esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Active Steering Control Strategy  
When the railway vehicle runs on the curved section, attack angle between the wheel and the rail are generated as shown in Figure 1 due to the lack of steering function of the wheel [13]. This causes unnecessary force in the driving direction and the lateral direction of the wheel, which is a major factor causing wear of the wheels and rails and the generation of noise.  
Therefore, in order for the railway vehicle smoothly run the curve section, the angle of attack becomes \"0\" if the wheelset is aligned with the center of curvature as shown in Figure 2 using active steering control technology. At this time, this geometric position is called the radial steering position.  
Figure 3 shows the geometrical relationship between the body and the bogies, assuming the vehicle is in the radial steering position in the curve section. In Figure 3, the angle (28) formed between two wheelsets is called the steering angle, and the angle (\(\theta\)) at which the bogie is rotated with respect to the vehicle body is called the bogie angle.  
When the bogie and wheelset are aligned with the radial steering position, the bogie angles of the front and rear bogies are the same. At this time, from the geometric relationship of Figure 3, the target  
Figure 1: Wheelset alignment of a conventional railway vehicle when running on curved section.  
Figure 2: Wheelset alignment of a railway vehicle with active steering technology when running on curved section.  
values for wheelset steering angle and bogie angle for the ideal wheelset steering in the curve section of radius R are derived as shown in Equations (1) and (2) [14; 15].  
\[2\delta =2d/R \tag{1}\]  
\[\theta =L/R \tag{2}\]  
where,  
\(2\delta\): steering angle(rad)  
R: radius of curve(m)  
\(2\)d: wheel base(m)  
L: semi-spacing of distance between bogie centers(m)  
\(\theta\): bogie angle between body and bogie(rad)  
When a railway vehicle is run on a curved section as above, it can ideally pass through the curve if it is located in a radial steering position. In other words, if steering control is performed by calculating the target steering angle by applying Equation (1), the railway vehicle will be located in the radial steering position. In order to apply Equation (1), information on the radius of the curved section passing through is required. Previously, a method of pre-input of the curve information according to the position into the controller was applied, but it was inefficient. In this paper, the method of estimating the radius of curvature in real time developed by the author was used, and this method of estimating the radius of curvature was experimentally verified [10].
# Rolling-Element Bearing Fault Diagnosis Using Advanced Machine Learning-Based Observer
###### Abstract  
Rotating machines represent a class of nonlinear, uncertain, and multiple-degrees-of-freedom systems that are used in various applications. The complexity of the system's dynamic behavior and uncertainty result in substantial challenges for fault estimation, detection, and identification in rotating machines. To address the aforementioned challenges, this paper proposes a novel technique for fault diagnosis of a rolling-element bearing (REB), founded on a machine-learning-based advanced fuzzy sliding mode observer. First, an ARX-Laguerre algorithm is presented to model the bearing in the presence of noise and uncertainty. In addition, a fuzzy algorithm is applied to the ARX-Laguerre technique to increase the system's modeling accuracy. Next, the conventional sliding mode observer is applied to resolve the problems of fault estimation in a complex system with a high degree of uncertainty, such as rotating machinery. To address the problem of chattering that is inherent in the conventional sliding mode observer, the higher-order super-twisting (advanced) technique is introduced in this study. In addition, the fuzzy method is applied to the advanced sliding mode observer to improve the accuracy of fault estimation in uncertain conditions. As a result, the advanced fuzzy sliding mode observer adaptively improves the reliability, robustness, and estimation accuracy of rolling-element bearing fault estimation. Then, the residual signal delivered by the proposed methodology is split in the windows and each window is characterized by a numerical parameter. Finally, a machine learning technique, called a decision tree, adaptively derives the threshold values that are used for problems of fault detection and fault identification in this study. The effectiveness of the proposed algorithm is validated using a publicly available vibration dataset of Case Western Reverse University. The experimental results show that the machine learning-based advanced fuzzy sliding mode observation methodology significantly improves the reliability and accuracy of the fault estimation, detection, and identification of rolling element bearing faults under variable crack sizes and load conditions.  
