esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Robust Representations of Spatial Networks Using Graph Neural Networks
## 4 Methods
### Constructing Heterogeneous Graph Representations

Table 2: Graph theory concepts and definitions. Figure 2: An abstract depiction of a heterogeneous graph representation of spatial entities. The triangles represents buildings, circles represents streets, parallelograms represents natural bodies. The relations between the entities is defined using relations such as the spatial _within_, spatial _intersacts_, spatial _adjacent_. stations [5]. Similarly, the type of relations we can define on spatial entities varies widely. For example, we could encode the relation between buildings using spatial measures such as euclidean distance between buildings _or_ buildings that share the same age range _or_ buildings whose household income fall in the same range. It could be said then that the type of relations considered in a heterogeneous graph representation depends on the task at hand. Ultimately, we want to be able to generate representations for spatial entities that strikes a balance between _adequate representation of the real-life perspective_ and _suitability to the task at hand_. In this article, we implement a new package that derives the heterogeneous graph representation for a place. We call this package HetSpatial. The package uses a place name and a set of relations to derive the heterogeneous graph representation for that place. At this time, HetSpatial supports streets and buildings as spatial entities and uses spatial relations to encode relations between the entities. The package is developed in the python programming language. We provide the algorithm that describes at a high level how the package works in Algorithm 1, where \(\mathbf{S}\) refers to a set of homogeneous spatial entities which could be presented in a generic format such as a table, \(\mathbf{R}\) is the set of relations to be considered, our implementation considers spatial relations. ```
Input: set of relations \(\mathbf{R}\) set of spatial entities \(\mathbf{S}\) relational operator \(\mathbf{f}\left(\cdot\right)\) Output: heterogeneous graph representation \(\mathcal{G}\) initialize \(\mathbf{M}\)\(\triangleright\) a data structure. for\(r\in R\)do for\(s_{i}\in S\)do for\(s_{j}\in S\)do if\(s_{j}\
eq s_{i}\)then \(\mathbf{M}_{r}\leftarrow\mathbf{f}\left(s_{i},s_{j};r\right)\)\(\triangleright\) We use spatial relations. else  pass  end if  end for  end for  initialize \(\mathcal{V}_{R}\), \(\mathcal{E}_{R}\) for\(r\in R\)do \(\mathcal{V}_{r}\)\(\mathbf{M}_{r}\) \(\mathcal{E}_{r}\)\(\mathbf{M}_{r}\) end for \(\mathcal{G}=\left(\mathcal{V}_{R},\mathcal{E}_{R}\right)\) return\(\mathcal{G}\)
```  
**Algorithm 1**Derive Heterogeneous Graph Representation  
Computational Complexity  
The run-time and memory complexity of Algorithm 1 is \(\mathcal{O}(|\mathbf{R}|*(|\mathbf{S}|*|\mathbf{S}|-1))\), where \(|\mathbf{S}|\) is the number of homogeneous spatial entities being considered and \(|\mathbf{R}|\) is the number of relations.
# Mobile Robot Path Planning with a Non-Dominated Sorting Genetic Algorithm
###### Abstract  
In many areas, such as mobile robots, video games and driverless vehicles, path planning has always attracted researchers' attention. In the field of mobile robotics, the path planning problem is to plan one or more viable paths to the target location from the starting position within a given obstacle space. Evolutionary algorithms can effectively solve this problem. The non-dominated sorting genetic algorithm (NSGA-II) is currently recognized as one of the evolutionary algorithms with robust optimization capabilities and has solved various optimization problems. In this paper, NSGA-II is adopted to solve multi-objective path planning problems. Three objectives are introduced. Besides the usual selection, crossover and mutation operators, some practical operators are applied. Moreover, the parameters involved in the algorithm are studied. Additionally, another evolutionary algorithm and quality metrics are employed for examination. Comparison results demonstrate that non-dominated solutions obtained by the algorithm have good characteristics. Subsequently, the path corresponding to the knee point of non-dominated solutions is shown. The path is shorter, safer and smoother. This path can be adopted in the later decision-making process. Finally, the above research shows that the revised algorithm can effectively solve the multi-objective path planning problem in static environments.  
