esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Methodology
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Morphometric Aspects** & **Morphometric Factors** & **Formula** & **References** \\ \hline Linear & Stream order (u) & Hierarchical ranking & [10; 27] \\  & Stream length (Lu) & Lu = L1 + L2 +...... + Ln & [11] \\  & Mean stream length (Lm) & Lm = Lu/Nu; Nu= Stream numbers & [11] \\  & & RI = Lu/L (u – 1); Lu = length of stream of order & \\  & Stream length ratio (RI) & u and L (u – 1) = stream length of the next & [9; 42] \\  & & lower order & \\  & Bifurcation ratio (Rb) & Rb = Nu /N (u + 1); Nu = Stream numbers of any & \\  & & given order and N (u + 1) = next higher order & \\  & & Drainage density (Dd) & Dd = L/A; L = total stream length and A = total & \\  & Stream frequency (Fs) & Fs = N/A; N = total stream numbers & [9] \\  & Drainage texture (Td) & Td = N/P; P = basin perimeter & [44] \\  & Length of overland flow (Lg) & Lg = \(\lambda\)\(\times\) Dd & [9; 24] \\  & Constant of channel maintenance (C) & C = 1/Dd & [11; 24] \\  & Form factor (Ff) & Ff = A/Lb\({}^{2}\); Lb = basin length & [9] \\  & Circularity ratio (Rc) & Rc = 4\(\pi\)A/P\({}^{2}\) & [45] \\  & Elongation ratio (Re) & Re = 2/Lb \(\times\) / (A/\(\pi\)) & [11] \\  & Shape index (Ish) & Ish = 1/Fs & [9; 29] \\  & Infiltration Number (If) & If = Fs \(\times\) Dd & [9; 29] \\  & Basin relief (R) & R = \(Z\)-\(Z\); \(Z\) = maximum basin elevation and z = & \\  & Basin slope (Sb) & Sb = \(Z\)/Lb & [45] \\ Relief & Relief ratio (Rr) & Rr = R/Lb & [11] \\  & Ruggedness number (Rn) & Rn = R \(\times\) Dd/1000 & [11] \\  & Melton ruggedness number (MRn) & MRn = R/\(\surd\)A & [46] \\  & Channel gradient (Gc) & Gc = R/\(\left(\pi/2\)\(\times\) Lb\(\right)\) & [19; 24] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Morphometric factors and the formulas used in this study.
## 3 Results
### In Situ FTIR
Figure 1b,c shows photos of the new setup for simultaneous FTIR and SANS analysis using the KWS-2: the components indicated by numbers in the scheme in Figure 1a are identified in the photographs, which show the complete experimental arrangement at the sample position of the KWS-2 between the \"collimation nose\" that holds the sample aperture on the left side of photos, and the entrance window of the evacuated SANS detector tank, which is visible on the right side of the images. The new FTIR setup is entirely built on a breadboard (Thorlabs GmbH, Bergkirchen, Germany) which is equipped with \"zero-point\" fixation systems (AMF GmbH, Fellbach, Germany) for pneumatic clamping (activated with pressurized air) for an easy and precise installation on the sample stage of the KWS-2. The control of the FTIR spectrometer, IR polarizer position (in beam and out of beam) and orientation were implemented in the NICOS instrument control software of the KWS-2 [24]. This is mainly meant for time-resolved investigations when FTIR measurements with the selected time acquisition can be carried out simultaneously with SANS measurements on samples in different external fields or composition conditions. The start of the FTIR acquisition is triggered by the detector start of the SANS instrument and runs until the SANS acquisition time is consumed. All relevant parameters for SANS: wavelength \(\lambda\), collimation length L\({}_{C}\), detection length L\({}_{D}\), beam-size on sample, etc. [15], sample field parameters (temperature), and the FTIR setup parameters (polarizer position and orientation, etc.) can be combined in a single complex experimental routine. For conventional SANS measurements, standard FTIR acquisitions can be carried out in parallel using the operation menu of the FTIR spectrometer. A precise synchronization of the results obtained with the two methods is possible by using the SANS data acquisition in list mode [8], which enables the assignment of the SANS pattern collected at a specific time to the corresponding FTIR spectrum. Proof-of-principle measurements were carried out by simultaneous use of FTIR in transmission geometry and SANS on soft matter systems that are relevant for the research topics developed using the KWS-2: (i) a hPEGDME solution in dTHF (\(\phi_{\text{pol}}=2\%\)) at 30 \({}^{\circ}\)C and 12 \({}^{\circ}\)C and (ii) a d-sPS film in co-crystalline \(\delta\)-form with hTol as guest molecules in the cavities between the sPS helices. Figure 1: Schematic presentation (**a**) and detailed (**b**) and general (**c**) views of the new operational setup for in situ FTIR analysis simultaneous with the SANS investigation using the KWS-2. The numbers indicate the main components, which can be identified in the photos on the (**b,c**) panels: the outlet and inlet beam ports (1,2), the windows (3,4), and the new ancillary equipment (5) for the control of the temperature on the sample (in-house designed and produced Peltier-based thermostated cuvette holder), while P indicates the IR polarizer.
