esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Total Cross Section
### Elastic Scattering  
In an elastic scattering process, the exciton remains in its ground state after the collision, meaning \(|\mathbf{k}_{i}|=\left|\mathbf{k}_{f}\right|\). As such, we can write  
\[q=2\left|\sin\left(\frac{\theta}{2}\right)\right|k_{i}. \tag{13}\]  
Additionally, we also have \(\chi_{f,i}(\mathbf{r})=\chi_{1s}(r)\), where we consider a simple variational _ansatz_[46; 47] based on the eigenfunctions of the two-dimensional hydrogen atom [48; 49] and given by  
\[\chi_{1s}(r)=\mathcal{N}_{1s}e^{-r\gamma_{1s}/2}, \tag{14}\]  
with \(\mathcal{N}_{1s}\) a normalization constant given by  
\[\mathcal{N}_{1s}=\left[\int rdrd\theta\left(e^{-r\gamma_{1s}/2}\right)^{2} \right]^{-1/2}=\frac{\gamma_{1s}}{\sqrt{2\pi}} \tag{15}\]  
and \(\gamma_{1s}\) a variational parameter. This variational parameter is computed by minimization of the energy expectation value of the Wannier Equation [50]  
\[H=-\frac{\hbar^{2}c^{2}}{2\mu}\
abla^{2}+V_{RK}(\mathbf{r}), \tag{16}\]  
with \(V_{RK}(\mathbf{r})\) the Rytova-Keldysh potential.  
With this _ansatz_, we can directly substitute the wave function into \(J(i\to f)\), given by Equation (10), and obtain  
\[J_{\text{elast}}(q)=J(1s\to 1s)=\gamma_{1s}^{3}\left[\frac{1}{\left[\left(\frac{ \sigma}{1+\sigma}q\right)^{2}+\gamma_{1s}^{2}\right]^{3/2}}-\frac{1}{\left[ \left(\frac{1}{1+\sigma}q\right)^{2}+\gamma_{1s}^{2}\right]^{3/2}}\right] \tag{17}\]  
after integration. This is then substituted into Equation (12), reading  
\[Q_{\text{elast}}(k_{i})=\frac{2\pi M^{2}(\hbar c\alpha)^{2}}{\hbar^{4}e^{2}k _{i}}\int_{-\pi}^{\pi}d\theta\left|\frac{J_{\text{elast}}(q)}{q(1+r_{0}q)} \right|^{2}, \tag{18}\]  
where \(q\) is given by Equation (13). This integral has no analytical solution and must be computed numerically.  
To finalize the computation of the elastic cross section, we must choose a set of material specific parameters. We consider those corresponding to monolayer hexagonal boron-nitride (hBN) encapsulated in fused quartz, with dielectric constant \(\epsilon=3.8\)[51]. The electron and hole masses in this material are \(m_{e}=0.83\,m_{0}\), \(m_{h}=0.63\,m_{0}\)[52], with \(m_{0}\) the electron rest mass, and screening length \(r_{0}=10\,\mathrm{\AA}\)[53]. This material was chosen, as both the screening length and the electron/hole masses are known accurately. The obtained variational energy for the \(1s\) excitonic state is \(E_{1s}=-58.9\,\mathrm{meV}\).  
Varying the initial relative wave vector, we obtain the plot of the total elastic cross section from Equation (12) in Figure 1. We can see that the cross section for electron scattering is always larger than that for hole scattering, as expected from the fact that the reduced mass of the system is larger when the free carrier considered is an electron. A very quick increase from zero relative momentum up to a global maximum is also observed, consistent with the results of [PERSON] and [PERSON] [41] for elastic scattering.
## 2 Power Internet and High Voltage Transmission Line Condition Monitoring Intelligent Sensor Design
### Smart Grid High-Voltage Transmission Line State Data Acquisition Technology
#### 2.2.2 Edge Computing Technologies  
The edge computing network model mainly consists of cloud computing, communication network, edge server, and power terminal. This computing network determines the overall data processing and data storage capabilities through the performance of devices in each layer. The data interaction across layers is completed through the communication network [23]. All users and devices of the power IoT collect data through terminals, and the collected terminal data is used as the input quantity of the system. The edge server operation framework is shown in Figure 2.  
