esa-sceva/satcom-chunk-collection huggingface dataset:
# Trajectory Optimization with Dynamic Drivable Corridor-Based Collision Avoidance
## 4 Simulation Results and Discussion
Figure 13: Comparison of optimization time between Planner 2 and the proposed method under increasing obstacle numbers: (**a**) this work with grid resolution of 0.3 m; (**b**) Planner 2; numbers 1 to 8 indicate the sequential addition of obstacles in the experimental scenario. plex geometric operations and point-by-point checks. Specifically, AABB collision detection only requires comparing the boundary values of the rectangles \((x_{\min},x_{\max},y_{\min},y_{\max})\), with a linear time complexity, significantly improving detection efficiency. Second, a reasonable grid resolution is selected and maintained within an appropriate range. On one hand, the resolution is fine enough to accurately represent obstacles in the environment; on the other hand, the resolution is not too low, thus avoiding significant compression of the passable space. This design ensures that the constructed safety driving corridor closely aligns with the real passable space, providing the optimizer with a reliable solution space. Third, we adopt a dynamic DC expansion scheme, which differs from the traditional step-by-step expansion used in Planner 2. In the initial expansion stages, where the available space is larger, we expand simultaneously in all four directions, significantly reducing the number of expansion steps and collision detections, thereby improving efficiency. Furthermore, to avoid issues related to the reduction of passable space, when a collision is detected, the expansion method switches to a more cautious one-direction-at-a-time approach, ensuring the integrity of the DC. This dynamic adjustment strategy significantly accelerates the construction speed of the DC. The simulation environment in this study is set in unstructured scenarios, where a simplified kinematic bicycle model is sufficient to meet the fundamental requirements of path planning. The core idea of the dynamic corridor (DC) method lies in constructing a collision-free driving corridor through geometric constraints that isolate the vehicle's trajectory from surrounding obstacles. A key feature of this approach is its generalized framework, which maintains functional independence from specific vehicle dynamics models. Theoretical analysis indicates that the method remains valid not only for simplified kinematic models but also for more complex dynamic models involving nonlinear tire forces, provided that appropriate safety margins and conservative treatments are introduced. The primary contribution of this work lies in accelerating the DC construction process through more efficient modeling techniques, thereby enabling rapid adaptation to diverse scenarios and dynamic models. Future research may explore the extension of this framework to more sophisticated vehicle dynamics, focusing on refined constraint formulations and enhanced computational efficiency.
# Research on Path and Sequence Planning for Multi-Cabin Onboard Transportation of Large Cruise Ships
## 1 Introduction
The cabin transportation problem can be classified as a type of path planning problem. Path planning refers to the process of selecting the optimal path to achieve a specific goal or maximize certain benefits under given constraints [13; 14]. The core challenge lies in  
Figure 1: PMCU transferred to fixed location. how to plan the shortest path for moving cabins from the hull opening to their designated installation locations, with the goal of minimizing transportation time as much as possible. Common algorithms used to solve path planning problems include [PERSON]'s algorithm [15], the A-Star algorithm [16], and [PERSON]'s algorithm [17], as well as various heuristic and intelligent optimization algorithms, such as Ant Colony Optimization (ACO) [18], Genetic Algorithm (GA) [19], Particle Swarm Optimization (PSO) [20], and Simulated Annealing (SA) [21]. These algorithms have helped researchers to effectively address path planning problems in various fields. Although these methods have proven highly effective in specific applications, such as waste transportation, 3D printing path optimization, and robot navigation, they may have limitations when applied to path planning for the multi-cabin transportation of large cruise ships. Due to the complexity of the ship's internal structure, path planning must address two types of obstacles: grid obstacles (such as non-prefabricated cabins on the cruise ship) and boundary obstacles (such as the horizontal and vertical bulkheads within the ship). Moreover, path blockages may occur during the actual cabin transportation process, further increasing the complexity of the problem. Once a blockage occurs, it not only disrupts the original cabin transportation plan, but also significantly increases the difficulty of transporting subsequent cabins.
