esa-sceva/satcom-chunk-collection huggingface dataset:
# Constructing More Complete Control Flow Graphs Utilizing Directed Gray-Box Fuzzing
## 4 Methodology
### DGF-Based Test Case Generation
#### 4.2.2 Fuzzing Loop  
Our fuzzing loop (the right part of Figure 4) is modified based on the classical gray-box fuzzing loop. The difference is that the purpose of the existing fuzzing is to find program vulnerabilities, while the purpose of our fuzzing is to generate test cases that exercise more indirect jumps. The workflow of our fuzzing loop is shown in Algorithm 2.  
```
Input: Initial seed set \(S_{in}\) Output: The evolved seed set \(S_{out}\)
1\(S=S_{in}\);
2 whilenot (timeout or abort-signal)do
3\(s=\) Choose(\(S\));
4\(p=\) AssignEnergy(\(s\));
5 foriffrom1 topdo
6\(tcs=\) Mutate(\(s\));
7\(info=\) Execute&Evaluate(\(tcs\));
8 Update(\(S\), \(tcs\), \(info\));
9
10 end for
11
12 end for
13\(S_{out}=S\);
14 output \(S_{out}\);
```  
**Algorithm 2**The fuzzing loop of test case generation.  
The fuzzing loop takes an initial seed set \(S_{in}\) as input. In each loop, one seed \(s\) is chosen from \(S\) and assigned energy according to the seed distance. The energy represents the priority of seeds in the fuzzing evolution. The seeds closer to the targets (indirect jump locations) can obtain more energy and have more chances to generate test cases  
Figure 5: Example of basic block-level distance calculation.  
_tcs_ by mutating. Therefore, the mutation may generate test cases closer to the indirect jump locations in the next loop. Then, the program instrumented with seed distance calculation executes with the test cases _tcs_ and evaluates each of them. The seed pool \(S\) is updated according to the results of the evaluation. By repeating the above steps, we can continuously update the seed pool to make the inputs closer to indirect jump locations. The fuzzing loop runs until a timeout or abort signal is received. Finally, all seeds in the evolved seed pool are output to the test case queue, which will be used to resolve indirect jump relations.
## 4 Results and Discussion
### Initial Tests and Result Collection
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Round-Trip Time (ms)** \\ \hline Mean & 115.381 \\ Median & 107.515 \\ Standard Dev. & 30.577 \\ Confidence Int. & 2.960 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Round-trip time of Mobile Hotspot-to-University network connection scenario including VPN.
## 3 Absorption Characteristics
### CST and HFSS Validation  
The proposed structure was designed, and its performance was studied using computer simulation software (CST). This research verified the suggested MMA unit cell performance using the high-frequency simulation software (HFSS). Figure 16 shows the acquired data from several simulation programs that show the results of comparing CST and HFSS. This research found that both HFSS and CST software show transverse electric (TE) and transverse magnetic (TM) modes of propagation. The suggested MMA has a maximum absorbance of 99.99% at 557 nm and an average of 91.82% from 450 to 650 nm at TE and TM mode when measured using CST. On the other hand, a wavelength range of 450 to 650 nm with an average absorbance of 96.45% in TE and TM mode when the suggested MMA unit cell performance was verified using the HFSS software. Finally, the plot shows that both CST and HFSS simulation data are acquired.
## 6 Results and Discussion
### Analysis of Energy Consumption  
Each node in WSNs is equipped with a limited power source or battery. Therefore, the nodes energy should be utilized in an efficient manner for better sustainability of the network. The lifetime of a node is directly proportional to the speed at which its energy is being consumed. In this work, the initial energy of each node is set to 0.5 Joule.  
Figures 13 and 14 represent the system residual energy (SRE) and energy consumption evaluation of DT, LEACH and LEACH-PRO for network size of 50, 100, 200 and 500 nodes respectively. As illustrated in the plotted results, LEACH-PRO is outperforming the other two protocols. DT has fast energy degradation rate as it does not use intermediate nodes for transmission towards the BS. LEACH uses its own probability function for selecting CHs, where each node has the same probability to become a CH, thus all nodes die quickly because of the blind CH selection strategy. LEACH-PRO, however, achieves the lowest degradation rate of energy due to its CH selection strategy that maximizes the energy efficiency of the network.  
