esa-sceva/satcom-chunk-collection huggingface dataset:
# Multimodal Video Analysis for Crowd Anomaly Detection Using Open Access Tourism Cameras
## 4 Results
### Collective Anomalies
#### 4.2.3 Comparison with PySAD  
To facilitate results comparison, the PySAD framework is employed. PySAD [[https://github.com/selimfirat/pysad](https://github.com/selimfirat/pysad)]([https://github.com/selimfirat/pysad](https://github.com/selimfirat/pysad)) (accessed on 24 November 2024) [29] is an open source Python framework designed for anomaly detection in univariate and multivariate streaming data, offering tools for experiment design and state-of-the-art models for supervised, semi-supervised, and unsupervised learning. Built on well-established frameworks like PyOD [30] and scikit-learn [31], PySAD enables advanced anomaly detection capabilities.  
For collective anomaly detection, the experimental setup specifies a data window size and sliding window size of 672 intervals (1 week) and an averaging window of 96 intervals (1 day). The models utilized include MCD [32] for the Detection Series and OCSVM [33] for the Heatmap Series.  
Table 1 presents a comparison of the PySAD framework and the proposed method in detecting collective anomalies across the Detection Series and the Heatmap Series. It displays the number of anomalies detected by each method for each series, along with a Cohen's Kappa Score as a metric for evaluating the level of agreement between the two methods for each series. The results indicate that both methods achieve moderate to substantial agreement in anomaly detection; however, the degree of alignment varies between the series, with higher agreement observed in the Detection Series compared to the Heatmap Series.
## 3 Test and Verification
### Test System Parameter Design
#### 3.1.3 Test Implementation  
After setting up the testing system, we begin to experiment: the laser falls on the PSD target surface after two reflection cycles, as shown in Figure 16. We read the initial temperature matrix value of the support frame, as shown in Figure 17. The laser spot of the PSD is set to zero as shown in Figure 18, while reading the displacement drift error of the spot, as shown in Table 1. After the support frame is heated up by about 10 degrees Celsius, it is first kept warm for 1 h. The temperature matrix value after the heating is read for the second time, and after waiting for half an hour, the final temperature matrix value is read for the third time, as shown in Figure 19. The three temperature change curves are obtained, as shown in Figure 20. The PSD spot deviates, and the displacement deviation is shown in Figure 21. The displacement sampling values are shown in Figure 22.  
\[T_{\textit{front}}=\begin{bmatrix}26.6&26.1&26.8\\ 27.0&26.9&26.1\\ 26.4&26.9&26.7\\ 26.5&26.1&27.2\\ 26.9&25.9&26.3\\ 28.2&27.7&26.9\end{bmatrix},\ \ T_{\textit{back}}=\begin{bmatrix}26.4&27.2&26.1\\ 26.7&26.7&26.3\\ 26.3&26.7&26.2\\ 26.4&27.0&26.5\\ 26.2&27.6&26.1\\ 27.7&26.5&27.3\end{bmatrix}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Sampling** & **X** & **Y** \\ \hline
1 & \(-0.014\) & \(-0.01\) \\ \hline
2 & 0 & \(-0.004\) \\ \hline
3 & \(-0.003\) & \(-0.019\) \\ \hline
4 & 0.017 & 0.024 \\ \hline
5 & \(-0.003\) & \(-0.004\) \\ \hline
6 & 0.012 & \(-0.047\) \\ \hline
7 & 0.005 & \(-0.04\) \\ \hline
8 & 0.008 & \(-0.007\) \\ \hline
9 & 0.012 & 0.014 \\ \hline Average drift error & 0.003778 & \(-0.01033\) \\ \hline \end{tabular}
\end{table}
Table 1: PSD Spot Coordinate Zeroing Sampling.  
Figure 17: Initial temperature matrix.  
Figure 18: Schematic diagram of PSD displacement drift.  
\[T_{\text{\tiny{free}}}^{{}^{\prime}}=\begin{bmatrix}31.46&30.17&32.32\\ 30.72&31.53&30.06\\ 30.72&30.06&30.21\\ 30.39&30.28&30.85\\ 31.93&30.21&30.08\\ 31.36&30.45&31.25\end{bmatrix},\quad T_{\text{\tiny{free}}}^{{}^{\prime}}= \begin{bmatrix}29.97&30.87&30.06\\ 30.48&30.21&30.22\\ 30.23&30.23&30.28\\ 30.17&30.05&29.99\\ 30.14&30.74&30.23\\ 30.06&30&30.09\end{bmatrix}\]  
Figure 19: Final temperature matrix.  