## 4 Experiment Environment
### Case 1: Frequency Control of the IEEE 14-Bus Testing System under Different DoS Attack Intensities
Figure 9: Frequency deviation, control output, BESS and power output deviation of the generator in IEEE14 bus testing system, \(\eta_{ji}=0.2\). (**a**) Frequency deviation. (**b**) Control output \(u_{i}\). (**c**) Power deviation of BESS. (**d**) Power deviation of generators. The maximum frequency deviation in Figure 11a is 0.13 Hz. During the simulation process, each bus has an oscillating trend inside and outside the stable interval and cannot converge to the stable interval. At this time, the control output (shown in Figure 11b), the power output deviation of BESS (shown in Figure 11c), and the power output deviation of the generator (shown in Figure 11d) also present an oscillating state with a large change range, respectively. Since the intensity of DoS attacks is too strong, each bus cannot obtain the state information from the adjacent buses. The ADES controller cannot accurately calculate the control output. As shown in Figure 12, the average training loss decreases gradually, as the data packet loss rate \(\eta_{ji}\) continues to increase. Compared with other values of \(\eta_{ji}\), the oscillation amplitude of the average training loss of convex neural network weights is larger when \(\eta_{ji}=0.6\). When the intensity of DoS attacks increases, the convergence speed of convex neural network weights decreases. After experimental calculations, each weight update time is about 6 ms (less than the sampling time 10 ms), and thus the control strategy update can be completed within one sampling time. According to the above experimental results, the proposed frequency control strategy can complete power grid frequency adjustment under a variety of DoS network attack intensity and load disturbances, and restrain the impact of load change disturbances on power grid frequency. Figure 11: Frequency deviation, control output, BESS and power output deviation of the generator in IEEE14 bus testing system, \(\eta_{ji}=0.6\). (**a**) Frequency deviation. (**b**) Control output \(u_{i}\). (**c**) Power deviation of BESS. (**d**) Power deviation of generators. Figure 12: Average training loss curve under different \(\eta_{ji}\).
# Light-Controlled Microbots in Biomedical Application: A Review
## 4 Aspects of Design
### Fabrication Process
#### 4.2.4 Fused Deposition Modeling (FDM)  
Fused Deposition Modeling is a type of Rapid Prototyping (RP) method that deposits thermoplastic layer by layer [165]. Initially, FDM was used to fabricate solid physical structures, but to make scaffolds that can be used as therapeutic substrates, it is necessary that there are pores and that the pores are interconnected [166]. Therefore, the adjustment of the parameters in the FDM apparatus results in the production of a porous three-dimensional scaffold. [PERSON] et al. used FDM to fabricate PCL scaffolds for the seeding of fibroblasts. The results showed that the fibroblasts had proliferated on the scaffold, and a large presence of extracellular matrix was observed, leading to the formation of bridges between the pores [166]. Similarly, different cell types can be cultured in substrates fabricated using FDM by changing the pore size and interconnectivity. Moreover, the orientation of cells can be maintained by manipulating the hierarchical organization of the scaffold. Recently, researchers successfully fabricated a poly(vinyl alcohol)/\(\beta\)-tricalcium phosphate composite scaffold for bone tissue engineering using FDM. The pre-determined architecture was useful in ensuring quick bone mineralization [167]. FDM's advantage over other conventional techniques makes it a viable candidate for both soft and hard tissue engineering [168].
## 3 Experimental Results and Analysis
### Experimental Evaluation Criteria  
In order to compare the performance of multi-pedestrian tracking algorithms more objectively, this paper uses the evaluation criteria commonly used in the field of multi-object tracking and object detection, and the main MOT indexes are shown in Table 1. In the table, an upward arrow indicates the higher the value, the better the algorithm performance, and a downward arrow indicates the lower the value, the better the algorithm performance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Evaluation Criteria** & **Meaning of Criteria** \\ \hline FP\(\downarrow\) & Rate of being misidentified as a positive sample, i.e., false detection rate \\ FN\(\downarrow\) & Rate of being mistaken for negative samples, i.e., missed detection rate \\ IDs\(\downarrow\) & Number of pedestrian ID switches, i.e., pedestrian identity changes \\ MOTA\(\uparrow\) & Tracking accuracy calculated by metrics, such as FP, FN, IDs \\ IDF1\(\uparrow\) & Accuracy and recall of tracking with constant ID \\ ML\(\downarrow\) & Tracking of failed pedestrians as a percentage of all pedestrians \\ MT\(\uparrow\) & Track of successful pedestrians as a percentage of all pedestrians \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation Criteria and their meaning.  
