esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analytical Results
### Summary  
In this section, we evaluate the proposed two-stage clustering method by comparing it with one-stage clustering in terms of the clustering performance, unit traffic patterns, and time series representations of the extracted unit traffic patterns. The comparison results indicate that the IoT traffic analysis with two-stage clustering can provide more detailed communication features for IoT devices. Moreover, we demonstrated that two-stage clustering can reduce the impact of an increase in traffic data volume on the processing load in clustering.  
In this paper, we implemented the proposed two- and one-stage clustering methods on a single computer to analytically verify whether they function properly. However, when implementing the proposed method in real environments, data communication occurs between the gateway routers in smart home environments and the cloud server to exchange  
Figure 11: Time series representations of six IoT devices with one-stage clustering applied. These results do not sufficiently represent how each IoT device communicates in each unit of time, since they consist of the same unit traffic pattern with the same color and/or zero traffic except for the 600-second unit of time.  
Figure 12: Time series representations of six IoT devices with two-stage clustering applied. Compared to Figure 11, the results of the two-stage clustering exhibit various colors and sufficiently represent the communication traffic behavior of each IoT device.  
the feature values of the divided traffic data pieces and their cluster features. In the future, we will investigate the impact of communication in real network situations.
## 1 Introduction
First, the ductility of the UHPC is much better than that of the historical masonry, and the favorable ductility helps to bring in good energy dissipation ability of the strengthened walls. Second, the tensile strength, the compressive strength, and the shear strength of the UHPC are much higher than those of the historical masonry respectively, and the higher strength ensures the strengthening effect under different stress states. Third, the UHPC has no coarse aggregate and then has good grouting ability. This feature has been verified by its successful applications in grouting the rebar-splicing couplers in prefabricated concrete members [23]. With the good grouting ability, the UHPC can flow into and fill the existing damages in the masonry substrates before hardening. Such a mechanism makes the UHPC deform well with the masonry substrates, and it relieves influence of the seasonal difference between the thermal strain of the UHPC layers and the masonry substrates. Although the UHPC layers are not completely removable after being applied, their thickness can be very small because they do not require reinforcement inside. When the advances in technology allow better strategies, they can be applied on top of the thin UHPC layers without changing the profile of the masonry walls. From this angle, the UHPC layers allow the realization of better strategies like those reversible retrofitting strategies do. The research about the strengthening effect of applying UHPC layers on historical masonry walls is not enough yet. To contribute to the field, the authors conduct pseudo-static experiments on historical squat masonry walls strengthened by UPPC layers and reinforced polymer mortar layer, and they compare the strengthening effect of the two kinds of layers. The structure of the paper is as follows. In Section 2, the authors present a detailed experimental scheme with special considerations on characteristics of the historical brick masonry walls. In Section 3, they present and compare the experimental results in terms of failure mode, in-plane shear resistance, stiffness degradation, and ductility of the walls. These results reflect the static and seismic performance of the walls quantitatively. In Section 4, the authors evaluate the strengthening effect of applying the UHPC layers, with emphasis on the favorable interaction between the UHPC and the masonry substrates. Finally, they summarize the research findings in Section 5.
# Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid
## 2 Mathematical Models of the System Components
### Wind Turbine System  
During wind power generation, the blow of the wind generates kinetic energy, which drives the blades allowing the turbine to rotate. The mechanical energy then gets converted into electricity by the generator. The wind turbine system is significantly influenced by the wind speed. The generated power of the \(WT\) system is obtained from the manufacturers as follows [3]:  
\[P_{WT}=\begin{cases}0&if\ V<V_{in}\ or\ V>V_{out}\\ P_{r}\bigg{(}\frac{V-V_{in}}{V_{r}-V_{in}}\bigg{)}^{3}&if\ V_{in}\leq V<V_{r} \\ P_{r}&if\ V_{r}\leq V\leq V_{out}\end{cases} \tag{2}\]  
where \(P_{WT}\) denotes the output power at a particular value of wind speed. \(P_{r}\) represents the rated capacity. \(V_{in}\), \(V_{r}\), \(V_{out}\) stand for the cut-in, rated, and cut-out speeds, respectively.
