esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning for EEG-Based Preference Classification in Neuromarketing
## 3 Literature Review
### Predictive Features for the Preferences  
This section reports on the studies that focused on the predictive features of neuroscience methods that can aid marketers in forecasting consumer preferences, as described in Table 4. Most of these studies employed distinctions of the standard regression analyses toward their prediction models. We classified these predictive features based on the EEG signal types: (1) rhythms; and (2) transient activities.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Year** & **Reference** & **Stimuli and Marketing Element** & **Signal Type** & **EEG Waves or ERP Component** & **Recording Modality** \\ \hline
2008 [17] & Viewing brands and products & ERP & P300 & BCI & \\
2009 [11] & Watching TV ads & EEG & Alpha (for emotional state) & EXG, BCI and GSR \\
2010 [36] & Viewing brands and images & ERP & N200 and P300 & BCI \\
2010 [37] & Watching TV ads & EEG & Alpha band frontal asymmetry & BCI \\
2010 [35] & Watching TV ads & EEG & Theta and gamma & Heart rate, BCI and GSR \\
2011 [28] & Watching TV ads & EEG & Asymmetrical increase in theta and alpha in PSD & BCI \\
2012 [38] & Viewing brand names & ERP & N400 & BCI and EOG for eye movement \\
2012 [39] & Viewing products and prices & ERP & EN400, LPC, and P200 & BCI \\
2013 [39] & Viewing products & EEG & Alpha, beta, theta, gamma, and delta & BCI Eye tracking \\
2014 [40] & Viewing products & ERP & P300 & BCI \\
2014 [41] & Watching TV ads & EEG & Theta and alpha & Heart rate, BCI and GSR \\
2015 [23] & Viewing products & ERSF and ERP & Theta, N200, and FFN & BCI \\
2016 [24] & Watching ads (movie trailers) & EEG (64 electrodes) & Beta and gamma oscillations & BCI and EOG for eye movement (2 electrode) \\
2017 [20] & Watching TV ads & dense-array EEG & Three epochs: 200-350, 350-500, and 500-800 & BCI \\
2016 [24] & Viewing brand names & ERP & LPP & BCI \\
2017 [43] & Viewing product images & ERP & N200, LPP, and FSW & BCI \\
2017 [30] & Watching ad videos & EEG & Theta and alpha & Heart rate, BCI and GSR \\
2018 [27] & Viewing product images & EEG & Delta, alpha, beta, and gamma & BCI \\
2017 [29] & Viewing product images & EEG & Alpha & BCI \\
2018 [44] & Viewing ads of products & EEG & Delta, theta, alpha, beta, and gamma & BCI \\
2018 [32] & Viewing products and prices & EEG & Theta & BCI \\
2018 [31] & Tasting drinks & EEG & Alpha & BCI \\
2019 [31] & Viewing and touching products & EEG & Alpha and theta & BCI \\
2019 [27] & Viewing product images & ERSF, ERP & Theta, beta and N200 & BCI \\
2019 [45] & Viewing tourism images, videos and words & EEG & Delta, theta, alpha, beta and gamma & BCI and GSR \\ \hline \hline \end{tabular}
\end{table}
Table 4: EEG-based neuromarketing studies.
## 4 Test Results and Discussion
### Enhanced Resistance to Sulfate Ingress
The bacterial count may be another index for the quantification of the soundness of a repair material so that it was evaluated after 28 days of immersion in the 5% sulfuric acid solution. The specimen with NPM had a concentration of _Rhodobacter capsulatus_ at 6.7 \(\times\) 10\({}^{6}\) cells/mL. On the other hand, that with SSM was 2.1 \(\times\) 10\({}^{6}\) cell/mL, indicating that the immobilization rate of NPM specimen was more than 3.0 times higher. The same tendency was repeated for the results of strength reduction and mass loss. Figure 10 shows the bio-slime layer at 90 \(\upmu\)m thickness identified through SEM analysis, and Figure 11 shows the difference in the growth of bacteria according to the culturing method and the change in the concentration of bacteria under the sulfate resistance test condition. Figure 10: Typical SEM image of slime formed on the surface of bio-coating material (NPM). Figure 9: Mass variation of the coated concretes immersed in 5% sulfuric acid solution. For the sulfate ion diffusion coefficient of the bio-coating material, the average value of three specimens was used, and the results are shown in Figure 12. The sulfate ion diffusion coefficient measured by diffusion cell test showed the highest value of 2.09 \(\times\) 10\({}^{-12}\) m\({}^{2}\)/s for the specimen with normal repair, 0.176 \(\times\) 10\({}^{-12}\) m\({}^{2}\)/s for the NPM specimen, and 0.202 \(\times\) 10\({}^{-12}\) m\({}^{2}\)/s for the SSM specimen--which is approximately 15% higher than that of NPM. This test result was also closely associated with the bacterial immobilization rate, and shows a consistent trend with the results in the reduction rate in compressive strength and mass. Figure 11: Results of the re-cultured bacteria from the coated concrete immersed in 5% sulfuric acid solution for 28 days: (**a**) re-cultured images; and (**b**) population of bacteria. Figure 5:0 Results of the sulfate ion diffusion coefficient.