As shown in Figure 14, DT consumes more energy in less number of rounds and exhibits low performance as the size of the network increases. LEACH and LEACH-PRO both show comparable performance at the start of communication operation and do not have much difference up to 50% of SRE. However, beyond this limit and up to 10% of SRE LEACH-PRO performs significantly better than LEACH. This supremacy of LEACH-PRO is inversely proportional to the increase of WSNs size as the performance gap between it and LEACH shrinks as the network gets larger.  
Figure 12: HND evolution under varying WSN densities.  
Figure 13: _Cont_.
# UAV Support for Mission Critical Services
## VMCS Concept
### VMCS Orchestration
#### 5.2.1 VMCS Cloud Orchestration  
Typically, the ETSI MANO [50] approach is used for network and orchestration in Clouds. It allows for the programmatic creating of a network in a distributed Cloud environment using virtual machines or containers [51]. Such networks consist of components of Virtual Network Function (VNF) or Cloud-native Network Function (CNF). The MANO orchestrator is responsible for the so-called life cycle management of created software networks (their installation, modification and uninstalling) and for the dynamic allocation of virtual infrastructure (called NFV Infrastructure (NFVI)) resources, which consists of computing resources, connectivity and memory.  
The MANO orchestration is already a part of the 5G network, for which it is responsible for creating virtual networks called network slices [52]. Unfortunately, the MANO solution cannot be the only orchestration solution in VMCS. This solution is highly centralised  
Figure 4: VMCS Cloud, Edge and Fog node placement (an example).  
Figure 5: VMCS hierarchical orchestration options.  
(typically, one MANO orchestrator is used for the whole network) and requires reliable, high-bitrate communication links and a stable infrastructure that usually comprises large and reliable data centres, the resource pool of which is typically slow-changing over time.  
The distributed Cloud infrastructure used by MANO has fibre-based connectivity typically and can also suffer in case of power grid failure or disaster (broken fibres, etc.). However, as long as the Clouds and their connectivity function, the Clouds orchestrated using ETSI MANO or Kubernetes should be part of VMCS with minor modifications. These modifications concern cooperation with Edge and Fog that should be done and Cloud OSS/BSS level.
###### Abstract  
The plaster-casting method to create a scoliosis brace consists of mould generation and rectification to obtain the desired orthosis geometry. Alternative methods entail the use of 3D scanning and CAD/CAM. However, both manual and digital design entirely rely on the orthoist expertise. Characterisation of the rectification process is needed to ensure that digital designs are as efficient as plaster-cast designs. Three-dimensional scans of five patients, pre-, and post-rectification plaster moulds were obtained using a Structure Mark II scanner. Anatomical landmark positions, transverse section centroids, and 3D surface deviation analyses were performed to characterise the rectification process. The rectification process was characterised using two parameters. First, trends in the external contours of the rectified moulds were found, resulting in lateral tilt angles of 81 \(\pm\) 3.8\({}^{\circ}\) and 83.3 \(\pm\) 2.6\({}^{\circ}\) on the convex and concave side, respectively. Second, a rectification ratio at the iliac crest (0.23 \(\pm\) 0.04 and 0.11 \(\pm\) 0.02 on the convex and concave side, respectively) was devised, based on the pelvis width to estimate the volume to be removed. This study demonstrates that steps of the manual rectification process can be characterised. Results from this study can be fed into software to perform automatic digital rectification.  
