esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Social DTN for Message Communication between SNS Group Members
## 3 How to Build a Social DTN
### How to Create and Share a Community Token
#### 3.3.2 How to Obtain Group Information and Share a Community Token  
Facebook has been used as an SNS for sharing a community token. An application that incorporates Facebook Graph API [12] has been developed. Nodes obtain Facebook group information, create a community token and manage (i.e., post and delete) it. A community token is created from a  
Figure 2: Proposed DTN architecture.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Overview** & **Usage** \\ \hline Group ID & The ID used in the social networking service (SNS) is used. & This is used to identify a group. \\ \hline Token ID & The initial value is the MAC (Media Access Control) address of the source node. & This is used to determine which community token is new. \\  & The value is incremented by one each time & If the value of a node becomes “0”, \\  & the community token is updated. & the node is eliminated from \\  & This is the maximum level of battery power & routing in order to avoid excessive \\  & reduced by the level of battery power & use of resources. \\  & consumed during the DTN communication. & This is used for routing. \\ \hline Source node address & This is the MAC address of the source node that has created the community token. & This is used for routing. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Contents of a community token.  
user-unique access token issued by Facebook. Therefore, users obtain a Group ID publicized in each Facebook group. Specifically, a node sends a GET request to the Facebook group node via Facebook Graph API [12]. The Facebook group node sends group information back to the user as a response. The application that has received it extracts a Facebook group ID from it. The sequence of steps needed for a node to obtain group information is shown in Figure 3a.  
To share the created community token with other participants, the user posts a message with a message ID added to the group. The message ID indicates that the message contains a community token. The character string \"Token\" is used as the message ID. When a group is created in Facebook, a Facebook group ID is assigned to it. It is a 16-digit number unique to the group. It is used to indicate which group the community token has been created for. The sequence of steps needed for a group to share information is shown in Figure 3b.
# Using Multiple Robots to Increase Suggestion Persuasiveness in Public Space
## 2 Sequential Persuasion  
Since the objective of this study was to find a practical application in a public space, interactions with the small humanoid robots needed to be spontaneous and random, not requiring an explanation or a briefing to start. In addition, because the range of conversation necessary for an autonomous agent to reply to people is too broad to be defined easily, assisted teleoperation was used as the only practical option to control the robots.  
With these limitations in mind, a well-known idea in human communication research is revisited in a novel way with multiple robots--sequential persuasion, which would entail a series of short interactions between small robots and a human, made in succession, separated by time and space, with the objective of persuading the human to engage in a specific behavior. Sequential persuasion itself is not a new word in human communication research. Giving requests in sequential ways has been regarded as persuasive for humans, for example, foot-in-the-door [21] and door-in-the-face [22] strategies. It is worth noting  
Figure 1: Small humanoid robot, CommU.  
that a meta-analysis on extensive studies revealed their limited effects [23]. It is also shown that the foot-in-the-door strategy is effective even in human-robot conversation [24].  
When beginning an interaction, the robot would be able to introduce its purpose, the promotion of a behavior, and continue its task in the next interaction, as exemplified in Figure 2. A system so designed would be able to use both autonomous or teleoperation approaches, with a limited scope of themes and answers, thus not requiring extensive flexibility.  
Such an interaction raises the question of how humans should be approached. For instance, when promoting a product to a customer, a vendor has the option to actively approach the customer or simply wait to be approached by them. Moreover, since the interaction is supposed to be short yet occur multiple times, there were options for using only one robot to convey every message or multiple robots with each responsible for one message.