As shown in Figure 2, the operation process of the system is divided into three processes: cloud computing, edge computing, and user devices. User devices interact with edge computing through a communication interface. Edge computing consists of three sub-systems: system control center, system data processing, and system data security for the control, operation, and maintenance of the whole system. The multiwavelet transform algorithm is defined in the edge layer and is responsible for data collection, which is presented in the next section. Moreover, edge computing could benefit the applications, computing resources and data provided by the cloud layer. Data transmission between edge and cloud happens when the data needs to be processed in the main datacenter. In this case, only the necessary information is transmitted in order to minimize latency.
# Enhancing Absorption Bandwidth through Vertically Oriented Metamaterials
## 3 Unit Cell Containing a Single VSRR  
Although the unit cell geometry is relatively straight forward, SRRs demonstrate a rich absorption spectrum due to the several resonant modes they support [46; 47; 48]. Consider a unit cell containing a single VSRR, defined by parameters in Table 1, centered on a wall.  
Inserted in an empty unit cell, a single VSRR produces a large absorption peak under a normally incident plane wave, polarized with the electric field parallel to the VSRR gap (Figure 2, top). Note that this VSRR is driven by both electric and magnetic fields of the illuminating plane wave, and this condition would not be possible in planar metasurfaces.  
The absorption peak (\(A=0.96\)) is located at 30.21 THz, and demonstrates a full-width at half-maximum (FWHM) of 6.96 THz. Figure 2 (bottom) shows the electric and magnetic field profiles (magnitudes) at the peak absorption frequency. The large absorption peak is being driven by the fundamental resonance of the VSRR, as indicated by the electric and magnetic field plots. The spectral absorption profile of the geometry is heavily dependent on the parameters illustrated in Figure 1. Many of these dependencies were explored as a part of this study but, for brevity, are not included here.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(h_{s}\) & \(w_{s}\) & \(t_{s}\) & \(w_{a}\) & \(w_{g}\) \\ \hline
2.57 & 1.08 & 0.05 & 0.10 & 0.088 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensions of the VSRR. Units are in micrometers.  
Figure 1: The parameters used to define: (**left**) a split-ring resonator (SRR); and (**right**) unit cell are illustrated.
## 3 Materials and Methods
### Min-Max Scaling for Normalization  
Because features are extracted from a variety of sources, they span wide ranges. On the other hand, the performance of machine learning algorithms is sensitive to un-scaled features, which makes re-scaling them necessary. In this article, a min-max normalization is successfully used to re-scale the features, limiting their value to a common range. The limited range is useful because it makes the feature values end up with smaller standard deviations, suppressing the effect of outliers in the different features. Min-max normalization entails a linear transformation of the original features.
# A Process-Oriented Method for Tracking Rainstorms with a Time-Series of Raster Datasets
## 1 Introduction
The measured indexes were used as a cost function to quantify the similarity between rainstorm objects in successive snapshots. Most of the above object-based and vector-based field methods used a linear regression model to infer the presence of rainstorm objects in snapshot T+1 that were present in snapshot T. However, few of these extrapolation models consider rainstorm evolution characteristics, e.g., initiation, maturity, and dissipation. To overcome this problem, [PERSON] and [PERSON] [9] adopted a regional growth method to match rainstorm objects in successive snapshots. However, as they used the size of the spatiotemporal neighborhood as the search radius, the tracking results were sensitive to the neighborhood size, which is difficult to determine. In spatial terms, a rainstorm is defined as a region including a group of pixels that meets a size criterion [6], while in temporal terms, it is defined as a specified duration with high values of rainfall intensity separated from other periods [19]. In reality, in spatial terms, rainstorms evolve from production through development to termination [9,17,20]. Thus, this paper designs a process-oriented algorithm for identifying and tracking a rainstorm, named PoAIR. The rest of this paper is organized as follows. Section 2 describes a semantic process of rainstorms and presents basic concepts. Section 3 designs a workflow for identifying and tracking rainstorms from a time-series of raster datasets, which includes the instantaneous retrieval of rainstorm objects, the linkage of rainstorm sequences, and the reconstruction of rainstorm processes. In Section 4, a simulated rainstorm process and a real precipitation dataset are used to evaluate our proposed algorithm. Section 5 summarizes the conclusions.