# FD-LTDA-MAC: Full-Duplex Unsynchronised Scheduling in Linear Underwater Acoustic Chain Networks
## 3 Simulation Scenarios
### Simulation Set-Up
#### 3.2.2 Simulation Model  
Statistical channel models of the scenarios described above are created using the BELLHOP beam tracing method, as described above. To achieve this, an array of the node positions for \(1-N_{sn}\) hop distance (\(N_{sn}\) ranges from 2 to 1000 depending on the scenario) is created with the first node as the sink node plus \(n\) other transmitting nodes. The \(N_{sn}\) transmitting nodes and the sink node are arranged as described in Section 3.1.  
The statistical channel model uses random node positions set to be within 10 m sphere around of \(N_{sn}+1\) random displacements in both source and receiver positions to generate underwater acoustic channel realisations for every possible hop distance. Then channel gain, delay, and delay spread for every link in the network scenario is used to generate a full network model based on a corresponding lookup table.  
Thereafter, a binary interference matrix (\(N\times N\)), \(\mathbf{I}\) is generated such that \(I[i,j]=0\) if \(i=j\) (i.e., not interfering nodes) or \(I[i,j]=1\) if \(i\
eq j\) with \(\text{SNR}\geq 0\) dB (i.e., the interfering nodes). The FD-LTDA-MAC schedule is derived by loading the pre-simulated BELLHOP channel data of the node set (1-50) on to the algorithm that runs the FD-LTDA-MAC. The key simulation parameters are summarised in Table 1.  
Figure 6: SNR as a function of range and depth for the source at 480 m depth, source level: 170 dB re 1 \(\upmu\)Pa at 1 m, 24 KHz centre frequency and 7.2 KHz bandwidth.
## 4 Numerical Results
The [PERSON]'s fairness index is defined as: \(f(x_{1},x_{2},...,x_{M})=\dfrac{\left(\sum_{i=1}^{M}x_{i}\right)^{2}}{M \cdot\sum_{i=1}^{M}x_{i}^{2}}\)[27], where \(x_{i}\) is the resource allocated to D2D groups \(i\), and \(M\) is the number of D2D groups. In Figure 7, fairness index of two schemes decreases with the increasing number of D2D groups in the network. Nevertheless, the fairness index of the proposed scheme always has higher fairness than referenced scheme since it considers a constrained multi-objective optimization problem. Figure 4: Power consumption and number of D2D links versus cell radius: (**a**) Impact of cell radius on power consumption; (**b**) Impact of cell radius on system capacity. Figure 5: Power consumption and number of D2D links versus signal-to-interference- plus-noise ratio (SINR) threshold of each CU: (**a**) Impact of SINR requirement of CUs on power consumption; (**b**) Impact of SINR requirement of CUs on system capacity. Figure 6: Power consumption and number of D2D links versus SINR threshold of each D2D link: (**a**) Impact of SINR requirement of D2D links on power consumption; (**b**) Impact of SINR requirement of D2D links on system capacity.
## 3 Results and Validation
### Parameterization Results
#### 3.3.5 Effects of the Thickness of the Layer of Adhesive on the Transmission Force and Deformation  
To highlight the most effective resin in terms of transmission, a dedicated FE model was used, including a bonding layer. The plot in Figure 25 shows the displacement field obtained by means of a piezoelectric patch with a tangential extension of 10 mm, bonded on the middle (45 degree) of the beam with a curvature of 20 m\({}^{-1}\). The resin used in this case was Loctite-Hysol-9394. The impact of the thickness of the adhesive and \(\Gamma\) parameter was also investigated in Figures 26 and 27. In particular, the \(k\) factor linearly decreased with the bonding thickness, with a decrease of about 1% when passing from a thickness of 80 \(\upmu\)m to a thickness of 120 \(\upmu\)m. A similar trend was also observed for the beam-transmitted strain over the free strain of the piezoelectric device (Figure 28). It should be noted that the adhesive layer possesses a curvature like that of the beam and the piezoelectric patch.  
