esa-sceva/satcom-chunk-collection huggingface dataset:
# A Tolerance Type Screening Method Based on Similarity Evaluation
## 3 Similarity Assessment Strategies
### Determination of Similar Elements
#### 3.2.1 Tolerance Element Similarity Elements  
The types of tolerance elements are tolerance points (center points of spheres), tolerance lines (derived element lines), spheres, planes (derived element surfaces), rotational surfaces, cylindrical surfaces, prismatic surfaces, spirals, and free-form surfaces. In the field of tolerance design, tolerance types have a mapping relationship with tolerance elements [25], as shown in Table 1 (\"\(\Delta\)\" represents a mapping relationship). For this purpose, the similar elements of the tolerance elements are transformed into the set of tolerance types linked to the tolerance elements.  
In practice, the relative size between the characteristic dimensions of tolerance elements and the overall dimensions of the part (referred to as relative scale, represented by \(\mathbf{Q}_{p}\)) also affects the selection of tolerance types. He described the importance of tolerance elements relative to the overall part, which has a guiding significance for determining geometric tolerance benchmarks. To achieve this, it is necessary to consider the relative scale and consider the relative scale of tolerance elements as an element of similar tolerance elements. Thus, the tolerance element similarity element is defined as \(E_{t}\) = (!olerance type! associated tolerance type), relative scale].  
The commonly used relative scale is calculated by Equation (1). Among them, for the convenience of calculation, simplified formulas are applied for cylindrical and conical surfaces: the calculation formula for the relative scale of cylindrical surfaces is shown in Equation (2); the relative scale calculation formula for a cone is shown in Equation (3).  
\[\mathbf{Q}_{p}=\frac{\sqrt{\mathsf{S}}}{\sqrt[3]{\mathsf{V}}} \tag{1}\]  
where \(\mathsf{S}\) denotes the area of the tolerance element and \(\mathbf{V}\) denotes the volume of the part.  
Cylinder:  
\[\mathbf{Q}_{p}=\frac{\mathbf{L}}{\mathbf{D}} \tag{2}\]  
where \(\mathbf{L}\) is the height of the cylindrical surface and \(\mathbf{D}\) is the diameter of the feature size of the cylindrical surface.  
Cone:  
\[\mathbf{Q}_{p}=\frac{\mathbf{H}}{\mathbf{D}_{1}-\mathbf{D}_{2}} \tag{3}\]  
where \(\mathbf{H}\) is the height of the cone, \(\mathbf{D}_{1}\) is the diameter of the large end of the cone, and \(\mathbf{D}_{2}\) is the diameter of the small end.  
The relative scale varies within a certain range. When treating relative scale as a similarity element, it is necessary to classify the relative scale. In this study, the relative scale is divided into 8 levels as shown in Table 2.
# Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks
###### Abstract  
Traditional low earth orbit (LEO) satellite networks are typically independent of terrestrial networks, which develop relatively slowly due to the on-board capacity limitation. By integrating emerging mobile edge computing (MEC) with LEO satellite networks to form the business-oriented \"end-edge-cloud\" multi-level computing architecture, some computing-sensitive tasks can be offloaded by ground terminals to satellites, thereby satisfying more tasks in the network. How to make computation offloading and resource allocation decisions in LEO satellite edge networks, nevertheless, indeed poses challenges in tracking network dynamics and handling sophisticated actions. For the discrete-continuous hybrid action space and time-varying networks, this work aims to use the parameterized deep Q-network (P-DQN) for the joint computation offloading and resource allocation. First, the characteristics of time-varying channels are modeled, and then both communication and computation models under three different offloading decisions are constructed. Second, the constraints on task offloading decisions, on remaining available computing resources, and on the power control of LEO satellites as well as the cloud server are formulated, followed by the maximization problem of satisfied task number over the long run. Third, using the parameterized action Markov decision process (PAMDP) and P-DQN, the joint computing offloading, resource allocation, and power control are made in real time, to accommodate dynamics in LEO satellite edge networks and dispose of the discrete-continuous hybrid action space. Simulation results show that the proposed P-DQN method could approach the optimal control, and outperforms other reinforcement learning (RL) methods for merely either discrete or continuous action space, in terms of the long-term rate of satisfied tasks.  
