esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methodology
### Training with the T5 Model
```
1:Set \(prefix\_summarize\) and \(prefix\_expand\) as \(P_{s}\) and \(P_{e}\)
2:for each \(batch\in\)\(gold\_batches\)do
3:\((article,summary)\leftarrow\)\(batch\);
4: fine-tune \(T\) with \((P_{s}\oplus article,summary)\) and \((P_{e}\oplus summary,article)\)
5:\(\triangleright\) Fine-tune with real text pairs
6:endfor
7:for\(epoch\in[1,nb\_epochs]\)do
8:for all\((a_{i}s_{i})\) such that \(a_{i}\in\)\(Articles\) and \(s_{i}\in\)Summariesdo
9:\((a_{i}^{idt},\ s_{i}^{idt})\leftarrow(T(P_{e}\oplus a_{i}),\ T(P_{s}\oplus s_{ i}))\)\(\triangleright\) Re-expand and re-summarize
10:\((L_{a}^{idt},\ L_{s}^{idt})\leftarrow(L(a_{i},a_{i}^{idt}),\ L(s_{i},s_{i}^{idt}))\)\(\triangleright\) identity mapping loss
11:\((s_{i}^{fake},\ a_{i}^{fake})\leftarrow(T(P_{s}\oplus a_{i}),\ T(P_{e}\oplus s_{ i}))\)\(\triangleright\) Generate fake summary and article
12:\((a_{i}^{cyc},\ s_{i}^{cyc})\leftarrow(T(P_{e}\oplus s_{i}^{fake}),\ T(P_{s} \oplus a_{i}^{fake}))\)\(\triangleright\) Restore article and summary
13:\((L_{a}^{cyc},\ L_{s}^{cyc})\leftarrow(L(a_{i},a_{i}^{cyc}),\ L(s_{i},s_{i}^{cyc}))\)\(\triangleright\) cycle consistency loss
14:\(Loss\leftarrow\lambda_{idt}L_{idt}(a_{i},a_{i}^{idt})+\lambda_{idt}L_{idt}(s_{i},s_{i}^{idt})+ \lambda_{cyc}L_{cyc}(a_{i},a_{i}^{cyc})+\lambda_{cyc}L_{cyc}(s_{i},s_{i}^{cyc})\)
15:
16: Back-propagation of Loss
17:endfor
18:endfor
```  
**Algorithm 2** Semi-supervised automatic text summarization with T5. Here, the hyperparameters \(\lambda_{iit}\) and \(\lambda_{cyc}\) control the weights of the two types of losses.
# Rectification for Stitched Images with Deformable Meshes and Residual Networks
## 3 Materials and Methods  
This paper proposes a deformable mesh structure for the initial prediction of irregular images, enhancing its adaptability in various spatial scene structures. In light of this mesh structure, the paper establishes two methods for mesh application. One approach involves predicting the rigid structure of the input image through a simple convolutional neural network. Based on the prediction results, the most suitable mesh shape for the image is selected (Figure 2a). Subsequently, the input image and the chosen mesh shape are input into a width residual network for initializing mesh prediction. Finally, the predicted initial mesh and the input irregular image are jointly used for image rectification regression, resulting in the output image.  
Another option is to input the input image and predefined target meshes for all shapes into a width residual neural network to generate initial mesh predictions (Figure 2b). Subsequently, image rectification regression is performed with the input image. The optimization is then based on the regression loss of the rectified image, selecting the one with the minimum information loss as the final output image.  
A model of image rectification algorithms based on deformable meshes and residual networks is illustrated in Algorithm 1, which consists of two strategies to choose from, namely, the image-based and loss-based strategies. The image-based strategy selects the grid shape that best suits the current image based on the number and distribution of rigid structures within the image. After that, the selected mesh shape and the input image are fed into the residual neural network to obtain the initialized prediction mesh. The initialized prediction grid and the input image are then subjected to rectangle regression together until it meets the accuracy requirements; otherwise, the network continues to be trained. The loss-based strategy, on the other hand, inputs the input image directly into the residual network to obtain initialized prediction grids for the three meshes. Then, after the rectangle regression of the three initialized prediction grids along with the input image, the rectangle image with the lowest loss is selected as the final output.  
Figure 2: Image rectification with different mesh shape selection modes. (**a**) Selecting a mesh shape based on the image. Predicting the most suitable mesh shape for an input image through a convolutional neural network. Subsequently, the predicted initial mesh based on this shape and the predefined mesh are jointly input into a residual network for image rectification. (**b**) Selecting all mesh shapes for loss comparisons. Applying all mesh shapes to the input image and jointly inputting them into a residual network for rectification. The rectified image with the lowest loss is selected as the final output.
