esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Accident Number Prediction
#### 3.2.3 Annual Forecast  
For the forecast of the annual data, due to the small number of data samples and the large fluctuations, it was found that although the error of the prediction result was small after the grey forecast, it did not fully reflect the real accident trend, and the result was accidental. Using quarterly data, rolling forecasts [29] are made starting in 2009 in a two-yearly forecast, three-yearly forecast, and four-yearly forecast, and then repeatedly starting in 2010, and so on. Using the results updated after each step to re-forecast and adding up the forecasted quarterly data to produce the hierarchical annual forecasts shown in Figure 7.  
The different results generated by each prediction path are calculated in correlation with the real data, and the results are normalized to obtain the weights of the predicted values under different paths, and the revised annual accident prediction results are generated. The correlation coefficient between rolling predicted and true values is 0.963, the fit of a single grey forecast to raw value is 0.792, and the rolling forecast is as high as 0.928. It can also be seen from Figure 8 that the rolling forecast of the accident trend has a higher degree of accuracy than the single grey forecast and can better reflect the actual accident occurrence prediction than the single grey forecast.  
Figure 8: Annual forecast for the number of accidents.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Year** & **Trend** & **Seasonal** & **Residual** & **Predictive Value** & **Raw Value** & **Relative Error** \\ \hline
2019 & 786 & 0 & \(-16\) & 770 & 773 & 0.39\% \\ \hline
2020 & 870 & 0 & 8 & 878 & — & — \\ \hline \end{tabular}
\end{table}
Table 5: Quarterly forecast and additional results of housing municipal engineering production safety accidents after trend decomposition.  
Figure 7: Rolling annual forecast results generation graph for accident numbers.  
This method of continuous forecasting by constantly revising the data for the near period is similar to short-term forecasting and is particularly suitable for situations where the accumulation of annual forecast data is limited. At the same time, it also compensates for the disadvantage that the results of short-term forecasts are greatly affected by the proximity value and the overall trend. This combined model avoids the defect that a single grey prediction is not suitable for target analysis and prediction with large fluctuations and verifies the validity of the prediction results.
# A CNN Regression Approach to Mobile Robot Localization Using Omnidirectional Images
## 1 Introduction
They estimate the camera motion trajectory based on the catadioptric projection model and create a parallel virtual space simulating the environment in the real world. Compared to these previous works, which use handcrafted features to describe the scenes, our paper presents a different approach. The main contribution of the present paper is a solution to the localization of a mobile robot using deep learning techniques. In this work, two different kinds of CNNs are trained. First, a CNN is trained for classification in order to solve a coarse localization, i.e., to obtain the room or area where the robot is located. Second, for each room or area, a regression CNN is trained to perform a fine localization. Each of these regression CNNs estimates the position of the robot in a specific room or area (X and Y coordinates). Finally, a splitting method for improving the localization in large areas is proposed. This method leads to more precise results in larger areas. The remainder of the paper is structured as follows. Section 2 presents state of art of CNNs in feature extraction and localization. Then, Section 3 describes the training process we propose training the CNNs with a twofold purpose, to solve the hierarchical localization problem: (a) detection of the room or area where the robot is and (b) estimation of the coordinates of the position of the robot in the previously retrieved zone. Section 4 presents the dataset used in our experiments, which is specially intended for testing localization algorithms under real operation conditions. Then, Section 5 shows the experiments performed related to the classification stage as well as fine localization. Some improvements to enhance the localization results are also displayed. Finally, conclusions and future works are presented in Section 6.
