esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Data Forwarding Strategy for a Drone Delay Tolerant Network with Range Extension
## 6 Conclusions  
This paper presented a novel approach to intelligent transportation system and data communications where well-known retail companies such as Amazon, UPS, and Dominos are focusing on providing a data delivery service on top of the drone logistics network. Heuristic Flight Path Planning (HFPP) considers the data packet's priority, time to live, and energy consumption for the transmission to weight them. Accordingly, the destination of high weighted packets is added to the drone's flight path such that a minimum energy inefficiency is achieved through a modified TSP solver. In the case of needing more energy to meet more waypoints, as long as the consignment delivery is not overdue, the drone can add the nearby wireless charging station to the flight path to refill the necessary energy to complete the trip. The results show that HFPP delivers up to 33% more packets against comparable methods. Also, HFPP reduces data delivery delays up to 85% while keeping the overhead in low ratio.  
[PERSON] and [PERSON] worked on the conceptualization and methodology. [PERSON] worked on software, formal analysis, visualization, writing the original draft and data curation. [PERSON] supervised the project and the administration. [PERSON] also investigated the performance of the model followed by the resources, and review and editing the final draft.  
This research received no external funding.  
The authors declare no conflict of interest.
## 6 Cost Analysis of Smart Contract Execution
### Cost Breakdown and Business Model Implications  
In the following, we discuss the cost of each smart contract function along with its deployment. These costs are reported in Table 1.  
Deployment Cost: The cost for deploying the smart contract is 461,322 gas, which translates to approximately USD 23. This deployment is a one-time cost incurred by the broker. Given its single occurrence, it is a manageable and reasonable investment to set up the system.  
Adding Clients (addClient function): We recall that this function can only be invoked by the broker to include a new publisher or subscriber in the system. Each time this function is invoked, the broker incurs a cost of about 68,401 gas, equivalent to USD 3.41. In our business model, we propose that this cost be covered by customers (publishers or subscribers) as a one-time fee upon joining the system. This cost is not prohibitive, so it is a feasible expense for new users.  
Storing Root Hash and IPFS Address (addRootIPFSAddress function): We recall that the addRootIPFSAddress function can only be invoked by the broker. Specifically, the broker invokes it at the end of each temporal round to store the root hash of the Merkle Hash Tree (MHT) along with its corresponding IPFS address. This function consumes approximately 47,016 gas, resulting in a cost of around USD 2.34 per execution. The broker bears this recurring expense since this function is called at the end of each round. The frequency of these calls depends on the length of the time round: shorter rounds mean more frequent costs, while longer rounds reduce the frequency but delay the validation of message integrity for subscribers.  
Integrity Violation Alerts (pullTheAlarm Function): We recall that the pullTheAlarm function signals a violation of the integrity of the data exchanged. This function costs about 26,763 gas per call, about USD 1.33. This cost can be borne by the publisher or the subscriber reporting the violation. Since this function is called only when an integrity violation is detected, it is not a regular expense, but rather an occasional one. Therefore, the cost appears sustainable  
Fetching IPFS Address (getIPFSAddress Function): The getIPFSAddress function is a view function, which means that it does not alter the state of the blockchain and therefore does not incur any gas costs. This function is regularly used by publishers and subscribers to retrieve the IPFS address of the MHT for verification purposes. Thus, the integrity verification operation has no cost for publishers/subscribers.  
Figure 2: Cost of invoking the smart contract functions in US dollars over the year 2023.
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## 5 Performance Results and Evaluation
### Trade-Offs and Inefficiencies
#### 5.5.3 Mitigation Strategies  
To address these trade-offs and inefficiencies, the following strategies can be implemented:  
* Hardware upgrade: Incorporating more powerful edge computing devices, such as NVIDIA Jetson Nano or Xavier, could significantly improve processing speed and reduce latency.
* Hybrid Processing: Offloading computationally intensive tasks to cloud-based servers while maintaining local processing for critical operations can balance efficiency and responsiveness [15].  
* Model Compression: Techniques such as knowledge distillation, model pruning, and quantization can reduce YOLOv5's resource demands while maintaining detection accuracy.
