esa-sceva/satcom-chunk-collection huggingface dataset:
# Gaining Insights into Conceptual Models: A Graph-Theoretic Querying Approach
## 5 Use Cases for OPM Queries
### Cause and Effect in Model-Based System Engineering
#### 5.1.1 Impact Analysis by Forward Chaining: The Case of the Aircraft Collision Avoidance System
Cyber-physical systems are prime examples, in which the sensors and indicators that constitute their \"cyber\" part, are commonplace, and they monitor and control the physical subsystems, especially the critical ones. [PERSON] and [PERSON] [38] demonstrated how the 1979 Three Mile Island 2 (TMI-2) nuclear accident could be mitigated using OPM as a conceptual modeling framework. A graph representation of the OPM model, which includes mainly procedural links, reveals an upstream sub-graph that explains step-by-step what went wrong, leading to the eventual core meltdown. The explanatory power of the graphical representation is enhanced by examining the OPL paragraph that describes the accident causes. In cyber-physical systems, such a description helps determine the cyber-physical gap--the gap between the physical and the cyber realities--leading to an improved understanding of the system vulnerabilities and how to tackle them.
# Detection of Crustal Uplift Deformation in Response to Glacier Wastage in Southern Patagonia
## 4 Results
### Tendency and Seasonality Analysis  
The GBGR-CS combined series improved the amount of data by 17%, covering a total of 49% of the study period. Figure 4 shows the result for the CHLT, VOGH, and GBGR-CS time series. In red is shown the original series without outliers, and in blue, the interpolation points are added to continue with the processing for seasonality estimation.  
To assess the seasonality of the series, the signals were decomposed through the IMFs. Figure 5 shows the results of the GBGR-CS, CHLT and VOGH stations' time-series without gaps. They were decomposed into three intrinsic mode functions: long period, medium,  
Figure 4: Time series GPS data with the application of interpolation to fill out the gaps.  
Figure 3: (**a**) Velocity vectors of the continuous stations GBCS and GRCS, as well as the episodic stations PUMA and LPAS. (**b**) Altimetric profile of the GBCS, GRCS, and we added CHLT and UNPA located in the Patagonian plateau. (**c**) Up time series of the four stations denoting the slope of each of the GPS series.  
and high frequency. According to these results, we decided to emphasize the long period component because it showed a positive linear trend, but at the same time it overlaps with an annual periodic component of 10 mm. These few millimeters of signal amplitude with an annual or semi-annual period are partially caused by atmospheric and hydrological loads, the thermal expansion of soil and monumentations, or variations in multiple paths [56]. It should be noted that the high and medium frequency components have not been used for the seasonality analysis. The combined continuous stations located at the SPI plateau (GBGR-CS) and VOGH in the periphery, exhibit a similar tendency and period behavior; however, the CHLT station shows a linear and positive trend with a very slight seasonality. The estimated slopes for the stations according to a linear fit with a 95% confidence interval were 36.3 mm a\({}^{-1}\) for GBGR-CS, 24.8 mm a\({}^{-1}\) for CHLT, and 21.4 mm a\({}^{-1}\) for VOGH.  
The results of the Fourier transform and amplitude in the IFM's long period signal show that in two stations, the frequency is associated with an annual variation (\(f\) = 1 [1/a]) (See Figure 6). This signal predominates in the magnitude of the spectrum at stations GBGR-CS and VOGH, which may be linked to the hydrological cycle. Analyzing interannual frequency, only VOGH shows a frequency at \(f\) = 2.0 [1/a] that corresponds to a period of six months and the other to a period of 4 months. Likewise, an analysis of the yearly variations shows that the amplitudes of the seasonal signal are 0.0081 m (0.0162 m peak to peak) for GBGR-CS, and 0.085 m (0.017 m peak to peak) for VOGH. Although the adjustment for CHLT is poor, the amplitude reached 0.0019 m (0.0038 m peak to peak). As is shown in Figure 6, the annual signal does not predominate over the interannual signal as in the other stations.
## 2 Materials and Methods
### Sparse Representation Theory  
Every image \(Y(Y\in R^{n})\) could be represented by the atomic basis. The expansion coefficients form the matrix \(X(X\in R^{K})\). If \(n<K\), there are some vectors that cannot be represented by atomic bases. The vector basis of \(\alpha_{i}\) is not complete. If \(n\leq K\), the space vectors are expressed by the vector basis of \(\alpha_{i}\). So, the vector basis of \(\alpha_{i}\) is an over-complete basis and the expansion coefficients have a variety of combinations.  
