esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Inference with BRB Using ER Approach
#### 3.2.1. Input Transformation and Individual Matching Degree Calculation  
The numerical values or subjective judgments used as input to the antecedent variables are transformed to belief distributions on corresponding referential term associated with each attribute.This transformation can be done using rule- or utility-based equivalence transformation techniques [32]. For example, the numerical input value \(x_{i}\) of the \(i\)th antecedent attribute is transformed as follows:  
\[\alpha_{i,j}=\frac{u(A_{i,j+1})-x_{i}}{u(A_{i,j+1})-u(A_{A_{i,j}})}\text{ {and }}\alpha_{i,j+1}=1-\alpha_{i,j},\text{ {if }}u(A_{i,j})\leq x_{i}\leq u(A_{i,j+1}) \tag{1}\]  
\[\alpha_{i,k}=0,\text{ {for }}k=1,\ldots,J_{i}\text{ {and }}k\
eq j,j+1 \tag{2}\]  
where \(A_{i,j}\) is the \(j\)th referential term of the \(i\)th antecedent attribute, \(u(A_{i,j})\) represents the utility value of \(A_{i,j}\)\(\alpha_{i,j}\) is the degree to which the input \(x_{i}\) belongs to the referential term \(A_{i,j}\) with \(\alpha_{i,j}\geq 0\), and \(\sum_{j=1}^{I_{i}}\alpha_{i,j}\leq 1\)  
Finally, the belief distribution over referential terms is represented as follows:  
\[S(x_{i})=\{(A_{i,j},\alpha_{i,j});\text{ }i=1,\ldots,M;\text{ }j=1,\ldots,J_{i}\} \tag{3}\]  
where \(M\) is the number of antecedent variable in the BRB and \(J_{i}\) is the number of referential terms for the \(i\)th antecedent variable. Through this distribution, each referential value of the antecedent variables in the BRB will get a belief degree known as the _individual matching degree_ that quantifies the amount of input value match with the associated referential values. When the individual matching degrees are assigned to the referential values of the \(k\)th rule, then the antecedent part of the rule can be defined as follows:  
\[(A_{1}^{k},\alpha_{1}^{k})\wedge(A_{2}^{k},\alpha_{2}^{k})\wedge\cdots\wedge( A_{M}^{k},\alpha_{M}^{k}) \tag{4}\]  
where \(A_{i}^{k}\in\{A_{i,j};j=1,..,J_{i}\}\) and \(\alpha_{i}^{k}\in\{\alpha_{i,j};j=1,\ldots,J_{i}\}\)
## 1 Introduction  
The proper functioning of an enterprise is an important element of maintaining its continuity. Therefore, it is necessary to constantly track and analyze the signals coming from both the company itself and its environment in order to quickly respond to changes, modify adopted strategies, and introduce innovative solutions so as to adapt the company's operation to the changing market conditions on an ongoing basis.  
Logistics processes and their effective planning, proper management and effective implementation are of key importance here. The article examines the supply logistics, i.e., the process of supplying raw materials necessary for the implementation of production tasks. The specificity of the examined waste processing company requires knowledge about the size of potential deliveries because the delivered waste, which in this case is the production raw material, must be properly managed and stored due to its toxicity to the natural environment.  
In the article, hidden Markov models are used to assess the level of supply. They are a statistical modeling tool used to analyze and predict the phenomena of a sequence of events [1]. It is not always possible to provide sufficiently reliable information with the existing classical methods in this regard. Therefore, the article proposes modeling techniques that use stochastic processes. In hidden Markov models, the system is represented as a Markov process with states that are invisible to the observer but with a visible output (observation) that is a random state function. In the article, the distribution of outputs from the hidden states is defined by a polynomial distribution.  