* Enhanced Cooling Systems: Implementing advanced thermal management solutions, such as active cooling or heat sinks, can mitigate thermal throttling and ensure consistent performance.
* Dataset Augmentation: Expanding the training datasets to include more varied industrial scenarios can improve the model's robustness, reducing false positives and negatives.
## 5 Computational Experiments
Notice that for large instances (e.g., 1600 jobs), our approach provides solutions in times less than 0.2 s using the FIFO deterministic strategy, while Anylogic requires times greater than five seconds of computation to provide the same solutions. Regarding the different sorting methods, Figure 2 summarizes the results provided in Table 1, where the vertical axis of the boxplot represents the gap obtained with respect to the FIFO strategy. Notice also that, after applying biased-randomized techniques on the original FIFO strategy, the average gap outperformed the former by about 5.52% on the average. The original FIFO strategy does not contain any optimization strategy, it just dispatches jobs in the original order. Hence, improvements are achieved as more-intelligent sorting methods are used to dispatch the jobs. In that sense, the BR-NEH sorting method, which is based on sorting the jobs by total processing time in decreasing order, is able to enhance the original heuristic by 13.81% on average. Notice that the setup times of each job-machine pair may induct to lose the logic behavior of the NEH heuristic, since jobs with short processing times and large setup times are not prioritized in the sorted list of jobs. Thus, to address this limitation, the BR-STNEH sorting method also considers the setup times of each job-machine pair during the sorting process, outperforming the BR-NEH by about 2.05% (obtaining an average gap of 15.86% with respect to the initial FIFO strategy). Regarding the computational times, on average, there were no significant differences between the BR-NEH and BR-STNEH sorting methods to reach the best-found solution. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \multicolumn{1}{c}{**AnyLogic**} & \multicolumn{2}{c}{**De-FIFO**} & \multicolumn{2}{c}{**BB-FIFO**} & \multicolumn{2}{c}{**BB-NEH**} & \multicolumn{2}{c}{**BB-STNNH**} & \multicolumn{2}{c}{**GAP-GA**} \\ \hline
**Instance**
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
## 4 Results
### Effect of Additional Future Samples  
Missing samples were the most problematic case for the single-delay scenario. The RMSE can be reduced by providing more than a single future sample for the reconstruction algorithm (Figure 13). Taking a second future sample for the noisy test signal improved the OSR by 18%, a third future sample by 23%. Adding a fourth future sample brought only marginal advantage. Margolis profited less from additional future samples with only 7% improvement by using three future samples. The case of three future samples corresponds to Akima with a fixed input window of \(\pm 3\) samples. Allowing 25 future samples would be identical to the group-delay scenario. The number of future samples that can be used depends on the acceptable group delay in the related application.  
Figure 12: Comparison of single-delay scenario with missing samples for Kriging with zero nugget (dotted lines) and nugget set according to the noise amplitude (solid lines).  
In case of zero future samples, a prediction for the current point of time is calculated before the next input sample becomes available. The prediction requires extrapolation instead of interpolation. The prediction error largely increases and requires the double OSR = 4.34. In case an input signal with missing samples and noise, the threshold of \(-40\) dB was not achieved without future samples.
## 2 Preparations: Materials and Methods  
### On-Site Data Acquisition  
The spacing of each site for panoramic photograph acquisition should be as dense as possible, aiming to replace on-site observations to a certain extent, by generating a virtual tour without blind spots. For example, when acquiring photographs in the corridor, one site per bay or one site per beam was selected; when acquiring data under outer eaves, one site per one bay was selected; for special locations, such as a stone tablet, surround acquisition was essential (Figure 4).  
During ground acquisition, a countdown of 3 s to 8 s was set so that the photographer could move to the appropriate position to avoid occupying too many areas for image acquisition. If possible, GPS functions were activated to auto-link hotspots in the virtual tour.  
During UAV acquisition, the stability needed to be ensured during exposure to avoid stitch problems.  
Three-dimensional scanning and detailed photo shooting could also use these sites.  
