esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Primary Survey Data  
In this research, a survey was conducted to gather the opinions of relevant professionals from the infrastructure and transport industry. As the survey target population was diverse and large, the questions comprehensively covered all aspects of the HSR domain. There were 51 multiple-choice questions based on the Likert scale, Boolean options, or general multiple-choice, comprising the following categories:  
1. Participant Information--The survey could be undertaken anonymously to reduce the barrier to participation. Hence, tracking the participants' information was crucial for making informed analysis decisions;
2. General Questions--These questions pertained to the HSR domain in general;
3. HSR Benefits--These questions aimed to capture the general benefits participants expected to realise following the implementation of HSR. This was also used as a control to capture any biases due to individuals who benefit an extraordinary amount;
4. Barriers to Implementation--This section aimed to capture the participants' perceived barriers to HSR implementation;  
Figure 4: Research Principal Methodology.  
Figure 5: Research Methodology Flowchart.  
Concept Phase Risks--This section aimed to capture project management-related risks, particularly from participants working in the industry's management space;
* Contract Structuring--This section aimed to capture the participants' sentiment towards a PPP and alternative traditional procurement methods;
* Other categories of questions focused on nuanced barriers to the implementation of HSR.  
Note that, due to the nature of the survey, the expertise of the survey participants cannot be validated; therefore, control questions were embedded. These control questions prompt a response that is already answered through a literature review. A comparison of these control questions ensures that participants are suitable candidates for the survey. Following [PERSON]'s Marketing Scales Handbook [47], the questions were formulated to avoid further unintentional biases.  
The survey was designed using the University of Sydney's Qualtrics platform to comply with the relevant privacy policy. Qualtrics is also easy to use and presents a professional appearance to participants. QR codes and shortened URLs were obtained and shared with the author's professional network. This was notable in engineering consulting, construction, and academia.  
The rigid survey structure, developed using the Marketing Scales Handbook, ensures a prominent level of repeatability should this research be reinvestigated. The survey data can only be used to compare and validate existing literature. In a research domain such as HSR, primary research is required to address the existing literature gap rather than merely validating it. This is a notable limitation of the survey. Hence, interviews were also employed in this research.
# Coastal Sargassum Level Estimation from Smartphone Pictures
## 4 Experiments and Discussion
### Exploitation Experiments  
From the results obtained in the previous section, an exploitation grid search was performed with the architecture that presented best results, VGG16. Table 2 shows the combination of hyperparameters tested in this set of experiments. The batch size of 100 and the number of epochs of 500 were kept as orthogonal factors. The hyperparameters that were varied throughout this grid search are: learning rate and the optimizer.  
The results obtained during these experiments are presented in Table 3. The adjustment in the parameters of the model showed an improvement in the classification with the combination of the optimizer Adam and the learning rate \(1\times 10^{-4}\), but as can be observed, it also increases the training time of the model. It also seems that the learning rate of \(1\times 10^{-4}\), regardless of the optimizer used, causes an increase in training time. Training time in all experiments ranged from approximately 2 h to 2:30 h. When the learning rate of \(1\times 10^{-3}\) was used in combination with the Adam optimizer, the classification accuracy drop to 36% and for 4 of the 5 classes, it was not possible to classify any image correctly, so the rest of the metrics could not be calculated.  