Tracking accuracy (MOTA), shown in Table 1, is the main evaluation index of the multi-pedestrian tracking algorithm. It can directly reflect the performance of the algorithm, and the calculation process is given in Equation (15), where GT represents the real number of pedestrians in the video frame image, and the MOT value ranges from negative infinity to 1. Since the detection network is a very important part of the tracking algorithm and is improved in this paper, it needs to be evaluated as well. The performance of the detection network is evaluated based on precision, recall, average precision (AP), and mean average precision (mAP) as shown in Equations (16)-(18), in which TP stands for true positive, FP stands for false positive, FN stands for false negative.  
\[MOTA=1-\frac{FN+FP+IDs}{GT}\in(-\infty,1] \tag{15}\]  
\[Precision=\frac{TP}{TP+FP}=\frac{TP}{AllDetections} \tag{16}\]  
\[Recall=\frac{TP}{TP+FN}=\frac{TP}{AllGroundTruths} \tag{17}\]  
\[mAP=\frac{1}{|Q_{R}|}\underset{q=Q_{R}}{\sum}AP(q) \tag{18}\]
## 5 Performance Evaluation
### Tuning of the Multihop P2 MP Settings  
Our first simulation scenario, shown in Figure 8, includes five adjacent eNBs, located at a distance of 400 m from each other. We initially assume that each eNB serves 30 UEs with P2 MP D2D capabilities, which are deployed randomly in a narrow strip along a straight line. UEs are assumed to be static. Their transmission power is 30 dBm in the UL and 15 dBm in the SL. The channel model includes Jakes fading and log-normal shadowing. Table 1 reports physical-layer parameters. We assume that broadcast messages transport a 4 byte payload, representing a code for indicating the type of the message. Considering the additional information for the originator's coordinates and the target radius discussed in Section 4, we assume a total length of 14 bytes. A message is generated by a random UE every second, starting a new broadcast. As we will show later, the duration of each broadcast is less than one second, hence we can consider each message dissemination as an independent event. For each configuration, we run one instance of 100 s and statistics are obtained by averaging 100 independent broadcasts. Confidence intervals at the 95% level are shown. In the following, we assume that UEs relay the same message only once and that the target radius is 1000 m, unless otherwise specified.  
Figure 7: SRA with coordination among eNBs.
## 3 Experimental Results and Discussion
### Overall Discussion  
To observe the performance of the newly proposed algorithm GSLEO-AEFA, proper analyses and a set of various experiments were conducted. Considering the original AEFA suffers from the insufficient diversification such powerful tools are added to overcome this issue as well as by allowing the algorithm to escape from local optima and to find solutions that are closer to the global optimum. By achieving this equilibrium between exploration and exploitation, high solution quality is achieved. However, there may be some limitations to consider. Since the proposed algorithm is a hybrid one and formed by multiple methods, the computational time of the algorithm is slightly worse than the other algorithms analyzed. It is because of the running each of the methods during the optimization process, respectively. However, for the ones who consider the precise solution quality, this limitation might be omitted. It is important to consider these factors carefully and to tailor the used of these techniques to the specific problems being solved.
# MSSP: A Blockchain Sharding Protocol Based on Multi-Shard Storage
## 6 Experiment
### Transaction Throughput  
Figure 6 compares the transaction throughput of traditional sharding and multi-shard storage schemes under different shard quantities. The experimental results show that compared with the other four shard designs, the multi-shard storage scheme increases transaction throughput by 1.4 to 3.1 times. When there are 14 shards, the maximum throughput is 2654 TPS. As the number of shards increases, the throughput of MSSP shows a nearly linear growth trend, demonstrating good scalability. In summary, multi-shard storage solutions have significant advantages over traditional shard technologies.  
