esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study
### Causal Network Inference Experiment  
To verify the effectiveness on causal inference of IMESS algorithm, we employ two datasets with known causal relationships to conduct comparative experiments. In the experiment, three advanced algorithms of PCMCI, NCE, and MESS are used as the baseline algorithms to be compared with IMESS.  
We selected two datasets in Refs. [33; 34] for experimental verification, which are denoted by Dataset1 and Dataset2, respectively. The true causal relationships in these two datasets are shown in Figure 8. Dataset1 comes from the physical system of the network, and Dataset2 comes from the industrial system.  
In our experiment, MESS and IMESS may infer bidirectional causality, and when bidirectional causality is inferred, we choose the causality with greater \(\tau_{\text{max}}\) as the final direction of causality, which is also logical.  
Three evaluation metrics, recall, precision, and F1-score, are introduced to evaluate the performance of the algorithm. Recall is used to measure undiscovered edges between nodes. Precision is used to measure edges that do not exist in the causal network graph that are added by mistake. F1-score is a combination of recall and precision that can evaluate the integrated performance of the causal network inference algorithm. Figure 9 shows the confusion matrix of causal inference experiment, Equation (18) gives the definition of the three metrics.  
\[\begin{array}{c}Precision=\frac{TP}{TP+FP}\\ Recall=\frac{TP}{TP+FP}\\ F1-score=2\times\frac{Precision\times Recall}{Precision+Recall}\end{array} \tag{18}\]  
Figure 8: Real causal network of Dataset1 and Dataset2.  
Figure 9: Confusion matrix of the anomaly detection experimentFigure 10 shows the inference results of the baseline algorithms and IMESS on two datasets. Table 2 shows the precision, recall, and F1-score of these causal inference algorithms. It should be noted that all four algorithms need to manually set the confidence level of the significance test or conditional independence test (which can be regarded as the \"hyperparameters\" of the algorithm), the inference results will also change with these hyperparameters. Therefore, we selected the results closest to the true causal network by grid search among all the results of the four causal inference algorithms for comparison. It can be concluded that IMESS has a higher F1-score than the baseline algorithms.
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 3 Related Works  
In the literature, the problem solving strategy for ranging errors in UWB due to the effects of NLOS and/or MP can be coarsely classified into two steps [9; 15]: (i) the NLOS identification process [16; 27; 28] and (ii) the NLOS mitigation process [9; 16; 29]. This paper is solely focused on the former case. In fact, there exists a method that bypasses the identification process and directly mitigates the ranging error using channel statistics and SVM as an ML-based classifier [18; 30]. However, this method restricts the flexibility to choose different positioning algorithms in the latter case since the mitigation technique is limited to a few compatible algorithms.  
The common approach is to detect the non-direct path signal (i.e., NLOS and/or MP) and use the detected information to modify the location algorithm in order to mitigate the biases caused by the NLOS and/or MP conditions [9; 10; 17; 18; 20; 21; 30]. In this manuscript, we divided the related works into two subsections: (i) conventional approaches without using ML techniques (Section 3.1) and (ii) ML-based approaches (Section 3.2). Our proposed technique regarding the multi-classes identification process in UWB was based on the ML-based approach.
# A Comparison of Faulty Antenna Detection Methodologies in Planar Array
## 6 Conclusions  
An investigation was carried out for several types of failure, such as random, row, column, and group. Array sizes varied between 6 \(\times\) 6 and 8 \(\times\) 8 arrays. A suitable method to identify faulty element location was analyzed for PS, SA, and PSO methods. The pattern search method was successful in giving 100% accuracy in finding all types of failures compared to other optimization methods in both array sizes.  
It is recommended that the PS method is used for all types of failure because the method detects the faulty elements in both 6 \(\times\) 6 and 8 \(\times\) 8 planar arrays accurately. Even though this method takes a relatively longer duration to converge compared to other methods, it detects failed elements in a planar array accurately. For the occurrence of faulty elements in far-field radiation patterns, the PS method can be applied in DSP to identify the failed locations in the planar array.  
Since the PS method gives good results, this method can be incorporated with other optimization methods to form a hybrid optimization method to investigate failed element detection in a planar array and correction of the failed radiation pattern in a planar array. This work can also be extended to include parameters of MIMO or 5G communication applications. The external interference and internal noise effects can also be studied in simulation for robust analysis. However, in real-time applications, digital filters can be used to remove the noise. In the future, we can work on detecting elements that are not completely failed.  