Figure 3 shows the confusion matrix obtained by the model using a learning rate of \(1\times 10^{-4}\) and Adam optimizer. the results can be better interpreted by looking at the normalized confusion matrix. It can be observed that the best performance of the model was for the excessive class, where 82 % of the images where correctly classified. This result was not expected since the number of images in this class is low in the dataset, but it is a good indicator that the model correctly identifies the characteristics in this type of images which is relevant for the detection of Sargassum when it is really abundant. The worst behavior of the model is for the class moderate for which less than half of the images were correctly classified, this is affecting the overall performance of the model since the metrics reported is the average performance over all classes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network** & **Feature Extraction** & **Fine Tuning** & **From Scratch** \\ \hline AlexNet & 0.522 & **0.556** & 0.467 \\ GoogleNet & **0.485** & 0.4752 & 0.436 \\ ResNet18 & 0.566 & **0.5860** & 0.512 \\ VGG16 & 0.522 & **0.5862** & 0.527 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Validation accuracy reached by each network for different training methods. Feature extraction freezes the weights in the convolutional layers. The best result for each network is remarked.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Learning Rate** & **Optimizer** \\ \hline
1 & \(1\times 10^{-3}\) & SGD \\ \hline
2 & \(1\times 10^{-4}\) & SGD \\ \hline
3 & \(1\times 10^{-5}\) & SGD \\ \hline
4 & \(1\times 10^{-3}\) & Adam \\ \hline
5 & \(1\times 10^{-4}\) & Adam \\ \hline
6 & \(1\times 10^{-5}\) & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 2: Exploitation grid search.
# Symbol-Level Selective Channel Estimation in Packet-Based OFDM Systems
## 3 Related Work
### TRFI Channel Estimation Scheme  
TRFI employs the frequency-time correlation characteristics of adjacent subcarriers and symbols. First, the \((m-1)\)-th received symbol \(Y(m-1,k)\) is divided by the channel estimate obtained from Equation (3).  
\[\hat{T}^{\prime}(m-1,k)=\frac{Y(m-1,k)}{\hat{H}(m,k)}. \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & \begin{tabular}{c} **Distance** \\ **between** \\ **TX \& RX (m)** \\ \end{tabular} & \begin{tabular}{c} **Velocity** \\ **(km/h)** \\ \end{tabular} & \
\begin{tabular}{c} **Doppler** \\ **Shift** \\ **(Hz)** \\ \end{tabular} &
\begin{tabular}{c} **Maximum** \\ **Excess** \\ **Delay (\(\mu\)s)** \\ \end{tabular} \\ \hline V2V & & & & & \\ Expressway & 300–400 & 104 & 1000–1200 & 0.3 \\ Oncoming & & & & \\ \hline V2I & & & & \\ Urban & 100 & 32–48 & 300 & 0.5 \\ Canyon & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for the channel model.  
Subsequently, the \((m-1)\)-th channel estimate \(H(m-1,k)\) is used to divide \(Y(m-1,k)\).  
\[\hat{\Upsilon}^{\prime\prime}(m-1,k)=\frac{Y(m-1,k)}{H(m-1,k)}. \tag{5}\]  
\(\hat{\Upsilon}^{\prime}(m-1,k)\) and \(\hat{\Upsilon}^{\prime\prime}(m-1,k)\) are demapped into \(\hat{X}^{\prime}(m-1,k)\) and \(\hat{X}^{\prime\prime}(m-1,k)\) in accordance with the modulation scheme, and the demapped symbols are subjected to a time reliability test. If the demapped symbols are matched to the same symbol, then the initial channel estimate \(\hat{H}(m,k)\) is determined as the final channel estimate \(H(m,k)\). Meanwhile, the final channel estimate \(H(m,k)\) that fails to pass the test is obtained through frequency interpolation using the channel estimates that have passed the test.  
The performance of TRFI highly depends on the accuracy of the initial channel estimate. As the SNR increases, the smaller demapping error improves the accuracy of the initial channel estimate. This enhances the accuracy of the channel estimates obtained by the time reliability test and frequency interpolation; thus, TRFI provides high performance. By contrast, as the SNR decreases, the demapping error caused by a considerable noise component lowers the accuracy of the initial channel estimate, which leads to a performance degradation.
