] and [PERSON] [41] to find a subset of decorrelated ambiguities. [PERSON] et al. [42] developed integer search estimation based on the lattice theory whose search algorithms were presented and proved to be many times faster than the least-squares ambiguity decorrelation adjustment (LAMBDA) [43] and modified LAMBDA [44]. [PERSON] and [PERSON] [45] developed a posterior probability test to validate each ambiguity subset, which can guarantee the correct fixing confidence. However, in the above-mentioned methods, the fixing and computing efficiency of high-dimensional ambiguity for the MBRTK has not been investigated. To address the aforementioned problems, a rigorous algorithm for MBRTK is presented in this paper to improve the RTK positioning accuracy and availability using multiple reference receivers, and a modified Kalman filter with parameter constraints is used to estimate the system states. Then, a PAR strategy for high-dimensional ambiguity is proposed to determine an ambiguity subset with high confidence. In order to validate and assess the performance of the proposed method, the positioning results of different satellite systems and reference stations are calculated and compared with the SBRTK. This paper is organized as follows. In Section 2, conventional single-differenced (SD) and equivalent double-differenced (DD) observation equations are introduced. Then, a modified Kalman filter with parameter constraints is proposed. The redundancy of MBRTK is given, along with the data processing flow. In Section 3, the static and kinematic experiments are carried out and the impact of satellite systems as well as the reference receivers are discussed. Finally, our conclusions are summarized in Section 4.
## 3 Results
### Data Processing  
We processed four datasets, and each dataset was randomly split into query and database networks. We calculated the similarity between every pair of database networks using EC, LCCS, and S3 metrics via NETAL and HubAlign, selecting the maximum values as the final similarity measures. These computed values served as self-supervised labels. The details of the dataset information are listed in Table 2.  
The datasets were split into training and test sets using a 7:3 ratio, with 70% of the data allocated for training and 30% for testing. To ensure the robustness of our model, we also applied five-fold cross-validation, which helps validate the model's performance across different data splits. In GBNSS, separate GNN models are trained for each species to capture species-specific biological features. This is essential, as biological networks differ significantly across species, not only in their topological structures but also in the biological processes they represent. We applied the same training procedure for each species, including Homo Sapiens, Bos Taurus, Mus Musculus, and Saccharomyces cerevisiae. This approach ensures that the models are trained on biologically relevant data, capturing both the unique topological patterns and biological characteristics specific to each species.
## 3 Decomposition Method for Countermeasures against Excessive Fault Currents  
### Motivation  
The increase in the load demand consequently results to increased levels of fault current that might exceed the breaking duty of CBs in the credible operating point. The critically excessive fault current levels can disrupt the proper operation of CBs, and hence they can lead to a widespread low voltage problem caused by sustained or permanent failures in some components in the system. The aim of this paper is to reduce the fault currents through applying bus splitting and installing CLRs in the power systems while keeping the fault current levels within the CB breaking duties. The countermeasures are not incorporated in the formulation of the FFLC-OPF in the previous section.  
Several countermeasures can be implemented against excessive fault current levels, as in [22; 23; 24]. This paper concentrates on bus splitting and equipping CLR. The implementation of them can basically change fault current levels at the applied locations and neighboring buses. In addition, it is desirable to consider the two types of countermeasures at the same time in the decision making process because both of them need to be considered in the operational planning procedure and their applications affect each other. However, the two types of countermeasures might degrade the operational efficiency of transmission systems in terms of system loss and transient stability because of the increase of electrical distances between local generators and the external system. Thus it would be better to minimize the number of bus splitting locations and CLRs equipped in the system. For the purpose, then, a certain tool is needed for the optimal allocation of the countermeasures ensures adequate level of fault current reduction as well as system operational efficiency.  