# Detection and Tracking of Moving Pedestrians with a Small Unmanned Aerial Vehicle
## 3 Target Tracking with IMM Filtering
### State Estimate and Covariance Update  
The state estimate and the covariance matrix of targets are updated as:  
\[\hat{\mathbf{x}}^{t}_{j}(k|k)=\hat{\mathbf{x}}^{t}_{j}(k|k-1)+W^{t}_{j}(k) \mathbf{v}^{t}_{m^{t}_{j}(k)j}(k),\ t=1,\ldots,n(k),\ j=1,\ldots,M, \tag{21}\]\[P_{j}^{t}(k|k)=P_{j}^{t}(k|k-1)-W_{j}^{t}(k)S_{j}^{t}(k)W_{j}^{t}(k)^{T}. \tag{22}\]  
If \(m_{j}^{t}(k)\) is equal to zero, i.e., no measurement exists in the validation region, the state estimate and the covariance become the predictions of the state and the covariance as:  
\[\hat{\mathbf{x}}_{j}^{t}(k|k)=\hat{\mathbf{x}}_{j}^{t}(k|k-1), \tag{23}\]  
\[P_{j}^{t}(k|k)=P_{j}^{t}(k|k-1). \tag{24}\]  
The mode probability is updated as:  
\[\mu_{j}^{t}(k)=\frac{\Lambda_{j}^{t}(k)\sum\limits_{i=1}^{M}p_{ij}\mu_{i}^{t}( k-1)}{\sum\limits_{j=1}^{M}\Lambda_{j}^{t}(k)\sum\limits_{i=1}^{M}p_{ij}\mu_{i}^{t} (k-1)}, \tag{25}\]  
\[\Lambda_{j}^{t}(k)=N\Bigg{(}\mathbf{0};\mathbf{v}_{\hat{m}_{j}^{t}(k)j^{\prime }}^{t}S_{j}^{t}(k)\Bigg{)}, \tag{26}\]  
where \(N\) denotes Gaussian probability density function. If no measurement exists in the validation region, the mode probability becomes:  
\[\mu_{j}^{t}(k)=\sum\limits_{i=1}^{M}p_{ij}\mu_{i}^{t}(k-1). \tag{27}\]  
Finally, the state vector and covariance matrix of each target are updated as:  
\[\hat{\mathbf{x}}_{t}(k|k)=\sum\limits_{j=1}^{M}\hat{\mathbf{x}}_{j}^{t}(k|k) \mu_{j}^{t}(k), \tag{28}\]  
\[P_{t}(k|k)=\sum\limits_{j=1}^{M}\mu_{j}^{t}(k)\{P_{j}^{t}(k|k)+[\hat{\mathbf{x }}_{j}^{t}(k|k)-\hat{\mathbf{x}}_{t}(k|k)][\hat{\mathbf{x}}_{j}^{t}(k|k)-\hat{ \mathbf{x}}_{t}(k|k)]^{T}\}. \tag{29}\]  
The procedures from Equation (10) to Equation (29) repeat until the track is terminated. Figure 3 is the block diagram of moving object tracking. The track is terminated when the track continuously fails to update its state with validated measurements for a certain number of frames. It is noted that when there is no measurement in the validation region, the track can update its state as Equations (23) and (24), not Equations (21) and (22). The terminated track is also considered false if the number of updates with validated measurements is too small, that is, it is assumed that the true target generates at least a certain number of validated measurements.
# A TCP Acceleration Algorithm for Aerospace-Ground Service Networks
## 4 Experimental Results
### Test in Actual Environment  
To validate the BoostTCP algorithm's performance in real-world network transmission, a real-world network test was conducted analyzing the data transmission throughput rate between ground stations and satellite user centers. The real-world network test results of BoostTCP and standard TCP are presented in Figure 16, where the data transmission performances of the two algorithms were compared for a network consisting of seven ground stations and a satellite user center. The relationship between the increase in data transmission throughput rate and the network's maximum bandwidth is depicted in Figure 17. The relationship between the speed-up ratio of data transmission throughput rate and network delay is shown in Figure 18.  
Figure 15: The preemptive test curves of the three congestion control algorithms at a network delay of 80 ms and a packet loss rate of zero.  
Figure 16: The actual network test results of the BoostTCP and ordinary TCP.  
Figure 17: The throughout enhancement versus the maximum network bandwidth.  
Figure 18: The speed-up ratio of data transmission versus the network delay.  