To avoid overexposure in bright parts, or no images in dark parts, if the light contrast is strong, 3D panorama photographs were taken in raw format. Raw files could be post-processed in Camera Raw, a plug-in of Photoshop. As a result, both the bright parts and dark parts preserved solid images (Figure 5).  
In the completely dark environment of the blindstory, raw photographs were taken with a Canon 6D camera with an 8 mm fish-eye prime lens, a 720 panoramic camera pan, and a group of fill lights (Figure 6).  
In all conditions, for both ground devices and UAVs, raw formats are most suitable because they are faster, saving on-site time and processing in the lab, i.e., stitching panoramas, adjusting the brightness.  
Figure 4: Sites (red spots) in the corridor, by the stone tablet, and under the outer eaves.
# Deep-Learning Based Positron Range Correction of PET Images
## 3 Results
### Model Deployment and Quantitative Analysis  
In order to test the model, the trained Deep-PRC network (PET, five slices) was applied to a simulated case considered in neither the training nor the validation process. The time required to obtain the PRC on the 80 slices of the whole volume was 5.14 s in a T4 GPU and 2.85 s in a V100 GPU. Although these results could be sped up by using multiple GPUs, they are already fast enough to be used in preclinical and clinical applications.  
Figure 5: Evolution of the loss function over the epochs during the training using the training and the validation datasets for the different cases evaluated.  
Figure 6: Example of a coronal view of one of the evaluated cases, corresponding to the \(\upmu\)-map obtained from the CT (**a**), and the reconstructed images of \({}^{18}\)F (**b**), \({}^{68}\)Ga (**c**), and \({}^{68}\)Ga after the PRC (**d**).  
Profiles along some organs of interest, such as the bladder, heart and tumor in the \({}^{18}\)F, \({}^{68}\)Ga, and \({}^{68}\)Ga with Deep-PRC images are shown in Figure 7. The significant impact of the PR in these cases is quite clear, as well as the capacity of the proposed method to correct for this effect. The differences in SUV units between the estimated images (\({}^{68}\)Ga with Deep-PRC) and the reference one (\({}^{18}\)F) are shown in Figure 8. Areas with high uptake, such as the bladder, still had some residual error (as it can be also seen in Figure 7a), but it was a small deviation in relative terms.  
The quantitative analysis of the results is shown in Table 2. From the table, it is clear that the \({}^{68}\)Ga images corrected by PR images were very similar to the \({}^{18}\)F images (with recoveries greater than 95% of the reference values). Additionally, the noise level of the estimated images was comparable to that of the reference ones, which indicates that the proposed method did not trade noise for resolution, as is the case in many deconvolution-based approaches for PRC.  
Figure 8: Images of the difference between the estimated images (\({}^{68}\)Ga with Deep-PRC) and the reference ones (\({}^{18}\)F) for each of the cases considered.  
Figure 7: Profiles across different regions in the \({}^{18}\)F, \({}^{68}\)Ga, and \({}^{68}\)Ga with Deep-PRC images: bladder (**a**), heart (**b**), and tumor (**c**).
## 4 Artificial Intelligence in Ultrasound in Symptomatic Carotid Plaque
### Application of AI for Vulnerability Plaque Assessment
The first application of AI in the automated recognition of plaque motion as a marker of vulnerability was proposed in one study conducted by [PERSON] et al. B-mode ultrasound images were obtained from a cohort of 77 patients with atherosclerotic carotid disease, 18 of which were symptomatic patients with carotid stenosis of 66% \(\pm\) 29%, 57 were asymptomatic with degrees of stenosis 73% \(\pm\) 22%, while in 2 patients, the symptomaticity and degree of stenosis were unknown. In this study, an automated machine learning random forest (RF) algorithm was used to recognize different plaque motion patterns (evaluating the displacements inside the plaque and between the plaque and wall) and relate them to vulnerability on the basis of US echogenicity, the degree of stenosis, and symptomaticity of the plaque. The study showed that plaque asynchronism was related to symptomaticity, echogenicity, and high-grade stenosis of plaque, proposing an accurate automated method (AUC of 0.81, 0.79, 0.89, and 0.90 for the association of plaque motion with echogenicity, symptomaticity, stenosis degree, and plaque risk, respectively) to detect vulnerable plaques for assessing stroke risk. The study was limited by the moderate size of the cohort and the heterogeneity of the dataset.
