esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Signal Processing Algorithm
### Peak and Zero-crossing Detection  
At this stage, all the peaks \(p_{i}\), all the minima \(q_{i}\) and all the zero crossings \(z_{i}\) of the cross-correlation \(i(n)\) are detected and registered. Every peak is labeled as \(p_{i}\) where its value \(p_{0}\)=\(n_{d}\) is zero order fringe peak position, \(p_{\text{-1}}\) is negative first order fringe peak position, \(p_{1}\) is positive first order fringe peak position in terms of sample number and so on. Also every zero crossing between peak \(p_{i}\) and minimum \(q_{i}\) is detected and labeled as \(z_{j}\) where \(j\)=1,2,3...in terms of sample number. Then zero crossing position \(z_{j}\) with sub-sample resolution is calculated [9] by using interpolation formula as shown in Equation 8, Equation 9 and Figure 4.  
\[z_{j}=r+\frac{i(r)}{i(r)-i(r+1)}\ \text{for}\ \left|i(r)\right|>\left|i(r+1)\right| \tag{8}\]  
and  
\[z_{j}=r+1-\frac{i(r+1)}{i(r+1)-i(r)}\ \text{for}\ \left|i(r)\right|<\left|i(r+1)\right| \tag{9}\]  
Linear interpolation is the reasonable method because the cosine function crosses the zero essentially as a straight line as shown in Figure 4.  
Then the zero crossing period \(b\) is calculated by fitting all the \(z_{j}\) into the form of:  
\[y(n)=bn+a\ \ \ \text{for n}=1,2,3,...\,N_{Z}\] (10a) by applying least square fitting to the distance between all pairs of zero ( \[z_{j}\] and \[z_{j+t}\] ) as shown Figure 5  where \[N_{Z}\] is the total number of zero crossings in \[i(n)\]. In Figure 5 the slope \[b\] of the \[y\left(n\right)\] is the estimated number of samples inside half fringe (or half the sample rate) and the estimated sample rate is given by: \[\hat{f}_{S}=\left[2b\right]\] (10b) where \[\left[2b\right]\] is the closest integer number of \[2b\].
## 3 Results and Discussion
### Validation and Annual Estimation  
The INLAND model was validated from atmospheric forcing and turbulent flux data collected at the Santa Maria experimental site from 1 January to 31 December 2018. Figure 15 shows the daily time series of the net radiation, latent and sensible heat fluxes, and the net ecosystem exchange of CO\({}_{2}\). In this comparison, we had 212 daily measurements observed to \(Rn\), \(LE\), \(H\), and 110 to \(NEE\). In general, we find a good fit between the simulated and observed data, indicating that the multi-objective calibration generated positive model simulation results. \(Rn\) and \(LE\) were slightly underestimated by the model (PBIAS \(-2.6\) and \(-4\), respectively) while \(H\) and \(NEE\) were overestimated (PBIAS 24 and 20) while maintaining low values (similar to those found from the calibration). In average terms the calibration of model can successfully represent the observed patterns (RMSE 15, 15, and 14 W m\({}^{-2}\) for \(Rn\), \(LE\), and \(H\), respectively, and 1.7 umol CO\({}_{2}\) m\({}^{-2}\)s\({}^{-1}\) for \(NEE\)), especially compared to the default simulation (RMSE 31, 22, and 14 W m\({}^{-2}\) for \(Rn\), \(LE\), and \(H\), respectively, and 6.4 umol CO\({}_{2}\) m\({}^{-2}\)s\({}^{-1}\) for \(NEE\), respectively).  
The good agreement of the simulations for the validation period is confirmed by the graphs of the average diurnal cycles of \(Rn\), \(LE\), \(H\), and \(NEE\) (Figure 16). Although there are still small discrepancies in the nocturnal period of the variables \(H\) and \(NEE\), it is possible to observe that the calibration  
Figure 15: Daily average net radiation (\(Rn\)), latent heat flux (\(LE\)), sensible heat flux (\(H\)), and net ecosystem exchange of CO\({}_{2}\) (\(NEE\)) observed (\(obs\)) and simulated by the model before calibration (\(def\)) and after the calibration (\(clb\)) to the validation period (January to December 2018). Daily average statistics are shown in the graphics area.  
substantially improves all the fluxes analyzed, especially in relation to \(Rn\) and \(LE\) for the whole day and \(H\) and \(NEE\) for the diurnal period.  
