esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The UWB-MIMO MTM-Based MAVA for Diversity Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Ports/Frequency** & **3.8** & **5.2** & **8** & **10** & **15** & **18** & **22** & **28** & **32** & **35** & **38** \\ \hline
**P12** & 9.30 & 9.98 & 9.95 & 9.98 & 9.86 & 9.99 & 9.99 & 9.99 & 9.99 & 9.99 & 9.98 \\ \hline
**P13** & 9.95 & 9.99 & 9.99 & 9.99 & 10 & 10 & 10 & 10 & 10 & 9.95 & 9.98 \\ \hline
**P14** & 9.94 & 9.99 & 9.99 & 9.99 & 9.99 & 9.99 & 9.98 & 10 & 9.99 & 9.98 & 10 \\ \hline
**P15** & 9.99 & 9.99 & 9.99 & 9.99 & 9.99 & 9.99 & 9.99 & 9.99 & 9.98 & 10 & 9.99 \\ \hline
**P16** & 9.94 & 9.88 & 9.99 & 9.99 & 9.99 & 10 & 10 & 10 & 9.99 & 10 & 9.98 \\ \hline
**P17** & 9.94 & 9.97 & 9.99 & 10 & 9.98 & 9.99 & 9.99 & 10 & 9.99 & 9.94 & 9.94 \\ \hline
**P18** & 9.89 & 9.99 & 9.98 & 9.99 & 9.99 & 10 & 10 & 9.98 & 9.99 & 9.97 & 9.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The simulated GD values for between Port 1 and other ports at different frequencies. Figure 23: Simulated and Measured performance for the channel capacity loss (CCL). Figures 24 and 25 depict the flexibility capability setup and the reflection coefficient result at various bending angles. It demonstrates that increasing the bending degree reduces the BW. Still, the decrease is minor, and the majority of the BW remains. Furthermore, when the degree increased, the amount of the reflection coefficient decreased. Apart from that, the creased substrate had no effect on the reflection coefficient findings. The bending conditions should be investigated since the proposed MIMO antenna is wearable. Furthermore, the robustness of a wearable antenna should be checked to be sure if the antenna's performance in terms of S-parameters and other radiation characteristics are not dramatically altered [75]. Figure 26 shows the transmission coefficient results of the antenna considering four different angles of bending. It demonstrates that the isolation of the antenna (shown by the S21 result) was improved by a smaller radius of bending until 50\({}^{\circ}\) due to the separation between radiating elements that get bigger as the degree bend increases. After it exceeded from 50\({}^{\circ
## 4 Experiment and Analysis
### SemanticKITTI
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**mIoU**} & \multirow{2}{*}{**OA**} & \multirow{2}{*}{**mIoU**} & \multirow{2}{*}{**mIoU**} & \multirow{2}{*}{**mIoU**} & \multirow{2}{*}{**mIoU**} & \multirow{2}{*}{**mIoU**} & \multirow{2}{*}{**mIoU**} & \multirow{2}{*}{**mIoU**} & \multirow{2}{*}{**mIoU**} & \multirow{2}{*}{**mIoU**} & \multirow{2}{*}{**mIoU**} & \multirow{2}{*}{**mIoU**} \\ \cline{3-14}  & & & & **Man-Made** & **Natural** & **HighVeg** & **Low Veg** & **Buildings** & **Hard Scep** & **Scanning** & **Cars** \\ \hline SnapNet & 59.1 & 88.6 & 82.0 & 77.3 & 79.7 & 22.9 & 91.1 & 18.4 & 37.3 & 64.4 \\ SEGCloud & 61.3 & 88.1 & 83.9 & 66.0 & 86.0 & 40.5 & 91.1 & 30.9 & 27.5 & 64.3 \\ RF\_MSSF & 62.7 & 90.3 & 87.6 & 80.3 & 81.8 & 36.4 & 92.2 & 24.1 & 42.6 & 56.6 \\ ShellNet & 69.3 & 93.2 & 96.3 & 90.4 & 83.9 & 41.0 & 94.2 & 34.7 & 43.9 & 70.2 \\ GAENet & 70.8 & 91.9 & 86.4 & 77.7 & 88.5 & 60.6 & 94.2 & 37.3 & 43.5 & 77.8 \\ SPG & 73.2 & 94.0 & 97.4 & 92.6 & 87.9 & 44.0 & 84.2 & 31.0 & 63.5 & 76.2 \\ KPConv & 74.6 & 92.9 & 90.9 & 82.2 & 84.2 & 47.9 & 94.9 & 40.0 & 77.3 & 79.7 \\ RandLA-Net & 77.4 & 94.8 & 95.6 & 91.4 & 86.6 & 51.5 & 95.7 & 51.5 & 69.8 & 76.8 \\ Our & 77.6 & 95.0 & 96.8 & 90.5 & 85.1 & 52.3 & 97.5 & 54.3 & 71.2 & 79.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of different methods in SemanticKITTI. Figure 6: Semantic segmentation of 3D point clouds in semanticKITTI.
