esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments  
In this section, we present a rigorous experimental framework designed to thoroughly assess the efficacy of our proposed FastSLAM-MO-PSO algorithm. The experimental setup is structured around four principal components to provide a comprehensive evaluation. Firstly, we have established a novel benchmark for evaluating FastSLAM and its variants, which encompasses three distinct environments--small, medium, and large--along with various environmental elements that are typically encountered in practical scenarios. This benchmark is crafted to challenge the algorithms with real-world complexities, thereby providing an accurate assessment of their performance under genuine conditions. Secondly, we conduct a comparative analysis involving the original FastSLAM, its single-objective variant FastSLAM-PSO, the differential evolution-based FastSLAM-DE [23], and the multi-objective variants FastSLAM-NSGA, FastSLAM-MODE, and our proposed FastSLAM-MO-PSO. This comparison underscores the performance benefits and the necessity of adopting a multi-objective approach in the context of FastSLAM-MO-PSO. Additionally, we conducted a time analysis across all algorithms to evaluate their efficiency. This analysis focuses on the inherent performance of the various algorithms, revealing their strengths in practical applications. This comprehensive evaluation is crucial for understanding not only how the algorithms perform in theory, but also how they can be effectively applied in practice. Lastly, we visually present the experimental results across different parameter settings. These visual representations serve as a graphical benchmark to intuitively demonstrate the effectiveness of our proposed algorithm. They allow for a direct comparison of performance across various experimental setups, effectively highlighting the performance disparities under different configurations. It is noteworthy that all the figures and visualizations presented throughout this manuscript have been generated using the Python language, ensuring reproducibility and facilitating further research in this domain.  
For the experimental configuration, several parameters were carefully adjusted to optimize the performance of the FastSLAM-MO-PSO algorithm. The social constant \(c_{2}\), which directs particles towards the swarm's best position, and the cognitive constant \(c_{1}\), responsible for steering particles towards their personal best positions, were both calibrated  
Figure 2: The flow chart of MO-PSO.  
to 1.5. These values are pivotal for effectively balancing exploration and exploitation within the search space. The inertia weight, denoted as \(w\), was set to 0.5 to maintain equilibrium between the global search for new solutions and the exploitation of already identified promising areas. To ensure a smooth start to the optimization process, the initial velocities of the particles, \(v_{i}\), were set to zero, preventing any erratic behavior at the beginning of the iterations. Lastly, to maintain the integrity of the map and prevent particles from venturing outside the defined boundaries, the algorithm enforces constraints on the particle distribution.
## 6 Experimental Simulations
### Picking
#### 6.1.5 Time Performance Analysis  
The results of the simulations in terms of the picking process are summarized in Figure 3 and Table 6.  
In Table 6, we can observe clearly that the TSP optimization has the best performance in terms of travel time, so our main interest is in estimating the extra time required by optimizing with the SOP solutions, and whether this extra time is worth being invested to have a better sequencing of the articles. When considering the optimal TSP solution, the picking operation takes 163.28 s on average, the optimal SOP solution takes 430.80 s, meanwhile the Relaxed SOP takes 272.48 s on average. Note that the SOP-based strategies preserve the articles' integrity by avoiding damage, although this factor is not captured by the times represented here. Considering the fact that in-store logistics operations are a major cost driver and human resources are required for all in-store activities [31], the Relaxed SOP offers a good tradeoff by sacrificing a bit more time than TSP while preventing damage to the products. The more constraining SOP is less competitive in this sense since it takes too long. Furthermore, the high amount of back and forward movements by store employees may interfere with regular in-shop customers and lower their satisfaction levels. Meanwhile, the time difference between the TSP and Relaxed
## Literature Review
### Development of Two-Sided Matching Theory  
Over the 50-plus years from the proposition of the two-sided matching theory in 1962, a large body of literature on this subject has been developed. The evolution of the theory and the new developments in its application can be summarized as representing three themes: first, the traditional research on two-sided theory that was presented by [PERSON] and [PERSON] (1962), in which money is not involved; second, the research that was presented by [PERSON] and [PERSON] (1972) and [PERSON] and [PERSON] (1982), wherein money was introduced into the theory; and third, the research on the theory of matching with contracts, as presented by [PERSON] and [PERSON] (2005), which incorporated the two-sided matching theories with and without money into one framework [9; 10; 11]. In a two-sided matching market where no money exists, stability is the most important element. Orders of preferences or orders of priority are the basis for the matching between the two sides and the deferred acceptance--the key feature of the G-S algorithm--ensures matching stability in the market. [PERSON] and [PERSON] (1972) creatively identified another research perspective and developed a new theoretical matching model that is referred to as the \"assignment game model\". Under this model, the two-sided matching market that contains money is defined and money is used to measure the utility in the market. The introduction of money connects two-sided matching theory with auction theory and competitive equilibrium theory. As one of the most important developments in matching theory over the past two decades, the theory of matching with contracts is at the forefront of the research on two-sided matching theory. This theory integrates the two-sided matching problem into markets with and without money [12].  