Based on the above theory, researchers propose to use the over-complete basis to form a learning dictionary [29]. The over-complete basis is highly redundant and it could be represented by a variety of coefficients. We could select the sparsest set of coefficients as the solution, assuming the learning dictionary \(D\) is  
\[D=[d_{1},d_{2},d_{3},\ldots d_{K}]\in R^{n\times K} \tag{1}\]  
The input image can be described by  
\[Y=DX \tag{2}\]  
where \(X\) is the matrix formed by sparse vectors. It can be described in  
\[X=[x_{1},x_{2},x_{3},\ldots x_{K}]^{T} \tag{3}\]  
\(K\) is much more than \(n\). The more sparse \(X\) is, the more concentrated the image energy is. We usually use \(l_{0}\) norm to represent the sparsity and it means the non-zero numbers of the vectors or matrix. We utilize the sparse representation of non-local correlation to split the whole image into many IR patches. The patches are similar and can form a group. These groups can form the image matrix with low rank. As a result, the base function in every group is over-redundant. Through the optimization of sparse representation, we could attain the sparse solution to recover all groups. The image noise has been reduced in all IR groups. We need solve the problem in  
\[min||X||_{0},Y=DX \tag{4}\]  
We can transform to a Lagrange formula without limitations, which is expressed by  
\[argmin||Y-DX||_{\rm F}^{2}+\gamma||X||_{0} \tag{5}\]  
The normalization parameter is \(\gamma\). The main solution to solve the optimal function is the convex optimization approximation and greedy track based on image match. If noises exist, the \(l_{0}\) norm has no means to represent the vector sparsity. It is an NP-hard problem  
Figure 1: The flow chart of adaptive GSR model in IR denoising.  
that cannot lead to an optimal solution. We could use slack convex \(l_{1}\) norm to finish the convex optimization approximation [30]. It is described in  
\[argmin(||Y-DX||_{F}^{2}+\gamma||X||_{1}) \tag{6}\]  
The image uses the atoms as the vector basis. The sparse vector forms the sparse solution. For separating the noises from IR images, optimization computation is needed.
# Using the \(\beta\)/\(\alpha\) Ratio to Enhance Odor-Induced EEG Emotion Recognition
## 3 Results
### EEG Spectral Analysis
#### 3.3.2 Analysis of Spectrograms
In the case of the alpha2 rhythm (Figure 3I), all ROIs except the T-ROI and C-ROI regions showed a significant enhancement due to SEO (F-ROI: \(t=-2.213\), \(p=0.016\); T-ROI: \(t=-0.898\), \(p=0.187\); C-ROI: \(t=-1.087\), \(p=0.141\); P-ROI: \(t=-1.908\), \(p=0.031\); O-ROI: \(t=-2.604\), \(p=0.006\)), with BEO affecting only the F-ROI (F-ROI: \(t=-2.151\), \(p=0.018\); T-ROI: \(t=-0.732\), \(p=0.234\); C-ROI: \(t=-1.075\), \(p=0.144\); P-ROI: \(t=-1.671\), \(p=0.051\); O-ROI: \(t=-1.646\), \(p=0.053\)). In conclusion, compared to RS, inhalation of either SEO or BEO resulted in varying degrees of increase in the PSD of \(\alpha\)1 and \(\alpha\)2 across all ROIs. The distinction lies in the alpha1 frequency band, where inhalation of SEO significantly increases PSD in most ROIs, while inhalation of BEO does so only at a few electrodes. Notably, the PSD of the \(\beta/\alpha\) ratio decreased across all ROIs in the SEO group, while it increased across all ROIs in the BEO group. In conjunction with the self-reported data, which indicated that inhalation of SEO and BEO induced low and high arousal emotions, respectively, these results suggest that the \(\beta/\alpha\) ratio has the potential to serve as an arousal marker [13; 14; 15]. These findings also provide objective evidence that inhaling SEO induces low-arousal emotions, while inhaling BEO induces high-arousal emotions.
## 3 Results and Discussion
### Statistical Analysis of Z. kerrii Volatile Components  
PCA was used to determine the differences in the obtained volatile components of _Z. kerrii_ according to the plant part and the extraction technique (Figures 1 and 2). The principal components (PC) were selected according to the highest significance and the explanation of the variation. In the PCA results, all samples were separated into three clusters in the PCA score plot according to the main components (Figure 1). The PCA score plot revealed that the essential oil compositions were associated with the plant part and the extraction method as well as PC1 and PC2. PC1 explained 44.5% of the total volatile component variation and PC2 accounted for 24.0%. The flowers and the leaves exhibited similar major components, but the rhizomes were different. The extraction methods markedly impacted the chemical compositions of essential oils. As shown in Figure 2, the negative axis was highly influenced by \(\alpha\)-pinene, \(\beta\)-pinene, (_E_)-caryophyllene, \(\beta\)-elemene, \(\alpha\)-zingiberene, valencene, (_E_)-\(\beta\)-cocimene, terpinen-4-ol, \(\alpha\)-humulene, linalool, and mycrene, all of which were present in large amounts. This was considered to be the strongest determinant of chemical composition identification.  