As presented in Figure 16, the BoostTCP algorithm performed significantly better than the standard TCP algorithm. Data transmission rates between the satellite user center and seven ground stations were significantly increased. For instance, the speed-up ratio was typically tenfold and could reach seventyfold. The result indicates that the BoostTCP algorithm significantly increased data transmission throughput and effectively increased network resource utilization.  
The relationship between the increase in data transmission throughput and the maximum network bandwidth is presented in Figure 17. As shown in Figure 17, when the network bandwidth increased from 300 MB to 2300 MB, the data transmission throughput rate increased significantly. The relationship between the speed-up ratio of the data transmission throughput rate and the network delay is displayed in Figure 18. When the network delay increased from 1 ms to 70 ms, the data transmission throughput rate's speed-up ratio increased proportionately. As a result, the greater the network bandwidth and delay were, the greater the performance advantage of the BoostTCP algorithm was. The measured data have conclusively demonstrated that the BoostTCP algorithm is more suitable for networks with high bandwidth and a long delay than the conventional TCP algorithm.
## 2 The TASIPPS Conceptual Framework
### Database Security  
The database security subcomponent, a crucial facet of the overarching security module, stands as the sanctuary in which critical data reside. Its purpose is to ensure the impervious security of the data stored within the database, thereby maintaining its confidentiality, integrity, and availability. The implementation of a robust database security framework is pivotal to blocking or frustrating any potential breach attempts, preserving data privacy, and maintaining the unwavering trust of stakeholders.  
The combination of Transparent Data Encryption (TDE), data masking, redaction, and Database Activity Monitoring (DAM) used within the database security subcomponent forges a resilient bastion for the data entrusted to the framework. These measures collectively uphold the sanctity of the data by fortifying their confidentiality, ensuring controlled access, and enabling a rapid response to potential threats. As the repository of sensitive information, the database security subcomponent stands as an unwavering bulwark against the diverse array of risks that seek to compromise the framework's most valuable asset--its data.
# Blockchain-Based Model for Incentivized Cyber Threat Intelligence Sharing
## 2 Related Work
In the case of success, the reputation score of the node is increased; in the opposite case, the reputation score of the node is decreased. The node is declared unfaithful if its reputation score is less than a predefined threshold. [PERSON] et al. [22] thoroughly reviewed the state and challenges of CTI data sharing with a focus on the perceived barriers. An analysis of almost ten years' worth of open CTI data was also performed. Based on the findings, [PERSON] et al. [22] raised the requirements for a CTI data-sharing architecture. These requirements are as follows: identity, bidirectionality, collaboration model, actionability, and safety. The collaboration model is an essential part of the architecture, and it is mapped to four sub-requirements. First, it must focus on decentralized networks. Second, data analytics must be possible. Third, it must include economic incentives. Finally, the model must be able to maintain reputation management. [PERSON] et al. [23] proposed a blockchain-based incentive CTI data-sharing mechanism to solve the problems of lack of trust and free riding. Evolutionary game theory was used to model the incentive mechanism. Two assumptions form the basis of the incentive mechanism: rewards are needed for sharing CTI data, and penalties are needed for not sharing CTI data within a designated time frame. These assumptions form the basis for the calculation of ratings of members. The rating of members is the basis for the trust in shared CTI. However, the reward given is not based on the quality of the CTI data, as it is not evaluated. The whole incentive mechanism is oriented towards the goal of \"sharing as much as possible and within a designated time frame\". There is no motivation to consume CTI data. The simulation of the obtained incentive mechanism was performed in MATLAB. The blockchain chosen for the implementation was Ethereum. A summary of the reviewed works is provided in Table 1.  
Thus, we can make the following conclusions:  
1. Many authors have used incentives to either enhance trust in CTI data or motivate sharing of CTI data, but never pursued both goals together. However, [PERSON] et al. [22] noticed that a CTI data-sharing model must include both economic incentives and the ability to maintain reputation management.
