esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions and Future Work  
In this work, we presented an approach that includes the use of social relationships as a criterion for disseminating messages in Opportunistic Networks. We propose the \"FriendShip and Acquaintanceship Forwarding\" (FSF) algorithm that considers two social characteristics: the social relationships between nodes, and the selfishness of relay candidate nodes. FSF performs two main tasks: first, FSF classifies the social bonds between nodes; second, FSF uses a reputation system to verify the egoism of the relay node by considering cases where, despite a strong social relation to the destination, the relay node may refuse to receive the message because it is selfish, or its device has resources' constraints at that time.  
To validate the proposed algorithm, we performed a set of experiments to determine the effectiveness of message delivery in four scenarios based on trace-based simulations by means of the ONE simulator. Compared to other routing algorithms of the same class, FSF achieved better results in the context of a set of standard metrics, namely the delivery rate, the average cost, in addition to achieving reasonable results in terms of delivery delays. In addition, we also conducted a set of experiments to determine the impact of three FSF variants. From the results obtained, it can be observed that FSF performance is strongly impacted by the presence of selfish nodes in the network. However, the results also showed that adding a buffer management algorithm can improve the delivery ratio, even in scenarios with the presence of selfish nodes in the network.  
As future work, we intend to carry out a comparative study of adopting other machine learning algorithms to classify the friendship strength among pairs of nodes. Moreover, we want to analyze the impact of selfishness in the routing process by using different thresholds for device resource restrictions.  
[PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] conceived the idea; [PERSON], [PERSON], [PERSON], [PERSON] designed and implemented the protocol; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] analyzed the data; all the authors wrote the paper.  
This work was partially supported by the \"[PERSON]/Programa Doutorado-sanduche no Exterior (PDSE)/Processo 88881.133931/2016-01\" and by the _Ministerio de Ciencia, Innovacion y Universidades, Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad, Proyectos l+D+1 2018_, Spain, under Grant RTI2018-096384-B-I00\".  
The authors declare no conflict of interest.
## 4 Applications of Intelligent Acoustic Monitoring
### Smart Cities and Urban Monitoring
#### 4.2.3 Traffic Monitoring with DAS  
In [110], [PERSON] et al. described an experiment using Distributed Acoustic Sensing (DAS) technology to monitor road traffic in real urban conditions, utilising existing communication fibre optics in Granada (Spain) as sensors. The aim was to determine whether DAS could be useful for the constant and exact observation of street traffic events. Analysing signals captured by optical fibres, they identified various types of moving objects, such as cars, and generated a feature vector using domain knowledge to more precisely separate acoustic events. Clustering methods were employed to identify the optimal frequency bands, key measurement points, and distinctive signal characteristics; exploratory data analysis (EDA) was also conducted. Apart from offering a valuable approach for processing DAS data in an urban setting, the study also opened the door for future research on automatic detection of anomalies and irregular urban traffic events employing artificial intelligence. Similar observations were made by [PERSON] et al. in [112] and [PERSON] et al. in [111]. [PERSON] et al. in [111] concluded that DAS technology could be effectively used for continuous and anonymous monitoring of urban traffic, enabling the automatic recognition of different types of traffic participants and generating data that helped plan innovative and sustainable cities. They also demonstrated that it was possible to obtain helpful information despite interference and changing environmental conditions by using appropriate signal processing.  
In [115], [PERSON] et al. developed and tested an algorithm for detecting and counting road vehicles based on the sound intensity analysis measured by a small, passive acoustic sensor placed on the road. The innovative element of the method was the use of two-dimensional sound intensity measurement (parallel and perpendicular to the roadway), which allowed not only the detection of the presence of a vehicle but also the determination of its direction of movement. The system was tested on a 24 h recording in real conditions and showed high efficiency (precision, sensitivity, and F-score of 0.95) in increased traffic conditions. Compared to other acoustic detection methods or technologies such as induction loops or radars, the authors' solution was distinguished by its simplicity, low cost, lack of wave emission and the ability to operate on weaker equipment without the need for installation in the road surface. Due to these features, the system could be widely used in traffic monitoring networks within smart cities. In the future, the authors plan to conduct further tests under more challenging conditions, such as increased traffic intensity, environmental noise, or vehicle obscuration phenomena.
