esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 5 Tests on New Methods for Gamma/Hadron Separation
### Tests on Gamma/Hadron Separation
The bootstrap hyperparameter is a Boolean value that indicates whether or not to use bootstrapping when building the decision trees. When bootstrap is set to True, bootstrapping is used to create the subsets of training data used to train each decision tree. When bootstrap is set to False, each decision tree is trained on the entire training set. Our best results, achieved for the Random Forest model and for all the sets of features considered, are as follows:* \(n\) _estimators_\(=100\). In our case, 100 decision trees were used, which was not the best choice for achieving the highest performance. However, we chose to keep the network simple to balance performance and computational complexity. We noted that there was little improvement in performance when using hundreds of trees, but using thousands of trees would result in a very heavy and slow network;
* _max features_\(=\) square root of the number of features used during fit;
* _max depth_\(=20\)
* _min samples split_\(=2\)
* _min samples leaf_\(=2\)
* _bootstrap_\(=\) True
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 3 Machine Learning Techniques
### Extra Trees  
Extra Trees are a fast and efficient Machine Learning algorithm, and they are well-suited to applications where the data are noisy or have a large number of features. They are similar to Random Forest in that they use multiple decision trees to make predictions. However, there are a few key differences between the two algorithms.  
In Extra Trees, the decision trees are trained on random subsets of the data and with random subsets of the features, rather than using the entire dataset and all the features. This makes Extra Trees more random than Random Forest and reduces the chances of overfitting to any particular pattern in the data.
# Study on the Evolution Law of Overlying Rock Collapse Induced by Mining Based on BOTDR
## 4 Analysis of Horizontal Optical Fiber Strain Monitoring in Slope
### Evolution Law of Horizontal Optical Fiber Strain in Rock Strata
Meanwhile, the middle section of the curve stabilizes, suggesting that the goat rock mass enters a compaction stage, where fallen rock fragments compress and fill voids under gravity, forming a more stable stress state. At 240 m advancement, both optical fibers H1 and H2 exhibit strain peaks exceeding 2000 \(\upmu\epsilon\), confirming that the roof has entered a critical failure stage. Experimental images clearly show large-scale rock collapse, roof instability, and a fully developed goat collapse zone. The strain curve continues to exhibit a double-peak pattern, but the strain in the H1 peak zone (50-120 m) and H2 peak zone (180-250 m) increases significantly, indicating that these areas correspond to major stress concentration zones or key collapse zones. At this stage, severe strain fluctuations reflect the instability and sudden failure of the rock mass, suggesting that stress accumulation in the surrounding rock leads to sudden failure events, posing serious safety risks for mining operations. The strain curves of optical fibers H1 and H2 exhibit a consistent pattern, effectively capturing the evolution of rock mass failure. Overall, at 115 m and 155 m advancement, the roof remains largely intact, characterized by localized fractures and stress adjustments. At 195 m advancement, the collapse zone expands, roof failure intensifies, and stress concentrations become more pronounced ahead of the working face. By 240 m, large-scale rock collapse occurs, roof structures become unstable, and surrounding rock stress fluctuates violently, marking the most critical failure stage. Therefore, in coal seam mining operations, special attention should be given to the stress evolution of surrounding rock beyond 200 m of advancement, with proactive support and monitoring measures implemented to mitigate sudden failures and enhance mining safety.
# Adaptive Dynamic Search for Multi-Task Learning
## 4 Experiments
### Results  
#### 4.3.1 Scenario 1  
We first applied the proposed method, ADM, to the first scenario and compared it with several recent approaches that can perform multiple tasks: NestedNet, PackNet, (A modified version of PackNet was applied as in [25]) and VirtualNet, including a multi-task learning baseline method. While NestNet and PackNet produce outputs corresponding to tasks under different computational budgets (number of parameters), VirtualNet and ADM can produce a larger number of outputs for tasks and budgets. Note that the number of outputs (models) with respect to computational budgets in VirtualNet should be the same as the number of tasks [25], which limits its applicability, whereas ADM allows the search network to choose the number of different computational budgets regardless of the number of tasks. (Here, the number of computational budgets \(c\) is set to three.) We used WRN-32-4 [36] for all compared approaches, whose total number of parameters was 7.4 million.  
