esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT-Enabled Vehicle Speed Monitoring System
## 5 Conclusions and Future Work  
In recent years, we have witnessed great technological innovations and inventions in society, which have impacted lives substantially. Road safety and traffic violation surveillance systems were not left out. In this paper, we propose an IoT-based approach to automatically monitor speeding vehicles on highways without having to deploy traffic personnel. The traditional speed checking systems in the transportation industry fail to report the majority of speeding violations due to their respective flaws. For instance, speed guns on roads work in isolation, and cannot measure the speeds of all vehicles at all spatial points and can only accurately detect speeds of vehicles in front of the camera. Thus, a huge number of speed guns would be required to solve that problem, which would make the system far more costly. The proposed system, with the help of the IoT, measures the average speed between two detection points with surveillance cameras. The measured data are sent to the cloud for further calculations to enforce speed limits. With the help of entrance and exit points, information about any uncertainty in a given region is noticed. For example, a car passing an entrance point that does not reach the end point can be highlighted.  
The system consists of a web network and a mobile phone application sharing real-time data, including details of the vehicles passing by, such as entrance time, photos, and registration numbers on license plates. Two different approaches (streaming a recorded MP4 video and installing Raspberry Pi with an IP camera) tested the efficiency of the proposed system. The advantages of such a system include installing comparatively few speed guns, monitoring vehicles even if they are not within the camera's line of sight, and minimum human intervention.
# A Software-Defined Networking Framework to Provide Dynamic QoS Management in IEEE 802.11 Networks
## 3 Proposed SDN-Based Framework for Implementing DEDCA
### Selecting Giving Terminals  
As said before, the aim objective of our solution is to change the way the available bandwidth is shared among the cameras in order to allow a _requesting camera_ to transmit the video with a higher resolution. In this scenario we want to improve the resolution from 4 CIF@12 fps to 720p@12 fps, which means to double the required data rate.  
If the default value of \(CW_{min}\) is set to 31 in all the cameras, from Equation (5), it can be deduced that the value of \(CW_{min}\) of the _requesting camera_ should be reduced in 16 units in order to double the probability of winning the channel.  
Because the _requesting camera_ improves its bandwidth, the rest of cameras will get worse. However, as it is studied by simulation in Section 4.1.1, if a subset of cameras are chosen to compensate the increase in the gain of the _requesting terminals_, the remaining nodes will not notice any change in their channel access rate. It does not matter how the compensation is done as long as the increment in the obtained bandwidth of the _requesting terminal_ is equal to the decrement in the obtained bandwidth of the _giving terminals_ (see Equation (7)).  
This behavior has been taking into account to define the algorithm that the controller applies to select the _giving terminals_ in this scenario and to obtain their required \(CW_{min}\) values. When a camera launches an Alarm, the SDN controller will consider as _requesting terminals_ this camera and its two neighbor cameras. This will enable to increase the video resolution of these cameras, offering to the security officer a more detailed view of the conflict area. In addition, the controller has to select the _giving stations_ and to obtain their new \(CW_{min}\) values. In this implementation, we have chosen that the _giving stations_ are the seven furthest cameras, as it is shown in Figure 5. Finally, applying Equation (7), the new \(CW_{min}\) value of these terminals is obtained.  
Figure 4: Communication flow of the implemented solution.  
In this case, the number of stations \(s=16\), and the number of _requesting stations_ and _giving stations_ are set to \(r=3\) and \(g=7\), respectively. As the objective of the requesting stations is to duplicate the obtained bandwidth, \(G^{+}=2\). Consequently, applying Equation (7) it can be obtained that the _giving stations_ have to increase their \(CW_{min}\) value in \(k=24\) units, so their \(CW_{min}\) value will be increased to 55.
## 4 Results and Analysis  
### CEIs  
The five indicators (NDVI, LAI, GPP, LST, and Wet) of the study area in 2010, 2015, and 2020 were obtained, respectively. Then, the PCA (Table 1) was carried out, and the distribution results of the CEIs in 2010, 2015, and 2020 are shown in Figure 4.  