## 2 State of the Art
### Ongoing Efforts in Standardization and Interoperability  
The World Wide Web Consortium (W3C) standards for Decentralized Identities (DIDs) and Verifiable Credentials (VCs) represent a promising approach that could transform interactions for individuals, applications and several industrial domains [72]. The academic credential verification is a paradigmatic use case for this novel identity management standard. In fact, the solution proposed in this article allows for direct integration of DIDs and VCs. Specifically, the use of DIDs and VCs for an academic verification system would require an issuer (e.g., a university) to issue a VC for holders (e.g., a student), allowing verifiers (e.g., third-parties, companies) to verify such VC.  
The issuer has its own DID, which represents the organization identity. When a student reaches a milestone (e.g., completing a degree), the university issues a VC that includes the corresponding merits, signed with the private key associated with its DID. The complete VC is stored off-chain (e.g., in a decentralized database) and its hash would be stored on the blockchain. To verify this VC, a third party can check:  
* That the VC's DID is the DID of the university.
* The validity of the VC's signature using the university's public key.
* The VC's hash, which is effectively stored on the blockchain.  
As it is detailed in the next Sections, the approach proposed by the DIDs and VCs standards is similar to the one of the solution described in this article, so its future adaptation to the use of VCs and DIDs is straightforward. In fact, several papers have already demonstrated similar approaches, showing the potential applicability of DIDs and VCs to diverse scenarios [73; 74].  
Finally, it is worth mentioning initiatives such as the Electronic Identification, Authentication and Trust Services Regulation (eIDAS) [75] or the European Blockchain Services Infrastructure (EBSI) [76], which are an ongoing effort to provide the necessary interoperability for the creation of cross-border platforms that can be formed by a great number of stakeholders (e.g., private companies, academic institutions). However, the presence of such initiatives in the literature is scarce [77]. For the interested reader, [PERSON] et al. [76] provide a comprehensive review on these initiatives, showing that such interoperability requires not only a complex implementation, but also a big effort from the different institutions to implement a successful European platform.
###### Abstract  
In order to guarantee the service performance of recycling asphalt mixture with reclaimed asphalt pavement (RAP), asphalt rejuvenator shall be added. In the last five years, vegetable oil-based rejuvenators have received more and more attention due to their green and regenerative advantages. The object of this paper is to investigate the feasibility of rejuvenating aged asphalt by a kind of waste vegetable oil (W-oil). The effect of W-oil on the performance of aged asphalt is characterized by a safety property test, aging property test, and pavement performance tests; the pavement performance tests included traditional tests and a rheological test. The results show that both the safety property and aging property of rejuvenated asphalt with W-oil meet the specification requirements. According to the results of traditional performance indexes (i.e., penetration, soften point, and ductility), the pavement performance of rejuvenated asphalt can be recovered to the level of virgin asphalt. According to the results of performance indices obtained from the rheological test, the optimum dosage of W-oil is determined to be 13.4 wt %. Compared with virgin asphalt, the rutting property of rejuvenated asphalt is equivalent to that of virgin asphalt, and the workability is slightly poorer; however, the fatigue property and low temperature property have been significantly enhanced. W-oil cannot only improve the pavement performance of aged asphalt, it can also guarantee good safety property and aging property. Therefore, W-oil is of great potential to serve as an asphalt rejuvenator for rejuvenating aged asphalt.  
Keywords: asphalt; rejuvenator; vegetable oil; pavement performance; traditional indexes; rheological indexes +
Footnote †: journal: Applied Mechanics and Physics Letters
## 6 Verification and Results
### Episode 1: Packet Forwarding and Provisioning (req: i, iii, iv, vi)  
This episode aims to provide a demonstration of the service provisioning. It also aims to show that the data packets are routed correctly and that end-to-end traffic tests and throughput tests are satisfying the requirements.
## 5 Synergistic Effects of Integrating IoT, LLMs, and Federated Learning
### Theme 2: Intelligent Interpretation and Action within Complex IoT Environments  
The Challenge: IoT environments produce data that is often complex, noisy, unstructured, and multimodal (e.g., raw sensor time series, machine logs, video feeds, acoustic signals) [14]. Traditional FL, while preserving privacy, often employs simpler models that struggle to extract deep semantic meaning or perform complex reasoning on such data [49]. Conversely, powerful LLMs, while capable of understanding complexity [15], lack the direct connection to the physical world for sensing and actuation and struggle with distributed private data access [107].  