## 2 Proposed Equations
### Soil Type C  
In this section, the proposed equation for the correlation factor when the colliding buildings are founded on soil type C is presented. Figure 6 presents the plot of the correlation factor when the colliding buildings are founded on soil type C versus \(T_{1}/T_{2}\). It can be seen that the plot is a piecewise function, and it is composed of three different functions. It can also be noticed that the correlation factor follows three different trends depending on whether \(T_{1}\leq 0.2\) s, \(0.2\) s < \(T_{1}\leq 0.4\) s, or \(T_{1}>0.4\) s (see Figure 6). Therefore, the proposed equation for the correlation factor when the colliding buildings are founded on soil type C is composed of three equations depending on whether \(T_{1}\leq 0.2\) s, \(0.2\) s < \(T_{1}\leq 0.4\) s, or \(T_{1}>0.4\) s (see Equation (12)). Figure 7 shows a comparison between the proposed equation (Equation (12)) and the actual values of the correlation factor obtained for different cases. Using Equation (9), the RMS error has been calculated as equal to 7.00% for \(T_{1}\leq 0.2\) s, \(2.98\%\) for \(0.2\) s < \(T_{1}\leq 0.4\) s, and \(6.31\%\) for \(T_{1}>0.4\) s.  
\[\rho=\left\{\begin{array}{ccc}\left(\frac{T_{1}}{T_{2}}\right)^{-1.225}&,&T_ {1}\leq 0.2\text{ s}\\ 854.668\Big{(}\frac{T_{1}}{T_{2}}\Big{)}^{6}-3093\Big{(}\frac{T_{1}}{T_{2}} \Big{)}^{5}+4428.7\Big{(}\frac{T_{1}}{T_{2}}\Big{)}^{4}-3195.3\Big{(}\frac{T_{ 1}}{T_{2}}\Big{)}^{3}+1232.8\Big{(}\frac{T_{1}}{T_{2}}\Big{)}^{2}\\ -250.62\Big{(}\frac{T_{1}}{T_{2}}\Big{)}+23.752&,&0.2\text{s}<T_{1}\leq 0.4 \text{ s}\\ 18.95\Big{(}\frac{T_{1}}{T_{2}}\Big{)}^{4}-51.456\Big{(}\frac{T_{1}}{T_{2}} \Big{)}^{3}+58.036\Big{(}\frac{T_{1}}{T_{2}}\Big{)}^{2}-31.526\Big{(}\frac{T_{ 1}}{T_{2}}\Big{)}+6.996&,&T_{1}>0.4\text{ s}\\ \end{array}\right. \tag{12}\]  
Figure 5: Comparison between the actual trend and the proposed equation for the correlation factor when the colliding buildings are founded on soil type B.  
Figure 6: The correlation factor when the colliding buildings are founded on soil type C versus \(T_{1}/T_{2}\). All lines correspond to the same thing (correlation factor). The graph of the correlation factor is not continuous.
# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 4 Routing Protocols for UAWSNs
### Tree-Based Routing
UAV-aided compressive data gathering for WSN (UAV-CDG): In UAV-CDG [50], the authors addressed the problem of data collection, in which data can be located in hard-to-reach areas and data collection is normally extremely energy-constrained, in order to design an effective energy-aware data collection mechanism. A UAV is used to collect data in dense WSNs by using projection-based compressive data gathering (CDG). CDG is employed to aggregate data from a large set of sensor nodes, in which the selected projection nodes act as cluster heads (CHs). The CDG method reduces the number of transmissions, leading to remarkable energy savings and prolonging network lifetime. After that, the UAV transfers the gathered data from the CHs to the base station, which avoids the need for long-range transmissions or multi-hop communications. In UAV-CDG, the deployed sensor nodes are divided into clusters, and a forwarding tree for clusters is constructed on the basis of the CDG technique, decreasing both the total transmission power in the network and the total UAV trajectory distance. Simulation results showed that UAV-CDG significantly improves the performance in terms of energy saving, flight distance, and total number of transmissions.
# A CNN-LSTM-Attention Model for Near-Crash Event Identification on Mountainous Roads
## 2 Preliminary of Near-Crash Event Identification
### Overall Framework  
The overall framework for identifying near-crash events based on naturalistic driving data are illustrated in Figure 1, comprising the following four main steps.  
Step 1: Data Collection (with more details introduced in Section 3.1)  
Using onboard sensors of a sport utility vehicle specialized for naturalistic driving, a total of 150 h and approximately 8000 km of raw data were collected on mountainous roads in Yunnan Province, China.  