Figure 1: Principal component analysis (PCA) plots of all plant parts and extraction techniques.  
Figure 2: PCA loading plots revealing the compounds present in _Z. kerrii_.  
Two-way ANOVA analysis was performed to define the variables of the chemical composition according to plant part and extraction method, revealing a significant (\(p<0.05\)) interaction between the plant part and the extraction method with the volatile components.
# Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals
## 6 Conclusions  
In this paper, a dual residual denoising autoencoder with channel attention (DRdA-CA) is designed based on CNN to deal with the problems caused by a low SNR in wireless communications. First, as a channel attention mechanism, the SE module with one residual connection is adapted and applied to the proposed DRdA-CA to improve the feature extraction ability of neural networks. Then, the other residual connection between the different coding (decoding) blocks is introduced to solve the network degradation problem, which promotes the fusion of model characteristic information at different depths, and further enhances the network noise reduction performance. Moreover, a dataset including eight modulation types is created under the AWGN channel model.  
The ablation experiments are done to verify the efficiency of the improved SE module and the dual residual connections, and the dual residual connections are more helpful in improving the denoising performance. Then, the experiments on SNR improvement, modulation classification, and demodulation are also done to verify the advantage of the proposed method. The simulation results show that the proposed DRdA-CA surpasses the traditional denoising algorithms and the DnCNN noise reduction method in both improving the SNR and recovering the original signals for all eight modulation modes. Taking GMSK and 8 PSK as examples, the proposed DRdA-CA improves the SNR by 16.84 dB and 9.47 dB on average, respectively. Compared to the DnCNN denoising method, the proposed DRdA-CA makes the recognition accuracy of each modulation signal increase significantly. Even the recognition accuracy of 16 QAM and 64 QAM goes up steadily with the increase in the SNR. Besides, demodulation performance is also dramatically improved after using the proposed DRdA-CA to reduce noise. Taking the SNR of 8 dB as an example, for 64 QAM signals, the proposed DRdA-CA lowers BER by 43.34% compared to the DnCNN method. These experiments further demonstrate our method's strength. In the future, we will extend our designs and make it suitable for the fading-channel scenario, where modulation signals are prone to be affected by multipath propagation and the Doppler frequency shift.  
Conceptualization, R.D. and Z.C.; methodology, R.D. and H.Z.; validation, R.D., Z.C. and H.Z.; formal analysis, H.Z. and X.W.; investigation, G.S.; resources, W.M.; data curation, Z.C.; writing--original draft preparation, R.D. and X.W.; writing--review and editing, Z.C. and W.M.; visualization, H.Z. and G.S.; supervision, W.M.; project administration, R.D. and H.Z.; funding acquisition, R.D. All authors have read and agreed to the published version of the manuscript.  
The work was supported by the Fundamental Research Funds for the Central Universities (BLX201623), Beijing Natural Science Foundation (L202003) and National Natural Science Foundation of China (No.31700479).  
Figure 11: Demodulation BER after denoising.
# Thermal Fatigue Properties of Ultrasonically Bonded Copper Joints
## 4 Conclusions  
In summary, the influence of the microstructure of ultrasonically bonded copper joints on thermal fatigue properties was evaluated. We achieved an improvement of the thermal fatigue resistance property of joints based on designing the bonding substrate. The main conclusions are as follows:  
* Microstructural observations of the C1020 joints confirmed that the bonding interface was formed by fine grains. As the grain boundary can be an obstacle to crack propagation, the cracks caused by thermal fatigue did not progress to the fine grain region and propagated by avoiding that region.
* C1940 joints with a harder substrate and finer grain size than C1020 joints resisted thermal fatigue fracture during thermal cycling tests. As the vicinity of the bonding interface was constituted by finer grains in the C1940 joint, crack propagation in the bonding interface of C1940 joints was suppressed because of the presence of more grain boundaries.
* Grain refinement was also confirmed at the terminal of the C1940 joint containing the same material as that used in the terminal of the C1020 joint. It was considered that the time for which the terminal was subjected to sliding friction increased owing to hardening of the substrate, and the grains of the terminal that were subjected to longer processing times were finer.  
