esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Crustal Uplift Deformation in Response to Glacier Wastage in Southern Patagonia
## 2 Geodetic and Satellites Input Data
### GPS Episodic Stations: Upsala Glacier  
Two episodic GPS stations (ES) installed on rocky outcrops located in the vicinity of the Upsala glacier front (LPAS and PUMA) were added to the geodetic network (Figure 1), following the fixed anchor State Survey Mark [32] minimizing the leveling error [33]. The minimum observation period at these stations was 48 h per campaign, corresponding to two 24 h observation sessions. The measurements in all periods were carried out at the same time of the year in order to reduce the influence of the seasonal effects on the estimation of site velocities. In total, the PUMA station recorded data during 13 days between 2015 and 2017. Likewise, LPAS registered 12 days during the same period. Table 1  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site** & **Type** &
\begin{tabular}{c} **Height** \\ **(m a.s.l.)** \\ \end{tabular} & **Year** & **Start Date** & **End Date** & **Total Days** \\ \hline LPAS & Episodic & 754 & 2015 & 18 April & 20 April & \\  & Episodic & 754 & 2016 & 23 October & 25 October & \\  & & & 2017 & 17 April & 19 April & \\ \hline PUMA & Episodic & 807 & 2015 & 18 April & 20 April & \\  & Episodic & 807 & 2016 & 22 October & 25 October & \\  & & & 2017 & 17 April & 19 April & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._shows the GPS site occupation time of each of the continuous and episodic stations of the geodetic network.
## 3 Method
### Patch Merging  
Image segmentation networks frequently employ average pooling or maximum pooling for feature downsampling. However, these operations inevitably result in the loss of resolution information. If a deep network struggles to learn effective features, it can significantly impair the model's performance. Taking inspiration from the Swin-Transformer [45], we adopted the Patch Merging operation to guarantee that no information was sacrificed during the downsampling process. This approach enabled us to adjust the number of network channels while decreasing resolution, as illustrated in Figure 4.  
Figure 3: The architecture of the multi-scale channel attention gate.  
Since each downsampling operation reduced the resolution by half in both the row and column directions, we adopted a specific approach. We selected every other element at a 2-pixel interval in both directions and combined them to form new patches. These patches were then concatenated to create a unified tensor. Next, each patch was expanded in each channel, resulting in a fourfold increase in the original channel dimensions. Afterward, we applied a normalization operation to the tensor. Finally, we adjusted the channel dimensions back to twice the original size through a fully connected layer. This process ensured that we preserved resolution while simultaneously incorporating spatial information. This enables us to make full use of pixel feature information, especially in grayscale images like breast ultrasound scans.
# Impact of Grounding Modeling on Lightning-Induced Voltages Evaluation in Distribution Lines
###### Abstract  
Lightning-induced voltages are one of the main causes of shutdown in distribution lines. In this work, attention is focused on the effects of wideband modeling of electric grounding in the overvoltage calculation along insulator strings due to indirect lightning strikes. This study is done directly in the time-domain with the grounding being represented with an equivalent circuit accounting for its dynamics. Results show that the adoption of commonly adopted simplified grounding models, such as low-frequency resistance, may lead to an underestimation of the overvoltage. According to the results, differences in the order of 25% can be found in some studied cases.  
distribution lines; lightning-induced overvoltages; grounding modeling; soil resistivity +
Footnote †: journal: Applied sciences  
implemented in the software developed in [22]. In this paper, as commonly proposed in the IEEE Standard [5], the coupling between the tower and the lightning channel and the coupling between the lightning channel and the grounding electrodes are neglected.  
The results illustrate that the induced voltages, considering the grounding modeled via \(R_{LF}\) are quite different from those results using \(Z(\omega)\), with perceptual differences reaching values of around 25%. It is noticeable that the differences increase with the soil resistivity and with the point of occurrence of the lightning (lightning striking closer to the DS increase the perceptual differences) for both first and subsequent return strokes. The paper is organized as follows: Sections 2-4 show the lightning field-to-line coupling problem equations, the tower and the grounding modeling, respectively; while Sections 5 and 6 present the test cases and the results. Section 7 is dedicated to the conclusions.