The continuous introduction of money and contracts into traditional two-sided matching models makes the theory more applicable to reality. The progress in the research on two-sided matching is not only demonstrated by the continuous evolution of the matching model, this research has also advanced market design and expansion. Examples include the matching between colleges and applicants, job matching in the labor market, matching kidney transplant donors and recipients, auctions of vehicle licenses, ratio spectrums, and internet advertisements. The stable matching concept originated first from practice and then its theoretical development followed. The earliest example of its practice was the National Resident Matching Program (NRMP), which was adopted in 1952 in the United States. Stable matching theory has now been applied to a range of academic subjects and markets. It has also been successfully adopted in a wide array of places, such as Canada, Britain, South Korea, the Netherlands, and Hong Kong.
## 4 Security Threat Landscape in 5G Networks
### Potential Attackers and Their Motivations  
Understanding the potential attackers in the 5G environment and their motivations is a critical step in identifying and mitigating potential threats. Potential attackers in the context of 5G may be categorized into three broad groups, based on their capabilities, objectives, and the resources at their disposal: cybercriminals, state-sponsored attackers, and insider threats [71; 87]. Cybercriminals typically launch attacks with the aim of achieving financial gain. These actors employ a range of methods to compromise 5G networks, including malware, ransomware, and DDoS attacks. They often exploit weak security controls, insecure interfaces, and poor user security practices [87]. State-sponsored attackers or APTs represent a significant threat to 5G networks due to their high level of sophistication and the substantial resources at their disposal. Their motivations may include espionage, disruption of critical infrastructure, or gaining a competitive advantage at a national level. These attackers often utilize advanced techniques to evade detection, remain persistent within the network, and achieve their objectives [72; 87]. Insider threats, although often overlooked, can pose a substantial risk to 5G security. Disgruntled employees, contractors with access to sensitive network information, or even employees unwittingly manipulated by external actors, can cause significant damage to network security. These attacks can be particularly challenging to detect and mitigate due to the inherent trust placed in these individuals. In order to effectively mitigate these threats, it is critical that security measures account for the various attackers' capabilities, strategies, and motivations and that they are adaptable to evolving threat landscapes.
# Analysis of Conflict Distribution Characteristics between Motor Vehicles and E-Bikes at Roundabouts
## 2 Materials and Methods
### Conflict Type Classification  
The types of conflict that may exist were sorted by the weaving of different turning movement trajectories [38]. The following Figure 7 shows the movement trajectories of vehicles and e-bikes at the Chenyangzhai roundabout.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**File** & **Number** & **frame** & **Type** & **t** & **x** & **y** & **v** & **a** & **\#v** \\ \hline
1 & 9 & 3961 & E-bike & 85.62 & 98.68 & \(-\)16.83 & - & - & - \\
1 & 9 & 3962 & E-bike & 85.66 & 98.52 & \(-\)16.83 & 4.07 & - & \(-\)179.89 \\
1 & 9 & 3963 & E-bike & 85.70 & 98.35 & \(-\)16.83 & 4.12 & 1.09 & \(-\)177.64 \\
1 & 9 & 3964 & E-bike & 85.74 & 98.19 & \(-\)16.84 & 4.14 & 0.79 & \(-\)177.95 \\
1 & 9 & 3965 & E-bike & 85.78 & 98.02 & \(-\)16.85 & 4.14 & 0.41 & \(-\)179.30 \\
1 & 9 & 3966 & E-bike & 85.82 & 97.86 & \(-\)16.86 & 4.12 & 0.02 & \(-\)171.98 \\
1 & 9 & 3967 & E-bike & 85.86 & 97.69 & \(-\)16.86 & 4.09 & \(-\)16.85 & \(-\)172.53 \\
1 & 9 & 3968 & E-bike & 85.90 & 97.53 & \(-\)16.87 & 4.05 & 20.15 & \(-\)179.51 \\
1 & 9 & 3969 & E-bike & 85.94 & 97.36 & \(-\)16.89 & 4.01 & \(-\)0.79 & \(-\)179.12 \\
1 & 9 & 3970 & E-bike & 85.98 & 97.20 & \(-\)16.90 & 3.96 & \(-\)0.92 & \(-\)179.14 \\ \hline \end{tabular}
\end{table}
Table 2: Trajectory information.  
Figure 6: Smoothing process of speed.  
