esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analysis of Results
### Analysis of Results
#### 4.4.3 Ablation Experiments  
To evaluate the performance of the MD-SERel model in capturing semantic and syntactic information, as well as the impact of the attention mechanism on the experimental results, an ablation study was conducted on the pesticide dataset PRD. The results are presented in Table 5. The specific experiments included replacing RoBERTa with BERT (-RoBERTa), excluding the syntactic information module (-GCN), and removing the multi-head self-attention mechanism (-ATT). The experimental results show that when RoBERTa was replaced with BERT, the F1 score decreased by 3.3 percentage points, indicating that RoBERTa performed better in handling domain-specific terminology and long-distance dependencies in the pesticide domain due to its larger training data and dynamic masking  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Relation** & **Precision** & **Recall** & **F1** \\ \hline TRE & 0.954 & 0.866 & 0.908 \\ CON & 0.983 & 0.960 & 0.971 \\ PES\_FORM & 0.983 & 0.960 & 0.970 \\ PES\_TYPE & 0.962 & 0.960 & 0.960 \\ PES\_BRA & 0.950 & 0.942 & 0.946 \\ PRT & 0.963 & 0.910 & 0.936 \\ TSYM & 0.983 & 0.952 & 0.967 \\ WUAC & 0.935 & 0.906 & 0.921 \\ PE & 0.974 & 0.847 & 0.906 \\ STMT & 0.990 & 0.950 & 0.970 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The prediction results of each relation type.  
strategy. When the multi-head self-attention mechanism was not used, the F1 score decreased by 1.1 percentage points, suggesting that the multi-head self-attention mechanism is crucial for capturing global contextual information and avoiding introducing redundant information during encoding, thus enhancing the model's overall performance. When the GCN was not used for syntactic information extraction, the recall dropped by 1.8 percentage points, and the F1 score decreased by 1.2 percentage points. This indicates that relying solely on semantic information is insufficient to fully capture the latent features of entities, while the integration of semantic and syntactic structural information improves the model's feature capture capability, improving recognition performance. The experimental results validate the importance of incorporating each module to enhance the model's performance.
## 4 Experimental Results
### Measurements
differences will be more obvious in more complex designs. Besides, the proposed array with multiple polarization modes has the most elements, and the highest G/T values, and can scan up to \(\pm\)70\({}^{\circ}\) in both azimuth and elevation planes.
# Centralized Energy Prediction in Wireless Sensor Networks
## 5 Experiments and Results
### Energy Consumption Model Accuracy  
The energy consumption model accuracy was evaluated using an N6705C DC power analyzer from Keysight. First, we measured the states and transitions average energy consumption using the power analyzer. Then, we deployed a network as shown in Figure 2 to obtain the energy consumption estimation from one node in an SDWSN and measure its energy consumption with the N6705C at the same time. The controller software runs on a desktop computer Intel Core i5 CPU 750 and 8 GB of memory RAM. All the nodes in the WSN are TelosB motes, including the gateway used to communicate the WSN and the SDN controller. Figure 3a depicts the connection to collect energy consumption measurements using the N6705C equipment and Figure 3b depicts the connection between the SDN controller and the WSN gateway.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Energy Information**} \\ \hline Approach & Packet size (B) \\ \hline Running on the controller & 89 \\ \hline Running on the WSN node & 15 \\ \hline Controller reply & \\ \hline Running on the controller & 12 + 2 \(\times\) hops \\ \hline \hline \end{tabular}
\end{table}
Table 2: Energy information packets’ sizes.  
Figure 2: Network deployed for energy consumption measurements.  
We compared the node estimation and the N6705C measurement to calculate the energy consumption model accuracy. During the testing, all the sensor nodes send temperature measurements to the sink and energy consumption measurements to the controller every 20 s. The experiment was replicated ten times, taking ten energy consumption measurements per time. We obtained an average error of 4.98% and a confidence interval of 1.53% using a significance level of 0.05.
## 5 Proposed Fleet Management System
### Local Communication with the Robots  
Local communication refers to the direct interaction with a single robot at a time, to understand how the data from each robot is processed. In this step, the focus was on studying the methods of communication with the robots individually, in order to collect their data.  