Figure 4 shows the experimental results, where ours give the average of the individual results of all instances for each task. At first, the solutions obtained by ADM improved over the baseline while using a lower number of parameters for both tasks. Overall, ADM produces three outputs of different computational budgets, and all of them outperformed other methods being compared while consuming lower computational budgets. Since the  
proposed approach performed adaptive dynamic search for each instance, it could produce a bespoke model and the solution for each instance rather than giving a single hand-crafted model for a task. Aside from that, it automatically selected an optimal structure, enabling performance improvement compared with other competitors that are based on a fixed manual model structure. Notice that the models explored by the search network were distributed in a region (5.3-6.7 million) in the computational budget. This is potentially because the probability of choosing the compositions of the intermediate number of building blocks was dominant among \(2^{8}\) possible compositions using \(g\) building blocks, whereas the probability of choosing extreme cases (i.e., compositions with few or most of the building blocks chosen) was scarce when optimizing the loss function (Equation (6)).
## 4 Case Study and Result
### Examples of Data Exploration with the Geovisual Analytical System
#### 4.2.4 Relationships between Inputs and Outputs  
Since the model configurations have significant impacts on the outputs of the model simulation [14], being able to identify the relationships between input parameters of a model configuration and the corresponding output is useful for adjusting model configurations to reduce the unstructured errors. To set up the 300 model runs of ModelE, scientists deliberately designed the values of the seven input parameters (shown under the \"Input Parameter\" Column in Figure 10).  
Figure 10: Interface for comparing model inputs and outputs.  
Figure 9: Client interface for evaluating the quality of simulation and the result of evaluation.  
The system provides a function that allows users to select multiple output parameters (shown under the \"Diagnostics\" column in Figure 10). For every pair of input and output parameters, the system will generate a scatter plot showing their relationship across 300 runs. The 5-year global mean value of the output parameter is used in creating the scatter plot. Given the number of selected inputs and outputs, one or more scatter plots can be shown on the interface at the same time for comparison. Through analyzing the trends in the plots, users can obtain the covariance between input and output. If the outputs are close to the observations, indicating better accuracy of the model configurations, scientists can narrow the range of inputs to further improve the model simulations.
## 5 Discussion and Conclusions
The turbine rotor diameter used in the study was evaluated as 120 m and the hub height was evaluated as 100 m by considering the installed wind turbines in Turkey and up-to-date wind turbine technology. In order to not adopt a brand- and model-dependent \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Wind Speed Range**} & \multicolumn{2}{c}{**Current Study**} & \multicolumn{2}{c}{**REPA**} \\ \cline{2-5}  & **Total Area** & **Wind Potential (P)** & **Total Area** & **Wind Potential (P)** \\  & **(km\({}^{2}\))** & **(MW)** & **(km\({}^{2}\))** & **(MW)** \\ \hline
6.8–7.5 & 965.28 & 6468.10 & 1511.42 & 7557.12 \\
7.5–8.1 & 364.64 & 3173.79 & 850.96 & 4254.80 \\
8.1–8.6 & 252.88 & 2701.24 & 284.51 & 1422.56 \\
8.6–9.5 & 174.92 & 2380.15 & 115.23 & 576.16 \\
3.9–5.5 & 65.48 & 1205.53 & 3.34 & 16.72 \\ Total & 1823.20 & 15,928.81 & 2765.47 & 13,827.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of current study with REPA. approach, no turbine model of any brand was taken as a reference. The approach used in this study can be applied considering different hub heights and predetermined turbine specifications. [PERSON] [52] mentioned problems that wind farm owners may encounter technically, such as a miscalculation of the power coefficient for the turbine, which may cause maintenance costs in the future. Therefore, more appropriate results can be obtained if the wind energy potential determination approach in this study is re-applied in projects to be realized in smaller areas according to the type of turbine determined, by taking into account meteorological conditions. This research received no external funding. Not applicable. The results presented in this paper are available upon request. Corine Land Cover (CLC) 2018 data is public available and can be sourced at [[https://land.copernicus.eu/pan-european/corine-land-cover/clc20187](https://land.copernicus.eu/pan-european/corine-land-cover/clc20187) tab=download]([https://land.copernicus.eu/pan-european/corine-land-cover/clc20187](https://land.copernicus.eu/pan-european/corine-land-cover/clc20187) tab=download) (accessed on 11 February 2023). The author declares no conflict of interest. Figure 10: Wind speed maps of Gömec, Gönen, Havran, and Ivrindi districts at a height of 100 m.