Figure 17 shows scatter plots for \(Rn\), the \(LE\) and \(H\) fluxes and the \(NEE\) value. The simulation of \(Rn\) from the calibrated model shows excellent adjustment in relation to the observed data. The angular coefficient of close to 1 (0.97) and the linear coefficient of close to zero (\(-3.5\)) show that the simulation closely reflects the observed values. Values of the coefficients are similar to those found by [13; 25], who used similar models to that used in this work. \(LE\) and \(H\) simulated after calibration also exhibit good adjustment in relation to the observed data. Both fluxes present high coefficients of determination (0.89 for \(LE\) and \(H\)). \(NEE\) is also well adjusted with values of coefficients of the regression line (angular 0.61 and linear \(-2.19\)) considered satisfactory.
# Seismic Upgrade of an Existing Reinforced Concrete Building Using Steel Plate Shear Walls (SPSW)
## 5 Seismic Response Results of the Retrofitted r/c Building
### Finite Element Model (FEM)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Horizontal Direction of**} & **Seismic** & \multicolumn{4}{c}{**Maximum IDR (\%)**} & \multirow{2}{*}{**Base Shear (kN)**} & \multirow{2}{*}{**Maximum RIDR (\%)**} \\  & **Motion** & & & **1 st Storey** & **2 nd Storey** & **3 rd Storey** & \\ \hline \multirow{4}{*}{X} & 1a & 0.72 & 0.67 & 0.20 & 626.1 & 0.09 \\  & 1b & 2.46 & 1.22 & 0.27 & 647.5 & 1.71 \\  & 2a & 1.05 & 0.73 & 0.24 & 604.6 & 0.13 \\  & 2b & 1.26 & 0.48 & 0.22 & 534.1 & 0.54 \\  & 3a & 0.76 & 0.76 & 0.30 & 735.3 & 0.09 \\  & 3b & 0.77 & 0.85 & 0.40 & 723.6 & 0.15 \\ \hline \multirow{4}{*}{\(\gamma\)} & 1a & 0.09 & 0.11 & 0.07 & 2680 & 0.01 \\  & 1b & 0.32 & 0.19 & 0.08 & 2248 & 0.21 \\  & 2a & 0.14 & 0.11 & 0.06 & 2104 & 0.02 \\  & 2b & 0.19 & 0.13 & 0.08 & 2406 & 0.07 \\  & 3a & 0.13 & 0.16 & 0.08 & 2558 & 0.01 \\  & 3b & 0.11 & 0.14 & 0.08 & 2096 & 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Maximum values for IDR, RIDR, and base shear. The Von Mises stresses and the maximum shear stresses of the SPSWs are listed in Table 8, demonstrating that with respect to the model of Figure 8, the SPSWs are certainly better utilized, and one of them yields (Figure 13). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Horizontal Direction of the Building**} & \multirow{2}{*}{**Seismic**} & \multicolumn{4}{c}{**Maximum IDR (\%)**} & \multirow{2}{*}{**Base Shear (kN)**} & \multirow{2}{*}{**Maximum RIDR (\%)**} \\ \cline{3-3} \cline{5-7}  & & **1 st Storey** & & & & **2 nd Storey** & **3 rd Storey** \\ \hline \multirow{4}{*}{X} & 1a & 0.25 & 0.35 & 0.25 & 1493 & 0.003 \\  & 1b & 0.34 & 0.49 & 0.39 & 1893 & 0.027 \\  & 2a & 0.26 & 0.37 & 0.28 & 1416 & 0.005 \\  & 2b & 0.25 & 0.36 & 0.29 & 1456 & 0.036 \\  & 3a & 0.26 & 0.36 & 0.28 & 1507 & 0.002 \\  & 3b & 0.29 & 0.41 & 0.32 & 1796 & 0.009 \\ \hline \multirow{4}{*}{\(\Upsilon\)} & 1a & 0.13 & 0.19 & 0.15 & 2066 & 0.013 \\  & 1b & 0.01 & 0.15 & 0.12 & 1688 & 0.006 \\  & 2a & 0.10 & 0.14 & 0.12 & 1613 & 0.009 \\  & 2b & 0.14 & 0.21 & 0.18 & 1838 & 0.013 \\  & 3a & 0.14 & 0.21 & 0.17 & 1977 & 0.001 \\  & 3b & 0.15 & 0.22 & 0.17 & 1666 & 0.010 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Maximum values for IDR, RIDR, and base shear. Figure 12: Plastic hinge formations—Finite Element Model (FEM). Figure 13: [PERSON] stresses (in MPa) to the SPSWs—Finite Element Model (FEM).