# A Comprehensive Survey on Knowledge-Defined Networking
## 6 Data Plane
### Forwarding Devices
#### 6.2.5 Wireless Access Points, Base-Stations, and Vehicles  
A wireless access point is a networking device that allows wireless devices such as laptops and mobile phones to connect to a wired network using Wi-Fi technology, which allows multiple devices to connect and share resources [427]. Similarly, a cellular base station in a mobile communication network manages call handoffs and communicates wirelessly using radio signals with mobile devices. Vehicles in a vehicular network communicate wirelessly, either using Dedicated Short Range Communication (DSRC) for Vehicle to Vehicle (V2V) communication or using mobile communication with the aid of a cellular base station for Vehicle to Infrastructure (V2I) communication [428]. Figure 10 depicts the KDN paradigm implementation in a heterogeneous network scenario consisting of a Wi-Fi network, a cellular network, a vehicular network, an optical network, and a wired network. Therefore, for all the types of wireless communication described above, when KDN is integrated into such networks, switching can occur through an access point in a Wi-Fi network, a base station or a wireless access point in a mobile network, or a vehicle or a Road Side Unit (RSU) in a vehicular network, as shown in Figure 10.  
Figure 10: A KDN heterogeneous network scenario.  
Note that, in Figure 10, combined controller refers to a physical entity whose management, control, and knowledge planes have been unified. However, note that, in KDN, these three planes are logically separated, even though they can be physically unified to be represented as a combined controller. Thus, wireless access points, base stations, and vehicles become forwarding devices in their corresponding wireless networks, which can be configured by a remote combined controller in the KDN paradigm. Software-Defined Radio (SDR) is the concept of controlling wireless transmission strategies in the physical layer using software [429]. Thus, SDR can be integrated with KDN to provide physical layer control of the wireless KDN switches, such as wireless access points, mobile base stations, vehicles, etc., using a combined controller. Thus, these wireless switches' physical parameters, such as link association, channel selection, transmission rate, etc., can be managed and controlled dynamically based on network statistics and network defined policies in KDN.
# Optimized Classification of Intelligent Reflecting Surface (IRS)-Enabled GEO Satellite Signals
## 4 Simulation Results
### Case IV: Classification of BPSK for Different Nos. of Samples  
Figure 6 shows the average classification accuracy for different numbers of samples. It is evident from the figure that increasing the number of samples will result in higher classification accuracy. Figure 6 is the ACA with the IRS, and the modulation scheme considered is BPSK. The ACA is also verified for different SNRs, i.e., 0, 5, and 10 dB.  
In Figure 6, the ACA for the BPSK case increases, i.e., from 90% to approximately 100% at an SNR of 10 dB, with an increasing number of samples, i.e., (512, 1024, 2048, 4096). It is also observed that as the number of samples rises, the computational complexity of the proposed algorithm also increases, which is a trade-off between the ACA and the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**SNR = 0 dB** & **QAM** & \begin{tabular}{c} **16-** \\ **QAM** \\ \end{tabular} & \begin{tabular}{c} **64-** \\ **QAM** \\ \end{tabular} & \begin{tabular}{c} **256-** \\ **QAM** \\ \end{tabular} & \begin{tabular}{c} **BPSK** \\ **QPSK** \\ \end{tabular} & \begin{tabular}{c} **QPSK** \\ **PSK** \\ \end{tabular} & \
\begin{tabular}{c} **16-** \\ **PSK** \\ \end{tabular} &
\begin{tabular}{c} **64-** \\ **PSK** \\ \end{tabular} \\ \hline
**QAM** & **97.23** & 1.77 & & & & 1.0 & & \\ \hline
**16-QAM** & 1.50 & **96.33** & 1.67 & & & 0.5 & \\ \hline
**64-QAM** & 1.42 & 2.18 & **95.48** & & & & 0.92 \\ \hline
**256-QAM** & 1.14 & 2.20 & 2.54 & **94.12** & & & \\ \hline
**BPSK** & & & & & **98.87** & 1.13 & & \\ \hline
**QPSK** & & 1.55 & & & **98.45** & & \\ \hline
**16-PSK** & & & & 1.21 & 1.29 & **96.78** & 0.72 \\ \hline
**64-PSK** & & & & & 2.2 & 0.57 & **97.23** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix for 8-class problem.  
Figure 6: Comparison of ACA for different nos. of samples.  
complexity. In this analysis, we have only chosen 1024 as the number of samples, resulting in low complexity and good classification accuracy.