rotating machine; bearing; decision trees; sliding mode observer; fuzzy technique; advanced sliding mode observer; fault detection; fault estimation; fault identification; machine learning
## 3 Results
### Random Undersampling Class Balancing  
The overall accuracy results from 5-fold cross-validation and unseen test data for each model type, by number of features and discretisation approach, are shown in Figures 13-15. With some exceptions, test accuracy is significantly lower than validation accuracy. With all test accuracies in the range of 60-75%, none of the variables show a dramatic impact on performance; however, some points are worth noting.  
Figure 11: Distributions of features and porosity condition for the defect-centric approach at 1.0 mm voxel size. The porosity sub-figure presents the count of voxels for each condition and the other sub-figures present the normalised distribution for each porosity condition. Blue = non-porous, red = porous.  
Figure 12: Distributions of features and porosity condition for the defect-centric approach at 2.0 mm voxel size. The porosity sub-figure presents the count of voxels for each condition and the other sub-figures present the normalised distribution for each porosity condition. Blue = non-porous, red = porous.  
Figure 13: Overall porosity prediction accuracy from 5-fold cross-validation and test data at 0.5 mm voxel size with random undersampling for class balancing. Each model has 3 or 7 input features and uses uniform or defect-centric discretisation.  
The highest test accuracy for each model type was achieved at 2.0 mm voxel size with seven features and uniform discretisation, although it is only a few percentage points higher than most other conditions. The neural network has the highest test accuracy and closest agreement between validation and test accuracy at these conditions, whereas it performed very poorly at 0.5 mm voxel size. The nearest neighbours model has very similar test and validation accuracies at 0.5 mm voxel size; as voxel size increases, the validation accuracy increases but the test accuracy does not.  
The number of input features has very little effect on either test or validation accuracy. The discretisation approach also has little impact on test accuracy but has a more visible impact on validation accuracy at 1.0- and 2.0 mm voxel sizes.
## 4 Case Study
### Procedures  
Subjects were instructed to walk seven times on an 8 m walkway. The first two laps were not measured to allow for familiarization with the task and instrumentation. The last five laps were assessed to catch four gait cycles, employing the right limb from the HG and the injured limb from the PG. We compared the ACLR to the right limbs only because most individuals are right leg dominant and we wanted to compare those limbs to a right control limb that is more likely to exhibit ideal and stable knee biomechanics [44; 45].  
A number of previous studies have compared differences in ACLR limbs to control limbs and the fact that both the right and left control limbs provided similar results and that we observed significant differences between the ACLR and right limbs indicates that we were able to find these differences by concentrating on these limbs [45; 46; 47].  
The trial was conducted in the Motion Lab at the University of Malaysia Perlis. Motion data were collected using five Qus cameras of a motion capture system (Oqus, Qualisys AB, Gothenburg, Sweden). 36 reflective markers were placed on the joint landmarks and segments of the lower limb as denoted in Figure 2. The cameras were mounted on tripods to capture the static trial of the subject in 2 s at 120 Hz, followed by the walking motion of the subjects. The dynamic trials were captured in 8 s at 480 Hz.  
Two force plates were reset for the next trial. The vertical ground reaction force was detected by two force plates with dimensions of 400 \(\times\) 600 mm (Bertec, Worthington, Ohio, OH, USA) while the subject performed several walks at his own pace. At least five success trials were collected and interpreted.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **HG** & **PG** & **\(p\)-Value** \\ \hline Age (years) & 27.73 \(\pm\) 0.80 & 25.8 \(\pm\) 5.41 & 0.1365 \\ \hline Body mass (kg) & 60.22 \(\pm\) 6.04 & 71.6 \(\pm\) 9.66 & 0.608 \\ \hline Height (cm) & 167 \(\pm\) 5.0 & 172.0 \(\pm\) 6.0 & 0.837 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Anthropometric data of the subjects and p-value of relationship between the healthy group (HG) and ACLR group (PG). Values stated as mean \(\pm\) standard deviation.  