3D scanning; adolescent idiopathic scoliosis; digital design; orthosis design; plaster-casting; scoliosis brace; sculpting software +
Footnote †: journal: Medical Engineering  
Article  
Characterising the Mould Rectification Process for Designing Scoliosis Braces: Towards Automated Digital Design of 3D-Printed Braces
## 3 Results  
### Evaluation Metrics  
For the objective evaluation of the proposed method, three widely recognized indicators were selected: mean intersection over union (mIoU), mean F1 score (mF1), and overall accuracy (OA). The evaluation metrics can be computed as follows:  
\[OA=\frac{TP+TN}{TP+TN+FP+FN} \tag{7}\]  
Figure 3: Improved vision transformer module.  
\[Pre=\frac{TP}{TP+FP} \tag{8}\]  
\[Rec=\frac{TP}{TP+FN} \tag{9}\]  
\[F1=\frac{2\times Pre\times Rec}{Pre+Pre} \tag{10}\]  
\[IoU=\frac{TP}{TP+FP+FN} \tag{11}\]  
where \(Pre\) is the precision, and \(Rec\) is the recall. The test results can be divided into \(TP\), \(FP\), \(TN\), and \(FN\), which signify true positive, false positive, true negative, and false negative values of a particular class.
## 4 Numerical Experimental Simulation and Analysis
### Algorithm Performance Analysis
#### 4.3.3 Comparison of Distribution Change After Matrix Preconditioning  
Due to the characteristics of sparse matrices, where most elements are zero and the distribution of non-zero elements is highly scattered, this poses a significant challenge to matrix solution efficiency. The G-Pre used in this section optimizes the matrix structure by employing a graph theory preconditioner, which adjusts the distribution of non-zero elements to concentrate them, thereby facilitating the solving operation. The changes in the distribution of the matrix before and after G-Pre preconditioning are illustrated in Figure 6.  
From the changes observed in different matrices, it can be seen that chunk map reordering made the non-zero elements appear in blocks of consecutive rows, with similar densities across these rows. This significantly improved the spatial locality of memory access patterns and promoted a balanced load distribution during parallel computation across different blocks. Particularly in Matrix 4, the reordered elements were closer to those of a dense matrix, which is advantageous for computational solving.  
However, in Matrices 1, 2, and 3, the preconditioning effect was less satisfactory. In the first two matrices, the distribution of elements after preconditioning remained relatively scattered, which hindered the improvements to the solution efficiency. This observation highlights that the current G-Pre preconditioning algorithm still has areas that require enhancement. Specifically, efficiently preconditioning small-sized matrices to achieve optimal results remains a key area for future improvement.  
Figure 5: Comparison of solution times for different models with no preconditioning, ILU preconditioning, and G-Pre preconditioning. (**a–d**) Matrices 1–4.
# One-Layer vs. Two-Layer SOM in the Context of Outlier Identification: A Simulation Study
## 2 The SOM Algorithm
### Sequential (Original) SOM  
In the original, so-called sequential SOM, observations are introduced one at a time, as opposed to the batch SOM.  
For the training phase, a metric is required for the distance between vectors in the input space, and the euclidean distance is typically applied when the variables are continuous and on equal scale [1]. For each input vector introduced, the closest (euclidean distance) weight vector is obtained. The so-called winning vector is denoted by the \(c\) subindex. Finally, all weight vectors are updated according to Equation (1).  
\[\omega_{k}(t+1)=\omega_{k}(t)+\alpha(t)h_{ck}(t)|x(t)-\omega_{k}(t)| \tag{1}\]  
The weight updating process is mainly driven by the learning rate, which should decrease monotonically along time. A standard option is an exponential decrease:  
\[\alpha(t)=\alpha_{1}e^{-\frac{t}{\alpha_{j}}}\,, \tag{2}\]  
where \(\alpha_{1}\) and \(\alpha_{j}\) are constants. The neighborhood function \(h_{ck}(t)\) provides the conservation of topology through weight updating, usually called the neighborhood Equation (3) function. It thus defines the region--around the winning node--affected by the updating process. It controls the extent to which an input vector adjusts the weight of the neuron \(k\) with respect to the winner \(c\) by means of the corresponding (in the map) intra-net distance between the two vectors. Moreover, the \(x(t)-\omega_{k}(t)\) term produces a stronger update--within nodes equally distant in the 2D map from the winning one--for that whose weight in the previous iteration is more distant from the new observation. Several functions have been proposed in this direction, and here, we applied a Gaussian neighborhood. This function is symmetric about the winner. Furthermore, it monotonically decreases with time due to \(\sigma(t)\) and with distance to the winner.  