For the AGV ABB TriBot, this process is carried out through a datalogger that records real-time information about the vehicle's state and position. To ensure a stable connection during the development phase, the AGV was initially connected to a computer via Ethernet, configuring the computer's IP address to match the robot's address range. Access to the data is made through a web page of the datalogger, where information such as the position, system status, and recorded events can be viewed. However, for more efficient communication, the datalogger is connected to a router, allowing remote data collection without the need for a direct physical connection, as shown in Figure 6.  
Automated data collection was implemented using Python 3.10 and Selenium 4.10.0, enabling the continuous extraction of information directly from the datalogger's interface without human intervention. After the data is collected, it is filtered to remove irrelevant information before being stored for later analysis.  
This approach ensures efficient communication with the AGV individually, allowing the continuous monitoring of the vehicle and optimizing its integration into the fleet management system.  
Figure 6: The local communication method with the AGV ABB TriBot.  
In terms of communication for the Aiten MP10S and SEER AMB-1000 JS AMRs, this is achieved through the TCP/IP, where the robot acts as a server, accepting and responding to requests from an external client. This method ensures efficient communication, allowing for data collection and the remote control of the robots, as shown in Figures 7 and 8.  
Figure 8: The local communication method with the AMR AMB-1000 JS.  
Figure 7: The local communication method with the AMR Aiten MP10S.  
The API used follows a TCP request-response model, where each message sent contains a fixed header and a data area in a serialized JSON format. The header identifies the basic packet information, such as the length and data type, while the data area contains the specific information of the request or response.  
Communication with these AMRs allows for detailed control and efficient data collection, facilitating the integration into the global fleet management system.
# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
## 3 Results
### MR Slice Reconstruction:  
MR slice reconstruction is performed to examine brain anatomy and surgery maneuverings as the modality provides high resolution. Thin-section images are 1 mm wide with a spacing gap of zero, while thick-section images are 4 mm to 6 mm wide with a spacing gap of 0.4 mm to 1 mm. The higher value of thickness leads to low resolution. The GAN and CNN are combined to reconstruct thin-section brain scans of newborns from thick-section  
Figure 7: Grouping of reconstruction methods.  
ones. The first stage of the network is a Least-Square GAN (LS-GAN) with a 3D-Y-Net generator. This stage fuses the images of the axial and sagittal planes and maps them onto thin-section image space. The cascade of 3D-DenseU-Net and a stack of enhanced residual structures removes image artifacts and provides recalibrations and structural improvements in the sagittal plane [121]. Unsupervised medical anomaly detection (MAD)-GAN uses multiple adjacent brain MRI slice reconstructions to locate brain anomalies at different stages of multi-sequence structural MRI [122]. The edge generator of Edge-guided GAN (EG-GAN) joins the missing edges of low-resolution images and masks produced from missing slices in the through-plane as input. A contrast completion network employs these connected edges to predict the voxel intensities in the missing rows [123]. Conditional deep convolutional generative adversarial neural networks (CDCGANs) are used to forecast the advancement of AD by producing dummy MR images in the series arrangement. The atrophy is measured using the cortical ribbon (CR) fractal dimension (box-counting method). The method uses only one coronal slice of a patient's baseline T1- image. A reducing fractal dimension ensures the progressing illness [124]. The brain multiplex image represents the brain connectivity status extracted from MRI scans. The association between the two brain regions of interest is quantified based on function, structure, and morphology. A single network, adversarial brain multiplex translator (ABMT), performs brain multiplex estimation and classification for the vision of gender-related distinction linkages. Brain multiplexes are constructed from a source network intra-layer, a target intra-layer, and a convolutional interlayer. The ABMT is the improved version of GT-GAN [125], which has pioneered graph or network translation. Contrary to conventional GAN, the generator (translator) of GT-GAN picks up the generic translation mapping from the source network to the target network simultaneously [126]. 3D CGAN and a local adaptive fusion method are used for quality FLAIR image synthesis. They synthesize each slice separately along the axial direction and concatenate them into a 3D image. This synthesis predicts the coronal and the sagittal direction by analyzing complete images or large image patches [127].
