esa-sceva/satcom-chunk-collection huggingface dataset:
# Assembly Quality Detection Based on Class-Imbalanced Semi-Supervised Learning
## 3 Results
### Experimental Results  
Figure 7 shows the differences in the representation of the training set between the supervised learning method (a) and the proposed method (b). T-SNE [40] projection with perplexity of 50 was used for visualization. In Figure 7a, the boundaries of the three classes were mixed. Therefore, under the condition of limited and imbalanced data, the model trained by the conventional supervised learning method was hard to learn discriminative data representation. The proposed method could form better class boundaries and obtain better classification performance. Table 2 shows the mean and standard deviation of bACC and GM for the above methods on the test set. The proposed algorithm achieved an average bACC of 93.67% and an average GM of 93.57% when the labeled fraction was 10%. When the labeled fraction was 20%, an average ACC of 98.83% and an average GM of 98.83% were achieved. When the labeled fraction was 50%, an average ACC of 99.17%, and an average GM of 98.99% were reached. The proposed method performed better than the supervised learning method and all the mean teacher methods with existing class-imbalanced learning strategies, indicating that the proposed method was effective in the case of limited labeled data with the imbalanced class distribution. In addition, with less annotated data, the proposed method had more advantages and had a higher accuracy than other methods. Figure 8 shows the error rates of all methods in the three classes. Figure 8a-c resulted from training the models under the labeled fraction \(\varepsilon\) of 10%, 20%, and 50%, respectively. The proposed method kept a low error rate in the majority and minority classes. When labeled data were few (Figure 8a,b), the supervised learning method showed high error rates in all classes. In contrast, although the mean teacher method achieved higher accuracy, its error rates in the minority classes did not decrease significantly because of confirmation bias. The mean teacher method combined with class-imbalanced learning strategies led to overfitting in the lead seal missing class, which was the greatest minority. Although it achieved a lower error rate, it sacrificed its fitting ability in the similar sub-minority class-both lead seal and gaskets missing. In addition, with the gradual increase of labeled data, the supervised learning algorithm had already obtained a low error rate, and the mean teacher method with class-imbalanced learning strategies did not observably improve the error rate. Figure 9 compares the accuracy and loss of the proposed method and the standard mean teacher method in the training process. The accuracy values were the validation accuracy after each epoch. A thousand uniform sampled loss values from all iterations were used to plot Figure 9(a2-c2). It can be seen that the proposed method converged faster and tended to be more stable, and a model with higher accuracy could be obtained with fewer iteration steps. In Figure 10, the accuracy and prediction variance (PV) during the training
###### Abstract
Since the IMU can directly detect the attitude and motion information of the object, in order to improve the accuracy of motion estimation, the SLAM system that integrates the IMU, camera, and LiDAR has also been widely studied. Ref. [5] used visual odometry and inertial measurement for state estimation, and LiDAR for mapping. However, since the point cloud information may be occluded, resulting in reduced estimation accuracy, Ref. [6] was used to solve this problem. V-LOAM [7] and DV-LOAM [8] utilize information from visual detections to provide initial pose estimates for LiDAR odometry. Ref. [9] uses the predicted value of IMU to roughly estimate the state of the system, and then further optimizes it with visual-inertial odometry (VIO) and LiDAR-inertial odometry (LIO). However, they are all loosely coupled systems, and sensor data may not be directly correlated between different time periods. They are sensitive to cumulative errors and difficult to handle loop closure. Later, tightly coupled methods were proposed, including LIC-fusion [10], LIC-fusion2.0 [11], LVI-SAM [12], R2 Live [13], etc., which can consider all variables at the same time, realize pose optimization, and use loop closure detection to regularly eliminate robot drift. R2 LIVE consists of an odometry module based on filtering approach and an optimization module based on a factor graph, and the LiDAR-inertial system is based on the FAST-LIO2 [14], which incorporates sparse visual features and estimates the robot's state in real-time by minimizing feature reprojection error, optimizing visual landmark points within the sliding window, and achieving good positioning accuracy and robustness in fast-moving environments with small viewing angle of LiDAR. However, they all use feature-based method in visual odometry, by extracting the feature points of the image for inter-frame matching and motion estimation, but an image has thousands of pixels, and the extracted feature points may only have a few hundred, this will cause most of the useful image information to be lost, especially in scenes with less texture, the number of feature points will be significantly reduced, resulting in invalid motion estimation. Based on the studies of many predecessors, this paper proposes a SLAM framework that integrates LiDAR, IMU, and camera to solve the problem of localization and mapping failure of unmanned vehicles when working in tunnels, mining areas, and open fields. The system tightly couples the texture information provided by vision and the geometric information provided by LiDAR, which uses VIO and LIO to jointly realize pose estimation and map construction. The difference is that this framework uses direct method on the visual front end to estimate pose by minimizing the photometric error between images. Compared with the feature-based method, direct method can utilize richer image information, which is suitable for environments lacking textures.
