esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Estimation of Generative Models Using Tukey Depth
## 5 Synthetic Experiments  
As mentioned in the introduction, the mean vector and covariance matrix are the most commonly used statistics in likelihood free estimation such as ABC. In this section we demonstrate how the use of Tukey depth statistics results in more flexible and effective ABC parameter estimation compared to using the mean vector and covariance matrix.  
We consider the problem of estimating the mixture parameter \(\theta\in[0,1]\) of the mixture distribution  
\[P(x|\theta)=\theta f(x)+(1-\theta)g(x),\;\;\;-\infty<x<\infty. \tag{9}\]  
The distribution \(f(x)\) refers to a bivariate distribution with standard normal marginal distributions and dependency given by the Gumbel copula with \(\gamma=2\). The distribution \(g(x)\) is equal to \(f(x)\) except that the dependency is by the Clayton copula with \(\gamma=2.13\). A short introduction to copulas and details on the Gumbel and Clayton copulas are given in Appendix A. Figure 4 shows contour plots of the distributions \(f(x)\) and \(g(x)\).  
Even though the two distributions look quite different, they have the same marginal expectations, variances and correlations.  
In order to estimate the mixture parameter \(\theta\) using ABC, it is essential that we can generate samples from the mixture distribution \(P(x|\theta)\), as given in Equation (9). The mixture parameter \(\theta\) controls the portion of samples that should be generated from \(f(x)\) and \(g(x)\)  
Figure 4: Contour plots of \(f(x)\) (**left**) and \(g(x)\) (**right**).  
Therefore, to generate a sample, first, a random number \(\eta\) is drawn from the uniform distribution on \([0,\,1]\). If \(\eta<\theta\), the sample is drawn from \(f(x)\) else, the sample is drawn from \(g(x)\).  
Since the mean, variance, and correlation are identical for \(f(x)\) and \(g(x)\), it is naturally impossible to estimate the portions of the samples that were drawn from each of the two distributions using these statistics in likelihood-free estimation. In terms of these statistics, the distributions \(f(x)\) and \(g(x)\) are identical. However, we will demonstrate that, rather than using TDCs as statistics, the mixture parameter can be estimated.  
We used the uniform distribution on \([0,1]\) as a prior distribution for \(\theta\). Given the observation \(\mathbf{x}=(x_{1},\ldots,x_{n})\), the resulting posterior distribution becomes as given by Equation (1).  
Let \(\widehat{\theta}=\widehat{\theta}_{1},\ldots,\widehat{\theta}_{m}\) represent samples from an approximation to the posterior distribution using ABC. To evaluate the quality of the approximation, we suggest comparing true posterior probabilities  
\[P_{I}=P(\theta\in J)=\int_{J}P(\theta|\mathbf{x})\,\mathrm{d}x \tag{10}\]  
with approximations based on the samples  
\[\widehat{P}_{J}=\widehat{P}(\theta\in J)=\frac{1}{m}\sum_{i=1}^{m}I\left( \widehat{\theta}_{i}\in J\right), \tag{11}\]  
where \(J\) refers to some sub-interval of \([0,1]\) and \(I(\cdot)\) the indicator function. In the experiments, we used ten different intervals \(J\).
# XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones
## 3 Threat Model
### Classification and Identification Demands  
DoS attacks of 6G NTN protocols, such as UPF DoS attacks, cause network service disruptions by disabling NFs. Thus, the key to defending against DoS attacks of 6G NTN protocols is to classify the attacks and identify the failed NFs. When a DoS attack is classified, the protocol traffic received by the identified failed NFs needs to be intercepted. It is easy to identify the failed NFs; for example, a failed UPF can be easily identified through logs in UPF DoS attacks. However, the lack of 6G NTN protocol attack data makes classifying UPF DoS attacks challenging. Thus, we design self-evolving classification methods to cover unclassified protocol attacks.  