\[h_{ck}(t)=exp(-|\omega_{c}-x_{k}|^{2}/\sigma^{2}(t)) \tag{3}\]  
\[\sigma(t)=\sigma_{1}e^{-\frac{t}{\sigma_{j}}} \tag{4}\]  
```
SOM 1-layer Require:\(K\geq 0\lor S\subset X\) sample \(\vee\) epochs number Ensure:\(\omega_{k}\)\(\forall K\)  Initialize \(\omega_{k}\)\(\forall k\in(1...K)\) randomly for all epoch do {random loop over all input vector \(x\in X\)} for each x do {update weight vector \(\omega_{k}\) according to eq. 1}  Note: Including winner and neighborhood function endfor  Note: Not update \(\alpha\) and \(\sigma\) because are function of the epoch endfor
```  
**Algorithm 1** SOM algorithm.
## 3 Electromagnetic Modeling of Balise for Underwater Debris
### Magnetic Field Strength of Conductive Media
Where \(\delta\), \(k\), and \(\gamma_{1}\) can be expressed as follows:  
\[\delta=\frac{4\mu_{0}\mu_{1}k\gamma_{1}e^{(k+\gamma_{1})h}}{(\mu_{0}\gamma_{1 }+\mu_{1}k)^{2}e^{2\gamma_{1}h}-(\mu_{0}\gamma_{1}-\mu_{1}k)^{2}} \tag{31}\]  
\[k=\sqrt{\xi^{2}+\eta^{2}} \tag{32}\]  
\[\gamma_{1}=\sqrt{\omega^{2}\mu_{1}\varepsilon_{1}-j\omega\mu_{1}\sigma_{1}} \tag{33}\]  
When the center \(P\) of the OAU is at (\(P_{x}\), \(P_{y}\), \(h\)), according to Equation (4), the magnetic flux \(\varphi_{water}\) received by the antenna is:  
\[ \begin{array}{ll}\varphi_{water}&=\int_{P_{y}-d}^{P_{y}+d}\int_{P_{x}-c}^{P_ {x}+c}B_{2z}(x,y,h)dxdy\\ &=\frac{2\mu_{0}I_{1}}{\pi^{2}}\int_{-\infty}^{\infty}\int_{-\infty}^{\infty} \frac{k\delta}{\xi^{2}\eta^{2}}\text{sin}(a_{\xi}^{\zeta})\text{sin}(b\eta) \text{sin}(c_{\xi}^{\zeta})\text{sin}(d\eta)e^{-k(h+h_{0})}e^{-j(P_{x}\xi+P_ {y}\eta)}d\xi d\eta\end{array}  \tag{34}\]  
Combining Equations (5) and (34), the mutual inductance \(M_{water}\) between the OAU and the balise for underwater debris can be expressed as:  
\[M_{water}=\frac{8\mu_{0}}{\pi^{2}}\int_{0}^{\infty}\int_{0}^{\infty}\frac{k \delta}{\xi^{2}\eta^{2}}\text{sin}(a_{\xi}^{\zeta})\text{sin}(b\eta)\text{sin} (c_{\xi}^{\zeta})\text{sin}(d\eta)\text{cos}\big{(}P_{x}\xi+P_{y}\eta\big{)}e^ {-k(h+h_{0})}d\xi d\eta \tag{35}\]  
Equation (35) represents a crucial parameter for quantitatively evaluating the electromagnetic coupling characteristics of underwater transponders. In conjunction with Equation (6), the voltage \(U_{water}\) received by the OAU is derived as:  
\[U_{water}=-j\omega nQ\varphi_{water}=\frac{8n\omega\mu_{0}I_{1}}{j\pi^{2}} \int_{0}^{\infty}\int_{0}^{\infty}\frac{k\delta}{\xi^{2}\eta^{2}}\text{sin}(a _{\xi}^{\zeta})\text{sin}(b\eta)\text{sin}(c_{\xi}^{\zeta})\text{sin}(d\eta) \text{cos}\big{(}P_{x}\xi+P_{y}\eta\big{)}e^{-k(h+h_{0})}d\xi d\eta \tag{36}\]
## 3 Results
### The Effect of Map Orientation on Spatial Knowledge Development
#### 3.2.3 Landmark Sequencing by Cognitive Style  
The results of mean differences in landmark sequencing accuracy according to cognitive style are given in Table 7. In the north-up map condition, the mean of participants classified as FD was lower (M = 3.846, SD = 1.068) than that of participants classified as FI (M = 5.65, SD = 1.182). The same trend was seen in the forward-up map condition, where FD individuals showed lower performance (M = 4.939, SD = 1.44) than FI individuals (M = 5.303, SD = 1.510). However, as shown in Figure 10, the mean difference in each map orientation condition among the FD group showed a larger discrepancy than the mean difference among the FI group.  