# Analysis of All-Optical Generation of Graphene Surface Plasmons by a Frequency-Difference Process
###### Abstract  
The generation of graphene surface plasmons (SPs) by a frequency-difference nonlinear (NL) process caused by the interaction of two optical beams was experimentally demonstrated several years ago by measuring the differential reflectance of the probe beam. However, the understanding of these results requires much larger second-order optical conductivities of graphene than calculations performed so far can yield. In this work, we carefully calculate the relevant NL conductivities and show that, indeed, the experimental observations of the differential reflectance must have originated from physical processes beyond the coherent frequency-difference generation of SPs described by the density-matrix perturbation theory approach, presumably by hot-electron effects. We also suggest an alternative way of detecting optically generated SPs, which can be feasible at lower powers of the optical pulses. Such additional experiments are expected to help understand the remaining discrepancy between the theory and the existing experimental data.  
graphene; light-matter interaction; surface plasmon; nonlinear conductivity +
Footnote †: journal: Applied Physics Letters  
1
Footnote 1: Centre of Physics—CF-UM-UP and Laboratorio de Fisica para Materiais e Tecnologias Emergentes (LaPMET), University of Minho, Campus of Guatar, 4710-374 Braga, Portugal 1
## 3 Methodologies
### An Integrated Analysis Method  
The features synchronous localization and identification of turbulent energy method is a combination of an integrated VMD method, a 1D continuous wavelet decomposition, and a special local measure of intermittency approach. Integrated features identification methods mainly consist of two parallel modules and the framework of a features identification and quantitation method, as shown in Figure 3.  
* Temporal features localization and quantitation. First, the measured turbulent shear fluctuations are decomposed into K IMF components associated with multiscale and multi-frequency bandwidths. Second, each IMF mode is calculated using the classical wavelet decomposition method to obtain the time and scale values of wavelet coefficients. Synchronously, the Hilbert transform (HT) is also utilized to get the phase values and unilateral frequency spectrum of IMF modes. Finally, the measure method of local intermittency is applied to identify the temporal intermittent events and scales correlations in the energy transfer process.
* Spatial features identification. First, original horizontal shear fluctuations in the time domain are transformed into vertical shear gradients according to the empirical [PERSON]'s frozen theorem: \(\partial U/\partial z=W^{-1}\left(\partial U/\partial t\right)\)([PERSON] and [PERSON], 2016), where \(W\) is the speed of the instrument, and the symbol \(U\) denotes the shear fluctuations' velocity. Second, the shear spectrum is obtained using the fast Fourier transform (FFT) for the processed vertical gradients data \(\partial U/\partial z\) with an interval of 4 s. The coherent spectrum calibration method proposed by [PERSON] et al. (2014) is used to eliminate the interference of instrument vibration and noise, and then, the Nasmyth standard spectrum is fitted to calculate the dissipation rate (\(\epsilon\)) of turbulent kinetic energy (TKE) iteratively, which is expressed as follows: \[\epsilon=\frac{15}{2}\
u\overline{\left(\frac{\partial U^{\prime}}{\partial z }\right)^{2}}=\frac{15}{2}\
u\int_{k1}^{k2}\Phi(k)dk\] (6) where \(\partial U^{\prime}/\partial z\) is the variance of the shear fluctuations' velocity, \(\
u\) (\(\
u\)=1.64 \(\times\) 10\({}^{-6}\) m\({}^{2}\) s\({}^{-1}\)) is the kinematic molecular viscosity, \(k_{1}\) is the lower integration limit, and \(k_{2}\) is the upper integration limit, which is resolved by the Kolmogorov cutoff wavenumber, where  
Figure 3: A brief framework of an identification and quantitation method.  
\(k_{2}=\frac{1}{2\pi}\left(\frac{\varepsilon}{v^{3}}\right)^{1/4}\). Finally, the spatial statistical characteristics of the turbulent energy dissipation are quantitatively studied using the cumulative probability distribution functions (CPDFs) to reveal the spatial correlations in the dissipation process [25,26]. The corresponding cumulative distribution function is defined as:  
\[\text{CDFs}(\varepsilon)\ =\ \Phi\bigg{(}\frac{\ln\varepsilon-\mu}{\sigma} \bigg{)} \tag{7}\]  
where the symbol \(\Phi\) denotes the cumulative distribution functions (CDFs) of a standard normal distribution.