# Improving Human Ground Control Performance in Unmanned Aerial Systems
## 2 State of the Art
### Shared Displays and Team Performance
The same group of researchers later conducted another experiment with an improved design in which all teams were trained on each other's roles [12]. They used shared and non-shared displays again and abstract displays that showed only the most relevant parts of the display for the other team members. In addition, they manipulated the workload. With low workloads, there was no difference in performance between conditions, but with higher workloads, teams with non-shared and abstract displays performed better than those with shared displays. This is surprising, especially the performance of teams with non-shared displays, as it suggests that TSA is inversely related to performance, which is contrary to all predictions in the existing literature. From this model of results, it is not clear how displays affect performance in M.  
Further work is needed to clarify the outcome of this experiment. Overall, the described experiments demonstrate the relevance of display sharing for TSA and team performance, but do not offer convincing evidence of the impact of shared displays and TSA on team performance. Our study was meant to further investigate this relationship by comparing shared and non-shared displays and routine and off-nominal situations. An experimental design will be used that offers greater experimental control and will therefore allow for more solid conclusions to be drawn.
# GAN-Based Image Dehazing for Intelligent Weld Shape Classification and Tracing Using Deep Learning
## 2 Methods
### Contour Detection for the Extraction of Weld Seams  
In image processing, contour detection helps in finding the same intensity points along the boundary of any object in the image. This is helpful in object detection and recognition applications. In this work, ten different weld seam shapes needed to be identified for the robot to trace the shapes accordingly. Hence, contour detection was implemented using OpenCV to identify the contours present in the weld plates. To improve the accuracy of detection, the weld image obtained from the live camera feed was converted into a grayscale image which was further cropped based on the predicted bounding box coordinates. To obtain better accuracy, the image was thresholded and then it was eroded and dilated [44]. Furthermore, contours were sorted, the largest contours present in the image were taken, and the contour lines were drawn around the weld seam, indicating boundaries of the same intensities or pixel values. Finally, all the contours for the particular shape were saved in csv format to convert the contour pixels into robot coordinates for robotic welding. The overall contour detection process flow diagram for shape number 5 is shown in Figure 7.  
The pseudocode for contour detection is as follows Algorithm 2:  
```
Begin Input: Image 640 \(\times\) 480 \(\times\) 3; I = f (u, v,3) Output: Contour Points C = (\(x_{cont}\), \(y_{cont}\)) for i = 1 to n Read test weld image or live camera image I = f(u, v,3) Extract the individual channels from RGB image, \(R\) = I (; ; ;1); \(G\) = I (; ; ;2); \(B\) = I (; ; ;3);  Grayscale Conversion, graylImage = (\(R\)+\(G\)+\(B\))/3 Perform Inverted Binary Image thresholding, set T = 100 if f (x, y) > T then f (x, y) = 0 else \(f\) (x, y) = 255 endif  Perform erosion and dilation  Find contours using C = cv2,findContours() Draw contours using C pixel points endfor Save contour points C to.csv end
```  
**Algorithm 2** Pseudocode of Contour Extraction of Weld Seam
## 2 Experiments and Methods
### t-SNE Algorithm for Dimensionality Reduction
The gradient of the cost function with respect to the low-dimensional coordinates is given by the following:  
\[\delta C/\delta y_{i}=4\sum_{j}\big{(}p_{ij}-q_{ij}\big{)}\big{(}y_{i}-y_{j}\big{)} \Big{(}1+\big{\|}y_{i}-y_{j}\big{\|}^{2}\Big{)}^{-1}, \tag{5}\]  
This gradient expression reveals the underlying force field where \(p_{ij}>q_{ij}\) generates an attractive force between points, while \(p_{ij}<q_{ij}\) results in a repulsive force. The optimization process employs gradient descent with momentum:  
\[y(t)=y(t-1)+\eta\frac{\partial C}{\partial y}+\alpha(t)(y(t-1)-y(t-2)), \tag{6}\]  
where \(\eta\) denotes the learning rate controlling the step size of each iteration, and \(\alpha(t)\) is the momentum coefficient that accelerates convergence and helps escape local optima. The perplexity parameter, which is crucial for t-SNE's performance, is related to the Shannon entropy \(H(Pi)\) of the conditional probability distribution:  
\[perp(pi)=2^{H(pi)}, \tag{7}\]  
Higher perplexity values emphasize the preservation of global structure, while lower values focus more on maintaining local features. In practice, perplexity values typically range from 5 to 50, requiring adjustment based on the specific characteristics of the dataset. Through iterative optimization, t-SNE progressively refines the low-dimensional embedding until the probability distributions in both spaces achieve optimal alignment, thereby creating a meaningful visualization of the high-dimensional data structure.