# A Review of Deep Learning Based Methods for Acoustic Scene Classification
## 6 Learning Paradigms
### Attention  
The temporal segments of an environmental audio recording contribute differently to the classification of its acoustic scene. Neural attention mechanisms allow neural networks to focus on a specific subset of its input features. Attention mechanisms can be incorporated at different positions within neural network based ASC algorithms. [PERSON] et al. incorporated gated linear units (GLU) in several steps of the feature learning part of the network (\"multi-level attention\") [13]. GLUsimplement pairs of mutually gating convolutional layers to control the information flow in the network. Attention mechanisms can also be applied in the pooling of feature maps [73]. [PERSON] et al. used self-determination CNNs (SD-CNNs) to identify frames with higher uncertainty due to overlapping sound events. A neural network can learn to focus on local patches within the receptive field if a network-in-network architecture is used [74]. Here, individual convolutional layers are extended by micro neural networks, which allow for more powerful approximations by additional non-linearities. Up to now, attention mechanisms have been rarely used in ASC algorithms, but often applied in AED algorithms, where the exact localization of sound events is crucial.
###### Abstract  
Ultra-short pulse laser interaction with diamond materials has attracted extensive interest in micro- and nano-machining, especially for the fabrication of micro tools, because of the straightforward method and high precision. Thanks to the development of chemical vapor deposition (CVD) technology, high-quality CVD diamonds are employed in more varieties of tools as performance-enhancing coatings. The purpose of the experiments reported here was to explore the machinability of CVD diamond coating under the irradiation of femtosecond (fs) pulsed laser. The factor-control approach was adopted to investigate the influence of scanning speed, single pulse energy and repetition rate on the surface quality and carbon phase transition of CVD diamond coating. The material removal rate and surface roughness were evaluated. The interaction mechanism of scanning speed, single pulse energy, and repetition rate were discussed, and the fs laser ablation threshold of CVD diamond coating was calculated. It was demonstrated that two ablation mechanisms (weak and intensive) were in existence as evidenced by the distinct surface morphologies induced under different processing conditions. A strong dependence on the variation of scanning speed and pulse energy is identified in the examination of surface roughness and removal rate. Lorentzian-Gaussian deconvolution of Raman spectra illustrates that fs laser irradiation yields a strong modification effect on the coating and release the compressive stress in it. Furthermore, a newly defined parameter referring to the fs laser energies applied to unit volume was introduced to depict the degree of ablation and the Taguchi method was used to figure out the significance of different parameters. The ablation threshold of CVD diamond coating at the effective pulses of 90 is calculated to be 0.138 J/cm\({}^{2}\).  
femtosecond laser ablation; CVD diamond coating; surface quality; material removal rate; Raman spectra deconvolution; interaction mechanism; ablation threshold
## 2 Related Work
### Noise Reduction Methods for Anomaly Detection
#### 2.2.3 Support Vector Machine  
The SVM model takes much time to learn data relatively and has a disadvantage. As retrains the number of features in the training data increases, the interpretability decreases. However, among machine learning methodologies, it is generally a model showing high performance and prediction accuracy. In addition, it has the advantage that the risk of overfitting is relatively low, even for data with a biased distribution [24; 25]. The SVM model should use the appropriate kernel and parameters to construct the decision boundary. As parameters, the margin for error tolerance, flexibility (gamma) for decision boundaries, and kernel function should be appropriately selected considering the complexity and scale of network data. For the kernel function (\(k(x,x_{i})\), for arbitrary data \(x\) and support vector \(x_{i}\), linear, polynomial(\((x\cdot x_{i}+1)^{d}\)), Gaussian radial basis function(\(exp(-\gamma||x-x_{i}||^{2})\)), sigmoid(\(tanh(\alpha x^{T}y+c)\)) are frequently used.