# The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review
## 3 6G-IoT: New Technologies and Emerging Services
### Sustainable End-to-End Network Architecture: The Role of Green Technologies  
Sustainability is a primary consideration for 6G networks, with an emphasis on the reduction of energy consumption and the employment of green technologies. The objective is to design an end-to-end network architecture that operates efficiently while minimizing its environmental impact. Consequently, the concept of energy optimization is not restricted to a single 6G network component. Instead, it incorporates the entire network architecture with the objective of reducing energy consumption and maximizing the use of renewable energy sources [88].  
In this context, it is anticipated that 6G networks will employ artificial intelligence to optimize energy consumption via intelligent power management processes. By employing AI and ML algorithms to monitor and optimize energy usage, it will be possible to reduce the energy consumption of 6G networks without impacting their performance [89]. This will not only reduce the ecological footprint of 6G networks, but it will also help extend the battery life of wireless IoT devices. Furthermore, it is anticipated that energy-efficient hardware will be one of the most significant green innovations utilized by 6G-IoT. As the demand for wireless communication increases, so does the energy requirement to power wireless devices. Through the use of energy-efficient hardware, it is possible to reduce the amount of energy required for data transmission, thereby reducing the carbon footprint of 6G networks.  
Energy harvesting is also an essential technology that is expected to be employed in 6G networks. This technology captures and transforms ambient energy sources like heat, light, and motion into electrical energy and is typically used for powering low-power devices or sensors in remote or inaccessible areas. Moreover, it is anticipated that 6G networks will make extensive use of renewable energy sources such as solar, wind, biomass, and hydroelectric power, which are becoming increasingly efficient and cost-effective. By utilizing renewable energy sources to power 6G networks, it will be possible to decrease their reliance on fossil fuels and, in turn, their carbon emissions [90; 91; 92].
# Method for Wind-Solar-Load Extreme Scenario Generation Based on an Improved InfoGAN
## 4 Case Study
### Analysis of the Effectiveness of Different Models  
To accurately evaluate the performance of the three models in capturing the characteristics of historical wind, solar, and load series data, we introduce extreme scene ensemble validity indexes, focusing on two primary metrics: extreme scene coverage and the average  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Categories** & **Parameter** & **Value** \\ \hline Wind power & Scale1 & 4.738 \\ Wind power & Variance \(\beta\)1 & 2.684 \\ Solar power & Scale \(\alpha\)2 & 0.326 \\ Solar power & Variance \(\beta\)2 & 1.764 \\ Load & Average \(\mu\) (MW) & 724.3701 \\ Load & Variance \(\sigma\) (MW) & 147.3181 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter settings of the distribution.  
width of power intervals. The formula for calculating the coverage rate is given in (22), while the formula for the validity index is presented in (23).  
\[C=\frac{T^{\prime}}{T}\times 100\% \tag{22}\]  
\[W=\frac{1}{T}\sum_{t=1}^{T}\left(P_{t}^{up}-P_{t}^{down}\right) \tag{23}\]  
where \(T^{\prime}\) is the number of moments when the real scene data is enclosed within the scene set; \(T\) denotes the total number of moments in the outgoing force sequence; \(C\) is the cover rate, the larger its value, the more reliable the generated set of scenarios; \(P_{t}^{up}\) is an upper bound on the set of scenes generated at time \(t\); \(P_{t}^{down}\) is the lower bound of the set of scenes generated at time \(t\); and \(W\) is the average width of the power interval of the generated scene, and the smaller its value, the closer the generated scene data is to reality.  