At least five success trials were captured and post-processed using Qualisys Track Manager with a sampling rate of 60 Hz. The 11 spatiotemporal parameters (Table 2) of the trial were generated in Visual 3D Pro v6. This includes variations in the step width, stride speed, swing time, stance time, stride time, step time, step width, stride length and step length.
## 2 Materials and Methods
### Feature Selection
#### 2.4.1 Mutual Information (MI)  
Filter-based selection: Statistical techniques are used in strategies to determine the link or dependence between independent features (input features) as well as the dependent feature (targeted feature). The filter methodology analyzes features based on common features of the data, regardless of learning methods. In contrast, the outcomes obtained by the various statistical analyses used to evaluate them are used to determine which features to employ. MI is a metric evaluating the dependency or decrease in uncertainties between two variables [28]. Below is a list of the steps MI feature selection takes to select features.  
Step 1: Input original Features as X_Original.  
Step 2: Determine the connection among input (feature) and output by computing gain ratio scores (target).  
Step 3: A strong correlation indicates a greater dependence on the target attribute. To develop the model, SelectKBest() was used to choose only the features with highest gain scores.  
Step 4: Finally, all the features that will be used in the classification model will be transferred as X_MI based on the optimal gain scores.
## 2 Theoretical Analysis of Air Refrigeration Cycles
Figure 1:3  
The COP of the two open-loop high-pressure cycles (C-CT cycle and CT-C cycle) can be determined by Equations (12) and (13), respectively:  
Figure 1:4  
Figure 1:5  
In the above case, where \(T_{\text{I}}\) = 300.15 K, \(T_{\text{O}}\) = 308.15 K, \(T_{\text{AC}}\) = 313.15 K, \(T_{\text{CI}}\) = 295.15 K, \(\eta_{\text{T}}\) = 0.8, \(\eta_{\text{C}}\) = 0.75, \(\eta_{\text{M}}\) = 0.99, variations of COP with expansion ratio for various cycles are shown in Figure 1:6 It can be observed that the COP of the closed cycle is lower than that of the open-loop cycles due to the loss caused by the finite temperature difference in the heat exchanger. The four two-stage open-loop cycles have higher COPs, especially when the expansion ratio is larger than 1.7. However, in a practical cycle, the working medium in the above open-loop cycles is wet air, and the energy loss caused by the condensed water cannot be ignored. When the expansion ratio is relatively large, the air temperature after the compressor and the expander may be lower than the dew point, and the water vapor is condensed. In order to control the humidity, the air containing condensed water cannot be directly supplied to the room. As the condensed water is removed, the mass flow rate is reduced, resulting in the loss of cooling capacity. Therefore, the cycle performance in a practical air refrigerator depends on the humidity and pressure of the ambient air, in addition to the losses in expanders and compressors, which are discussed in the following section. In order to consider the effect of air temperature and absolute humidity, the enthalpy of wet air can be calculated by Equation (14):  
Figure 1:7  
where \(c_{p,a}\) is the specific heat at constant pressure of dry air, which is mostly related to temperature, and \(c_{p,p}\) is the specific heat at constant pressure of water vapor. \(L\) is the latent  
Figure 1:8 Variations in COP as a function of expansion ratio for various air refrigeration cycles. heat of the water vapor. The above data can be obtained from the NIST REFPROP database to fit the corresponding correlations; \(d\) is the absolute humidity of the wet air. The following part takes the TC cycle as an example to illustrate the thermodynamic analysis on the actual system performance of the open-loop cycle. Although the internal expansion process in the turbo-expander is too fast for the water to condense, the moist air in the downstream pipeline gradually reaches the equilibrium state as the water vapor is condensed. Therefore, the latent heat of vapor condensation needs to be considered. In the calculation, and it is assumed that no water is condensed inside the expander, and the influence of water on the isentropic efficiency of the expander is ignored. The calculation process is shown in Figure 1:9 In the process, the outlet temperature of the expander \(T_{\text{EO}}\) and the supplied temperature \(T_{\text{sup}}\) are solved iteratively, according to the change in enthalpy.