Conceptualization, T.Y. and A.H.; experiments and data analyses, T.F., Y.T. and T.M.; discussion, all authors; writing, T.F. and T.M.  
This research received no external funding.  
The authors declare no conflicts of interest.
## 4 Model of Electric Motors and Concurrent Efficiency Maps  
All the selected electric motors were a DC permanent magnet (PM) type, and their characteristics are expressed in the Appendix A section. For the in-wheel electric motor architecture, each of the in-wheel motor powers equaled 17 kW, and the total power of the vehicle was determined as 68 kW. On the other hand, for the architectures of 1G, 2G, and CVT, the motors had a separate power of 34 kW, and the total powers of the vehicles were the same for all the architectures. However, the power of the downsized electric motor was 30 kW, and the total power of the vehicle was 60 kW, less than that of the other architectures. The equations of the consumed and recovered currents are expressed in Equation (15), and the energy consumption of the fuel cells was activated during the driving case, while the state of charge of the supercapacitor was run during braking conditions.  
\[I=\left\{\begin{array}{ll}\frac{T\omega_{EM}}{uV_{C}},&\mbox{ {driving situation}}\\ \frac{uT\omega_{EM}}{V_{C}},&\mbox{{ brake situation}}\end{array}\right. \tag{15}\]  
Here, the term \(I\) represents the consumed and recovered current, \(T\) is the motor shaft torque, \(\omega_{EM}\) represents the angular speed of the electric motor, \(u\) denotes the efficiency of the electric motor with respect to the torque and angular speed, and \(V_{C}\) indicates the boost converter voltage. The efficiency of the electric motor is expressed as follows:  
\[\mu=100\frac{T\omega_{EM}}{T\omega_{EM}+P_{0}+k_{e}T^{2}+k_{w}\omega_{EM}{}^{2}} \tag{16}\]  
where \(P_{0}\) is the fixed power loss, \(k_{e}\) represents the torque-dependent electrical loss coefficient, and \(k_{w}\) is the speed-dependent iron loss coefficient.  
\[T_{peak}=k_{T}D_{EM}^{2}L_{EM} \tag{17}\]  
The peak torque value can be found using Equation (17), where \(k_{T}\) is described as a torque constant, \(D_{EM}\) is the diameter of the motor, and \(L_{EM}\) denotes the length of the motor.  
Note that, the motor shaft torque T and angular speed \(\omega_{EM}\) are exposed to gear ratios and their efficiencies given in Table 2. The efficiency map of CVT is adopted from [24].
# Investigating and Modeling of Cooperative Vehicle-to-Vehicle Safety Stopping Distance
###### Abstract  
Dedicated Short-Range Communication (DSRC) or IEEE 802.11p/OCB (Out of the Context of a Base-station) is widely considered to be a primary technology for Vehicle-to-Vehicle (V2V) communication, and it is aimed toward increasing the safety of users on the road by sharing information between one another. The requirements of DSRC are to maintain real-time communication with low latency and high reliability. In this paper, we investigate how communication can be used to improve stopping distance performance based on fieldwork results. In addition, we assess the impacts of reduced reliability, in terms of distance independent, distance dependent and density-based consecutive packet losses. A model is developed based on empirical measurements results depending on distance, data rate, and traveling speed. With this model, it is shown that cooperative V2V communications can effectively reduce reaction time and increase safety stop distance, and highlight the importance of high reliability. The obtained results can be further used for the design of cooperative V2V-based driving and safety applications.  
Keywords:collision avoidance; DSRC; stopping distance; software defined radio; testbed; V2V; consecutive loss; vehicular communication; ADAS +
Footnote †: journal:  
_Article_
## 4 Performance Evaluation and Discussion
### Robustness to Ambient Noise  
In this subsection, the STR of the proposed audio marker system was measured in noisy environments to evaluate its robustness to ambient noise according to different noise levels. To this end, the audio marker receiver and transmitter were placed in the middle of an office with a distance of 2 m, and a monitor speaker was installed in a corner of the office. Specifically, three different ambient noises that had been recorded in a bus-stop, street, and cafeteria were played out through the monitor speaker, where the SPL ranged from 70 to 100 dB at a step of 10 dB. In this experiment, 100 different audio markers were repeatedly transmitted through the proposed audio marker system for each ambient noise condition.  