By applying the validity indexes to analyze the generated scenes, the validity indexes for the scene data produced by the three models were obtained through comparative experiments, as shown in Table 4. This comparison highlights the differences in the models' ability to fit extreme scene data and their effectiveness in capturing variations in power intervals.  
By analyzing Table 5, it can be observed that the data generated by the proposed improved InfoGAN model in Case 3 for wind power, solar power, and electric load scenarios demonstrate a higher coverage index \(C\) compared to the other cases. Additionally, Case 3 exhibits a smaller average width index \(W\) of the power intervals than the other models. This comparison shows that the InfoGAN model in Case 3 achieves higher coverage while maintaining a lower average width of power intervals, indicating that the proposed model generates scenario data that more realistically and reliably aligns with real-world conditions.
###### Abstract
Based on a literature review, an industry survey, and expert knowledge, a refined maturity model is herein proposed. This model accounts for the necessary correlations when assessing maturity, allowing for more precise recommendations to manage data challenges. This work attempts to answer the following research question:  
RQ: Which capabilities and process dimensions should be part of a data management maturity model that can be used in practice as a basis for interviews to determine the status quo in the transition to a data-driven organization? To achieve the objective of this study, the paper proceeds as follows: Section 2 defines key terms related to the background and provides an overview of the methodology. It also examines the current research status of maturity models and identifies the research gap. Section 3 outlines the development phases of the maturity assessment in detail, followed by a discussion of the findings and limitations. Section 4 also offers an outlook on future research plans.
# Managing Emergency Situations in VANET Through Heterogeneous Technologies Cooperation
## 3 Architecture and Communication Protocol
### Protocol Communications
#### 3.2.4 High Layer Protocol Messages
* _Map Update Message_ (MapUpMsg): it is sent by HRTM to LRTMs periodically along the ILLI interface to allow LRTM to keep updated the weights of the covered roads. In this way vehicles can update their map to perform rerouting actions when triggered. More details are given in Figure 9. In this message, the data length field indicates the number of road information that message contains. The weight coefficient field, instead, reports the new weight coefficient for the related lane identified by the \(Road\_1D\) and \(Lane\_1D\) fields. * _Rerouting Order Message_ (ROMsg): This message is generated by LRTMs. It sends ROMsgs through the LLI interface reaching RSUs. RSUs spread this message to vehicles. In order to limit flooding and saving resources, this message has two kinds of priority Global and Local. In case of Global Priority the message has been generated because of HRTM decisions. In this case, all vehicles will receive this message. In case of Local Priority only a limited set of vehicles will receive the message. This last set is composed of vehicles covered by LRTM area of interest. Message will change weights coefficient for some edges to follow LRTM or HRTM decisions. Once received, interested vehicles will evaluate an alternative path to reach their destinations. More details about message format are given in Figure 9. Here is reported the message format. In particular, on the basis of the value of the Priority field is possible to infer the information that message brings up. If the priority field is set to Global this means that the ROMsg is sent by HRTM to actuate a global rerouting. If the priority field, instead, is sent by LRTM this means that is valid only in the area of interest of the LRTM and the rerouting is made only by vehicles that are traveling in this area. Moreover, the content of the message includes also local policies made by LRTM about lanes interested by decision making policies. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Field Name** & **Type of Data** & **Length in Byte** & **Description** \\ \hline _LRTM\_1D_ & Unsigned int & 2 & ID of LRTM device \\ \(Road\_1D\) & Unsigned int & 2 & ID that represents the road globally in the ITS \\ \(Lane\_1D\) & Unsigned short & 1 & ID of road lane involved in update status \\ _Type of Data_ & Enumerate & 1 & 0-Inhibit & 1-Update \\ _Weight data field_ & Float & 2 & new lane metric value: \(w_{i,j}\) \\ _Density Factory_ & Float & 2 & Density factory: \(\pi_{i,j}\) \\ _Traveling Delay_ & Float & 2 & Delay value: \(\delta_{i,j}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Street Status Message (StrStsMsg) : Message fields specification.