# Distribution and Deposition of Cylindrical Nanoparticles in a Turbulent Pipe Flow
###### Abstract  
Distribution and deposition of cylindrical nanoparticles in a turbulent pipe flow are investigated numerically. The equations of turbulent flow including the effect of particles are solved together with the mean equations of the particle number density and the probability density function for particle orientation including the combined effect of Brownian and turbulent diffusion. The results show that the distribution of the particle concentration on the cross-section becomes non-uniform along the flow direction, and the non-uniformity is reduced with the increases of the particle aspect ratio and Reynolds number. More and more particles will align with their major axis near to the flow direction, and this phenomenon becomes more obvious with increasing the particle aspect ratio and with decreasing the Reynolds number. The particles in the near-wall region are aligned with the flow direction obviously, and only a slight preferential orientation is observed in the vicinity of pipe's center. The penetration efficiency of particle decreases with increasing the particle aspect ratio, Reynolds number and pipe length-to-diameter ratio. Finally, the relationship between the penetration efficiency of particle and related synthetic parameters is established based on the numerical data.  
cylindrical nanoparticles; distribution; deposition; turbulent pipe flow; numerical simulation +
Footnote †: journal: Applied Sciences
## 1 Introduction
The basic principle of these methods is to use mechanical braking to provide a rather steady and large amount of braking force and let regenerative braking force compensate the error of demanded and mechanical braking force, no matter whether improving the traditional ABS with regenerative braking [20; 21], braking allocation based on tire-road recognition [22; 23], or using an advanced braking controller based on sliding mode, a low-pass filter, or phase plane theory [24; 25; 26; 27]. As for tracked vehicles, studies on emergency braking are relatively rare. A few accessible ones related to combined braking systems are mostly applying hydraulic-mechanical braking systems because hydraulic retarders are widely used in modern high-speed tracked vehicles [28]. Research on traction control of tracked vehicles using the sliding mode method has been conducted long before [29], but since ABS is rarely used on tracked vehicles, the response of a typical hydraulic system is normally slow, and the braking force of the hydraulic retarder is a function of speed, which cannot be controlled actively [28; 30]. Braking stably on a low adhesion surface is comparatively difficult for a traditional tracked vehicle. With the development of DDTVs, cooperative control of motors, hydraulic retarders, and mechanical brakes may leads to a better performance of stability, but current studies are still mainly focused on how to generate enough braking force while regenerating more energy [30; 31; 32]. Thus, this paper comes up with a novel emergency braking control strategy for dual-motor electric drive tracked vehicles based on regenerative braking to improve braking stability. The braking performances have been improved significantly adopting this strategy compared with traditional ways. The organization of this paper is as follows. In Section 2, the structure of a DDTV and the models related to its braking process are introduced. In Section 3, the design of the braking controller, which includes a slip ratio controller and a braking force allocation method is performed. MATLAB/Simulink simulations of the proposed controller with a typical emergency braking process, as well as some other control strategies are shown and analyzed in Section 4. Section 5 introduces a series of controller-driver-in-the-loop experiments based on a dSPACE SCALEXIO platform. Finally, conclusions are given in Section 6. The nomenclature is listed in Appendix A.
# Triple-Band Notched Ultra-Wideband Microstrip MIMO Antenna with Bluetooth Band
## 2 Design of an Ultra-Wideband Antenna Element
### Step 3: UWB Antenna with Dual-Band Notch at WiMAX Band and WLAN Band
Therefore, the WLAN band notch is located between two very high sharpness edges (high selectivity poles), one at the beginning of the WLAN band at a frequency of \(f\) = 4.94 GHz and the other one located at the end of the band at a frequency of \(f\) = 6.2 GHz. Thus, the WLAN band notch resembles a rectangular shape. This rectangular notch band improves the radiating characteristic of the antenna more than the conventional notch antenna (i.e., single U-shaped slot UWB radiator). Figure 7: UWB antenna with dual-band notches: design steps. (**a**) Case 7; (**b**) Case 8; (**c**) Case 9.