## 4 Payload and Subsystem Architectures and Design
### Attitude Determination and Control Subsystem  
The architecture of the ADCS proposed in the nanosatellite to accomplish the proposed mission is presented in the block diagram of Figure 18.  
In this ADCS configuration, the MagneTorquers (MTs) used are integrated into solar panels. This type of Embedded MTs was chosen because of its low power consumption. As mentioned previously, the MCU is integrated into high-performance and low-power 32-bit ARM9.  
Figure 17: OBC block diagram.
## 3 RS Applications in Ocean
### Ocean Wave Height (OWH)
#### 3.3.5 Marine Radar  
Since the 1980s, marine radars have been mainly developed to measure ocean surface parameters such as OWH. This is because of their high temporal and spatial resolutions as well as the distinguishability of ocean waves in the corresponding images [188]. Several texture-analysis-based, spectral-analysis-based, and coherent radar algorithms have been so far developed for OWH estimation using marine radar data.  
Spectral-analysis-based algorithms acquire wave spectra information from a series of radar images to generate OWH information. These methods can be generally divided into three groups, namely, the 3D discrete Fourier transform-based algorithms [189; 190], 2D continuous wavelet transform-based algorithms [191], and the array beamforming algorithms [192]. Regarding texture-analysis-based algorithms, wave parameters are directly generated from image texture information based on seven different methods, including the probability of illumination [193], statistical analysis [194], tilt-based [195; 196], shadowing-based [197], support vector regression [198], empirical orthogonal function [199], and ensemble empirical mode decomposition [200]. Finally, coherent radar algorithms have been developed based on the X-based coherent marine radars [201]. The image intensity and the radial velocity of the ocean surface scatters are two main advantages of coherent marine radars. These systems enable coherent radar-based algorithms to directly estimate OWH information from the radar data without calibration [188]. Based on this feature, several empirical [202], semiempirical [203], and nonempirical [204] methods have been developed for OWH estimation.
# Bayesian Uncertainty Inferencing for Fault Diagnosis of Intelligent Instruments in IoT Systems
## 8 Conclusions  
In an IoT system, innovative solutions are required to handle the numerous unknowns related to sensors, problem determination, and symptoms. Fault diagnosis for equipment in IoT system must not only consider incomplete fault information and sensor data, but also address uncertainties arising from multiple types of faults. In this study, to tackle these challenges, a novel BN-based fault diagnosis approach for industrial instruments in an IoT system, was presented. This approach places particular emphasis on managing uncertainties in the construction of a BN model. To achieve this, the TrIFN-based entropy method was utilized in order to aggregate the priority probability, and the leaky-OR gate was employed to calculate the CPT. The proposed model was applied to an intelligent pressure transmitter, and a sensitivity analysis was conducted to evaluate its effectiveness. The results highlight the innovative nature of the proposed approach, which was shown to successfully manage fault diagnosis uncertainties, and enhance the accuracy of BN model diagnosis and fault detection.  
Although expert knowledge is compiled using intuitionistic fuzzy numbers, there are no standards for expert selection. For simplicity, the node state in this study had two states; however, node signals may have multiple states, or use continuous values. Another drawback is that, while the IPT fault tree covers practically all fault types, current IoT systems can only monitor a portion of the IPT fault tree's root nodes. Therefore, it would be more useful to adjust this method according to monitored IoT parameters.  
Conceptualization, Q.L. and C.W.; methodology, Q.L.; investigation, C.W. and Q.L.; data curation, C.W. and Q.W.; writing--original draft preparation, Q.L.; writing--review and editing, Q.L. and Q.W.; project administration & funding acquisition, Q.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Program of China 2020 YFB2009400; the Zhejiang Special Support Program for High-Level Personnel Recruitment of China 2019R52017; and The National Natural Science Foundation of China 52175257; \"Pioneer\" and \"Leading Goose\" R&D Program of Zhejiang 2022C03179.  
Not applicable.  
Data supporting reported results is already available in the content.  
The authors declare no conflict of interest.