# Construction of IsoVoc Database for the Authentication of Natural Flavours
## 3 Results and Discussion
### Database Creation
In addition, the broad range of \(\delta^{13}\)C values in the literature data for \(\gamma\)-decalactone, \(\alpha\)-ionone, \(\beta\)-ionone is most likely due to the low number of samples and, consequently, the smaller variation in \(\delta^{13}\)C values in samples used in our study. The \(\delta^{13}\)C values for \(\gamma\)-decalactone and \(\delta\)-decalactone in _prunus_ fruits such as peaches, apricots, and nectaries ranged from \(-38.4\) to \(-34.0\%\)[11] and were lower compared to strawberries, where the \(\delta^{13}\)C values of \(\gamma\)-decalactone ranged from \(-31\) to \(-28\%\)[7]. In this study, the \(\delta^{13}\)C values for \(\gamma\)-decalactone in organically produced strawberries range from \(-29.7\) to \(-28.2\%\) and cannot be distinguished from other natural strawberry samples. Raspberry has also been subjected to isotope analysis [13]. The \(\delta^{13}\)C values for \(\alpha\)-ionone, \(\beta\)-ionone, and \(\alpha\)-ionol in these samples ranged from \(-36.6\) to \(-30.3\%\), which shows that it is possible to differentiate between natural and synthetic derived compounds. Although compounds appearing in peach and raspberry are well researched, many more samples are needed to represent better the actual variation in \(\delta^{13}\)C values for those compounds. A database should not contain only the isotopic values of authentic natural samples. For example, we observed several overlaps in the isotopic values of synthetic samples, which meant that it was not possible to differentiate between natural and synthetic samples, mainly (E)-2-hexanal, 2-methylbutyl acetate, amyl acetate, E-nerolidol, ethyl acetate, ethyl hexanoate, hexyl acetate, and linalool \(\delta^{13}\)C values. In addition, having only natural aroma compounds in the database is likely to result in samples being misclassified. Different  
Figure 4: \(\delta^{13}\)C values of VOCs of natural and synthetic samples (this study) and literature values. market brands should also be analysed to increase the range of \(\delta^{13}\)C values observed in synthetic samples. Despite this overlap, the method could successfully separate 25 of the 33 VOCs for which we have data for the synthetic compounds (\(\gamma\)-decalactone, \(\alpha\)-ionone, \(\beta\)-ionone from the literature).
## 5 Experimental Setup and Results
### Experiment 4--Processing Variations in Same Hand Gestures  
In this section, the variation in hand gestures is processed through the proposed communication system. In Figures 14-16, two variants of letters from the PSL dataset are processed, and the detection accuracy is calculated. In this experiment, three letters from PSL are selected, and the hand gestures of these letters are varied slightly and then processed through the system. The results show that the accuracy is between 80% and 100% for the first letter in Figure 14. This variation is due to the distance between the hand and the LMD. A part of the index finger of the image on the left is outside the camera vision.  
Figure 14: Processing variation in same hand gesture scenario 1.  
Figure 13: Processing images of different quality for the same hand gesture.  
A similar experiment is repeated in Figure 15, where the accuracy is 100% due to minimal variation between the images. The results shown in Figure 16 are similar to that in Figure 14. Again, the low accuracy for the image on the right side in Figure 16 is due to the distance of the hand from the LMD as well as the variation in the gesture.  
In Figure 17, a letter from the ASL dataset is shown with a detection accuracy exceeding 90%. The accuracy for the two images is high for the letter selected from the ASL dataset, mainly because the distance between the hand and camera for both images is similar, and the variation between the two gestures is minimal.  
Figure 16: Processing variation in same hand gesture scenario 3.  
Figure 17: Processing variation in same hand gesture scenario 4.  
Figure 15: Processing variation in same hand gesture scenario 2.  