The Synergy (IoT + LLM + FL): LLMs bring sophisticated natural language understanding, reasoning, and generation capabilities to the table [1], allowing the system to interpret intricate patterns, correlate information across different IoT modalities, and even generate human-readable explanations or reports [105]. FL provides the means to train these powerful LLMs collaboratively using the relevant complex IoT data distributed across the network [61]. Crucially, IoT devices provide the physical grounding, acting as the sensors collecting the complex data and potentially as actuators executing decisions derived from LLM insights [3]. Furthermore, LLMs can enhance the FL process itself by intelligently guiding client selection based on interpreting the relevance or quality of their IoT data, or even assisting in designing personalized FL strategies [15].  
Emergent Capability: The combination allows for systems that can deeply understand complex physical environments and interact intelligently within them. This goes beyond simple data aggregation or pattern matching:  
Contextual Anomaly Detection: Identifying subtle anomalies in IIoT machine behavior by correlating multi-sensor data and unstructured logs, understood and explained by an LLM trained via FL [108]. Causal Reasoning in Smart Cities: Using FL-trained LLMs to analyze diverse IoT data (traffic, pollution, events) to infer causal relationships and predict cascading effects [14; 106]. Goal-Oriented Dialogue with Physical Systems: Enabling users to interact with complex IoT environments (e.g., a smart factory floor) using natural language, where an LLM interprets the request, queries relevant IoT data (potentially involving FL for aggregation), and generates responses or even commands for actuators [15].
## 2 An Optimization Model for Wind Farms with an ESS  
### Coordinated Model of Wind Farms with an ESS  
This study examined a coordinated model of an onshore wind farm with an ESS. The farm was connected to an electricity grid via the PCC as shown in Figure 1.  
Power generated from wind turbines can flow in two directions. One way is toward the ESS, where the power is used to charge the storage system. The rest of power is transmitted to the grid via the PCC. In other words, the power charge of the ESS comes only from wind power generation.  
This model allows the wind farm operator to transmit and sell the generated electricity via the PCC to the main grid. The revenue from selling the electricity to the grid is comprised of two parts: first, is the income from wind power generation, excluding the power for battery-charging; and second, is the income from the ESS discharge.  
This model assumes that the operator is not responsible for mitigating the resource variability since there are no penalties or incentives dependent on fluctuating wind power generation under the current policy.
## 4 Implementation and Discussion
### The Network Performance Result
#### 4.2.1 Delay
This protocol has the ability to promptly adjust to dynamic alterations within the network, facilitated by its on-demand route discovery mechanism, enabling swift responses to network modifications. The AODV protocol selects the minimal path based on node count between source and destination. In our authentication scheme, the sender is the vehicle and the receiver is the TAS. When the network has two TASs, one TAS may become more heavily loaded than the other, leading to load imbalance. The reason for this is that the vehicle's hop distance to the TAS depends on its mobility and dynamic network topology. Although AODV can dynamically adjust routes if the current route becomes invalid during its path maintenance stage, the newly adjusted route may still not account for equal load balancing. This load imbalance can result in higher authentication delays. Several factors contribute to the increased authentication delays, such as load imbalance, including server processing time and queuing delay. Each authentication request requires time for the server to process, which involves the encryption and decryption of Kerberos authentication messages, message generation, and other authentication steps. If the number of authentication requests exceeds the processing time capacity, the time needed to handle the request will increase. On the queuing delay side, if one server accepts many requests at the same time, then the request has to be in the queue until the server needs to process it. The larger the queue, the more time is required for each request, thereby increasing the overall authentication delay in the TAS with higher authentication requests. A summary of the simulation network prerequisites is illustrated in Table 5. The \"\(\check{\check{\check{\check{\check{\check{\check{\check{\check{\check{\check{\check{ \check{\check{\check{\check{\check{\