LEO satellite edge networks; offloading decision; resource allocation; hybrid action space; P-DQN +
Footnote †: journal: _Sensors_**2023**, 23, 9885. [[https://doi.org/10.3390/s23249885](https://doi.org/10.3390/s23249885)]([https://doi.org/10.3390/s23249885](https://doi.org/10.3390/s23249885))
## 7 Sustainability of V2X Technology  
V2X technology plays a significant role in promoting sustainability in the transportation sector. By enabling vehicles to communicate with each other and with surrounding infrastructure, V2X technology offers numerous benefits that contribute to a more sustainable and efficient transportation ecosystem [54].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Smart** & & **Delaborative** & **Delaborative** & **Driver Support** & **Driver Warning** \\
**Infrastructure** & **Driving Strategy** & **Decision Making** & **Driving** & **Driver Support** & **Driver Warning** \\ \hline Static maps & Global path & Swarm intelligence & Shared data & N/A & Optimal status \\ \hline Dynamic data & Local trajectory & Static sensors & - & N/A & Warnings \\ \hline \hline \end{tabular}
\end{table}
Table 2: Application of the proposed method of mapping ODD elements on automated driving functions for LoA 4 valet parking with V2X.  
Figure 3: Steps and procedures of the proposed methodology visualized as a flow chart.  
One key aspect of V2X technology is its potential to improve traffic flow and reduce congestion. With real-time communication, the vehicles can share information about the traffic conditions, the road hazards and the optimal routes. This facilitates smoother traffic management and reduces unnecessary idling, resulting in lower fuel consumption, reduced greenhouse gas emissions and improved air quality. V2X technology also supports eco-driving practices by providing the drivers with information on the optimal acceleration, deceleration, and speed adjustments to maximize the fuel efficiency. By promoting eco-friendly driving behaviors, V2X contributes to reduced fuel consumption and lower carbon dioxide emissions.  
V2X technology enables smart charging and grid integration for the Electric Vehicles (EVs) [55]. Through the Vehicle-to-Grid communication, EVs can interact with the electrical grid, allowing for optimized charging schedules based on the grid demand and the renewable energy availability. This bi-directional energy flow helps balance the grid, maximize the utilization of renewable energy sources, and reduce the reliance on conventional power generation.
# Research on the Path Planning Algorithm of a Manipulator Based on GMM/GMR-MPRM
## 5 Simulation and Experimental Analysis  
### Simulation and Analysis  
In order to verify the effectiveness of the algorithm, this paper constructed a simulation scenario in MATLAB. The purpose of the experiment was to plan a motion path close to the task trajectory, and there were three obstacles around the task trajectory. The task trajectory was a cosine curve of two periods, that is: \(y=1.5\times\cos(x)\), \(x\in[0,4\pi]\).  
In order to simulate the uncertainty in the demonstration data, the task trajectory was first enlarged by 0.9, 0.96, 1, 1.03, and 1.1 times, and then, 6 dB of Gaussian noise was added to obtain the teaching data, as shown in Figure 4. Finally, the GMM algorithm was used to model the loose teaching data, as shown in Figure 5. The number of Gaussian components in the GMM was set as \(K=4\), and it can be seen that the data expressed by the GMM were relatively compact.  
After using the GMM algorithm to transform the demonstration data into a compact probability model, the GMR algorithm was used to fit the model into a smooth path with variance as the resample space for path planning. However, there will be a certain difference between the recurrent scene and the teaching scene. Two obstacles were added to the recurrent scene to indicate the difference from the teaching scene. The recurrent scene can seen in Figure 6; it can be seen that the path planned using GMR will collide with the obstacle.  