# Experimental Study on Fluid Dissipation Effects in Core Samples by NMR Measurement
## 5 Discussion
### Crude Oil of Different Viscosities  
Utilizing the analytical methodology illustrated in Figure 6, the variations in crude oil signals throughout the second dissipation phase are examined, as shown in Table 3. The findings indicate that the residual crude oil content in the samples diminishes throughout the second dissipation phase, reflecting the dynamic alterations of crude oil components during dissipation, particularly the depletion of lighter components. This understanding is essential for comprehending the dissipation process of crude oil within geological formations.  
The proportion of residual crude oil content at various dissipation intervals is statistically analyzed and represented, as indicated by the dashed lines in Figure 9. The correlation between the proportion of residual crude oil content and dissipation time exhibits a strong logarithmic relationship.  
\[\text{Sample A}:\ p\text{ }=-0.063 ln(t)+0.9729\ \ R^{2}=0.9832 \tag{8}\]  
\[\text{Sample B}:\ p\text{ }=-0.038 ln(t)+0.9496\ \ \ R^{2}=0.9872 \tag{9}\]  
where \(po\) denotes the residual crude oil content per unit volume, t represents the dissipation time, and R signifies the fitting correlation coefficient, exceeding 0.9. Subsequent analysis indicates that for Samples A and B, which possess similar porosity and permeability characteristics, as well as initial saturation states, the dissipation rate of the low-viscosity crude oil Sample A is markedly higher than that of the high-viscosity crude oil Sample B. By the conclusion of the second stage, the residual proportion of crude oil in sample A is considerably lower than that in sample B. This observation suggests a correlation between the dissipation process of crude oil and its viscosity, where increased viscosity results in slower dissipation.  
Figure 9: The relationship between the remaining mass percentage of crude oil and exposure time.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Oil Viscosity** & **T2 Center** & **T1 Center** & **4 h (\%)** & **8 h (\%)** & **16 h (\%)** & **32 h (\%)** & **64 h (\%)** & **96 h (\%)** \\ \hline A & 42 & 16.3 & 60.2 & 88.0 & 85.5 & 80.0 & 73.9 & 71.8 & 68.8 \\ B & 240 & 6.8 & 67.1 & 89.1 & 87.2 & 85.3 & 82.1 & 79.2 & 77.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Percent change of crude oil in core with different dissipation times.  
As shown in Figure 10, by comparing the changes in T\({}_{2}\) and T\({}_{1}\) values of crude oil components in the cores after 4 and 96 h of dissipation (see Table 4), it is observed that the T\({}_{2}\) values of crude oil components decreased, while the T\({}_{1}\)/T\({}_{2}\) ratio markedly increased. This phenomenon indicates the depletion of lighter components in the crude oil, leading to decreased fluidity.
# Data-Driven Predictive Modeling of Neuronal Dynamics Using Long Short-Term Memory
## Appendix B Simulation Results on Full State Predictions of Hodgkin-Huxley Model
### Regular Bursting  
In this section, we show the simulation results on predicting the dynamics of all the 9 states of HHCA1 Py over a long-time horizon using the deep LSTM neural network for the regular bursting regime (\(I\in[0.24,0.79)\) nA). Figures 11-14 show the comparison between the state's dynamics simulated using the Hodgkin-Huxley model and the deep LSTM neural network model developed for 1 timestep, 50 timesteps, 100 timesteps, and 200 timesteps (equivalently, \(N_{p}=1,50,100,200\)) predictive horizon, respectively.  
As shown in these figures, the performance of the deep LSTM neural network model in predicting state dynamics significantly improved between 1 timestep predictive horizon (Figure 11) and 200 timesteps predictive horizon (Figure 14) across all the states except \(q_{sAHP}\) for the similar reason we provided for the regular spiking and irregular bursting regimes. More importantly, the LTSM network predicted the temporal correlations with high accuracy over the time-horizon of 300 ms for \(N_{p}=200\). The extrapolation of these results suggest that increasing the predictive horizon beyond \(N_{p}=200\) could improve the prediction beyond 300 ms of time-horizon.  
