esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### High Accuracy  
In the case of high accuracy in the localization measurement, the results are distributed as the following example:  
1. Number of clusters: 15;
2. Size of smallest cluster: 16;
3. Size of largest cluster: 1175;
4. Mean cluster size: 359;
5. Median cluster size: 350;
6. Number of points that are not part of any cluster: 6.  
In this case, Figures 14 and 15 show that the neural network predicts 95% of the data correctly, and 5% of the data were mislabeled in the validation dataset.  
Another example of a lambda value lower than 0.05 is the following:  
1. Number of clusters: 10;
2. Size of smallest cluster: 12;
3. Size of largest cluster: 1784;  
Figure 14: This figure shows the trend of the training progress in the case of a subarea in which the lambda value is equal to 4%.  
Figure 15: This figure shows the confusion matrix in the case of a subarea in which the lambda value is equal to 4%.  
Mean cluster size: 299;
* 5 Median cluster size: 121;
* 6 Number of points that are not part of any cluster: 18. In this last case, \(\lambda=0.03\). In this case, Figures 16 and 17 show that the neural network predicts 95% of the data correctly, and 5% of the data were mislabeled in the validation dataset.  
which, with the neural network, we set a precision of a few meters (\(\sim\)5 m). The accuracy of the measurement drops drastically to values around 55%. This phenomenon is explained by the \"crowding index\", which in this case assumes values of 2.16, significantly higher than the critical threshold. In Table 4, the accuracy results obtained with different values of lambda are represented. The outcome of the simulation with different lambda values confirms what was previously stated. In any case, the proposed solution is substantially at no cost, as it does not require implementations of dedicated networks but uses networks that have already been created (for example gas meters, electricity meters, water meters, etc.) simply by integrating a few lines of code on each firmware, an operation that can be performed remotely. In [37], a performance comparison of RSS- and TDoA-based location approaches in an outdoor public LoRa network is presented. The raw location estimates of the TDoA approaches outperform all RSS approaches examined, with median errors on the order of 200 m versus median errors on the order of 1250-2500 m [3]. With the present work, we instead demonstrated that, by using a neural network applied to an RSS dataset, it is possible to go far below that threshold. A completely different approach is presented in [17], where through the OwLL, a LoRa localization system, it is possible to reach an accuracy of a few meters \(\sim\)9 m. Obviously, this is a specific and proprietary solution and which by its nature has costs associated and needs a specific business case analysis.
# Trajectory Clustering and k-NN for Robust Privacy Preserving Spatiotemporal Databases
## 2 Materials and Methods
### Useful Definitions  
We consider points in a \(d\)-dimensional space \(\mathcal{D}\). Given two points \(a\) and \(b\), we define as \(dist(a,b)\) the distance between \(a\) and \(b\) in \(\mathcal{D}\). In this paper, we utilize the Euclidean distance metric, which is defined as  
\[dist(a,b)=\sqrt{\sum_{i=1}^{d}\left(a[i]-b[i]\right)^{2}}\]  
where \(a[i]\) and \(b[i]\) denote the values of \(a\) and \(b\) along the \(i\) dimension in \(\mathcal{D}\).  
