esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparing New Wireless Sensor Network Protocols through Simulation and Data Analysis +
## 4 Results and Analysis
### Routing Overhead  
We will look at each scenario and compare both protocols with regard to the number of nodes and the kind of scenario in order to examine the findings for the routing overheads of DSR and OLSR in both scenarios with varied numbers of nodes. When we take into account the situation depicted in Figure 6a, which does not involve any nodes failing, we see that the DSR operates very differently in the case of 25 nodes compared to the case of 50 nodes. Because it uses source routing, the routing overhead for a smaller network, which consists of control messages, is low. This is also owing to the fact that it operates in a proactive manner. A ROUTE REQUEST message is sent whenever a route is required, and the ratio of ROUTE ERROR messages is low. However, as the size of the network expands, the routing overhead for the protocols that perform real-time routing (on time) undoubtedly increases. As a result, this caused a comparatively bigger overhead in the case of the 50-node scenario. When considering OLSR's performance, it becomes immediately apparent that the protocol excels in both small and big networks. This is because it uses predetermined routes for each destination (node), which is why we have come to this conclusion. So, the only overhead that it shows are routing updates, topology control messages, and hello messages that are used to communicate that network, link, and node conditions have lowered after failure, which causes it to show a greater overhead to identify routes to different nodes. After a certain amount of time, the overhead continues to decrease due to a decrease in the number of executions of ROUTE REQUEST and ROUTE ERROR messages. Keeping route cache assists in overlooking the dead nodes, which in turn leaves retransmission to a higher tier.  
In this paper, we have examined the simulated outcomes and performed some analyses on them. We ran simulations on wireless sensor networks with a variety of topologies and degrees of complexity and then reviewed the metrics obtained from those simulations. The primary metrics that have been taken into consideration throughout this paper are end-to-end delay, throughput, and routing overheads. We have simulated different network scenarios, including scalability and the failure of individual nodes, using both fixed and mobile node networks. For the purposes of the simulation, we have established specific parameters, and the results of the simulation have been displayed. In each of the test cases, the DSR and OLSR protocols were each given a separate implementation (simulation) so that their performance could be evaluated in relation to the proposed network in the context of scalability and the loss of nodes, as Figure 8.
## 3 Proposed Approach  
Based on the leader/follower configuration, two different DRL controllers are designed. While the leader's goal is to approach the target taking into consideration the location of the followers, the aim of these ones is to keep a fixed relative location concerning the leader for a given geometric formation. These relative reference positions are located on each side of the leader at some relative angle and distance. Both DRL controllers output the linear velocities of the left and right wheels, \(v_{L}\) and \(v_{R}\), according to Figures 3 or 4.  
The Deep Deterministic Policy Gradient (DDPG) is selected for the DRL controllers. This is an improvement derivation from the Q-learning and Deep Q-learning Controller (DQN) formulations for continuous-time states and actions based on the following recursive Bellman optimality equation [38]:  
\[Q(S[k],A[k])=Q(S[k],A[k])+\alpha\] \[(\underbrace{R[t+1]+\gamma\cdot\max_{\begin{subarray}{c}a\\ \text{numerical search target}\end{subarray}}Q(S[k+1],a)-Q(S[k],A[k])}_{\text{ numerical search target}}) \tag{8}\]  
Figure 5: Positionformation control.  
which, after convergence, gives the maximum reward-to-go \(Q\) as an output with inputs \(S[k]\) and \(A[k]\), the current observed state and action. The discount factor \(\gamma\) accounts for future attenuation, and learning rate \(\alpha\), also a design parameter, is used for accelerating or slowing down the convergence. \(R[k+1]\) is the reward obtained from applying \(A[k]\) at \(S[k]\) and transitioning to \(S[k+1]\). The associated neural network is called Critic. Another net is used to approximate the control policy, and it is called an actor. It is interesting to highlight that DRL is by design an optimal controller that directly benefits from this underlying theory, but it differs from other optimal techniques in that it can be trained forward-in-time while interacting with the environment, using the plant's real dynamics. Its performance will be shaped by reward function in any way, similar to all other optimal approaches. Figure 6 shows an Actor and Critic neural networks block diagram, corresponding to the Control Law block in Figures 3 and 4 for the followers and the leader.