Attacks such as hijacking UE capabilities and sequence number desynchronization, which involve tampering with protocol sequences or fields, are more covert in 6G NTN environments than in other networks. Degraded NFs, such as those of UE, may not correspond to the entities actually sending or receiving the attack protocol message. Consequently, the protection target differs from the failed NF. The critical defense strategy is to identify anomalous protocol sequences, analyze their transmission paths, and intercept and monitor the corresponding NF and protocol messages. Here, we design online methods for identifying anomalous protocol sequences.
## 2 Background Work  
### Previous Work  
Opportunistic systems have been previously discussed in [2; 18; 19; 20], where their essential parts are described. Specifically, such papers indicate that an OEC system is defined as a decentralized and adaptable architecture that allows IoT/IIoT devices to take advantage of computing, storage, and communication resources available in their nearby environment without relying on continuous connectivity or predefined infrastructure. Its operation is based on the ability to dynamically detect and use nearby nodes or gateways (static or mobile) to execute critical tasks in the absence of stable network coverage.  
In order to clarify how an opportunistic system works, an example of a generic architecture is shown in Figure 1. At the bottom, the IoT network layer is composed of different IoT networks whose end devices are capable of exchanging data with the upper layer. Such end devices can also communicate with other devices in their same network, thus acting as relays. What distinguishes the IoT network layer from other layers present in generic IoT architectures is the fact that the communications with the upper layer are not always possible, so they need to be carried out opportunistically. Therefore, the upper layer (labeled in Figure 1 as OEC Smart Gateway Layer) is made up of gateways capable of providing services opportunistically with reduced latency, thanks to their proximity to the IoT end nodes. Moreover, the proposed architecture allows the received data to be stored in a distributed manner among all the gateways. Finally, the Cloud provides services that gateways cannot provide, such as intensive processing or the storage of large amounts of data.  
An example of the use of the previously described communications architecture is detailed in [2]. Such a paper proposes an OEC system in which IoT devices deployed in remote areas or with limited connectivity (e.g., environmental or industrial sensors) use Bluetooth 5 to dynamically connect to edge gateways based on Single-Board Computers (SBCs) (e.g., Raspberry Pi) when these are within their communication range. Equipped with Bluetooth 5 modules and WiFi/4G connectivity, such gateways act as intermediary nodes that provide local storage and processing services, reducing the dependency on a  
Figure 1: Generic OEC communications architecture.  
continuous Internet connection. The system employs a peer-to-peer (P2P) network based on Kademlia DHT to store data on other nearby gateways or route them to available destinations, ensuring service continuity even when a node loses its connectivity and data locally. The communication between gateways is primarily performed via WiFi or 4G networks, but in the event of interruptions, the system turns to the Cloud as a backup to maintain connectivity between geographically dispersed IoT networks.
## 3 Human-Centered Knowledge Graph-Based Concept towards Collaboration in Manufacturing
### Human-Robot Collaboration and Key Performance Indicators  
This section delves into the various categories of workstations and collaboration scenarios that are of significance within the context of the case study under consideration. In addition, a concise summary of the primary performance metrics related to human-centered, ergonomic, and human-robot collaboration is provided. Depending on whether the actors involved are human or robotic, different types of workstations can be identified: manual, collaborative, and automatic [53]. Within a collaborative workstation setup, further categorization is established based on the nature of the interaction between human and robot actors during work tasks, aiming to yield a more comprehensive case study. Figure 3 illustrates three different types of collaboration [42,54]:  
1. Separate work: Human and robot tasks are kept apart, and they do not share workspaces, tools, or workpieces.
2. Sequential collaboration: Although the human and robot actors are in a shared process flow of a workpiece, tasks are completed in succession. The workspaces, tools, and workpieces may be shared, but the tasks are strictly serialized such that any sharing is temporally separated.
3. Simultaneous collaboration: Human and robot tasks are executed concurrently and, moreover, may involve working on different parts of the same workpiece but are focused on achieving separate task goals.