# Continuous Adaptation with Online Meta-Learning for Non-Stationary Target Regression Tasks
## 6 Conclusions  
We proposed CORAL, a computationally efficient method to adapt to non-stationary targets. CORAL builds upon Bayesian linear regression with a sliding window in order to follow the target variation and online meta-learning to improve its prediction accuracy even for the non-stationary target scenario. We evaluated our method in two tasks. First, we studied a simple linear regression problem with varying generative model coefficients. Then, we tackled a more complex type 1 diabetes (T1D) blood glucose level (BGL) prediction  
Figure 9: Blood glucose level (BGL) prediction RMSE for the non-stationary target model (adult#001 \(\leftrightarrow\) adult#003) with three base models—RLS, ALPaCA and CORAL. The x-axis shows its prediction time in minutes, which shows how far into the future it predicts.  
task. The evaluation results showed the effectiveness of CORAL for non-stationary tasks and how the online meta-learning mitigates the performance loss due to short window sizes.  
Regarding the limitations, CORAL assumes the sliding window size is known and fixed. Although the meta-learning mitigates the drawbacks of using a short window, we believe that an adaptive window size would perform better. Another aspect that requires attention is that the use of analytic recursive update formulas comes with the limitations of Gaussian models (and the predictive posterior is limited to a multivariate normal distribution). Finally, the non-stationary target adaptation is achieved by discarding (forgetting) old data and never utilising the discarded data in the future, even if it becomes relevant again. To recycle the past data, we might require a _context_ detector to categorise and re-using the data when the current _context_ matches with the data category.  
For future work, we will address some of the limitations above by exploring algorithms for sliding window adaptation. In this work, we assumed the window size was given and fixed. However, the model will become much more flexible by having the ability for the size to be set in an adaptive manner. In a sense, this adaptation algorithm is related to the _context_ detector mentioned above. We can view it as identifying how far the past data still belongs to the current _context_.  
T.Y.: Conceptualisation, methodology, software, validation, formal analysis, investigation, data curation, writing--original draft preparation, visualisation; R.S.-R.: Conceptualisation, validation, writing--review and editing, supervision; P.F.: writing--review and editing, supervision. All authors have read and agreed to the published version of the manuscript.  
This work was supported by SPHERE Next Steps Project funded by the U.K. Engineering, and Physical Sciences Research Council (EPSRC) under Grant EP/R005273/1. RSR is funded by the UKRI Turing AI Fellowship EP/V024817/1.  
Not applicable.  
This study did not involve any underlying data.  
Thanks are due to [PERSON] for providing useful comments on a draft of the paper.  
The authors declare no conflict of interest.
## 4 Results
### Data Profiling
The BMP280 sensor performed as expected, generating a complete set of data with no missing values, and showing a temperature range from 4.2 \({}^{\circ}\)C to 46.8 \({}^{\circ}\)C, with an average temperature of approximately 22.09 \({}^{\circ}\)C. This indicates a wide fluctuation in temperature, which may correspond to varying operational phases within the monitored environment that will be observed in a later stage. The SI7021 temperature dataset exhibits a similar trend, with temperatures ranging from 5.0 \({}^{\circ}\)C to 46.0 \({}^{\circ}\)C, though it suffers from a significant amount of missing data (15.1%). Humidity levels, as recorded by the SI7021 humidity sensor, range from 35% to 99%, with an average humidity of around 63.26%. The high variability in humidity suggests the environment undergoes substantial changes in moisture content, likely influenced by external factors or operational cycles (e.g., usage of a water-cooling system, scheduled filling of water tanks and others). However, the received data also has 14.5% missing data, which may affect the overall interpretation of trends. The dew point data, with values ranging from \(-1.0\)\({}^{\circ}\)C to 32.0 \({}^{\circ}\)C, provide insight into the moisture saturation level of the air, with an average dew point of 15.44 \({}^{\circ}\)C. This, combined with the temperature and humidity data, could help in assessing the comfort and safety of the environment for both animals and workers. Air quality data, particularly PM2.5 and PM10 levels, reveal potential concerns. The PM2.5 values range from 0 to 128 \(\upmu\)g/m\({}^{3}\), with an average of 6.18 \(\upmu\)g/m\({}^{3}\), while PM10 levels range from 1 to 348 \(\upmu\)g/m\({}^{3}\), averaging 13.45 \(\upmu\)g/m\({}^{3}\). The occasional spikes in particulate matter could be indicative of activities such as cleaning or feeding, which may stir up dust and affect air quality. Interestingly, the CCS811 TVOC and eCO\({}_{2}\) datasets, which measure total volatile organic compounds and equivalent carbon dioxide, respectively, show a broad range of values. TVOC levels span from 0 to 29,206 ppb, with an average of 981.6 ppb, suggesting periodic increases in airborne chemicals, possibly from animal waste or cleaning agents. The eCO\({}_{2}\) levels, ranging from 400 to 32,768 ppm with an average of 1902.7 ppm, indicate variable CO\({}_{2}\) concentrations, potentially reflecting changes in ventilation or animal respiration. The use of fans in the barn, the condition of the doors and windows--open or closed--and other weather conditions such as wind strength and direction have a particular influence.