# A Source Anonymity-Based Lightweight Secure AODV Protocol for Fog-Based MANET
## 4 Source Anonymity-Based Lightweight Secure AODV Protocol
### Simulation and Analysis  
Simulation analysis is performed using a Network Simulator (NS-2), which is the most common tool for the simulation of network scenarios and topologies. We simulated AODV, SAODV, and SAL-SAODV protocol, and made a comparison in terms of energy consumption, throughput, and BPUE (Bits-Per Unit of Energy). BPUE is a multi-parameter joint evaluation metric based on the transmission distance and modulation level [30]. The simulation parameters are shown in Table 2:  
The energy consumption of the three kinds of protocol is compared in Figure 5a. It can be seen from the diagram that the AODV protocol has the largest energy consumption, and the network energy consumption tends to be constant at about 700 s, which means that most of the nodes witness energy depletion and the network stops working; the SAODV protocol has the minimum energy consumption, and the SAODV and SAL-SAODV deal is still in a rising status in the 800 s, which means that the nodes have residual energy and the network can continue working.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Number of nodes & 50 \\ Initial energy of nodes (J) & 2 \\ Simulation area (m\({}^{2}\)) & 1000 \(\times\) 1000 \\ Node movement speed (m/s) & 0 \\ Simulation time (s) & 800 \\ Transmission range(m) & 200 \\ Antenna Type & Omni antenna \\ Mobility Model & Random Way Point \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 4: Random Delayed Transmitting Scheme.  
The three protocols' throughputs are compared in Figure 5b. It shows that the AODV's throughput is bigger than SAODV and SAL-SAODV. It reaches its maximum at about 700 s, and its throughput is no longer high due to the fact that the network stops working, whereas SAODV's throughput is the minimum.  
Since AODV has no security schemes, its operating speed is the fastest. Meanwhile, its energy consumption and throughput exhibit the largest values. Instead, the SAODV protocol joins the security scheme and is more complicated than the SAL-SAODV protocol. Therefore, it exhibits the minimum energy consumption and throughput. The energy consumption and throughput values of the SAL-SAODV protocol are between the AODV and SAODV protocol. It can be concluded from the Figure 5a,b that the SAL-SAODV protocol can be used to reduce energy consumption by an average of 30% in terms of SAODV when they have the same throughput.  
The BPUE of the three kinds of protocol is compared in Figure 5c. It can be seen from the diagram that the AODV protocol has the biggest BPUE, while SAODV displays the minimum; the BPUE of SAL-SAODV is between that of the AODV and SAODV protocol. It can be seen from Figure 5c that the BPUE metric is about 55% higher than that of the SAODV protocol when it remains steady. Figure 5a confirmed our viewpoint that the SAL-SAODV protocol improves the energy efficiency of SAODV.
# A Graph Convolution Collaborative Filtering Integrating Social Relations Recommendation Method
## 5 Experiment
### Parameter Analysis  
For the proposed model, there are two crucial parameters: the number of layers \(l\) and the embedding size \(d\). In this section, we first change only one parameter and fix the others, and then observe how the performance changes.  
**Effect of the number of layers \(l\)**. Take Gowalla and Epinions for example, we set the \(l\) from 1 to 5 to measure the impact of different layers, and then we can obtain the performance with the different number of layers that showed in Figure 7. We observed that performance increases and then decreases as the number of layers grows. When the number of layers grows from 1 to 4, performance of SRRA is improved. However, performance starts to become worse when the number of layers is 5. It demonstrates that too many layers may cause over smoothness that is a common problem existing in graph convolution methods. Thus, in order to prevent this, we need to use the proper number of layers.  