This paper adopts a decomposed approach to tackle the problem of decision making on bus splitting and equipping CLRs. Decomposition methods can be used for a complicated optimization problem which can be divided into a main problem and a subproblem. Decomposing the problem of application of appropriate countermeasures, in the paper, facilitates to deal with the discretized characteristic of them. The main decision variables of the original problem are integers. In the case of bus splitting, the variables are binary; in the case of placement of CLRs, they are also discrete values with a step increase of the reactance. The main problem is to decide whether to apply bus splitting and to install CLRs that would reduce fault currents further. That is, the main problem obtains the correction of the decision making variables. The subproblem is to evaluate the system operational efficiency in terms of fault current levels with the given decision variable settings from the optimal solution to FFLC-OPF. The main problem and subproblem are repeatedly performed until the final solution is obtained.
# BearingCog: A Bearing Fault Diagnosis Method under Variable Operational Conditions
## 3 Methodology  
### BearingCog Overview  
In the flowchart (see Figure 1), the procedure of bearing fault detection is conducted in four stages: the signal acquisition, the signal processing, the feature extraction, and the fault classification. Firstly, the vibration signals are acquired from the fault bearings. Then, the VMD is utilized to decompose the original signal and obtain finite intrinsic mode functions (IMFs). Based on the decomposition results, the PCA is introduced to extract the features of IMFs. The main purpose of this step is to obtain the most representative fault characteristics. Finally, the fault classification is achieved by the classifier of SVM, which is based on the extracted fault features.
## 4 Discussion
### Analysis of Results Under Different Initial State-of-Charge Levels  
To verify the effectiveness of optimizing the initial \(SOC\) in improving the economic performance of the wind farm, this study analyzes comparison cases with initial \(SOC\) levels set at 0.5, 0.9, and the optimized value of 0.1. Figures 7 and 8 show the \(SOC\) variation curves when the initial \(SOC\) is set to 0.5 and 0.9, respectively. Table 7 compares the expected revenues of the wind farm under different initial \(SOC\) levels.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicator** & **Scheme 1** & **Scheme 2** & **Scheme 3** \\ \hline Initial \(SOC\) & 0.1 & 0.1 & 0.1 \\ Expected Revenue (CNY) & 16,233.78 & 16,903.17 & 14,894.26 \\ Minimum Scenario Revenue (CNY) & 15,532.48 & 14,445.90 & 14,192.96 \\ Maximum Scenario Revenue (CNY) & 16,263.00 & 17,721.76 & 14,923.48 \\ CVaR (CNY) & 15,903.29 & - & 14,563.77 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of day-ahead economic benefits and risk indicators under three scheduling schemes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **CVaR Considered** & **Peak–Valley Arbitrage Considered** \\ \hline
1 & Yes & Yes \\
2 & No & Yes \\
3 & Yes & No \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of different scheduling schemes.  
As shown in Table 7, when the initial \(SOC\) is set to 0.1, the wind farm achieves the highest expected revenue of CNY 16,233.78. In contrast, the expected revenues at initial SOC levels of 0.5 and 0.9 are CNY 16,044.70 and CNY 15,923.12, respectively, both lower than that of the 0.1 case. Further insights from Figures 7 and 8 show that during valley periods, a lower initial SOC facilitates the use of surplus wind power for energy storage charging, thereby reducing the cost associated with the initial \(SOC\). During peak periods, the storage system discharges to take advantage of higher electricity prices, thus improving overall revenue through arbitrage. Therefore, optimizing the initial state of charge is of great significance for enhancing the overall economic performance of the wind farm.