## 2 Methods
### Indicators to Evaluate Node Importance
#### 2.3.3 Node Efficiency  
The node efficiency (NE) is defined as the degree of decline in network efficiency before and after the removal of a given node. The greater the decline, the more significant the node's impact on the overall network, and thus, the more important the node is. Its expression is as follows:  
\[NE_{i}^{(m)}=\frac{E^{(m)}-E_{i}^{(m)}}{E^{(m)}}, \tag{12}\]  
where  
* \(V_{i}^{(m)}\): Represents a node within \(V^{(m)}\);
* \(V^{(m)}\): Denotes the finite, non-empty set of nodes that constitute the \(RSCN^{(m)}\);
* \(E^{(m)}\) and \(E_{i}^{(m)}\): Represent the network efficiency before and after the removal of node \(V_{i}^{(m)}\), respectively. The corresponding expressions are as follows:  
\[E^{(m)}=\frac{1}{N^{(m)}\big{(}N^{(m)}-1\  
\[EPR_{i}^{(m)}(t)=\alpha\sum_{j=1}^{N^{(n)}}\left[\left(TPM_{ji}^{(m)}+a^{T}\frac{e }{N^{(m)}}\right)\cdot\frac{CCM_{ji}^{(m)}}{\sum_{i,j=1}^{N^{(m)}}CCM_{ij}^{(m)}} \cdot EPR_{j}^{(m)}(t-1)\right]+(1-\alpha)\frac{1}{N^{(m)}}, \tag{15}\]  
where  
* \(\alpha\): The damping factor, which typically takes a value in the range _(0, 1)_ and is usually set to 0.85, but should be tested under different scenarios;
* \(TPM_{ij}^{(m)}\): Represents the transition probability matrix of \(RSCN^{(m)}\);
* When \(TPM_{ij}^{(m)}\) contains a column of all zeros (indicating the node's out-degree is 0), it is replaced with \(a_{i}=\)1;
* \(N^{(m)}\): The number of rows/columns in the matrix \(TPM_{ij}^{(m)}\), which is also equal to the number of nodes;
* \(CCM_{ij}^{(m)}:\) Represents the transition probability matrix of \(RSCN^{(m)}\);
* \(e\): A matrix of size \(N^{(m)}\times N^{(m)}\), filled entirely with ones.
# A BiGRU Model Based on the DBO Algorithm for Cloud-Edge Communication Networks
## 3 Short-Term Traffic Load Prediction Based on DBO-BiGRU
### DBO-BiGRU Model
#### 3.2.1 Structural Design of the BiGRU Prediction Model
Since BiGRU is used in this paper, the value of \(num_{directions}\) is 2. The number of neurons in the hidden layer has a profound effect on the approximation potential and generalisation ability of the neural network. If the number of neurons is too high, it may lead to an overly complex model and overfitting, while too small a number may limit the expressive ability of the network, making the model unable to adequately fit the data. Therefore, the number of neurons in the hidden layer needs to be set reasonably. Figure 4: Structure of BiGRU traffic prediction model.