To analyze the statistical effect of map orientation on landmark sequencing score regarding cognitive style, a repeated measures ANOVA was conducted with map orientation (North-Up vs. Forward-Up) as a within-subject variable and cognitive style (FD vs. FI) as a between-subject variable. There was no significant main effect of map orientation for landmark sequencing test scores [F (1, 35) = 2.891, \(p=0.099\)], but there was a significant \"map orientation \(\times\) cognitive style\" interaction: F (1, 35) = 6.27, \(p=0.018\) (see Table S5 in the Supplementary Materials).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Map Orientation** & **Cognitive Style** & **Mean** & **Standard Deviation** & **Min** & **Max** \\ \hline \multirow{2}{*}{North-up} & FD & 3.85 & 1.07 & 2 & 5 \\ \cline{2-6}  & FI & 5.65 & 1.18 & 3 & 7 \\ \hline \multirow{2}{*}{Forward-up} & FD & 4.94 & 1.44 & 3 & 7 \\ \cline{2-6}  & FI & 5.3 & 1.51 & 2 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Descriptive statistics for landmark sequencing test in the map orientation conditions by cognitive style. Note: “FD” stands for field dependence and “FI” stands for field independence.  
Figure 9: Mean of two factors for sketch maps in different map orientation conditions by cognitive style (Mean of the canonical organization on the left- and angle accuracy on the right-hand side). Note: “FD” stands for field dependence and “FI” stands for field independence.  
Supplementary Materials). Post hoc comparisons for landmark sequencing revealed a significant difference between cognitive style groups (FD vs. FI) in the north-up map condition (\(p\) = 0.002). Also, a significant difference between map orientation (north-up vs. forward-up) among the FD group was found (\(p\) = 0.044) (See Table S6 in the Supplementary Materials).
# Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers
## 3 Methodology
### Optimization  
The optimization process utilizes the cross-entropy loss function, which is defined as follows:  
\[\mathcal{L}(X,S)=\frac{1}{T}\frac{1}{M}\sum_{t=1}^{T}\sum_{i=1}^{N}\sum_{j=1}^ {M}-P(\varphi_{j}^{i}|X_{t},S_{t})\log\tilde{P}_{\theta}(\varphi_{j}^{i}|X_{t },S_{t}), \tag{13}\]  
where \(X\) represents the set of attribute vectors over \(T=10\) time steps, and \(S\) denotes the set of spatial tensors. The total number of opponent pieces is \(N=6\), and the total amount of hexagonal grids is \(M=3366\). The term \(P(\varphi_{j}^{i}|X_{t},S_{t})\) is the true probability of the \(i\)-th piece being located on the \(j\)-th grid given the input features \(X\) and \(S\). Conversely, \(\tilde{P}_{\theta}(\varphi_{j}^{i}|X_{t},S_{t})\) denotes the predicted probability of the \(i\)-th piece being on the \(j\)-th grid, and it is generated by the proposed ELP-Net with the set of model parameters \(\theta\). The complete algorithm is presented in Algorithm 1. Further complexity analysis of our model is provided in Appendix C.  
