esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analysis
### Experimental Results and Analysis
#### 4.5.6 Scalability and Computational Complexity  
When using the data for the first time, data preprocessing is required. The average duration for preprocessing the raw data is 13 h. After preprocessing, the entire MTQADC model and the data size are approximately 3.5 GB, with an average training duration of 2.5 h for the model.  
From Figure 10, it can be seen that as the size of the dataset increases, the total Hits @ 1 value shows a gradual upward trend, increasing from 0.24 at 20% to 0.342 at 100%. This indicates that the scale of the dataset has a significant impact on the model's performance, as larger datasets can provide more information, thereby enhancing the model's accuracy. Among the different data types, the \"Entity\" and \"Single\" types perform relatively well, particularly when the dataset reaches 100%, with Hits@1 values of 0.43 and 0.406, respectively. This demonstrates the model's effectiveness in handling entity and single questions. In contrast, the improvement for the \"Multiple\" type is minimal, increasing from 0.156 at 20% to 0.185 at 100%, suggesting that the model's performance gains are limited when addressing multiple questions. Although the Hits@1 value for the \"Time\" type also increases with the dataset size, it remains at a relatively low level.  
Overall, the size of the dataset positively influences the model's performance, especially in handling entity and single questions. Future research could further explore ways to enhance the model's performance on multiple questions and time-related issues.  
Figure 10: The impact of dataset size on model performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Question** & **Answer** & **Predicted Answer** & **Answer Type** & **Time** & **otype** & **glable** \\ \hline
1 & When did [PERSON]\#[PERSON] first visit China? & \$’8 April 200\%’ & 27 April 200\%’, \$ April 200\%’, \$AoS’ & time & day & first\_last & Single \\
2 & Who would wish to visit Oman on the same & \$’Sandi Arabia’ & \$’Tran’, \$[PERSON], \$’Unidad Arab Emirates’ & entity & month & equal\_multi & Multiple \\
2 & month of the Businessperson of Uzbekistan? & \$’December 200\%’ & 14 December 2006\%’, \$’November 2005’, \$’12 August 200\%’ & time & month & equal & Single \\
3 & In which month did [PERSON] vist Swainland? & \$’December 2006’ & \$’14 December 2006’, \$’7 November 2005’, \$’12 August 200\%’ & time & month & equal & Single \\
4 & Before China, with whom did the UN Security & \$’France’ & \$’Japan’, \$’tran’, \$China’ & entity & day & before\_last & Multiple \\
5 & Who was the first to paise China after [PERSON] & \$’Ma [PERSON] & \$’Japan’, \$’South Korea’, \$Vietnam’ & entity & day & after\_first & Multiple \\
5 & \$’Common? & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Error analysis.
# Satellite Image Super-Resolution by 2D RRDB and Edge-Enhanced Generative Adversarial Network +
## 3 Materials and Methods  
For our proposed approach, EEGAN [5] is adopted as the basic framework and some modifications are made based on it. As shown in Figure 1, we can divide the generator G into two sub-networks: ultra-dense subnet (UDSN) and edge-enhanced subnet (EESN). The UDSN is made up of several dense blocks and a reconstruction layer which can be used to generate intermediate HR image, while the EESN is used to enhance the edges extracted from the intermediate SR image by removing artifacts and noise. Then, the clean edges from EESN will replace the noisy edges in the intermediate SR image and output the final SR image. A perceptual loss function is tried to be added into the model to enhance visual quality [29; 30; 31]. We find a set of parameters that can achieve better performance through the process of parameter adjustments in conducted experiments. The obtained HR images are also compared with the ones generated by other SR models. We will describe more details in the following subsections.
## 4 Simulation Results
### Noise Tolerance Analysis
#### 4.3.2 Noise Tolerance--MEO  
The effect of background noise in QKD links is strongly dependent on the links with overall efficiency; therefore, we shall expect a great influence on the noise in the achieved key rates for MEO-to-ground QKD links, where end-to-end loss is much higher. Figure 9 presents the distilled SKR over a single MEO satellite pass when considering different sky radiance values for (a) \(D_{r}=1\) m and (b) \(D_{r}=2.3\) m, of which both had SMF-coupled SNSPD detection. As discussed before, in the MEO-QKD case, the source's iQBER was set to 1.5%, whereas the overall number of transmitted bits was set to \(10^{12}\) bits. It was observed that MEO-to-ground QKD links are indeed very unstable in terms of variations in the sky radiance. Since the end-to-end loss was already relatively close to the key distillation limits, variations in the background noise were able to completely interrupt the QKD communication. It was evident that, as shown in Figure 9b, even with a much bigger receiver with a 2.3 m aperture diameter, key exchange was only possible under clear night and new moon conditions. In conclusion, this limitation would severely affect the availability of MEO satellites for delivering keys to the ground segment. To tackle this, a combination of narrower filters along with shorter detection time windows may  
Figure 8: SKR over time for a single satellite pass when varying the sky radiance values of (a) \(D_{r}=1\) m and SMF-coupled SNSPD detection, (b) \(D_{r}=1\) m and SMF-coupled SPAD detection, and (c) \(D_{r}=1\) m and free-space SPAD detection.  