Step 2: Data Preprocessing (with more details introduced in Section 3)  
1. Data Filtering. An adaptive filtering algorithm is applied to the collected naturalistic driving data for noise reduction, enhancing data quality for subsequent analysis.
2. Data Labeling. A dual approach, combining the threshold method and manual video verification, is applied to label the raw data accurately and generate a subset of near-crash events.
3. Feature Selection. The random forest method is employed to assess the importance of each feature in the near-crash event dataset, with those features deemed most significant chosen as inputs for the identification model.
4. Normalization. Prior to model training, the dataset is normalized to eliminate discrepancies in scale among features. At the same time, it accelerates the convergence speed of the model and can help avoid overfitting to some extent. The normalization function is: \[x_{new}=\frac{x-x_{min}}{x_{max}-x_{min}}\] (1) where \(x_{max}\) and \(x_{min}\) represent the maximum and minimum values of the sample data, respectively.
5. Dataset Division. The data are randomly divided into 60%, 20%, and 20% for training, validation, and test purposes, respectively. The training set is used for model parameter optimization, the test set for the final assessment of model performance, and the validation set for model tuning.  
Step 3: Model Construction and Training (with more details introduced in Section 4)  
The CNN-LSTM-Attention model for near-crash event identification is developed using the Keras deep learning framework. The Adam optimizer [23] is employed for  
Figure 1: The overall framework for near-crash event identification.  
updating model weights, and binary cross-entropy is used as the loss function for model optimization. Binary classification accuracy is utilized as the evaluation metric for assessing model performance.  
Step 4: Model Performance Evaluation (with more details introduced in Section 5)  
The CNN-LSTM-Attention model's effectiveness is assessed against various models, including CNN, LSTM, Attention, CNN-LSTM, CNN-Attention, and LSTM-Attention. It provides a comprehensive evaluation of the CNN-LSTM-Attention model's accuracy in identifying near-crash events.
# Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains +
## 3 Methodology
### Experiment Design  
As part of a collaboration between BT and Ulster University, an eye-tracking experiment was undertaken, with participants using the BT Player app while a Tobii eye-tracking device monitors the gaze positions of each participant's eyes. This experiment was set up to develop a richer understanding of how customers use BT Player, which could potentially be used to improve the design and usability of future versions of BT's TV service [11]. An overview of the experiment setup can be seen in Figure 3 below, which shows how the TV screen and eye-tracking device were set up for each participant [11]. Each participant must be sitting the same distance from the screen, with the eye-tracking device positioned near them on an adjustable stand to account for different participant heights, so the eye-tracker can be a fixed distance from their eyes, as the maximum gaze angle must be 35 degrees. All of these measurements must be fixed across the experiment to ensure consistent results.  
Data from the eye-tracking software has been collected by Ulster University during an experiment where 14 individuals each spent a period using BT Player while being monitored by eye-tracking software. Each individual completed a survey asking for demographic information such as gender, age, and familiarity with BT Player and other popular TV services. The individuals were required to complete a series of tasks throughout this period, which were based on the requirements provided by BT.
## 3 Results and Discussion
### Hardened Characteristics: Compressive Strength and UPV
#### 3.2.1 Influence of MgO, MWCNT and rGO on Compressive Strength Development
Therefore, it can be concluded that 0.3% is an optimum dose for MWCNTs/rGO to achieve better compressive strength. These results indicate that the compressive strength was decreased by adding a higher amount (>0.3% wt) of MWCNTs/rGO, although this effect can be compensated/reversed by using MgO-MWCNT/rGO combinations. Previous studies by [PERSON] et al. [40] also confirmed the compressive strength enhancement of fly ash-based geopolymer composites with the addition of low dosages (0.2 wt%) of MWCNTs. An increase in the flexural strength, modulus of elasticity, and toughness was also observed with the use of 0.35% of rGO as a nano-filler in geopolymer composites [17]. This was attributed to the strong dispersion of rGO sheets between fly ash particles due to their malleable property causing them to fill voids and produce a denser matrix. A lower dosage of MWCNTs/rGO can be uniformly dispersed in the matrix, which in turn will improve the compressive strength, as observed in [17]. Although an appropriate amount of rGO can be evenly dispersed in the matrix, overlap and agglomeration may still occur in excess, limiting the development of  
Figure 10: Evolution of compressive strength with age for MgO-MWCNT/rGO-incorporated (**a**) binary and (**b**) ternary AAEC mixes with reagents 1 and 2.  