Table 4 shows that, in the first principal component (PC1), the indicators with larger coefficients are GPP, LAI, and NDVI, and the indicators in the three periods are positive and large. In the second principal component (PC2), LST is the index with larger coefficients in the first two periods: 0.955 and 0.984 in the two periods, respectively, while NDVI is the index with a larger coefficient in the third period: 0.709. Since the cumulative variance contribution rate of the first two principal components of the data in 2020 did not reach80%, the first three principal components were selected. In the third principal component (PC3), LST has a larger coefficient of 0.787.  
Figure 4 shows that the CEIs in the middle of the study area are high, i.e., Jiuyquan City, Jiayuguan City, Zhangyce City, and Haibei Tibetan Autonomous Prefecture. However, the CEIs in the southeast and northwest of the study area are low, i.e., Lanzhou City, Baoji City, Pingliang City, Urumqi City, Turpan City, and Changji Hui Autonomous Prefecture.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Indexes**} & \multicolumn{2}{c}{**2010**} & \multicolumn{2}{c}{**2015**} & \multicolumn{2}{c}{**2020**} \\ \cline{2-9}  & **PC1** & **PC2** & **PC1** & **PC2** & **PC1** & **PC2** & **PC3** \\ \hline GPP & 0.955 & 0.008 & 0.930 & 0.048 & 0.941 & \(-\)0.215 & 0.151 \\ \hline LAI & 0.912 & \(-\)0.219 & 0.931 & \(-\)0.079 & 0.795 & \(-\)0.458 & 0.344 \\ \hline LST & \(-\)0.072 & 0.955 & \(-\)0.113 & 0.984 & \(-\)0.254 & 0.560 & 0.787 \\ \hline NDVI & 0.933 & \(-\)0.030 & 0.941 & 0.002 & 0.546 & 0.709 & \(-\)0.331 \\ \hline Wet & 0.744 & 0.387 & 0.763 & 0.182 & 0.826 & 0.389 & \(-\)0.042 \\ \hline Eigenvalue & 3.172 & 1.111 & 3.213 & 1.009 & 2.564 & 1.224 & 0.872 \\ \hline Contribution rate of variance (\%) & 63.446 & 22.217 & 64.262 & 20.183 & 51.272 & 24.486 & 17.448 \\ \hline Accumulated variance contribution rate (\%) & 63.446 & 85.663 & 64.262 & 84.445 & 51.272 & 75.758 & 93.206 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The contribution rates and characteristic values of the principal components.  
Figure 4: Distribution of CEI in 2010, 2015, and 2020.
## 4 Evaluation
### Failure Detection Evaluation--Results and Analysis
DBSCAN obtained an excellent performance for marking outliers over all the tests with a FAR lower than 6%, a perfect Recall and an Accuracy, Precision, and F-Score greater than 99% for the most extensive dataset. Furthermore, DBSCAN does not require a re-setting of the parameter eps when the outliers increase. Note that IF got the highest Precision and lowest FAR. However, the IF's contamination parameter needs to be re-set when the outliers raise to keep high performance. The IF's false negatives will increase significantly if the real number of outliers is lower than the contamination. In turn, the false positives of IF will increase when the actual number of outliers is higher than the contamination. This re-setting is unpractical because it is hard to know the percentage of outliers previous to the algorithm's training. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & & **Accuracy** & **Recall** & **Precision** & **F-Score** & **FAR** \\ \hline \multirow{3}{*}{1\% Outliers} & DBSCAN (e = 0.2–0.6) & 100 & 100 & 100 & 100 & 0 \\  & IF (c = 0.01) & 100 & 100 & 100 & 100 & 0 \\  & SVM (nu = 0.01) & 100 & 100 & 100 & 100 & 0 \\ \hline \multirow{3}{*}{5\% Outliers} & DBSCAN (e = 0.2–0.6) & 99.65 & 100 & 99.64 & 99.82 & 7.14 \\  & IF (c = 0.07) & 97.56 & 97.45 & 100 & 98.71 & 0 \\  & SVM (nu = 0.09) & 95.83 & 95.62 & 100 & 97.76 & 0 \\ \hline \multirow{3}{*}{10\% Outliers} & DBSCAN (e = 0.1–0.2) & 99.65 & 100 & 99.62 & 99.81 & 3.45 \\  & IF (c = 0.2) & 89.93 & 88.80 & 100 & 94.07 & 0 \\ \cline{1-1}  & SVM (nu = 0.1) & 98.96 & 99.61 & 99.23 & 99.24 & 6.90 \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison Per_Day dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & & **Accuracy** & **Recall** & **Precision** & **F-Score** & **FAR** \\ \hline \multirow{3}{*}{1\% Outliers} & DBSCAN (e = 0.3–0.8) & 100 & 100 & 100 & 100 & 0 \\  & IF (c = 0–0.04) & 100 & 100 & 100 & 100 & 0 \\  & SVM (nu = 0.01–0.1) & 83.33 & 82.61 & 100 & 90.48 & 0 \\ \hline \multirow{3}{*}{5\% Outliers} & DBSCAN (e = 0.3–0.8) & 100 & 100 & 100 & 100 & 0 \\  & IF (c = 0.05–0.08) & 100 & 100 & 100 & 100 & 0 \\  & SVM (nu = 0.09–0.1) & 100 & 100 & 100 & 100 & 0 \\ \hline \multirow{3}{*}{10\% Outliers} & DBSCAN (e = 0.2) & 100 & 100 & 100 & 100 & 0 \\  & IF (c = 0.09–0.1) & 100 & 100 & 100 & 100 & 0 \\  & SVM (nu = 0.09–0.1) & 79.16 & 80.95 & 94.44 & 87.18 & 33.33 \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison Per_Hour dataset.
## 3 Signal Models for Direct Position Determination  
### Time-Domain Signal Model  
Consider an emitter and \(N\) base stations intercepting the transmitted signal. Each base station is equipped with an antenna array consisting of \(M\) elements. The transmitter's position is denoted  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Explanation** \\ \hline \(\otimes\) & [PERSON] product \\ \(\odot\) & Schur product \\ diag \([\cdot]\) & a diagonal matrix with diagonal entries formed from the vector \\ blkdiag \([\cdot]\) & a block-diagonal matrix formed from the matrices or vectors \\ \([\cdot]^{\dagger}\) & Moore-Penrose inverse of the matrix \\ \(\mathbf{I}_{n}\) & \(n\times n\) identity matrix \\ \(i_{n}^{(k)}\) & the \(k\)th column vector of \(\mathbf{I}_{n}\) \\ \(\mathbf{O}_{n\times m}\) & \(n\times m\) matrix of zeros \\ \(\mathbf{1}_{n\times 1}\) & \(n\times 1\) vector of ones \\ \(\lambda_{\text{max}}\{\cdot\}\) & the largest eigenvalue of the matrix \\ \(||\cdot||_{2}\) & Euclidean norm \\ \(<\cdot>_{n}\) & the \(n\)th entry of the vector \\ \(<\cdot>_{\text{max}}\) & the \(n\)th entry of the matrix \\ Re\(\{\cdot\}\) & real part of the argument \\ Im\(\{\cdot\}\) & imaginary part of the argument \\ Pr\(\{\cdot\}\) & probability of the given event \\ E\([\cdot]\) & mathematical expectation of the random variable \\ var\([\cdot]\) & variance of the random variable \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notational conventions.  
