esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Defect Detection Model for Aero-Engine Components Based on Improved YOLOv5
## 2 Methods and Principles
### YOLOv5 Improvement
#### 2.2.3 BiFPN Module  
The BiFPN module is derived from the Google team's 2019 EfficientDet network [27]. It strengthens higher-level feature fusion in the processing path, processing each bidirectional path (top-down and bottom-up) as a feature network layer, through the fusion of weighted features. The importance of different input features is learned, and differentiated fusion is carried out for different features [28]. Figure 3a,b show the FPN and PANet feature fusion structures used in the original YOLOv5 network. FPN carries out multi-scale feature fusion in a top-down manner, and PANet adds a bottom-up path on the basis of FPN. Figure 3c shows the BiFPN module, which is the feature fusion part of the EfficientDet network. It receives five effective feature layers P3-P7 from the backbone feature extraction network, carries out upsampling and downsampling feature fusion for these feature layers successively and sets weights for each node to balance features of different scales. In the figure, the blue route conveys high-level semantic information from the top down, while the red route conveys low-level location information from the bottom up.  
Figure 2: Diagram of ECA-Net module. For the aggregated features, ECA-Net generates channel weights by performing a fast 1D convolution of kernel size k, where k is proportional to the channel dimension.  
FPN typically uses the same weight when fusing features of different scales, but these features contribute differently to the final output, so BiFPN adds additional weight to each input feature and lets the network know how important each feature is. The fast normalized fusion weighted fusion method is adopted, as shown in Equation (4):  
\[\mathcal{O}=\sum_{i}\frac{w_{i}}{\epsilon+\sum_{j}w_{j}}\cdot I_{i}, \tag{4}\]  
The Relu activation function is used for each weight to ensure that \(w_{i}\geq 0\), and \(\epsilon=0.0001\) is a small quantity used to keep the value stable. The process of cross-scale connection and weighted feature fusion in BiFPN is shown in Equations (5) and (6):  
\[P_{6}^{td}=Conv(\frac{w_{1}\cdot P_{6}^{in}+w_{2}\cdot Resize(P_{7}^{in})}{w_{ 1}+w_{2}+\epsilon}), \tag{5}\]  
\[P_{6}^{out}=Conv(\frac{w^{\prime}_{1}\cdot P_{6}^{in}+w_{2}\cdot P_{6}^{ld}+w^ {\prime}_{3}\cdot Resize(P_{5}^{out})}{w^{\prime}_{1}+w^{\prime}_{2}+w^{\prime }_{3}+\epsilon}), \tag{6}\]  
where \(P^{ld}\) is the middle layer of the top-down feature fusion process; \(P^{in}\) and \(P^{out}\) are the bottom-up input and output features, respectively; and Conv represents the convolution process.
## 5 Experimental Results and Analysis
### Non-Protocol Method  
Using this approach, we published and retrieved the data payloads from the Tangle by only publishing the sensor data in the form of zero-value transactions [48], which are transactions that only contain data and no cryptocurrency, and without using any IOTA protocol. The proposed work was performed using Jupyter Notebook [49] which  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Processor** & **Action** & **Payload** & **Trials** & **Avg.** & **St. Dev.** & **Variance** & **Min** & **Max** \\  & & **Size (KB)** & & **Time (s)** & & **(s)** & **(s)** & **(s)** & **(s)** \\ \hline Intel(R) CoreTM & 7-8565U & Create & 145 & 300 & 0.49 & 0.13 & 0.017 & 0.415 & 1.34 \\ Intel(R) CoreTM & 7-8565U & Create & 330 & 300 & 0.7 & 0  
is an open-source web tool for creating and sharing documents with live code, equations, visualizations, and machine learning. Two Python scripts were written, where one script was configured to publish the sensor data, i.e., creating and attaching the transactions to the Tangle, while the other one was used to fetch the data from the Tangle. These two scripts utilized the official Python library for IOTA, called Pyota. Jupyter Notebook, running on the IOTA client, executed the two scripts to perform the abovementioned tasks.  
With this system in place, the IOTA client published and fetched the sensor data from the Tangle with the help of the address whence the transactions were sent. The results of this approach are shown in Table 3.  
We concentrated our investigation on the tasks that caused significant time delays for publishing and retrieving the messages. The three acts were studied: create, attach, and fetch. First, based on our findings, we discovered that the execution time for the message creation task was precise and was dependent on both the processor and the size of the payload. Second, we found that the average time for the attack process had a strong relationship with the size of the payload. Because the attaching stage involves the proof-of-work [50], which was conducted remotely by the private IOTA Tangle, a large variance and high correlation to payload size were expected. Thirdly, the average time to fetch a message from the private Tangle showed a high correlation to the payload size.  