## 4 Algorithms
### Audio Pre-Processing  
At first, each audio track \(x_{i}(n)\) is converted into the related spectrogram by computing the short-time Fourier transform:  
\[X_{i}(f,m)=\sum_{n=-\infty}^{+\infty}x_{i}(n)\ w(n-mL)\ e^{-\beta\pi fn} \tag{1}\]  
where \(w(\cdot)\) is a windowing function and \(L\) is the stride parameter. Coefficients \(X_{i}(f,m)\) are computed for a finite set of \(N_{f}\) frequencies \(f\) and a finite set of \(M\) windows \((m=0,\ldots,M-1)\). In our experiments,  
Figure 2: Block diagram of the proposed algorithm. The input consists of the digitalized audio tracks \(x_{i}\), \(i=1,\ldots,N\), and the output is the estimated audio phylogeny tree (APT).  
we adopted Hamming-windowed frames of 4096 samples, with an overlap rate of 0.75, and a set of \(N_{f}=512\) linearly-spaced frequencies, ranging from 0 to 6 kHz. This latter choice allows one to reduce the computational burden (by reducing the size of images to process by a factor of four) without affecting the system performance, given that most of the spectral information is usually found at a low frequency.  
By associating each spectrogram coefficient to the pixel of a grayscale image \(P_{i}(u,v)\), we obtain a \(N_{f}\times M\) gray level image, where the pixel intensity is obtained by converting the value \(|X_{i}(f,m)|^{2}\) into an 8-bit integer. In order to remove part of the background noise, if \(|X_{i}(f,m)|^{2}<\delta\), the pixel \(P_{i}(u,v)\) is set to zero.  
From the obtained spectrogram image \(P_{i}(u,v)\), a set of keypoints \(\mathcal{K}_{i}=\{(u_{k},\
u_{k})\}\) with the related descriptors is computed by using the speeded-up robust features (SURF) algorithm [35].  
After the pre-processing step, each image pair \((P_{i},P_{j})\), \(i=1,\ldots,N\), \(j=1,\ldots,N\), \(i\
eq j\), is passed to the next modules.
## 3 System Overview
### Ceres Scan Matching  
SLAM uses a scan matcher to process sensor data and to obtain pose. Then, the pose obtained is used as a reference coordinate to insert scan data into the submap. This estimated pose is computed using a comparison between current scan states of LiDAR and submap. During vehicle motion, the scan always has familiarity with the previous scan.  
Moreover, a scan matcher filters the residual difference of these consecutive scan data and obtains the translation with the rotation to estimate the pose. The scan matcher responsible in this SLAM is called a Ceres scan matcher [24], which works to find optimal probability values in scan point in the grid-based submap. In the process of scan matching to the submap, due to the presence of multi-distance scan scheduler, it is unnecessary to use a larger search window to match the scan into the submap. The smaller scan distance requires a smaller search window size which increases processing speed.  
When the scan uses a maximum LiDAR distance and generates \(\mathcal{F}_{max}\),  
\[\mathcal{F}_{max}\in W_{max}\ \ \sum_{k\ =\ 1}^{K}(1-M_{smooth}(T_{\mathcal{F}}h_{ k}))^{2} \tag{3}\]  
When the scan uses smaller LiDAR distance and generates \(\mathcal{F}_{min}\),  
\[\mathcal{F}_{min}\in W_{min}\ \ \sum_{k\ =\ 1}^{K}(1-M_{smooth}(T_{\mathcal{F}}h_{ k}))^{2} \tag{4}\]  
where \(h_{k}\) is a scan frame transformed by \(T_{\mathcal{F}}\) to the submap frame, \(M_{smooth}\) is a bi-cubic interpolation smooth filter for probability values of submap, \(W_{max}\) and \(W_{min}\) are the search window areas of the scan matcher where their size follows the size of frame \(\mathcal{F}\) which contains the width and height applied around the last transformed frame pose \(h_{k}\) location.
