esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Concept and Design: The Function Design of the CBTC System
### The Function Design of the CBTC System for the Solutions to the Practical Problems
#### 2.2.2 Efficiency Problem: The Transport Efficiency Problem  
The distance and speed information of the transport units are collected by the ATO (Automatic Train Operation) system and transmitted to the ATP (Automatic Train Protection) system, and they both together control the speed of all transport units to maximize the efficiency of the intelligent underground mines. In 2019, [PERSON] and [PERSON] [35] emphasized the crucial role of ATO system in security analysis, and they put forward their viewpoint that although the ATP system is the core system of the security of the CBTC system, the ATO system provides the significant information of speed and the distance of the transport units. The vital information not only works in the security monitoring of the ATP system but also plays an important role in the supervision and tracking of the ATS system. To put it simply, all thanks to the useful information of the speed and distance of the transport units provided by the ATO system, the ATP system and ATS (Automatic Train Supervision) system can achieve their respective control and monitoring functions.  
As is known, the transport efficiency of the underground mine is determined by the speed of transport units to some extent. Therefore, both the ATO system and the ATP system are indispensable to make the underground mine transportation achieve its maximum transport efficiency. It is worth mentioning that calculating the speed of each transport unit when the maximum transport efficiency can be achieved is what the ATP system or ATO system could not accomplish, so it is essential to draw support from the calculating function of AI computing.  
To solve the problem, first, it is necessary to establish a single analysis of the fault event on the basis of the Dynamic Fault Tree analysis method: (1) Has the underground mine achieved maximum transport efficiency? Through the monitoring and control functions of the ATP system, the information-providing function of the ATO system, and other auxiliary functions, transport efficiency problem could be solved with the assistance of the Dynamic Fault Tree analysis method and AI Computing: (1) The ATO system provides the significant information of speed to the ATP system, and the ATP system controls the speed of each transport unit until the underground mine transportation achieves the maximum transport efficiency by the sustained calculating progress of AI Computing.  
With the improvements based on the Dynamic Fault Tree analysis method and AI Computing, the functions and working principle of the ATO system and ATP system efficiency adjustment function in the intelligent underground mine are shown in Figure 5.  
Figure 4: The control function and working principle of the ATP system in the intelligent underground mine.
## 4 Experiments
### Performance Comparison of Different Models  
We compare the MCNN model with other models that demonstrate the importance of various parts of our approach. The three comparison models are the multiscale pooled convolutional neural network (MPCNN), the convolutional neural network with single-scale (CNN), and the fully connected neural network (FNN). The three models can be  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Case** & **Fault Location** \\ \hline case 1\_fault & preheating zone \\ case 2\_fault & high-temperature zone \\ case 3\_fault & high-temperature zone \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fault location for three cases.  
Figure 16: Temperature trend prediction results for three cases on fault data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Time Consumption** & **Speedup** \\ \hline CFD & 1470 s & \\ POD-MCNN & 0.2 s & 7350 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model time speedup performance.  
interpreted as degenerate versions of the MCNN model. MSCNN retains the pooling operation of convolutional neural networks, CNN uses a convolution kernel of one size, and FNN does not use convolution operations.  
The training process adopts the same batch size, training times, initial learning rate, and weight decay as the MCNN training. Using the mean squared error on the test set to measure the prediction accuracy of the model:  
\[MSE=\frac{1}{N}\sum_{n=1}^{N}\left(\hat{Y}-Y\right)^{2} \tag{18}\]  
where \(\hat{Y}\) is the predicted value of the network output and \(Y\) is the true label value. The experimental results are shown in Table 6. The proposed model has the smallest mean square error on the test set. Using convolution instead of the full connection greatly reduces the number of parameters of the network. At the same time, the over-fitting phenomenon is improved, and the error is reduced by 35%. Using multi-scale instead of single-scale convolution makes the features extracted by the convolution more abundant, and the error is reduced by 15.5%. Discarding the pooling operation keeps all the features extracted by multi-scale convolution, and the error is reduced by 27.2%.  
In conclusion, we use a multiscale convolutional neural network for surrogate modeling. Using multi-scale convolution kernels can extract features of different finesse and improve the feature learning ability of the model. No pooling operation is required in the model, which allows the data features extracted by the convolutional layers to be preserved, giving the network higher accuracy. Based on high-fidelity simulation and artificial intelligence technology, DT can follow the data of the physical world for real-time updates, providing a data basis for the monitoring, evaluation, optimization, and maintenance of field equipment.