Figure 20: Temperature sampling curve for installing the base plate.  
Figure 21: Laser spot displacement sampling for PSD devices.
# Lossless Compression of Large Aperture Static Imaging Spectrometer Data +
## 4 Experimental Results
### Rearrange Method  
The first experiment examines the impact of the data rearrange method introduced in Section 3.1 on the compression performance. The inter-frame correlation coefficients for the LASIS, LAMIS, and LADIS data cubes are calculated using Equation (3), where \(M\) is the number of rows, \(N\) is the number of columns in each frame, and \(\overset{-}{f_{i}}\) and \(\overset{-}{f_{j}}\) are the mean values of the \(i\) and \(j\) frames, respectively.  
\[c_{i,j}=\frac{\sum_{x=1}^{M}\sum_{y=1}^{N}\left(f_{i}(x,y)-f_{i}\right)\times \left(f_{j}(x,y)-f_{j}\right)}{\sqrt{(\sum_{x=1}^{M}\sum_{y=1}^{N}\left(f_{i} (x,y)-f_{i}\right)^{2})(\sum_{x=1}^{M}\sum_{y=1}^{N}\left(f_{j}(x,y)-f_{j} \right)^{2})}} \tag{3}\]  
Figure 7 depicts the correlation coefficients between each band of the LASIS, LAMIS, and LADIS and the average inter-frame correlation coefficient value computed except, for diagonal elements; the blue-to-red color map corresponds to the correlation coefficient values, ranging from 0 to 1. It can be seen that the correlation between each band of the LADIS is significantly higher than that of the LAMIS and LASIS, which will significantly improve the prediction accuracy, resulting in a much higher compression ratio, as shown in Figure 8. Under all four prediction mode and local sum type combinations of the CCSDS-123 standard, the rearranged LADIS data yield the best results by a considerable amount under all combinations compared with the LAMIS and LASIS data, and the original LASIS data produce the worst performance. The best performance of the proposed compression scheme is approximately 32.9% higher than that of the scheme that combines the traditional arrange approach with the standard. In the following experiments, we aim to optimize the relevant parameters of the proposed compression scheme.
## 3 Corrosion Modelling  
### Uniform and Grooving Corrosion  
The grooving corrosion mechanism was investigated by [PERSON] et al. [29]. Rapid heating and cooling of welding can produce a sulphide-enriched portion surrounding the MnS inclusions, which dissolves anodically concerning MnS inclusions and the base metal. Corrosion pits were initiated immediately on the MnS inclusions. They were then developed into grooving corrosion with the aid of the macro-cell between the anodic weld and cathodic base metal. In addition, the protective coating around the weld may not be as good as that on the base metal because of the existence of weld defects, which can lead to localized grooving corrosion [14]. The grooving corrosion can also be attributed to an unfavourable local corrosive environment and the residual stress caused by welding, which leads to stress corrosion degradation and a greater corrosion depth around the weld than that away from the weld [16]. The high stress arising at the hot spot caused by external loading can also accelerate the corrosion degradation rate and lead to non-uniform corrosion [7]. [PERSON] et al. [30] conducted some continuous immersion tests in seawater and found that the grooving corrosion occurring on welded joints is the consequence of galvanic corrosion and stress corrosion.  
The uniform corrosion and grooving corrosion are modelled as shown in Figure 6. The outer surface of the tubular joint is uniformly removed due to uniform corrosion whose corrosion depth is d. The modelling of grooving corrosion is mainly based on the work of [PERSON] et al. [30]. It is assumed that the weld metal and base metal are both subjected to  
Figure 5: SCFs of weld toe on the chord under (**a**) axial loading; (**b**) in-plane bending (IPB); and (**c**) out-of-plane bending (OPB).  
uniform corrosion, but their corrosion rates are different; the corrosion rate of weld metal is 1.4 times that of the base metal, i.e., the corrosion depth of the weld metal \(\mathrm{d_{g}}\) equals 1.4 d. The above assumptions are consistent with the observations in [30]. The corrosion degradation rate of the heat-affected zone is assumed to be linearly varied from the weld metal to the base metal, and the width of the heat-affected zone \(\mathrm{b_{g}}\) is assumed to be 3 mm. It has been shown that plastic deformation is the main reason for stress corrosion [30]. Because plastic deformation is limited in practice, the contribution of stress corrosion to grooving corrosion is ignored. The reference points for the determination of the hot-spot stress are placed concerning the new weld toe and plate thickness due to corrosion. The FE models of uniform and grooving corrosion are shown in Figure 7.