**Effect of embedding size \(d\)**. In this subsection, take Gowalla and Epinions for example, we analyze how the embedding size of \(e_{u}\) and \(e_{i}\) affect proposed model. On these  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Recall@10**} \\ \cline{2-7}  & & **LightGCN** & **DSCF** & **DiffNet** & **GraphRec** & **DICER** & **SRRA** \\ \hline
**Brightkite** & 0.1642 & 0.1895 & 0.1962 & 0.2172 & 0.2235 & **0.2293** \\ \hline
**Gowalla** & 0.2083 & 0.2253 & 0.2399 & 0.2779 & 0.2886 & **0.3011** \\ \hline
**Epinions** & 0.2269 & 0.2613 & 0.2874 & 0.2845 & 0.3155 & **0.3341** \\ \hline
**LastFM** & 0.2519 & 0.2742 & 0.2932 & 0.2876 & 0.3059 & **0.3272** \\ \hline \hline \multicolumn{7}{c}{**NDCG@10**} \\ \hline
**Datasets** & **Methods** & **LightGCN** & **DSCF** & **DiffNet** & **GraphRec** & **DICER** & **SRRA** \\ \hline
**Brightkite** & 0.1321 & 0.1393 & 0.1539 & 0.1612 & 0.1672 & **0.1701** \\ \hline
**Gowalla** & 0.1355 & 0.1482 & 0.1667 & 0.1724 & 0.1744 & **0.1782** \\ \hline
**Epinions** & 0.1425 & 0.1598 & 0.1642 & 0.1709 & 0.1737 & **0.1824** \\ \hline
**LastFM** & 0.1431 & 0.1563 & 0.1628 & 0.1862 & 0.1953 & **0.2086** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison between SRRA and baselines.  
Figure 7: Effect of #layers \(l\) on Gowalla and Epinions.  
two datasets, Figure 8 compares the performance of our proposed model when varying its embedding size \(d\).  
Accordingly, as embedding size increases, performance first become better, then worse. If the size grows from 8 to 64, the performance improves obviously. However, the performance of SRRA starts to deteriorate when the embedding size is 128. It demonstrates that a large embedding size is likely to produce powerful representations. Nevertheless, if the length of embeddings is too long, our model will become more complex. Thus, we must find an appropriate embedding size to make a trade-off, and we find that 64 is the optimal value.
## 4 Methods
### Parallel IO and Asynchronous IO  
The default IO mode in WRF is serial IO, in which the master process collects data from other processes via Gatherv and formats it to be written to a disk. The default serial IO mode shows good performance when the data size is small. However, when the data size is large, the memory size of the master process may become a bottleneck and the communication overhead of collecting data is high when receiving data. Therefore, it is necessary to use parallel IO to improve the overall performance.  
Parallel IO technology subdivides the original process into multiple process groups, also called aggregation groups. Each process group has a master process that is responsible for receiving data from other processes in the group, formatting the data and then transferring them to a disk. This greatly reduces the communication overhead caused by a single process collecting other data in serial IO mode [6; 12].  
The master process in both serial and parallel IO modes collects data through MPIs Gatherv, formats the data, and writes it to a disk. During this process, other processes are blocking and waiting. In the asynchronous IO mode, the processes are divided into computing and IO processes, where the computing process is responsible for computing and the IO process is responsible for writing data to a disk. Asynchronous IO solves the problem of waiting overhead for other processes when serial and parallel IO write data, improving the overall performance.  
The WRF parallel IO method allows the following two steps to be implemented: install PNetCDF before compiling, and compile WRF with the appropriate path; second, before running WRF, in the WRF/run directory of namelist.input to modify the io_form_history, io_form_restart value (the default is 2, NetCDF formatted output), the value is modified to 11 is PNetCDF parallel IO mode. It should be noted that the parallel IO output of the data is not compressed.  
The developers of WRF provide methods for setting up asynchronous IO. In the namelist_quilt module in namelist.input, we can modify the values of nio_tasks_per_group and nio_groups. nio_groups refers to the number of groups in the IO process group, and nio_tasks_per_group refers to the number of processes in each IO group. By default, nio_tasks_per_group=0 (for non-asynchronous IO). The number of asynchronous IO process groups and the number of processes in each group are set according to the following rules.  
* Total number of cores = (nproc_x x nproc_y + nio_groups x nio_tasks_per_group) x threads;  
Figure 4: Two-dimensional process partition of different domain decomposition.  
* \(\text{nio\_tasks\_per\_group}<\text{nproc\_y}\);
* \(\text{nproc\_y}\) and \(\text{nio\_tasks\_per\_group}\) are preferably multiplicative.
# Sentiment Classification Using Convolutional Neural Networks
## 2 Background
### Deep Learning for Sentiment Classification  
The deep learning technique, one of the machine learning techniques, has been recently widely used to classify sentiments. [PERSON] et al. [34] designed a new model, namely the Adaptive Recursive Neural Network (AdaRNN), that classifies the Twitter texts into three sentiment labels: positive, neutral, and negative. By experimental results, the AdaRNN achieved 66.3% accuracy. [PERSON] et al. [35] proposed Hierarchical Long Short-Term Memory (HLSTM) and obtained 64.1% accuracy on Weibo tweet texts. [PERSON] et al. [36] introduced a new variant of the RNN model, the Gated Recurrent Neural Network (GRNN), which achieved 66.6% (Yelp 2013-2015 data) and 45.3% (IMDB data) accuracies. All of these studies commonly assumed that there were three or more sentiment labels.  