In order to plan a path that is not only able to avoid new obstacles, but is also similar to the original trajectory, the original trajectory was divided into the collision-free path and the collision path. According to the distance between the obstacles and the points in the sampling space, variable density resampling was carried out to generate uneven sampling points and remove the points that collide with obstacles. A roadmap was constructed according to the EPD, then Dijkstra algorithm was used to search the feasible path. Finally, the planned path was pruned and smoothed to generate a smooth path. In the simulation, the quantity of resampling based on covariance was set as \(n_{\Sigma}=100\), the quantity of resampling based on distance was set as \(n_{0}=300\), and the adjustment coefficient of the Euclidean and probability distances was \(\lambda~{}=0.7\), while the order of the B-spline was set as \(p=3\). The path planned by the GMM/GMR-MPRM is shown in Figure 7, and it can be seen from the figure that the GMM/GMR-MPRM can still plan a feasible path when the recurrent scene is not the same as the teaching scene.  
Figure 4: Demonstration environment.  
Figure 5: GMM encoding result.
# MPVT: An Efficient Multi-Modal Prompt Vision Tracker for Visual Target Tracking
## 3 Experimental Results and Discussion
### Ablation Study  
To analyze the effectiveness of the proposed components, ablation experiments were conducted on the three datasets. The experimental results are shown in Table 3. MPVT, due to the use of efficient training paradigms such as prompt learning, achieved the best performance in RGB-T, RGB-D, and RGB-E, although only 0.9% of the parameters were used for training. First, if the fully connected head network module was removed and the traditional convolutional head network was used instead (MPVT-FC), the performance decreased by 0.01 to 0.02. This indicates that fully connected head networks can effectively avoid the inductive bias of traditional convolutional head networks. Second, if the dynamic adaptive prompt fusion module was removed based on MPVT-FC (MPVT-PF), the model just utilized a full-parameter fine-tuning strategy. In this case, each index decreased by 0.04 to 0.05 on all three datasets. This indicates that the fusion module is crucial for efficient parameter learning and can efficiently transfer from a single-modal pre-trained model on large-scale RGB data to multi-modal scenarios such as RGB-T, RGB-D, and RGB-E. Finally, if the input enhancement module was further removed (MPVT-IE), the perfor  
Figure 4: Precision in 15 degraded scenarios in the VisEvent dataset.  
mance decreased by about 0.01, proving that the proposed position and type embedding enhancement can contribute to accurate target tracking.
# Engineering Challenges Ahead for Robot Teamwork in Dynamic Environments
## 3 Teamwork Challenges
### Shared Knowledge  
We already mentioned that our viewpoint of robot teamwork is closely linked to mutual awareness in the robots, i.e., teammates have--in addition to their local knowledge--some knowledge about their colleagues in the team. This awareness often, but not necessarily, is based on the provision of a shared global knowledge base for the entire team. The knowledge base, which typically would be implemented as a distributed replicated knowledge store, contains the concepts, objects, and relations known to the robots, as well as the fused perceptions of the state of the execution environment. In many works, the individual local view of a robot is called the _local world model_, while the shared team knowledge base is called the _shared world model_[57].  
Potentially, such a shared knowledge base with frequently consulted thousands of objects and relations is a very resource-consuming and a performance-critical element of the teamwork. This creates several challenges, as listed below.  
* How do we formally define the shared knowledge such that reasoning at run time satisfies performance requirements?
* How do we equip robots with commonsense knowledge that human beings would have implicitly about the environment?
* How can we integrate individual, heterogeneous knowledge representations of heterogeneous robots into a shared world model?
* What kind of consistency guarantees are necessary and feasible for the distributed, replicated storage of the (dynamic) contents of the knowledge base?
* How robust and scalable is the common knowledge base in view of, e.g., unreliable communication connections, imprecise sensor data, and predefined time barriers?
* Can the knowledge base structure be adapted and extended at run time?  
While there is a large set of publications focusing on knowledge representation techniques for robotic applications (e.g., [58; 59; 60]), little was published specifically on distributed knowledge bases for multi-robot systems. One thread of research--in particular for service robots--looked at offloading the knowledge base to the cloud [61]. The viability of such an approach clearly depends on the application requirements in terms of access performance and availability. Other approaches exploit a decentralized storage of knowledge [57] leading to well-known questions of consistency in data replication.  