allow for the MEO QKD communication to become feasible for increased background solar radiance values.
# Evaluation of a Reputation Management Technique for Autonomous Vehicles
###### Abstract  
Future autonomous vehicles will rely heavily on sharing and communicating information with other vehicles to maximize their efficiency. These interactions, which will likely include details about the positions of surrounding vehicles and obstacles on the road, are essential to their decision-making and the prevention of accidents. However, malicious vehicles--those that intentionally communicate false information--have the capacity to adversely influence other vehicles in the network. This paper presents and evaluates a reputation management system, capable of identifying malicious actors, to mitigate their effects on the vehicle network. The viability of multiple report weighting schemes to calculate reputation is evaluated through a simulation, and a blockchain-based backend for the reputation management system to securely maintain and communicate reputation data is proposed. Storage and computational challenges are considered. This paper shows that weighting schemas, related to the number and reputation of witnesses, positively affect the accuracy of the model and are able to identify malicious vehicles in a network with consistent accuracy and scalability.  
autonomous vehicles; reputation; trust; cybersecurity; transportation; blockchain +
Footnote †: journal: Future internet  
In order to provide these benefits, autonomous vehicles' communications and system decision-making must be secure [12]. The reputation of individual vehicles is crucial to the integrity of a network of autonomous vehicles (AVs). A vehicle's reputation may be used to inform other vehicles' decisions about which details and transactions to accept. It also has potentially broader uses, such as for rating purposes by insurance companies, where premium prices might be decreased for vehicles that demonstrate stable performance.  
This paper presents and evaluates a reputation management system for AVs, based on the use of blockchain technology. The system is proposed and assessed, via simulation, to characterize its efficacy at performing its primary function: accurately identifying malicious vehicles under various environmental conditions.  
It continues by reviewing prior related work, in Section 2. Then, in Section 3, the system design is presented. Following this, the operational environment is explored with a traffic simulator. Next, the experimental design that is used for this work is discussed. In Section 6, the trust model that is used for this work is assessed. Following this, the results of the experimentation are presented and evaluated. Finally, the paper concludes with a discussion of the work presented herein and key topics for future study.
# An Efficient Probe-Based Routing for Content-Centric Networking
## 4 Performance Evaluation
### Experimental Results
#### 4.4.3 The Impact of Variation of Cached Contents  
Variation of cached content is used to simulate different application scenarios and to evaluate the applicability of our _probe_-based approach. Consider IoT sensor data, video aggregator data, and social network data: their data contents vary fast, and consumers request sequential data, so cached data in a router is quickly replaced, resulting in a high cache miss rate. Intuitively, a high cache miss rate causes FIB to invalidate content providers so the FIB miss rate increases in tandem with the cache miss rate.  
Figure 7 exhibits the effect of the cache update ratio on the performance of basic CCN and our _probe_-based approach. For all three approaches, the number of forwarded interest packets, the number of timeout packets, and the average response time gradually increase as the cache update ratio increases. Again, _probe_-based approaches perform better than basic CCN. The results of the comparison in Figure 7 are comparable to those in Figure 6.  
The slight difference is that when the cache update ratio exceeds 15%, the _FIB probe_ performs worse than the _PIT probe_ in terms of the number of forwarded interest packets and timeout interest packets. This is due to the fact that the _FIB probe_ updates the entry in  
Figure 7: Impact of variation of cached contents. (**a**) Total number of forwarded interests; (**b**) number of timeout interests; (**c**) average response time.  
FIB with the maximum cost yet the entry may not have been accessed recently. With the increase of time, the entry's efficacy falls dramatically, especially if the cache update ratio is high. In other words, when the FIB entry is accessed, the content providers stored in it become invalid.  