# A New Propagation Channel Synthesizer for UAVs in the Presence of Tree Canopies
## 7 Conclusions  
We have presented a propagation channel synthesizer which enables the generation of a site-specific time series for the case of a signal propagating from a UAV towards its ground terminal, or vice versa, in the presence of tree canopies. This also includes any BLoS relay. Such scenarios can often be found in both urban and rural environments, for example, an alley of trees or a forest, respectively. Due to the size of the scenarios addressed, which may include several grown trees, full-wave solutions in electromagnetic simulators are not always possible.  
However, we have introduced a new model which enables efficiently obtaining the coherent scattered fields based on PO, while accounting for the field passing through tree canopies. The incoherent scattered field is obtained by means of MST within the proposed synthesizer.  
As a consequence, the generated time series are site-specific, correctly represent attenuation and diffraction effects caused by the presence of tree canopies, and follow the continuity of the electromagnetic field within the scenario of interest. Apart from the narrowband characteristics, the capability of the synthesizer to provide the wideband characteristics was demonstrated as well.  
The synthesizer was validated using an artificial scenario, as well as an actual experimental dataset obtained with the use of a blimp as a UAV. An excellent agreement with the expected and actual results was found. With an average computation speed of one output value per 2 s for the case of a very large scenario including several grown trees, the proposed channel synthesizer proved to be a very efficient tool when the influence of vegetation on the UAV links is to be addressed.  
**Acknowledgments:** This work was supported by the Czech Science Foundation grant no. P102/14-01527S \"Basic Research of Propagation of Electromagnetic Waves in the Atmospheric Boundary Layer for Low Elevation Links\" and by the European Association on Antennas and Propagation (EurAAP).  
**Author Contributions:** [PERSON] and [PERSON] designed and implemented the channel synthesizer, [PERSON] conducted the experimental trials and simulations. [PERSON] wrote the manuscript, [PERSON] and [PERSON] contributed to the drafting of the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Heuristic Approach for a Real-World Electric Vehicle Routing Problem
## 4 Solution Approach
### Penalty Functions  
Both feasible and infeasible solutions are allowed in the ALNS using a generalized cost function \(f_{\text{gen}}\)(s) to evaluate a solution \(s\). In addition to a term \(f_{\text{obj}}\)(s) denoting the objective value, penalty terms for time window violations \(f_{\text{tw}}\)(s), capacity violations \(f_{\text{cap}}\)(s), and battery capacity violations \(f_{\text{batt}}\)(s) are taken into consideration  
\[f_{\text{gen}}(s)= f_{\text{obj}}(s)+\alpha f_{\text{tw}}(s)+\beta f_{\text{cap}}(s)+ \gamma f_{\text{batt}}(s) \tag{1}\]  
Factors \(\alpha\), \(\beta\), and \(\gamma\) are used to weight the penalties. They are initialized with (\(\alpha_{0}\), \(\beta_{0}\), \(\gamma_{0}\)) and dynamically adjusted between a given lower (\(\alpha_{\text{min}}\), \(\beta_{\text{min}}\), \(\gamma_{\text{min}}\)) and upper bound (\(\alpha_{\text{max}}\), \(\beta_{\text{max}}\), \(\gamma_{\text{max}}\)).  
To achieve a good balance between diversification and intensification, the weights are multiplied by a factor \(\omega\) if a penalty occurred in the last iteration, and are divided by \(\omega\) if no penalty occurred in the last iteration.  
A time window violation occurs at a customer if the vehicle arrives later than the latest allowable time for starting service which equals the arrival time minus the latest time for starting service. The total time window violation \(f_{\text{tw}}(s)\) is the sum of violations of all customers. Because multiple trips and customers with delivery or pickup requests are allowed in a route, a capacity violation occurs if the maximal demand loaded by the vehicle exceeds its capacity before returning the depot. The capacity violation during one trip equals the maximal demand minus the capacity of vehicles. The total capacity violation \(f_{\text{cap}}(s)\) is the sum of violations on all trips. A battery capacity violation occurs at a charging station or the depot if the distance traveled by the vehicle exceeds its battery capacity after the last charge. The total battery capacity violation \(f_{\text{batt}}(s)\) is the sum of violations of charging stations and the depot.