**Definition 1**.: \(k\)_-NN: Given a point \(b\), a dataset \(X\) and an integer \(k\), the \(k\) nearest neighbors of \(b\) from \(X\), denoted as \(k-NN(b,X)\), is a set of \(k\) points from \(X\) such that \(\forall p\in k-NN(b,X)\) and \(\forall q\in\{X-k-NN(b,X)\}\), \(dist(p,b)<dist(q,b)\)._  
**Definition 2**.: \(k\)_-NN Classification: Given a point \(b\), a training dataset \(X\) and a set of classes \(Cl_{X}\) where points of \(X\) belong, the classification process produces a pair (\(b\),\(cl_{b}\)), where \(cl_{b}\) is the majority class \(b\) belongs._  
**Definition 3**.: _Clustering: Given a finite dataset \(\mathcal{P}\) = {\(a_{1}\), \(a_{2}\),..., \(a_{N}\)} in \(R^{d}\), and number of clusters \(K\), the clustering procedure produces \(K\) partitions of \(\mathcal{P}\) such that among all \(K\) partitions (clusters) \(C_{1}\), \(C_{2}\),..., \(C_{K}\) it finds one that minimizes_  
\[\arg\min_{C_{1},C_{2},\ldots,C_{K}=\mathcal{P}}\sum_{c=1}^{K}\sum_{a\in C_{c} }\left\|a-\frac{1}{|C_{c}|}\sum_{a_{j}\in C_{c}}a_{j}\right\|^{2}\]  
_where \(|C_{c}|\) the number of points in cluster \(C_{c}\)._
# Alignment Method of an Axis Based on Camera Calibration in a Rotating Optical Measurement System
## 4 Experiment  
### Experimental Setting  
We carried out experiments to verify our method. The experimental setup is shown in Figure 8a. The rotation control system is composed of a CCD camera (model: Bammer camera TXG50; resolution: \(1224\times 1025\) pixels) with a 16 mm focus length imaging lens (model: MA1214M-MP), two translation stages (Zolix TSA50-C electric translation platform, with a repositioning precision less than 5 \(\upmu\)m; PI-M406, with a repositioning precision of 0.078 \(\upmu\)m) and a turntable (Zolix RAP200 electric rotation platform, with a repositioning precision less than 0.005\({}^{\circ}\)). The translation stages are fixed on the rotation platform, and the camera is fixed on a translation stage. The stepper motor controls the camera to move along the \(Y_{w}\) and \(Z_{w}\) axes and rotate with the turntable, respectively. During the experiment, the exposure time of the camera is 12,000 \(\upmu\)s. The checkerboard image (the size of a square is 14.175 \(\times\) 14.175 mm) is displayed on an LCD screen (Philips 190V with a resolution of 1440 \(\times\) 900 pixels; the dot pitch is 0.2835 mm/pixel). The LCD display screen keeps still during the calibration process. Since the LCD display has a completely pure plane and the screen glass is very thin, the refraction phenomenon can be ignored. Of course, a checkerboard target with a higher machining accuracy can also be employed for providing feature points with higher accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Fitted Center/mm** & **Fitted Radius/mm** \\ \hline \multirow{3}{*}{Without noise} & \(Or=(-50.00,\,900.00)\) & r = 50.00 \\  & \(Og=(-50.00,\,900.00)\) & r = 65.00 \\  & \(Ob=(-50.00,\,900.00)\) & r = 80.00 \\ \hline \multirow{3}{*}{3.5\% random noise} & \(Or=(-49.96,\,900.04)\) & r = 49.96 \\  & \(Og=(-50.01,\,900.03)\) & r = 65.19 \\ \cline{1-1}  & \(Ob=(-49.96,\,900.01)\) & r = 79.99 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fitted centers and radii correspondence table.  
Figure 8: (**a**) Experimental setup; (**b**) one set of the patterns for calibration.
# Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector
## 4 Results and Discussion
### Experiment 2: Impact of Data Augmentation on the Performance  
#### 4.2.1 Comparison Results  
The weight of the coded portion of the VGG16 convolution autoencoder was replaced by the initial weight of VGG16 in the SSD model before training, while the model trained with the proposed method was tested under the same test set as the previous section. To analyze the effect of data expansion, SSD_CAE was selected for comparison with this method. Table 5 shows the statistics of the data augmentation method under the test set. Table 6 shows the evaluation results of the two models.  
In this experiment, the data set for training SSD_CAE in Experiment 1 was named the original data set. The data set of 5000 samples obtained using data augmentation was used to train the SSD_CAE networks.  
The trained model is applied to the Hurricane Sandy scenario to detect and evaluate disasters. Figure 9 shows the result.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **mF1** & **mAP** \\ \hline
**Original data set** & 56.03 & 44.83 \\ \hline
**Data augmentation** & 67.89 & 77.27 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Impact of data augmentation using the SSD_CAE method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Label** & **Debris** & **Mild** & **Miss** & **Total** & **Recall** \\ \hline
**Debris** & 85 & 23 & 31 & 139 & 61.151\% \\
**Mild** & 21 & 56 & 11 & 88 & 63.636\% \\
**False detection** & 1 & 1 & - & - \\
**Total** & 107 & 80 & & - \\
**Precision** & 79.439\% & 70.000\% & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of the data augmentation method in the test dataset.  