# Research on High-Performance Fourier Transform Algorithms Based on the NPU
###### Abstract  
Backpack computers require powerful, intelligent computing capabilities for field wearables while taking energy consumption into careful consideration. A recommended solution for this demand is the CPU + NPU-based SoC. In many wearable intelligence applications, the Fourier Transform is an essential, computationally intensive preprocessing task. However, due to the unique structure of the NPU, the conventional Fourier Transform algorithms cannot be applied directly to it. This paper proposes two NPU-accelerated Fourier Transform algorithms that leverage the unique hardware structure of the NPU and provides three implementations of those algorithms, namely MM-2 DFT, MV-2 FFTm, and MV-2 FFTv. Then, we benchmarked the speed and energy efficiency of our algorithms for the gray image edge filtering task on the Huawei Atlas200l-DK-A2 development kits against the Cooley-Tukey algorithm running on CPU and GPU platforms. The experiment results reveal MM-2 DFT outperforms OpenCL-based FFT on NVIDIA Tegra X2 GPU for small input sizes, with a 4- to 8-time speedup. As the input image resolution exceeds 2048, MV-2 FFTv approaches GPU computation speed. Additionally, two scenarios were tested and analyzed for energy efficiency, revealing that cube units of the NPU are more energy efficient. The vector and CPU units are better suited for sparse matrix multiplication and small-scale inputs, respectively.  
backpack computer; wearables; NPU; Fourier Transform +
Footnote †: journal: Applied sciences
## 7 RF-EH Circuit
### Antenna Configurations  
One of the most critical parts of a rectenna system's front-end is the antenna. The antenna serves as the RF power receiver and converts them into a DC signal via other components of the rectenna system over the following stages. The effectiveness of the deployed antenna significantly influences the complexity, size, and performance of a  
Figure 2: Taxonomy of literature on radio frequency energy harvesting.  
retenna system. The desirable properties of an antenna include a broad range of operating frequency, low-profile, omnidirectional radiation pattern, high gain, compact size, and circular polarization (CP) [77].
# Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11 ah
## 7 Conclusions  
In this paper, we propose a novel traffic-adaptive RAW optimization algorithm (TAROA) to adjust the RAW parameters in real time based on the current traffic conditions, optimized for sensor networks with mainly upstream traffic in which each station is assumed to transmit packets with a certain (predictable) frequency. The TAROA algorithm improves upon the state of art in three ways, including supporting dynamic and heterogeneous traffic conditions, only using information available on the AP side and real-time execution. The combination of these three factors allows TAROA to be deployed in realistic environment. The TAROA algorithm is executed at each target beacon transmission time (TBTT), it first estimates the packet transmission interval of each station only based on packet transmission information obtained during the last beacon interval, and then determines the RAW parameters and assigns stations to RAW slots based on this estimated transmission frequency.  
The simulation results reveal three key points on TAROA. First, it scales much better than EDCA/DCF in dense networks in terms of throughput under static traffic conditions. Second, it is resilient to dynamic traffic conditions in which topology or transmission interval change over time. In the scenario under evaluation, TAROA only starts to experience throughput degradation when topology changes at Poisson rate 5. Third, TAROA gains additional advantages over EDCA/DCF when hidden nodes are present under both static and dynamic traffic conditions. Hidden nodes highly degrade the performance of EDCA/DCF, while TAROA is hardly affected. In summary, for dense IoT networks, TAROA can easily adapt to current traffic conditions in real time and significantly improves throughput.  
In future work, we will further improve the performance of the TAROA algorithm, and, in particular, the traffic estimation under high load. Moreover, TAROA will be extended to support adaptive MCS.