## 3 Results
### Functional Enrichment Analysis  
Following RNA-seq analysis, a comprehensive functional enrichment analysis was performed using the g:Profiler server. For overexpressed genes, enriched categories included response to stimuli (GO:0050896, padj = 5.210 \(\times\) 10\({}^{-10}\)), cellular response to chemical stimuli (GO:0070887, padj = 1.173 \(\times\) 10\({}^{-7}\)), alditol:NADP+ 1-oxidoreductase activity (GO:0004032, padj = 4.895 \(\times\) 10\({}^{-6}\)), ketosteroid monooxygenase activity (GO:0047086, padj = 4.956 \(\times\) 10\({}^{-6}\)), daunorubicin metabolic process (GO:0044597, padj = 1.551 \(\times\) 10\({}^{-6}\)), cellular response to jasmonic acid stimulus (GO:0071395, padj = 2.154 \(\times\) 10\({}^{-6}\)), polyketide metabolic process (GO:0030638, padj = 3.088 \(\times\) 10\({}^{-6}\)), oxidative stress response (GO:0006979, padj = 7.294 \(\times\) 10\({}^{-5}\)), photodynamic therapy-induced NFE2L2 (NRF2) survival signaling (WP:WP3612, padj = 1.406 \(\times\) 10\({}^{-5}\)), and ferroptosis (WP:WP4313, padj = 4.043 \(\times\) 10\({}^{-5}\)) (Figure 3B). Conversely, underexpressed genes targeted epithelial development (GO:0060429, padj = 1.552 \(\times\) 10\({}^{-9}\)), morphogenesis of anatomical structures (GO:0009653, padj = 1.776 \(\times\) 10\({}^{-9}\)), cell differentiation (GO:0030154, padj = 4.236 \(\times\) 10\({}^{-5}\)), cell periphery (GO:0071944, padj = 4.558 \(\times\) 10\({}^{-7}\)), extracellular matrix (GO:0031012, padj = 2.898 \(\times\) 10\({}^{-5}\)) and external encapsulating structure (GO:0030312, padj = 2.976 \(\times\) 10\({}^{-5}\)) (Figure 3C). To further the analysis, a protein-protein interaction network functional enrichment analysis was carried out. The results of this analysis resulted in the construction of a network that agrees well with the functional enrichment results (Figures 4 and 5). In particular, annotated keywords associated with overexpressed genes in the network prominently include NADP (strength 0.92, false discovery rate 0.00038), whereas protein domains are predominantly linked to the NADP-dependent oxidoreductase domain superfamily (strength 1.62, false discovery rate 0.00067). Furthermore, a substantial portion of the overexpressed genes were observed to play a regulatory role in cytokine signaling in the immune system (strength 0.35, false discovery rate 0.0109).