## 4 Simulations and Results Analysis  
The simulations based on Matlab/Simulink were performed with the proposed H2/H\(\infty\) control strategy to identify the 4 WS vehicle's yaw rate tracking ability and path tracking performance. Moreover, the H\(\infty\) controller and the traditional proportional controller were also designed to contrast the performance of the proposed H2/H\(\infty\) controller. A proportional controller is usually used to control real 4 WS vehicles, and the ratio of rear wheel angle to front wheel angle is defined as follows [38].  
\[K=\frac{\delta_{r}}{\delta_{f}}=\frac{-l_{r}+\frac{ml_{r}V^{2}}{C_{r}L}}{l_{f} +\frac{ml_{r}V^{2}}{C_{r}L}}\left(L=l_{f}+l_{r}\right)\text{.} \tag{37}\]  
The parameters utilized in the numerical simulations are shown in Table 1.  
A H2/H\(\infty\) controller was designed for the 4 WS vehicle at a velocity of 90 km/h. The LMI (linear matrix inequality) Control Toolbox was used to obtain the H2/H\(\infty\) controller, which can guarantee that the \(H2\) performance index \(\|T(z_{2}\text{w})\|<0.61\) and the \(H\infty\) index \(\|T(z_{\infty}\text{w})\|<0.27\). In order to verify the performance of the robust H2/H\(\infty\) controller applied in the 4 WS vehicle, four steering maneuvers (step steering maneuver, sinusoidal steering maneuver, fishhook maneuver, and double-lane change maneuver) were simulated in a MATLAB/Simulink environment using the nonlinear 8 DOF vehicle model.  
Figure 14: Standard H2/H\(\infty\) design problem of the 4 WS vehicle.
Then, at the selected sensitive wavelengths, the spectral data were resampled with a 1 nm sampling interval to explore the impact of spectral resolution on the estimation accuracy, so as to determine the best spectral resolution. Finally, the noise-free spectral data simulated by soil radiative transfer model was added with Gaussian random noise to change the SNR. The influence of different SNR values on the estimation accuracy was studied, and the SNR range suitable for soil moisture content measurement was obtained. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Sensitive Wavelengths of**} \\  & **Soil Moisture Content (nm)** & **Author** \\ \hline correlation analysis & 880, 901–903, 924–929 & [PERSON] et al., 2009 [10] \\ \hline correlation analysis & 400–410, 1400–1850 & [PERSON] et al., 2011 [11] \\  & and 2050–2220 & [PERSON] et al., 2017 [12] \\ \hline correlation analysis & 712, 807, 1142, 1570, 1850, 2221 & [PERSON] et al., 2017 [12] \\ \hline \multirow{2}{*}{CARS} & 443–449, 1408–1456, 1916–1943, 2209–2225 & [PERSON] et al., 2016 [13] \\ \hline \multirow{2}{*}{wavelet transform and CARS} & 400–500, 1320–1461, 1851–1961, 2125–2268 & [PERSON] et al., 2017 [14] \\ \hline stepwise regression and & 1460, 1950 and 2200 & [PERSON] et al., 2020 [15] \\ grey correlation & 1460, 1950 and 2200 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The selection of sensitive wavelengths for soil moisture content.