## Appendix A  
Harvesting efficiency results obtained in the 2\({}^{4}\) full factorial design experiment for aluminum sulfate, ferric sulfate, and chitosan.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Flocculant**} & **Dosage** & **Price of Flocculant (\&) to** & \\  & **(mg\(\cdot\)L\({}^{-1}\))** & **Price (\&)** & **Harvest 100 m\({}^{3}\) of** & **Reference** \\  & & & & _C. vulgaris_ & \\ \hline Aluminum sulfate & 250 & 0.20 & 4905 & [34] \\ \hline Ferric sulfate & 150 & 0.29 & 4350 & [35] \\ \hline Chitosan & 10 & 1.86 & 1856 & [36] \\ \hline \hline \end{tabular}
\end{table}
Table 8: Economic analysis of the price of flocculant needed to harvest 100 m\({}^{3}\) of _C. vulgaris_ culture.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Dosage**} & \multicolumn{2}{c}{**Settling**} & \multicolumn{2}{c}{**RM**} & \multicolumn{2}{c}{**SM**} & \multicolumn{2}{c}{**A \(\times\) B**} & \multicolumn{2}{c}{**A \(\times\) C**} & \multicolumn{2}{c}{**A \(\times\) D**} & \multicolumn{2}{c}{**B \(\times\) C**} & \multicolumn{2}{c}{**B \(\times\) D**} & \multicolumn{2}{c}{**C \(\times\) D**} \\ \cline{3-13} Aluminum sulfate & Effect & 4.181 & 2.356 & 48.806 & 48.681 & \(-\)2.319 & 0.256 & 0.756 & \(-\)2.019 & 2.106 & \(-\)44.669 \\ sulfate & \(p\)-value & 0.002 & 0.061 & 0.000 & 0.000 & 0.064 & 0.831 & 0.531 & 0.104 & 0.091 & 0.000 \\ \hline Ferric sulfate & Effect & 0.069 & 0.206 & 49.719 & 47.831 & \(-\)0.131 & 0.556 & \(-\)0.406 & 0.169 & \(-\)0.094 & \(-\)47.406 \\ sulfate & \(p\)-value & 0.264 & 0.002 & 0.000 & 0.000 & 0.040 & 0.000 & 0.000 & 0.010 & 0.133 & 0.000 \\ \hline Chitosan & Effect & \(-\)0.337 & 0.463 & 49.450 & 49.100 & \(-\)0.562 & 1.475 & \(-\)0.950 & 0.350 & \(-\)0.150 & \(-\)48.337 \\ \(p\)-value & 0.214 & 0.093 & 0.000 & 0.000 & 0.044 & 0.000 & 0.002 & 0.198 & 0.575 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effects and factor interaction analysis for _C. vulgaris_ flocculation with aluminum sulfate, ferric sulfate, and chitosan.
## 2 Proposed Method
### Intersection Points Calculation  
There is a large number of similar patterns in the straight-line segments of the trajectory obtained based on the maximum reconstruction error method. To speed up the intersection point calculation process and enhance the road network pattern, this paper first merges the straight-line segments with similar patterns. Then, the pairs of segments that do not have the possibility of intersection points are excluded using bounding box filtering and orthogonal filtering. Finally, the intersection points are calculated in the filtered set of straight-line segments.
# Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding
## 5 The Proposed Approaches
### Discussion  
The major contributions of the work include:  
1. We combine the recently proposed image processing algorithms and the traditional GLCM to propose different approaches to segment the reflected laser lines. Their performances including accuracy and processing time are evaluated and compared thoroughly in this paper, which is critical in implementing the on-line weld pool monitoring system;
2. The image processing algorithms proposed previously are explained theoretically in this paper, which serves as a complementation to the previous research [23];
3. More efficient segmentation approaches for images captured under mild welding parameters with relatively high quality are proposed in this paper.  
Image segmentation is fundamental and challenging in many machine vision applications. The most effective methods are usually obtained from the formulated mathematical model and address the characteristics of the captured image sequences well. As a result, the best solution usually needs to combine different image processing algorithms and forms a heuristic approach to achieve the best accuracy and required efficiency. As a typical example of visual intelligent sensing, the research conducted in this work might benefit other researches that need to automatically and robustly extract visual information from the image sequences in different industrial applications.