mobile robot; static environments; path planning; multi-objective optimization; NSGA-II; evolutionary operators +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion  
### Hydraulic Conductivity  
3.1.1. Hydraulic Conductivity of Olivine-Admixed Soil after Curing  
In order to assess the hydraulic conductivity variations of the carbonated and cured specimens, the hydraulic conductivity tests were conducted for different curing durations, using the same olivine content obtained from the experimental design. Figure 3 depicts the effect of olivine content (10%, 20%, and 30%) on the hydraulic conductivity of the treated clay specimens after three, seven, and 14 days of curing. The hydraulic conductivity of the natural clay (8.26 \(\times\) 10\({}^{-10}\) m/s) is presented in Table 1. As observed in Figure 3, the hydraulic conductivity declines steadily with an increase in olivine content and curing time. Nevertheless, the reduction was still within the same order of magnitude (\(\times\)10\({}^{-10}\)) as with the natural clay, except for the 30% olivine-admixed specimen after 14 days of curing, which recorded a value of 9.08 \(\times\) 10\({}^{-11}\) m/s. The hydraulic conductivity reduced from 8.26 \(\times\) 10\({}^{-10}\) m/s for the natural clay to 8.09 \(\times\) 10\({}^{-10}\) m/s, 7.89 \(\times\) 10\({}^{-10}\) m/s, and 5.97 \(\times\) 10\({}^{-10}\) m/s for 10%, 20%, and 30% olivine-admixed clay samples, respectively, after three days of curing. This finding suggests that, for the test conditions, the stabilization process began after 72 h of curing. The decrease in the size of the inter-aggregate pores due to the formation of the hydration products (M-S-H and M-A-H) is responsible for reducing the hydraulic conductivity at the early stage of curing (three days curing) [35]. This concurs with the trivial decrease in the hydraulic conductivity of the treated specimens relative to the untreated sample.  
Similarly, the hydraulic conductivity of the olivine-admixed samples decreased further after seven and 14 days of curing (see Figure 3). The hydraulic conductivity declined from 8.26 \(\times\) 10\({}^{-10}\) m/s for the natural clay to 2.74 \(\times\) 10\({}^{-10}\) m/s, 1.02 \(\times\) 10\({}^{-10}\) m/s, and 9.08 \(\times\) 10\({}^{-11}\) m/s for 10%, 20%, and 30% olivine-admixed clay samples, respectively, after 14 days of curing. This finding also indicates that, with an increase in curing duration, the sizes of the inter-aggregate pores decrease, due to the excessive formation of the hydration products, which fills the pores and bind the clay particles together, forming packed and compacted clay particles, thus decreasing the hydraulic conductivity.  
Figure 3: Hydraulic conductivity of the olivine-admixed clay at different curing durations (3, 7, and 14 days).
## 3 Testing Results and Analysis
### Strength Characteristics Testing
#### 3.1.2 Splitting Strength Experiment  
The splitting strength test results for phosphogypsum-modified lime-ash stabilized soil are presented in Figure 6. Several observations can be derived from Figure 6: (1) The splitting strength of phosphogypsum-modified lime-ash stabilized soil initially increases and then decreases with an increase in phosphogypsum content. When the phosphogypsum content is 4%, the splitting strength of the lime-ash stabilized soil reaches its maximum value (0.73 MPa), which is 4.3 times higher than the strength of the unmodified lime-ash stabilized soil (0.17 MPa). The early splitting strength of the lime-ash-stabilized soil is significantly improved. However, when the phosphogypsum content is 24%, the splitting strength of the lime-ash stabilized soil at different curing periods is lower than that of the unmodified lime-ash stabilized soil. (2) With an increase in the curing period, the splitting strength of phosphogypsum-modified lime-ash stabilized soil gradually increases. In the early curing period, the reaction between soil particles and the cementitious materials has not yet fully occurred, resulting in a slower rate of splitting strength growth. However,  
Figure 5: Variation Trend of Improved Soil UCS with Different Phosphategypsum Contents.  
as the curing period increases, the soil particles gradually become more stable and firmly bonded together. Additionally, certain active components within the soil gradually become effective during the curing process, accelerating the growth of the splitting strength of the lime-ash stabilized soil [22]. It is worth noting that, under different phosphogypsum contents and curing periods, the splitting strength of the lime-ash stabilized soil is highest at a phosphogypsum content of 4%. Combining this result with the UCS test results, this study considers a 4% phosphogypsum content as the optimal content.