From the statistics of different turning movement trajectories, we summarized the results as follows:  
1. The conflicts between vehicles and e-bikes mainly occurred at exit areas, which accounted for 86.2% of the total conflicts at this roundabout. Those conflicts could be classified into two types: The first is the conflicts between the vehicles driving toward exits with a right-turn movement and the entering e-bike that needed to go straight or turn left, as shown in the black dashed box in Figure 7; The second is the conflicts between vehicles who intend to go straight or turn left released during the previous cycle and the left-turning e-bikes, as shown in the red dashed box in Figure 7. The two types of conflicts between vehicles and e-bikes total 103 times, which means a large possibility of collision.
2. Apart from the two conflict types mentioned above, conflicts caused by the behavior of lane changing at interweaving areas and the circulation area account for 4.5%, and those that are due to the abnormal behavior of e-bikes (such as the left-turning e-bikes not following the traffic lights and crossing directly through the second stop line) account for 9.3%.
# Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks
## 3 FSO-Based Backhauling for NTNs  
### Backhaul System Architectures  
The specific backhaul system architecture depends on the use case. While single-layer backhaul systems are sufficient for providing coverage in rural areas, multi-layer designs might be required to support connectivity in urban areas where LoS links are difficult to establish [103]. HAPS fleets operate at high altitudes on predetermined tracks (e.g., elliptical, circular), providing broad coverage. Rotary-wing UAVs operate at lower and medium altitudes, complementing the HAPSs.  
As an example, Figure 11 illustrates a single-layer aerial backhaul. The HAPSs follow a circular track at a fixed speed in the stratosphere. To ensure continuous aerial coverage, multiple circular tracks are considered. The entire coverage area of a cellular system is divided into smaller cells, each represented by a hexagon shape. Each cell has a ground base station that covers a specific radius. The HAPS is equipped with several steerable laser sources, each serving as a dedicated link to a base station within its coverage area. Additionally, it has a Pointing Acquisition-Tracking (PAT) system that enables establishing a FSO link with the base station it serves.  
Figure 12 illustrates a multi-layer backhaul system for urban areas. In addition to HAPSs operating at high altitudes, rotary-wing UAVs are utilized in the medium- and low-altitude layers. The altitude of the middle layer is restricted to a few km, due to aerodynamic considerations [104]. Unlike HAPSs, which follow circular trajectories, rotary-wing UAVs possess the capability to hover in a semi-fixed position. The middle layer comprises UAVs, each of which serve several ground base stations. In addition, low-altitude UAVs are deployed to serve specific links in case of blockages.
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 3 Background in Programmable Data Plane and 5G and Beyond
### Network Function Virtualization, Network Slicing, and Multi-Access Edge Computing  
#### 3.3.1 Network Function Virtualization (NFV)  
Network function virtualization (NFV) [38] is an architectural approach for designing and deploying network services by virtualizing the functionality of traditional dedicated hardware, such as routers, switches, firewalls, and load balancers, into software running on standard servers, storage, and networking equipment.  
This architecture has been standardized by ETSI (European Telecommunications Standards Institute) [39] defining functional building blocks and interfaces to manage virtualized network services. 5G systems incorporate virtualization technology to enable the implementation of various network services, including 5G core network functions, such as the AMF as virtual network functions. This means that 5G network functions can be executed on standard servers, which can be located in centralized data centers or cloud-based infrastructures.  
The NFV architecture presents a structural basis for constructing adaptable and versatile networks through the virtualization of network functions and their separation from the underlying hardware. This permits network operators to enhance scalability and optimize operational expenses by leveraging a shared infrastructure to cater to multiple network services effectively.
# IoT Sensor Challenges for Geothermal Energy Installations Monitoring: A Survey
## 2 Sensor Technologies
### Measurement of Mechanical Parameters
#### 2.3.3 Tilt Sensors  
These sensors are used to obtain information about slope movements and deformations. Tilt monitoring systems combine tilt sensors and LPWAN technology to measure slope movement and transmit collected data wirelessly. Data from tilt sensors and other additional parameters (rainfall data, groundwater level) are analyzed to predict slope failures [55].
# Enhancing IoT Data Dependability through a Blockchain Mirror Model
###### Abstract  
The Internet of Things (IoT) is a remarkable data producer and these data may be used to prevent or detect security vulnerabilities and increase productivity by the adoption of statistical and Artificial Intelligence (AI) techniques. However, these desirable benefits are gained if data from IoT networks are dependable--this is where blockchain comes into play. In fact, through blockchain, critical IoT data may be trusted, i.e., considered valid for any subsequent processing. A simple formal model named \"the Mirror Model\" is proposed to connect IoT data organized in traditional models to assets of trust in a blockchain. The Mirror Model sets some formal conditions to produce trusted data that remain trusted over time. A possible practical implementation of an application programming interface (API) is proposed, which keeps the data and the trust model in synch. Finally, it is noted that the Mirror Model enforces a top-down approach from reality to implementation instead of going the opposite way as it is now the practice when referring to blockchain and the IoT.  