The spectrum of diverse requirements in multi-robot applications seems to be so large, that a harmonization of techniques for knowledge representation and storage is out of reach. On the other hand, a vast body of know-how is available for knowledge representation, reasoning, distributed data storage systems, and replication and consensus protocols. In this situation, it is worth considering the question of whether the variety of existing solutions could be narrowed down to a few solution patterns, which would satisfy the majority of application scenarios.
## 3 Fundamentals of GLI-Split
### Interworking between GLI-Domains and the Classic IPv6 Internet  
For future Internet routing architectures, it is extremely important that the communication with the non-upgraded part of the Internet is possible and that it does not imply any restrictions. In the GLI-Split architecture, the interworking between single-homed GLI-domains and classic IPv6 domains is natively supported. In multihomed networks, some additional mechanisms are required, which are explained in Section 4.  
In the single-homed case, when node 11 (_2001:db8::11_) in the classic IPv6 Internet wants to initiate a communication to the GLI-node 3 in a GLI-domain (see topology in Figure 5), it uses its IPv6 address  
Figure 4: Communication process with horizontal address translation between two GLI-domains. GLI-node 1 sends a packet to node 3 in a different GLI-domain and node 3 replies.  
as source address and the global GLI-address of node 3, which was obtained through the DNS, as destination address. According to the global locator N in the global GLI-address, the packet is delivered to the GLI-gateway of the destination GLI-domain. There, the GLI-gateway queries the local mapping system for the local locator of the destination GLI-node and replaces the global GLI-address in the destination field with the returned local GLI-address. The packet is then forwarded according to the local locator to the destination node. At GLI-node 3, the local GLI-address on the network layer is replaced by an identifier address before handing the packet up to the transport layer. This is the vertical address translation mechanism which is explained in Section 3.2. In return packets, GLI-node 3 simply swaps source and destination address on network layer and sends the packet to its default GLI-gateway. There, the local GLI-address in the source field is replaced with a global GLI-address indicating the global locator of the GLI-gateway. After the translation, the packet is forwarded according to the classic IPv6 address in the destination field and eventually arrives at node 11 in the classic IPv6 domain.  
In the opposite direction, when a GLI-node initiates a communication to a node in the non-GLI IPv6 Internet, it uses its own identifier address as source and the conventional IPv6 address as destination on the transport layer. The source address is, as usual, replaced by a local address, but the destination address is not changed when passing the packet from the transport to the network layer. The packet is carried to a GLI-gateway which then substitutes the local source address by the global address reflecting the global locator of that GLI-gateway. Eventually, the packet is delivered to the destination node. This node can respond to that packet by simply swapping source and destination address. The receiving GLI-node can map the packet as a response to its initial request because the identifier GLI-address and the non-GLI IPv6 address are used on the transport layer.  
Figure 5: IPv6 node with IP address 2001:db8::11 in a non-GLI domain communicates with GLI-node 3 in GLI-domain.
## 5 Performance Analysis  
The performance of the proposed IM-MIMO-OFDM system was evaluated through Monte Carlo simulations to obtain achievable BER metrics and compare them with MIMO-OFDM systems. The simulations considered a \(2\times 2\) MIMO system (Experiments 1, 2, and 3) and a \(4\times 4\) MIMO system (Experiments 4 and 5). A MIMO-V2V channel with selective Rayleigh fading in frequency with a maximum delay spread (\(\tau_{rms}\)) of \(0.4\)\(\mu\)s and Doppler frequency (\(f_{D}\)) of \(1\)\(\mathrm{KHz}\) was considered. Each OFDM-IM symbol block consists of \(N=64\) subcarriers with the addition of a cyclic prefix of size \(CP=16\). For proper comparison, channel state information was estimated using a two-dimensional orthogonal basis expansion model (2D-BEM) for all evaluated receivers. The remaining system and channel parameters are shown in Table 3.