# Local Community Detection in Dynamic Graphs Using Personalized Centrality +
## 5 Dynamic Communities from Personalized Centrality
### Real Graphs
These results show that our dynamic algorithm helps more in terms of performance over time, without sacrificing the quality of the communities produced. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Graph**} & \multirow{2}{*}{**Batch Size**} & \multicolumn{2}{c}{**Performance**} & \multicolumn{2}{c}{**Quality**} \\ \cline{3-7}  & & \(T_{S}\)/\(T_{D}\) & \(I_{S}\)/\(I_{D}\) & **Recall** & \(\phi_{S}\)/\(\phi_{D}\) & \(f\)/\(f_{D}\) \\ \hline \multirow{3}{*}{slash-threads} & 10 & 52.94\(\times\) & 34.02\(\times\) & 0.93 & 0.99 & 1.03 \\  & 100 & 26.88\(\times\) & 21.46\(\times\) & 0.96 & 1.00 & 1.01 \\  & 1000 & 39.65\(\times\) & 31.09\(\times\) & 0.96 & 1.00 & 1.00 \\ \hline \multirow{3}{*}{enron} & 10 & 75.42\(\times\) & 45.04\(\times\) & 0.97 & 1.00 & 1.00 \\  & 100 & 63.61\(\times\) & 41.28\(\times\) & 0.98 & 1.01 & 0.98 \\  & 1000 & 46.20\(\times\) & 29.57\(\times\) & 0.96 & 1.01 & 0.98 \\ \hline \multirow{3}{*}{digg} & 10 & 54.29\(\times\) & 29.41\(\times\) & 0.86 & 0.97 & 1.18 \\  & 100 & 47.64\(\times\) & 25.69\(\times\) & 0.90 & 0.98 & 1.07 \\  & 1000 & 50.64\(\times\) & 26.87\(\times\) & 0.97 & 0.99 & 1.02 \\ \hline \multirow{3}{*}{wiki-talk} & 10 & 56.02\(\times\) & 36.68\(\times\) & 0.95 & 1.00 & 1.02 \\  & 100 & 48.87\(\times\) & 31.46\(\times\) & 0.91 & 0.99 & 1.19 \\  & 1000 & 56.22\(\times\) & 36.95\(\times\) & 0.96 & 1.00 & 1.02 \\ \hline \multirow{3}{*}{youtube- u-growth} & 10 & 56.47\(\times\) & 27.66\(\times\) & 0.96 & 1.00 & 0.94 \\  & 100 & 50.00\(\times\) & 26.58\(\times\) & 0.96 & 1.00 & 1.00 \\ \cline{1-1}  & 1000 & 40.17\(\times\) & 20.44\(\times\) & 0.91 & 1.00 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average summary statistics over time on real graphs for all batch sizes. Columns are graph name, batch size, speedup in time, speedup in iterations, recall, ratio of conductance scores, and ratio of normalized edge cut scores.
## 1 Introduction
### Related Work
#### 1.1.2 Experimental Studies and Wireless VR Testbeds
Powerful new hardware and novel software techniques are stated as being desirable to enable wireless high-quality VR applications. In [18], an end-to-end wireless VR platform is proposed offering low latency remote rendering. An Intel Core i7 based PC equipped with an NVIDIA TITAN X GPU is used as rendering server. The server holds a Mellanox 10 Gbps network interface card that is connected through a 10 Gbps Ethernet cable with a Netgear Nighthawk X10 WiGig AP. A ThinkPad X1 Yoga laptop, equipped with an Intel i7-7600U CPU and an HD 620 integrated GPU with H.264 hardware decoder ASIC, is used as client. The laptop uses a Qualcomm QCA6320/QCA6310 WiGig module to wirelessly connect to the WiGig AP and connects to an HTC Vive HMD through HDMI and USB cables. The proposed parallel rendering and streaming mechanism reduces streaming latency while the proposed VSync driven rendering technique minimizes display latency. It is shown that this wireless VR platform supports 2K VR resolutions at a refresh rate of 90 Hz with less than 16 ms and 4K VR resolution with 20 ms end-to-end latency. In [19], a TPCAST wireless VR testbed is presented that allows for studying temporal data delivery issues of video frames. In particular, the TPCAST wireless adapter and the underlying WirelessHD standard is used with the HTC Vive HMD. The testbed allows for monitoring and recording of traces from which video freezes at the wireless HMD can be detected. It also records traces that capture information about the input traffic from the HMD and hand controls to the rendering computer. The TPCAST wireless VR testbed was used in [20] to measure, analyze, and model network-induced temporal disturbances in VR applications with focus on freeze durations. The freeze length distributions deduced from the measurement campaign were modeled as geometric distributions.