## Appendix A Group Velocity Calculation Method of the AE Signal at the Dominant Frequency
### Initiation of Location and Group Velocity  
We consider the coordinates of the AE source initial location, \(\hat{x}_{s_{0}}\) and \(\hat{y}_{s_{0}}\) as uniformly distributed random variables. Their means and variances are expressed below and are calculated based on geometrical considerations [13; 37]:  
\[\hat{\mathcal{X}}_{s_{0}}=\frac{\left(\mathcal{X}_{s\mathrm{I}}+\mathcal{X}_{s \mathrm{L}}\right)}{2},\ \hat{\mathcal{Y}}_{s_{0}}=\frac{\left(\mathcal{Y}_{s\mathrm{I}}+\mathcal{Y}_{s \mathrm{L}}\right)}{2} \tag{17}\]  
\[\sigma_{\mathcal{X}_{s_{0}}}^{2}=\frac{1}{12}\left(\mathcal{X}_{s\mathrm{I}}- \mathcal{X}_{s\mathrm{L}}\right)^{2},\ \sigma_{\mathcal{Y}_{s_{0}}}^{2}=\frac{1}{12}\left(\mathcal{Y}_{s\mathrm{I}}- \mathcal{Y}_{s\mathrm{L}}\right)^{2} \tag{18}\]\(\mathcal{X}_{sL}\) and \(\mathcal{Y}_{sL}\) represent the location of the master sensor (sensor 1) located at the origin of the coordinate system (the lower bound). \(\mathcal{X}_{sL}\) and \(\mathcal{Y}_{sL}\) represent the location of sensor 3 (the upper bound).  
The starting estimates of the group wave velocity \(\hat{V}_{g_{0}}\) and its corresponding variance \(\hat{a}_{\hat{V}_{m}}^{2}\) are calculated using the Monte Carlo simulation. This is performed by assuming that the functional form \(V_{g}=F(fd)\) is known and deterministic. The frequency \(\overline{f}\) is assumed as a Gaussian random variable of mean and variance defined in Equations (10) and (12), respectively. Recall that we are interested in determining the probability density of \(V_{g_{0}}\) given the probability density of \(\overline{fd}\), where \(d\) represents half the thickness of the plate (\(d=e/2\)). Once the dominant Lamb wave mode is determined, the Monte Carlo simulation is performed by randomly generating 150,000 samples of the random variable \(\overline{fd}\)[37].
# Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods
## 3 Overall Procedure for Real Driving Cycle-Based SOC Estimation  
This paper proposes a method to predict SOC using a real-world driving cycle. The proposed SOC prediction method using real-world driving cycle is as shown in the Figure 1.  
The tested driving routes run from a fixed site to the other, i.e., from the homeplace of one of the authors to a parking lot in the Hanyang University campus. The route consists of several intersections between the start point and the destination point. The driving data were collected onboard while the author was driving, using an application named Torque Pro running on an Android device connected to a Bluetooth dongle, which supports the OBD-II specifications. The OBD data were comprised of various second-by-second temporal data, such as GPS latitude, longitude, velocities, etc., and were saved in a Comma-Separated-Value (CSV) format. The route is highlighted, as shown in Figure 2. The route was extracted and drawn on the map using the Google API with the GPS latitude and longitude information from the OBD data.  
Figure 1: Procedure of Real Driving cycle based SOC estimation.  
Since the overall driving cycles are highly dependent on the operations of en-route traffic lights, to enhance the accuracy of the predicted driving cycles, we first divided each of the end-to-end routes into sub-routes by the traffic lights (as can be seen from Figure 2 as circular markers). The segments obtained from different instances of the route were then compared to one another. To find the most similar intervals across all of the instances of the route, a Dynamic Time Warping (DTW) algorithm was employed. After grouping the segments of the route by their similarity, different Machine Learning (ML) algorithms were used to learn from them and predict future driving cycles.  