# Ramjet Nozzle Analysis for Transport Aircraft Configuration for Sustained Hypersonic Flight
## 3 Conceptual Design of a Ramjet Nozzle
### Nozzle Geometric Configurations  
Historically, the shape of the diverging section of the nozzle has been primarily conical owing to the simplicity and the ease of construction. However, in time, a better understating of the aerothermodynamics of convergent-divergent nozzles has led to the substitution of the conventional conical nozzle with more sophisticated shapes in order to minimise fluid dynamic losses. In addition to the conical nozzle configuration, the bell and dual bell nozzle shapes are described in the following sections owing to their suggested superior performance. The conical nozzle was studied as a reference point for the later comparison of the other two more advanced types.
# Recent Advances in the Heterologous Biosynthesis of Natural Products from _Streptomyces_
## 2 In Silico Prediction of BGCs
The top-down approach focuses on the production of natural products without the knowledge of BGCs. In contrast, the bottom-up approach first focuses on BGCs and then uses genetic techniques to produce natural products from target BGCs [50]. Molecular networking was introduced in 2012 [51] and was built using simplified tandem mass spectrometry (MS/MS) data for molecular network analysis. The mass spectrometry data of putative compounds can reveal similarities in the fragmentation patterns of a single compound using the Global Natural Products Search (GNPS) website. The putative structure of molecules is closely linked to the structure of compounds found in the GNPS. The GNPS is a powerful tool for the discovery of new drugs and metabolites [52]. Molecular networking entails the computational analysis of metabolic fragments. It enables the rapid identification of compounds in a broad diversity of unknown natural products related to potential drugs, where even the compounds are in the low titer or difficult to separate. Based on the MS/MS data, bioactive natural products were explored by visualizing and organizing them using untargeted mass spectrometry. The advantage of molecular networking is the possibility of detecting analog compounds and identifying the biosynthetic pathways. Molecular networking permits finding unknown compounds and connecting \"standard networks\" for the annotation of new compounds [52]. For example, a saponin glycoside from the British Bluebell, a map of matlystatin congeners in various media, and the proposal of bottromycin pathway were explored via molecular networking [53; 54; 55].
# A Lab-Made E-Nose-MOS Device for Assessing the Bacterial Growth in a Solid Culture Medium
## 2 Materials and Methods
### E-Nose Setup
#### 2.4.3 Data Acquisition, Feature Extraction and Signal Treatment  
As previously described [32], the resistance signals (in ohms) generated due to the interaction between the VOCs emitted by each bacterium (_E. coli_, _P. aeruginosa_, _E. faecalis_, or _S. aureus_), grown in solid medium, and the nine MOS sensors, were acquired by a data logger (Agilent 34970A) and then recorded by the Agilent BenchLink Data Logger software. For each analysis and sensor, a total of 37-38 resistance values were recorded (signals acquired during 2.5 min each 4 s). In total, seven feature extraction methods [32; 34] were applied to obtain a representative E-nose fingerprint for the volatile profile of the compounds emitted during the growth of each of the four bacteria under study: last response point (LP) acquired; integral of each E-nose signal response curve (INT); maximum resistance response (MAX); minimum resistance response (MIN); the sum of the response curve (SUM): the sum of all the resistance signals recorded during the analysis time-period; the average of the resistance signal curve (MEAN); and standard deviation (SD) of the response curve. In this context, for each independent sample a response database comprising 9 sensors \(\times\) 7 feature extraction method, is generated, being the basis for the statistical analysis.