## 3 System Model
### Simulations Results
#### 4.3.7 Throughput with Different Node Density  
In Figure 11, we investigated the graphs that represent the throughput comparison of ATEAR, Enhanced-ATEAR, SHR, MRRP, and TARA protocols having a different number of nodes, i.e., 5-13 at simulation time 1000 s. The immediate observation from the graph is that even with the varying network size, ATEAR and Enhanced-ATEAR showed improved performance than MRRP, SHR, and TARA in terms of network throughput. Moreover, it can be analyzed that both ATEAR and Enhanced-ATEAR protocols had a persistent network throughput. In contrast, MRRP, TARA, and SHR the network throughput dropped to 50%, 70%, and 20%, respectively. It can be observed from the graph that the throughput of ATEAR and Enhanced ATEAR was 93% at 1000 s with five nodes. As the throughput is investigated with different networks and node density, it did not drop from the value of 85%, because of the steadiness of temperature and energy consumption the nodes.
# Enhanced Routing Algorithm Based on Reinforcement Machine Learning--A Case of VoIP Service
## 1 Introduction
The e-RLRP provides an overhead reduction of up to 18% compared to RLRP. The case study demonstrates that e-RLRP can provide a VoIP communication quality improvement of more than 90% if compared to OLSR, and up to 8% if compared to RLRP. The remainder of this paper is structured as follows. In the Section 2 a theoretical review is presented. The proposed routing algorithm based on RL is described in Section 3. In Section 4, the different steps of the experimental setup are described. Section 5 presents the experimental results. Finally, the conclusions are presented in Section 6.
## 3 Results and Discussion
### Preparation and Characterization of Rubber Composites
#### 3.2.5 Mooney Rivlin Plot  
The stress-strain curves were converted into the Mooney-Rivlin plots, shown in Figure 7, where the reduced stress given by Equation (4)  
\[\sigma^{\star}\left(\sigma^{\star}=\sigma/(\alpha^{2}-\alpha^{-1})\right) \tag{4}\]  
was plotted versus the reciprocal of the extension ratio \(\alpha\).  
The Mooney Rivlin plot reveals the stress upturn, which is considered to be due either to the entanglements which behave as effective crosslinking points at higher elongations. [82] or to the finite extensibility of the network chain segments [83; 84] or to the strain induced crystallization [82; 83; 84; 85; 86; 87; 88].  
Figure 7: (a) Mooney–Rivlin plots of reduced stress versus reciprocal extension ratio of unfilled NR and nanocomposites based on either HSAG or HSAG-SP. Only representative curves are reported. (b) Strain at the minimum reduced stress. This parameter is conventionally taken as indication of the stress upturn point.  
Curves in Figure 7 refer to the NR gum stock and the composites based on HSAG and HSAG-SP, with different filler contents. In the graph in Figure 7a, a remarkable decrease in stress at low strain levels can be observed, attributed to the Payne effect in the case of filled rubbers. Furthermore, NR and composites with low amount of filler (5 phr) show a nearly linear region, followed by a stress upturn. The higher the filler content, the less definite is the central linear region of the plot, which turns into a smooth curvature. It appears that the upturn occurs at a lower extension ratio for the HSAG-based nanocomposites: the larger the amount of filler, the lower is the extension ratio. At 5 phr of filler content, the upturn occurs at an appreciably lower strain in the case of HSAG-SP.  