Conceptualization, N.B., A.K.R., and F.N.; methodology, N.B., A.K.R., and F.N.; software, N.B. and F.N.; validation, N.B., F.N., and A.K.R.; formal analysis, N.B.; investigation, N.B.; resources, A.K.R.; data curation, N.B.; writing--original draft preparation, N.B.; writing--review and editing, N.B., A.K.R., and F.N.; visualization, N.B.,and A.K.R.; supervision, A.K.R., and F.N.; project administration, A.K.R.; funding acquisition, A.K.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by UNITEN BOLD, grant number J510050002/2021064. The APC was funded by Universiti Tenaga National (UNITEN) BOLD Publication Fund.  
Not applicable.  
Conflicts of Interest: The authors declare no conflict 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.
# Comparison of Dimensionality Reduction Approaches and Logistic Regression for ECG Classification
## 3 Materials and Methods
### Logistic Regression (LR)
#### 3.2.1 Frequentist Approach (LR-ML)  
In the frequentist framework, we used a one-vs-rest multiclass logistic regression with maximum likelihood estimation. The method estimates the probability of each class based on a logistic link function. While interpretable, LR-ML assumes linear boundaries, which may not hold in complex ECG classification tasks [16].
## 5 Experiment  
### Experimental Setup  
The one-legged robot platform is built to verify the optimization results of the DAG and FMAG, as shown in Figure 4a. The frame (1.6 m \(\times\) 1 m \(\times\) 1.8 m) is built by aluminum extrusions (6060). Two cylindrical linear rails (16 mm diameter) are fixed vertically on the frame. Four slide blocks (SBR16) are fixed symmetrically on the horizontal rack. The robot body, the horizontal rack and slide blocks weigh about 7.2 kg in total. Therefore, another 3 kg load is fixed on the rack to match the mass \(m_{0}\) in Table 1. An angular transducer is installed to measure the displacement of the knee joint.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Optimized Design Parameters**} \\ \hline DAG & \(a_{1}=0.2883\;\mathrm{m},\;a_{2}=0.0462\;\mathrm{m},\;\beta_{1}=4.8^{\circ},\; \beta_{2}=-7^{\circ}\) \\ FMAG & \(l_{AB}=0.0665\;\mathrm{m},l_{BC}=0.0468\;\mathrm{m},l_{CD}=0.0819\;\mathrm{m},l _{AD}=0.0627\;\mathrm{m}\), \\  & \(l_{BE}=0.3538\;\mathrm{m},\theta_{ABE}=15.15^{\circ},\beta_{1}^{\prime}=12.2^{ \circ},\beta_{2}^{\prime}=29.56^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimized results of the design parameters in the DAG and FMAG.  
Because the landing stage is not considered in this paper, to prevent the undesired impact at the foot of the leg, the robot is locked by a locking mechanism after reaching the highest height. The locking mechanism is mainly composed of a roller chain and a single speed freewheel (20 teeth), which works as a ratchet mechanism. The close-up views of the knee joint structures of the DAG and FMAG are shown in Figure 4b,c. The structure of the rotational toe is shown in Figure 4d. To increase the friction force between the toe and ground and prevent the sliding during the take-off phase, an elastic tape with high adhesion ability is stuck at the bottom of the toe.  
A fixed displacement pump with 2 ml/rev is driven by a brushless servomotor at 800 r/min. The system pressure is set as 10 MPa by the relief valve. To absorb the fluid shock and decrease the instant pressure drop, an accumulator with 0.4 L volume and 6 MPa initial pressure is added to the hydraulic system. The hydraulic circuit is shown in Figure 5. Both pressures in the cylinder two chambers are measured by the pressure transducers. The analogue signals of the knee joint angle and the fluid pressures are fed back to a computer through the data acquisition card (Advantech PCI-1716L).  
Figure 4: One-legged vertical hopping robot platform. (**a**) Prototype of the one-legged robot and the test platform; (**b**) Close-up view of the DAG; (**c**) Close-up view of the FMAG; (**d**) Close-up view of the toe.