###### Abstract  
In the fault diagnosis field, the fault feature signal is weak and contaminated by the noise. The lock-in amplifier is a useful tool for weak signal detection. Aiming to the amplitude error of the lock-in amplifier caused by frequency deviation between the measured signal and the reference signal, a DFT-based automatic signal frequency estimation method is studied to improve the frequency accuracy of the reference signal. Based on this frequency estimation method, a software digital lock-in amplifier method is proposed to detect the multiple frequencies signals. This proposed method can automatically measure the frequency value of the measured signal without prior frequency information. Then, the reference signals are generated through this frequency value to make the digital lock-in amplifier estimate the amplitude of the measured signal. Moreover, an iterative structure is used to implement the multiple frequencies signal measurement. The frequencies and amplitudes measurement accuracies are tested. Under different SNR conditions, the frequency relative error is less than 0.1%. In addition, the amplitude relative error with different signal frequencies is less than 1.7% when the SNR is \(-1\) dB. This proposed software digital lock-in amplifier method has a higher signal frequency tracking ability and amplitude measurement accuracy.  
Keywords:weak signal detection; digital lock-in amplifier; signal frequency estimation; multiple frequencies signal measurement +
Footnote †: journal: Applied sciences
# Cluster-Based Routing Protocol with Static Hub (CRPSH) for WSN-Assisted IoT Networks
###### Abstract  
The Internet of Things (IoT) is an evolving concept that has achieved prominence in the modern era. An autonomous sensor-equipped device is the major component of WSN-assisted IoT infrastructure. These devices intelligently sense the environment, automatically collect the data, and deliver the information to paired devices. However, in WSN-assisted IoT networks, energy depletion and hardware faults might result in device failures. Additionally, this might affect data transmission. A reliable route significantly reduces data retransmissions, which can help in congestion reduction and energy conservation. Generally, the sensor devices are typically deployed densely throughout the WSN-assisted IoT networks. A high number of sensor devices covering a monitoring area might result in duplicate data. The clustering method can be used to overcome this problem. The clustering technique reduces network traffic, whereas the multipath technique ensures path reliability. In CRPSH, we used the clustering technique to reduce the duplicate data. Moreover, the multipath approach can increase the reliability of the proposed protocol. CRPSH is intended to minimize the overhead associated with control packets and extend the network's lifetime. The complete set of simulations is carried out using the Castalia simulator. The proposed protocol is found to reduce energy consumption and increase the lifetime of IoT infrastructure networks.  
Internet of Things; wireless sensor network; routing; green IoT +
Footnote †: journal:  
0
Footnote 0: _Sustainability_
## 7 Results and Analysis
### Discussion
#### 7.12.2 Security Advancements  
Our ZTA reduces attack success rates to 11% (Figure 13), compared to 87% under RBAC [15] and 100% with no defenses. Against data poisoning, the framework maintains 94% global accuracy with up to 15% malicious participants (Figure 14), outperforming the 67% reported by [11]. Behavioral continuous authentication cuts unauthorized RIC access by 89% (Figure 15), and our autoencoder-based detector achieves a 94% F1-score, which is 16% higher than prior xApp detectors [45]. These gains stem from the combined use of micro-segmentation, runtime monitoring, and unsupervised anomaly detection within a unified ZTA fabric.
## 3 Results  
### Feature Importance and Preprocessing Results  
Figure 4 shows the results of the ranked feature importance from the XGBoost analysis. Hemoglobin had the highest importance value, followed by age, SFA, NRI, etc. Based on the XGBoost feature importance analysis, 20 features were selected (Table 2).  
Two expert clinicians ([PERSON] and K.W.K.) selected 13 clinically significant features from the 47 features. All 13 clinically significant features selected by the two expert clinicians were included in the 20 features selected based on XGBoost.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Features** & **Based on XGBoost** & **Based on Experts** & **Selected for AI Model** \\  & **(n = 20)** & **(n = 13)** & **(n = 15)** \\ \hline Age & O & O & O \\ Sex & O & O & O \\ Height & O & X & O \\ BMI & O & O & O \\ SFA & O & O & O \\ VFA & O & O & O \\ SMA/height\({}^{2}\) & O & O & O \\ SMA/BMI & O & O & O \\ NRI & O & O & O \\ Cholesterol & O & O & O \\ Hemoglobin & O & O & O \\ Albumin & O & X & O \\ Protein & O & X & X \\ Type of operation (total vs. & O & O & O \\ distal gastrectomy) & O & O & O \\ Type of anastomosis & O & O & O \\ TNM stage \({}^{1}\) & O & O & O \\ Distal resection margin & O & X & X \\ Proximal resection margin & O & X & X \\ Number of metastatic lymph nodes & O & X & X \\ Number of retrieved lymph nodes & O & X & X \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of features for the artificial intelligence model training.  