In the literature, the Mooney-Rivlin plot has been elaborated upon for NR/GRM composites from latex blending, containing a low amount of GRM. With 1 phr of either GO or chemically reduced GO [89], the upturn occurred at a lower strain than in the case of NR, in particular for the reduced GO. Decrease of stress upturn was found by increasing the amount of reduced GO from 1 to 2 phr [43], Abrupt upturn was observed with 0.5 phr of reduced GO [46]. These findings were attributed to the strain-induced crystallization, a phenomenon that was documented to be favored by such a level of GRM content [90]. However, when the content of reduced GO was increased up to 5 phr [46], a gentle instead of an abrupt upturn was observed and attributed mainly to limited extensibility of the network chains. In this study, this latter interpretation could be adopted to explain the results of Figure 7. The difference observed between HSAG and HSAG-SP based composites, at 5 phr as the filler content, could be explained by the larger mobility of polymer chains in the latter.
# Robot Navigation Based on Human Trajectory Prediction and Multiple Travel Modes
## 3 Results and Analysis  
### Human Trajectory Prediction  
The improved socially conscious model is an LSTM network. An embedding layer, whose dimension is 64, for the spatial coordinates, is used as the input to the LSTM. The dimension of hidden states for all the LSTM models is 128. The embedded layer uses ReLU (rectified Linear Units) for non-linearization. 80% of the data is used for training and the remaining 20% for verification. 50 epochs were trained totally. During the model training, the learning rate is set to 0.001, dropout is set to 0.8, and the RMS-prop in [23] is used as the optimizer. The proposed socially conscious model is implemented on a single GTX1080 GPU. The model reads the newly input data and generates the predicted data obeying the trained two-dimensional normal distribution through the sampling function in Numpy. In order to improve the stability of the prediction data, multiple generation operations are performed, and the average value is calculated as the final prediction result.  
Similar to Social-LSTM [8], eight time steps (3.2 s) are observed and 12 time steps (next 4.8 s) are predicted. In this paper, we compare our proposed improved socially conscious model with the state-of-the-art Social-LSTM in [8] with the datasets, ETH [24] and UCY [25]. A total of five sub-datasets are included in this paper, and the leave-one-out method is adopted. Prediction performance is evaluated with two different metrics [24]: Average displacement error (ADE) and final displacement error (FDE). Table 2 shows the prediction error reduction percentage, which shows that our proposed improved socially conscious model has a smaller average displacement error and final displacement error, and proves the effectiveness of our proposed model.  
Compared with the social-LSTM [8], the improved socially conscious model has a higher prediction accuracy in all datasets. One reason is that the social-LSTM considers the influence of pedestrians in a small area around the target pedestrian, and does not take into account the relative directions of pedestrians. However, our proposed model considers more pedestrians in larger areas and filters out the pedestrians with negligible influences as judged by their relative directions. Additionally, the introduction of the pooling layer between the current pedestrian trajectory sequence input and position estimation output, which enhances the effect of a pedestrian's own history trajectory, can significantly reduce the prediction error of the model.
# Graph Neural Networks for Routing Optimization: Challenges and Opportunities
## 7 Challenges and Opportunities
### Opportunities
#### 7.2.1 Exploration of Novel GNN Architectures  
One research opportunity is the exploration of additional GNN structures for routing applications to enhance learning capabilities in complex scenarios. GNNs have grown into large families of variants. However, most of the surveyed studies use only early-stage GNNs, such as GNN [67] and MPNN [74]. The application of GCN and GAT models, which are extremely successful in other domains, is rarely seen in the existing studies for routing optimization, which leaves a large research gap for exploration. Other specific research ideas include advancements in model interpretability, scalability to handle larger networks, and integration with real-time data for dynamic routing.
# Enhancing Detection of Arabic Social Spam Using Data Augmentation and Machine Learning
## 2 Background and Related Work
### Spam Detection Techniques  
Most research in the area of spam detection is focused on English text, while little research has been done on Arabic text. This is mainly due to its morphological complexity and the limited availability of compatible Arabic language tools and a dataset. The English Twitter-sphere has a lot of spam detection research (e.g., [36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47]). However, there has been work on detecting undesirable Arabic content such as advertisement [48; 49; 50], adult-content [51; 52], offensive, hate, and vulgar speech [53; 54; 55; 56], spam reviews [57; 58; 59], and detect spam accounts [60; 61; 62]. Table 1 summarizes existing Arabic spam-detection studies.  