hidden Markov models; distribution logistics; environmental safety; enterprise management
# Unsupervised Feature Selection for Outlier Detection on Streaming Data to Enhance Network Security
## 3 Requirements Engineering and Comparison with Related Work
### Requirements with Respect to Feature Selection for Outlier Detection on Streaming Data
The count of features is denoted as \(d\) (dimension) and \(\mathbf{x}_{t}\) the \(n_{t}\)-_th_\(d\)-dimensional most recent incoming data instance at time \(t\). In the field of cybersecurity and the monitoring of network data, the number of features is fixed [R-FS06] and can be defined _a priori_ by an expert since incorporating domain knowledge can help select relevant features to improve learning performance greatly. For network-based features, one may distinguish between basic features (derived from raw packet headers (meta data) without inspecting the payload, e.g., ports, MAC or IP addresses), content-based features (derived from payload assessment having domain knowledge, e.g., protocol specification), time-based features (temporal features obtained from, e.g., message transmission frequency, sliding window approaches), and connection-based features (obtained from a historical window incorporating the last \(n\) packets) [15]. Those features are streaming from a single source (single-view) [R-FS07], e.g., a raw network interface, as statistics from network switching elements or in the form of log-files from devices. However, a human expert cannot be expected to recognize correlations from the variety and multitude of multivariate features [R-FS08] occurring in high-dimensional real-world applications. Typically, this manual feature engineering is a time-consuming ad hoc process composed of trial and error to determine the features most relevant to the detection problem, which inhibits the application of ML to network security [3; 16]. Thus, FS is needed, whose output can either be a ranked list of the input features or a dedicated subset. The former allows a better insight into which features contribute the most in order to select the top-performing-features [R-FS09].
## 2 Modeling and Implementation
### Note on the Determination of Plane Parameters  
We present a brief tutorial on how to determine the plane parameters and consider three appropriate points \(\mathbf{x}_{i}\in\mathbb{R}^{3},i\in\{1,2,3\}\) that can be used to construct the plane. First, we define the parameter form with \(\mathbf{x}_{1}\) as the support vector and obtain  
\[P:\mathbf{x}=\mathbf{x}_{1}+\lambda(\mathbf{x}_{2}-\mathbf{x}_{1})+\mu( \mathbf{x}_{3}-\mathbf{x}_{1}).\]  
In this context, we further require that the direction vectors are not collinear, which means that the condition  
\[\beta(\mathbf{x}_{2}-\mathbf{x}_{1})\
eq\mathbf{x}_{3}-\mathbf{x}_{1},\quad \beta\in\mathbb{R}\]  
must be satisfied to ensure that they are not multiples of each other and thus do not point in the same direction. Otherwise, it would not be possible to span a plane. Then, we determine normal vector \(\mathbf{n}\) using the cross product, i.e.,  
\[\mathbf{n}=(\mathbf{x}_{2}-\mathbf{x}_{1})\times(\mathbf{x}_{3}-\mathbf{x}_{ 1})\]  
and insert it into the coordinate form in Equation (1). The remaining parameter, \(d\), is obtained by inserting support vector \(\mathbf{x}_{1}\). Finally, the parameters result in  
\[a =n_{1} c =n_{3}\] \[b =n_{2} d =n_{1}x_{11}+n_{2}x_{21}+n_{3}x_{31}.\]  
This approach can be used to determine the parameters of an inclined plane.
## 2 Cross-Domain Collaborative Task Allocation Model
### Mathematical Model of Target Allocation
#### 2.3.1. Based on Comprehensive Optimizing Effectiveness  
Most traditional task allocation models use the task success probability as the objective function [39]. According to the task allocation scenario setting, combined with the performance index of the load agent and the characteristics of the target agent, the probability of the load agent damaging the target agent is given and its damage probability is taken as the optimization goal of the model. After reviewing and summarizing the actual task results, it is evident that ensuring the task's sustainability and cost-effectiveness is essential. To achieve this, the efficiency/cost ratio and threat level of each task must be incorporated into the objective function, along with the overall efficiency and cluster workload. The economic benefit of cross-domain collaborative tasks mainly depends on the value of the target agent. Therefore, the coefficient of the target value and target threat degree was introduced to make more rational use of the resources and enhance the practical significance of the allocation scheme. The objective function was mainly determined by the damage probability of the load agent to the target agent, the threat degree, and the economic value of the target agent, as shown in Equation (2):  
\[P_{1}=-\sum_{j=1}^{c}v_{j}\times E_{j}\times(1-\prod_{i=1}^{d}(1-p_{ij})) \tag{2}\]  
where \(p_{ij}\) is the damage probability of the load agent \(M_{i}\) to the target agent \(T_{j}\); \(v_{j}\) is the threat coefficient of the target agent \(T_{j}\); \(d\) is the number of load agents \(M_{i}\) in the cluster; and \(E_{j}\) is the amount of value of the target agent \(T_{j}\).