4. Supportive collaboration: Humans and robots work together on the same piece of work to complete a common task.  
The key performance indicators (KPIs) focused on human factors are outlined in Table 1 across six distinct categories: time behavior, physical measures, HR physical measures, efficiency, effectiveness, and ergonomics. Furthermore, the Operator 4.0 typologies [46] have been incorporated into these KPIs in the second column, illustrating the potential to facilitate the establishment of a harmonious relationship between humans and automation. A study that assesses the quality of human-robot interaction [15] has been partially referenced, although it does not offer a comprehensive overview of the subject matter. Additionally, a paper on ontology-driven KPI metamodeling [14] has been taken into account in this case study, focusing on a semantic technology perspective.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**KPI Description** & **Operator 4.0 Type** \\ \hline \multicolumn{2}{c}{Time behavior category} \\ \hline Average time to complete task & Analytical operator \\ Collaboration time—Type-3 and Type-4 & Collaborative operator \\ Functional delays & Analytical operator \\ Human operation time & Analytical operator \\ Interaction time & Collaborative operator \\ \hline \hline \end{tabular}
\end{table}
Table 1: The categorized human-centric KPIs for the case study.  
Figure 3: Separate (**I.**), sequential (**2.**), simultaneous (**3.**), and supportive (**4.**) types of human–robot collaborations [42].
## 2 Approach to Fuel-Cell System Analysis
### Balance of Plant Description
The retained stream from the O\({}_{2}\) separation membrane (mainly composed of N\({}_{2}\)) is directed to a turbine to recover as much energy as possible. Instead, the permeate stream (i.e., the O\({}_{2}\)-enriched air) arrives at the fuel cell stack again after mixing with water in the outlet of the humidifier. Figure 3: Typical balance of plant layouts for atmospheric and supercharged fuel cells. In the H\({}_{2}\) stream, the first component is a supply valve from the 700-bar tank in the vehicle such as in the standard system, although controlled to a higher pressure (\(\tau_{H_{2}}\)). The low-temperature H\({}_{2}\), characterized by its high specific heat capacity of 14.3 kJ/kgK, compared to air's 1 kJ/kgK at 25 \({}^{\circ}\)C, functions as a cooling medium for the first two heat exchangers. The principal objective of these exchangers is to increase the H\({}_{2}\) stream temperature. This helps to raise the H\({}_{2}\) temperature before it arrives at the turbine side of the turbocharger, where it is expanded to provide the power required for the first air compression. Considering the layout of the balance of plant proposed, other parameters that need to be taken into account are the isentropic efficiencies of the existing compressors and turbines for energy recovery, as well as the expansion ratio in the turbine corresponding to the H\({}_{2}\)-fired generator set. Table 1 shows a summary of all the parameters explored, the values initially adopted for each of them (which are defined using values close to the average values within the usual working range for each parameter and are called base case in the following section), as well as the ranges that were explored later in the sensitivity studies carried out for each of the variables. Finally, the net efficiency of the balance of plant was obtained as follows:  
\[\eta_{net}=\frac{N_{stack}-N_{comp}+N_{turb}}{\dot{m}_{H_{2}}HHV} \tag{4}\]  
where \(N_{stack}\) is the power delivered by the fuel cell stack, \(N_{comp}\) is the power consumed combining both compressors of the cathode stream, \(N_{turb}\) is the power delivered by both H\({}_{2}\) and N\({}_{2}\) turbines, and \(HHV\) is the higher H\({}_{2}\) heating value (142.5 MJ/kg). In the next sections, the system efficiency reached at different O\({}_{2}\) enrichment levels is compared with the one corresponding to an atmospheric fuel cell such as the one described in Figure 3a. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Base Value** & **Range** \\ \hline \(\lambda_{\text{O}_{2}}\) & 1.5 & [1.2–2.1][39] \\ \(\tau_{mem}\) & 4 & [2–5][40] \\ \(\eta_{comp}\) & 0.65 & [0.3–0.9][41] \\ \(\eta_{turb}\) & 0.75 & [0.6–0.9][41] \\ \(\tau_{H_{2}}\) & 5 & [3–9] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Design parameters and operative conditions of fuel cell with O\({}_{2}\)-enriched air. Figure 4: Proposed layout for an O\({}_{2}\)-enriched fuel cell system. The reason for this selection is that, as mentioned before, it is assumed that the permeate flow in the O\({}_{2}\) separation membrane is approximately atmospheric.