# SDS: Scrumptious Dataflow Strategy for IoT Devices in Heterogeneous Network Environment
## 1 Introduction
### Smart Homes
#### 1.1.1 IoT-Based Transport  
Sensors and cognitive information processing systems can be used by IoT-based transportation apps to govern everyday traffic in the city. The primary aims of intelligent traffic systems are to reduce traffic congestion, make parking easier and stress-free, appropriately route traffic, and eliminate accidents by identifying intoxicated drivers. GPS sensors for position information, accelerometers for speed, gyroscopes for direction, RFID for vehicle identification, and infrared for counting passengers and cars are examples of IoT devices with sensor technologies for these sorts of applications, as well as sensors and cameras for documenting traffic and vehicle movements.
## 4 Interference Monitoring
### Carrier to Noise Ratio  
Carrier to noise ratio (\(C/N_{0}\)) is the ratio of received signal power to noise power spectral density. Carrier to noise ratio affects the performance such as the acquisition, tracking and data demodulation of receivers, which will ultimately affect the positioning precision of receivers. When the inference exists, it superimposes noise on the GNSS signals, thus reducing the carrier to noise ratio of receivers and increasing the measurement errors of pseudo range and pseudo range rate. This will result in the increase of the error of navigation positioning. When the interference power is too high, the carrier to noise ratio will fall below the tracking threshold, making receivers lose their capabilities of obtaining the navigation information from satellite signals. Therefore, carrier to noise ratio can be used as an interference monitoring indicator.  
When inferences exist, the carrier to noise ratio is given by:  
\[\left(\frac{C_{s}}{N_{0}}\right)_{eff}=\frac{\frac{C_{s}}{N_{0}}\int\limits_{- \beta_{r}/2}^{\beta_{r}/2}G_{s}(f)df}{\int\limits_{-\beta_{r}/2}^{\beta_{r}/2}G _{s}(f)df+\frac{C_{l}}{N_{0}}\int\limits_{-\beta_{r}/2}^{\beta_{r}/2}G_{l}(f)G_ {s}(f)df} \tag{18}\]  
where \(\beta_{r}\) is the bandwidth of receiver front-end and processing filter, \(N_{0}\) is the noise PSD, \(C_{s}\) is the carrier power of navigation signals, \(G_{s}(f)\) is the normalized PSD of navigation signals, \(C_{l}\) is carrier power of the interference signals, and \(G_{l}(f)\) is the normalized PSD of interference signals.
# Towards Incidence Management in 5G Based on Situal Awareness
## 5 Analysis and Decision-Making
### Analysis
#### 5.1.7 Countermeasure Tracking  
The countermeasure tracking component conducts comprehensive monitoring of the actions proposed by the decision-making modules for dealing with situations. This allows for identifying ineffective countermeasures that may lead to new diagnosis or prevention of counterproductive situations. On the other hand, the countermeasure tracking stage allows for the development of an immune memory. Therein, all situations that have been resolved or are being processed are stored. This allows knowing how a problem was previously solved, offering added value to the decision-making. In addition, it provides information about all similar problems that are being processed, which may facilitate the correlation of incidences. In order to enhance the countermeasure tracking tasks, the proposed architecture implements an Issue Tracking System (ITS). The ITS assigns a ticket to every situation tracked in the protected environment. Tickets are running reports on a particular problem, which contains their nature, history and other relevant data. They are continuously analyzed to provide real-time status of the situation, and they record all the countermeasures implemented over time, as well as their effectiveness. The ITS is illustrated in Figure 6, where the original situation to be treated is provided by a use case, or it is detected from the network map. Then, the incident is analyzed, and a token with the results is sent to the decision stages. If countermeasures should not be applied, the ticket is close; in this case, it is assumed that the problem is fixed, and the solution is stored in the immune memory. Otherwise, countermeasures are applied. Then, the evolution and effectiveness of such actions are studied and added to the ticket history field; as in the previous case, the progress is also stored in the memory. The resulting ticket is sent back to the decision-making module, and this process is repeated until the problem is fixed.