## 2 Materials and Methods
### Methods
#### 2.2.2 Building Damage Attention Module Based on Dilated Convolution and Direction Convolution  
The building damage attention module consists of an ASPP (Atrous Spatial Pyramid Pooling) module [22] and a DFE (Direction Feature Extraction) module [23]. Firstly, since our dataset includes both UAV and satellite imagery, the difference in the resolution scale between the two types of images can be very large. Therefore, an ASPP module was initially employed within the feature extraction module to enhance the network's capability to extract features at various scales. The design of the ASPP module is inspired by DeepLabv3 [24] and DeepLabv3+ [25]. Because of the different resolutions between UAV images and satellite remote sensing images, this module enhances the receptive field of the convolutional kernel by incorporating an adaptable dilation rate, enabling the capture of features at varying scales. The augmentation allows the network to carry out comprehensive mapping across different scales effectively. Furthermore, the entire feature map is transformed into a fixed-length feature vector that retains global information while minimizing spatial dimensions. This modification strengthens the network's capacity to derive both local and global information from images.  
Secondly, in addition to the debris accumulation around damaged houses, there is evidence of random collapse directions and distinct textural characteristics compared with the surrounding ground. To take advantage of this distinctive feature, we incorporated a Direction Feature Extraction (DFE) module for extracting direction features. As shown in  
Figure 2: Label dilation technology.  
Figure 3, the DFE module comprises two branches. In the first branch, a 1 \(\times\) 1 convolution is initially applied to reduce dimensionality, followed by directional convolutions that employ four 1 \(\times\) 1 convolutions to perform convolutions in the up, down, left, and right directions. This process generates four local spatial feature weight maps. Subsequently, the weight maps are concatenated to form a single feature aggregation weight map encompassing local spatial features from all four directions. The second branch incorporates global average pooling, 1 \(\times\) 1 convolution, batch normalization (BN), and rectified linear unit (ReLU) activation, which collectively yields global spatial information weight maps. Finally, the local spatial information weight map and the global spatial information weight map are integrated using the element-wise multiplication (\"mul\" operation). \(F_{i}\) represents the input features processed by the convolution layer, and \(F_{j}\) represents the learned damaged buildings feature map. By incorporating building damage heterogeneity features such as random collapse directions, unique texture attributes compared to the surrounding ground, and debris accumulation around the affected structures, the network gains a deeper understanding and enhanced utilization of these features. Consequently, this leads to improved accuracy and robustness in the pixel-level classification of building damage.
# Probabilistic Resilience Analysis of the Icelandic Power System under Extreme Weather
## 1 Introduction
### Literature Review & Contributions  
Relying on Monte Carlo simulations to evaluate the effect of extreme weather events on the electrical power system is a standard approach in the literature. Most notably, in a series of publications, [PERSON] et al. use the so-called fragility curves to model the relationship between wind speed and line failure probability [4; 5; 6]. Further, these authors rely on AC optimal power flow computations to sequentially assess the viability of the power system states simulated by randomly drawing component outages and repairs. In a similar style, [PERSON] et al. also use the fragility curve concept and focus on exploiting Latin Hypercube Sampling and scenario reduction techniques while expressing viability through DC optimal power flow computations [7].  
Our work benefits from the availability of historical records of sustained component failures at different wind speeds for the Icelandic power system. We rely on such data and the seminal work of [PERSON] [8] to compute transmission line failure probabilities in function of the forecasted wind speed throughout the storm event, as developed in [3]. Moreover, instead of optimal power flow computations, we evaluate the resulting randomly generated power system states by means of a bi-level attacker-defender model. The bi-level attacker-defender model has been used in several applications to expose worst-case vulnerabilities of the power system infrastructure [9; 10; 11; 12; 13]. We argue for the suitability of such model for probabilistic power system resilience assessment studies, since it provides a measure of the ability of the system to contain the impact of any additional component failure. We study this measure over characteristic phases in the anticipated evolution of the storm, differentiated in terms of the survival probability of the system components. In this way, we verify the resilience of the Icelandic power system, whose ability to mitigate the impact of any additional failure does not considerably change at the different phases of the storm.