## 2 Theory and Analysis
### Radiation Emission Components
Peak components represent the series of peak signals formed by signals such as sinusoidal waves \(N_{j}(f_{k})\) and square waves \(H_{i}(f_{h})\). These components are the amplitude and frequency corresponding to a series of peaks in the test data, which is reflected in  
Figure 2: Model of radiated emission generation mechanisms within electronic equipment using four basic waveforms. the fact that the equipment under test has an oscillator working on the corresponding frequency or an equivalent circuit of the oscillator working at the same resonant frequency. Broadband components represent waveforms with a bandwidth formed by signals such as damped oscillations \(E(f)\) and spike waves \(S(f)\). Since electronic equipment generally works in a specific frequency band, even if the internal circuit module has the influence of nonlinearity and other factors, its unique radiation emission spectrum will appear in one or more specific frequency bands. Therefore, after eliminating all peak emissions, 3-5 frequency bands with the unique radiation emission spectrum of electronic equipment can be extracted as broadband components. Considering the more global characteristics of the spectrum, the trend components are introduced into the whole spectrum. All other less significant parts of the spectrum are regarded as trend components \(v_{t}(f)\). In addition, some peak components and broadband components are also included in these trend components because they are not significant enough. All the remaining spectra are divided into a series of segments as trend components; we took ten segments as examples in this paper. A typical radiation emission dataset is shown in Figure 3. The radiation emission components of the three groups are also shown in the figure. Through the work in Section 2.1, a large number of complete spectrum data were converted into about two dozens of radiation emission components.
## 4 Performance Evaluation  
### Simulations  
In this simulation, to explain Vertical Handover schemes we use the same scenario for different mathematical theories.  
On the network side, we consider two available networks (WLAN and Wimax). Both are ideal partners for operators to deliver convenient and affordable services, they are open IEEE wireless standards built from scratch for IP-based applications. We used the same scenario as in [16]. On the user side, we consider four types of applications with different QoS requirements including Conversational, Streaming, Interactive and background. Each traffic class is combined with four different QoS parameters or attributes: Throughput, e2e Delay, Jitter, and Ber. The four traffic classes have different QoS requirements. Therefore, different weights have been assigned to the AHP method for the same attribute with different traffic classes. We use our own weights which are shown in Table 3. Although the simulation is maintained for 10 min, \(100,000\) packets are supposed to be sent and the decision is made every 5 s in realtime.  
In case of \(N\) different available networks, the terminal needs to apply the methods for the four QoS classes. For each method, the terminal perform \(4\) x \(N\) computations to select the optimal network, the total of \(3\times 4\times N\) computations in the case of all decision schemes.  
To calculate the time \(T\) the terminal will require selecting the optimal network, we translated the complexity of the algorithms \(\mathcal{O}(n^{2})=max(\mathcal{O}(SAW),\mathcal{O}(TOPSIS),\mathcal{O}(MEW))\). Typically, for high quality and seamless handover, the packet delay must not exceed \(150\) ms. Assuming the terminal performs one billion operations per second, the approximate running time of each algorithm where \(n<5\) is \(250\) ns. Accordingly, the time to find the rank of all the available network is \(\sim 1\) ms for the four types of applications  
The following section details the development of the network through the simulation.
## 4 Experimental Results  
### Validity of the Gaussian Propagation Model  
Figure 6 shows the experimental setup for measuring the ROPs of the divergent Gaussian beam. In the experiment, the half-divergent angle of the Gaussian beam is 10\({}^{\circ}\) and the transmission distance is 2.5 m. At the receiver, a zoomable receiving collimator is fixed on a laterally fixed rail, and the ROPs of different receiver angles are obtained by  
Figure 5: Focus shift of the receiving collimator and beam radius at the focus of the receiving collimator. (**a**) Focus shift versus transmission distances; (**b**) Beam radius at the focal length of the receiving collimator versus transmission distances.  