Section 3.1 discusses the system model of MSSP, in which the types of blockchain nodes are divided into s-nodes and m-nodes. S-nodes are only responsible for processing internal transactions within their respective shards, while m-nodes can directly handle cross-shard transactions involving related shards. Figure 7 shows the transaction throughput at different m-node proportions. It can be seen from the figure that when the proportion of m-nodes is between 20% and 25%, the throughput reaches its maximum value. If the proportion of m-nodes is too large or too small, it will lead to a decrease in throughput.  
When the proportion of m-nodes is too low, it may cause an imbalance in the load of cross-shard transactions, with some nodes bearing too large a transaction processing burden while others are relatively idle, thereby reducing overall throughput. When there are too many m-nodes, cross-shard transactions require more inter-node communication  
Figure 6: Comparison of transaction throughput between MSSP and four other sharding protocols at different numbers of shards.  
Figure 7: Transaction throughput at different percentages of m-nodes in MSSP.  
and synchronization, leading to a decrease in throughput due to increased communication overhead.
## 5 Simulation, Experiment, and Comparison
### The Experiment
#### 5.2.2 The Fuzzy Logic Design  
The dataset collected above is processed to create the rule base of the fuzzy impedance controller as presented in Section 4.2. The number of regions of inputs and output is \(i=9\), \(j=9\), and \(k=49\). The maximum magnitudes of the inputs and output are \(A_{m}=10\), \(B_{m}=0.028\), and \(C_{m}=140\). The total number of rules created is 30.
# A Blockchain Self-Sovereign Identity for Open Banking Secured by the Customer's Banking Cards
## 4 Proposed Solution  
This section provides an overview of the components and actors used to build a demonstrative implementation, Figure 1 shows the SSI-based Blockchain identity systems components. The aim of utilizing banking cards is to balance the trusted industry security behind the day-to-day usage of personal payment cards and the user's experience, and to provide ease of use without relying on additional hardware tokens. As an extension, the proposed architecture supports smart cards with biometric fingerprints and other form factors such as wearable tags and banking bracelets. This section describes the designed flow, framework components, and interaction between the parties. This implementation shows the feasibility and attractive values for adoption by banks and other financial institutes to secure and improve open banking integrations, and to provide convenience to customers, who can control their identity and sensitive information sharing.  
This work proposes an architecture based on the SSI and Blockchain implementations, Ethereum with Veramo and smart contracts. Specifically, a decentralized framework is used that stores private information off-chain while the integrity and authenticity proofs are stored on-chain. Our proposal provides a friendly interface as a mobile application or via web access integrated with a payment card for a seamless user experience, which allows third parties to verify the validity of identity data. The Veramo SDK layer provides public profile data to be stored on the IPFS; this means the user has to interactively manage the sharing of their private identity with other parties using web links or QR codes. Mnemonics and QR code technology allow presenting the secret keys in mnemonic terms to help with memory retention. Seed phrases are analogies for mnemonic words to ease portability and retrieve the key parts.  
Figure 1: Blockchain SSI-based identity system components.  
Furthermore, new banking cards in the market are equipped with biometric fingerprints [26], as shown in Figure 2, which improve the user experience by eliminating PIN entry for each identity transaction.
# A Random Subcarrier-Selection Method Based on Index Modulation for Secure Transmission
## 4 Simulation Results  
In this section, the simulation results of the random degree, the computational complexity, and the BER performance are depicted to describe the performance of RSCS-IM. In this simulation, system parameters were chosen as follows: reference frequency \(f_{c}=2.404\) GHz [14], signal bandwidth \(B=20\) MHz, the number of total subcarriers \(N_{s}=512\) or \(1024\), \(E_{b}/N_{0}=9\) dB, the number of antenna array elements \(N_{T}=64\), the legitimate receiver Bob was located at \((30^{\circ},100\) m), and we employed a QPSK constellation. The constant \(\varphi_{0}=0\) in (13). These parameters are listed in Table 3.
## 2 Integrated Driving Hardware-in-the-Loop (IDHIL) Simulator
### Implementation of Vitual Roads for Simulation  
Virtual roads were built using dSPACE ModelDesk based on the measured data of real roads, as shown in Figure 5. GNSS/INS equipment (OXTS RT3100) was used to measure the real roads, including urban roads and motorways. To create roads using ModelDesk, it is necessary to quantify and simplify measured road information. We separated roads based on slope, traffic light locations, and intersections. Each segmented road is represented as a road segment, and this information is used directly by the EMS. Road segment information consists of length, slope angle, curvature, and limit speed.  