compressive strength. Furthermore, the rGO with a wrinkled texture cross-linked around geopolymer particles altered the morphology and reduced the matrix's porosity, which positively impacted its fracture toughness [17]. Also, a smaller-sized MWCNT resulted in a higher mortar compressive strength, as smaller MWCNTs were distributed at a much finer scale, filling nanopore space within the matrix more efficiently [40].
# From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
## 5 QAOA Mappings: Orderings and Schedules
### Example: Traveling Salesperson Problem (TSP)  
ProblemGiven a set of \(n\) cities, and distances \(d:\left[n\right]^{2}\rightarrow\mathbb{R}_{+}\), find an ordering of the cities that minimizes the total distance traveled for the corresponding tour. A _tour_ visits each city exactly once and returns from the last city to the first. Note that we defined \(\left[n\right]=\left\{1,2,\ldots,n\right\}\) and \(\left[0,n\right]=\left\{0,1,\ldots,n\right\}\).  
While for expository purposes, we call these numbers distances, the mapping works for any cost function on pairs of cities, whether or not it forms a metric or not; the distances are not required to be symmetric, or to satisfy the triangle inequality.
## 2 Methods
### Selection of the Emission Wavelengths by the Monte Carlo Method  
The P training samples with known concentrations of each component are used to establish the prediction concentration model. Presumably \(k\) excitation wavelengths are retained for each component by the method given in Section 2.1, the \(k\) emission spectra corresponding to specific excitation wavelengths are unfolded into a one-dimensional emission spectrum in the ascending order of the excitation wavelengths, that is,  
\[X=(X_{11},\ldots,X_{i1},\ldots,X_{\Pi},X_{12},\ldots,X_{i2},\ldots,X_{\Pi 2},\ldots,X_{1k},\ldots,X_{ik},\ldots,X_{\Pi k})^{T} \tag{2}\]  
Each sample has \(I\) emission wavelengths, leading to a total of \(I\times k\) emission wavelengths. There are \(P\) unfolding emission spectra for \(P\) training samples.  
The Monte Carlo method combined with PLSR is applied to select the emission wavelengths from \(P\) unfolding emission spectra as Formula (2). For each iteration, \(I\) emission wavelengths are randomly picked out from each \(\mathbf{X}\), a \(I\times P\) sub-emission matrix \(\mathbf{X}_{I}\) is formed with \(P\) unfolding spectra. The prediction concentration model is constructed with \(\mathbf{X}_{I}\) based on PLSR [10,11]. The root mean square error of correction (\(RMSEC\)) is calculated by Equation (3):  
\[RMSEC=\sqrt{\frac{\sum\limits_{c=1}^{C}\left(\overline{y_{c}}-y_{c}\right)^{2} }{C}} \tag{3}\]  
where \(y_{c}\) is the known concentration of component \(c\), \(\overline{y_{c}}\) is the predicted concentration of \(c\), and \(C\) is the number of components. The reliability of the prediction for each iteration is calculated by \(d_{j}=1/RMSEC\).  
After 20,000 iterations of above step, if the \(i^{\text{th}}\) emission wavelength is selected \(Q\) times, the reliability score \(S_{i}\) of the \(i^{\text{th}}\) emission wavelength is summed by  
Figure 1: Schematics of the excitation wavelengths clustering. Initially there are \(J\) excitation wavelength classes.  
\[Si=\frac{1}{Q}{\sum_{j=1}^{Q}d_{j}} \tag{4}\]  
The reliability scores of the \(I\times k\) emission wavelengths are calculated and the top 10% emission wavelengths are reserved. These selected emission wavelengths will be used to predict concentrations in the test samples.