# Mixed-Integer-Based Path and Morphing Planning for a Tensegrity Drone
## 5 Mixed-Integer Convex Path and Morphing Planner  
In this section, a path and morphing planner cast as a single mixed-integer convex program is described. The main difference between the proposed approach and the traditionally employed method for path planning over a union of convex regions is the introduction of another set of choices related to the shape of the robot. The fact that the shape of the robot appears as a linear term in the constraints (6) allows the use of a big-M type mixed-integer selector, similar to the convex region selector. This requires writing a region-fit constraint for each node, each configuration, each possible region, and each step. The resulting optimization problem is given below:  
\[\begin{array}{ll}\underset{\mathbf{x}_{k},\ c_{k},\ s_{q,k}}{\text{minimize}}&|| \mathbf{x}_{1}-\mathbf{x}_{\text{start}}||^{2}+||\mathbf{x}_{K}-\mathbf{x}_{ \text{goal}}||^{2}+w\cdot\sum\limits_{k=1}^{K-1}||\mathbf{x}_{k+1}-\mathbf{x} _{k}||^{2},\\ \text{subject to}&\begin{cases}\mathbf{A}_{t}(\mathbf{P}_{t}\mathbf{r}^{q}+ \mathbf{x}_{k})\leq\mathbf{b}_{t}+(1-c_{t,k})M\cdot\mathbf{1}_{t}+(1-s_{q,k})M \cdot\mathbf{1}_{t},\ \forall i,k,t,q\\ \sum\limits_{t=1}^{T}c_{t,k}=1,\ \forall k\\ \sum\limits_{q=1}^{Q}s_{q,k}=1,\ \forall k\\ c_{t,k},\ s_{q,k}\in\{0,\ 1\}\end{cases}\end{array} \tag{13}\]  
where \(Q\) is the number of configurations, \(K\) is the number of steps, \(\mathbf{x}_{\text{start}}\) is the desired starting position of the center of mass of the structure, \(\mathbf{x}_{\text{goal}}\) is the desired end position of the center of mass, \(\mathbf{1}_{t}\) is the vector of the same dimensions as \(\mathbf{b}_{t}\) whose elements are all ones, and \(w\) is a weight-adjusting relative importance of our primary objective (starting from the point \(\mathbf{x}_{\text{start}}\) and finishing at the point \(\mathbf{x}_{\text{goal}}\)) and our secondary objective (making the footsteps evenly spaced). Furthermore, note that \(i=1,\...,\ n;k=1,\...,\ K;t=1,\...,\ T;\)\(q=1,\...,\ Q.\)  
The use of big-M relaxation leads to \(n\cdot T\cdot Q\cdot K+2K\) constraints and \(K\cdot(T+Q)\) binary variables. Binary variables only appear in the constraints, which they enter linearly. The constraints are linear equalities and inequalities; the objective is quadratic, meaning that the proposed problem is a mixed-integer quadratic program. Problems of this type can be solved with Gurobi and MOSEK commercial solvers, among others [48]. Let us note that the number of solvers that handle mixed-integer quadratic problems is greater than for more general case mixed-integer convex optimization problems [48].
# Road Crack Detection by Combining Dynamic Snake Convolution and Attention Mechanism
## 2 Base Model and Proposed Model
### Proposed Model  
For road crack datasets, the target pixels are relatively sparse, categorizing road crack detection as a task of detecting small objects. Additionally, an analysis of road crack patterns reveals that cracks often exhibit branching structures, and their color tends to be darker compared to the surrounding environment. To address the characteristics of crack shape, color, and small object detection, this study integrates the CBAM into the foundational U-Net network model and DSC module. This enhancement assists the network in accurately capturing crack features across both channel and spatial dimensions.  