The average time was calculated for all three actions i.e., create, attach, and fetch, respectively, and are displayed in Figures 10-12. It can be seen that the MAM protocol performs far better than using any non-protocol method for publishing and retrieving sensor data from the private Tangle.
## 4 Discussion  
In this feasibility study, we applied for the first time the MRI-based QSM + qBOLD approach for OEF and CMRO\({}_{2}\) estimation to a prospectively recruited collective of GBM and cMET patients in order to distinguish the two entities based on their respective cerebral oxygenation and perfusion. Since the MRI protocol employed in this study for estimating cerebral tissue oxygenation and perfusion does not require the administration of an intravenous contrast agent per se, it offers the potential to facilitate metabolic imaging of cerebral tumors as well as non-invasive differential diagnosis of GBM and cMET. We demonstrated for the two patient groups that (i) OEF in the enhancing tumor was significantly lower in GBM than in cMET, (ii) the differences in perfusion and oxygenation between CET and NET2 were only significant for the cMET group, and (iii) the ratios of CBF and CMRO\({}_{2}\) in CET divided by NET2 were significantly higher for cMET patients than for GBM patients. In order to differentiate the two entities, a support-vector machine classifier was trained on oxygenation and perfusion parameters in CET and NET2. A support-vector machine was chosen since it emerged as the best classifier with the highest accuracy, in line with previous investigations that compared it to different approaches such as naive Bayes, weighted k-nearest neighbor and decision trees for binary classification of glioblastoma and cerebral metastasis [4; 23; 38]. The described procedure could identify OEF in CET and the ratio of CMRO\({}_{2}\) in CET versus NET2 as the most promising features for distinguishing GBM from cMET, achieving the highest discriminative power. With an accuracy of 93% and an AUC of 0.94, the two entities could be successfully differentiated. Our results suggest that differentiation of GBM and cMET based solely on OEF in CET is good but not sufficient for reliably distinguishing the two groups. AUC and accuracy were higher than for any other individual parameter in CET or NET2 but lower than those obtained from using the ratios of CBF or CMRO\({}_{2}\) in CET versus NET2. This may be explained by the tumor microenvironment, in particular angiogenesis, that is similar in the contrast-enhancing part of GBM and many hematogenous cMETs [17]. In both entities, a disruption of the blood-brain barrier is caused by tumor growth and angiogenesis: the hypoxic state of the tumor and its surroundings lead to hypoxia-inducible factors being activated and their gene product VEGF being expressed [39; 40]. The improved classification performance with the ratios of CET versus NET2 is likely attributable to the dissimilarities in NET2 tissue structure between GBM and cMET. Higher vascularization and neoplastic cell growth are hallmarks of the region surrounding contrast-enhancing GBM [5; 6; 41], while the peritumoral edema around cMET is caused purely by vasogenic
# Equivalent Linearization Methods for a Control System with Clutching Inerter Damper
## 2 Analysis of SDOF System with CID  
### Equation of Motion of SDOF with IMD  
In order to compare the CID with a traditional IMD, the equation of motion of a structure with a traditional IMD is derived in this section. Figure 1 shows a SDOF system with a traditional IMD based on gear and flywheel. The IMD is supported by a rigid frame, whose deformation can be ignored. The IMD, which is made up of a gear with a radius and a flywheel with a radius supported by the frame through the fixed spiale. When the structure begins to move, the translational motion of the beam drives the gear to rotate and the flywheel rotates at the same time.  
The equation of motion of the system under seismic load is  
\[m\ddot{x}+c\dot{x}+kx+F_{IMD}(t)=-m\ddot{x}_{g} \tag{1}\]  
Figure 1: A single-degree-of-freedom (SDOF) system with an inertial mass damper (IMD): (**a**) Front view; (**b**) Side view; (**c**) Detailed view.  
where \(m\), \(c\), and \(k\) are the mass, damping, and stiffness of the SDOF system, respectively; \(x\) is the relative displacement response of the SDOF system, representatively; \(\ddot{x}_{g}\) is the acceleration of ground motion; and \(F_{IMD}(t)\) is the force provided by the IMD, which can be derived by the following analysis.  