# Geometrical Degrees of Freedom for Cellular Structures Generation: A New Classification Paradigm
## 2 Design and Geometrical Consideration
### RVE Variation Methods  
#### 2.3.1 RVE Morphology Variation  
A CSs can vary in terms of the type of RVE (unit cell) present throughout the design space. The first column of Table 1 is dedicated to illustrating cases where there are RVE morphology variations. For example, [PERSON] et al. [55] have proposed a method to vary the morphology of the CSs based on the location within the design space (see Figure 7). The authors were able to ensure a continuous transition between morphologies using control points. Varying the morphologies of the RVE based on the location can pave the way toward mimicking natural structures such as tissues by increasing the degrees of freedom required to locally control the properties of the material.  
[PERSON] et al. [56] have presented an interesting way of changing the morphology by superimposing two TPMSs. Varying the volume fraction of one TPMS based on the location is like changing the morphology of the resulting structure.
# P D N: A Priori Dictionary Network for Fashion Parsing
## 3 Experiments
### ModaNet Dataset
#### 3.2.2 Results  
As shown in Table 2, we compared our model with other SOTA methods, and the PDN with the ResNet-101 backbone yielded a result of 77.91% in mIoU, which is 8.36% higher than TANet. To validate the effectiveness of the PD, we also trained the PDN with a VGG-16 backbone. Although this weaker model only achieved 44.80% in mIoU, it still achieved a superb performance among a series of VGG-16-based models. This result unveils that our proposed design brings great benefit to the CNN models in fashion parsing tasks.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Method** & **Backbone** & **mIoU** \\ \hline
[31] & FCN-32s & VGG & 35.36 \\
[40] & FCN-16s & VGG & 36.93 \\
[13] & FCN-8s & VGG & 38.00 \\
[41] & DANet & ResNet-101 & 68.16 \\
[42] & Deeplabv3 & ResNet-101 & 68.57 \\
[43] & PSPNet & ResNet-101 & 68.80 \\
[44] & CCNet & ResNet-101 & 69.22 \\
[29] & TANet & ResNet-101 & 69.55 \\  & PDN (VGG-16) & VGG & 44.80 \\  & PDN (ResNet-101) & ResNet-101 & 77.91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of experiments on ModaNet dataset.  
We compared the per-class IoU of our model with TANet, and the results are listed in Table 3. The comparison results show that the PDN can generate higher quality segmentation maps for most of the classes except belts and sunglasses, since belts and sunglasses only occupy 6.0% and 3.8% of all the targets, respectively, and they tend to be smaller compared to other types of targets. It is difficult to capture sufficient features for these small objects, leading to difficulties for the PD in storing templates of the latent representations. However, when the backbone can capture sufficient features, our method still significantly improves fashion parsing accuracy.  
In Figure 8, we present the testing results on the ModaNet dataset. The first column shows input images, the second column shows ground truth, and the third column shows the segmentation results obtained using the PDN method. It is evident that the PDN achieves segmentation results closest to the ground truth, both in terms of category classification accuracy and shape segmentation similarity. Particularly noteworthy is its performance on components such as scarves or bags, where the PDN exhibits notable accuracy.  
Figure 8: The testing samples of PDN on ModaNet dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **TANet (IoU\%)** & **PDN (ResNet-101) (IoU\%)** \\ \hline Background & 98.38 & 98.33 \\ Bag & 77.49 & 79.24 \\ Belt & 55.28 & 40.55 \\ Boots & 50.97 & 81.39 \\ Footwear & 56.35 & 69.34 \\ Coat/Jacket & 73.0.7 & 85.81 \\ Dress & 70.23 & 90.79 \\ Sunglass & 61.56 & 44.96 \\ Pants & 81.41 & 91.69 \\ Top & 72.05 & 85.67 \\ Shorts & 73.60 & 83.72 \\ Skirt & 71.50 & 92.65 \\ Headwear & 73.35 & 74.22 \\ Scarf/Tie & 58.42 & 69.88 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Per-class IOU on ModaNet.