The CBAM module is commonly utilized to enhance the representation capabilities of convolutional neural networks across both channel and spatial dimensions. By adaptively computing and learning attention weights in these two dimensions, the network can better focus on crucial features while suppressing non-essential information. Specifically, in road defect images, this allows the network to prioritize crack features while disregarding extensive road backgrounds. The specific structure and operational processes of CBAM in this network are illustrated in Figure 4.  
Figure 3: The difference between using standard and dynamic snake convolutions.  
Figure 5 shows partial feature maps during the downsampling process of ADSC-U-Net. From the figure, it is evident that the original input image has a high proportion of background, with the surrounding pixels being lighter in color compared to the target pixels. Additionally, the cracks are predominantly oriented horizontally and vertically. As seen in Figure 2, as downsampling progresses, semantic information becomes increasingly abstract. At this stage, the network needs to allocate more attention to specific spatial locations and channel regions to identify abstract cracks effectively. The CBAM continuously computes and optimizes weights for important features across the channel and spatial dimensions, ensuring the effective preservation of road crack information in the ADSC-U-Net network. This capability enables the network to remain effective in handling high-level semantic information. Furthermore, the CBAM module assigns higher weights to crack features from the initial stages of the network, ensuring continuous focus on minor cracks during the subsequent downsampling processes. This characteristic enhances the network's ability to detect small cracks effectively.
## 6 Discussion  
M2M Service networks are inherently multiple stakeholder systems, which usually consist of parts evolving in different timescales. For example, the generations of cellular radio systems may evolve 10 years, while novel M2M applications may be born even every month. However, the M2M service system lifecycles are required to be even longer than 20 years. If a part of the system is dependent on a single provider, then it is a strong risk for system being operational for such a long lifecycle. Therefore, the system should be based on open standards, and horizontal layering shall be kept clear. If autonomic M2M solutions are developed for the systems where horizontal layers are mixed, the challenge with such solutions is that their application is likely to be limited to the special case only.  
In this survey we categorize the available technologies into M2M Information and services, M2M communication and M2M security. According to the review, this categorization could act as a starting point for horizontal architectures of the M2M service networks. However, it is seen that principles and guidelines for the architectures are needed to be defined in order to establish a solid basis for multiple stakeholder system. In addition, it is seen that both end-to-end Internet approach, and M2M gateway based approach are needed to enable horizontally capable M2M service networks. These are needed for solving the heterogeneity of technologies to enable communication between objects and applications, which are not initially been designed to communicate together. It is also seen that the horizontal architecture of the system should be defined first, and then proceeding into the solving of complexity would be feasible. Otherwise, the solutions for solving the complexity may prevent the horizontal approach and thus the basis for a multiple stakeholder system.
# Integrity Monitoring of PPP-RTK Positioning: Part II: LEO Augmentation
## 4 LEO Augmentation
### User Positioning
#### 4.3.2 Positioning Results
Mis-modeled effects such as multipath or unmodelled ionospheric interpolation errors would bias the results and could further hamper the convergence. The shortened convergence time is related to the rapid change in geometry of the LEO satellites. For this aspect, it is similar to shortening the PPP convergence time with the LEO augmentation. Figure 12: Improvement (\(f_{\rm H}\)) of the formal horizontal precision by adding the LEO augmentation with (solid lines) and without the PAR enabled (dotted lines). The simulated data from (**a**) Network 1 with a \(\sigma_{i_{ij,\rm km}}\) of 1 mm and (**b**) Network 3 with a \(\sigma_{i_{ij,\rm km}}\) of 5 mm are used. For the ambiguity-fixed solutions, the formal precision that could be reached after FAR shows the potential of the PPP-RTK positional performance. Figure 13b shows the formal horizontal precision after 1 h of the processing with the PAR enabled. With the LEO augmentation, the ambiguity-fixed horizontal precision is also improved due to the increased model strength. The formal horizontal precision at 1 h is reduced to within 1 cm for all constellation combinations and under both scenarios with LEO augmentation.