## Appendix A GNSS Basics
In the second case, when the propagation is affected by multipath and the satellite is NLoS, we use the signal model in (10), considering that the random variable \(b\) is a zero-mean, complex Gaussian random variable, with variance \(2\sigma_{b}^{2}\), and the expression of the CRLB for the delay estimation is:  
\[CRLB_{\text{with NLoS multipath}}=\frac{1}{4\pi^{2}C\sigma_{f}^{2}}, \tag{14}\]  
where:  
\[C=2(2\pi)^{2}\frac{E_{r}}{N_{0}}\frac{1}{1+N_{0}/E_{r}}\text{ with }E_{r}=2 \sigma_{b}^{2}E_{c}. \tag{15}\]Therefore, in this case, the \(CRLB\) depends on the effective bandwidth of the transmitted signal \(\sigma_{f}\) and on the signal-to-noise ratio, \(SNR\), as in the first case, but also on the variance of the NLoS multipath fading \(\sigma_{b}^{2}\). Numerical simulations prove that the NLoS multipath propagation can deteriorate more than 100 times the precision of localization. In the case of real-life GNSS signals, the correlation peak detected in the acquisition sub-system fluctuates at each time instant. Because an error of 1 ms in the code delay produces an error of around 300 km in pseudorange, the tracking sub-system is necessary. The tracking sub-system is composed of a set of loops permitting the tracking of signals which were detected. The Delay Lock Loop (DLL) is in charge of the delay's tracking. Based on multiple correlators, it estimates the delay between the received signal and the locally generated replica. Knowing in this way the code phases at the moment when the signal was sent by the satellite and at the moment when the signal was received, the tracking sub-system finds the signal flying time. This time is then transformed into a pseudorange measure by multiplication with the speed of light. The role of the Frequency Lock Loop (FLL) is to maintain a good estimation of the received signal frequency. The measurement of the carrier phase (Phase Lock Loop-PLL) permits the pseudorange estimation with a better precision, but its utilization is delicate (2\(\pi\)-ambiguity). Using the pseudorange measures realized by the tracking sub-system, the navigation sub-system computes the receiver position using the principle of triangulation. Position calculations involve range differences, and where the ranges are nearly equal, small relative errors are greatly magnified in the difference. This effect, brought about as a result of satellite geometry (the range differences are small if the satellites used for triangulation are close to each other), is known as Dilution of Precision (DOP). The position measurement errors require the utilization of filters able to weight the measurements in function of the confidence granted. The most used algorithm is the Kalman filter or one of its variants, such as the Extended Kalman filter (EKF).
# Collective Communication Performance Evaluation for Distributed Deep Learning Training
## 7 Summary of Experimental Results
### PyTorch
#### 7.2.2 Deep Learning Experiments  
Table 11 presents the results of the minimum latency in deep learning experiments. Similar to the previous collective communication experiments, NCCL's superior performance is evident once again. The difference lies in the gathering subroutine, where NCCL demonstrates lower latency compared to other libraries. This indicates that GPU-based collective communication libraries exhibit outstanding performance in the deep learning execution phase. However, there are drawbacks, such as the necessity of using GPUs for the parameter server and the resulting GPU wastage within nodes.  
Table 12 illustrates the worst results in deep learning experiments. For the broadcast and gather subroutines used in the parameter server approach, GLOO's performance was consistently the lowest in all environments except for the cross-docker environment. However, due to the reduction in latency observed in the cross-docker environment, MPI's performance was the lowest. Additionally, in the results of the all-reduce experiments, GLOO exhibited the highest latency.  