# Beacon-Based Hybrid Routing Protocol for Large-Scale Unmanned Vehicle Ad Hoc Network
###### Abstract  
In this paper, we designed a beacon-based hybrid routing protocol to adapt to the new forms of intelligent warfare, accelerate the application of unmanned vehicles in the military field, and solve the problems such as high maintenance cost, path failure, and repeated routing pathfinding in large-scale unmanned vehicle network communications for new battlefields. This protocol used the periodic broadcast pulses initiated by the beacon nodes to provide synchronization and routing to the network and established a spanning tree through which the nodes communicated with each other. An NS3 platform was used to build a dynamic simulation environment of service data to evaluate the network performance. The results showed that when it was used in a range of 5 - 35 communication links, the beacon-based routing protocol's PDR was approximately 10% higher than that of AODV routing protocol. At 5 - 50 communication links, the result was approximately 20% higher than the DSDV routing protocol. The routing load was not related to the number of nodes and communication link data and the protocol had better performance than traditional AODV and DSDV routing protocol, which reduced the cost of the routing protocol and effectively improved the stability and reliability of the network. The protocol we designed is more suitable for the scenarios of large-scale unmanned vehicle network communication in the future AI battlefield.  
communication technology; beacon; large-scale; unmanned vehicle; ad hoc network; hybrid routing protocol +
Footnote †: journal: Electronics
## 3 Evaluation of PM\({}_{10}\) Generated in Cutting Operation through Chamber Experiment (Level 2)
### Experimental Results and Analysis
#### 3.2.4 XRD Analysis  
The characteristics of the PM generated from the asphalt concrete specimen-cutting activity were analyzed by using XRD. As a result of the XRD analysis, the main peaks were SiO\({}_{2}\) and CaCO\({}_{3}\) of the crystalline material due to the aggregate component, and it was found that many residual peaks of crystalline minerals combined with a large amount of elements such as Ca, Si, O, Al, Mg were also found (Figure 11).  
The asphalt binder is composed of amorphous materials unlike aggregates. The XRD pattern of asphalt consists of four major peaks: Y -peak, (002)-graphene peak, (10)-band, and (11)-band. The peaks of pure asphalt and mixtures appear near the 20 values in the range of 20-25\({}^{\circ}\). The resulting \(\gamma\)-peak originated from the X-rays scattered by aliphatic chains or condensed naphthenic rings. It has been reported that these peaks were mainly derived from paraffin [40]. The (002)-graphene peak at 20 values is caused by the X-ray diffraction of the aromatic sheet. The (10)- and (11)-band peaks are also expressed as (100) and (110). They appear because of the structure of the aromatic molecule in the plane. They represent the first and second elements in the ring compound.  
Among the four asphalt peaks, the peak mainly observed in the PM was the (002)-graphene peak. The \(\gamma\)-peak also appeared, although its intensity was small. Mean  
Figure 11: X-ray diffraction analysis of PM obtained from asphalt concrete cutting.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{10}{c}{**Weight (\%)**} \\ \cline{2-13}  & **C** & **O** & **Na** & **Mg** & **Al** & **Si** & **S** & **K** & **Ca** & **Ti** & **Fe** \\ \hline Type 1 & 30.2 & 35.6 & 0.96 & 0.53 & 3.18 & 15.8 & 0.48 & 1.16 & 4.9 & 0.7 & 6.49 \\ \hline Type 2 & 6.46 & 49.6 & 2.88 & - & 8.43 & 27.9 & - & 0.73 & 3.20 & - & 0.80 \\ \hline Type 3 & 12.9 & 39.4 & 2.66 & 6.25 & 10.9 & 15.4 & 0.28 & 0.62 & 5.97 & 0.4 & 5.22 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Elemental composition by average weight (%) from mapping analysis.  
while, the (10) and (11) peaks were not verified owing to the very low strength of the aromatic sheet [41].  
The results of XRD analysis of the PM generated from the asphalt concrete specimen cutting-activity showed that the main components of the aggregates were detected more easily than the components of the asphalt binder. Furthermore, a similar trend to the SEM and EDS analysis results presented in the previous section was observed.
# Model-Based Virtual Components in Event-Based Controls: Linking the FMI and IEC 61499
## 1 Introduction  
The complexity of instances of cyber-physical systems, like smart grids, calls for mature testing and validation methods in order to ensure correct operation in safety-critical situations [1]. In this context, model-based techniques allow representing parts of the overall system as virtual components and to simulate their dynamic behavior using numerical models, e.g., for evaluating control algorithms based on a virtual model of the physical system [2]. Such applications require the coupling of (time-continuous) simulation models with automation infrastructure, forming hybrid discrete/continuous systems. Integration of discrete and continuous dynamics remains a challenging task [3], due to the need for non-trivial time synchronization in combination with (distributed) event handling and accuracy requirements. Hardware-in-the-Loop (HIL) simulation, where some parts of the control loop are physical hardware, imposes even stricter requirements, like real-time capabilities. The accuracy of HIL setups strongly depends on the timing and synchronization properties, e.g., to avoid instabilities [4].  