## 3 Discussion  
This study has proposed a novel independently biased 3-stack GaN HEMT configuration to overcome inherent drawbacks of traditional design methods for both the LNA and PA of transceiver systems. For the design of the LNA, the proposed configuration becomes an excellent candidate since it exhibits superior stability, isolation, power gain, and noise figure compared with other configurations. Both the power gain and noise figure can be simultaneously improved by an adaptive control of the gate bias voltage of the first transistor. Despite having a slight degradation of power gain and noise figure compared with that of the single-stage and independently biased cascode configurations at the high-frequency region, it still exhibits the best compromise of overall performance. For the design of the PA at an operation frequency of 3.5 GHz, it shows simultaneous improvement of efficiency and linearity by appropriately controlling the gate bias voltages of the individual transistors. The LS performance of the proposed configuration has been compared to that of the other configurations. The compared results indicate that the proposed topology offers the best compromise in efficiency and linearity improvement. Despite having poorer linearity than the independently biased cascode configuration, the proposed topology can deliver better efficiency at the same linearity level. Generally, although the independently biased cascode configuration has the similar ability of independent control of bias condition for each transistor, it cannot efficiently optimize both the SS and LS performance at the same time compared with the proposed configuration. Therefore, the proposed configuration becomes the best choice among the configurations for use in modern wireless communication systems.  
[PERSON] and [PERSON] performed simulation and analyzed the data; [PERSON] contributed analysis tools; [PERSON] and [PERSON] wrote the paper; [PERSON], [PERSON] and [PERSON] modified the paper.  
This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.04-2018.14.  
The authors declare no conflict of interest.  
Figure 16: Power added efficiency (PAE) bandwidth comparison among configurations.
## 3 Communication Performance
### Packet Design  
Each packet consisted of a 200 ms-long linear frequency modulated (LFM) waveform, 100 ms zero padding period, followed by 32 SC-FDE blocks, each of which was 128 ms long. Each packet lasted 4.428 s and included 16,510 symbols. The total transmitted signal included five different packets, and its duration was about 22.14 s. We transmitted identical signals at each location. The LFM waveform at the beginning of the packet was used for synchronization and Doppler shift estimation. The coded data rate is about 2.79 kbps if we consider the synchronization LFM waveform, the additional zero-padding and the extra PN length before the first block. The structured signal was transmitted as shown in Figure 4.  
The detailed parameters of the communication system used in the lake experiment are shown in Table 1. The order of feedforward and feedback filters in HTFDE were both 32, respectively. We used the fractionally spaced equalizer (two samples per symbol) for the feedforward filter. The RLS forgetting factor \(\lambda\) in the DFE was 0.999. We corrected the residual phase offset using a second-order digital phase locked loop (DPLL) embedded in the DFE. Both \(K_{1}\) and \(K_{2}\) in DPLL were 0.005. The first PN sequence is used as training sequence.
## 4 Numerical Example
### Bridge Description  
A simply supported hollow slab beam in service was selected as the case study [20]. The bridge was 13.0 m long and 9.0 m wide. It was composed of nine concrete slab segments, as shown in Figure 6.  
The performance function corresponding to the failure mode of each slab segment was established using Equation (25):  
\[Z_{i}=g(R_{i},G_{i},Q_{i})=R_{i}-G_{i}-Q_{i},i=1,2,\ldots,9,. \tag{25}\]  
where \(Z_{i}\) is the performance function of the \(i\)th slab segment; \(R_{i}\), \(G_{i}\), and \(Q_{i}\) are random variables that represent the resistance, dead load effect, and live load effect of the \(i\)th slab segment, respectively.  
The resistance \(R\) refers to the ability of the structure to withstand the load effects. The dead load effect \(G\) is the internal force generated by the dead load on the structure, where the dead load refers to the load whose value does not change with time. The live load effect \(Q\) is the internal force generated by the live load on the structure, where the live load refers to the load whose value changes with time. For the calculation method of the resistance, dead load effect, and live load effect, please refer to References [2; 45; 46]. The resistance, dead load effect, and live load effect of each slab segment follow a normal distribution [20]. The statistical parameters of these random variables are listed in Table 3.  
Figure 6: Cross-section for the case bridge (unit: m).  
According to the performance function of each component and the statistical parameters of the random variables, the reliability index \(\beta\) of each slab segment was calculated using the first-order second-moment (FOSM) method [47]. Then, the failure probability \(P_{f}\) could be obtained using the equation \(P_{f}=\Phi^{-1}(\beta)\), in which \(\Phi^{-1}(\cdot)\) represents the cumulative density function of a standard normal distribution. The results of the failure probability are shown in Table 4.
# Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips
## 3 Evaluation Framework
### Methodology  
We propose a Multi-Layer Geographical Linkstream (MLGLS) to organize data from different sources to assess the impacts of using multimodal hybrid routes for urban trips. We study the impacts of replacing taxi-only trips in New York with hybrid options using our proposed methodology, such as reducing time spent on traffic jams, which leads to an increase in QoE during the trip. These hybrid routes contrast to the traditional approach of multimodal routing, which consists of different options using single modes (e.g., one public transportation, one driving, and one walking option). The hybrid approach that was proposed in this work also considers routes that started with one of the traditional modes and finished in another (e.g., started in public transportation and finished in a taxi). The purpose of this section is to describe the methodology used to evaluate the hybrid routes. The process is divided into seven steps: (i) reduce data about trips from Yellow and Green Taxis datasets and find main mobility flows; (ii) create and populate an MLGLS with trip data and with other transportation modes (e.g., bus stations, bike dockers); (iii) evaluate the initial driving options that contain traffic information; (iv) identify congested segments in the initial options; (v) select mode transition points and combine them; (vi) trace route alternatives and save them; and, (vii) evaluate the metrics of the alternatives. Steps i, ii, and vii are executed once for the whole dataset. In contrast, the other steps are repeated to every mobility flow in Step i. For each one of these flows, the trip is evaluated when considering the spatio-temporal \(\langle\)origin, destination\(\rangle\) pair. Additionally, changes in the scenario during the trip are not accounted for. Figure 2 outlines the methodology.  
Figure 1: Overview of proposed methodology.  
Section 3.3 describes Step i, Section 3.4 give details about the MLGLS used in Step ii. Section 3.5 comprises Steps iii. iv, v, and vi. Section 5 explains the outcomes that are produced in Step vii, and other important intermediary results, such as the individual route selection evaluation using the model described in Section 4.
# Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things
## 5 Experimental Study
### Analysis of Experimental Results
#### 5.5.4 Performance under Different Shot  
To evaluate the influence of varying sample sizes on the algorithm, we sequentially set the number of samples for each class in the target domain support set to 5, 10, 15, 20, 50, and 100. In this experiment, we chose a task from the DCDF scenario with lower accuracy for testing. The source domain operating conditions were Load 0, Load 2, and Load 3, with fault types being normal, inner circle fault, and outer circle fault, respectively. The target domain operated under Load 1 conditions, with fault types of normal and ball fault. We specifically selected this task to explore whether increasing the sample size could enhance the accuracy of more challenging diagnostic tasks.  
The experimental results are depicted in Figure 8. The observed trend reveals a noteworthy relationship as the number of samples in each class of the support set varies. Initially, as the sample size increases from 1 to 10, there is a discernible improvement in diagnostic accuracy, rising from 0.82 to 0.85. This correlation suggests that, up to a certain point, increasing the sample size indeed enhances the diagnostic performance of FedCDAE-MN. However, when the sample size is further increased to 100, a turning point emerges. In particular, the accuracy of diagnosis begins to gradually decrease and stabilize. This observation implies that, for FedCDAE-MN, a larger sample size does not necessarily translate into improved diagnostic performance. Instead, it suggests the presence of the optimal number of samples converging around 10. This finding carries practical implications for implementing FedCDAE-MN in real-world scenarios, as it highlights the importance of carefully considering the size of labeled data utilized. This observation suggests that, for FedCDAE-MN, a larger sample size does not necessarily lead to a better diagnostic performance. Moreover, the observed performance with a sufficient sample size highlights an essential aspect of FedCDAE-MN's suitability. Specifically, it affirms that FedCDAE-MN is particularly well-suited for addressing fault-diagnosis problems characterized by limited samples. In these scenarios, where acquiring a vast amount of labeled data may be challenging or costly, FedCDAE-MN offers a robust and effective solution. In practical applications, the observed relationship between sample size and diagnostic accuracy emphasizes the critical need to consider the sample size wisely and select the appropriate algorithm accordingly.
# Research on Optical System of Dim Target Simulator Based on Polarization Stray Light Suppression
## 2 Analysis and Design of Optical System of Dim Target Simulator  
Principle and Design Index of Optical System of Dim Target Simulator Based on Polarization Stray Light Suppression  
For the simulation of dim targets, the overall structure of the dim target simulation optical system is proposed, which consists of two parts: depolarization optical engine and high-image quality collimation optical system. After the light emitted by the light source passes through the homogenization, collimation, and beam splitting, two spatial light modulators (LCOS) are lit at the same time, and the high-image quality collimation optical system projects the display content of the two LCOS located in its conjugate focal plane position to infinity. Among them, the depolarization spectroscopic prism eliminates the polarized stray light and ensures the energy balance and position accuracy of the simulated target through strict aberration correction. The principle is shown in Figure 1. The main technical indicators of the system are shown in Table 1.