Through deep learning experiments, we analyzed the actual performance of collective communication libraries at the application level. Comparing the findings with experiments conducted on random data, we observed similar trends for each library. Based on the results presented in this paper, we anticipate that it will contribute to selecting the optimal collective communication library for each server environment and architecture.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **GPU Allocation Type** & **Environment** & **Library** & **Latency (s)** \\ \hline Broadcasting & multi-GPU per container & singularity & NCCL & 1.06 \\ Gathering & multi-GPU per container & single docker & MPI & 3.18 \\ All-reduce & multi-GPU per container & single docker & NCCL & 0.64 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Worst results of each experiment (PyTorch).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **GPU Allocation Type** & **Environment** & **Library** & **Latency (s)** \\ \hline Broadcasting & single GPU per container & cross docker & MPI & 2.60 \\ Gathering & single GPU per container & cross docker & NCCL & 5.45 \\ All-reduce & single GPU per container & cross docker & MPI & 2.85 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Worst results of each experiment (PyTorch).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **GPU Allocation Type** & **Environment** & **Library** & **Latency (s)** \\ \hline Broadcasting & multi-GPU per container & singularity & NCCL & 9.98 \\ Gathering & multi-GPU per container & singularity & NCCL & 356.06 \\ All-reduce & multi-GPU per container & single docker & NCCL & 94.10 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Best results of each experiment (PyTorch deep learning).
# A Systematic Review of Virtual Reality Interfaces for Controlling and Interacting with Robots
## 5 Future Directions
### Visualization  
#### Improving 3D visualization of the robot's environment inside a VR headset  
At the moment, there are two main techniques for 3D visualization of the robot's environment inside a VR headset: using pre-designed 3D virtual models that mimic the real-world or using visualization data from the robot, i.e. point-cloud data. Both techniques though have issues that still need to be addressed. Currently, using a modeled environment of the world requires that the environment be both known ahead of time and static. While using real-time visualization from the robot can be computationally expensive and require large amounts of bandwidth to stream data from robot to VR headset. Ref. [21] presents a method that uses model-based background segmentation of point-cloud data to reduce bandwidth, but their method requires multiple depth sensors, knowledge of background models, and does not work with mobile platforms. Therefore, there is room for additional work in point-cloud streaming and rendering inside VR headsets.
# Detection and Quantification of Damage in Metallic Structures by Laser-Generated Ultrasonics
## 2 Background
### Generation and Modulation of Laser-Generated Ultrasonics  
The ultrasonics technique has been used for damage detection for a long time, and the generation of ultrasonic waves by using laser pulses was developed in the 1960s [21]. When the surface of a material is illuminated by a laser pulse, the material absorbs a part of the laser energy, resulting in a rapid temperature rise in the local area. Elastic waves are generated due to the thermal expansion. Based on the power density of the irradiating pulse laser, there are two thermal models for the generation of ultrasonic waves, namely, ablation generation and thermo-elastic generation [23]. For the latter, the power density of the laser pulse is lower than the ablation threshold of the irradiated material, which indicates non-destructive wave generation. In this paper, thermo-elastic generation is considered.  
After irradiation by a laser pulse, a broadband ultrasonics is generated. The ultrasonics is then used as an excitation signal to detect the damaged structure. When the wave is propagating in the structure, nonlinear wave modulation will occur because of the damage and new frequency components are generated as shown in Figure 2. Taking a closer look at Figure 2, the high-order nonlinear modulation signals are easily submerged by the original signal because of the broadband characteristic of the input signal, which means that traditional nonlinear damage detection technologies are unsuitable for this situation. State-space predictive models [22; 24] are introduced to tackle this issue.  
Figure 1: The illustration of nonlinear modulation of two ultrasonic waves.