However, concerns of class imbalance for the Arabic spam detection dataset, as well as spam class recall, are not addressed. In this research, we replicate the state-of-the-art data augmentation techniques on a non-text domain and show that there is still a lot of opportunity for improvement by implementing data augmentation techniques in the text domain. Furthermore, Arabic studies did not cover all categories of spam content at once, or the dataset labeling step is unclear or depends on the English translation.
# Evaluation of the Digital Data Performance Transmission on a Fiber-Radio System
## 4 Conclusions  
Using a set of numerical simulations, the digital data performance transmission for a fiber-radio system was successfully evaluated. All the parameters considered in the numerical simulations correspond to standard values used in optical and wireless commercial systems. VPI Photonics software was used to simulate the transmission of a digital signal at a bit rate of 2.4 GHz through an optical link of 25 km of SM-SF. At this point, it is very important to remark that the effect of the chromatic dispersion was not a significant influence on the transmission through the optical fiber. MATLAB software was utilized to emulate a wireless channel considering four main phenomena inherent to this channel, such as multipath and slow fading, co-channel interference, and AWGN. Moreover, this modeling considered a user in movement within its coverage area cell. The constellation diagram and BER and SINR statistical tests were used to evaluate the performance of this approach. Results demonstrated good robustness of the system, which remains functional even in the presence of noise. In addition, all possible positions of the reference user inside the coverage area were considered. The chosen QPSK format provided good robustness for the wireless transmission. In summary, this numerical simulation finds potential applications in the evaluation of fiber-radio systems, in particular, cellular telephony, the IoT, or any other mobile system.  
Figure 11: SINR (dB) vs. AWGN standard deviation.  
Figure 12: BER percentage and SINR (dB) relationship.  
Figure 10: BER percentage vs. AWGN standard deviation.  
**Author Contributions:** Conceptualization, investigation, methodology and writing - original draft, R.C.-T., J.C.-C. and I.E.Z.-H.; resources, writing-review & editing, R.C.-T., J.C.-C., I.E.Z.-H. and G.A.M.-H.; formal analysis, J.C.-C., I.E.Z.-H. and G.A.M.-H.; supervision, J.C.-C. and I.E.Z.-H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** One of the authors, [PERSON] would like to thank the CONACyT for the student scholarship number 679055.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# AOA-Based Three-Dimensional Positioning and Tracking Using the Factor Graph Technique
## 6 Results
### Convergence  
In this subsection, the system convergence behavior in terms of the FG iteration number is evaluated. The sensor deployment is assumed to be the same as the previous simulation, while both \(\sigma_{\phi,\theta}=5^{\circ}\) and \(\sigma_{\phi,\theta}=20^{\circ}\) are set for comparison. According to Figure 6, the tracking errors in terms of RMSEs can be much reduced only after five FG iterations. The performances will be further improved and stable with 10 iterations. Moreover, even the standard deviations of the AOA measurement are different, and the convergence behavior is found to be very similar.