From Figure 1c, it is obvious that the IMD satisfies the following moment equilibrium equation:  
\[F_{IMD}(t)\cdot r=J\cdot\ddot{\theta}(t) \tag{2}\]  
where \(J=MR^{2}/2\) represents the moment of inertia of the IMD and M is the flywheel mass of the IMD; \(\ddot{\theta}(t)\) is the angular acceleration of the IMD. Equation (2) is considered to be established ideally, ignoring the gear friction. The translational motion of the beam drives the gear to rotate. It can be obtained that  
\[\ddot{x}(t)=\ddot{\theta}(t)\cdot r \tag{3}\]  
Substituting Equation (2) and Equation (3) into Equation (1), \(F_{IMD}(t)\) can be expressed as  
\[F_{IMD}(t)=b\ddot{x}(t) \tag{4}\]  
where \(b=MR^{2}/2r^{2}\) is the inertance of the IMD, also called apparent mass by [PERSON] et al. [20]. Substituting Equation (4) into Equation (1), the equation of motion of the IMD system becomes  
\[(m+b)\ddot{x}+c\dot{x}+kx=-m\ddot{x}_{g} \tag{5}\]  
The ratio of \(b\) to \(m\) is known as the inertance-mass ratio, which is expressed as  
\[\mu=\frac{b}{m} \tag{6}\]  
Equation (5) can be normalized by dividing by \(m+b\) on both sides:  
\[\ddot{x}+2\frac{\xi}{\sqrt{1+\mu}}\frac{\omega}{\sqrt{1+\mu}}\dot{x}+\left( \frac{\omega}{\sqrt{1+\mu}}\right)^{2}x=-\frac{\ddot{x}_{g}}{1+\mu} \tag{7}\]  
where \(\omega\) and \(\xi\) are the natural circular frequency and damping ratio of the SDOF system, respectively:  
\[\omega=\sqrt{\frac{k}{m}};\xi=\frac{c}{2\sqrt{km}} \tag{8}\]
## 3 Strategy of Cold-Sky Calibration for Synthetic Aperture Radiometers
The visibility function of the synthetic aperture radiometer is performed through the process of cross-correlation with every pair of elements in the array [9]. The angle (\(\theta_{a}\), \(\varphi_{a}\)) of the relationship of the direct/reflected sun vector, the cold-sky region, the Earth-sky horizon, the land-ocean fraction, and the director cosine (\(\xi\), \(\eta\)) domain is established, as shown in the following equation. The brightness temperatureimage reconstruction of the synthetic aperture radiometer of the scene is formed by taking the inverse Fourier transform. \[(\xi,\eta)=(\sin\theta_{a}\cos\varphi_{a},\sin\theta_{a}\sin\varphi_{a}) \tag{10}\]  
Due to the variations and the effect of the external thermal influence and other space environment factors after launching, there are some differences between on-ground antenna patterns and the in-flight antenna patterns. The accuracy of synthetic aperture radiometer cold-sky calibration is affected by visibility function differences from a flat target, which has a brightness temperature error against a known cold (deep)-sky scene because of the effect of antenna pattern characterization errors. By adding some errors between the on-ground characterizations and in-flight ones, the pixel bias can be evaluated by the brightness  
Figure 7: Cold-sky calibration strategy criteria. temperature of the 'in-flight' cold-sky scene and the'real' cold-sky scene. The impact of the antenna error on cold-sky calibration performed using the strategy in Figure 7 can be determined via the following formula:  
\[\Delta\text{T}_{bias}=\frac{1}{N}\sum_{circle}\left(\hat{T}_{B}^{sim}(\xi,\eta) -T_{real}\right) \tag{11}\]
# Human Activity Signatures Captured Under Different Directions Using SISO and MIMO Radar Systems
## 5 Discussion
In all likelihood, the TV radial velocity distribution \(p_{ii}(v,t)\) generated by the proposed \(2\times 2\) MIMO radar system would ameliorate the classification performance upon appropriate data fusion. Therefore, a learning network based on the proposed \(2\times 2\) MIMO radar data would be able to recognize different activities in different directions. For a computationally efficient machine learning algorithm, we can extract multiple features from the TV radial velocity distributions \(p_{11}(v,t)\) and \(p_{22}(v,t)\). For instance, we have computed a key parameter known as TV mean radial velocity \(\bar{v}_{ii}(t)\), which is shown by the black dashed line in Figures 2, 3, 8, and 9. Higher-order parameters and features can be readily computed for the data obtained from the proposed \(2\times 2\) MIMO radar system. Alternatively, a separate convolutional neural network (CNN) [57] may be adopted to extract features from the data of Radar\({}_{1}\) and Radar\({}_{2}\). Then, the obtained features corresponding to Radar\({}_{1}\) and Radar\({}_{2}\) can be merged using a deep neural network for the classification of human activity. Conceivably, the proposed \(2\times 2\) MIMO solution combined with a machine learning or deep learning-based classifier would mitigate the concerns regarding a direction-independent HAR system.