# Non-Ionizing Radiation Measurements for Trajectory Radars
## 4 Results and Discussion
### Measurement Results  
By measuring the average power level of the radar signal, it is possible to calculate the power density level of the emission, which has a limiting value as specified by ICNIRP [3] and Anatel [4]. Figure 8 shows the spectrum of the received signal at point P01 and its average power value measured by the Anritsu MS2720T spectrum analyzer.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**No.** & **Item** & **Specification** \\ \hline
1 & Spectrum Analyzer 1 & Anritsu-MS2720T \\
2 & Spectrum Analyzer 2 & Agilent-N9010A \\
3 & RF Generator & Rohde \& Schwarz-SMR20 \\
4 & Power Meter & Agilent-N1913A \\
5 & Power Sensor & Agilent-N8481A \\
6 & Oscilloscope & Agilent-DSO9104A \\
7 & Schottky Diode Detector & Agilent-8473C \\
8 & Directional Antenna & Hyper Gain-HG5822G, gain: 22 dBi \\
9 & RF Cables & 50 \(\Omega\); SMA-SMA; Attenuation: 2.4 dB \\
10 & Signal Cables & 50 \(\Omega\); BNC-BNC \\
11 & Connectors & Type: SMA-N; Attenuation: 0.05 dB \\
12 & Attenuators & 10 dB/20 dB/30 dB \\
13 & Wood Tripod & Maximum Height: 2 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of Equipment.  
Figure 7: (**A**) Radar signal in time domain; (**B**) Radar signal spectrum. Adapted from [21].  
From the average power values, we calculate the average power at the terminals of the receiving antenna by using Equation (2).  
\[P_{rx}=P_{i}+A_{tt}+P_{off} \tag{2}\]  
where \(P_{rx}\) is the average power at the terminals of the receiving antenna (dBm), \(P_{i}\) is the average power measured by the instrument (dBm), \(A_{tt}\) is the total attenuation between the measurement instrument and the terminals of the receiving antenna (dB) and \(P_{off}\) is the instrument power offset (dB). The calculated values of the average power at the terminals of the receiving antenna are shown in Table 3. From these values, we calculate the power density values by using Equation (3).  
\[S=\frac{P_{rx}4\pi}{\lambda^{2}G_{r}} \tag{3}\]  
where \(S\) is the power density (W/m\({}^{2}\)), \(P_{rx}\) is the average power at the terminals of the receiving antenna (W), \(G_{r}\) is the receiving antenna gain and \(\lambda\) is the signal wavelength (m). The calculated power density values are also shown in Table 3.
## Nomenclature  
\begin{tabular}{l l} \(A\), \(B\) & alphabetic symbols used to simplify equations, (-) \\ Ar & Archimedes number (\(=gd_{\text{P}}^{3}\rho_{\text{g}}\big{(}\rho_{\text{s}}-\rho_{\text{g}}\big{)}/ \mu^{2}\)), (-) \\ \(C_{1}\) & coefficient of the viscosity term in the Ergun equation, (-) \\ \(C_{2}\) & coefficient of the inertial term in the Ergun equation, (-) \\ \(C_{3}\) & multiplicative correction coefficient in Equation (24), (-) \\ \(d_{\text{P}}\) & particle size, (m) \\ \(F_{\text{mf}}\) & drag force of the incipient fluidized state, (N) \\ Fr & Froude number (\(=U_{0\text{mf}}/\sqrt{gd_{\text{P}}}\)), (-) \\ \(G\) & gravity force of particles, (N) \\ \(g\) & acceleration of gravity, (9.8 m/s\({}^{2}\)) \\ \(h\) & height of the specified section of the conical cylinder, (m) \\ \(H\) & total height of particles, (m) \\ \(N\) & total number of points in a time-series signal, (-) \\ \(P\) & pressure drop, (kPa) \\ \end{tabular}  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multicolumn{5}{c}{\(H_{0}\) **(m)**} \\ \cline{2-6}  & **Material Mass** & **Material Mass** & **Material Mass** & **Material Mass** \\  & **2 kg** & **2.5 kg** & **3 kg** & **3.5 kg** & **4 kg** \\ \hline \#1 & 0.0460 & 0.0565 & 0.0667 & 0.0765 & 0.0861 \\ \#2 & 0.0483 & 0.0593 & 0.0699 & 0.0802 & 0.0902 \\ \#3 & 0.0483 & 0.0592 & 0.0698 & 0.0801 & 0.0900 \\ \#4 & 0.0507 & 0.0622 & 0.0733 & 0.0840 & 0.0944 \\ \#5 & 0.0491 & 0.0603 & 0.0710 & 0.0815 & 0.0916 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Calculated \(H_{0}\) values of all experiments.