Figure 4: Feature importance of XGBoost. Among 47 features, 20 features were selected through a comparison of feature importance of XGBoost. BMI = body mass index.  
Of the 20 features selected from XGBoost, 5 features (i.e., protein, distal resection margin, proximal resection margin, number of metastatic lymph nodes, and number of retrieved lymph nodes) were excluded, because there were missing data in the external validation datasets. The following 15 features for predicting survival probability were finally selected:  
* Continuous features: age (year), height (cm), \(\Delta\)BMI\({}_{\text{standard}}\), \(\Delta\)cholesterol\({}_{\text{standard}}\), \(\Delta\)hemoglobin\({}_{\text{standard}}\), \(\Delta\)albumin\({}_{\text{standard}}\), \(\Delta\)NRI\({}_{\text{standard}}\), \(\Delta\)SFA\({}_{\text{standard}}\), \(\Delta\)VFA\({}_{\text{standard}}\), \(\Delta\)SMA/height\({}^{\text{2}}\)standard, and \(\Delta\)SMA/BMIstandard;
* Categorical features: sex, type of operation, type of anastomosis, tumor, nodes, and metastases (TNM) stage.  
The preprocessing results for the time-dependent continuous variables are shown in Supplementary Table S2. No statistically significant difference was observed among the development and validation sets.
## 4 Experimental Validation
### Metrics and Implementation Details
#### 4.3.2 CNN Network  
The CNN network used in our work consists of an encoder and decoder, following a U-Net shape [49; 50]. Compared to the U-net introduced in [51], we use a seven-layer network. Each layer in the network consists of a convolution operation, followed by batch normalization and the Rectified Linear Unit (ReLU) activation function. The encoder is composed of four layers, with each layer being followed by a downsampling operation. Following the bottleneck layer, we have the decoder. In order to facilitate training with larger batch sizes on the pollen dataset, we aim to minimize model parameters. To achieve this, we simplify the decoder section by reducing the number of layers to two, each preceded by an upsampling operation, compared to the classic U-Net architecture. And a ResNet50 (pretrained on ImageNet) is used as an encoder for the canopy images.  
During the training, random rotate90, flip, transpose, and brightness contrast adjustment are used as data augmentation methods.
## 3 Methods
### Communication Model Design
#### 3.1.1 Interconnection Communication Model  
According to Equation (1), the values of \(d_{n}^{pr}\), \(d_{n}^{w}\), and \(d_{n}^{q}\) can be considered negligible compared to \(d_{n}^{p}\). Therefore, the \(l_{n}^{com}\) can be approximately regarded as \(d_{n}^{p}\). To obtain the \(d_{n}^{p}\), we first need to obtain the signal-to-noise ratio (\(SNR\)). In wireless networks, the  
Figure 1: This is the communication model design. Users ask LLM medical questions from their own devices, then devices send the token via networks. Devices receive intermediate parameters sent via networks and complete computation for partial layers, and finally, all devices finish computation; LLM generates the final medical answers to the user.  
signal power between devices is denoted as \(S_{n}\), and the noise power can be approximately regarded as white Gaussian noise, denoted as \(\omega_{n}\), and the \(SNR_{n}\) is calculated as  
\[SNR_{n}=\frac{S_{n}}{\omega_{n}}, \tag{2}\]  
where \(\omega_{n}=kTB_{n}\) is white Gaussian noise, \(k\) is the Boltzmann constant, \(T\) is the temperature in kelvin, and \(B_{n}\) is the channel bandwidth.  
After obtaining the \(SNR_{n}\), we still need to calculate the channel capacity for obtaining the \(d_{n}^{p}\). According to Equation (2), the channel capacity \(C_{n}\) can be calculated as  
\[C_{n}=B_{n}\log_{2}(1+SNR_{n}), \tag{3}\]  
then, according to Equation (3), we calculate the \(d_{n}^{p}\). The data volume of medical services is denoted as \(D_{n}\). The \(d_{n}^{p}\) can be calculated as  
\[d_{n}^{p}=\frac{D_{n}}{C_{n}}, \tag{4}\]  
and now, we obtain the value of \(d_{n}^{p}\) through the computation.