## 5 Conclusions  
This paper addresses the challenge of establishing a secure communication for the SR network. This network consists of BS broadcasting to the environment, an HT in a symbiotic relationship with a BS, a WiFi access point used by HT for energy harvesting, a jammer cooperating with the HT and BS, an information receiver, and eavesdroppers trying to access the information of HT and BS. Firstly, four different scenarios (mutualism scenario, idle scenario, interference scenario, silent BS-active HT scenario) are presented in the proposed framework, depending on the activity of the BS and the sensing error of the HT. Secondly, the secrecy rate equation of each scenario is derived and the average secrecy rate is found. Finally, the average secrecy rate is maximized over the time parameters and the power reflection coefficient. The superiority of the proposed system over similar approaches in the literature, the advantages of using the channel symbiotically and the impact of including the sensing error in the calculations on the performance are graphically illustrated. In a future study, the system will be considered as a model with more than two HTs, and the secrecy rate of the system will be evaluated within the scope of different resource allocation algorithms.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(\beta_{0}\) (s)} & **Symbiotic Scenario with** & **Symbiotic Scenario** & **Nonsymbiotic Scenario** & **Nonsymbiotic Scenario** \\  & **Sensing Errors (bps/Hz)** & **Without Sensing Errors** & **with Sensing Errors** & **Without Sensing Errors** \\  & & (bps/Hz) & (bps/Hz)** & (bps/Hz) \\ \hline
0.1 & 20.72 & 26.2 & 18.42 & 24.72 \\ \hline
0.2 & 17.67 & 23.2 & 15.1 & 21.71 \\ \hline
0.3 & 14.61 & 20.2 & 11.76 & 18.7 \\ \hline
0.4 & 11.53 & 17.19 & 8.4 & 15.68 \\ \hline
0.5 & No solution & 14.18 & No solution & 12.66 \\ \hline
0.6 & No solution & 11.17 & No solution & 9.64 \\ \hline
0.7 & No solution & 8.16 & No solution & 6.6 \\ \hline
0.8 & No solution & No solution & No solution & No solution \\ \hline
0.9 & No solution & No solution & No solution & No solution \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(R_{\text{sec}}\) value for four different scenarios according to spectrum sensing time.
# BEAC-Net: Boundary-Enhanced Adaptive Context Network for Optic Disk and Optic Cup Segmentation
## 5 Experiments
### Evaluation Metrics  
We took a task-specific approach to the range of evaluation metrics, aiming to allow each experiment to be compared on the basis of having the same environmental settings. Accuracy, Dice coefficient (_Dice_), Intersection over Union (_IoU_),and Hausdorff Distance (H\({}_{\text{d}}\)) are our evaluation metrics used to measure the semantic performance for segmenting OD and OC relative to the ground truth. These parameters are formulated as follows:  
\[Acc=\frac{TP+TN}{TP+FP+TN+FN} \tag{9}\]  
\[Dice=\frac{2 TP}{2 TP+FP+FN} \tag{10}\]  
\[IoU=\frac{TP}{TP+FP+FN} \tag{11}\]  
\[\text{H}_{\text{d}}(\text{X}_{\text{Y}})=\text{max}\{\text{d}_{\text{XY}}, \text{d}_{\text{YX}}\}=\text{max}\{max_{x\in\text{X}}min_{y\in\text{Y}}\text {d}(\text{x},\text{y}),max_{y\in\text{Y}}min_{x\in\text{X}}\text{d}(\text{x},\text{y})\} \tag{12}\]  
Figure 7: OD and OC segmentation in fundus images into three classes: disk (green), cup (red), and background (black).  
where \(TP\) is true positive, \(FP\) is false positive, \(TN\) is true negative, and \(FN\) is false negative, respectively. \(X\) and \(Y\) are the number of pixels in the predicted and labeled binary mask images, respectively. The value of the Dice coefficient is between [0, 1]; the closer to 1, the better the segmentation result. The value of the IoU is also between [0, 1]; the larger the value of IoU, the more the region predicted to be the OD and OC overlaps with ground truth and the better the segmentation result.
# Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS
## 3 Proposed Methodology
### Metrics Used by Our Proposed Algorithm
#### 3.1.5 Distance between the Base Station and Each Sensor  
Calculate the distance between the base station to each sensor nodes, where, (XBS, YBS, ZBS) and (x1, y1, z1) are the coordinate positions of the base station and each sensor node, respectively, [2] as computed by Equation (5).  
\[Dist=\sqrt{((\text{XBS}-\text{x1})\,\text{\char 22}+(\text{YBS}-\text{y1}) \,\text{\char 22}+(\text{ZBS}-\text{z1})\,\text{\char 22})} \tag{5}\]