To validate the performance of the prediction of the SoC consumption, we generated the eventual SoC values by an Electric Vehicle (EV) simulation model, implemented based on Functional Mock-up Units (FMU). The FMU-based EV simulation model reads as inputs the velocities, calculates the electric current from power consumption and regeneration by acceleration and deceleration, respectively, and writes as outputs the resultant SoC values to a file at each second. As expected, the test results showed that the SoC values could be predicted with high accuracy, as long as the driving cycle was predicted with high accuracy.  
The paper is structured as follows: first, we proposed the preprocessing method using the DTW algorithm to group the driving intervals by similarity. Secondly, we proposed a deep learning algorithm that accurately predicts the speed after learning from similar driving cycles. The speed was also predicted using the existing Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Finally, to evaluate the models, the predicted speeds by both algorithms were used as input to a simulation model, which computes the SOC of the driving cycle.
## 4 Numerical Simulation
### Numerical Solution  
The commercial computational fluid dynamics (CFD) package software, Ansys Fluent, is used for this study.  
The numerical grid was created in Ansys meshing and the final one it's consisting of -7 million tetrahedral cells (Figure 6). Further on, the numerical grids were converted into polyhedral cells in the Ansys Fluent software.  
Polyhedral cells have the main advantage of combining the capabilities induced by hexahedral numerical grid as low numerical diffusion with those corresponding to the tetrahedral numerical grid as fast automatic generation. More important is that the polyhedral cells can overcome the disadvantages of both the above mentioned hexahedral and tetrahedral meshes. In the case of polyhedral mesh, each individual cell has a lot of neighbors compared with tetrahedral and hexahedral meshes (due to the larger number of faces for each cell), so the gradients can be very well approximated. Another advantage is that polyhedral cells are also less sensitive to stretching than tetrahedral, which results in better mesh quality (whose effect can be found in the improved numerical stability of the studied case). Not least, another advantage is that the numerical diffusion is reduced due to mass exchange with the neighboring cells over the numerous faces of the given cell which will leads to a more accurate solution obtained with less cells for the numerical grid [29].  
The final mesh was the result of an independence mesh study for the current computed domain. A boundary layer of eight layers with a growth factor of 1.1 is applied on all of the  
Figure 6: (**a**) Numerical grid in a longitudinal section across the computed domain, and (**b**) detail with the mesh in the SCs zone.  
solid-fluid interfaces. The resulting polyhedral numerical grid consisted in a number of \(\sim\)3 million polyhedral elements (Figure 7).  
The SST k-\(\omega\) turbulence was adopted for the current numerical simulation model [30]. This turbulence model is a two-equation eddy-viscosity model being a hybrid model combining the Wilcox k-\(\omega\) and the k-\(\varepsilon\) models. A blending function, F1, activates the Wilcox model near the wall and the k-\(\varepsilon\) model in the free stream. The k-\(\omega\) viscous model is well suited for modelling the flow in the viscous sub-layer and the k-\(\varepsilon\) viscous model is appropriate for simulating flow behavior in regions away from the wall. The blending function ensures that the appropriate turbulence model is utilized throughout the flow field.  
The maximum value of y+ obtained in the computed domain is 1.68 considered to be reasonable from our experience [31]. We know that values of y+ < 2.5 are desirable for SST k-\(\omega\) viscous model [32]. Also, the error could be higher when the first cell center from the wall is located in the region of y+ between 5 and 11, despite the use of correct boundary conditions and setup.
# Maintenance of Discovered High Average-Utility Itemsets in Dynamic Databases
###### Abstract  
High-utility itemset mining (HUIM) is an extension of traditional frequent itemset mining, which considers both quantities and unit profits of items in a database to reveal highly profitable itemsets regardless of their size. High average-utility itemset mining (HAUIM) is designed to find average-utility itemsets by considering both their utility and the number of items that they contain. Thus, average-utility itemsets are obtained based on a fair utility measurement since the average utility typically does not increase much with the size of itemsets. However, most algorithms for discovering high average utility itemsets are designed to extract patterns from a static database. If the size of a database decreases or increases over time (e.g., as a result of transaction insertions), the database must be scanned again in batch mode to update the results. Thus, previously discovered knowledge is ignored and the time previously spent for pattern extraction is wasted. We thus present an incremental HAUIM algorithm for transaction insertion (FUP-HAUIM) to maintain information about patterns when a database is updated, based on the FUP concept. An average-utility-list (AUL)-structure is first built by scanning the original database. Then, FUP-HAUIM selects high average-utility upper-bound itemsets and categorizes them according to four cases. For each case, itemsets are maintained and updated using a specific updating procedure. While traversing the enumeration tree representing the search space in a depth-first way, a join operation is performed to quickly and incrementally update the AUL-structures. Several experiments were carried to evaluate the runtime, memory usage, number of potential patterns (candidates), and the scalability of the designed approach. Results show that the performance of FUP-HAUIM is excellent compared to the state-of-the-art HAUI-Miner algorithm running in batch mode and the state-of-the-art incremental high-utility pattern mining (IHAUPM) algorithm for incremental average-utility pattern mining.  