## 4 System Architecture Evaluation
### Safety Analysis
For the current case, considering \"Hands and fingers\" as body region, the ISO/TS 15066:2016 specifies a maximum transferred energy equal to 0.49 J for contact area of approximately 1 cm\({}^{2}\) (see Table 3). The energy transfer threshold value for the current contact scenario is used to identify the maximum speed at which the robot could move within the collaborative workspace according to the following equation:  
\[E=\frac{F^{2}}{2k}=\frac{1}{2}\mu v_{rel}^{2}; \tag{2}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{3}{*}{**Body Region**} & \multirow{3}{*}{**Specific Body Area**} & **Quasi-Static Contact** & \multicolumn{2}{c}{**Transient Contact**} \\ \cline{3-5}  & & **Maximum** & **Maximum** & **Maximum** \\  & & **Permissible** & **Permissible** & **Permissible** \\  & & **Pressure** & **Force** & **Pressure** \\  & & **N/m\({}^{2}\)** & **N** & **Multiplier** & **Force Multiplier** \\ \hline  & Forefinger pad D 1 & 300 & & 2 \\ \cline{2-5}  & Forefinger pad ND 2 & 270 & & 2 \\ \cline{2-5}  & Forefinger end joint D 1 & 280 & & 2 \\ \cline{2-5}  & Forefinger end joint ND 2 & 220 & & 2 \\ \cline{2-5}  & Thenar eminence & 200 & 140 & 2 \\ \cline{2-5}  & Palm D 1 & 260 & & 2 \\ \cline{2-5}  & Palm ND 2 & 260 & & 2 \\ \cline{2-5}  & Back of the hand D 1 & 200 & & 2 \\ \cline{2-5}  & Back of the hand ND 2 & 190 & & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: ISO/TS 15066:2016—Bio-mechanical limits for hands and fingers (1 D = dominant body side; 2 ND = non-dominant body side). \begin{table}
\begin{tabular}{c c} \hline \hline
**Body Region** & **Maximum Transfered Energy /** \\ \hline Skull and forehead & 0.23 \\ Face & 0.11 \\ Neck & 0.84 \\ Back and shoulders & 2.5 \\ Chest & 1.6 \\ Abdomen & 2.4 \\ Pelvis & 2.6 \\ Upper arms and elbow joints & 1.5 \\ Lower arms and wrist joints & 1.3 \\ Hands and fingers & 0.49 \\ Thighs and knees & 1.9 \\ Lower legs & 0.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ISO/TS 15066:2016—Energy limit values based on the body region model. For the assumption behind the calculation of these limit values, the reader could refer to the ISO/TS 15066:2016. We simply recall here that \(\mu\) is the reduced mass of the two-body collision model described by the technical specification, whereas \(v_{rel}\) is the relative speed between the robot and the human body region. In conclusion, the limit values of force, pressure and speed for the current contact scenario (i.e., contact between operator's hands and the robotic arm) are set and reported in the Table 4. The relative speed is limited to 1200 mm/s since this value brings the transfer energy to \(0.42\) J/cm\({}^{2}\) which is less than the limit value based on conservative estimates and scientific research on pain sensation [12]. This computation takes into account the simplified mass distribution model that the technical specification provides, and considers an effective mass of 20 kg.
# Operation Risk Assessment of Urban Dense Cable Passageway Based on Fuzzy-Analytic Hierarchy Process
## 2 Operational Risk Parameters for Urban Dense Cable Corridors
### Cable Channel Risk Parameters
#### 2.2.2 Cable Channel Overcapacity  
Overcapacity of a cable channel means that the current-carrying capacity of a cable in a cable channel exceeds its designed or permitted maximum carrying capacity. This situation may lead to overheating of the cable and deterioration of the insulation material, and it may even cause fires and other safety hazards.
## 2 Materials and Methods
### Modeling  
For the model training, a Random Forest classifier with 100 trees was used. The reason behind the choice of the model is to retain uniformity and accuracy during the experimental process. Random Forest was selected and used during both the training and the testing of each individual model (audio and visual) [43] as well as for the Late Fusion meta-classifier training in our previous works [18; 25]. For an understanding comparison and integrity of the results, the same classifier was used. Also, as before, a 5-fold cross-validation process to evaluate the model's generalizability was implemented. Given the number of data points, a 10 \(\times\) 10 inner and outer cross-validation fold could provide more accurate results, but this also could potentially overfit the data into the model and could impede the ability to have a true representative image of the final results. For training the model, a test-train split of 80-20 was implemented, where 80% of the data was used for training and 20% of the data was used for testing.  