by an \(L\times 1\) vector of coordinates \(\mathbf{p}\). In practice, \(L\) is equal to two or three, and cannot be larger than three. We consider the case where there is no multipath or non-line-of-sight (NLOS) phenomenon. The complex envelopes of the signal observed by the \(n\)th base station are then modeled by [29]  
\[\mathbf{x}_{n}(t)=\beta_{n}\mathbf{a}_{n}(\mathbf{p})s(t-\tau_{n}(\mathbf{p}\,)-t_{0})+\mathbf{e}_{n }(t)\quad(1\leq n\leq N)\,, \tag{1}\]  
where  
* \(\mathbf{a}_{n}(\mathbf{p})\) is the \(n\)th array response to the signal transmitted from position \(\mathbf{p}\),
* \(s(t-\tau_{n}(\mathbf{p}\,)-t_{0})\) is the unknown signal waveform transmitted at unknown time \(t_{0}\),
* \(\tau_{n}(\mathbf{p}\,)\) is the signal propagation time from the emitter to the \(n\)th base station (i.e., distance divided by signal propagation speed),
* \(\beta_{n}\) is an unknown complex scalar representing the channel attenuation between the transmitter and the \(n\)th base station,
* \(\mathbf{e}_{n}(t)\) is temporally white, circularly symmetric complex Gaussian random noise with zero mean and covariance matrix \(\sigma_{e}^{2}\mathbf{I}_{M}\).  
Assuming the observation vector \(\mathbf{x}_{n}(t)\) is sampled with period \(T\), the \(k\)th sampled data can be expressed as  
\[\mathbf{x}_{n,k}=\beta_{n}\mathbf{a}_{n}(\mathbf{p})s(kT-\tau_{n}(\mathbf{p}\,)-t_{0})+\mathbf{e}_ {n,k}\quad(1\leq k\leq K)\,, \tag{2}\]  
where \(K\) is the number of snapshots.
## 4 Stress Intensity Factor of Weld Crack
### Stress Intensity Factor Analysis
#### 4.2.2 Stress Intensity Factor after Considering Residual Stress  
According to Section 3.2, the transverse residual tensile stress generated at the connection between the deck and the U-rib has the greatest impact on fatigue cracks, which will accelerate crack development. This paper uses FRANC 3D to add lateral residual tensile stress to finite element analysis. Under the most unfavorable vehicle load and residual stress, the stress intensity factor at the midspan weld details is shown in Figure 15. The maximum value of the crack K\({}_{\text{I}}\) at the weld toe is 319 MPa\(\cdot\)mm\({}^{1/2}\) at the deepest point of the crack, which is about 6.9 times higher than that of the crack under vehicle loading only. The maximum value of the crack K\({}_{\text{I}}\) at the weld root is 246 MPa\(\cdot\)mm\({}^{1/2}\), which is about 10.7 times higher than that under vehicle loading only. Therefore, it can be found that the transverse residual stress has a significant impact on the fatigue crack at the weld joint. Therefore, when analyzing the crack at this location, the residual stress should not be ignored. Under the combined action of residual stress and vehicle load, the cracks at the welding details are also dominated by Type I composite cracks, and K\({}_{\text{I}}\) is much larger than K\({}_{\text{II}}\) and K\({}_{\text{III}}\).  
Figure 14: Stress intensity factor of a crack under vehicle only. (**a**) Weld toe; (**b**) weld root.  
Figure 15: Stress intensity factor of a crack considering residual stress. (**a**) Weld toe; (**b**) weld root.
###### Abstract  
The transportation of freestanding packages, such as steel coils and prefabricated building components, poses significant safety challenges due to their susceptibility to dynamic responses like rocking, sliding, and overturning under vehicle acceleration. This study focuses on the critical rocking motion, which can lead to catastrophic accidents if the rocking angle exceeds a critical threshold. The package is modeled as a rigid block on a moving base, and its motion states, including static equilibrium, bouncing, rocking, and detachment, are analytically determined, with specific conditions identified for each state. A novel numerical algorithm, combining a time transformation method with the Runge-Kutta method, is proposed to efficiently determine collision time instants and analyze the piecewise-smooth rocking response. This approach partitions the solution into smooth intervals between impacts, significantly improving computational efficiency while maintaining accuracy. Experimental validation under sinusoidal and numerical analysis with random excitations confirms the algorithm's effectiveness in predicting rocking behavior. The results highlight the importance of the slenderness ratio and restitution coefficient in governing motion states and demonstrate the algorithm's superiority over traditional methods in terms of precision and efficiency. This work provides a robust framework for assessing transportation safety and optimizing package design, with implications for reducing overturning risks in practical scenarios.  