# Time Series Prediction of Sea Surface Temperature Based on an Adaptive Graph Learning Neural Model
## 4 Discussion
### Differences between Bohai Sea and South China Sea Data sets  
By comparing the performance of each model in different sea areas seen in Figure 3, we can find some similarities of laws. Under the same prediction scale, the prediction index values of each model in the Bohai Sea data set are significantly higher than those in the South China Sea data set. The experimental results show that the SST of the South China Sea is easier to predict than that of the Bohai Sea.  
Located in tropical and subtropical regions, the South China Sea is characterized by significant tropical maritime climate and small seasonal temperature variations, resulting in the weaker temporal and spatial correlation of the SST data in the South China Sea. Because the Bohai Sea is located in the north temperate zone, it is greatly affected by temperate monsoons, continental climate, ocean currents and other factors, so the SST data will show a stronger spatial correlation. Through practical analysis, it is more difficult to predict the SST in the Bohai Sea area affected by more factors, which is consistent with the experimental results in this paper.
## 1 Introduction
Benefiting from the research of probability density evolution method (PDEM) in recent years [39; 40; 41], TTBIM system reliability studies open a new way. [PERSON] et al. [42; 43] and [PERSON] [44] et al. studied the phenomenon of wear on the rail surface and its distribution characteristics as well as the influence of vehicle parameters' randomness on the dynamic response of a vehicle-bridge coupling system by PDEM. [PERSON] et al. [45] developed a probabilistic model to select representative and realistic track irregularity sets from numerous data with higher efficiency and accuracy. [PERSON] et al. [46; 47] used the spectral representation of a stochastic function to simulate random track irregularities in the time domain and developed a nonlinear vehicle-track coupled dynamic system stochastic analysis model under random irregularity excitations based on PDEM. In the actual operation line, bridge deformation is a common factor in high-speed railways, and the track stochastic irregularity directly affects the running quality of high-speed trains. Due to the variation of passenger capacity and service time, the parameters of running vehicles are also different. These problems are related to the safety and comfort of train operations. Therefore, it is necessary to comprehensively consider the influence of bridge deformation amplitude, track stochastic irregularity, the variation coefficient of vehicle parameters (VCVP), and the operation speed on driving safety. The aim of this paper was to explore the limit value of bridge structure deformation in order to ensure driving safety and comfort at 350 km/h and high operation speed. Considering the effects of track stochastic irregularity, the VCVP, bridge deformation amplitude, and operation speeds, this study constructed a vehicle-track-bridge coupling dynamic model, described in Sections 2.1 and 2.2 Because the model only considered the vertical behavior of the train, we considered the reduction rate of wheel load and the acceleration of the vehicle as evaluation indexes. Section 2.3 introduces the PDEM to calculate operation safety reliability, and then model validations are performed in Section 3. Section 4 illustrates the influence of these factors on operation safety. Finally, some representative conclusions are given in Section 5. The results of this paper can provide a reference for the operation and maintenance as well as the maintenance limits of a high-speed railway at 350 km/h and also provide a basis for the construction of faster higher-speed rails in the future.
# Multi-Band Unmanned Aerial Vehicle Antenna for Integrated 5G and GNSS Connectivity
## 2 Dual-Port Inverted T-Shaped MIMO Antenna and Analysis
### Gain Parameter Analysis  
An antenna's 3D gain indicates how successfully it sends and receives signals in every direction in space. It functions similarly to a measurement of its total efficacy in three dimensions. Antenna performance in capturing or transmitting signals from different angles is improved with a higher 3D gain. The lower frequency of the GNSS band of the single-patch antenna achieved a gain of 2.9 dB, of the MIMO antenna--3.45 dB, while that of the MIMO antenna with an FSS was 5.44 dBi, as shown in Figure 10. Similarly, for the higher frequency band of 5G, the antennas achieved a gain from 4.9 to 6.43 dBi.