# Inversion of Rayleigh Wave Dispersion Curve Extracting from Ambient Noise Based on DNN Architecture
## 2 Materials and Methods
### Generate Training Dataset
#### 2.1.2 Calculate Dispersion Data  
Next, we generated the fundamental mode Rayleigh wave phase velocity with a period range of 0.08 to 0.48 s with the Computer Programs in Seismology (CPS) software package [30]. The corresponding frequency range was 2.0 Hz-2.5 Hz. The reason for choosing this range was to keep consistent with measured data obtained in our previous research using ambient noise technology.  
In our study, we calculated 22,527 sample data pairs via MC and forward calculation. Ten groups of sample pairs were randomly selected to test the rationality (Figure 1). From Figure 1, we can see three typical velocity structures. Hence, this method could provide sufficient and diverse training data for the following research.
## 4 Discussion
conducted a landmark-based error analysis of 26 patients who underwent mandibular reconstruction using VSP-derived cutting and drilling jigs with patient-specific plates, reporting an average error of approximately 2 mm at key anatomical points, including the condesettings. The learning curve associated with VSP and the need for specialized expertise in both the planning and execution phases may also limit widespread adoption. Furthermore, the current inability to modify SLM-printed customized plates intraoperatively presents a significant challenge when unexpected anatomical variations or complications are encountered during surgery. Looking towards future perspectives, several key areas warrant further investigation. Future studies should focus on larger patient cohorts with longer follow-up periods to validate our preliminary findings. Multicenter randomized controlled trials comparing this technique with conventional approaches would be particularly valuable in establishing its clinical efficacy and cost-effectiveness. More homogeneous patient groups with specific defect types and standardized flap designs would help establish clearer guidelines for different clinical scenarios. The standardization of VSP protocols is needed to minimize errors and improve reproducibility across different centers [28]. Additionally, research into optimal customized plate designs should continue, with a particular focus on strategies to prevent hardware complications, as evidenced by our modification following the screw-loosening incident. Another important aspect that needs to be discussed is the potential for the automation of the VSP process, including through artificial intelligence and machine learning algorithms [29, 30]. While the current approaches rely heavily on manual planning and human expertise, AI-assisted planning could optimize the plate design, predict potential complications, and suggest ideal cutting planes based on accumulated case data. This would not only improve the accuracy but also reduce the planning time and associated costs.
# Multimodal Chinese Sarcasm Detection Integrating Audio Attributes and Textual Features
## 5 Experimental Results and Analysis  
### Main Experimental Results  
Table 4 presents the performance comparison between our proposed Multimodal Multi-Scale Convolutional Sarcasm Neural Network (MMCSNN) and several competitive baselines on the MCSh dataset. The baselines include models based on single modalities (text or audio) and multimodal fusion strategies.  
In the single-text modality, BERT achieves the best performance due to its strong contextual understanding.  
For the audio-only modality, the combination of OPENL3 and SVM achieves excellent results, indicating that audio cues such as pitch and tonal variation play a crucial role in sarcasm detection.  
After fusing the two modalities, a significant performance improvement can be observed, which demonstrates that combining textual and audio features indeed enhances sarcasm detection capabilities. Among them, our proposed MMCSNN model outperforms all the baseline methods across all the metrics, achieving an accuracy of 86.88%, an F1-score of 88.20%, and a Macro F1 of 86.17%.  
These results directly reflect the advantages of our model from a quantitative perspective. Further analysis of its structural improvements is provided in Section 5.2.  
To further strengthen the comparison, we additionally incorporate a recent multimodal baseline: Wav2 Vec2.0+BERT with cross-modal attention (CMA). This model leverages pre-trained encoders for both audio and text, followed by attention-based cross-modal fusion. As shown in Table 4, this method achieves strong performance (F1 = 83.20) but still lags behind our proposed MMCSNN across all the key metrics.  