Figure 4: The graph convolution module structure.  
```
0: batch of sampled data (\(X\in\mathbb{R}^{b\times T\times d_{x}},S\in\mathbb{R}^{b\times T\times P\times M}\)); initialized graph learning layer \(f_{A_{t}}(\cdot)\); initialized temporal convolution module \(f_{T}(\cdot)\); initialized graph convolution module \(f_{G}(\cdot)\); parameter set \(\theta\); learning rate \(\gamma\).
1:repeat
2: process piece attribute features using the transformer: \(Y=\text{Trans}(X)\).
3: perform spatial feature transformation: \(S^{\prime}=\text{Conv}2\text{D}(S)\).
4: construct the adjacency matrix of pieces: \(A_{t}=f_{A}(S^{\prime};S^{\prime T})\).
5: process spatial features with temporal convolution: \(S^{\prime\prime}=f_{T}(S^{\prime})\).
6: aggregate spatial information using graph convolution: \(S^{\prime\prime}=f_{G}(S^{\prime\prime};A_{t};A_{t}^{T})\).
7: integrate temporal and spatial features for all pieces:
8:for\(n\) in \(1:N\)do
9:\(Z_{list}=Z_{list}.append(\text{GRU}(concat(Y;S^{\prime\prime})))\).
10:endfor
11: stack outputs across all pieces: \(Z=stack(Z_{list})\).
12: compute the loss: \(\mathcal{L}=(\tilde{P}(Z;\theta);P)\).
13: update model parameters \(\theta\) according to gradients and learning rate \(\gamma\).
14:until convergence
```  
**Algorithm 1** The predictive algorithm of ELP-Net
## 3 Performance Analysis of Direct Detection CPolSK System  
### Direct Detection CPolSK System Model  
Figure 2 shows the block diagram of a direct-detection CPolSK system using a GSM beam. The beam propagation is simulated by the extended Huygens-Fresnel integral; the scintillation index is computed using the standard Rytov variance formula for a GSM beam. First, a phase diffuser converts the incident light into a GSM beam, which then passes through a polarization controller (PC). Thus, a linearly polarized GSM beam oriented +45\({}^{\circ}\) relative to the optical axis is modulated. By controlling the terminal voltages of a birefringence crystal-based external polarization modulator (PM), +45\({}^{\circ}\) or \(-45^{\circ}\) linearly polarized lights are obtained. A quarter-wave plate converts a left- or right-circular polarization into two orthogonal linear polarizations, which are then separated by the PBS for detection. Finally, after passing through a quarter-wave plate (QWP), the emitted light is transmitted via an antenna as either left- or right-circularly polarized light. In this work, beam propagation is modeled by the extended Huygens-Fresnel integral, and the scintillation index is calculated via theoretical expressions.  
At the receiver, a QWP converts the incoming signal into linearly polarized light without requiring polarization coordinate alignment between the transmitter and receiver. After passing through the polarization beam splitter, the \(\pm 45^{\circ}\) linearly polarized light is received by two photodetectors (PDs). Finally, the signals are demodulated using a differential circuit. The output current of the receiver can be expressed as follows.  
\[i=i_{D2}-i_{D1}=(i_{R}+i_{n2})-(i_{L}+i_{n1})=\left\{\begin{aligned} i_{s}+i_{n}\text{ {symbol ``1''}}\\ -i_{s}+i_{n}\text{{symbol ``0''}}\end{aligned}\right. \tag{21}\]  
where i\({}_{L}\) and i\({}_{R}\) are the detected currents of the left and right circularly polarized optical signals, respectively; \(i_{n1}\) and \(i_{n2}\) are the thermal noise currents. Taking equal normal distributions \(N(0,\,\sigma_{n}{}^{2})\) for i\({}_{n1}\) and \(i_{n2}\), we obtained \(i_{n}=i_{n2}-\)i\({}_{n1}\), \(N(0,2\sigma_{n}{}^{2})\).