# A Flexible Session-Based Recommender System for e-Commerce
## 2 Methods and Literature Review
### Tested SBRS Methods and Practical Scenarios
#### 2.5.7 SBRS and the Purchase Intent Task
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**All Sessions (21.896)**} & \multicolumn{2}{c}{\
\begin{tabular}{c} **Purchase Sessions** \\ **(689)** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Cart Abandonment** \\ **Sessions (1305)** \\ \end{tabular} } \\ \hline  & **Occurs** & **Frequency** & **Occurs** & **Frequency** & **Occurs** & **Frequency** \\ \hline CATEGORY & 141,561 & 54.8\% & 2760 & 20.5\% & 9956 & 40.6\% \\ \hline VIEW PRODUCT & 83,299 & 32.3\% & 3149 & 23.4\% & 6511 & 26.5\% \\ \hline HOME & 6139 & 2.4\% & 316 & 2.3\% & 360 & 1.5\% \\ \hline ASK QUESTION & 4027 & 1.6\% & 150 & 1.1\% & 479 & 2.0\% \\ \hline ORDER & 730 & 0.3\% & 730 & 5.4\% & 0 & 0.0\% \\ \hline CONTACT & 4264 & 1.7\% & 42 & 0.3\% & 133 & 0.5\% \\ \hline ADD CART & 3380 & 1.3\% & 1113 & 8.3\% & 2267 & 9.2\% \\ \hline VIEW CART & 10,722 & 4.2\% & 5011 & 37.2\% & 4483 & 18.3\% \\ \hline SEARCH & 69 & 0.0\% & 0 & 0.0\% & 0 & 0.0\% \\ \hline CONCERNED & 1172 & 0.5\% & 115 & 0.9\% & 183 & 0.7\% \\ \hline ACCOUNT & 2068 & 0.8\% & 75 & 0.6\% & 155 & 0.6\% \\ \hline RECOMMEND & 670 & 0.3\% & 5 & 0.0\% & 9 & 0.0\% \\ \hline  & 258.101 & 100\% & 13.466 & 100\% & 24.536 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: User action types. To summarize, our purchase intent system is modeled as a multilabel classification problem that determines, for each session action, the result of the rest session. The following four scenarios outline four different outputs:  
1. A user adds item(s) to her/his cart but does not make a purchase finally. This is the cart abandonment scenario. 2. A user adds item(s) to the cart and makes a purchase (purchase scenario). 3. None of the add-item and make-order events happens (browse-only scenario). 4. A user makes a purchase.
## 3 Cross Correlation Analysis to Diagnose the Sizes of Wheel Flats  
### Definition of Cross Correlation  
The cepstrum analysis is effective in detecting the existence of flats, but not their sizes. Therefore, another signal-processing technique must be applied to diagnose the sizes of wheel flats.  
The magnitude and width of a waveform signal depend on the size of a flat and the speed of a vehicle [17]. Cross correlation analysis is one of the signal processing techniques that effectively consider the signal waveform area of a wheel flat. As shown in Equation (8), this technique is to calculate the overlapping area of the kernel signal (g) with the measurement signal (f), while kernel signal (g) moves along with the time axis. In the case of a discrete function rather than a continuous function, the equation is given as in Equation (9). [18]:  
\[(f\bigstar g)(t)=\int_{-\infty}^{\infty}f(\tau)g(t+\tau)d\tau \tag{8}\]  
\[(f\bigstar g)(n)=\sum_{-\infty}^{\infty}f[m]g[m+n] \tag{9}\]  
In the cross correlation analysis, the maximum peak value can be obtained using the same kernel signal as the measurement signal. In this paper, we used the flat signal of the simulation result as the kernel signal.
## 3 Experiments and Comparisons
### Experimental Results Analysis
#### 3.4.1 Analysis of Comparative Experimental Results
In contrast, the clustering effect of the ChebyAT model is particularly outstanding: it demonstrated clear cluster boundaries and very few errors in the classification process, and the number of cluster groups and category labels of the data set correspond completely, showing excellent feature extraction accuracy and classification ability, and highlighting the strong adaptability and robustness of the ChebyAT model in dealing with complex graph data structures. Figure 6: Confusion matrix visualization experimental results of six models. (**a**) ChebyNet. (**b**) GAT. (**c**) GCN. (**d**) GIN. (**e**) GraphSAGE. (**f**) ChebyAT. In summary, the ChebyAT model has demonstrated considerable advantages in comparative experiments. It not only outperforms other graph neural network models with an accuracy of up to 0.9975, but also remains efficient and stable under changing working conditions. As shown by the confusion matrix evaluation, ChebyAT demonstrates superior recognition capabilities in complex, imbalanced data sets, while t-SNE visualization further highlighted its high accuracy in feature extraction and classification, demonstrating its high reliability in handling data from variable working conditions.