Figure 23: The \(k\) factor’s % of deviation vs. the adhesive shear modulus.  
Figure 24: Beam axial strain over piezo free strain vs. adhesive shear modulus.  
Figure 26: The \(k\) factor vs. adhesive thickness.  
Figure 25: Displacement results obtained for the Loctite-Hysol-9394 resin.  
As evident, the physical bonding layer brings about an important reduction in the transmission factor as the adhesive becomes softer. The case with the shear modulus equal to 10 can be considered representative of perfect bonding, leading to 100% transmission, although this is not realistically achievable due to the current limitations of the adhesives.  
Figure 28: Beam axial strain over the piezo free strain vs. \(\Gamma\).  
Figure 27: The \(k\) factor vs. the shear modulus of the resin.
###### Abstract  
The recently introduced plane-wave compounding method based on multiple plane-wave excitation has enabled several new applications due to its high frame rate (>1000 Hz). In this paper, a new efficient transmit delay calculation method in plane-wave compound imaging for a curved array transducer is presented. In the proposed method, the transmit delay is only calculated for a steering angle of 0\({}^{\circ}\) and is shifted along the element of the transducer to obtain other transmit delays for different steering angles. To evaluate the performance of the proposed method, the computational complexity was measured for various transmission conditions. For the number of elements and plane-wave excitations of 128 and 65, respectively, the number of operations was substantially decreased in the proposed method compared with the conventional method (256 vs. 8320). The benefits of the proposed method were demonstrated with phantom and in vivo experiments, where coherent plane-wave compounding with 65 excitations provided larger CR and CNR values compared to nine excitations (\(-\)22.5 dB and 2.7 vs. \(-\)11.3 dB and 1.9, respectively). These results indicate the proposed method can effectively reduce the computational complexity for plane-wave compound imaging in curved array transducers.  
Keywords:ultrafast ultrasound imaging; plane-wave compounding; curved array transducer; transmit delay +
Footnote †: journal: Applied
## 3 Learning-Based Path following Control with Parametric Uncertainties
### Learning with Dynamics Randomization  
Dynamics randomization [8; 9; 10] allows robust policies to be trained in cases where dynamics parameters are uncertain, difficult to measure, or frequently change over time. When dynamics randomization is applied, the dynamics parameters are sampled from a specific distribution at the beginning of each training episode. Figure 3 depicts this for a representative parameter \(\xi\). Often, a uniform distribution \(\mathcal{U}(a,\ b)\) within a predefined parameter range between the values \(a\) and \(b\) is chosen [8]. This enables the agent to learn a successful control performance for the entire uniformly distributed parameter range without favoring specific parameter values. In [8] it was shown that dynamics randomization can be interpreted as a trade-off between optimality and robustness. Therefore, the ranges in which the parameters are randomized need to be thoughtfully chosen to prevent the agents from learning overly conservative control behaviors.  
In this work, three agents are trained, whereby each agent experiences randomization for a different parameter of the vehicle model. More specifically, the parameters that are being randomized are the vehicle mass \(m\), the yaw inertia \(I_{z}^{\mathrm{C}}\) of the vehicle, and the tire-road friction coefficient \(\mu\) (cf. Appendix B), which are often randomized in such setups [8; 9]. More specifically, in this work, these particular randomizations are considered for the path-following control task based on the following reasons: the overall system mass changes every time a different load is placed inside the vehicle and also depends on whether a passenger is onboard. The inertia value is often difficult to measure and can be only estimated roughly. Furthermore, the tire-road friction frequently changes depending on the current weather.  
Besides training the three agents experiencing dynamics randomization, an agent with the nominal vehicle dynamics parameters is also trained for the same PFC task, which serves as a benchmark. To allow a straightforward comparison, the agents experiencing randomization are set to have the same reward as well as the same action and observation  
Figure 3: Dynamics randomization scheme at the beginning of each training episode for a representative dynamics parameter \(\xi\). First, the parameter \(\xi\) which should be randomized is selected and forwarded to the dynamics randomization module (top arrow). Afterwards, the randomized value \(\xi_{\mathrm{md}}\) of the parameter \(\xi\) is returned to the vehicle model (bottom arrow).  
spaces as the nominal agent experiencing no randomization. The different ranges of the uniform distributions \(\mathcal{U}(a,b)\) in which the vehicle mass \(m\), the yaw inertia \(J_{z}^{\text{C}}\) and the tire friction \(\mu\) are randomized are introduced in the following.