# Research on a Mixed Gas Classification Algorithm Based on Extreme Random Tree
###### Abstract  
Because of the low accuracy of the current machine olfactory algorithms in detecting two mixed gases, this study proposes a hybrid gas detection algorithm based on an extreme random tree to greatly improve the classification accuracy and time efficiency. The method mainly uses the dynamic time warping algorithm (DTW) to perform data pre-processing and then extracts the gas characteristics from gas signals at different concentrations by applying a principal component analysis (PCA). Finally, the model is established by using a new extreme random tree algorithm to achieve the target gas classification. The sample data collected by the experiment was verified by comparison experiments with the proposed algorithm. The analysis results show that the proposed DTW algorithm improves the gas classification accuracy by 26.87%. Compared with the random forest algorithm, extreme gradient boosting (XGBoost) algorithm and gradient boosting decision tree (GBDT) algorithm, the accuracy rate increased by 4.53%, 5.11% and 8.10%, respectively, reaching 99.28%. In terms of the time efficiency of the algorithms, the actual runtime of the extreme random tree algorithm is 66.85%, 90.27%, and 81.61% lower than that of the random forest algorithm, XGBoost algorithm, and GBDT algorithm, respectively, reaching 103.2568 s.  
machine olfaction; gas recognition; extreme random tree; dynamic time regulation; random forest; feature engineering +
Footnote †: journal: Applied Sciences
## 2 Related Work
### Basic Classification of MSIMP  
MSIMP can be classified into multi-satellite static mission planning and dynamic mission planning according to the mode of operation. MSIMP aims to obtain the optimal planning scheme that meets the satellite resource constraints and mission constraints through an optimization algorithm before mission planning is executed [13]. Multi-satellite static mission planning has poor anti-interference abilities and is not able to address emergency mission requirements and unexpected operating environments. Comparatively speaking, multi-satellite dynamic mission planning can be adjusted according to real-time environment changes and the mission requirements, providing a timely response and dynamic adjustment to the target mission [14]. Multi-satellite dynamic mission planning is more adaptable to complex mission environments and real-time changing requirements, thus enhancing the flexibility of MSIMP mission execution.  
MSIMP can be divided into area coverage task planning, target identification task planning, and event task planning, according to the spatial dimension. Area coverage task planning focuses on covering a specific geographical area and is suitable for application scenarios such as land resource assessment and environmental monitoring [15]. Target identification task planning focuses on locating specific targets within a broad coverage area and is particularly suited for detecting targets at various spatial locations with high precision [16]. Event task planning, on the other hand, prioritizes the allocation of resources to observe the region when an anomaly occurs [17]. This classification method helps to clarify the core requirements of different tasks and optimize the imaging strategy and resource allocation, thus enhancing the overall efficiency of MSIMP.  
MSIMP can also be divided into centralized mission planning, distributed mission planning, and autonomous mission planning, according to the decision-making method. Centralized mission planning consists of a central node that manages all mission information and satellite resources in a unified manner, and a ground control center maximizes the coordination of each satellite mission based on global information [18]. Distributed mission planning employs a decentralized management strategy, distributing mission and satellite resource data across multiple nodes. These nodes share information and coordinate tasks through communication and collaboration, offering enhanced flexibility and fault tolerance [19]. Autonomous mission planning integrates the advantages and disadvantages of centralized and distributed mission planning and empowers satellites with autonomous planning capabilities, where each satellite reasonably evaluates its own resources and mission requirements, with high autonomy and real-time responsiveness [20]. This classification method helps to systematically investigate different planning methods for multi-satellite imaging missions to better meet the needs of different application scenarios and improve the autonomy of MSIMP mission execution.