Figure 23 shows another example of an object captured with a hand gesture. The proposed system is unable to correctly detect the hand gesture here. In this experiment, the  
Figure 21: Detecting other objects—wristwatch scenario 2.  
Figure 22: Detecting other objects—ring.  
Figure 20: Detecting other objects—wristwatch scenario 1.  
object, which is a pen, is detected by the system, but the system is unable to detect the hand gesture correctly. This is due to the shape of the object and also how the user is holding it.
## 3 Research Design and Methodology
### Asynchronous Advantage Actor-Critic (A3C) Model
#### 3.2.3 Training the Asynchronous Advantage Actor-Critic (A3C) model  
In this section, we provide a concise overview of the training procedure for A3C-L4S.  
Algorithm 3 outlines the pseudo-code of the A3C model training process in a continuous action observation space. The model goes through a total of 100 episodes during the entire training phase. Every episode commences by resetting the environment and the training session with a fresh random data point. Every episode teaches the model to learn the relationship between action, states and reward. Every episode executes 100 actions to enhance the overall episode reward or to minimize the queuing delay throughout the entire episode. Our model is trained using network environment data collected over long testing periods in our existing artifact. During deployment, the local model or agent that governs each AQM instance asynchronously updates its parameters using the model weights provided by the global network after a certain time interval. Due to exchanging information from kernel space to user space, the delay is introduced, which is typically under one millisecond but may vary depending on system configuration and workload.  
```
1:Set discount factor gamma \(\gamma=0.99\).
2:Set the global update interval \(t_{\text{args\_update\_interval}}=5\).
3:Set the actor learning rate \(a_{\text{actor}}=0.0005\).
4:Set the critic learning rate \(a_{\text{critic}}=0.001\).
5:Set the entropy regularization term \(\beta=0.01\).
6:Initialize global shared parameter vectors \(\theta\) and \(\theta_{v}\), and a global shared counter \(T=0\).
7:Initialize thread-specific parameter vectors \(\theta^{\prime}\) and \(\theta^{\prime}_{v}\).
8:Initialize the thread step counter \(t\gets 1\).
9:repeat
10:Reset gradients: \(d\theta\) and \(d\theta_{v}\) to zero.
11:Synchronize thread-specific parameters: \(\theta^{\prime}\leftarrow\theta\) and \(\theta^{\prime}_{v}\leftarrow\theta_{v}\).
12:Set \(t_{\text{start}}=t\).
13:Retrieve current state \(s_{t}\).
14:repeat
15:Select action \(a_{t}\) based on policy \(\pi(a_{t}|s_{t};\theta^{\prime})\).
16:Retrieve reward \(r_{t}\) and observe new state \(s_{t+1}\).
17:Increment counters: \(t\gets t+1\) and \(T\gets T+1\).
18:until terminal state \(s_{t}\) or \(t-t_{\text{start}}=t_{\text{args\_update\_interval}}\)
19:for\(i\) from \(t-1\) down to \(t_{\text{start}}\)do
20:Update accumulated reward\(R\) recursively: \(R\gets r_{i}+\gamma R\).
21:Accumulate gradients w.r.t. \(\theta^{\prime}\): \(d\theta\gets d\theta+\
abla_{\theta^{\prime}}\log\pi(a_{i}|s_{i};\theta^ {\prime})(R-V(s_{i};\theta^{\prime}_{v}))\).
22:Accumulate gradients w.r.t. \(\theta_{v}\): \(d\theta_{v}\gets d\theta_{v}+\frac{\partial(R-V(s_{i};\theta^{\prime}_{v} ))^{2}}{\partial\theta_{v}}\).
23:endfor
24:Update weights of policy and value function asynchronously: \(\theta\leftarrow\theta+d\theta\) and \(\theta_{v}\leftarrow\theta_{v}+d\theta_{v}\).
25:until\(T>T_{\text{max}}\)
```  
**Algorithm 3**A3C Pseudocode