In previous work, the Late Fusion meta-classifier consisted of 465 instances of non-aggressive classes and 531 instances of aggressive classes. Class distribution during training of the Early Meta-Classifier resulted in 465 aggressive class instances and 465 non-aggressive class instances after the data curation and merging. For a fair comparison of the two meta-classifiers, the undersampling technique in the majority class of the Late Fusion model was applied, balancing the instances for both aggressive and non-aggressive classes at 465 instances for each class. This approach ensures that both classifiers have the same instances for both of their classes, thus ensuring a fair performance comparison with more robust and detailed data results (**S1.1**, **S1.2**, **S1.6**).
## 3 Proposed Method
### Principle of Gaussian Pyramid  
In light of the fact that data collection is limited, it is hard to assemble ample training samples for the large-scale network. A classical decomposition method known as the Gaussian pyramid [39] is exploited herein. The Gaussian pyramid decomposes the input data into a lower dimensionality contemporary, suppressing the redundant noise from the raw data. Specifically, assuming that the input image has a dimension of \(M\times N\), the original image is convolved by the Gaussian kernel and is downsampled into lower-dimensional images. The arbitrary pixel at spatial location (\(i\), \(j\)) can be determined as follows:  
\[I_{0}^{(l+1)}(i,j)=\sum_{u=-2}^{2}\sum_{v=-2}^{2}w(u,v)I_{0}^{(1)}(2i+u,2j+v) \tag{1}\]  
where \(0\leq i\leq\frac{M}{2^{i}}\), \(0\leq j\leq\frac{N}{2^{i}}\) is the size of the downsampled image, and \(w(u,v)\) represents the Gaussian window, which can be obtained by the following equation:  
\[w(u,v)=\frac{1}{2\pi\sigma^{2}}e^{-(u^{2}+v^{2})/2\sigma^{2}} \tag{2}\]  
where parameter \(\sigma^{2}\) refers to variance. Here, the Gaussian kernel is applied as \(5\times 5\) to provide global information simultaneously and to retain important details for the training model. Multiple low-pass filters are applied to extract discriminant features from high-dimensional data and provide multiscale sub-images \(\{l_{0}^{1}\), \(l_{0}^{2}\), \(l_{0}^{3}\),..., \(l_{0}^{n}\}\) for the training model, where 1, 2, 3,..., \(n\) represent the levels of the Gaussian pyramid. Here, a two-level Gaussian pyramid is utilized to decompose the source dataset into two different resolution sub-images, as shown in Figure 1, and the reason behind the application of a two-level Gaussian pyramid will be discussed in Section 5.1.
## 2 Proposed Seismic Damage Prediction Model for a Building Structure
### Generation Data Labels
#### 2.1.1 Dynamic Response Analysis Model for the Target Structure  
The seismic response of the structure is obtained from the numerical simulation of a planar-reinforced concrete moment-resisting frame (RCMRF). As shown in Figure 1a, the building model is a four-storey, three-span office building in Nansha District, Guangzhou City. The load designed by the code [42], dead load and live load on the 1-3 floors are 5.0 kN/m\({}^{2}\) and 2.0 kN/m\({}^{2}\), respectively, and the dead load and live load on the roof are 6.0 kN/m\({}^{2}\) and 0.5 kN/m\({}^{2}\), respectively. The sizes and reinforcement of beams and columns from the bottom and standard floor are shown in Figure 1c,d, respectively. In addition, the distance for both horizontal directions between the columns is 6.0 m. The floor level of the first floor is 4.5 m, and the remaining floors are 3.3 m. The columns, beams, and slabs are made of C30 concrete and HRB400 steel. The RC slab and protection layer thicknesses are 120 mm and 40 mm, respectively. The code for the seismic design of buildings is considered [43; 44], and the main design parameters include the following: seismic intensity (7), the basic design peak ground acceleration of 0.1 g (10% in 50 years), site class II, and seismic design group (2). In this paper, the intermediate frame shown in Figure 1b is selected for seismic analysis using OpenSees [45] finite element analysis software. Additionally, there is more information about this model in the study [46]. The effectiveness of the model has been proven in studies [47].