In Figure 10, we show the root mean squared error between the states of HHCA1 Py and the deep LSTM neural network model as a function of simulation time over 5000 random realizations, for \(N_{p}=1,50,100,200\). As shown here, the root mean squared error decreased with the increased predictive horizon of the LSTM network (i.e., \(N_{p}=1\) to \(N_{p}=200\)), which is consistent with the results of the regular spiking and irregular bursting regimes.  
Figure 11: Comparison between the Hodgkin-Huxley model (“HH Model”) states’ dynamics and the iterative predictions of states’ dynamics using the 1 timestep predictive horizon-based deep LSTM neural network (“LSTM Network”) in response to \(I=0.5\) nA.  
Figure 12: Comparison between the Hodgkin-Huxley model (“HH Model”) states’ dynamics and the iterative predictions of states’ dynamics using the 50 timesteps predictive horizon-based deep LSTM neural network (“LSTM Network”) in response to \(I=0.5\) nA.  
Figure 13: Comparison between the Hodgkin-Huxley model (“HH Model”) states’ dynamics and the iterative predictions of states’ dynamics using the 100 timesteps predictive horizon-based deep LSTM neural network (“LSTM Network”) in response to \(I=0.5\) nA.  
Figure 14: Comparison between the Hodgkin-Huxley model (“HH Model”) states’ dynamics and the iterative predictions of states’ dynamics using the 200 timesteps predictive horizon-based deep LSTM neural network (“LSTM Network”) in response to \(I=0.5\) nA.  
Figure A15: The root mean squared error (RMSE) versus simulation time for 5000 independent realizations, drawn from the predicted membrane potential trajectories of 50 randomly selected stimulating currents from a Uniform distribution \(\mathcal{U}(0.24,0.79)\) and 100 random initial conditions for each stimulating current.
# Benchmarking Deep Learning Methods for Aspect Level Sentiment Classification
## 2 Overview of ABSA
### Aspect Level Sentiment Classification (ALSC)  
The aspects discussed in the ALSC task can be either implicit or explicit aspects. An implicit aspect expresses an opinion about some feature of a product without explicitly using a target term for that feature. In contrast to this, the explicit aspect expresses an opinion about some feature of a product by explicitly mentioning the target term. Thus, ALSC is often interchangeably called Target dependent sentiment classification as well. Essentially, the terms 'aspect' and 'target' are used interchangeably by many researchers. For a better understanding of these two terms, consider the sentence in Figure 3: \"The Mobile Phone is quite bulky, but the camera is great\". Here we have two types of aspects of the mobile phone handset, explicit and implicit [2]. The first phrase, \"The Mobile phone is bulky\" talks about the implicit aspect 'weight' while the second phrase, \"the camera is great\", describes the explicit aspect \"camera\". So, the camera is an explicit aspect term as well as the target word.  
Figure 2: A sample sentence for explaining ABSA.  
In short, we can use target and aspect interchangeably when we are dealing with explicit aspect terms. Finally, the task of ALSC is to map these aspects with suitable sentiment polarity. As per Figure 3, the polarity of explicit aspect \"Camera\" is positive while the polarity of implicit aspect \"bulky\" refers to the feature of mobile phone, and the polarity is negative. Most of the research in ALSC is been carried out for explicit aspects and thus the sentiment classification of implicit aspect is out of the scope of this study.
## 2 Related Works  
Multiple studies have explored both feature-based and CNN-based approaches for ego-motion estimation in autonomous systems. Researchers have investigated feature extraction techniques, such as SIFT, SURF, and ORB, along with their variants, to capture essential motion information from visual data. Additionally, recent works have emphasized the use of deep learning architectures, particularly Convolutional Neural Networks (CNNs), to enhance accuracy and robustness in ego-motion estimation. These efforts have advanced the state of the art by improving feature representation, improving network architecture design, and optimizing computational strategies for real-time applications.