Figure 9 shows the STR of the proposed audio marker system for three different noise conditions according to four different noise levels. As shown in the figure, the proposed audio marker system operated perfectly, with an accuracy of 100% at 70 dB\({}_{\text{SPL}}\). Additionally, its STR was relatively high between 80% and 90%, when the noise level was under 90 dB\({}_{\text{SPL}}\). However, the STR largely dropped when the noise level reached 100 dB\({}_{\text{SPL}}\).  
To investigate the reason why the STR dropped at 100 dB\({}_{\text{SPL}}\), the power spectral density (PSD) of the LFU signal was compared with that of bus-stop noise with levels of 90 and 100 dB\({}_{\text{SPL}}\). As shown in Figure 10, side-lobe components of the LFU signal were distinguishable at the 90 dB\({}_{\text{SPL}}\) condition (as indicated by a yellow box), but they were totally overlapped with the PSD of noise at the 100 dB\({}_{\text{SPL}}\) condition. This was because the ambient noise of 100 dB\({}_{\text{SPL}}\) subsequently interfered with the LFU signal generated by the proposed audio marker system. This implied that the proposed audio maker system could robustly operate under ambient noises at a level of up to 90 dB\({}_{\text{SPL}}\), which would be considered as excessively large in every-day life.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method Measure** & **False Negative Rate** & **False Positive Rate** \\  & **Inside of MTZ (\%)** & **Outside of MTZ (\%)** \\ \hline Conventional audio marker [14] & 2.24 & 93.28 \\ Proposed audio marker & 2.08 & 14.28 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the average transmission accuracy (%) for the conventional and proposed audio marker system inside and outside the maximum transmitting zone (MTZ).  
Figure 9: Successful transmission rate of the proposed audio marker system for different ambient noise conditions.
## 2 Materials and Methods
### Gan
V(D,~{}G)=\mathbb{E}_{\mathbf{X}\sim p_{data}(\mathbf{x})}~{}[\log(D (\mathbf{X}))]+\mathbb{E}_{\mathbf{Z}\sim p_{Z}(\mathbf{z})}~{}[\log(1-D(G( \mathbf{Z})))] \tag{7}\]  
where \(p_{data}(\mathbf{x})\) denotes the true data distribution from which real samples \(\mathbf{x}\) are drawn, \(p_{Z}(\mathbf{z})\) is the prior distribution over the latent space, typically chosen as a multivariate Gaussian or uniform distribution, \(G(\mathbf{Z})\) is the synthetic data generated from noise \(\mathbf{Z}\), and \(D(. )\) represents the discriminator's estimated probability that an input sample is real. The generator \(G\) aims to minimize this value function by producing samples that maximize \(D(G(\mathbf{Z}))\), thus \"fooling\" the discriminator into misclassifying fake samples as real. Conversely, the discriminator \(D\) tries to maximize this objective by correctly identifying real and synthetic inputs. The optimization process alternates between updating the discriminator and the generator. The discriminator's loss function, which quantifies its ability to separate real from fake samples, is given by Equation (8) [42]:  
\[L_{D}=-\Big{(}\mathbb{E}_{\mathbf{X}\sim p_{data}(\mathbf{x})}~{}[\log(D( \mathbf{X}))]+\mathbb{E}_{Z\sim p_{Z}(\mathbf{z})}~{}[\log(1-D(G(\mathbf{Z}))) ]\Big{)} \tag{8}\]  
where \(L_{D}\) aims to maximize the likelihood of correctly classifying real samples while minimizing the likelihood of incorrectly classifying fake samples. In contrast, the generator's loss, expressed in Equation (9), is designed to enhance the realism of synthetic outputs:  
\[L_{G}=-\mathbb{E}_{Z\sim p_{Z}(\mathbf{z})}~{}[\log(1-D(G(\mathbf{Z})))] \tag{9}\]  
which encourages the generator to produce samples that the discriminator classifies as real. Here, \(L_{G}\) minimizes the negative log-likelihood classifying generated samples as authentic. Within the architecture depicted in Figure 2, the generator \(G\) is typically composed of multiple fully connected layers or transposed convolutional layers, depending on the complexity and structure of the desired output space. The generator gradually transforms a low-dimensional noise vector into a high-dimensional synthetic sample that matches the characteristics of real data. Meanwhile, the discriminator \(D\) consists of several neural network layers designed to extract hierarchical features from its inputs, culminating in a final sigmoid-activated neuron that outputs the probability of the sample being real. The competitive yet cooperative dynamic between \(G\) and \(D\) ultimately results in a generative model capable of approximating highly complex data distributions, an ability that is critical for enhancing the diversity and robustness of datasets in IoT intrusion detection applications [43].