translating the receiving collimator along the rail axis. Figure 7 showcases a comparison between the numerically simulated transverse ROP distribution and the experimentally measured discrete ROP values at intervals of \(1^{\circ}\). The theoretically predicted curve and the experimentally measured curve with linear interpolation exhibit the same trend, with the ROP variance between them not exceeding 1 dB at any receiving angle. The result verifies the validity of employing geometric optics and uniform reception to describe the propagation of Gaussian beams in terms of intensity within short-range indoor OWC. Since the simulation only considers geometrical path loss, it can be inferred that the fiber coupling loss is effectively eliminated through the compensation of focus shifts. It is important to note that due to the narrow FOV of the receiving structure, the orientation of the collimator needs to be adjusted accordingly to align transceivers at varying transverse distances, potentially introducing ROP errors. Furthermore, due to constraints in the length of the rail, only positive transverse distances along the lateral axis were measured. Nevertheless, the spatial intensity distribution of Gaussian beams has a circular symmetry. However, the inhomogeneity of the ROP is as high as 10 dB.
## 3 Methods
### Bus Scenario Patterns  
In this section, we present our bus scenario patterns. We used Beijing transportation smart card data from 3 August 2015 to 30 August 2015 and then divided the metropolitan area of Beijing into \(32\times 32\) grids, each grid representing a region. The size of each region is 0.625 km \(\times\) 0.625 km, and the time interval is 30 min. Then, using Definition 1, we obtained the bus alighting flow and boarding flow of each region at 30 min time intervals. We chose bus boarding flow and alighting flow from 6:00 to 22:00 each day as observational data, and there were 32 time intervals every day.  
Figure 3 shows one week's boarding flow in a region, that is the flow of each 30 min from 6:00 to 22:00 every day of one week. The region is located in the Beijing Central Business District, which is one of the busiest areas of Beijing. From the figure, we can find that there are two boarding-flow peaks every day during the weekday, and the boarding-flow curve is smoother during the weekend. Thus, there are obvious daily periodicity patterns. Figure 4 shows boarding flow in the same region of different time intervals, which is from 6:00 on 3 August 2015 to 22:00 on 3 August 2015. There are  
Figure 2: Improved residual block.  
896 time intervals. We can find that its period is 224 time intervals, i.e., one week. These all indicate that bus traffic flow has specific scenario patterns.  
Figure 5 presents total bus traffic flow of all regions at each time interval. Each subgraph in the figure represents the total traffic flow of one day, which is the sum of bus alighting flow and boarding flow in all regions at each time interval. The horizontal coordinate in each subgraph represents different time intervals from 6:00 to 22:00, and the vertical coordinate in each subgraph represents the total traffic flow of the current time interval. There are a total of 28 subgraphs, representing 28 days from 3 August 2015 to 30 August 2015. The seven subgraphs of each column in the figure are all from Monday to Sunday.  
From the figure, we can find that the bus traffic flow has two significant features that we define as bus scenario patterns. First, its total traffic flow volume is especially large and it has two peaks every day, which are from 7:30 to 9:30 in the morning and from 17:00 to 19:00 in the evening. Second, there are obviously different modes between workdays and weekends; the total traffic flow on the weekend is relatively small compared with the workday, and the traffic flow change is relatively smooth on the weekend compared with the workday. Therefore, it is crucial to capture the bus scenario patterns for traffic flow prediction.  
Figure 4: Boarding flow of different time intervals in a region. The time interval is 30 min, starting from 6:00 on 3 August 2015.  
Figure 3: One week’s boarding flow in a region.