IoT; blockchain; IoT security; IoT data dependability; Mirror Model; representation and trust models; API gateway +
Footnote †: journal:  
_Future Internet_
# Cyberattack Models for Ship Equipment Based on the MITRE ATT&CK Framework
###### Abstract
A key requirement of the regulatory and type approvals related to maritime cybersecurity is the implementation of a systematic cyber risk management process for protecting core ship systems, such as bridge systems, cargo handling and management systems, and power control systems, by referring to ISO27001 or the NIST Cybersecurity Framework [11]. In this process, threats and security vulnerabilities are assessed and identified to develop detection and protection measures to reduce the likelihood that vulnerabilities are exploited. Furthermore, a plan is established to respond to cyber risks and the procedures for maintaining continuity during cybersecurity incidents are developed. Identifying threats and vulnerabilities is an essential step in managing ship cyber-security risks [12]. One ship consists of 700 to 900 units of propulsion control, steering, navigation, and communication equipment, which are provided by 80 to 100 manufacturers. Therefore, it is important to identify the cyber threats and vulnerabilities of equipment supplied by manufacturers and installed on ships. In this sense, a ship cybersecurity vulnerability/threat sharing platform would be an effective way for multiple stakeholders related to ships to update their information on cyber threats and security vulnerabilities associated with ship equipment and supply chains. Therefore, with this study, we aim to gain insight by explaining the ship hacking model based on the MITER ATT&CK framework, based on the known ship security research and security vulnerabilities. In this study, 15 major studies on ship hacking are analyzed using the MITRE ATT&CK framework. According to the findings of the analysis, common security measures to be considered in modern ships are identified, and a new matrix of security mitigation measures for the ship environment is proposed. The remainder of this paper is structured as follows. Section 2 provides background on hacking case analysis methodology. Section 3 analyzes the four cases in detail and summarizes the analysis results. Section 4 draws the conclusions.
## 2 Precise Calculation of the Aluminum Extraction Flow Rate
### Unscented Kalman Filter
#### 2.2.1 State Prediction  
Sigma Point Generation: Sigma points are generated by expanding the current state mean and covariance matrix. The degree of expansion is controlled by the Unscented Transformation parameters. For a state \(\mathbf{x}\) and covariance matrix \(\mathbf{P}\), the generated sigma points are as follows [20]:  
\[\chi_{0}=\mathbf{x} \tag{1}\]  
\[\chi_{i}=\mathbf{x}+\left(\sqrt{\left(n+\lambda\right)}\cdot\mathbf{P}\right) _{i}\text{, for }\quad i=1,\ldots,n \tag{2}\]  
\[\chi_{i+n}=\mathbf{x}-\left(\sqrt{\left(n+\lambda\right)}\cdot\mathbf{P} \right)_{i}\text{, for }\quad i=1,\ldots,n \tag{3}\]  
where \(\lambda\) is the expansion parameter and \(\left(\sqrt{\left(n+\lambda\right)}\cdot\mathbf{P}\right)_{i}\) is the \(i\)-th column of the covariance matrix.  
State Prediction: the state transition model is used to predict all sigma points:  
\[\chi_{i}^{\prime}=f(\chi_{i}) \tag{4}\]  
where \(f(\mathbf{x})\) is the state transition function.  
The predicted mean and covariance are calculated:  
\[\mathbf{x}^{\prime}=\sum_{i=0}^{2n}W_{i}^{m}\chi_{i}^{\prime} \tag{5}\]  
\[\mathbf{P}^{\prime}=\sum_{i=0}^{2n}W_{i}^{c}(\chi_{i}^{\prime}-\mathbf{x}^{ \prime})(\chi_{i}^{\prime}-\mathbf{x}^{\prime})^{T}+\mathbf{Q} \tag{6}\]  
where \(W_{i}^{m}\) and \(W_{i}^{c}\) are the weights of the mean and covariance, respectively, and \(\mathbf{Q}\) is the process noise covariance matrix, which influences sensitivity to system dynamics. A larger \(\mathbf{Q}\) indicates that the filter assumes greater variability in system dynamics, reducing reliance on the state prediction model and increasing trust in measurement values. This is suitable for systems with frequent, unpredictable changes or those that exhibit difficulties in accurate modeling. Conversely, a smaller \(\mathbf{Q}\) suggests smoother system dynamics, prompting greater reliance on the state prediction model and less frequent corrections from measurements.