Figure 9: _Cont_.
# Algorithm for Extraction of Reflection Waves in Single-Well Imaging Based on MC-ConvTasNet
## 3 Results
### Wave Separation for the Double-Interface Model
#### 3.3.2 CSG Signals  
Figure 10 shows the CSG and extracted reflected waves for the double-interface model at a depth of 6 inches. Figure 10a,b show the full waveform and reflected waveforms, respectively. Figure 10c,d show the filtering results of MC-ConvTasNet and parameter estimation, respectively. Compared with the parameter estimation, MC-ConvTasNet suppresses the direct wave amplitude better. When using MC-ConvTasNet for wave separation, only the waveforms of the CSGs need to be input. It does not require advance acquisition of wellbore velocity information or re-selection of parameters. After training, MC-ConvTasNet quickly extracts the reflected waves from the full wave. On the NVIDIA GEFORCE GTX1050 Ti GPU (NVIDIA, Santa Clara, CA, USA), it takes 27.3 s to load the MC-ConvTasNet model and process 100 full waveforms, each with a recording time of 14.4 ms and 1024 time samples. Therefore, in the case of continuous lifting of the acoustic logging tool, it is expected to separate the reflected waves of the eight receivers in real time.  
Figure 10: _Cont._  
Figure 9: Full waveforms and extracted reflected waves for the double-interface model. **(a,b)** are, respectively, COG full waveforms and reflected waveforms obtained by forward simulation. **(c–e)** are the reflected waves separated by MC-ConvTasNet, median filter and F-K filter, respectively. On trace 1 in **(b)**, the arrivals are a reflected S-wave from interface 2 (red solid line), a reflected P-wave (blue dashed line), a converted wave (green dashed line) and a reflected S-wave (yellow dashed line) from interface 3. The average R\({}_{\text{std}}\) for MC-ConvTasNet, the median filter and the F-K filter is 32.3 dB, \(-\)19.1 dB and \(-\)19.5 dB, respectively.
# A Review of Edge Computing Technology and Its Applications in Power Systems
## 2 Review of Edge Computing Architectures
### Fog Computing
#### 2.3.2 Smart City  
[PERSON] et al. [49] studied vehicular congestion identification techniques based on fog computing. Communication in the system becomes more effective, allowing more nodes to be involved in communication without affecting performance. [PERSON] et al. [50] proposed an architecture for the development of fog computing applications in a vehicular ad hoc network (VANET) environment. Furthermore, they presented two fog computing applications for this system, and their results showed that the fog applications can provide reliable real-time information while minimizing traffic. [PERSON] et al. proposed a fog computing-based air quality monitoring system where air quality monitoring stations are located in the edge layer and provide sensor data to the fog layer for data processing and calculating [51]. [PERSON] et al. [52] proposed a fog computing-assisted path planning system for smart shopping. Fog nodes are placed in the shopping mall and collect the data from shoppers' mobile devices. The fog layer simulates and recommends promotions for customers. Experiment results show that customers receive the recommendations in a shorter time and shopping speed is noticeably increased.
## 4 Results Analysis
### Stress Evolution and Failure Mechanism
Based on the analysis of crack development and stress evolution throughout the failure process of the sample, the following failure mechanism can be summarised. The failure of the first rock step occurs before the peak, and the crack initiation is mainly driven by the tensile stress. A point-load stress state is experienced by the initial rock step, which frequently leads to splitting failure. The interlocking characteristics of the rock steps allow the initially fractured step to continue bearing a portion of the shear force. The stress distribution among the remaining rock steps is then changed by this initial collapse, putting them in a complicated compressive-tensile stress condition. The rock steps' mode of failure shifts to a combination of shear and tensile processes in the post-peak stage as displacement grows and the impact of shear friction becomes more noticeable.