## 3 Results  
For each mode, a significant decrease in light irradiance was observed comparing zero distance with distances above 4 mm, 2 mm distance with distances above 6 mm, and 4 mm with distances above 8 mm--Figure 2. The greatest power loss with increasing distance was observed in the soft mode, the irradiance of which was statistically significantly lower at each distance than in the other modes, and at a distance of 8 mm, it did not even  
Figure 1: Experimental set-up.  
reach 600 mW/cm\({}^{2}\). The standard mode achieved the highest values during the entire experiment; statistically significant differences were found at all tested distances compared to the other modes--Table 1. The irradiance values obtained when testing the pulse mode were like those in the standard mode.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Standard (A)**} & \multicolumn{2}{c}{**Soft (B)**} & \multicolumn{2}{c}{**Pulse (C)**} \\ \hline
**Distance** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & **Significant Differences** \\ \hline
0 & 1018.1 & 101.9 & 1007.4 & 95.0 & 1017.4 & 99.4 & A vs. B, B vs. C \\ \hline
2 & 996.5 & 95.1 & 985.1 & 89.7 & 993.7 & 94.4 & A vs. B, B vs. C \\ \hline
4 & 906.5 & 79.4 & 884.7 & 74.8 & 893.0 & 75.6 & A vs. B, A vs. C \\ \hline
6 & 720.9 & 61.1 & 709.4 & 68.4 & 722.0 & 70.0 & A vs. B \\ \hline
8 & 601.8 & 42.8 & 567.9 & 64.2 & 589.6 & 48.2 & A vs. B, B vs. C \\ \hline Significant Differences & 0 vs. 4, 0 vs. 6, 0 vs. 8, 2 vs. & 0 vs. 4, 0 vs. 6, 0 vs. 8, 2 vs. & 0 vs. 4, 0 vs. 6, 0 vs. 8, 2 vs. & 0 vs. 4, 0 vs. 6, 0 vs. 8, 2 vs. & \\  & 6, 2 vs. 8, 4 vs. 8 & 6, 2 vs. 8, 4 vs. 8 & 6, 2 vs. 8, 4 vs. 8 & 6, 2 vs. 8, 4 vs. 8 & 6, 2 vs. 8, 4 vs. 8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The comparisons of irradiance values for different polymerisation modes and light curing distances (significant differences for the post-hoc Friedman ANOVA test).  
Figure 2: The box plot presents the changes in light irradiance (mW/cm\({}^{2}\)) depending on polymerisation mode and light curing distance.
## 7 Conclusions  
Our research enabled an accurate classification of GNSS signal disruptions using XAI techniques on top of ML models. The significance of time and frequency domain features was considered to enhance the understanding of GNSS signal behaviour under different conditions. Moreover, accurate positioning can be achieved effectively by utilizing XAI autonomous signal quality monitoring in GNSS data processing. At an earlier stage, the root causes of an abnormality can be easily found without post-processing the signal, that is, before feeding it to the receiver. The techniques used emphasized the most discriminating part of the disruption, and the application of the XAI technique ensured transparency and interpretability in our classification, thereby taking care of trustworthiness in model development and providing confidence in the results of signal quality analysis and prediction. Overall, this study provided a robust framework for interpreting and analyzing GNSS signals. The prediction results using the LIME model show that the SVM has performed well overall, but the prediction of clean signals remains a major concern, as most of the time, these signals are incorrectly classified. Similarly, the classification results using the SHAP algorithm indicate that chirp and clean signals have the problem of misclassification, particularly between these categories. To address these challenges, careful attention needs to be paid to designing a GNSS monitoring setup equipped with sophisticated ML models that can provide more robust signal prediction/classification capabilities.  
The two authors equally contributed to the XAI-based GNSS Signal Quality Assessment, Methodology--A.E.; Validation--R.J.L.; Investigation--A.E.; Writing, review and Editing--A.E., R.J.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data that support the findings of this study are available upon reasonable request from the authors.  
The authors declare no conflict of interest.
## 3 Results and Discussion  
### Characterization of Nanocomposites  
Figure 2 shows the microstructure of epoxy/GNPs nanocomposites. It can be observed that the achieved nanoferforcement distribution was quite homogeneous. The disaggregation of the agglomerates and good dispersion of the two types of graphene nanoplatelets in the resin were reached using the calendering process. This homogeneous dispersion was achieved even for the higher percentages of 8 wt.% (Figure 2b). The graphene was homogeneously distributed throughout all the resin, and no areas free of nanoferforcement or porosity were observed.  