# Topp-Leone Modified Weibull Model: Theory and Applications to Medical and Engineering Data
## 4 Three Different Estimation Methods
### Bayesian Estimation
The conditional posterior distributions are as follows:  
\[\Pi(\alpha|\beta,\lambda,\theta,x)\propto\alpha^{n+q_{1}-1}e^{-\alpha(2\sum _{i=1}^{n}x_{i}+w_{1})}\prod_{i=1}^{n}\left(1-e^{-2(\alpha x_{i}+\beta x_{i}^{ \theta})}\right)^{\lambda-1}, \tag{37}\]  
\[\Pi(\beta|\alpha,\lambda,\theta,x)\propto\beta^{n+q_{2}-1}e^{-\beta(w_{2}+2 \sum_{i=1}^{n}x_{i}^{\theta})}\prod_{i=1}^{n}x_{i}^{\theta-1}\left[1-e^{-2( \alpha x_{i}+\beta x_{i}^{\theta})}\right]^{\lambda-1}, \tag{38}\]  
\[\Pi(\lambda|\alpha,\beta,\theta,x)\propto\lambda^{n+q_{3}-1}e^{-w_{3}\lambda} \prod_{i=1}^{n}\left[1-e^{-2(\alpha x_{i}+\beta x_{i}^{\theta})}\right]^{ \lambda-1}, \tag{39}\]  
and  
\[\Pi(\theta|\alpha,\beta,\lambda,x)\propto\theta^{n+q_{4}-1}e^{-u_{4}\theta}e ^{-2\beta\sum_{i=1}^{n}x_{i}^{\theta}}\prod_{i=1}^{n}x_{i}^{\theta-1}\left[1-e ^{-2(\alpha x_{i}+\beta x_{i}^{\theta})}\right]^{\lambda-1}. \tag{40}\]  
For further information, read [47]. The credible intervals (CIs) for Bayesian estimates are also known as the highest posterior density (HPD) intervals. Researchers utilized a well recognized approach to generate HPD ranges for unknown distribution characteristics. To generate estimates, samples are drawn using the proposed MH algorithm in this method; for information on the HPD algorithm, see [47].
## 2 Materials and Methods
### Experimental Setups  
#### 2.2.1 Terahertz Time-Domain Spectrometer  
Times of flight (TOFs) of the THz pulses transmitted through gasoline samples were measured by means of a stand-alone commercial THz-TDS spectrometer from Toptica A.G. [19]. The two different quasi-optical beam delivery arrangements, 4\(f\) and 2\(f\), were used with the spectrometer, as depicted in Figure 1. The differences between them are described in detail in the following Section 2.2.2.  
A pair of THz transmitter (Tx) and receiver (Rx) photoconductive antennas operating on the principle of an Auston switch were used in the spectrometer [20; 21]. They employed a state-of-the-art InAlAs/InGaAs multilayer structure technology [22; 23]. The optopair was excited and subsequently probed by the Erbium fiber laser of 1.56 \(\upmu\)m wavelength. The duration and repetition rate of the laser pulses were, respectively, 80 fs and 100 MHz. On the transmitter side, the laser delivered an average power of 20 mW, generating 30 \(\upmu\)W average THz power. The duration of the individual THz pulses was approx. 2 ps.  
The probe pulses of the laser were delayed with respect to the excitation pulses by means of the two-stage delay line. The first one (not shown in Figure 1), the long-travel delay line, introduced a constant time offset (up to 1 ns) of the pulses to compensate for the optical path length. The second delay line (shown in Figure 1), the fast delay line, provided time stamping for the precise sampling of the waveforms. Its readout was synchronized with the signal from the Rx receiver during a continuous forward and backward scan of the delay line mirror. In this way, the dead time of the system was reduced, allowing the THz pulse sampling to be completed in a twice shorter time. Sampling rates of approximately 12 pulses/s by time windows of 100 ps were achievable, allowing for less than 1.5 min acquisition times by applied averaging over 1,000 waveforms. Due to the high-precision position sensor of this delay line, which provided 1.3 fs and stable timing resolution, the system was characterized by the high accuracy of the time scale, resulting in a typical dynamic range of 98 dB when multiple traces were averaged [24]. Such a high dynamic range combined with the operational stability of the TDS spectrometer were crucial for obtaining low, ppm-level admixture detection limits during the measurements. An example of the time profiles and power spectra of the THz pulses registered with the spectrometer in ambient air is shown in Figure 2.
# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
## 6 Test Campaigns and Results  
A set of meaningful results are collected hereafter from the HANSEL test campaigns performed during the project. For each tested technology the experimental setup is contextually described.