## 4 Experimental Simulation Analysis
### Influence of Different Replication Times Ps on System  
According to the signal frame structure of the SR-ODBR-DCSK system, the length of the information time slot is \(\beta\) (\(\beta=PR\)), where \(P\) is the number of reference signal replication and \(R\) is the length of the reference signal. When \(P\) is changed, the information slot length of the system is also changing, and the reference slot is relatively shorter. This section explores the influence of different replication times \(P\) on the system.  
Figure 6 shows the tent mapping of the SR-ODBR-DCSK system with an initial value of 0.6. In the case of \(\beta=32\) and \(\beta=128\) respectively. The influence of \(Ps\) on the system error rate is explored. It can be seen from Figure 6a,b that when \(\beta=32\) and \(\beta=128\), the error performance of the system when is better \(P\) = 2 than that when \(P\) = 4, and when \(\beta=128\), the error performance is better than that when \(\beta=32\).
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 1 Introduction
### Related Work
#### 1.1.3 IEEE 802.11 ah  
IEEE [7,8] provides a wireless LAN standard that operates at sub-1-GHz license-exempt bands. The work is conducted by the IEEE 802.11 ah Task Group (TGah). Compared to IEEE 802.11 (operating at 2.4 GHz and 5 GHz), 802.11 ah supports a longer transmission range up to 1 km at the default transmission power of 200 mW. Depending on the bandwidth assigned, 802.11 ah can operate at 4 Mbps or 7.8 Mbps. If the channel condition is good enough, 802.11 ah can provide a hundreds of Mbps data rate, thanks to the novel modulation and coding schemes brought from 802.11 ac.
## 4 Discussion
### Limitations and Future Directions  
Using the model-averaging approach arguably increases our confidence in the forecasts. However, the forecast still has some uncertainty due to various factors. Based on the results of the model-averaging obtained, the decrease in CUE indicates that the adaptability of vegetation in the Qinghai-Tibet Plateau to climate change is relatively weak and that vegetation cannot adapt to the speed of climate change. If climate change exceeds the forecast, the CUE of the ecosystem will decrease faster, and ecosystem production will face the risk of decreasing in the Qinghai-Tibet Plateau. Therefore, it is very important to implement energy conservation and emission reduction and control the global temperature rise within 1.5 degrees. In addition, this study provides a CUE parameter reference for GPP and NPP simulations in the Qinghai-Tibet Plateau to improve the accuracy of model simulations in this region.  
The resolution of model data is low in space, which will affect the accuracy of CUE forecast to a certain extent, thus downscaling analysis should be considered in follow-up research. There is a lack of CO\({}_{2}\) data of relevant models and there are too few CO\({}_{2}\) and climate monitoring stations to calibrate the models for this particular study area, leading to insufficient data integrity. Although the CO\({}_{2}\) concentration differs little on a large spatial scale, there is uncertainty in the results when used for an impact factor analysis, which needs to be improved in later studies. The simulation results are uncertain among different models due to the different structures, parameters and driving factors of them and this uncertainty increases with the length of time. Thus, it is necessary to supplement the real data to further verify the accuracy of the CUE calculated by model-averaging in the follow-up study. In addition, the alpine climate caused by the unique geographical environment of the Qinghai-Tibet Plateau increases the instability of the forecast. Due to the limitation of understanding the mechanism of the carbon cycle for this particular area, we should consider more factors on CUE and explore how they affect CUE to improve the understanding and impact mechanism analysis of CUE in the Qinghai-Tibet Plateau.
# Synthesis of Nitrogen-Doped Graphene Quantum Dots from Sucrose Carbonization
## 2 Materials and Methods  
### Synthesis of GQDs  
The synthesis of the GQDs was carried out from sucrose, obtained from commercial refined sugar. First, a sucrose solution (1 g/mL) in deionized water with a maximum conductivity of 18 M\(\Omega\)/cm was obtained. Concentrated sulfuric acid (purity > 95%) was added to the solution in the proportion of 2 mL/mL to carry out the initial carbonization of sucrose. This step was carried out at room temperature, lasted only a few minutes, and gave rise to a carbonaceous solid material (Precursor A) after washing, filtering, and drying.  
To obtain the colloidal suspension containing the undoped GQDs, 3.5 g of Precursor A was added to 25 mL of DMSO (purity > 99.9%). The mixture was kept under magnetic stirring for 4 h, under reflux at 60 \({}^{\circ}\)C, in a water bath. At the end of the process, a washing and filtering step was carried out to obtain the undoped GQDs.  