# A Regret-Enhanced DEA Approach to Mapping Renewable Energy Efficiency in Asia's Growth Economies
## 2 Methodology
### Regret Theory Extension of Cross-Efficiency DEA Model
#### 2.4.2. Regret Utility Cross-Efficiency DEA Model  
Assume \(DMU^{+}\) is the best and reference DMU; the regret utility of \(DMU_{k}\) corresponding to \(DMU^{+}\) is considered the loss value of \(DMU_{k}\). According to Definition 2, the loss values \(\left(RE_{jk}^{output},RE_{ik}^{input}\right)\) of the output utility \(\left(y_{jk}\right)\) and input utility \(\left(x_{ik}\right)\) of \(DMU_{k}\) are determined as follows:  
\[RE_{jk}^{output}=R\left(y_{jk}-\max_{k\in K}\left(y_{jk}\right)\right)=1-e^{- \lambda\left(y_{jk}-\max_{k\in K}\left(y_{jk}\right)\right)} \tag{15}\]  
\[RE_{ik}^{input}=R\left(\min_{k\in K}\left(x_{ik}\right)-x_{ik}\right)=1-e^{- \lambda\left(\min_{k\in K}\left(x_{ik}\right)-x_{ik}\right)} \tag{16}\]  
According to Remark 1, because \(y_{jk}\leq\max_{k\in K}\left(y_{jk}\right)\), \(RE_{jk}^{output}=R\left(y_{jk}-\max_{k\in K}\left(y_{jk}\right)\right)\leq 0\) is the loss value of the decision maker selecting the \(DMU_{k}\) instead of \(DMU^{+}\) corresponding to \(jth\) output. Hence, the total loss value of the decision maker selecting the \(DMU_{k}\) instead of \(DMU^{+}\) corresponding to all outputs can be considered \(\sum_{j=1}^{J}v_{jk}RE_{jk}^{output}\). On the other hand, because \(\min_{k\in K}\left(x_{ik}\right)\leq x_{ik}\), \(RE_{ik}^{input}=R\left(\min_{k\in K}\left(x_{ik}\right)-x_{ik}\right)\leq 0\) is the loss value of the decision maker selecting the \(DMU_{k}\) instead of \(DMU^{+}\) corresponding to \(ith\) input. Hence, the total loss value of the decision maker selecting the \(DMU_{k}\) instead of \(DMU^{+}\) corresponding to all inputs can be considered \(\sum_{i=1}^{J}u_{ik}RE_{ik}^{input}\). Therefore, the total loss value or rejoic utility of the decision maker selecting the \(DMU_{k}\) instead of \(DMU^{+}\) corresponding to all inputs and outputs can be determined as follows:  
\[RE_{k}=\sum_{j=1}^{J}v_{jk}RE_{jk}^{output}+\sum_{i=1}^{I}u_{ik}RE_{ik}^{input} \tag{17}\]  
Because \(\sum_{j=1}^{J}v_{jk}RE_{jk}^{output}\leq 0\) and \(\sum_{i=1}^{I}u_{ik}RE_{ik}^{input}\leq 0\), \(RE_{k}\leq 0\). In the other words, the less total regret utility there is, the bigger \(RE_{k}\) is. In general case, decision makers prefer to minimize the total regret utility or maximize the value of \(RE_{k}\) by finding the optimal weights \(v_{jk}\) and \(u_{ik}\). Hence, the rejoic utility cross-efficiency DEA model can be constructed as follows below.  
\[\max RE_{k}=\sum_{j=1}^{J}v_{jk}RE_{jk}^{output}+\sum_{i=1}^{I}u_{ik}RE_{ik}^{ input}\]  
_subject to_  
\[\begin{array}{ll}\sum_{j=1}^{J}v_{jk}y_{jk}=E_{kk}^{*}&\qquad\qquad\quad k=1, \ldots,K\\ \sum_{i=1}^{J}u_{ik}x_{ik}=1&\qquad\qquad\quad k=1,\ldots,K\\ \sum_{j=1}^{J}v_{jk}y_{jl}-\sum_{i=1}^{I}u_{il}x_{il}\leq 0&\qquad\quad l=1, \ldots,K\\ u_{ik}\geq 0&\qquad\quad i=1,\ldots,I;\;k=1,\ldots,K\\ v_{jk}\geq 0&\qquad\quad j=1,\ldots,J;\;k=1,\ldots,K\end{array} \tag{18}\]