## 3 Deployment Test of Panels  
### Test Setup with Zero-G Device  
The deployment test was conducted with Zero-G device, which compensates the effect of gravity by hanging test articles with bungee cables with frictionless air bearings similar to other works [27; 28; 29]. Deployment testing of panels on the ground is routinely required to assure deployment success in space orbit for many space missions. One of the most critical factors of ground-based testing is the realization of zero gravity equal to space. Figure 6 shows the Zero-G device consisting of vertical and lateral frames, two sliding bars with air-bearings, and a dummy wing comprised of three open rectangular panels to minimize the effect of air-drag. The size of each dummy panel was 800 mm \(\times\) 800 mm, and the total mass of the three panels made of aluminum 6061 was 10.5 kg. To minimize the friction caused by deflection of the sliding bars during the deployment test, wire cables coupled with springs and air-bearings were installed. Besides, if the deployment test device was tilted or was not straight horizontally or vertically, the test device had to be leveled before the deployment test to avoid undesirable friction. To minimize such concern, a two-axis digital leveler [32] was utilized to adjust all possible misalignment angles of the frames less than 0.1\({}^{\circ}\). The stowed and deployed configurations of the panels in the deployment test device are shown in Figure 6b,c. To record the motion of panels during deployment, a motion capture camera of OQUS500 [33] was installed at the ceiling of the test facility.  
Figure 5: Expression function for the implementation of the path-dependent stiffness of TS hinges in RecurDyn.  
For the deployment test, three types of TS hinges were prepared to check the deployment performance with five peak values of the moment-rotation profile: A, B, C, D, and M (see Figure 2b) summarized in Table 1. The moment and rotation behavior of all hinges were measured three times, respectively. For simulation, averages were taken over the measured data. The bracket parts of the TS hinges were made of aluminum 6061, and the TS hinges were fabricated by the tape measures of the KMC-27S product of the KOMELON company [34], but it had two types of the radius (R), thickness (t) and subtended angles (\(\alpha\)): (11.5 mm, 0.08 mm, and 55\({}^{\circ}\)) and (13 mm, 0.13 mm and 70\({}^{\circ}\)).
# A Relative Coordinate-Based Topology Shaping Method for UAV Swarm with Low Computational Complexity
## 5 Numerical Results
### Global Energy Consumption  
In order to analyze the global energy consumption, 100 sets of simulations are conducted. In each simulation, the initial topology is randomly distributed in the 3D space of 1000 m \(\times\) 1000 m \(\times\) 1000 m, and the target topology is an aircraft shape as shown in Table 1.  
To evaluate the performance of proposed algorithm, the global energy consumption of the Auction algorithm, the KM algorithm, the improved KM algorithm and random mapping are compared. It is worth noting that the random mapping randomly selects a set of mapping relationships in each simulation. Simulation results are shown in Figure 8. From the simulation results, it can be seen that the average global energy consumption of the proposed method is reduced by about 98.4% compared with that of the random mapping. On the other hand, the LAPJV algorithm can achieve the same global energy consumption compared with that of the KM algorithm, the improved KM algorithm, and the Auction algorithm.  
Figure 7: The side perspective of topology shaping in cube shape.