# An Adaptive Process Monitoring Method Based on an Improved ADALINE Neural Network
## 4 Experiment and Results Analysis
### Implementation of the Proposed Approach
#### 4.2.3 Adaptive Adjustment of Control Limits  
Then, according to the method shown in Figure 5, the initial control limit is obtained, and the result is shown in Figure 10. The red dot in Figure 10a represents the statistical characteristics of different batches and the same variable in different time series and further uses Equations (6) and (7) to obtain \(\mu\) and \(\sigma\), respectively. Then, 3\(\sigma\) is used to calculate the process initial control limit, as shown in Figure 10b.  
The effect of using historical data to train the adaptive control limit update model is shown in Figure 11. We observe that the control accuracy gradually improves as the training proceeds. Since this experiment involves a simple cutting process, the volume of data is adequate. In cases where the data volume in other application scenarios is insufficient, to address the issue of data scarcity, we can employ data augmentation techniques. For instance, we can utilize generative adversarial networks and variational autoencoders to process the data. Additionally, we can leverage transfer learning. By using the model parameters that have been pre-trained on other similar tasks, transfer learning can reduce the reliance on large-scale data.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Experiment**} & \multicolumn{3}{c}{**Sampling Interval**} & \multicolumn{3}{c}{**Monitoring Sample Size**} & \multirow{2}{*}{**Success Rate P**} & \multirow{2}{*}{**Monitoring Efficiency E**} \\ \cline{2-2} \cline{5-8}  & \(n_{1}\) & \(n_{2}\) & \(n_{3}\) & \(h_{1}\) & \(h_{2}\) & \(h_{3}\) & \\ \hline
1 & 9 &  
Through the utilization of the aforementioned methods, we formulate dynamic control limits capable of being adjusted in accordance with the accumulation of knowledge and  
Figure 11: The accuracy training process of the control limit.  
Figure 10: Obtaining the initial control limits: (**a**) partially expanded historical data; (**b**) the initial control limit.  
the evolution of quality trends on the production line. This enables us to promptly monitor the quality drift during the production process, thereby contributing to the enhancement of product quality.
# Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening
## 3 Results
### Substrates
#### 3.2.2 Influence of the Number of Rotational Bonds  
Figure 9 presents the relative distribution of all active ligands in the DUD-E dataset taking into consideration the number of rotational bonds present. There is a higher prevalence in molecules with 4 to 7, and 8 to 11 rotational bonds, representing 73% of the dataset. The remaining 27% corresponds to molecules with 0 to 3 and higher than 12 rotational bonds.  
Ligands with more rotatable bonds presented a higher challenge for docking because they could adopt a larger number of possible conformations. Discriminating actives with many rotatable bonds from decoys with many rotatable bonds hence became more difficult, because correctly identifying the real pose of the ligand was more challenging. Hence, ligands with a higher number of rotational bonds were placed at the worst position in the database, when comparing with the ligands with fewer rotatable bonds. In this study, this was observed for all studied families.  
In Figure 10, the data for nuclear receptors and GPCRs are presented. For both families, AutoDock was able to rank more ligands early on. While in GPCRs there was a clear difference in the discrimination ability between Vina and AutoDock, for nuclear receptors, there was a similar behavior between both alternatives (exception--compounds with 4 rotatable bonds in nuclear receptors). According to our study, molecules with 5 to 10 rotational bonds ensured a better prediction with both AutoDock and Vina.  
Figure 8: Influence of MW on (**a**) GPCR and (**b**) Kinase ligands ranking position with Vina and AutoDock.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Vina** & **AutoDock** \\ \cline{2-3}  & **No. of Ligands within Top 1\%** & **No. of Ligands within Top 1\%** \\ \hline
**Total** & 2002 (8.9\%) & 1935 (8.6\%) \\
**\textless{400 Da**} & 536 (2.4\%) & 395 (1.8\%) \\
**400–500 Da** & 885 (3.9\%) & 1043 (4.7\%) \\
**\textgreater{500 Da**} & 581 (2.6\%) & 497 (2.2\%) \\ \hline \end{tabular}
\end{table}
Table 5: Influence of the molecular weight for actives found in the top 1% of the database.