# Hybrid Modulation Strategy to Eliminate Current Distortion for PV Grid-Tied H6 Inverter
## 4 System Analysis and Control  
### Common-Mode Voltage and Leakage Current Analysis  
[PERSON] et al. (2018) describes the common-mode voltage \(v_{cm}\) of single-phase grid-tied inverter as  
\[v_{cm}=0.5(v_{an}+v_{bn})-0.5v_{ac} \tag{2}\]  
and the leakage current as  
\[i_{cm}=C\frac{dv_{cm}}{dt} \tag{3}\]  
where, \(v_{an}\) and \(v_{bn}\) represent the pulse voltages between the two bridges midpoint and the DC bus negative terminal, respectively, which are varying at the inverter switching frequency. C represents the stray capacitance of PV module to ground. According to Equation (3), we can see the \(i_{cm}\) is dependent on the \(\Delta v_{cm}\). Note the component of common mode leakage current due to the grid voltage \(v_{ac}\) can be ignored due to its low frequency of 50/60 Hz. Thus, the common-mode voltage can be approximately written as  
\[v_{cm}\approx 0.5(v_{an}+v_{bn}) \tag{4}\]  
The main common-mode leakage current \(i_{cm}\) can be eliminated as long as the common-mode voltage \(V_{cm}\) maintains a constant value. For the H6 grid-tied inverter with HMM, the common-mode voltage can be analyzed for five operation modes. This analysis shows that the common mode voltage \(V_{cm}\) for this case can be maintained at a constant value \(0.5\times E\), no matter which mode the system is in, and hence there is no common-mode leakage current present.
## 2 Materials and Methods
### Performance Evaluation
#### 2.7.2 Parameter Optimization  
None of the three types of relative power feature extraction methods used free parameters. As for the classifiers, LDA involves no free parameters, and both SVM and CK-SVM have two parameters to be adjusted, including the penalty weight \(C\) and kernel parameter \(\sigma\). We searched for the optimum parameters by performing the LOPO-CV procedure combined with a grid search. The parameters were searched in the following sets: \(C=\{1,\,10,\,20,\,50,\,80,\,100,\,120\}\), \(\sigma=\left\{1.025^{d}\middle|d=-100:1:300\right\}\). Therefore, there was a total of 2807 (\(7\times 401\)) parameter grids, and for each grid, the LOPO-CV procedure was performed once. The optimum parameter grid resulted in the highest classification accuracy. The results of SVM and CK-SVM reported here were the ones obtained from the most optimal values of the parameters.
## 2 System Model
### Power Consumption Model
#### 2.2.2 Circuit Power Consumption Model  
The power consumption of circuits except PA is divided as follows:  
\[P_{Cir}=P_{BB}+N_{t}P_{rf}, \tag{11}\]  
where \(P_{BB}\) is the power consumption of baseband (BB). Each parameter is a function of bandwidth, \(B\), and we set \(B\) as 10 MHz. We only listed the components, which depend on \(N_{t}\). The \(P_{rf}\) can also be divided as follows:  
\[P_{rf}=P_{dac}+P_{m}+P_{filt}, \tag{12}\]  
where \(P_{dac}\) is the power consumption of the digital-to-analog converter (DAC), \(P_{m}\) is the power consumption of the mixer, and \(P_{filt}\) is the power consumption of the TX filter [23]. The power consumption parameters for a low power TX are given in [24].  
To get the \(P_{BB}\), we use the following floating point operations per second (Glops), \(\chi\) that was presented in [25; 26; 27; 28]:  
\[\chi(Gflop) = N_{t}\cdot\frac{(T_{u}B)}{T_{s}}\cdot\log_{2}(T_{u}B)+N_{t} \cdot\frac{(T_{d}B)}{T_{sl}}\cdot\tau_{r}\cdot\log_{2}(\tau_{r}) \tag{13}\] \[+N_{t}\cdot K\cdot(T-\tau_{r})\cdot\frac{(T_{d}B)}{T_{sl}}+N_{t} \cdot K^{2}\cdot\frac{(T_{d}B)}{T_{sl}},\]  
where \(\tau_{r}\) is the length in the samples of the uplink sequences, and the description of each parameter is shown in Table 2. We took the parameters from [6] and from a current 3 GPP LTE system [29]. The first part of (13) is to operate the FFT/IFFT operation, the second part is to implement precoding/decoding, the third part is to correlate the pilot signals with pilot sequences, and the last part is to contain the additional pseudo inverse for zero forcing (ZF) precoding.  