Figure 2: SEM images of the dispersion achieved in the epoxy/graphene nanoplatelets (GNPs) nanocomposites: (**a**) With 5 wt.% of nonfunctionalised GNPs and (**b**) 8 wt.% of functionalised GNPs.  
The density values and Vickers microhardness of epoxy/GNPs nanocomposites as a function of GNP content are shown in Figure 3. The density values (blue circles in Figure 3) increased slightly as the content of GNPs in the epoxy resins increased. However, it can be considered that density remained roughly constant. For the material with 8 wt.% of GNPs, the density only increased from 1.17 g/cm\({}^{3}\) to 1.19 g/cm\({}^{3}\). For the potential applications of these materials, density values are important.  
Microhardness was evaluated to study its possible effect on the wear behaviour of materials. When the content of the two types of GNPs increased (columns in Figure 3), the microhardness values enhanced slightly. However, these differences were very small. This increase was only greater (around 25%) for percentages of 8 wt.% of GNPs. Comparing the influence of the type of GNPs, it can be observed that the microhardness values were similar for the same content of graphene nanoplatelets. In summary, as the microhardness values obtained were similar for all epoxy/GNP nanocomposites studied, we can conclude that this variable had no influence on wear properties, at least for contents lower than 8 wt.%.
## 1 Introduction
Recently, RNN-based Seq2 Seq learning [21] has proven to be an outstanding technique for mapping one sequence to another one, especially in the field of VC [22], text-to-speech synthesis (TTS) [23], and natural language processing [24]. As far as we know, converting spectral features using a Seq2 Seq model with attention has been attempted for the first time in [25]. However, in his work [26], the author mentioned that the proposed model cannot use its own predictions to generate correct output predictions. A gated CNN-based Seq2 Seq spectrum conversion method has been proposed in [27]. Due to the lack of an attention mechanism in their model, they still use the DTW algorithm to obtain aligned feature sequences when preparing training data. The work [28] proposed a method based on a Seq2 Seq learning with attention and context preservation mechanism for voice conversion tasks. In their method, taking into account the attention module and the context preservation losses makes it possible to further stabilize the training procedure. A recent method was proposed in [29] for the enhancement of whisper-to-normal speech based on a Seq2 Seq model. In this method, taking into account the attention technique makes it possible to further stabilize the training procedure, outperforming the conventional methods. Nevertheless, significantly improving the intelligibility and the naturalness of the ES by overcoming the previously cited problems remains challenging. In this work, we propose a novel esophageal-to-laryngeal VC system based on a Seq2 Seq mapping model combined with an attention mechanism. Our work is inspired by the latest work [29] and the first work [25]. The strength of the proposed method is that it can adaptively characterize the nonlinear mapping between features of the original esophageal speech and its laryngeal counterpart. Additionally, our method does not require any temporal alignment during the training phase, which avoids erroneous mappings and significantly reduces the computing time. Furthermore, to preserve the identity of the target speaker, the excitation and phase coefficients are estimated from the target training space structured as a binary search tree. The remainder of the paper is organized as follows. Section 2 presents the proposed methods. Section 3 details the experimental setup. Section 4 presents the results and discussion. Finally, a conclusion is given in Section 5.
# Intelligent Radio Frequency Identification for URLLC in Industrial IoT Networks
## 1 Introduction
### Outline and Notations  
The remainder of this paper is organized as follows. In Section 2, the general system model is introduced in detail. Then, the novel architecture of RFI based on SSL is investigated in Section 3. Section 4 outlines mathematical framework performance analysis of RFI-aided URLLCs with massive MIMO. Section 5 investigates the experimental and simulation results to verify the performance of proposed RFI-aided URLLCs with massive MIMO. Lastly, in Section 6, conclusions are drawn in detail.  