Figure 4: Performance maps: (**a**) engine; (**b**) MG1; (**c**) MG2.  
Figure 5: Process of virtual road implementation.
# Effect of Terahertz Antenna Radiation in Hypersonic Plasma Sheaths with Different Vehicle Shapes
## 2 The Plasma Sheath and the THz Array Antenna
### The Plasma Sheaths Corresponding to Blunt-Coned Vehicle and Sharp-Coned Vehicles
The impact of plasma sheaths on antennae can be reflected by the permittivity of plasma, which affects electromagnetic wave propagation. According to the Appleton-Hartree formula [18], the relative permittivity of plasma can be obtained with the following equation. \[\epsilon_{r}=1-\frac{\omega_{\mathrm{p}}^{2}}{\omega^{2}{+}\
u^{2}}-\mathrm{ j}\frac{\omega_{\mathrm{p}}^{2}\
u}{\omega(\omega^{2}{+}\
u^{2})} \tag{1}\]  
Figure 1: (**a**): The electron density distribution in the plasma sheath of the blunt-coned vehicle. The red arrow is the antenna radiation path. The left zoom area shows the plasma sheath layer distribution along the antenna radiation path in detail. The layers are not perpendicular to the radiation path. (**b**): The electron density distribution in the plasma sheath of the sharp-coned vehicle. The red arrow is the antenna radiation path. The right zoom area shows the plasma sheath layer distribution along the antenna radiation path in detail. The layers are perpendicular to the radiation path. (**c**): The structure of the 0.14 THz array antenna. where \(\epsilon_{r}\) is the relative permittivity, \(j=\sqrt{-1}\) and \(\omega\) is the angular frequency of radiation. \(\omega_{p}\) is the angular frequency of plasma, which can be obtained with  
\[\omega_{p}=\sqrt{\frac{N_{e}e^{2}}{\epsilon_{0}m_{e}}} \tag{2}\]  
where \(N_{e}\) is the electron number density, \(m_{e}\) is the mass of electrons, and \(\epsilon_{0}\) is the permittivity of vacuum. The antenna can only work when the operation frequency is higher than plasma cut-off frequency, which is obtained by:  
\[f_{p}=\frac{\omega_{p}}{2\pi}=\sqrt{\frac{N_{e}e^{2}}{\epsilon_{0}m_{e}}} \tag{3}\]  
Thus, the antenna must operate at higher than 75 GHz for the blunt coned vehicle since the peak electron density is \(7\times 10^{19}\) m\({}^{-3}\), according to Figure 2. As for the sharp coned vehicle, the antenna operating frequency should be higher than 98 GHz since the peak electron density is \(1.2\times 10^{20}\) m\({}^{-3}\).
## 3 Results and Discussion  
### Energy Content Analysis  
The results of the Gross Calorific Value (GCV and \(\Delta\)GCV%) are presented in Figure 1a. As can be seen, the GCV of plastic waste is 167% higher than the one of lignite, and in absolute values, it exhibits a GCV of 33.88 MJ/kg versus 12.68 MJ/kg of lignite. The GCV of plastic waste is in agreement with literature-reported values [34]. This value is comparable to or even higher than well-established liquid fuels; e.g., ethanol has a GCV of \(\sim\)27 MJ/kg [35]. This rather high value of the single fuel (plastic waste) is responsible for the respective increase of the GCV of the blended fuel, which is observed with the increase of the plastic waste content. Though the plastic waste is composed of millimeter-sized particles of various polymers, and thus extensive heterogeneity is expected, interestingly, the increase of the GCV value increased linearly with the content of plastic waste, as can be seen in Figure 1b. Also, as presented in Figure 1c, the experimental GCV values of the blended fuels are highly correlated with the theoretical values calculated from the values of the single fuels. This lack of deviations cannot be translated to a lack of heterogeneity and could be attributed to the similarly high GCV of each polymer that exists in the plastic waste.  
Figure 1: _Cont._