On the contrary, _PIT probe_ is like a greedy algorithm devoted to shortening the pending interests by selecting the most popular entry in PIT as a _probe_, which means the updated entry in FIB is accessed right away, and its effectiveness is less influenced by time. Therefore, the cache update ratio has less impact on the _PIT probe_.
## 7 Performance Evaluation and Simulation Results
#### 7.4.2 Very High Social Media Interaction.  
Node's Density = 200 Nodes/km\({}^{2}\)  
The same conclusion as in the previous case also applies in this case which even can be seen more clearly. For this low \(\sigma=1\) (compared to the average \(\mu=4\)) nodes show very similar TS values regarding the other nodes. Therefore, the best \(w_{TS}\) is 0, since the \(\overline{TS}\) measured is very near to 4 in all the cases. The lowest percentage of packet losses (15%) is obtained for \(w_{TS}=0\) (i.e., when we select the path with best QoS performance), which in this case produces a \(\overline{TS}\) = 4.  
Concluding, for this higher nodes' density, it is specially important to consider the QoS performance of the available paths to select the best one. Furthermore, since the TS is very high in all the nodes, the selected path will offer a high \(\overline{TS}\) for any \(w_{TS}\).  
Figure 15: Average end-to-end packet delay. Scenario with high social media interaction (TS normal distribution with \(\mu=3\) and \(\sigma=1\)). Nodes’ density = 200 nodes/km\({}^{2}\).  
Figure 16: Percentage of packet losses and average tie strength of the forwarding path. Scenario with very high social media interaction (TS normal distribution with \(\mu=4\) and \(\sigma=1\)). Nodes’ density = 100 nodes/km\({}^{2}\).  
Figure 17: Average end-to-end packet delay. Scenario with very high social media interaction (TS normal distribution with \(\mu=4\) and \(\sigma=1\)). Nodes’ density = 100 nodes/km\({}^{2}\).
## 2 Materials and Methods  
### Study Area and In Situ Survey  
#### 2.1.1 Study Sites  
In this study, GPP was measured at two forest observation stations located in the Saihanba Forest Farm in Chengde, Hebei Province, China (Figure 1). The first station (SHB1) is located in a larch (_Larix principis-rupprechtii_ Mayr) forest (117\({}^{\circ}\)18\({}^{\prime}\)52.61\({}^{\prime}\)E, 42\({}^{\circ}\)24\({}^{\prime}\)35\({}^{\prime\prime}\)N), which is an artificial forest that is approximately 60 years old, with a tree height of approximately 22 m. The forest is composed of a single tree species with a high canopy density and sparse shrubs and grasses. As the land cover type is larch, the monitored larch at this observation site is highly representative. The second station (SHB2) is located in a Scots pine (_Pinus sylvestris_ var. _mongolica_) forest (117\({}^{\circ}\)22\({}^{\prime}\)12\({}^{\prime\prime}\)E, 42\({}^{\circ}\)23\({}^{\prime}\)28\({}^{\prime\prime}\)N), which is also an artificial forest but approximately 10 years old. The forest is composed of a single species with high understory herb coverage. The canopy height of Scots pine ranges from 3 to 5 m, and the forest area is relatively small (250 m \(\times\) 500 m). The area around the Scots pine forest includes grasslands, larch, and birch (_Betula platyphylla_) forests, making the underlying surface of the EC flux footprint relatively complex. Saihanba experiences a temperate continental monsoon climate within a cold and semiarid zone, characterized by an average temperature of \(-\)1.2 \({}^{\circ}\)C and an annual precipitation of 452 mm. The predominant soil type in the area is sandy loam [30].
# Long-Range Wireless Radio Technologies: A Survey
## 2 Long-Range Wireless Technologies
### ISM Band-Based Technologies  
In contrast to mobile IoT technologies, ISM-based LPWAN technologies are using unlicensed parts of the spectrum to carry communications. Unlicensed bands are usable by everyone without any specific authorization. In return, legal restrictions must be respected, e.g., a given duty cycle that must not be overrun.