In this paper, lowercase bold letters denote vector; uppercase bold letters denote matrices; \((\cdot)^{-1},(\cdot)^{*},(\cdot)^{T}\) denote the inverse, conjugate and transpose, respectively; \(\mathcal{CN}\big{(}0,\sigma^{2}\big{)}\) denotes a typical Gaussian distribution noise of channel; \(\sigma^{2}\) denotes variance in Gaussian distribution; \(\|\cdot\|\) denotes the spectral norms of a matrix; \(Q(\cdot)\) represents the Gaussian \(Q\) function; \(\mathbb{E}[\cdot]\) denotes the expectation operator; \(\mathbb{P}[\cdot]\) denotes the probability of a variable; and \(log(\cdot)\) represents the normal natural logarithm.
###### Abstract  
Currently, wireless acoustic sensor networks (WASN) are commonly used for wild bird monitoring. To better realize the automatic identification of birds during monitoring, the enhancement of bird audio is essential in nature. Currently, distributed beamformer is the most suitable method for bird audio enhancement of WASN. However, there are still several disadvantages of this method, such as large noise residue and slow convergence rate. To overcome these shortcomings, an improved distributed minimum variance distortionless response (IDMVDR) beamforming method for bird audio enhancement in WASN is proposed in this paper. In this method, the average metropolis weight local average consensus algorithm is first introduced to increase the consensus convergence rate, then a continuous spectrum update algorithm is proposed to estimate the noise power spectral density (PSD) to improve the noise reduction performance. Lastly, an MVDR beamformer is introduced to enhance the bird audio. Four different network topologies of the WASNs were considered, and the bird audio enhancement was performed on these WASNs to validate the effectiveness of the proposed method. Compared with two classical methods, the results show that the Segmental signal to noise ratio (SegSNR), mean square error (MSE), and perceptual evaluation of speech quality (PESQ) obtained by the proposed method are better and the consensus rate is faster, which means that the proposed method performs better in audio quality and convergence rate, and therefore it is suitable for WASN with dynamic topology.  
bird audio enhancement; wireless acoustic sensor networks; IDMVDR; local average consensus algorithm +
Footnote †: journal: Applied Soft. 2019, 9, 3153; doi:10.3390/app9153153
# Study of a Small Robot for Mine Hole Detection
## 3 Mechanical Structure Design of Small Robots for Mine Holes
### Power System Design  
The power system is a pipeline robot's core and provides the robot's direct drive. When the power system is too complex, it becomes an essential factor that causes the robot's size to increase. In order to reduce the radial size of the robot to adapt to narrow mine holes while ensuring the stability of the robot power transmission, this paper carries out a unique design of the power system that abandons the use of motors to drive the crawler wheel directly and uses motors, drive shafts, and gears that cooperate to transmit power to the crawler wheel. As shown in Figure 5, The radial rotation of the motor is converted into tube rotation with level gears, and the level gears are connected coaxially with cylindrical spur gears so that the two rotate concentrically and at the same speed. The transmission arm should not only function in power transmission but should also have a certain length of stretching space to meet the needs of the mine hole-detection robot to change the size of the function. It is designed as a cabin: the four cylindrical spur gear transmission structure constitutes the internal structure of the extension arm, and the power is transmitted to the crawler wheel via the mesh transmission of the cylindrical spur gears. The geared motor is used to drive the two crawler wheels, which reduces power consumption and guarantees the safety of the robot's work.  
At the crawling wheel and transmission arm coaxial connection, surface knurling treatment is used to increase friction. The crawling wheel diameter size is directly related to the working life of the bearings. For the same crawling speed, when the crawling wheel diameter is smaller, the rotational speed is higher and the more severe the wear. When the traveling wheel is too large, the revolution will not be able to be wholly retracted inside the shell, and this will lead to the rest of the structure being too compact. After careful consideration, the crawling wheel diameter was designed as 50 mm.  
Figure 4: The general structure of the robot.