Keywords:freestanding packages; rocking motion; motion state; numerical algorithm; transportation safety; overturning risk _Article_
## 3 Discussion
Most of the characteristic volatiles in 'Jinguanyin' black tea could be detected in the freeze-dried tea of 'Jinguanyin' with relatively high contents. The floral fragrance of 'Chunyuz' black tea was mainly attributed to the high levels of certain alcohols, namely 2-[(1S)-4-methylcyclohex-3-en-1-yl]propan-2-ol, 3,7-dimethylocta-1,6-dien-3-ol, 2-[(_2S_,_5S_)-5-ethehenyl-5-methyloxolan-2-yl]propan-2-ol, 6-ethenyl-2,2,6-trimethyloxan-3-ol, 2-ethylhexan-1-ol, and heptan-2-ol, which were also abundantly present in 'Chunyuz' green tea. The similarity of alcohol composition to 'Jinguanyin' black tea (Figure 4) and relatively high content of alcohols could be plausible explanations for the floral aroma of 'Chunyuz' black tea. Thus, it seems the scent types of green tea and black tea could be influenced by fresh tea leaves as raw materials. The significant correlations between the contents of floral odorants in black tea and fresh tea leaves indicated that the tea cultivar is the key factor in the formation of floral scents since intense enzymatic reactions such as oxidation and polymerization occur during the processing of black tea. The original contents of floral odorants in fresh tea leaves importantly affect their levels in dry teas. Tea cultivar is an important factor to the chemical composition of fresh tea leaves. Based on the co-expressions analysis of 27 shared volatiles in all the tea samples, the contents of 3,7-dimethylocta-1,6-dien-3-ol, (_2E_)-3,7-dimethylocta-2,6-dien-1-ol, 2-[(_2S_,_5S_)-5-ethenyl-5-methyloxolan-2-yl]propan-2-ol, (_5E_)-3,7-dimethylocta-1,5,7-trien-3-ol, nonan-1-ol, (_E_)-4-(2,6,6-trimethylcyclohex-1-yl)but-3-en-2-one, and 3-methyl-2-[(_Z_)-pent-2-enyl] cyclopent-2-en-1-one in freeze-dried sample had correlations with their contents in both green tea and black tea, which means these compounds are more cultivar-dependent compared with the influence of processing methods. Since 3,7-dimethylocta-1,6-dien-3-ol, (_2E_)-3,7-dimethylocta-2,6-dien-1-ol, 2-[(_2S_,_5S_)-5-ethenyl-5-methyloxolan-2-yl]propan-2-ol, (_5E_)-3,7-dimethylocta-1,5,7-trien-3-ol, nonan-1-ol, trimethylcyclohex-1-yl)but-3-en-2-one, 3-methyl-2-[(_Z_)-pent-2-enyl]cyclopent-2-en-1-one are associated with floral fragrance [2,9,11,16,29], it verified that the formation of the floral scent of dry teas were essentially attributed to the tea cultivar used, although the intensity of floral aroma may be impacted by processing methods. 3,7-Dimethylocta-1,6-dien-3-ol and its oxide 2-[(_2S_,_5S_)-5-Ethenyl-5-methyloxolan-2-yl]propan-2-ol showed congruous change trend in both green tea and black tea, since 2-[(_2S_,_5S_)-5-ethenyl-5-methyloxolan-2-yl]propan-2-ol is the oxidization product of 3,7-dimethylocta-1,6-dien-3-ol [22]. For the comparison between different tea cultivars, the high level of 3,7-dimethylocta-1,6-dien-3-ol in fresh tea leaves led to the abundant generation of its oxides such as 2-[(_2S_,_5S_)-5-ethenyl-5-methyloxolan-2-yl]propan-2-ol, while the wax-and-wane phenomenon between 3,7-dimethylocta-1,6-dien-3-ol and its oxides could be ignored, compared with the impact of tea cultivar.