# Minimum Variance Distortionless Response--Hanbury Brown and Twiss Sound Source Localization
###### Abstract  
Sound source target localization is an extremely useful technique that is currently utilized in many fields. The Hanbury Brown and Twiss (HBT) interference target localization method based on sound fields is not accurate enough for localization at low signal-to-noise ratios (below 0 dB). To address this problem, this paper introduces Minimum Variance Distortionless Response (MVDR) beamforming and proposes a new MVDR-HBT algorithm. Specifically, for narrowband signals, the inverse of the correlation matrix of the sound signal is calculated, and a guiding vector is constructed to compute the MVDR direction weights. These direction weights are then used to weight the correlation function of the HBT algorithm. Subsequently, the MVDR-HBT algorithm is extended from narrowband signals to broadband signals. As a result, the directivity of the HBT algorithm is optimized for wide- and narrowband signals, resulting in improved localization accuracy. Finally, the target localization accuracy of the MVDR-HBT algorithm is analyzed through simulation and localization experiments. The results show that the MVDR-HBT algorithm can accurately determine the direction of a sound source, with localization errors at different positions that are smaller than those produced by HBT. The localization performance of MVDR-HBT is considerably better than that of HBT, further verifying the simulation results. This study provides a new idea for target localization within an acoustic propagation medium (air).  
source localization; adaptive beamforming; microphone array; signal processing; HBT interference
## 3 Results and Discussion
### Cross-Domain Evaluation  
In contrast, the model trained on original images (Model 1) but tested on the test set of GrabCut-Watershed images (Model 1-CDE in Table 2) achieved a classification accuracy of approximately 82.77%, while the model trained on GrabCut-Watershed images (Model 2) but tested on the test set of original images (Model 2-CDE in Table 2) achieved an accuracy of approximately 34.15%. In the cases of Models 1 and 2, the variability in training and validation loss and accuracy decreased over the epochs, eventually stabilizing; this behavior indicates good convergence and reliability of the generated classification models. Moreover, by simply fine-tuning a pre-built PyTorch model, we were able to develop an image-based growth stage classification model that achieved over 97% accuracy, demonstrating that deep learning models can provide effective growth stage classification even with relatively small datasets.  
However, the results of Model 1-CDE and Model 2-CDE demonstrate a substantial decline in classification accuracy when there is a domain shift between training and testing images, compared to experiments conducted within the same domain. Although we initially anticipated that Model 1-CDE would significantly outperform the baseline, given that the original images contain richer and more complex information than the preprocessed images, its performance was unexpectedly low, achieving only around 80% accuracy. This discrepancy may be attributed to the presence of multiple crops and higher visual density in the original images, whereas the GrabCut-Watershed preprocessed images typically contain a single, isolated crop, resulting in reduced complexity for the classifier.  
Additionally, while the model trained on original images and tested on GrabCut-Watershed images still achieved an accuracy above 80%, the model trained on GrabCut-Watershed images and tested on original images barely exceeded 30% accuracy. These findings indicate that generalizing from features learned on preprocessed images to classify original images is a more challenging task.  
Therefore, additional improvements in experimental conditions may be required to enhance the model's performance in such cross-domain scenarios. Furthermore, these results suggest that the features learned from the preprocessed images are insufficient to fully capture the diversity caused by the complex background information and noise present in the original images, clearly demonstrating that domain differences have a negative impact on classification performance.
# Internet of Things and Long-Range-Based Smart Lampposts for Illuminating Smart Cities
## 4 Hardware and Software Description
### Software Development  
In any system, the hardware node hardly stores the encryption keys and, therefore, it is challenging for the hardware node to handle and manage the encryption simultaneously. However, in parallel with its neighbors, network designers can provide the encryption facility in the node hardware. To overcome this challenge, we have a Request-Response communication model, and it is shown in Figure 7. In this communication model, the client generates the request to the server and the server acknowledges the request. Concerning the request from the client, the server retrieves the data, formulates the response, and sends the response to the client.  
IoT comprises six different levels of the architecture in the aspects of hardware complexity, several nodes, observer nodes, and cloud servers. Our architecture is framed based on level 6 IoT and it is shown in Figure 8. The level 6 architecture consists of independent end nodes, observer nodes, devices, controller service, local server, and cloud server. The independent end nodes sense the data of the physical elements, and communicates with the cloud server. Generally, this architecture is applicable where a large amount of data is generated from the final nodes. As the number of data points is huge, analytic tools are required for validating the data. The analytic tools will be applied to the data, which are present in the cloud server. A cloud-based application is available in this architecture for monitoring the end nodes in a graphical user interface.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sensor Name** & **Characteristics** \\ \hline LDR sensor & 6 V, 1012 \(\Omega\) \\ DHT sensor & 20–90\% RH, 0–50 \({}^{\circ}\)C \\ Cox sensor & 200–300 \({}^{\circ}\)C, 5 V \\ NOx sensor & 120 \(\Omega\), 12 V to 32 V \\ Pi camera & 5 Megapixels, 2592 \(\times\) 1944 pixels \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hardware Specifications.  
Figure 7: Request Response Communication Model.