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 4 Applications of LLMs in Maritime Operations
### Case Studies
#### 4.5.2 Automated Failure Reporting  
Large Language Models (LLMs) can effectively analyze maintenance logs and sensor data to generate detailed failure reports, which are crucial for diagnosing equipment malfunctions. These models excel in tasks such as log parsing, anomaly detection, and report generation, enhancing the accuracy and efficiency of log analysis [146; 147]. By automatingthe analysis of logs, LLMs streamline communication between onboard engineers and shore-based support teams, facilitating faster repairs and minimizing downtime [148,149]. Automated failure reports improve communication by providing clear and structured information about malfunctions, which helps in coordinating repair efforts and decision-making processes [150]. This approach reduces the time required to diagnose and fix issues, thereby minimizing operational disruptions and enhancing the reliability of maritime operations [115]. The integration of AI-driven predictive maintenance further supports this by enabling early fault detection and maintenance planning, which is critical for reducing downtime in the maritime industry [117,151].  
While these applications demonstrate the transformative potential of LLMs, their adoption is not without challenges. The following section critically examines the limitations and risks associated with LLM integration in maritime safety.
## 3 Results
### The Performance of ROM for Heat Flux Prediction
The relative errors of 1000 data points have the narrowest range in outlet heat flux prediction of the nanofluid-filled absorber tube: the prediction errors of 95% samples are lower than 0.99% and greater than \(-\)0.84% (presented with short vertical lines); 50% of the samples fall within the range between \(-\)0.1% and 0.39% (presented with blue box). At the same time, the smallest dataset with 500 data points has the largest relative error range, between \(-\)4.7% and +5.4%. The average accuracies of all the validation data with the four sizes of dataset are shown in Figure 13. Although based on the results of the 81 samples, the 1000- and 1500- data-point datasets show higher coefficients of determination and a narrower range of relative errors, the differences are very small. In addition, the ROM trained by 2000 data points shows the highest average accuracy on the validation dataset, which means it performs better in heat flux prediction compared to the ROMs trained by 500, 1000 or  
Figure 13: Accuracy of the outlet heat prediction on validation dataset. 1500 data points. All the results in the section show a huge potential of the proposed ROM to replace the CFD solver for outlet heat flux prediction of nanofluid-filled absorber tubes. Furthermore, we compare the heat flux of the absorber tube for four fin geometries, as listed in Table 4. The fin adjacent angle is fixed at 45\({}^{\circ}\), the fin width is set at 5 mm, and the fin height is from 5 mm to 20 mm. Firstly, the four cases' relative errors are very small, less than 0.2%, which confirms the accuracy of the proposed model. Then, the \(\mathrm{q_{out}}\) values vary with the change in fin height. In detail, the \(\mathrm{q_{out}}\) decreases when the height of the fin augments. This is reasonable, as we fixed the inlet velocity. Thus, the flow rate is proportional to the cross-sectional area of the fluid domain. When the height of the fin increases, so does the surface of the fin. Consequently, the cross section of the fluid domain decreases, then the rate of flow decreases. Therefore, there is not enough working fluid to carry the heat out, and the \(\mathrm{q_{out}}\) reduces.
# Optimal Prediction of Individual Vessel Trajectories Based on Sparse Gaussian Processes
## 7 Conclusions and Future Work  
This study aimed to develop a prediction model for individual ship trajectories. The application of SGPR and GA to historical AIS data resulted in the development of models that enable the presentation of Bayesian-based posterior probabilistic results rather than  
Figure 7: Accuracy comparison with increasing prediction time.  
deterministic ones. This study highlights the mitigation of FGPR's limitations in terms of storage cost and time complexity by using SGPR and GA, providing a foundation for further exploration in this area.  
Additional research is required to enhance the precision of long-term ship motion forecasts, enabling accurate decision making for collision avoidance between vessels through trajectory prediction. Furthermore, research aimed at identifying a resolution should be incorporated to ensure that the performance of the prediction system remains stable, even when traversing lengthy curved routes like the one leading to Mokpo Port.  
The proposed model facilitates the implementation of appropriate measures to avoid vessel collisions that might arise from misinterpreting the maneuvers of the encountering vessel. This facilitates timely and rational decision making, enabling a navigator to initiate avoidance maneuvers earlier through the provision of an exact predictive trajectory in situations where collision risk is present. Vessel traffic service operators (VTSOs) may also utilize it to monitor vessel traffic and deliver navigational safety information while at sea. Moreover, it will prove beneficial for research pertaining to autonomous surface vessels in the maritime domain that generates routes to prevent collisions.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The author declares no conflict of interest.