Typically, simulation models and automation systems are coupled using tool-specific and/or custom-built interfaces, which require significant development effort and offer little reusability [5].  
In contrast, interfaces based on established technology standards covering a broader variety of simulation models and tools offer the ability to develop more generic virtual component interfaces that are reusable across different setups.  
In this work, a concept for a generic virtual component for bridging the gap between the automation systems and simulation domain is devised. This generic virtual component is based on established standards from each domain, in particular the IEC 61499 and Functional Mock-up Interface (FMI) standard, and implements two different approaches for time synchronization and event mapping.  
The article is structured as follows: Section 2 briefly describes the state-of-the-art, and Section 3 introduces the concept of generic virtual components in detail. An experimental evaluation of the studied concepts is presented in Section 4, and concluding remarks are given in Section 5.
## 3 Results and Analysis
### Performance of Models
The number of samples correctly predicted by MobileNet V2 was the largest; 1426 out of 1501 samples were correctly predicted, and 39 samples were incorrectly predicted as interlayer weight. While DenseNet-121 correctly predicted the fewest number of samples, and DenseNet-121 correctly predicted 1361 samples, 92 samples were incorrectly predicted as interlayer debonding, and 48 samples were incorrectly predicted as interlayer water seepage. For the category of interlayer debonding, the number of correct predictions is between 4406 and 4456. The number of samples correctly predicted by DenseNet-121 was the largest, 4456 out of 4505 samples were correctly predicted, and 34 samples were incorrectly predicted as interlayer loosening; 15 samples were incorrectly predicted as interlayer water seepage. On the other hand, MobileNet V2 correctly predicted the fewest number of samples, correctly predicted 4406 samples; 64 samples were incorrectly predicted as interlayer loosening, and 35 samples were incorrectly predicted as interlayer water seepage. For the category of interlayer water seepage, the number of correct predictions is between 3532 and 3567. The number of samples correctly predicted by ResNet-6 was the largest; 3567 out of 3607 samples were correctly predicted, and 30 samples were incorrectly predicted as interlayer loosening, and 10 samples were incorrectly predicted as interlayer debonding. On the other hand, MobileNet V2 correctly predicted the fewest number of samples, and MobileNet V2 correctly predicted 3532 samples; 68 samples were incorrectly predicted as interlayer loosening, and 7 samples were incorrectly predicted as interlayer debonding. It can be seen from the confusion matrixes that there are more mispredictions related to interlayer loosening, while the mispredictions between interlayer debonding and interlayer water seepage are relatively infrequent. The reason may be that both interlayer debonding and interlayer water seepage exhibit similar characteristics in part of samples to interlayer loosening as shown in Figure 3.
## 4 Conclusions  
In this work, we compared two different mixers within the framework of wireless optical THz communications. The main finding of the study shows that, under adequate optical LO and power conditions, InGaAs mixers can achieve results as good as, or even better than, GaAs mixers in terms of the BER and EVM. The reason lies in the superior signal purity achieved through optical pumping compared to electrical pumping, compensating for the higher inherent conversion loss of InGaAs mixers. In addition, InGaAs devices require approximately ten times less LO power than GaAs, making them highly attractive for photonic-based systems. This opens the possibility to merge optical and electrical devices, paving the way for the future integration of both technologies. Moreover, our simulations suggest that modifying the epitaxial structure of InGaAs diodes could further reduce the conversion loss gap between GaAs and InGaAs by 2 dB.  
Additionally, we emphasize the importance of proper phase noise estimation algorithms for achieving optimal results. Without a dedicated DSP, the system performance degrades regardless of the mixer technology or pumping method used. This work highlights the potential of InGaAs technology, particularly when combined with optical pumping, as a great candidate for next generation of THz receivers, challenging the dominance of GaAs in high-data-rate wireless communications.  