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline
**Modality** & **Model** & **Acc (\%)** & **Pre (\%)** & **Rec (\%)** & **F1 (\%)** & **Macro Pre (\%)** & **Macro F1 (\%)** \\ \hline \multirow{3}{*}{Text} & TextCNN & 63.11 & 63.28 & 63.11 & 63.19 & 63.28 & 63.19 \\  & TextCNN+LSTM & 59.35 & 69.53 & 59.35 & 64.03 & 62.66 & 62.81 \\  & BERT & 68.37 & 69.73 & 68.37 & 69.04 & 69.73 & 69.04 \\ \hline \multirow{3}{*}{Audio} & VGGISH & 65.40 & 67.78 & 67.33 & 67.78 & 67.33 & 67.33 \\  & MFCC+SVM & 67.52 & 77.93 & 67.52 & 72.35 & 70.63 & 70.63 \\  & OPENL3+SVM & 74.71 & 80.24 & 74.71 & 77.38 & 74.12 & 74.12 \\ \hline \multirow{3}{*}{Text+Audio} & TextCNN+VGGISH & 73.33 & 73.33 & 76.85 & 75.09 & 73.91 & 73.91 \\  & BERT+VGGISH & 73.33 & 73.76 & 82.85 & 77.85 & 76.66 & 76.66 \\ \cline{1-1}  & Wav2 Vec2.0 + BERT + CMA & 83.74 & 81.13 & 85.37 & 83.20 & 81.26 & 82.89 \\ \cline{1-1}  & MMCSNN (Ours) & **86.88** & **84.01** & **92.85** & **88.20** & **86.24** & **86.17** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Main experimental results of unimodal and multimodal models on the MCSH dataset. **Bold values indicate the best results in each column.**
## 2 Results and Discussion
### Study of Extraction Temperature and Time  
The effect of temperature on the DI-SPME sampling is less than HS-SPME due to its mass transfer mechanism. However, to obtain an accurate quantification, it is necessary to evaluate this effect. For this propose, the effect of sample temperature was examined in the range of 20-60 \({}^{\circ}\)C. The results showed that the extraction efficiency increased with temperature up to 50 \({}^{\circ}\)C and then started to decline slightly (Figure 4). The initial rise in response can be attributed to the enhancement of migration through the diffusion layer near the fiber surface [28]. However, further raising the temperature did not favor the sorption and resulted in a decrease in the efficiency for both the DI- and EE-DI-SPME methods, due to exothermic nature of the sorption process. Hence, 50 \({}^{\circ}\)C was selected as the optimum value for further studies.  
To investigate the effect of extraction time, different experiments were conducted with varying times over the range of 5-40 min. The results revealed that peak areas were increasing up to 10 min for EE-DI-SPME and up to 25 min for DI-SPME (Figure 5). Using extraction times longer than 10 and 25 min for EE-DI-SPME and DI-SPME, respectively, resulted in relatively constant peak areas. Therefore, 10 and 25 min were selected as the best extraction times for the EE-DI- and DI-SPME-GC-FID methods, respectively.  
Figure 4: Study of the extraction temperature in the DI- and EE-DI-SPME sampling methods for the extraction of nicotine in aqueous media (condition: sample solution pH: 6; extraction time: 20 min; stirring rate: 500 rpm; applied voltage in EE-DI-SPME: \(-\)1 V).  
Figure 5: The efficiency of DI- and EE-DI-SPME-GC-FID procedures in different extraction times (condition: sample solution pH: 6; extraction temperature: 50 \({}^{\circ}\)C; stirring rate: 500 rpm; applied voltage in EE-DI-SPME: \(-\)1 V)
## 2 Methods  
### Experimental Design  
This study adopts Grounded Theory as a qualitative research approach, integrated with the SOR theoretical framework, to explore drivers' interaction preferences with voice assistants and the underlying dialogue-awakening mechanisms under varying fatigue states. As an inductive methodology, Grounded Theory emphasizes theory generation from empirical data rather than relying on preconceived assumptions, thereby enhancing the scientific validity and practical relevance of the findings. Through systematic analysis of interview data, this approach facilitates the construction of a theoretical model that captures the dynamic and complex nature of drivers' interaction behaviors with voice assistants.  
Within the framework of Grounded Theory, this study follows a three-stage coding process: open coding, axial coding, and selective coding. Initially, open coding is used to conduct a preliminary analysis of the interview transcripts, identifying key words and behavioral patterns. Axial coding then organizes these elements into categories reflecting drivers' interaction needs and preferences across different fatigue states. Finally, selective coding integrates these categories into a coherent theoretical model [25].  
To further understand the mechanisms of driver responses under different fatigue conditions, this study incorporates the SOR framework [26]. In this model, Stimulus refers to external factors influencing driver decision-making, such as the road environment, fatigue level, and the content and frequency of voice assistant interactions. Organism represents the internal psychological and physiological states of the driver--fatigue severity, cognitive load, and alertness--that mediate information processing. Response captures the driver's behavioral reactions to the voice assistant, including acceptance, interaction needs, and preferences regarding content, tone, and speech rate.