# Digital Art and the Metaverse: Benefits and Challenges
## 1 Introduction
Further, extended reality services (e.g., virtual reality and augmented reality) and AI provide to modern artists new technics, tools for showcasing their creations to their audiences in 3D digital platforms. As the development of the Metaverse continues, more possibilities for fine art artists are available. The utilisation of non-fungible tokens (NFTs) is, thereby, empowering (digital) artists, including those working in visual and audio media, to establish their creatorship of digital content [14]. This revolutionary technology enables artists to prove ownership and safeguard their intellectual property rights, which is of paramount importance in the world of art. Additionally, NFTs allow artists to sell their designs as unique, one-of-a-kind digital assets, thereby providing publicly recognisable ownership [15]. Hence, the ability to authenticate authorship and to sell digital art as distinctive assets are of immense significance in the art industry, and NFTs have opened up new possibilities for artists to monetize their talent and creativity. Though the expected benefits for artists and the cultural society as a whole appear to be evident, less is known about the actual consumer preferences and opinions regarding AI-generated artwork exhibited in digital galleries. Hence, in this article, a novel focus is provided through reflection on the nexus between technologies related to the Metaverse, digital art, AI and NFTs. This nexus can be considered as emerging, with relatively untapped potential. Thus, the following research questions are put forward, from which the aggregated findings can be used to investigate what benefits and challenges may emerge as the technology develops: (1) To what extent are consumers currently aware of digital art and the Metaverse? (2) To what extent will the Metaverse be beneficial for artists? (3) How will consumers perceive AI-generated art? and (4) What role does AI play for digital art and the Metaverse? Answering these questions will prime an understanding of how artists could potentially use digital platforms to create and share digital art and explore the role of digital art in the context of society (and its potential social and cultural implications for consumers). The method used to answer the research questions relies on a synthesis of works in the existing literature and a mixed-method questionnaire surveying a community of consumers by developing an AI-based virtual art gallery. This narrative will contribute towards what is an unexplored topic through both a systematic view and methodological primary data collation process (for one specific use case) that can serve as a platform for subsequent research. The remainder of this article is as follows. Section 2 provides a background on the Metaverse concept and current state of the art within this domain. Section 3 discusses the methodology used for the experimentation and survey process. Section 4 provides the findings and answers the research questions, with a discussion of the key benefits and challenges outlined in Section 5. The article is concluded in Section 6.
# Detection of Geomagnetic Signals as Precursors to Some Earthquakes in China
## 4 Discussion
An important phenomenon is that when the relationship between magnitude and epicentral distance satisfy the relationship of \(M\geq 0.011R+4.175\), some seismomagnetic anomaly signals are more likely to appear before an earthquake. Statistical results showed that 50% of the earthquakes have seismomagnetic signals before them among the earthquake samples that meet the condition of \(M\geq 0.011R+4.175\) in Figure 6a. We also noticed that most earthquakes which met the condition of \(M\geq 0.011R+4.175\) but did not show pre-earthquake magnetic anomaly occurred near the GLM station. Figure 6b shows that some earthquakes near GLM station were removed, and 67% of the earthquakes that met the condition of \(M\geq 0.011R+4.175\) had seismomagnetic signals before them. Therefore, we preliminarily believe that earthquakes near GLM stations do not easily generate pre-earthquake magnetic signals, which may be related to the geological structure or earthquake types of the observatory. This preliminary conclusion is only based on the results given by the 37 earthquakes in the paper, and more detailed relationships and causes require more samples in future work. In terms of the seismo-electromagnetic emissions mechanism, some researchers have proposed many mechanisms for the generation of seismo-electromagnetic emissions, such as induced magnetic effect, piezomagnetic effect, electrokinetic effect, thermomagnetic effect, and micro-rupture mechanism. [PERSON] and [PERSON] proposed a quasi-static model, in which the conductive fault zone acts as an antenna to couple with the external geomagnetic field to generate the observed geomagnetic anomalies [7, 31]. Precursory changes in fault-zone conductivity lead to precursory changes in observed geomagnetic field.