# A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning
## 3 System Diagram
### Transmitter: LED  
The semiconductor light sources known as light-emitting diodes (LED) emit light when current flows through them. This is conceivable because of the electroluminescence phenomenon, in which the forward current causes semiconductor electrons to rejoin electron holes and release energy as photons. The energy required for electrons to traverse the band gap of the semiconductor determines the wavelength of the emitted light. Phosphor LEDs and Red Green Blue (RGB) LEDs are the two most commonly used types of LED to generate white light: (i) by using a blue LED with yellow phosphor, white light is produced and (ii) by using the RGB-based LED, which does not use phosphorus and is sustainable, high speeds can be reached in domestic environments [72].
# Dynamic Response of Slender Vertical Structures Subjected to Thunderstorm Outflows
## 3 Numerical Application
### Structure Case Studies  
Two slender vertical structures are considered: a steel lighting pole (Figure 1a) and a reinforced concrete telecommunications tower [18] (Figure 1b).  
The steel lighting pole (Figure 1a) is composed of a shaft, covered at the base with a prismatic hollow element, and a lighting device at the top. The shaft consists of two parts, both with a truncated conical shape and a tubular octagonal section, with a constant thickness of 4 mm. Its height above the ground is 14 m, of which 2.2 m corresponds to the first part, and 11.8 m to the second one. The outer diameters at the base and at the top are 280 and 80 mm, respectively. It reaches a total height of 15.76 m. The top portion, a length of 1.76 m, holding the lighting device, presents a decagonal cross section until a height of 14.33 m, after which it becomes dodecagonal. The lighting device has a mass of 145 kg and a center of gravity at 14.9 m above the ground. Considering its modest height, it can be regarded as a low-rise structure.  
The reinforced concrete telecommunications tower (Figure 1b) is 98.00 m high and can be regarded as a mid-height structure. It is composed of three superimposed shafts. The first shaft, up to 3.90 m above the ground, is made up of two concentric tubular circular sections linked by six radial walls. The second one, from 3.90 to 80.50 m, has a circular tubular section with an outer diameter of 6.50 m and thickness of 0.50 m; in its upper part, from 59.50 to 80.50 m, there are seven steel platforms with a constant distance of 3.50 m that carry transmission parabolas. The third shaft, from 80.50 m to 98.00 m, has a circular tubular section with an outer diameter of 3.00 m and thickness of 0.25 m; its outer surface carries four tubular steel uprights that support other parabolas.  
In Table 1, the properties of the two structures are reported, including the first three fundamental frequencies, modal masses, and the damping ratio, while the first three mode shapes and the width along the height are reported in Figure 2.
# A Geometric Error Measurement System for Linear Guideway Assembly and Calibration
## 2 Measurement Principles
### Straightness Measurement Module  
The proposed straightness measurement module is composed of a PSD (Dual lateral), a signal processor, a data acquisition card (USB-6210, National Instruments Co., Taipei, Taiwan), and a laser source, as shown in Figure 4. The straightness error is the extent of the actual path deviating from a straight line, which can be divided into horizontal and vertical straightness errors [19].  
The linear least-squares method is used for analyzing the geometric errors in this study, which is to find the equation of the straight line that minimizes the sum of the squares of deviations calculated from PSD-measured points:  
\[y=ax+b \tag{1}\]  
The straightness error \(\varepsilon_{l}\) can be expressed as:  
\[\varepsilon_{l}=\left(E_{i}\right)_{max}-\left(E_{i}\right)_{min} \tag{2}\]