The relationship between \(P_{BB}\) and \(\chi(Gflops)\) can be represented as  
\[P_{BB}(W)=\frac{\chi(Gflops)}{v(Gflops/W)}, \tag{14}\]  
where \(v\) is the VLSI processing efficiency.  
Let us assume \(T_{g}=T_{d}\) for short guard interval and fast pilot correlation. When we use matched filtering (MF) precoding, the last part of (13) becomes zero, since MF precoding does not need to perform the pseudo inverse [25].  
The success of an energy efficient LSAS depends on how small the \(P_{Cir}\) is in \(P_{sum}\). If \(P_{RF}\) occupies too much of \(P_{sum}\), the increase of \(N_{t}\) is of little help in increasing \(EE\). That is, there is little room for \(P_{PA}\) to reduce sum power.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(B\) & Bandwidth & 10 MHz \\ \(T_{sl}\) & Slot length & 0.5 ms \\ \(T_{p}\) & Pilot length in one slot & 0.214 ms \\ \(T_{s}\) & Symbol duration & 71.4 us \\ \(T_{g}\) & Guard Interval (GI) & 4.7 us \\ \(T_{u}\) & Symbol without GI & 66.7 us \\ \(T_{d}\) & Delay spread & 4.7 us \\ \hline \hline \end{tabular}
\end{table}
Table 2: System Parameters.
# Damage Analysis of Reinforced Concrete Columns Based on Fiber Beam-Column Element
###### Abstract  
The method of damage analysis of reinforced concrete columns based on the fiber element model is established by combining the uniaxial damage constitutive model of concrete and steel with the fiber beam-column element model. The quasi-static test and the shaking table test of reinforced concrete columns are simulated and analyzed, respectively. The results show that the established fiber element model can predict nonlinear mechanical behavior and the damage distribution of reinforced concrete columns with good accuracy. Furthermore, the model can effectively simulate the evolution process of damage of columns, and describe weak parts of columns, and has high computational efficiency and solution accuracy, thus providing a practical analysis method for the simulation of building structures during earthquake.  
fiber beam-column element; damage constitutive model; damage analysis; reinforced concrete columns +
Footnote †: journal: Applied Sciences
## 5 Numerical Simulations
### RANS Simulations
#### 5.1.2 Verification  
A total run-time of 700 and 1500 s for each simulation proved to give a converged solution with respect to time for the Vestas and Samsung turbines, respectively. A case with a reduced time step was also run to check the simulations sensitivity to a reduction in residuals. It showed a minimal effect on both wake, thrust, and power for both turbines. Residuals along with results for velocity deficit, turbine thrust, power, and time convergence can be found in Appendix A.  
A mesh refinement study was performed to ensure that grid convergence for the RANS simulations was achieved. Three meshes of increasing refinement named coarse, orig, and fine were initially run for both turbines with minimum cell length \(dx\) in the refined region. The cell length \(dx\) was changed by a factor of \(r=1.25\) between each mesh for the Vestas turbine and \(r=1.33\) for the Samsung turbine. Based on these results, a fourth mesh, named medium, with a refinement level in between coarse and orig was proposed and verified for the V80 turbine, while the orig mesh was deemed adequate for the Samsung turbine. Figure 5a,b show the results of a GCI study performed using the medium and fine meshes, while output for power and thrust from the rotors is presented in Table 6.  
Shear in the inflow profile proved to have a pronounced effect on the velocity deficit in the wake, as can be seen in Figure 6. The shear profile was therefore included in subsequent iterations of the models. A no-slip boundary condition and wall functions were applied at the bottom wall, and, in order to capture the steep gradient, cells were refined in this lower  
Figure 4: RANS mesh for the Samsung turbine shown in the (**a**) \(yz\)-plane and (**b**) \(xz\)-plane.  
Figure 5: Plot of downstream velocity deficit with error bars from GCI study for (**a**) V80 and (**b**) Samsung turbines.  
\begin{table}
\begin{tabular}{l l l} \hline  & **2 MW Vestas V80** & **7 MW Samsung** \\ \hline \(L_{x}\), \(L_{y}\), \(L_{z}\) [m] & 1400, 500, 250 & 2435, 1000, 600 \\ \(N_{x}\), \(N_{y}\), \(N_{z}\) & 190, 61, 55 & 241, 81, 71 \\ \(dl_{turbine}\) [m] & 6.0 & 9.0 \\ \hline \end{tabular}
\end{table}
Table 5: Mesh parameters for RANS simulations.  
region. A sufficiently low value for the non-dimensional wall distance y+ needed for an accurate near-wall solution was never achieved. However, a plot of the wind velocity profile for locations downstream of the inlet and upstream of the turbine shows good agreement with the input velocity profile, especially in the region where the turbine operates; see Figure 7. This analysis was done for the Vestas turbine because of the availability of inflow data from [PERSON] et al. [7]. Changes based on the analysis were made to both turbine models.