## 6 Analysis of Results  
The BiLSTM models were shown to outperform other prediction models such as ELMAN, RNN and LSTM. The accuracy improvement percentages ranged between 2% and 6% for traffic volume prediction in both travel directions for up to 60 min into the future. The accuracy ranged between 96% and 99% for multiple prediction horizons for both directions. A greater improvement can be noticed for speed prediction, ranging between 9% and 12% for both travel directions. The accuracy ranged between 99.80% and 99.93% for up to 60 min into the future. This study also focused on the impact of weather on traffic prediction performance. When rain intensities (in mm) are added to the inputs, the flow performance is not affected for a prediction horizon of up to 30 min. However, a slight improvement can be noticed for the 60-min prediction horizon through an increase from an accuracy of 96.86% to 97.13%. On the other hand, no improvement was noticed for speed predictions, noting that the previous accuracy was above 99% and it is hard to outperform that by adding more features, such as weather information. Another experiment included adding both rain and wind speed to the traffic flow and speed inputs. However, no improvement could be observed in model performance for both speed and flow for multiple prediction horizons. The last experiment included adding multiple types of weather information (rain intensity, wind speed, humidity and air temperature) to the traffic information inputs. The results showed a minimal improvement in traffic flow prediction from 97.54% to 97.60% for the 30-min prediction horizons and from 96.86% to 97.60% for the 60-min prediction horizons. For speed prediction, the only improvement noticed was for the 60-min prediction horizon, with a minimal enhancement from 99.75% to 99.77%. This shows that adding weather information can enhance the model only if the initial traffic information is not performing well as inputs to the model. Adding weather information data to the inputs also provided good accuracy overall, similar to the initial results, with minor enhancements to a few traffic prediction horizons for both speed and flow. However, adding weather information to the model has practical implications for the public, who might want to know how their travel is impacted by severe weather conditions on their travel routes. In this study, the BiLSTM model demonstrated superior performance in predicting traffic flow compared to the unidirectional LSTM. A comparison with other neural network models, such as RNN, Elman, and DLBP, is provided to contextualise these findings further, as shown in Tables 3-6. This comparative analysis allows a clearer understanding of BiLSTM's advantages and limitations in various forecasting scenarios. The performance disparities between the LSTM and BiLSTM models observed in this study can be attributed to their architectural differences and how each model processes
## 6 Simulation Results
The explanation is twofold: (i) The proposed OTPA algorithm for the CoPC problem outperforms that of the NoCoPC problem owing to the lower cooperative optimal transmit power \(p_{m}^{C}(t)\) obtained by the CoPC problem compared  
Figure 9: Optimal transmit power vs. the slot \(\tau_{rp}\) (\(r=0.5,l=250\) iterations). NoCoPC: noncooperative power control; CoPC: cooperative power control; PB: power balancing. with the noncooperative optimal transmit power \(p_{m}^{NC}(t)\) by the NoCoPC problem; (ii) The reduction of the data sequence of SSR is inversely proportional to the optimal transmit power of selected SSNs. Figure 12 shows the comparison of the reduction of data sequence of SSR with respect to selected SSNs in the internal buffer of bottlenecked SU \(b\), versus the slot \(\tau_{rp}\) under the condition of discount factor \(r=0.3\), for the proposed congestion mitigation approach and PB algorithm with 250 iterations. As  
Figure 6.0 Reduction of data sequence of spectrum sensing results for of selected spectrum sensor nodes vs. discount factor \(r\) (\(\tau_{rp}=12\) ms, \(l=250\) iterations). NoCoPC: noncooperative power control; CoPC: cooperative power control; PB: power balancing. Figure 6.1 Transmitted data sequence of SSR vs. discount factor \(r\) (\(\tau_{rp}=12\) ms, \(l=250\) iterations). NoCoPC: noncooperative power control; CoPC: cooperative power control; PB: power balancing. seen from Figure 11, the reduction of data sequence of SSR through PB algorithm has converged to the same constant value after the 250 iterations. This is the direct influence that the optimal transmit power of selected SSN will converge to an expected equilibrium point by using PB algorithm. As expected, the reduction of data sequence of SSR by the proposed congestion mitigation approach with discount factor \(r=0.3\) is larger than that of PB algorithm with 250 iterations. This is because our proposed approach can obtain smaller optimal transmit power than PB algorithm. According to Equations (42) and (47), the smaller optimal transmit power will lead to the more reduction of data sequence of SSR with respect to selected SSNs. It is also interesting that with the growth of the slot \(\tau_{rp}\) the reduction of data sequence of SSR by our proposed approach will gradually increase, and the reduction by the CoPC problem is much higher than that of the NoCoPC problem. This result can be interpreted by the fact that the optimal transmit power of selected SSNs by the CoPC problem under discount factor \(r=0.3\) is clearly lower than that of the NoCoPC problem with the growth of the slot \(\tau_{rp}\). This result further gives rise to the larger reduction of data sequence of SSR.