# Study of the Applicability of Thermochemical Processes for Solid Recovered Fuel
## 3 Results
### Comparative Analysis: Gasification vs. Combustion  
When comparing gasification and combustion of SRF, it is important to consider both the energy efficiency and the environmental implications of each technique. According to the energy balance results, SRF combustion demonstrates superiority in terms of energy efficiency. When burning completely dried SRF, combustion allows recovering up to 1.6 MJ per kg of processed wet SRF (with initial moisture content of 77.3 wt% before drying), while air gasification allows of around 1.4 MJ per kg of wet SRF. This performance is consistent with the results shown in previous studies found in the literature concerning the combustion of SRF in facilities such as WtE plants or cement kilns, in which energy benefits close to 1.8 MJ per kg of processed SRF have been reported, depending on operational conditions and the specific type of SRF used [24; 25]. On the other hand, the energy benefit obtained in this study for the air gasification of dried SRF (1.6 MJ/kg of processed SRF) is slightly higher and comparable to those found in the literature for the co-gasification of SRF (produced from different residues) with biomass or plastic wastes (0.5-0.6 MJ/kg) [30; 52]. Such differences may be attributed to variations in SRF composition, gasifier characteristics, and operational conditions, such as the equivalence ratio or the gasification temperature. In contrast, SRF air/steam gasification, under the operating conditions set in this study, does not seem to be a useful process for energy recovery, as the energy balance of the entire process resulted in an energy benefit of 0 MJ. Analysing the product yields and energetic efficiency of air/steam gasification when feeding SRF with lower moisture content could be an interesting work to explore.  
From an energy efficiency standpoint, SRF combustion represents the most effective option and is suitable for applications where maximising the recovered energy is the primary objective. However, air gasification, while less efficient in terms of recovered energy, offers substantial benefits regarding sustainability and potential for integrating cleaner technologies. Therefore, gasification is preferable in contexts where emission reduction and integration with renewable energy sources are critical. Recent studies highlight that gasification, as a partially oxidative process, produces a synthesis gas that, while less energetic compared to combustion, has a lower direct environmental impact [17; 27]. This aligns with previous studies that highlight gasification as a viable and preferable alternative in scenarios where sustainability and emission reduction are priorities, despite its lower intrinsic energy efficiency [35]. Consequently, the choice between combustion or gasification should be informed by a comprehensive analysis that considers not only immediate energy efficiency but also long-term sustainability and adaptability to future energy and environmental requirements.
## 6 Plate Partitioning Optimization
### Optimization Approaches
#### 6.2.1 Successive Plate Partitioning  
The idea of SucPP follows the routine which the design engineer used to determine the plate partitioning manually. The calculation of the plate lengths is achieved in an incremental, three-step manner.  
1. Determine the lengths of the top plates: Since no other lengths are set yet and the bulkheads are not welded to the top plates, the length of each top plate is chosen as long as possible, i.e., is set to _MaxLength_, or, in the case of the last plate, is determined such that the sum of all plates is equal to _TotalLength_, the length of the middle section.
2. Determine the lengths of the bottom plates: The lengths of the bottom plates are restricted by the positions of the bulkheads. Since the lengths of the side plates are not determined yet, these constraints can be neglected at this point. Thus, the length of each bottom plate is determined to be as long as possible, yet avoiding conflicts with the bulkheads. In particular, if the gap between the welding seam of two bottom plates and the welding seam of a bottom plate and a bulkhead would be smaller than _MinGap_, the bottom plate is shortened to an appropriate length. As for the top plates, the last bottom plate is assigned the appropriate length to reach _TotalLength_.
3. Determine lengths of side plates: All determined parts have to be taken into account, meaning that the length of each side plate is restricted by the lengths of the top and bottom plates, as well  
Figure 8: Middle piece of a BTB with indicated variables and constraints for the plate partitioning.  
as the position of the bulkheads. As in Point 2, if any two welding seams are too close to each other, the side plate is shortened to an appropriate length. As above, the length of the last side plate is chosen to reach _TotalLength_.  
This strategy for calculating the lengths of the single plates has the advantage of being transparent and comprehensible. However, once the length of a plate is determined, it cannot be changed, even if it were advantageous for the determination of other lengths.  
SucPP does not use the information of the optimized boom configuration. Instead, this information is only used afterward to determine the thicknesses of the resulting plates as the thickest one among the segments that the plate spans.