## 4 Experiments and Results  
### Evaluation Metrics and Implementation Details  
For deep learning algorithms, commonly used evaluation metrics include Accuracy (_Acc_), Precision (_P_), Recall (_R_), F1-score (_F1_), Average Precision (_AP_), and mean Average Precision (_mAP_). _mAP_ represents the average precision across all classes. _mAP_50 refers to the mAP value at a 50% Intersection over Union (IoU) threshold. The closer these evaluation metrics are to one, the better the algorithm's performance. Their calculation methods are shown in Table 3.  
The experimental platform uses the following hardware and software configuration: CPU: 12 th Gen Intel(r) Core(tm) i7-12700 KF, GPU: NVIDIA GeForce RTX 3080, RAM: 64 GB, Operating System: Windows 10, Python Version: 3.8, PyTorch Version: 2.2.1+cu121
## 5 Discussion  
### Model Accuracy Evaluation  
To evaluate the accuracy of the model, the reconstructed results of three data from the ISPRS benchmark dataset were compared with ground truth models. The evaluation used Equation (9) to calculate the unsigned deviation between planes. Figure 15 shows the unsigned distance deviations using colors and plots the statistical histogram. Among them, the deviation in TUB1 data is mainly distributed in the range of 0-5 cm, the mean of distance deviation is about 1.3 cm, the root mean square (RMS) of distance deviation is about 2.2 cm. The deviations in TUB2 data were concentrated in the range of 0-10 cm, the mean of distance deviation was about 3.1 cm, the RMS of distance deviation was about 6.3 cm. The deviation in UOM data is concentrated in the range of 0-9 cm, the mean of distance deviation is about 4.3 cm, the RMS of distance deviation was about 7.3 cm. Compared with other methods submitted to the ISPRS indoor modeling benchmark such as [45], our methods have a higher reconstruction accuracy for TUB1 and TUB2. Meanwhile, the accuracy of the reconstructed models for the UOM dataset is slightly lower.  
Since the other four datasets do not have real ground truth models, the unsigned distance deviation between the reconstructed models and the original point cloud is calculated for quantitative evaluation. Figure 16 shows the unsigned distance deviations using colors and plots the statistical histogram. Among them, the deviations in Synth are concentrated between 0-3 cm, the mean of distance deviation is about 2.1 cm, the RMS of distance deviation was about 4.8 cm. The deviations in the data Room are concentrated in the range of 0-10 cm, the mean of distance deviation is about 3.4 cm, the RMS of distance deviation was about 7.2 cm. The deviations in the data Office1 are concentrated in the distribution of 0-10 cm, the mean of distance deviation is about 5.2 cm, the RMS of distance deviation was about 9.7 cm. The deviations in the data Office2 are concentrated in the range of 0-5 cm, the mean of distance deviation is about 3.9 cm, the RMS of distance deviation was about 7.5 cm.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Data** & **Indoor Space Decomposition** & **Navigation Network** & **Model (Hide Ceiling)** \\ \hline TUB1 & & & \\ TUB2 & & & \\ UOM & & & \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._
# Interference of Spread-Spectrum EMI and Digital Data Links under Narrowband Resonant Coupling
###### Abstract  
In this paper, the effects of electromagnetic interference (EMI) coupled to a radio-frequency (RF) communication channel by resonant mechanisms are investigated and described in the framework of Shannon information theory in terms of an equivalent channel capacity loss so that to analyze and compare the effects of non-modulated and random Spread Spectrum (SS) modulated EMI. The analysis reveals a higher EMI-induced capacity loss for SS-modulated compared to non modulated EMI under practical values of the quality factor \(Q\), while a modest improvement in the worst case capacity loss is observed only for impractical values of \(Q\). Simulations on a 4-quadrature amplitude modulation (4-QAM) digital link featuring Turbo coding under EMI resonant coupling reveal that SS-modulated EMI gives rise to higher bit error rate (BER) at lower EMI power compared non-modulated EMI in the presence of resonant coupling for practical values of \(Q\), thus suggesting a worse interfering potential of SS-modulated EMI.  
Spread Spectrum; DC-DC power converters; digital communications; channel capacity; resonant coupling +
Footnote †: journal: Electronics Letters