## 3 Results
### Evaluation of the Prerequisites for Survival in GIT  
LAB isolated at days 121 and 150, i.e., those that are ingested in high numbers with the product, including the enterococcal isolates devoid of virulence characters, were tested for their ability to survive in acidic conditions (pH 2.5 and 3.0) in the presence of bile salts (0.3%, 0.5% and 1%) and for their ability to adhere to CaCo-2 cells. These cells are generally used as a model of the intestinal epithelial cells [29]. _L. casei_ ATCC 393, a strain isolated from cheese and already tested for tolerance to acidity, bile salts and adhesion properties [30], was used as a reference to interpret the characteristics in the isolates examined in this study by comparison with previous work. The results are reported in Table 2.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Isolate**} & \multicolumn{2}{c}{**pH Tolerance**} & \multicolumn{2}{c}{**Bile Tolerance**} & \multicolumn{2}{c}{**Adhesion to**} \\  & \multicolumn{2}{c}{**(\% Survival after 2 h)**} & \multicolumn{2}{c}{**(\% Growth vs. Control)**} & \multicolumn{2}{c}{**CaCo-2 Cells**} \\ \cline{2-7}  & **2.5** & **3** & **0.3** & **0.5** & **1** & \\ \hline _Lacticuseibacillus casei_ & & & & & & \\ ATCC 393 & 0 & 94.60 \(\pm\) 2.12 & 27.00 \(\pm\) 0.71 & 12.00 \(\pm\) 0.97 & 5.00 \(\pm\  
At pH 3.0, all isolates tested exhibited good tolerance, while at pH 2.5 only ten were able to survive. Most isolates showed significantly higher tolerance than the reference strain to all tested bile salt concentrations. In the cell adhesion test, six enterococci, seven _L. brevis_ isolates and _L. plantarum_ L22150 showed better adhesion capacity than the reference strain. All the _L. paracasei_ isolates showed a low cell adhesion capacity.  
Patterns of tolerance to GIT conditions and adhesion capacity were not species-specific, as shown by correlation clustering of average data series for the isolates listed in Table 2 (Figure 2).  
Figure 2: Correlation clustering based on average values of tolerance to low pH and bile salts and adhesion capacity reported in Table 2 for LAB isolates at 121 and 150 days.
# Attention-Based Active Learning Framework for Segmentation of Breast Cancer in Mammograms
## 3 Method
### Basic Segmentation Model for Breast Cancer  
Although skip connections in UNet could avoid the loss of detailed information caused by continuous down-sampling, they cannot capture multi-scale information with strong restrictions on locality. To obtain multi-scale feature maps, we cascade the feature maps at multiple layers with different receptive fields, where skip connections are used across different layers.  
On the basis of the UNet segmentation model, the proposed basic model further involves the strength of the promotion feature module (PFM) [32] for the features of each output layer, which fuses multi-scale feature maps to enhance their representation capability. Note that the PFM works as a feature fusion and enhanced block in our former work, which not only fuses the features from multiple scales, but also selectively forgets useless information and enhances informative information, thus constructing more effective feature representation.  
Specifically, an input image \(I\) in the labeled breast cancer set is sent to the proposed basic segmentation model for feature extraction:  
\[F^{i}=Seg_{i}^{i}(I), \tag{3}\]  
where \(F_{i}\) refers to feature maps corresponding to the \(i\)th output layer of the UNet model, and the function \(Seg^{i}()\) refers to the segmentation model during the \(t\)th iteration of retraining.  
Figure 1: Framework of the proposed framework, which consists of (**A**) Basic Segmentation Model, (**B**) Attention-based Sampling, and (**C**) Active-Learning-based Labeling.  
Then, feature maps of multiple layers are sent to different PFMs for fusion and enhancement:  
\[F_{E}^{i}=G_{e}^{i}(G_{f}^{i}(F^{i})),\textit{where}\;i=1,2,...,n \tag{4}\]  
where \(F_{E}^{i}\) represents the output feature map after processing of the \(i\)th PFM, functions \(G_{e}\) and \(G_{f}\) represent enhance and forget operations in PFM, and \(n\) is the total number of layers in the segmentation model.  
In the later fusion step, the original feature map \(F^{i}\) corresponding to the \(i\)-th layer is fused with its enhanced version \(F_{E}^{i}\) via skip connections:  
\[F^{i-1}=F_{E}^{i}\oplus X_{UP}(F^{i}),\textit{where}\;i=1,2,...,n \tag{5}\]  
where \(F^{i-1}\) refers to the output feature map of the \(i-1\)th layer, and the function \(X_{UP}()\) is an up-sampling operation.  
Finally, the generated feature map \(F_{P}^{1}\) combines the high-layer semantic features with shallow features, thus enhancing representation capability for segmentation via multi-scale feature extraction fusion. We set \(n=5\) for all experiments in this paper.