Meanwhile, [PERSON] et al. [37] utilized Long Short-Term Memory (LSTM) [38; 39; 40; 41] for binary classification of sentiment and obtained 82.1% accuracy on the movie review data [27]. [PERSON] [1] had a result of a maximum of 89.6% accuracy with seven different types of data through their CNN model with one convolutional layer. [PERSON] et al. [42] proposed three-way classification and obtained a maximum 94.2% accuracy with four datasets, where their best three-way model was NB-SVM. [PERSON] and [PERSON] [43] employed a pretrained Word2 Vec for their CNN model and achieved 84.79% (phrase-level) and 64.59% (message-level) accuracies with SemEval-2015 data. The CNN model used in [43] was essentially the same as the model of [1]. [PERSON] et al. [44] trained the CNN model that had a combination of two convolutional layers and two pooling layers to classify tweet data of four languages sentimentally and obtained a 67.79% F1 score. In the study of [PERSON] et al. [45], the CNN model, which had three convolution/pooling layer pairs, was proposed, and the model outperformed other previous models including the Matrix-Vector Recursive Neural Network (MV-RNN) [46]. We conducted experiments with several of our CNN models that had different structures, and two of our models (e.g., the seventh and eighth models) were similar to the model of [44; 45].  
As summarized above, for the sentiment classification, there are two dominant types of deep learning technique: RNN and CNN. In this work, we propose a CNN model, the structure of which was carefully designed for effective sentiment classification. In the next subsection, we explain the advantages of the CNN in analyzing text.
## 4 Dynamic Damage Constitutive Model
### Comparative Analysis with ZWT Model  
ZWT model [34; 35] is a nonlinear viscoelastic constitutive model and is suitable for the strain rate of \(10^{-4}\) s\({}^{-1}\)-\(10^{3}\) s\({}^{-1}\). Researchers have conducted some studies on the application of ZWT model in the dynamic constitutive model of concrete. The ZWT model is shown in Figure 12.  
The ZWT model is composed of two Maxwell medium and a nonlinear elastomer parallelly. The two Maxwell models (\(E_{1}\), \(\varphi_{1}\) and \(E_{2}\), \(\varphi_{2}\)) describe the viscoelastic response of BFRC under quasi-static and high strain rates load respectively. The ZWT model expression is as follows:  
\[\sigma=E_{0}\varepsilon+\alpha_{0}\varepsilon^{2}+\beta_{0}\varepsilon^{2}+E_ {1}\int_{0}^{t}\varepsilon(\tau)\exp(-\frac{t-\tau}{\varphi_{1}})d\tau+E_{2} \int_{0}^{t}\varepsilon(\tau)\exp(-\frac{t-\tau}{\varphi_{2}})d\tau \tag{9}\]  
where, \(E_{0}\), \(E_{1}\), \(\varphi_{1}\), \(E_{2}\), \(\varphi_{2}\) are material constants, \(E_{0}\), \(\alpha_{0}\), \(\beta_{0}\) are elastic constants, \(E_{1}\), \(E_{2}\) are linear elastic modulus, \(\varphi_{1}\), \(\varphi_{2}\) are relaxation times.  
The first part of nonlinear elasticity in the above equation can be regarded as linear elasticity and only the first term can be taken due to the strain of the specimens is tiny and the stress-strain curve under the quasi-static load is approximately linear at elastic range. The strain rate (\(\dot{\varepsilon}\)) of the SHPB test can be approximately regarded as a constant. Therefore, the expression of ZWT model can be written as follows:  
\[\begin{split}&\sigma=(1-D)\bigg{[}E_{0}\varepsilon+E_{1}\varphi_{ 1}\dot{\varepsilon}(1-e^{-\frac{\varepsilon}{\varphi_{1}}})+E_{2}\varphi_{2} \dot{\varepsilon}(1-e^{-\frac{\varepsilon}{\varphi_{2}}})\bigg{]}\\ &=\exp[-\frac{\varepsilon^{m}}{\  
Compared with the ZWT model, the visco-elastic damage constitutive model has the following characteristics:  
1. The V-E model needs fewer parameters to fit the test curve well.
2. The expression of the V-E model is simpler, so it has better practicability.
3. The deficiency of the V-E model is the need for strain rate data, while the ZWT model only needs average strain rate (\(\dot{\epsilon}\)), except for stress and strain data.