Conceptualization, J.M.-G.; Methodology, J.M.-G., I.B.-A. and J.T.; Validation, J.M.-G. and I.B.-A.; Investigation, J.M.-G.; Resources, J.L.F.E.; Writing--original draft, J.M.-G.; Writing--review and editing, J.M.-G., I.B.-A. and J.T.; Supervision, D.M.-M.; Project administration, C.C.R., A.S. and O.C.; Funding acquisition, C.C.R., A.S. and O.C. All authors have read and agreed to the published version of the manuscript.  
Horizon 2020 Marie Skodowska-Curie Actions TERAOPTICS (956857) and the German Federal Ministry of Education and Research under Project 6 GEM under Grant 16 KISK039.  
Data Availability Statement: Data are contained within the article.  
Figure 12: Simulated conversion loss (straight line) of GaAs mixers using highly doped epitaxy versus InGaAs mixers using high- and low-doped epitaxies and experimental measured results (dotted line) of the high-doped GaAs and low-doped InGaAs. In the current study, we used the low-doped InGaAs and high-doped GaAs.  
Conflicts of Interest: Author [PERSON], [PERSON] and [PERSON] were employed by the company ACST GmbH. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 The EA-CA Model  
### Mechanism of EA-CA  
The definition of CA is described based on the set theory as follows: Denote the dimension of the cell space as \(d\), the neighbor radius of the cell as \(r\), and time as \(t\). The cell state is a value in the finite state set \(S\), and \(Z\) is a set of integers in one dimension. In distributed ECM scenarios, two-dimensional CA [14] are commonly used for modeling. The state of a cell at the next moment is determined by the current states of its neighboring cells. Thus, the state value of a cell at \(t\) can be summarized as  
\[S_{i,j}^{t}=f(S_{i,j}^{t-1},S_{i-1,j}^{t-1},S_{i+1,j}^{t-1},S_{i-1,j-1}^{t-1}, S_{i-1,j+1}^{t-1},S_{i,j-1}^{t-1},S_{i+1,j+1}^{t-1},S_{i+1,j-1}^{t-1}) \tag{1}\]  
Let \(G(V,E,A)\) represent the diagram of communication topology structure of the CA model. The elements in the set \(V=1,2,\ldots,n\) are used to mark the corresponding \(n\) agent members in turn, and the state of agent \(i\) is denoted by \(x_{i}(t)\). The set of communication edges between members is represented by \(E=\{(i,j):i,j\in V,i\
eq j\}\).  
According to the rules of CA, the consensus algorithm [15] of the system can be defined as  
\[\dot{x}_{i}(t)=-\sum_{i,j\in E}a_{ij}(t)\big{(}x_{j}(t)-x_{i}(t)\big{)} \tag{2}\]where \(a_{ij}\) is an element of the adjacency matrix \(A(t)=[a_{ij}(t)]\) at \(t\geq 0\). The weight assigned to the edge of the graph \(G\) is defined as  
\[(i,j)\in E\Longleftrightarrow a_{ij}>0,(i,j)\
otin E\Longleftrightarrow a_{ij} =0,a_{ii}=0 \tag{3}\]  
Then, the adjacency matrix \(A\) represents the communication network between all agents in the system. Given that \(G\) is an undirected graph, the degree of the agent \(i\) is  
\[d(i)=d_{in}(i)=d_{out}(i)=\sum_{j=1}^{n}a_{ji} \tag{4}\]  
and the degree matrix can be denoted by \(D=\text{diag}\{d(i),i\in V\}\); the elements of \(D\) can be written as  
\[d_{i}=\left\{\begin{array}{c}\sum_{j=1}^{n}a_{ji},i=j\\ 0,i\
eq j\end{array}\right.. \tag{5}\]  
Combining Formulas (3) and (5), the Laplacian matrix of the figure G can be obtained by \(L=D-A\). The element of \(L\), \(l_{ij}\), is given by  
\[L=[l_{ij}](i,j=1,2\ldots,n)=\left\{\begin{array}{c}\sum_{j=1}^{n}a_{ij},i=j \\ -a_{ij},i\
eq j\end{array}\right.. \tag{6}\]  
The consensus algorithm of the system based on CA can be written in matrix form \(\dot{x}=-Lx\), \(x=[x_{1},x_{2},\ldots,x_{n}]\). It can be seen from the evolutionary rules of CA that the state of each agent is determined by the states of its neighbors. Therefore, when \(t\rightarrow\infty\) and \(\left|x_{i}(t)-x_{j}(t)\right|\to 0\), the system achieves global consistency, i.e., the states of all agents converge to a common value.