# Landslide Susceptibility Modeling Using a Deep Random Neural Network
###### Abstract
Deep learning ensemble methods have been developed in order to improve the prediction capability of individual deep learning models [40; 41; 42; 43]. Several neuron-free deep learning methods are also successfully applied in LSM, such as the deep Boosting model and the deep learning tree [44]. Compared with shallow machine learning methods, deep learning methods can further improve landslide prediction accuracy due to the deep and interconnected structures [45]. In particular, the CNN plays an important role in addressing feature learning problems [33]. The CNN is a neural network that simulates the mechanism of the human visual system. The core feature encoding the structure of the CNN is the convolution filter, which establishes local connections for the input data. By using stacking convolution and pooling layers, CNN can extract robust and diverse representations [46]. The convolution-based deep learning models have been widely applied to various fields, including image classification and segmentation [38], video summarization [47], remote sensing [48], object detection [49], and natural language processing [50]. CNN also brings a revolution to landslide susceptibility modeling, which can mine deep-level relationships between landslide-related factors and landslides and explore the synergic effects of factors on landslides through convolution operators [33]. In LSM, CNNs are found to be superior to shallow machine learning methods. For example, [PERSON] et al. (2022) compared the CNN with three conventional machine learning models of RF, logistic regression, and SVM. They showed that CNN not only achieves preferable performance but also reduces the salt-and-pepper effect [51]. Although deep learning methods achieve impressive performance, they rely on massive sample data for training and learning [69]. Given that the LSM training data are relatively limited, the designed model should not be complex, e.g., decreasing the depth of
# VLSI-Friendly Filtering Algorithms for Deep Neural Networks
## 1 Introduction  
The need for high-speed processing of large amounts of information stimulates the development and use of highly effective data processing systems. In such systems, the primary requirement for implementing computing methods is to minimise the time of data processing, ensuring the ability to fulfil the planned task within the allocated time for this application. This requirement is especially relevant in the implementation of algorithms for processing digital information in deep neural networks (DNNs) [1; 2; 3; 4; 5]. As is known, in deep neural networks, the primary and time-consuming operation is digital convolution. The need to quickly calculate digital convolution arises in both convolutional and capsule neural networks. Digital convolution calculations can be accelerated by algorithmic and hardware methods. In general, algorithmic methods primarily focus on minimising the number of arithmetic operations involved. One widely employed strategy for reducing the computational complexity of the digital convolution operation is utilising the Fast Fourier Transform (FFT) algorithm. This approach has found application in some deep neural networks [6; 7; 8; 9; 10; 11]. However, modern convolutional and capsule neural networks use small filters more often than the traditionally used large filters computed using the FFT approach. The [PERSON]'s minimal filtering algorithm [1; 12; 13; 14; 15], which has recently gained significant popularity, is widely regarded as well-suited for such scenarios. This approach exhibits enhanced efficiency, specifically when employing small filters and tile sizes. In such cases, it performs linear convolution with minimal computational complexity. Indeed, this method calculates the dot products of adjacent vectors obtained from a sliding time window in the current data stream. It employs a third-order finite impulse response filter (FIR) for this purpose.
## 7 Challenges and Limitations
### Model Interpretability  
One of the key challenges in AI deployment in critical network operations is the interpretability of AI models. Many AI models, especially deep learning models, are often considered \"black boxes\" making it difficult to understand how decisions are made. This lack of transparency is particularly problematic in mission-critical applications, such as network security, where understanding the rationale behind an AI decision can be crucial to preventing security breaches [138]. For instance, in network traffic anomaly detection, an AI model might flag a packet as suspicious, but without a clear explanation, network administrators may hesitate to act. Explainable AI (XAI) techniques, which aim to make AI models more transparent, are crucial in addressing this issue. However, XAI techniques often come with trade-offs in terms of model complexity and performance [139].  
Table 12 summarizes the impact of different explainability methods on model performance:  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Accuracy** & **Privacy Protection** & **Computational Overhead** \\ \hline Centralized & 95\% & Low & High \\ Learning & Federated Learning & 90\% & High & Medium \\ \hline \hline \end{tabular}
\end{table}
Table 10: Impact of federated learning on accuracy, privacy, and computation.  
This table shows the performance trade-offs between different explainability methods for AI models in communication networks, helping to decide which technique balances interpretability and accuracy best for a given application.
## 3 Materials and Methods
### Implementation Tools  
The experiments were implemented on an Ubuntu 18.04 platform, utilizing Python 3 as the programming language. For deep learning functionalities, the experiments employed Keras [42] along with TensorFlow [43] as the backend framework, as described in the study. The hardware configuration used to conduct the experiments involved an Intel CPU with 8 cores, specifically an i7 950 running at 3.07 GHz. Additionally, the experiments made use of a Nvidia GeForce GTX 960 GPU with 4 GB of memory. This hardware setup was utilized to facilitate the training, validation, and testing phases of the research.