Keywords:high average-utility mining; dynamic database; transaction insertion; FUP +
Footnote †: journal: Applied Sciences
# Triple-Attention Mixed-Link Network for Single-Image Super-Resolution
## 3 Proposed Method
### Triple Attention
#### 3.3.1 Channel Attention  
Channel attention could help the network gain the ability of modeling and selecting the information among channels. As shown in Figure 3a, the channel attention module consists of a global average pooling layer [20] which squeezes the features spatially to grab the global information among channels. Then two \(1\times 1\) convolutions named ConvD and ConvU generate a bottleneck. Finally, a Sigmoid layer used to normalize the information and the output is used to reweight the original output to generate a self-learned channel-wise attention. The process could be calculated as the following formulas:  
\[S_{q}(x_{c})=\frac{1}{HW}\sum_{i}^{H}\sum_{j}^{W}x_{c}(i,j) \tag{6}\]  
where \(S_{q}(.)\) represents the spatial squeeze with global average pooling. H and W denote the height and width of the feature map. \(x_{c}\) means channel c of the input feature map \(x\).  
\[A_{c}(x)=\sigma^{s}(W_{u}\sigma^{p}(W_{d}S(x)))*x \tag{7}\]  
where \(A_{c}(.)\) denotes the channel attention, \(\sigma^{s}\)means the sigmoid activation function and \(\sigma^{p}\) means the PReLU activation function, \(W_{u}\) and \(W_{d}\) means the two convolution layers with \(1\times 1\) kernel size.  
Figure 3: Channel attention and kernel attention, ‘O’ denotes add or concatenate operation.
# FOXS-GSC--Fast Offset Xpath Service with HexagonS Communication
## 4 FOXS-GSC Performance Evaluation  
This section presents a performance evaluation conducted to validate the proposed FOXS-GSC solution. Simulations varying the number of RSUs and vehicle density were carried to analyze the efficiency of the proposed multi-hop communication protocol and route suggestion technique.  
The experiments were divided into (i) network communication, (ii) scenario coverage evaluation, and (iii) traffic efficiency evaluation.  
1. The network communication experiment was used to evaluate the impact of different vehicle densities on FOXS-GSC and other solutions (Flooding, DESTINy [16]) from the literature.
2. The scenario coverage evaluation analyzed FOXS-GSC with respect to the impact of scenario coverage by varying the number of RSUs and their relationship with the scenario density.
3. The traffic efficiency evaluation assessed the traffic efficiency of FOXS-GSC by varying the scenario coverage.  
To conduct the experimental simulations, we used OMNeT++ 5 (Objective Modular Network Testbed) [34] for the network simulator and SUMO 0.25.0 simulator (Simulation of Urban Mobility) [35] for the traffic simulator. SUMO is an open-source traffic simulator framework which controls and models objects in a lane scenario. For the vehicular network, we used the Veins 4.3 (Vehicles in Network Simulation) framework [36]. Veins implements the IEEE 1609.4 DSRC/WAVE and IEEE 802.11p protocols and includes a signal attenuation  
Figure 6: State diagram for sending aggregated information message.  
model to consider obstacles. CO\({}_{2}\) emissions and fuel consumption metrics were gathered by the EMIT model (described in HBEFA ([[http://www.hbera.net](http://www.hbera.net)]([http://www.hbera.net](http://www.hbera.net)), (accessed on 30 May 2023)), Handbook Emission Factors for Road Transport) integrated into SUMO. The evaluation was based on realistic scenarios, as the SUMO simulator can import real city maps through Open Street Maps (Open Street Maps is a free collaborative mapping project ([[http://www.openstreetmap.org/](http://www.openstreetmap.org/)]([http://www.openstreetmap.org/](http://www.openstreetmap.org/))), (accessed on 30 May 2023)).