# Image Anomaly Detection Using Normal Data Only by Latent Space Resampling
## 2 Related Work
### Reconstruction Based Method  
The assumption of this kind of method is that normal images can be reconstructed from latent space better than anomalous images. Early defect detection works utilized sparse dictionary [31; 32] for reconstruction. Deep learning techniques have extended the toolbox of reconstruction-based methods. Autoencoder-like models are the commonly-used model for these kinds of methods. Generally, AE-like based methods can be summarized as (i) training AE from only normal samples; and (ii) anomaly segmentation based on reconstruction error of input samples, which may have anomalies. [PERSON] et al. [10] applied AE directly to detect pathologies in Brain MR Images. In particular, VAE attempts to detect anomalies through the generated perspective. Some work [8; 11] assumes that VAE trained on normal images will only be able to reconstruct normal images, thus getting larger reconstruction errors in anomalous regions. Since the essential dimensions of the data are not known, information may be lost after a bottleneck and even normal parts may not be reconstructed correctly, resulting in false detections. Several approaches have been proposed to address this issue. [PERSON] et al. [9] thought \(\ell^{p}\)-distance would enlarge slightly reconstruction errors and replaced it with SSIM. [PERSON] et al. [33] are the first to use uncertainty estimate based on Monte Carlo dropout for lesion detection. They perform dropout operation on AE and then take the variance of result as anomaly score. Both [PERSON] et al. [34] and [PERSON] et al. [35] implemented Grad-CAM [36] into AE, aiming to replace reconstruction errors with the attention mechanism and achieving outstanding performance. [PERSON] et al. [37] treated anomaly detection as an inpainting problem that uses AE trained on the normal dataset to generate patches clipped on the image. [PERSON] et al. [38] argued that learning only on normal manifold does not guarantee generalization outside of the manifold, which will lead to unreliable reconstruction. They used iterative energy-based projection to sample the closest normal image to the anomalous input on normal manifold. Another is to apply Generative adversarial networks (GAN), and [PERSON] et al. [39] used GAN to model normal OCT images of retina distribution. At the time of the prediction, searching the latent space for suitable latent codes makes the generator yield the normal sample closest to the anomalous input, and then use \(\ell^{1}\) distance as anomaly score. Soon after, [PERSON] et al. [40] optimized the search process before the generator. Similarly, GANomaly [41] adds GAN's discriminator to AE and encodes the reconstructed image for detecting the anomalies in X-ray images by summing the errors of latent code, reconstruction error, and adversarial loss as anomaly scores. However, GAN suffers from pattern collapse that may yield plausible samples and cause false alarms.
# An Ensemble Feature Selection Approach to Identify Relevant Features from EEG Signals
## 3 Results
### Selecting Relevant Features from the EEG Dataset
#### 3.2.2 The Detector of Epileptic Activity  
The SVM model built in the previous step was included as part of a detector of epileptic events to support the automatic reading of EEGs. Then, following the approach described in Section 2.6, we built a detector capable of decomposing an EEG signal into channels and segments; the segments were analyzed by the SVM model and classified as normal or abnormal.  
The detector was developed to evaluate the relevance of the EFS approach in the classification of EEG signals. One of the main reasons that motivated this research was to help diagnose epilepsy by supporting the automatic detection of epileptic events in EEG signals. To achieve this, we proposed improving the classification process by including only the relevant features that describe an EEG signal in the learning process.  
To validate the detector, a set of 100 EEG records taken from 100 pediatric patients were read by the detector. The 100 EEG records are part of the EEG repository built in this research. For the test, each EEG record with epileptic activity describes the beginnings and ends of the epileptic abnormalities. These descriptions were used to validate the detections made by the detector.  
Table 8 describes the results of the reading of the 533,909 segments extracted from 100 EEGs, where 6806 segments are epileptiform events.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Predicted**} \\ \hline  & & **Abnormal** & **Normal** \\ \hline \multirow{2}{*}{True} & Abnormal & 95.79\% & 4.2\% \\ \cline{2-4}  & Normal & 7.51\% & 92.48\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Confusion matrix of the detector.  
Figure 7: Precision–recall.  
According to the confusion matrix, the detector's accuracy, sensitivity, specificity, NPV, and PPV were 92.53%, 95.57%, 92.48%, 92.49%, and 95.80%. The rate of false negatives was 4.20%, and the rate of false positives was 7.51%.