## 1 Introduction
### Contribution of the Paper  
Based on the problem statement defined in Section 1.1, the contribution of this paper can be summarized by the following:  
* An analytical study is carried out which highlights the impact of the original hop penalty when the hop count increases up to 10 nodes.
* The design and implementation of a test-bed suitable for implementing and testing BATMAN routing protocol with original hop penalty in a real environment.
* The design and implementation of a novel split-hop penalty for transmission quality of the routing protocol. The developed split-hop penalty was analyzed on a test-bed in an indoor environment using IoT prototypes.
* A comparison of the discussed collected results by testing the original hop penalty with the modified split-hop penalty in various network configurations.  
The rest of the paper is organized as follows: Section 2 provides details of the methodology used to diagnose the vanilla version of BATMAN and the production of the split hop penalty. Experimental setup, software configuration, and assumptions made to make the experiment tractable and reproducible are presented in Section 3. Results and comparisons are presented and discussed in Section 4, while Section 5 concludes the findings and presents remarks on the performance of the proposed method.
# Multi-Objective Ship Route Optimisation Using Estimation of Distribution Algorithm
## 3 Problem Formulation  
Consider a ship sailing in the tidal water area \(SA\) (see Figure 1):  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Problem Features**} & \multicolumn{3}{c}{**Algorithm Features**} \\ \cline{2-7}
**Method** & **Combinatorial** & **Multi-Objective** & **Non-Stationary** & **(Explicit)** & **Local** & **Deterministic Local Search** \\  & **Optimisation** & **Problem** & **Problem** & **Distibution** & **Search** & **(with Tabu** \\ \hline Our method & YES & YES & YES & YES & YES \\ \([\)2, 4, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 3, 5, 16, 28, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,  
* With the (tidal) stream (a horizontal flow of water caused by the rise and fall of the tide) given by the following velocity vector field: \[\mathbf{v}_{t}(x,y,t)=\Big{(}v_{t_{x}}(x,y,t),v_{t_{y}}(x,y,t)\Big{)}^{T},\] (1)
* From point \(A\) to \(B\), along the path/route: \[\widetilde{AB}=\mathbf{p}_{AB}(t)=(x_{AB}(t),y_{AB}(t))^{T},\] (2)
* Under a specific control input (that corresponds to path \(\widetilde{AB}\)): \[\mathbf{u}_{\widetilde{AB}}(t)=(c_{AB}(t),v_{s_{AB}}(t))^{T},\] (3) where \(c(t)=\{c_{i}(t):c_{i}(t)\rightarrow\mathbf{p}_{AB}(t_{+})\in SA\ \forall t\in[0,t_{AB})\}\) denotes the ship course, \(v_{s}\in[v_{s_{min}},v_{s_{max}}]\) is the ship relative speed, and \(SA=\{(x,y):x_{min}\leq x\leq x_{max}\) and \(y_{min}\ \leq\ y\ \leq\ y_{max}\}\) is the sailing area.  
The route optimisation problem under consideration is to find a set of admissible control inputs \(\{\mathbf{u}^{*}\}_{i}\)--each causing the ship to follow an admissible path \(\mathbf{p}^{*}\)--which minimise in [PERSON]'s sense both the passage time (duration) \(t_{AB}\) and fuel consumption \(fc_{AB}\), i.e.,  
\[\underset{\mathbf{u}_{AB}}{\text{minimise}}\ (t_{AB},fc_{AB}). \tag{4}\]  
**Remark 1**.: _We assume that the values of both \(t_{AB}\) and \(fc_{AB}\) can be found only through simulation (i.e., it is a black-box problem). The (simulation) model is described in Appendix A._