# Multi-Relational Graph Convolution Network for Service Recommendation in Mashup Development
## 4 Experiments
### Discussion
#### 4.6.4 Impact of the Threshold  
When constructing graphs with functional relation and tagging relation, we set the number of edges for each node to the threshold \(\tau_{1}\) and \(\tau_{2}\), respectively. To study the parameters' effect on the recommendation performance, we adjust \(\tau_{1}\) and \(\tau_{2}\) from 5 to 40 with a step size of 5. The results are shown in Figure 7. We can see that, for the functional relation, the performance increases when \(\tau_{1}\) increases from 5 to 30, which suggests that exploiting more functional relation of mashup and service brings more beneficial information for recommendation. However, when \(\tau_{1}\) further increases, the performance plateaus and starts to decrease. Perhaps too many neighbors for a mashup or service introduce noisy information and harm the GCN learning. Therefore, we set \(\tau_{1}\) as 30 in the experiment. A similar trend can be seen for \(\tau_{2}\) when constructing the tagging relation. We can see that  
Figure 5: Impact of propagation layer number in GCN. (**a**) Recall@K; (**b**) NDCG@K.  
Figure 6: Impact of feature dimensions in GCN. (**a**) Recall@K; (**b**) NDCG@K.  
optimal value of \(\tau_{2}\) (15 in the experiment) is generally smaller than that of \(\tau_{1}\), which might indicate that the tagging relation is more susceptible to noisy links than functional relation.
# Fire Performance of Self-Tapping Screws in Tall Mass-Timber Buildings
## 4 Design Proposal
### Verification and Validation from Heat Transfer Modelling
Figure 16: Numerical modelling of \(\O\Omega 10\) mm full-thread screws under standard fire CAN/ULC S101 [6]: (a) Heat transfer model at two hours of fire exposure; (b) Comparison of numerical and experiment of time–temperature curves up to two hours of fire exposure. In an attempt to validate the heat transfer model response under standard fire exposure, the glulam residual cross-section can be compared using the one-dimensional charring rate in CSA O86-19 [8] and by considering all areas with temperatures lower than 300 \({}^{\circ}\)C, as proposed previously. By applying the 300 \({}^{\circ}\)C isotherm of the model, as shown in Figure 16a, the charring rate estimated numerically of the opposite (unheated) side of the O10 mm full-thread STS (i.e., where no influence of heat transfer of fastener into glulam specimen is observed) was 0.66 mm/min and 0.73 mm/min under one and two hours of fire exposure, respectively. This is consistent with the one-dimensional charring rate of 0.65 mm/min from CSA O86-19 [8]. However, and as expected, applying the standard temperature-time curve at the top of the STS head and glulam surface resulted in higher predicted temperatures when compared to that of the small-scale fire tests, namely for the two-hour exposure. Under the standard fire exposure, the heat flux at the top surface becomes rapidly greater than that of the small-scale fire test exposure, which increases the heat transfer through the fastener and results in higher temperatures into the glulam. Influenced by the incident heat flux and high conductivity of steel, longer exposure times increase the temperature distribution along the fastener shank to much higher temperatures inside the timber. This effect was observed when comparing the temperature-time curve from the heat transfer model and those from Equations (9) and (10) to the measured temperatures reported in this study, as shown in Figure 17. While the curves of Equations (9) and (10) predict the thermal response well, the heat transfer model under standard fire exposure was expected to be higher than the measured temperatures. In our study, the measured temperatures are for a constant heat flux of 50 kW/m\({}^{2}\), corresponding to approximately 10 to 15 min of the standard temperature-time curve as previously discussed by [PERSON] [34]. To develop a better numerical prediction of measured temperature profiles under the standard fire exposure, further studies should be conducted in the future by considering the parameters discussed in this study and for standard fire exposure up to two hours. Otherwise, the heat transfer model under a constant heat flux of 50 kW/m\({}^{2}\) predicts well the temperature profiles behind the char layer. The proposed calculation model can also be used to predict the heat-affected layer used for evaluating the residual resistance of STS.