## 4 Applications
### Enzymatic Biosensors
#### 4.1.1 Glucose
[99] reported on the fabrication of a flexible and highly selective enzymatic OECT-based sensor using a gate electrode functionalized by an alternation of positively charged (a PANI film) and negatively charged (graphene flakes/Nafion) bilayer films (Figure 13). The authors compared the sensitivity of their device towards several analytes (uric acid, lactic acid, glucose, cholesterol and H\({}_{2}\)O\({}_{2}\)) compared to a more classical bare Pt gate electrode. They showed that the nature of the electrode participated in the decrease of the detection limit for most analytes, of around three orders of magnitude except for H\({}_{2}\)O\({}_{2}\). This GOx-GO/PANI/Nafion/graphene/Pt gate electrode was used to detect glucose with a very low detection limit of 30 nM. In a very recent paper, [PERSON] and [PERSON] [130] described the fabrication of an OECT integrated with a microfluidic device. First, PtNPs then GOx/chitosan were sequentially deposited onto the gate electrode. A low detection limit of glucose (0.1 \(\upmu\)M) was achieved thanks to PtNPs catalytic oxydation of H\({}_{2}\)O\({}_{2}\) on the gate. The use of a microfluidic channel presented several advantages such as a low analyte consumption (30 \(\upmu\)L) and a very short detection time (1 min), of real interest for a real-time, non-invasive and portable glucose sensor. Figure 13: Schematic diagram of an OECT with a UOx-GO/PANI/Nafi on-graphene/Pt gate Reproduced from [99] with permission from John Wiley and Sons, Copyright © 2014.
# Security Scheme Based on Parameter Hiding Technic for Mobile Communication in a Secure Cyber World
## 3 Proposed Security Scheme  
The proposed scheme was designed to protect original identification information such as IMSI and RNTI that are transmitted as plain texts when UE tries an initial attach in the network. It is composed of four cases in total, according to UE's initial access types, and eNB and MME are assumed to be a secure channel along with the backbone network.
# Generation of Stereo Images Based on a View Synthesis Network
## 3 The Proposed Approach
### Translation and Rotation Computation  
When computing translations, the sign of disparities from the left-eye view to the right-eye view is crucial. We used a form of the conventional warping method [21] to compute the disparity of each object. The equation is defined as follows:  
\[d=\frac{B(D-f)}{D}\]  
where \(d\) is the disparity, \(D\) is the depth, \(B\) is the baseline, and \(f\) is the focal length. Thus, when \(D<f\), \(d\) carries a negative sign, and a positive sign otherwise.  
Figure 3: Segmenting the input image into objects: (**a**) the input image; (**b**) after clustering; (**c**) the background after erosion; (**d**) the pillows; and (**e**) the bed.  
We use this equation to compute disparities for both objects and background. For objects, \(D\) is the average depth of the covered region, and we use the same disparity to translate all pixels in the region. For the background, \(D\) is the depth of each pixel that translates with its own disparity. The left-eye view is translated to the right-eye view using a simple warping equation, defined as follows:  
\[I_{t}(x,y)=I_{s}(x-d,y)\]  
where \(d\) is the disparity, \(I_{t}(x,y)\) is the value of pixel \((x,y)\) in the result, and \(I_{s}(x-d,y)\) is the value of pixel \((x-d,y)\) in the input image.  
The rotation of an object is related to its depth. Between the left- and right-eye views, the rotation is larger if the object is closer. In addition, because we generated the right-eye view from the left-eye view, the rotation of the object was always clockwise. After obtaining the rotation limit, the rotation of an object is computed using smoothing. The equation for computing the rotation of an object is as follows:  
\[r_{i}=\left\{\begin{array}{ll}3,&\text{for $D_{avg}\leq D_{min}$}\\ \frac{(3-1)(D_{max}-D_{avg})}{D_{max}-D_{min}}+1,&\text{for $D_{min}\leq D_{avg} \leq D_{max}$}\\ 0,&\text{otherwise}\end{array}\right.\]  
where \(r_{i}\) is the index of the rotation, which is one of the inputs to the view synthesis network; \(r_{i}\in(0\cup\)[1,3]). The corresponding angles of rotation are \(0^{\circ}\) and \([5^{\circ}\), \(15^{\circ}]\), and \(D_{avg}\) is the average depth of an object. The values of \(D_{max}\) and \(D_{min}\) depend on the range of the predicted depth map. In general, \(D_{max}=2.5\) and \(D_{min}=0.8\), and both are restrictions for ignoring objects that are either too far or too close.