## 3 Work-Flow
### From Unstructured Text to Semantic Flows
(SNAP: Semantic Networks Analysis Project) (1) Original text of a single document uploaded for processing, (2) Natural Language Processing (NLP) step processes text by stemming, tokenizing, sentence splitting, part-of-speech tagging, lemmatizing and named entity recognition, (3) content narrowing by stop-word and frequency threshold token removal (TF-IDF—term frequency-inverse document frequency), (4) lexical chaining generation of semantic networks using sliding window (3 token window shown) and the merger of sub-networks into a document wide network, (5) Social Network Analysis of the document network. Detected communities of nodes (nodes with the same color) represent semantic concepts. Node’s size proportional to its Eigenvector centrality, (6) 3D network of semantic flows is constructed by connecting matching semantic concepts across multiple documents. The illustration shows breakup of a yellow semantic concept into two sub-concepts in the subsequent document. The other two semantic concepts are propagated with minimal change across two sample documents.
## 5 System Simulation
### Energy Calculations
This stresses the importance of correctly setting the different parameters, and specifically the activity period, which specifies the speed of updating the network and detecting errors at the expense of spending more energy. Also Figure 10b shows similar results to that of Figure 10a, with the difference that here the energy consumption spikes at the first period to around 30 mJ and then averages to around 1.7 to 2 mJ/period for the remaining network lifetime. This increase is due to the added authentication bytes communicated by the nodes. Table 8 shows that the energy dissipated to transmit and receive normal sensor packets is around 6 mJ to 8 mJ/period. Thus, the communication energy overhead is still minor when compared to the normal functioning of the sensor network. Figure 10: (**a**) Transmission energy without authentication in \(\upmu\); (**b**) transmission energy with PBC authentication in \(\upmu\). \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Pass**} & \multicolumn{3}{c}{**Hello**} & \multicolumn{3}{c}{**Reports**} & \multicolumn{3}{c}{**SUB**} & \multicolumn{3}{c}{**OH**} & \multicolumn{3}{c}{**Normal**} \\ \cline{3-11}  & **Tx** & **rx** & **tx** & **rx** & **tx** & **rx** & **rx** & **tx** & **rx** & **tx** & **rx** \\ \hline \multirow{3}{*}{2} & Network & 3494.40 & 3216.00 & 207,782.40 & 467,531.36 & 9772.80 & 7900.64 & 221,049.60 & 478,648.00 & 0.00 & 0.00 \\  & Av. Per Node & 43.68 & 40.20 & 2597.28 & 5844.14 & 122.16 & 98.76 & 2763.12 & 5983.10 & 0.00 & 0.00 \\  & Worst Case (42) & 48.00 & 53.60 & 2822.40 & 6619.60 & 556.80 & 375.20 & 3472.20 & 7048.40 & 0.00 & 0.00 \\ \hline \multirow{3}{*}{8} & Network & 0.00 & 0.00 & 18,585.60 & 15,994.24 & 9926.40 & 8072.16 & 28,512.00 & 24,066.40 & 193,536.00 & 168,089.60 \\  & Av. Per Node & 0.00 & 0.00 & 232.32 & 199.93 & 124.08 & 100.90 & 356.40 & 300.83 & 2419.20 & 2101.12 \\  & Worst Case (32) & 0.00 & 0.00 & 1267.20 & 1350.72 & 624.00 & 423.44 & 1891.20 & 1774.16 & 12,902.40 & 13,807.36 \\ \hline \multirow{3}{*}{14} & Network & 0.00 & 0.00 & 18,585.60 & 15,994.24 & 9705.60 & 7809.52 & 28,291.20 & 23,803.76 & 193,536.00 & 168,089.60 \\  & Av. Per Node & 0.00 & 0.00 & 232.32 & 199.93 & 121.32 & 97.62 & 353.64 & 297.55 & 2419.20 & 2101.12 \\  & Worst Case (50) & 0.00 & 0.00 & 1910.40 & 2068.96 & 336.00 & 230.48 & 2246.40 & 2299.44 & 193,533.60 & 21,011.20 \\ \hline \multirow{3}{*}{20} & Network & 768.00 & 3044.48 & 18,585.60 & 15,994.24 & 10,771.20 & 8961.92 & 301,124.80 & 2800.00 & 64,1515.200 & 126,067.20 \\  & Av. Per Node & 9.60 & 38.06 & 232.32 & 199.93 & 134.64 & 112.02 & 376.56 & 350.01 & 1814.40 & 1575.84 \\  & Worst Case (32) & 9.60 & 42.88 & 1382.40 & 1479.36 & 710.40 & 493.12 & 2102.40 & 2015.36 & 10,483.20 & 11,256.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Transmission Energy in (\(\mu\)J) without Authentication.