To obtain nitrogen-doped GQDs (N-GQDs), 7 g of Precursor A was added to 25 mL of azeotropic nitric acid. The mixture was refluxed for 4 h and heated in a water bath at 60 \({}^{\circ}\)C. Afterward, the nitric acid was evaporated, and a solid powder (Precursor B) was obtained. N-GQDs were then obtained after mixing 3.5 g of Precursor B into 25 mL of DMSO. The mixture was kept under magnetic stirring for 4 h and under reflux at 60 \({}^{\circ}\)C. At the end of the process, washing, filtering, and drying steps were carried out to obtain the N-GQDs.
# Improvement of Heterogeneous Transfer Learning Efficiency by Using Hebbian Learning Principle
## 2 Related Works
### Transfer Learning  
Deep learning algorithms have achieved excellent performance with large amounts of labeled data [19]. Without sufficient amount of data for training, one cannot expect good performance in deep learning. People have been using procedures like data augmentation to increase the amount of available data that can be used for training deep learning models. However, we know that humans can learn from a small amount of data, using good analogy, experience, and knowledge acquired in the corresponding domain.  
Transfer learning is a way of transferring knowledge learned from one task to another task [51; 52; 53; 54]. A machine learning model trained on a source dataset can boost the performance of the model training on a different homogeneous or heterogeneous target dataset. For example, a deep learning model can be developed and trained for one image classification task, and then used on a second related image classification task to maximize the classification accuracy after fine-tuned with target task's training data. To bridge the various gaps for the transfer of knowledge for CNN models, an efficient transfer method is adapting a pre-trained model for a new task, called fine-tuning [55]. In the standard transfer learning example, a model is trained with a large volume of data, and learns model parameter weights and bias. Then, the model is embedded to a new model for target task that can be initialized with pre-trained weights and fine-tuned with target dataset [56].  
The target applications for Hebbian transfer learning can be anything, such as image segmentation, object recognition for robotic manipulation, pedestrian or obstacle detection for autonomous vehicles, among others.
## 3 Case Studies
### Dams with High Groundwater Level  
The Shangmo reservoir which is a small-scale project mainly for urban water supply and flood control, is located on the Jinjiahe River to the west of Tianshui city. The dam of the Shangmo reservoir is a composite geomembrane slope-wall rock-fill dam with a crest width of 6 m, a crest length of 202.8 m, and a crest elevation of 1637.1 m. The ground surface elevation of the dam is 1600 m, and the upstream and downstream comprehensive slope ratios are about 1:2.5 and 1:1.75, respectively. A composite geomembrane two fabrics with a membrane (250 g/1 mm/250 g) is laid on the dam slope upstream of the dam body and riverbed surface.  
According to the dam design information provided by the Design Institute [45; 46], two calculation conditions are listed. The normal water level is 1632.21 m, and the corresponding water level downstream is 1603.19 m; the designed flood level is 1634.68 m, and the corresponding water level downstream is 1603.19 m.  
Typical cross section and dam axis sections of the Shangmo dam are shown in Figure 7, and the permeability coefficients of the materials are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Position** & **Material Partition** & **Permeability Coefficient (cm/s)** \\ \hline \multirow{4}{*}{Foundation} & Strong permeable layer & \multirow{2}{*}{\(2.0\times 10^{-3}\)} \\  & 100 Lu \(\leq\) q & \\ \multirow{4}{*}{Foundation} & Medium permeable layer & \multirow{2}{*}{\(3.0\times 10^{-4}\)} \\  & 10 Lu \(\leq\) q \(<\) 100 Lu & \\ \multirow{4}{*}{} & Weak permeable layer & \multirow{2}{*}{\(5.0\times 10^{-5}\)} \\  & 5 Lu \(\leq\) q \(<\) 10 Lu & \\ \multirow{4}{*}{} & Relative impermeable layer & \multirow{2}{*}{\(2.0\times 10^{-5}\)} \\  & q \(<\) 5 Lu & \\ \hline \multirow{4}{*}{Dam} & Composite geomembrane & \(1.0\times 10^{-9}\) \\  & Dam body & \(2.0\times 10^{-1}\) \\ \cline{1-1}  & Concrete slab & \(1.0\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Permeability coefficient of the materials in each area.  
Figure 7: Sections of the Shangmo dam: (**A**) a typical cross section; (**B**) the dam axis section.