# Time Classification Algorithm Based on Windowed-Color Histogram Matching
## 4 Experimental Results
### Dataset  
The dataset for time classification used a total of 450 images and divided them to 150 images for the training dataset, 300 images for the validation dataset, and 300 images for the test dataset. Images are collected by web crawling and various time scenes are captured from movies. The train and validation dataset are used to extract the sky region using Mask R-CNN. They consist of images, annotations of sky regions, and are classified by time variance: dusk or dawn, day, night. The validation dataset is used only for sky detection using the Mask R-CNN. After training the Mask R-CNN model, the reference histogram of each time class is created to compare the color and brightness of the sky region. Figure 5 shows a part of our dataset for each time period.
# FLaMAS: Federated Learning Based on a SPADE [PERSON]
## 3 Multi-Agent Learning Based on Federated Learning
### Agent Interaction Model
#### 3.3.1 Normal Execution Cycle  
The normal execution cycle stage of an agent in _FLaMAS_ is made up of four states, as shown in Figure 1 (independently of the role it is playing):  
1. SETUP STATE: In this state the agent configures parameters, such as \"agent name\", \"XMPP Server IP\", \"name of the database to train\", and any other configuration parameters required by the developer.
2. TRAIN STATE: In this state, if the agent is playing the Client role, the training of the deep learning model is performed, and the weights and the losses parameters of the model are extracted. If the agent is playing the Server role, it calculates the average of the weights of the different models received from the Client agents. These weights are then used by the next state.
3. SEND STATE: This state is responsible for encapsulating the different weights (weights and losses), using the XMPP message structure, and sending them. If the agent isplaying a Client role, they will be sent to the Server agent, otherwise, the Server agent will send them to the agents playing the Client role.
4. RECEIVE STATE: If the agent is playing the Client role, this state receives the message coming from the Server agent, which has the new weights. This same state is in charge of introducing these new weights into the model that is being trained. On the other hand, if the agent is playing the Server role, in this state it has to wait for all the agents playing the Client role, until the Presence state indicates they are active.  
At each iteration of the normal execution cycle of the _FLaMAS_ system, there are the following steps and messages (the last ones are shown in Figure 2):  
1. The Server agent uses the presence of all the Client agents to wait for the model trained by all the active Client agents.
2. Each active Client agent trains its model.
3. Each active Client agent sends its trained model to the Server agent.
4. After receiving all the trained models of the active Client agents, the Server agent calculates the average of all the models.
5. The Server agent sends the average model to each active Client agent.
6. Each active Client agent deploys the average model received.
# Improving Energy Efficiency on SDN Control-Plane Using Multi-Core Controllers
###### Abstract  
Software-defined networks have become more common in data centers. The programmability of these networks is a great feature that allows innovation to be deployed fast, following the increasing number of new applications. This growth comes with a cost of more processing power and energy consumption. Many researchers have tackled this issue using existing routing techniques to dynamically adjust the network forwarding plane to save energy. On the control-plane, researchers have found algorithms for positioning the controller in a way to reduce the number of used links, thus reducing energy. These strategies reduce energy consumption at the expense of processing power of the controllers. This paper proposes a novel approach to energy efficiency focused on the network's control-plane, which is complementary to the many already existing data-plane solutions. It takes advantage of the parallel processing capabilities of modern off-the-shelf multicore processors to split the many tasks of the controller among the cores. By dividing the tasks among homogeneous cores, one can lower the frequency of operations, lowering the overall energy consumption while keeping the same quality of service level. We show that a multicore controller can use an off-the-shelf multicore processor to save energy while keeping the level of service. We performed experiments based on standard network measures, namely latency and throughput, and standard energy efficiency metrics for data centers such as the Communication Network Energy Efficiency (CNEE) metric. Higher energy efficiency is achieved by a parallel implementation of the controller and lowering each core's frequency of operation. In our experiments, we achieved a drop of 28% on processor energy use for a constant throughput scenario when comparing with the single-core approach.  
software-defined network; energy efficiency; control-plane; multicore; controller Article